知识热榜

  • 014618
    天幕红尘(《遥远的救世主》姊妹篇)
    跳出认知陷阱,逆转人生困局!从谋于术,到观于道,最后行于势——豆豆三部曲收官之作,继《遥远的救世主》后重磅政治小说! 复制别人的路,永远走不出自己的命!高手从来都是看透规律、顺势而为。 苏联解体时代剧变,石油商人罗家明因投资失利,背负105万美元巨债绝望自尽。蜗居柏林,东西方世界的边缘人、马克思主义哲学的信奉者叶子农,因一句“见路不走”的箴言以身入局,直面旁人无解的债务死局。 他不循世俗老路、不照搬固有经验,立足现实精准破题,以跨国劳务输出盘活危机,完美诠释核心智慧:见路不走。 所谓“见路”,是盲从经验的致命陷阱;“不走”,是实事求是的通透抉择。 不懂规律,努力只是原地踏步;脱离现实,选择皆是豪赌。 可同样信奉“见路不走”,为何有人惨败落幕,有人顺势翻盘? 普通人又如何真正吃透“见路不走”,活出自成一派的人生? 翻开本书,参透顶级认知思维,拿回人生主动权!豆豆
  • 021.4万
    遥远的救世主(《天道》影视原著)
    穷人改命顶级阳谋,现象级神剧《天道》原著! 认知决定命运,改命不必拼命!一段灵魂共鸣的智性爱恋、一场算无遗策的商海博弈、一个证道文化属性的社会实验。 突然解散私募基金、放弃巨额利润的商界“怪才”丁元英孑然一身,隐居避世,这位传统文化的叛逆者,与古城女警芮小丹不期而遇。俗世的高人与“天国的女儿”双双坠入一场灵魂交响的智性爱恋。 芮小丹不滞于物、不昧因果,只向丁元英要一件礼物——一个扶贫神话。 当“弱势文化”的牺牲品、“务实文化”的代表、“强势文化”的赢家、“觉悟文化”的化身齐聚一堂,不同认知的众人将走向怎样不同的命运? 弱者扒着井沿看一眼世界又掉回去,不是运气差,是认知配不上机遇; 强者改命逆袭,不是手段高,是对规律的敬畏和对自己下刀子的狠劲。 可杀富济贫,真的能救贫吗?大爱无爱,是否有违天道? 翻开这本触及灵魂和人性的觉醒之书,探索强势文化改命的秘密。豆豆
  • 033633
    背叛(《天道》原著作者豆豆开山之作)
    普通人乱世立身的终极法则,《天道》原著作者豆豆开山之作! 一段世纪之交的个人生命史,一部时代浪潮中的灵魂忏悔录! 一次受人所托的探监,聪颖果决的记者夏英杰“慧眼识穷途”,爱上了入狱避祸的宋一坤。为了这段不被世人理解的感情,宋一坤随她避居海岛,从一本小说、一个商机起手,悄然撬动一场千万级商业风暴。 他出身贫寒,却有看透机会的洞察与绝处逢生的胆识; 她家境优渥,却不为名利所惑,只愿追随内心。 一个靠天才与谋略织就财富迷局,一个为爱奔赴、为信念坚守底线。 可当境外资本步步围猎,利益、友情、爱情与国有资产被同时推上赌桌,他们必须作出最残酷的选择。 是追逐利益,还是坚守正义? 是信奉弱肉强食,还是守住为人的底线? 翻开本书,见证一场义与利交织的惊心博弈,看良知如何照亮命运!豆豆
  • 042642
    豆豆三部曲(普通人的改命路线图)
    普通人如何破局?答案不在救世主手里,而在认知、底线和规律之中。 作为豆瓣9.1分现象级神剧《天道》的原著,豆豆三部曲讲透了一件事:认知决定命运,改命不必拼命! 为什么有人越拼命越穷,有人却能轻松改命? 因为命运从不奖励最辛苦的人,只奖励最清醒的人。 改命,先改脑;逆袭,须守心。良知立身、认知觉醒、落地践行,缺一不可。 有人懵懂无知,机会摆在眼前却识别不出;有人错把平台当能力,想复刻别人的路,却走不出自己的命;有人知行合一、坚守本心,终走出一方天地。 从守底线、破认知,再到循规律,读豆豆,不是打鸡血,是换脑子;不是教你更拼,是教你别再白拼。认知差一旦拉开,命运差就会复利。如果你正困在越努力越无力的循环里, 翻开这套命运、人性与规律的清醒之书,探索属于你的道路。 内含:《遥远的救世主》《天幕红尘》《背叛》豆豆
  • 055116
    第一性原理:普通人的人生高效手册
    为什么你明明很努力,却始终感觉在原地踏步? 大多数人终其一生,都在用“类比思维”过日子。别人怎么做,我就怎么做;过去怎么做,现在还怎么做。这种思维方式省力、安全,但也给你设了一道无形的天花板。 这本书要做的,就是把第一性原理从哲学家和企业家的书斋里,搬到你的日常生活中。 什么是第一性原理?简单说就是:把问题一层层剥开,直到看到最核心的基本事实,然后从那里出发,重新构建解决方案。小到今晚吃什么,大到人生方向怎么选,都可以用。 书里给出了一整套可操作的工具箱:四种拆解方法(连续追问、成本拆解、假设审查、类比消除)和四种重构方法(约束反推、极端假设、要素重组、验证回路)。并用SpaceX、香农信息论、Costco、指数基金、费曼学习法五个跨领域案例,完整演示了这套方法如何在真实世界中发挥作用。 读完这本书,你会获得一种能力:不再被别人的经验绑架、不再被过去的自己束缚。 你会学会如何拆掉思维里的那堵墙,看到问题真正的样子。武从文
  • 061726
    住小房子的女人
    一场关于女性如何安身立命的终极撕扯。 《我才不想做家务》作者最新力作! 32岁的离婚律师路佳与男友恋爱三年,因偷偷给自己买了套40平的小房子,关系就此陷入僵局; 30岁的妹妹路怡未婚先孕,男友东拼西凑在北京买了套58平的老破小。公婆来带娃后,她大战“烟人”公公,公媳矛盾硝烟四起; 59岁的母亲陈文娟丧偶独居,招赘未遂,患上重度抑郁症——女儿“嫁出去”之后,母亲就成了被剩下的人? 去父留子、两头婚、招赘、冠姓权……人们总说女性已经解放了,人生很广阔。可事到临头她们才发现,女性能走的路其实很少。理论是理论,现实是现实。 当房价下跌、少子化、AI冲击、女性主义浪潮同时席卷而来,一群半新半旧的男女,如何在主义与现实中既博弈又退让,重新建立联结? 翻开本书,且看她们如何找到答案,自洽地幸福。纪静蓉
  • 071069
    财经(2026年第19期)
    新政正式终结了“高杠杆、高负债、高周转”的房地产旧模式。制度切换不会自动解决旧问题,转型阵痛难免财经
  • 082661
    首席御医(神医官场流开山之作)
    神医官场流开山之作!挽救你的生命,即挽救你的政治生命!冬奥冠军王濛推荐,银河九天口碑神作,出版名《首席医官》。 实习报到第一天,曾毅就干了件轰动省人民医院的事:直言国内顶尖专家治疗方案错误。 老专家们拍桌怒斥,他直击病症核心,几剂中药,领导夫人病根尽除! 望闻问切出神入化,针灸正骨化有形为无形,国医大师也对他青眼有加。 凭借一身中医绝学,曾毅成为领导的健康顾问,一步步走进权力中心。 省医疗保健专家、卫生局长、招商主任,直至主政一方。面对官场中的利益博弈与人情考验,他以医术立身,以智慧破局,在风云变幻的仕途中不断成长。 “上医医国,中医医人,下医医病。” 看一介中医,如何凭一身医术闯入官场核心,为百姓谋福祉,为国医争尊严!银河九天
  • 09550
    看世界(2026年第19期)
    "1999年底,由广州日报市委宣传部、市新闻部出版局、市社科联等单位进行的穗版期刊评比中,我刊以总比分第一名的成绩被评为""穗版优秀期刊"";在2000年广东省第三届优秀期刊评选活动中,我刊获""广东省优秀期刊""。2002年又被评为新闻出版社总署""双效期刊""。1999年6月,中央电视台""经济半小时""对报刊的发展之路给予较高的评价和充分报道;《新闻出版社》分别在1999年12月、2000年5月和8月三度刊文正面分析我刊的成功经验。2001年~2002年由香港亚视授权本期刊独家推出""《看世界》版百万富翁、《看世界》版各出奇谋""栏目,并成功地于2001年9月27日在广州天河宏城广场与亚视联手举办了""我与《看世界》有个约会""大型宣传活动,香港著名艺员陈启泰应邀主持,同事还有近20家香港传媒记者莅临参加,这次活动在省港两地引起了较大反响。2002年元月15日,开中国期刊宣传先河之举的""广州--北京""""《看世界》号列车""首发。该列车上所有的宣传标志均由《看世界》提供,列车上也只卖《看世界》等几种指定期刊。2002年获得国家""双效""期刊奖。2003年年初又荣获第二届国家期刊奖百种重点期刊!2005年年初又荣获第三届国家期刊奖百种重点期刊!"看世界
  • 10936
    人性的弱点
    本书是卡耐基经典人际关系著作,剖析人性,结合真实案例传授沟通处世法则,帮助读者改善社交关系,收获信任,实现个人成长与生活顺遂。[美]戴尔·卡耐基
  • 111868
    执掌风云
    深处基层的萧峥无意中抓住一个机会,经历了从潜龙在渊到辉煌腾达的人生历程。 萧峥曾是西部小镇安监站里最不起眼的干部——下乡被人当空气,冒雨去给女友母亲庆生,推门却见另一个男人稳坐主位。那一夜风雨骤起,惊雷劈开山体,也劈开了他认命的人生:这条路上,能护住你的从来不是背景,是敢担事的脊梁、做实事的硬气。 雨夜救人,救出仕途第一个贵人。从安监站到组织部,从边远小镇到改革前沿的深市——他查带病提拔、破塌方腐败,把当年看不起他的人一个个甩在身后,也把每一个想干事的人稳稳护在身边。他不站队、不媚上,却让越来越多的人愿意把后背交给他。 当他站上贸易战的谈判桌,代表国家寸土不让时,那个曾被晾在门外的镇干部,早已是别人仰望的风云。权力是照妖镜,也是试金石——一个清白的人,究竟能在这浑浊的宦海里走多远? 一步一个脚印,写尽一个基层干部从泥泞到巅峰的登顶之路。笔龙胆
  • 12643
    钱外之物:普通人的理财清醒指南
    这是一本写给普通人的财商课。作者没有堆砌金融术语,而是把自己和身边人真实踩过的坑写了下来:一位建筑行业的朋友在2021年房价高点掏空家底连买两套房,最终被现金流拖垮;作者自己把几乎全部积蓄押在一只港股上,亏掉七位数;还有人在消费分期、体面社交、盲目加杠杆中,一次次把未来的选择权提前典当。 全书从"现金流才是家庭安全感的底盘"讲起,逐层拆解体面消费、分期付款、赚钱能力、财富的本质、投资不加杠杆、不懂不买、行业集中与资产流动性等关键议题。作者提出,资产可分为使用型、经营型、共识与信仰驱动型三类,看懂这一点,才能分辨自己手里握着的究竟是财富还是负担。 书的核心观点是:赚钱靠能力、机会和努力,管钱则是另一套完全不同的能力。学财商不是为了把自己变成收益率更高的赚钱机器,而是为了在消费、负债、职业和投资的每一个关口,都能多想一步、少被数字绑架。正如书末所写:真正的自由,不只是拥有更多选择,更是有能力放弃其中一个。珈一
  • 13734
    读者(2026年第17期)
    《读者》,原名《读者文摘》,是由读者出版传媒股份有限公司主办的中文版半月刊物。《读者》杂志发掘人性中的真、善、美,体现人文关怀。《读者》在刊物内容及形式方面与时俱进,追求高品位、高质量,力求精品,并以其形式和内容的丰富性及多样性,赢得了各个年龄段和不同阶层读者的喜爱与拥护。发行量稳居中国期刊排名第一,亚洲期刊排名第一,世界综合性期刊排名第四。被誉为“中国人的心灵读本”、“中国期刊第一品牌”。 《读者》收录的文章融思想性、知识性、趣味性为一体,追求高质量、高品位,篇篇精品。这里有正确的思想、高尚的道德、崇高的理想、生活的哲理,使读者在轻松愉快的阅读中陶冶情操、净化心灵!具有深广的影响力与历久弥新的力量!读者
  • 14439
    看天下(2026年第24期)
    《看天下》是一本综合类新闻杂志,创刊于2006年,也是经全球华文媒体BPA认证国内发行量最大的新闻期刊。内容丰富多元,包含时政、财经、科技、文化、娱乐、教育、心理等多个领域。立志于为读者展现更广阔的世界和人生的更多可能。看天下
  • 15187
    那年那信
    本书是一部用家书串起的家族故事,作者敬一丹以“信中信”的方式娓娓道来,从1950年到2026年,涵盖了76年里家中四代、十几口人各自的成长历程和整个家庭的岁月变迁。 全书分为两部分,“那年那信”为第一部分,从1700余封家书中精选了数十封,将其凝练为30篇文章,通过夫妻、亲子、手足之间在不同年代的通信,折射一家人生活、学习、思想的方方面面,既有宏阔的时代背景,又有细微的日常小事,生动地勾勒出变迁的世间图景;“这年这信”为第二部分,新增的11篇文章,是对“那年那信”隔着时空的回应,也是家族中第二代、第三代乃至第四代,对已逝的第一代老人和整个大家族的深深缅怀与无限热爱。 这样的家庭,是千千万万中国家庭的缩影,他们的生活、家族的历史,点点滴滴、喜怒哀乐,始终伴随着亲人的温情、家风的传承。家书之于一个家,是当年当日的叮咛,也是此时此刻的回望,还将是未来可依的溯流。敬一丹
  • 16244
    商界(2026年第9期)
    2026年8月,吉利汽车一纸公告,把“接班”这件事再次推到了所有人面前。 李书福退了。不是交给儿子,不是交给女儿,而是交给跟他干了 30 年的“老会计”安聪慧。副主席桂生悦说得直白,这是“去家族化”的明确信号,企业从创始人驱动,正式迈向制度驱动。 一个中国民营车企的掌舵者,把权杖交给了职业经理人—这件事本身就值得让人多看两眼。 但吉利的从容,远不是全貌。 当创一代们陆续步入老年,摆在他们面前的是一道无法回避的选择题 :子女愿接,如何接?子女不接,谁来接?接过去的企业,又该如何在时代的巨变中活下去? 有人接过重担,在阵痛中求生 ;有人走向融合,用数字化找到新路 ;也有人选择体面离场。冰冷的数据印证着这种撕裂 :仅30% 的民企能传至第二代,能传至第三代的骤降至13%。 有人接班,有人拒绝,有人交给职业经理人,有人卖厂退场。传承从来不是一道单选题。 当无人接班从个例走向常态,中国民企必须重新理解“传承”的重量:它不再是血脉的延续,而是治理能力的迭代 ;不再是财富的交接,而是商业系统的重生。 本期策划将深入这场大考——不想接的企二代在想什么?想接的又接成了什么样?而答案正在无数车间、会议室和谈判桌上,一点点浮现。商界
  • 171076
    桃花案:财迷孤女X清冷状元
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10天前更新
  • 01
    PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics
    World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into downstream applications. Existing methods typically require robot action labels to learn action-conditioned 3D dynamics, which excludes web video data from the training pool. We study 3D point track completion as a pre-training objective for learning transferable 3D dynamics without robot data. GivenBardienus P. Duisterhof
  • 02
    In-Context Robot Learning with VLM Agents
    Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstrations can cover every task and situation a robot will encounter, making the ability to learn from context at deployment essential for generalization. Such in-context learning (ICL), however, remains largely beyond the reach of existing robotic policies. The broad agentic capabilities of commercial vision-language models (VLMs), such as GPT-6 Astra, raiDongzhou Cheng
  • 03
    Dreaming the Sound of Contact: Leveraging Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation
    Recent advances in video generation allow robots to learn manipulation trajectories from generated videos. However, these approaches produce purely kinematic trajectories that lack force information, causing failures in contact-rich tasks where appropriate contact forces are essential for success. In this work, we explore augmenting generated video with audio to shape a bounded, time-varying desired-force profile using the loudness of generated contact sounds. We present a pipeline that jointlyGuanhua Ji
  • 04
    rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference
    Factory work is a promising early scenario for embodied AI: assigning repetitive manual jobs to robots has clear economic payoff, and a structured station keeps the jobs tractable for current policies. Vision-Language-Action (VLA) models now dominate as the policy paradigm for these robots. The inference latency of VLA models directly affects robot responsiveness and motion smoothness. However, existing VLA inference frameworks do not fully exploit the characteristics of embodied workloads or acKaijun Zhou
  • 05
    ElastiQP: An Always-Feasible QP Solver for Constrained Robot Control
    As robot capabilities increase, quadratic programming (QP)-based controllers must account for a similarly increasing number of constraints to ensure safe, reliable operation. Yet, with each added constraint, this introduces more chances of momentary conflict: in which case, a QP solver that returns an "infeasible" status leaves the controller with nothing to execute. To address this, we introduce ElastiQP, a modified dual active-set QP solver that relaxes every inequality constraint with an exacDaniel Morton
  • 06
    "What's going to happen after I'm gone?": Parent Perspectives on Technology in Supporting Independent Living for Adults with Intellectual Disabilities
    Adults with intellectual and developmental disabilities (IDD) are increasingly transitioning from family homes towards semi-inde\-pendent living. As parents hand off the role of primary caregiver, they face numerous challenges in arranging consistent and quality support. Our research centers on understanding these caregiving routines. By focusing on the unique lived experience of parents, who possess extensive explicit and tacit knowledge of their adult child's requirements, we aim to map the maAlexander Tyshka
  • 07
    Learning Holistic Whole-Body Loco-Manipulation with a Bipedal Mobile Manipulator
    Bipedal loco-manipulation enables robots to interact with objects beyond the nominal workspace of their arms by coordinating locomotion and manipulation. Realizing this capability requires a low-level whole-body controller that translates task-level manipulation goals into coordinated arm and leg motions while maintaining balance. We present a unified whole-body controller trained with reinforcement learning that directly maps 6-DoF end-effector targets to coordinated actions for the bipedal basZhongyu Chen
  • 08
    CaSCo: Cascade-Aware Soft-Collision Motion Planning
    Conventional motion planning treats collision as a binary constraint, although contact with different objects can have drastically different consequences. A robot may safely brush against a cardboard box while even minor contact with a glass, laptop, or unstable object may be undesirable. Moreover, a direct robot--object collision can move the contacted object and trigger secondary object--object collisions, making the risk of a motion depend on the physical evolution of the scene rather than onShivaram Kumar
  • 09
    Examining the Difference in Human Behavior Between Virtual and Real-World Human-Robot Teaming
    Prototyping and evaluating human-robot teaming (HRT) scenarios in the real-world is costly. Virtual simulation of HRT scenarios has been adopted as an alternative to conducting user studies in the real-world to investigate user perceptions, behaviors, and performance during human-robot interactions. The consistency of human behavior between the real and virtual-worlds is integral to the validity of utilizing such virtual experimentation. This paper presents a user study examining the differenceSean Dallas
  • 10
    SOL-SLAM: Inverse Compositional Gauss-Newton Direct Registration for Fast Sonar-Only Local SLAM
    Autonomous underwater navigation typically relies on complex and expensive multi-modal sensor suites designed to prioritize global Simultaneous Localization and Mapping (SLAM) accuracy. However, local reactive behaviors such as coarse navigation and obstacle avoidance require only local consistency---a capability that should be feasible using only a Forward-Looking Sonar (FLS), yet remains largely unaddressed, leaving a critical gap in FLS-only local SLAM. Moreover, existing acoustic SLAM framewKalvik Jakkala
  • 11
    Body-Motion Control of a Simulated Aerial Swarm from a First-Person View
    First-person-view (FPV) teleoperation of aerial swarms requires an operator to coordinate collective translation, viewing direction, and formation spacing. We present an upper-body interface that maps torso inclination, hand position, and head rotation to five continuous command dimensions. Neutral postures and motion ranges are calibrated for each participant. In a within-subject study, 14 participants navigated a simulated 15-agent swarm through three-dimensional obstacle courses using this inYang Chen
  • 12
    KINO: A Keyframe Interface for VLM Planning and Whole-Body Control in Humanoid Loco-Manipulation
    Humanoid loco-manipulation requires robots to interpret task instructions and scene semantics while executing coordinated whole-body motions. We propose a hierarchical framework that uses motion keyframes as an intermediate representation between Vision-Language Model (VLM) planning and Reinforcement Learning (RL) control. Each keyframe specifies a target whole-body robot pose and, when applicable, an object pose. Given a language instruction, scene observations, and execution feedback, the VLMSitong Chen
  • 13
    Towards Interaction Regulation from Human Feedback via Free Energy Minimization
    A central challenge across control and learning is the design of mechanisms regulating the interactions between humans and autonomous agents. Inspired by the free energy principle from computational neuroscience, we introduce a control-theoretical framework to integrate human preferences online into an agent policy. We turn the framework into an open control architecture and validate our approach using a human-in-the-loop experimental testbed involving a rover navigating via onboard sensing. TheMaria Paula Diaz Monfort
  • 14
    SEAM: Submap-Anchored Evidence for Lifelong LiDAR Mapping under Trajectory Deformation
    We propose SEAM, a LiDAR-based lifelong mapping framework. Instead of relying on a single anchor spanning the entire session, SEAM generates evidence based on a trajectory optimized with submap-level anchors, and performs dynamic object removal and change detection. Through submap-level reprojection, the generated evidence remains usable even if the trajectory is subsequently modified by a new session, eliminating the need to recompute the entire process from scratch. SEAM suppresses geometricalKyuwon Kim
  • 15
    Asymptotically Optimal Multi-Robot Task and Motion Planning
    Multi-robot task and motion planning (MR-TAMP) requires jointly reasoning about discrete task decisions and continuous collision-free motions of multiple interacting robots. Although asymptotically optimal algorithms have been developed for task and motion planning, extending these guarantees to the multi-robot setting introduces an important challenge: different task transitions may involve different subsets of robots and therefore impose constraints of different dimensions on the composite conThi Thuy Ngan Duong
  • 16
    AdaGeoVLN: Selective Geometry Across Representation Depth and Navigation Time for Vision-Language Navigation
    Vision-language navigation requires aligning language with visual observations while maintaining spatial understanding over time. Geometry foundation models (GFMs) expose intermediate representations throughout their hierarchy, but how navigation policies should use these features and retain historical geometric evidence remains unresolved. We introduce \method{}, a streaming VLN framework that addresses these questions across \textbf{representation depth} and \textbf{navigation time}. HierarchiQuan-Dung Pham
  • 17
    A Convergence Framework for Deep $V$-Learning: Error Propagation and Sharp Action-Gap Bounds
    We establish convergence bounds for deep $V$-learning with horizon $H$. The algorithm fits a scalar value function to targets from executed transitions and selects actions using a predictive model and the value function. For current observed-successor targets with fresh true-kernel outcomes, the conditional mean is $\mathcal{T}^βV$, which averages over behavior-policy actions. The Bellman optimality update is $\mathcal{T} V$. We decompose the update error into six residuals: fitting, transitionYury Kolomeytsev
  • 18
    TRACER: Adaptive Multi-Robot Social Navigation via Joint Human-Response Prediction and Interaction-Aware Replanning
    Multi-robot navigation in human-shared spaces is inherently interactive: coordinated robot motions influence how nearby entities respond, while those responses provide valuable information for subsequent robot decisions. However, existing methods typically address action-conditioned prediction, multi-robot planning, or online adaptation separately, and therefore lack a unified mechanism for modeling joint robot-entity interactions and adapting future decisions from executed interaction outcomes.Lan Hu
  • 19
    Gated Residual Body-Hand Coordination for Whole-Body Humanoid Teleoperation
    Whole-body humanoid teleoperation commonly combines a motion-tracking policy with a separate dexterous-hand retargeter. However, independently generated commands do not explicitly preserve body-hand geometric relations, leading to mismatches in relative wrist poses and fingertip positions during bimanual interaction. We present a gated residual coordination framework that keeps both modules frozen and applies bounded corrections to their outputs. A motion-conditioned action gate allocates correcRuiming Wu
  • 20
    QMSR: Query-Conditioned Mask-wise Expert Routing for Robust Open-Vocabulary Underwater Object Retrieval
    Open-vocabulary object retrieval remains challenging in complex underwater environments. Although underwater image enhancement (UIE) can improve visual quality, fixed UIE strategies may even underperform the Raw representation in retrieval, indicating that enhancement should not be applied as a uniform preprocessing step. To address this problem, we propose \textbf{QMSR}, a query-conditioned mask-wise expert routing framework for underwater open-vocabulary retrieval. Specifically, QMSR selects oFuming Zhang
  • 21
    Active perception for robotic harvesting: 3D reconstruction and localisation of tomatoes hidden within clusters in a Mediterranean greenhouse
    Automating robotic harvesting in intensive agriculture within Mediterranean greenhouses requires overcoming significant challenges related to the geometric complexity of plants and occluded fruits. Although existing literature offers solutions targeting crops that grow in isolation (e.g., apples, sweet peppers, or peaches), the fundamental challenge lies in cluster-growing vegetables, where fixed sensors mounted on robotic systems fail to detect fruits hidden behind the visible surface. To addreFernando Cañadas-Aránega
  • 22
    PASSAGE: Scaling Scene-Aligned Motion Learning for Perceptive Humanoid Traversal in Cluttered Environments
    Humanoid robots can step over, squeeze past, and duck under obstacles, but learning to select and coordinate these behaviors from onboard perception remains challenging. Many existing approaches rely on task-specific reinforcement-learning objectives or curated motion libraries, making broad behavioral coverage costly. We present PASSAGE, a perception-conditioned planner--tracker framework for humanoid traversal. Using virtual reality and inertial motion capture, we collect 100 h of scene-aligneYuxuan Ma
  • 23
    Calibrated Probabilistic Obstruction Reasoning with Vision-Language Models for Grasping in Clutter
    Retrieving a target from clutter requires deciding whether to grasp the target, remove a blocker, or defer. Existing methods typically commit to a single obstruction graph or removal strategy, ignoring uncertainty across alternative scene interpretations. They also rely on miscalibrated vision-language model (VLM) predictions and can produce pairwise obstruction relations that are jointly inconsistent. Moreover, current approximations provide no guarantees about the impact of discarded hypotheseThanh-Tuan Tran
  • 24
    Toward 3D Printable Non-Planar Electroadhesive Structures for Active Anchoring
    This paper investigates multi-material 3D printing as a method to fabricate non-planar structures with 3D-printed electrode patterns for electroadhesion. We printed flat electroadhesion pads as a planar benchmark and cylindrical pads as a non-planar demonstration, with conductive interdigitated electrodes 3D printed as part of the structure. Normal-force measurements showed voltage-controlled modulation in both geometries. At 3 kV, the flat pads generated approximately 0.11-0.13 N, while the cylMostafa A. Atalla
  • 25
    WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning
    Reinforcement learning allows robots to acquire complex skills, but producing policies for geometrically complex manipulation remains difficult. A promising approach is to learn on top of collision-avoidant controllers, such as geometric fabrics. However, these approaches have relied on static, hand-specified representations of the scene. Integrating active, online 3D perception into massively parallel RL training has so far been inaccessible. We introduce a GPU-accelerated method that reconstruRemo Steiner
  • 26
    M$^3$P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation
    Multi-Modal Motion Planning (M$^3$P) requires joint reasoning over continuous motions and discrete mode transitions, making it difficult to solve efficiently. For instance, a bipedal robot may walk to a target location and then use its arms to grasp an object. This scenario captures both mode transitions and continuous dynamics, yielding feasible paths that neither purely discrete nor continuous planners can handle. While Mixed-Integer Programming (MIP) offers a principled framework, constructinXingpeng Sun
  • 27
    VLA-ULAP: Interleaving Cloud VLA Calls with Ultra-Lightweight Local Action Prediction at the Edge
    Billion-parameter vision--language--action (VLA) policies demand substantial onboard power, while communication delays in remote inference hinder timely responses. We propose VLA-ULAP, which interleaves remote VLA calls with an Ultra-Lightweight Local Action Predictor (ULAP). With approximately 7.4M parameters including the frozen vision encoder, ULAP combines current views, proprioception, and executed action history to predict chunks in one pass. Trained independently, it requires no VLA hiddeDeyu Cao
  • 28
    FIERCE: From Generalist Robot Policies to Fast Specialists via Progress-Failure Feedback
    Generalist robot policies offer useful initialization, but refining compact specialists through limited physical interaction requires informative learning feedback. We present FIERCE, a generalist-initialized reinforcement learning framework centered on a unified, task-adaptive progress-failure evaluator. Its architecture shares an observation-language representation between an observed-progress head and an action-conditioned latent predictor whose past and current predictions feed a causal sequRunjia Tan
  • 29
    From Gameplay to Policy: Towards Scalable Robot Data Collection via Gamified Robot-Free Interaction
    Learning generalizable robot manipulation policies requires large-scale and diverse interaction data, yet collecting real-world demonstrations remains costly and difficult to scale. Existing approaches to data collection are either dependent on specific robot hardware that limits crowdsourcing and transferability, or suffer from incomplete annotation and limited behavioral diversity. Inspired by how games sustain long-term human engagement, we explore an alternative paradigm that turns data collZheng Li
  • 30
    Benchmarking Visual-Inertial Odometry in Subterranean Environments Under Sensor Degradation, Miscalibration, and Dynamic Occlusion
    Visual-inertial odometry (VIO) is a core capability for autonomous operation in GPS-denied subterranean environments, yet its reliability can degrade sharply under sensor drift, calibration errors, and dynamic occlusion. Existing evaluations mainly emphasize nominal-condition accuracy, offering limited insight into when practical deployment failures occur. In this work, we present a failure-centric stress-test benchmark for VIO in underground environments using the CERBERUS dataset. We systematiYueying Zhu
  • 31
    FIVE-VLA: Fast and EffectIVE Autonomous Driving with Recurrent Action Memory
    State-of-the-art vision-language-action models (VLA) for autonomous driving face critical limitations: excessive parameter counts, inefficient high-resolution image processing, and lack of temporal memory. We introduce Fast and EffectIVE VLA (FIVE-VLA) to address these through two key contributions. First, we employ an efficient vision encoder that processes high-resolution ($448 \times 896$) images while generating only 98 tokens, over $5\times$ fewer than existing approaches, and bypass text gKemal Oksuz
  • 32
    DeformSmith: Physics Harness-Guided Hierarchical Generation of Deformable Assets for Robot Manipulation
    Creating deformable assets for robot manipulation requires jointly specifying their geometry, appearance, and physical properties. This is especially challenging for deformable objects, since text and images provide limited evidence about how they deform and respond to contact, yet these responses directly affect their suitability for interaction. Automated generation therefore needs to resolve coupled physical requirements and use interaction evidence to guide construction and refinement. We prCan Li
  • 33
    GroundingVLN: Reasoning and Acting with Grounding for Vision-Language Navigation
    Although vision-language models (VLMs) possess strong visual understanding and reasoning capabilities, existing vision-and-language navigation (VLN) agents struggle to connect semantic reasoning with spatial execution. Two coupled gaps remain in this connection, as intermediate reasoning is not explicitly anchored to visual evidence and high-level decisions lack precise spatial goals to guide low-level motion. Cognitive science suggests that human navigation bridges these levels hierarchically bKailing Li
  • 34
    DynoFluxBench: Benchmarking Kinodynamic Space-Time Planners in Dynamic Environments
    Robots that leave structured, static environments must plan motions that are kinodynamically feasible and safe among moving obstacles. However, there are no dedicated benchmark frameworks that combine both aspects. To overcome this, we present DynoFluxBench, a framework to compare kinodynamic planners in known, dynamic environments with unbounded arrival time. To demonstrate its utility and establish strong baselines, we develop three dedicated planners, named ST-Db-RRT, ST-GBRRT, and KIST, thatFranz Queißner
  • 35
    HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction
    Egocentric motion forecasting has primarily focused on hands and manipulated objects, leaving future human head motion comparatively underexplored. During manipulation, the head both redirects perception toward the target to acquire task-relevant evidence and coordinates with body and hand motion. We therefore formulate future six Degree of Freedom (6-DoF) head-motion prediction conditioned on observed hand motion and inferred target context, and propose HAP, a Hand-Driven Active Perception framYunji Feng
  • 36
    ActiveScale: Scaling Active Perception for Robots across Model, Data, and Hardware
    Active perception is essential for robotic manipulation when fixed viewpoints leave task-relevant information occluded or unobserved. However, enabling vision-language-action (VLA) models to reason across changing viewpoints and actively acquire informative observations remains challenging. We present ActiveScale, a framework that advances active perception through coordinated model, data, and hardware designs. Our model augments a VLA with historical video observations and explicit camera-poseShuai Zhou
  • 37
    InterMASH: A Unified Geometric Representation for Grasp Synthesis
    Grasp synthesis aims to generate stable and physically plausible hand--object interactions, and has become a fundamental problem in both human hand modeling and robotic manipulation. However, a unified representation across human and robotic hands is still lacking, mainly due to differences in hand morphology and surface modeling. Prior methods typically rely on either contact maps or dense implicit descriptors to represent interaction, but these representations are often incomplete or computatiXuanze Yang
  • 38
    TAO-Force: Unifying Force-Aware Perception and Fast-Slow Control for Contact-Rich Manipulation
    Vision-Language-Action (VLA) models have demonstrated strong performance across diverse robotic manipulation tasks, yet their predominantly vision-centric perception and position-controlled execution remain insufficient for contact-rich manipulation. Visual observations alone often provide limited evidence of contact onset and interaction magnitude, while position-control policies cannot respond compliantly to rapidly changing contact dynamics. To bridge both the perception and control gaps, weBohan Gan
  • 39
    ActionPiece: Rethinking Action Tokenization for Autoregressive Vision-Language-Action Models
    Action tokenizers play a central role in autoregressive vision-language-action (VLA) models, determining both the targets for policy training and the executable commands recovered from predicted tokens. Their fidelity is commonly evaluated using pointwise reconstruction metrics such as mean squared error (MSE), yet small individual errors do not fully characterize how faithfully action adjustments across demonstrations are preserved. After compression, similar actions may still cluster around aShijie Lian
  • 40
    Real-Time Bounded Catenary Solver for UAV Tether Modeling
    For non-stationary tethered multirotor UAVs in real-world conditions, simulating the forces imposed on the drone by the aerodynamic drag of the tether becomes crucial, with online use cases placing a hard bound on the maximum solve time. In previous work, a quasi-analytical catenary tether model reached a mean solve time of 0.51 ms using a general-purpose root finder, but without any worst-case guarantees or proven convergence. In this work, we reformulate the inner solver by reducing the catenaMax Beffert
  • 41
    ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects
    Ropes, cables, and other deformable linear objects appear in tasks from untangling to cable routing and suturing, yet controlling their shape remains a challenge in robot manipulation. We study model-based shape control in a general setting: the object lies unfixated on a support surface and two arms may grasp and move it anywhere along its length. Because each arm chooses a grasp point, direction, and magnitude, the joint action space is combinatorially large, and the dynamics model's per-prediTim Missal
  • 42
    VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation
    We present a hierarchical UAV navigation framework that aligns natural-language intent with dynamically feasible flight behaviors in cluttered indoor environments. To bridge the gap between abstract semantics and low-level control, we employ a parallelized ensemble of six behavior-conditioned Model Predictive Path Integral (MPPI) planners. Crucially, by designing mode-specific guiding costs and sampling biases, we induce distinct trajectory modes that converge to unique behavioral means, yieldinHanbing Zhang
  • 43
    Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving
    Safe motion planning in automated driving requires anticipating evolving traffic risks and deciding when to revise the current planned trajectory. We introduce RiskWorld, a risk-aware world modeling framework for shared occupancy forecasting and selective trajectory replacement. Spatial risk fields and temporal actor context are fused with visual bird's-eye-view features. Flow-guided evolution transports occupancy and scene features, while signed residuals correct occupancy after transport. OneRongxiang Zeng
  • 44
    WetRobo: A Reproducible Robot Kit for Coding Agents in Biological Laboratories
    Automating biological research requires general-purpose, reproducible robot systems that allow individual wet-lab researchers to delegate robot tasks without performing teleoperation or neural-network training. Vision-language-action policies have been proposed for general-purpose arms, but can lose performance when their operating environment changes. We therefore built WetRobo, a robot kit that can readily transfer between laboratories. It consists of one robot arm, laboratory equipment (an inYuna Oikawa
  • 45
    Hardware-Free Robotics Laboratories in Mixed Reality
    Teaching robotics relies on screen-based simulation, showing robot motion in an abstract coordinate frame rather than at real scale in the learner's own space, while access to physical hardware is limited by cost, safety, and scheduling constraints. We present MR-Robotics LAB, a mixed-reality (MR) platform that replays MATLAB-generated robot trajectories at real scale within the learner's physical environment. A browser-based service validates a MATLAB workspace file (.mat), normalizes units, anSantiago Berrezueta-Guzman
  • 46
    DetAug: Obstacle-Blind Trajectory Augmentation for Zero-shot Obstacle Avoidance
    Policies for robotic manipulation are produced by training on large teleoperated datasets. These datasets typically consist of free-space trajectories, making them difficult to transfer to test-time environments with obstacles. Previous methods for closing this gap have largely fallen into two groups. Dataset augmentation addresses it at training time but needs obstacle geometry in advance, whereas steering an existing checkpoint at inference time avoids that requirement but is limited in flexibReece O'Mahoney
  • 47
    DistAL: Distance-based Advantage Learning for VLA Fine-Tuning
    Vision-language-action models (VLAs) have trans- formed the field of robotic manipulation in recent years by combining the semantic understanding of LLMs with the precise control of flow-matching policies. Advantage conditioning is a recent technique that iteratively improves VLAs by training a value function on deployment data and using this to train an advantage-conditioned policy. Previous works have only applied simple, low-information success/failure rewards, which leave the value functionReece O'Mahoney
  • 48
    Decoupling Vision, Language, and Action for Efficient Multi-Task Robot Policies
    Vision-Language-Action (VLA) models attach an action module to a Vision-Language Model (VLM) with billions of parameters and pay for that backbone at every control step. For a low-level manipulation policy, this cost may be unnecessary: the VLM supplies vision and language embeddings, and recent standalone vision encoders and encoder-only language models now match or exceed large VLMs on visual embedding and language understanding benchmarks. We study this question with a controlled experiment.Xiatao Sun
  • 49
    RecMorph: Topology-Guided Spatial Recurrence for Generalized Morphology Control
    Generalized morphology control requires a single policy to transform information across limbs with different physical roles, coordinate whole-body motion, and remain efficient as body size grows. Existing communication mechanisms address these requirements only partially. We introduce RecMorph, a topology-guided spatial recurrent architecture that uses recurrent sequence computation to jointly perform cross-limb communication and representation transformation. A depth-first traversal converts thQuanrui Rao
  • 50
    GraphPoint: Semantic Entity Graphs and Point Trajectories for Compositional Robot Manipulation
    Robot manipulation policies often struggle to generalize beyond their demonstrations, even when new instructions involve familiar objects and behaviors. When language and scenes are strongly correlated during training, a policy can learn a fixed visual-action mapping rather than respond to the requested behavior. We investigate compositional reuse at two levels: within a subtask, combining familiar entities, action types, and action modifiers; and across subtasks, reusing learned subtasks in unsKang Luo
  • 51
    UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents
    Unmanned aerial vehicles (UAVs) extend embodied intelligence into continuous three-dimensional space, where perception, reasoning, physical embodiment, and action are tightly coupled through flight and environmental interaction. Recent advances in foundation models, world models, and AI agents are shifting UAV autonomy from task-specific perception and control toward systems that can interpret human intent, understand open environments, reason about physical consequences, and organize complex beYonglin Tian
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  • 172764
    白夜行
    1999年,东野圭吾41岁正值盛年,写作出道已14年,在笔力、技巧、体力和雄心上都炉火纯青,于是洋洋洒洒写出了这部鸿篇巨制《白夜行》。这种规模宏大的长篇作品在职业作家一生的创作中极为罕见,完成后基本都被视为生涯代表作。《白夜行》一经推出即成为东野圭吾的长篇小说代表作,被万千书迷视为东野圭吾作品中的无冕之·王,畅销至今。《白夜行》中文版发行量超600万册,和《嫌疑人X的献身》《恶意》《解忧杂货店》并称为东野圭吾四大杰作。东野圭吾
  • 182682
    白鹿原
    同名电视剧由张嘉译、何冰、秦海璐、刘佩琦等人主演。当代文学里程碑之作,茅盾文学奖获奖作品。一部记录“民族的秘史”的长卷,一部渭河平原50年变迁的雄奇史诗,一轴中国农村斑斓多彩,触目惊心的长幅画卷。主人公白嘉轩六娶六丧,神秘的序曲预示着不祥。一个家族两代子孙,为争夺白鹿原的统治代代争斗不已,上演了一幕幕惊心动魄的活剧:巧取风水地,恶施美人计,孝子为匪,亲翁杀媳,兄弟相煎,情人反目……大革命、日寇入侵、三年内战,白鹿原翻云覆雨,王旗变幻,家仇国恨交错缠结,冤冤相报代代不已。陈忠实
  • 191079
    桃花案:财迷孤女X清冷状元
    【欢喜冤家+玄学探案+轻松搞笑】高口碑甜宠《夺嫡》作者月落最新古言甜宠力作!当 “有仇必报小半仙”遇上“铁面无私新县令”,风水玄学大战朝堂律法。 父兄蒙难,家产百万,全家等着吃绝户? 面对虎视眈眈的亲戚,顾九桃冷笑一声,决定先下手为强。 她手握铜钱,口念法诀,不仅吓退了贪官,还顺手捡了个“铁面无私”的新任县令当靠山。 本以为这位裴县令是个不通人情、只认律法的木头,没想到...... “顾小姐,本官不信鬼神。” “哦?那大人信我吗?” “.……信。” 为了赢下三个月内让棺材铺盈利的赌约,顾九桃开坛做法、画符捉鬼,将生意做得风生水起。 却不想,一桩河堤贪腐案牵扯出数十条人命...... 待硝烟落定,桃花再开时,她只愿与他,守一间小店,换岁岁安稳。月落
  • 201485
    素食者【2024诺贝尔文学奖得主】
    为了逃避来自丈夫、家庭、社会和人群的暴力,她决定变成一棵树。在英惠的丈夫郑先生的眼中,“病”前的英惠,是个再普通不过的女子:不高不矮的个头、不长不短的头发,相貌平平,着装一般,温顺、平淡、文静。正如他所希望的那样,英惠完美地扮演了平凡妻子的角色——料理家务,伺候丈夫,就像千千万万的传统妇女一样。[韩]韩江
6天前更新
  • 01
    Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design
    Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable programmatic oracle. We introduce a continual adaptation framework in which a frozen frontier model operates professional design software through more than 230 tools, while an external procedural memory of natural-language skills accumulates and refines reusable design procedures from experience. The memory widens by acquiringHongyang Du
  • 02
    MintAct: A Unified Visual Agent for Digital Environments
    We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use, trained at 2B, 4B, and 8B scales. Through careful design of our environments, data, and training recipes, MintAct models match the performance of per-domain specialists across all of these capabilities. To enable this, we develop a scalable environment and reinforcement learning (RL) infrastructure. On the environment side, we host hundredsMingfei Gao
  • 03
    OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation
    Reference-to-video (R2V) generation is evolving toward increasingly general and versatile reference control, giving rise to the emerging paradigm of omni R2V generation. However, existing benchmarks fall short of these emerging capabilities: their test cases cover limited reference types and compositions, and their evaluation protocols largely assess holistic reference consistency, overlooking whether reference factors are properly preserved, disentangled, and routed. Meanwhile, the high cost ofWenxue Li
  • 04
    Traffic Sign Recognition for Autonomous Driving Using Branched YOLOv2 and Geometric Features
    Traffic sign recognition (TSR) is an important perception task for autonomous driving and advanced driver-assistance systems, where a system must both localize traffic signs and determine their semantic classes efficiently. This work presents a TSR system based on YOLOv2 for simultaneous detection and classification. Two complementary modifications are studied. First, YOLOv2 is extended with intermediate prediction layers, forming a branched architecture that can terminate inference early for eaArefeh Rezaei
  • 05
    PRIME: Perception Feedback with Situational Memory Embeddings in VLA Models
    Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--planning hierarchy. While modern architectures maintain temporal recurrence within the perceptual module, early perception remains blind to downstream reasoning and navigation goals, processing visual inputs agnostically without prioritizing cues informed by prior decisions. To bridge this gap, this paper introduces PRIME, a learned feedback mechaniErik Deinzer
  • 06
    GALA: Geometry-Aware Latent Action Modeling for Vision-Language-Action Model Pretraining across Embodiments
    Learning large-scale vision-language-action (VLA) models from multi-embodiment datasets remains challenging due to heterogeneous action spaces across end effectors. Although latent action models (LAMs) can learn embodiment-agnostic action representations from diverse video data, existing image-based LAMs often fail to capture fine-grained end-effector articulation, particularly finger-level geometric changes in human and dexterous robot hands. To address this limitation, we propose GALA, a GeomeYichen Liu
  • 07
    Info3R: Information-Adaptive Test-Time Training for 3D Reconstruction
    Transformer-based models have recently achieved strong performance on 3D reconstruction from images, and recent works extend them to process video streams in an online manner for real-world deployment. However, existing methods overlook two key signals when handling long image streams: the importance of each incoming frame and the information saturation of the model's internal state. In this paper, we propose Info3R, a novel information-adaptive test-time training method for the online 3D reconsSunghyun Baek
  • 08
    The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds
    Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomass estimation. Existing segmentation methods typically exclude radiometric features (e.g., intensity, return number) to maximize cross-sensor compatibility. We challenge this design choice by evaluating cross-site and cross-platform generalization: training on the public Heidelberg dataset (terrestrial TLS, 1550nm) and testing on a novel dataset frRoman Kaharlytskyi
  • 09
    Catena: A Comprehensive Software Suite for Large-Scale Connectomics
    The gold standard datasets for mapping connectomes are electron microscopy volumes of densely labeled neural tissue at nanometer resolution. Yet reconstructing and proofreading neuronal arbors and annotating all synapses requires pipelining multiple software tools that are often fragmented, inconsistently maintained, or proprietary, hindering reproducibility and automation. Here, we introduce Catena, an open-source, comprehensive, developer-centric software suite for connectomics that integratesSamia Mohinta
  • 10
    Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognition
    Vision-Language Models (VLMs) have recently been proposed as promising tools for face recognition, as they can produce natural language explanations alongside similarity scores. This capability is considered appealing for face comparisons in forensic contexts, which require decisions to be transparent and auditable. However, existing evaluations of VLMs for that use case focus mostly on recognition accuracy, while the validity of generated explanations remains unquantified. In this work, we intrLaurent Colbois
  • 11
    Chronosphere: Space-Time Tessellation of Local Climate Experts
    We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate. A central challenge in geographic representation learning is modeling environmental processes whose spatial and temporal complexity varies widely. Yet existing location encoders typically fix a single level of detail everywhere. Global bases such as spherical harmonics spread capacity uniformly across space and time. Localized bases resolve only predefined regions. Learned tessellations adapt, but aDaniel Cher
  • 12
    Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support
    Wafer map defect recognition is commonly formulated as a fixed-taxonomy classification problem that assigns each wafer to a single defect class. However, some wafers exhibit morphologies near class boundaries, for which forcing a single prediction may be less informative than providing plausible diagnostic alternatives. This paper proposes a morphology-aware ambiguity learning framework that supports three diagnostic actions: automatic single-class diagnosis, assisted diagnosis with two plausiblSeungjun Chu
  • 13
    The Weight Is Over - Interactive Diffusion on Consumer GPUs
    On-device inference is booming, but the momentum is almost all in language models. Diffusion pipelines are memory hungry, latency-sensitive, and require orchestrating an embedder, a transformer, a decoder, and often further postprocessing that is not as standardized as LLM inference loops are. We navigate the trade-off between performance, quality, and model footprint to reach as many client devices in the wild as possible. We make three contributions: an embedding translator that maps a small tFrieder Ganz
  • 14
    Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty
    While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limited by annotation incompleteness. Across four widely used datasets (COCO, Pascal VOC, Cityscapes, KITTI), re-annotation reveals substantial increases in annotated objects (e.g., up to +60% on KITTI and +40% on COCO), driven primarily by previously unlabeled small, occluded, or densely packed instances. While some differences arise from dataset-spSarina Penquitt
  • 15
    How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing
    In classification-oriented adaptive sensing, posterior samples characterize uncertainty at the current measurement state and can serve two roles: they may guide the next sensing direction, while their class labels provide votes for the candidate classes and determine whether sensing should continue. We focus on the stopping layer that turns these votes into a declaration, without modifying the posterior sampler or sensing directions. A natural plug-in rule declares when the observed vote share eVincent Corlay
  • 16
    Classification-oriented adaptive sensing via posterior sampling
    Recent advances in diffusion models have enabled high-performance, instance-adaptive compressed sensing through posterior sampling, without task-specific policy training. Existing methods select sensing probes by maximizing total posterior signal variance and are therefore primarily reconstruction-driven. We introduce a classification-driven extension motivated by the closed-form posterior covariance of a class-conditional Gaussian mixture model, which decomposes into within-class and between-clAndriy Enttsel
  • 17
    MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention
    Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity. To address these challenges, we propose MIST, multimodal survival prediction with genomic-guided histology attention. MIST represents genomic features as tokens and allows them to query compact foundation-model-derived histology context tokens before survival prediction. This desMuhammet Sami Yavuz
  • 18
    VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph
    Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visual methods rely on appearance and become unreliable under these changes; localizing one frame at a time from object classes and geometry instead leaves sparse, ambiguous evidence. We introduce VideoReloc, whose adaptive clips use odometry to gather spatial evidence until object and motion criteria are met, adapting query length to the observed sceQianru Li
  • 19
    A Principled Approach to Unsupervised Anomaly Detection
    Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of the anomalies. Yet the nature of an anomaly is often as important as its presence. We reformulate UAD as a Bayesian inverse problem, in which the objective is to infer the most probable corruption responsible for each observation. Our framework yields a probabilistic anomaly score as the energy of the inferred corruptJames Myles
  • 20
    PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection
    3D object detection from LiDAR point clouds faces a fundamental dilemma: voxel-based methods achieve efficiency at the cost of geometric quantization, while point-based methods preserve fidelity but suffer from prohibitive computational bottlenecks. Specifically, point-based architectures are crippled by slow downsampling strategies (e.g., FPS) and expensive dynamic neighbor queries (e.g., k-NN) coupled with costly continuous interactions. To tackle these systemic inefficiencies, we propose PoinXuanming Shang
  • 21
    XCalib Depth-Guided Geometric Optimization for Dense Thermal-Visible Video Registration
    Image registration is a vital preprocessing step in multimodal perception tasks, including image fusion, object detection, and semantic segmentation. In Advanced Driver- Assistance Systems (ADAS), spatial misalignment between visible (RGB) and infrared (IR) cameras -caused by non-coincident optical axes and field-of-view differences- introduces non-uniform parallax and visual ghosting. Classical keypoint-based methods are restricted to global homographies that fail under dynamic depth, while uncAurelien Godet
  • 22
    Beyond Benchmark Scores: Auditing Medical Vision-Language Models for Chest X-Ray Tuberculosis Screening
    A medical model's benchmark score does not establish that the same conclusion holds under a different evaluation. This study tests whether claims about model ranking, score reliability and screening performance survive changes in cohort, prompt, negative spectrum, specified prevalence and operating threshold. We audit three medical vision-language models (BioMedCLIP, CheXficient, and MedSigLIP) and a general-domain OpenCLIP comparator on 12,200 chest radiograph records from four datasets (MontgoMushir Akhtar
  • 23
    SFVO: Decoupled Confidence-Guided Stereo-Flow Visual Odometry with Bidirectional PnP
    Deep learning-based visual odometry (VO) has achieved significant progress, yet most existing methods focus on a monocular approach, which suffers from scale ambiguity. Stereo VO provides real metric by its nature, but remains less studied in deep learning VO due to its high computational cost and modeling complexity. Recent advances in stereo matching and optical flow estimation have made dense visual correspondence increasingly accurate and reliable, but their complementary geometric informatiKai Zhang
  • 24
    Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation
    Entropy minimization is a standard objective for test-time adaptation (TTA), but it can fail in imbalanced binary segmentation. Unlike image classification, dense segmentation aggregates thousands of pixel predictions, allowing the larger predicted class to dominate the update, pull minority predictions toward itself, and produce a degenerate mask as predictions saturate and their entropy gradients vanish. We theoretically establish this collapse in a shared-shift model. This analysis motivatesZhengshan Wang
  • 25
    ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation
    Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three unresolved requirements: faithfully reproducing a mixed fisheye-pinhole rig at native resolutions; reconciling causal, per-timestep interaction with long-horizon stability and low latency; and preserving scene identity when a location is revisited. We present ZYT-World, a single architecture that natively generates foBoni Hu
  • 26
    SignGPT: Toward LLM-Mediated Sign Language Interaction through Gloss-Free Translation and Generation
    Large language models (LLMs) provide limited support for sign language interaction. Unifying sign language translation (SLT) and generation (SLG) to enable sign language as both input and output can reduce switching between separate models during sign-text interaction. We present SignGPT, a unified, pose-based framework for gloss-free SLT and SLG. SignGPT integrates part-aware hierarchical representations of body, hand, and facial motion into a shared language model and employs asymmetric multi-Ronghui Li
  • 27
    Diffusion-Based Tumor Inpainting for Renal Segmentation under Clinical Data Scarcity
    Deep learning segmentation of renal tumors requires large annotated datasets, yet clinical deployments typically offer only a handful of tumor-positive cases from the target site. We propose a diffusion-based inpainting framework that synthesizes anatomically plausible renal tumors within healthy CT scans, requiring no additional annotation, and provide the first systematic comparison of 2D, 2.5D, and full 3D (MAISI) synthesis strategies for this task. Training the diffusion model on public dataEkaterina Sedykh
  • 28
    Listen Before You Speak: Response Planning from Listener Facial Reactions for Conversational Speech Generation
    Conversational speech depends on dialogue context and the listener's immediately preceding behavior. We propose ReACT-TTS, a two-stage framework that uses a one-second pre-response listener facial sequence to plan the next utterance's emotion and prosody before speech realization. On a strict dyadic MELD protocol, Temporal conditioning yields higher mean macro-F1 and VAD concordance than Text-only across ten seeds, while accuracy remains essentially unchanged. Ablations show that temporal modeliYunji Chu
  • 29
    DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning
    Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural-language expression space. Consequently, they inherently incur excessive linguistic overhead, leading to information dilution and weak visual grounding. To address this challenge, we propose Dense Reasoning Trace (DRT), a paradigm that departs from natural-language-centered CoT by expressing reasoning as compact structured traces, which include cWan Xu
  • 30
    Configurable Multi-Stage Vision Pipeline for Crop Disease and Pest Diagnosis
    Farmer.Chat is Digital Green's farm advisory service for smallholder farmers. When something looks wrong with a crop, the farmer takes a photograph and sends it, and that photograph is the whole question: no symptom described, no crop named, often no text at all. The service has to determine whether the picture can be used, what crop it shows, and what is wrong with it, from images taken on cheap phones in a field, in poor light and with a moving camera. The system doing this today cannot be adjNaga Ganesh
  • 31
    Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images
    Multi-Channel imaging (MCI) data differs fundamentally from natural images, as each channel records a semantically distinct signal rather than a colour band. To adapt vision encoders to MCI data, Multi-Channel Vision Transformers (MC-ViTs) tokenize each channel independently and concatenate the resulting tokens into one sequence, and the channel count is no longer fixed by the architecture. Self-attention is then computed across all channel-patch tokens with no restriction on which channels atteUmar Marikkar
  • 32
    Detection is solved, delineation is not: what governs tooth segmentation on panoramic radiographs
    Automatic tooth segmentation and FDI numbering on panoramic radiographs underpins computer-assisted dental diagnosis, yet which factors govern performance remains unclear. We assemble a corpus of 1,422 panoramic radiographs containing 42,142 expert-delineated tooth polygons across the 32-class FDI taxonomy, annotated by 30 dental practitioners and independently reviewed by two others, and use it to isolate input resolution, architecture and anatomical priors under a single evaluation protocol. FMuhammad Rehan
  • 33
    Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video
    Problematic internet use affects a growing share of the population, yet common interventions, e.g., time limits, blocking, forced breaks, are coercive and easily circumvented. We explore a less restrictive alternative: adapting the emotional intensity of visual content. Prior work has shown that optimization can steer an image's affective content, but its per-image optimization cost makes it impractical for real-time deployment. We instead learn a model that predicts this transformation in a sinMusa Rochi
  • 34
    HAT: Hypothesis-Anchored Tracking for Video Monocular Spacecraft Pose Estimation
    Monocular 6-DoF pose estimation of non-cooperative targets is important for on-orbit servicing and debris removal. A single-image estimator can confuse near-symmetric spacecraft orientations, and tracking can preserve an incorrect pose. We present Hypothesis-Anchored Tracking (HAT), a causal framework that uses inter-frame motion to select among competing CAD-based pose hypotheses before alignment and fusion. Rather than independently choosing the highest-scoring hypothesis in each image, HAT reAndré Lopo
  • 35
    Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction
    In-context learning using Large Language Models (LLMs) offers a compelling path to training-free post-OCR correction, yet its effectiveness for Devanagari script remains entirely unexplored. We present the first systematic evaluation of LLMs (3B-32B) for post-OCR correction in Hindi and Marathi, comparing three in-context example retrieval strategies: domain-random selection, dense semantic retrieval, and our proposed CharBM25, which retrieves examples by character n-gram BM25 similarity over OCAbhishek Bhandari
  • 36
    A benchmark dataset and baseline methods for four-dimensional STEM diffraction patterns
    Four-dimensional scanning transmission electron microscopy (4D-STEM) records a two-dimensional diffraction pattern at each electron-probe position, yielding spatially resolved reciprocal-space information but large, heterogeneous data volumes. Here we describe 4D-ImageNet, a collection of 174,000 diffraction patterns comprising 145,000 experimental patterns selected from 29 acquisitions and 29,000 multislice simulations. The experimental data cover acquisition-level labels for Ag, Au, mixed Au-AYuyan Guan
  • 37
    GestureFAR: Streaming Co-Speech Gesture Generation with Flow Autoregression
    Generating natural co-speech gestures from streaming speech is essential for embodied conversational agents, where motion must be produced while a user is still speaking. Recent streaming gesture systems make online generation possible by autoregressing over discrete motion tokens, but this design compresses high-dimensional continuous motion into finite codebooks and can limit the realism and diversity of generated gestures. To preserve both causality and continuous expressiveness, we propose \Pinxin Liu
  • 38
    From Retrieval to Recognition:How Vision--Language Models Become OCR Specialists
    Does a general vision--language model acquire specialized OCR ability by developing a new reading circuit or by reusing an existing mechanism? We address this question in the setting of full-sequence OCR, rather than local-answer retrieval. Using an evidence-grounded protocol with held-out causal interventions, we identify sparse and stable OCR-head sets in GLM-OCR, MinerU2.5, and PaddleOCR-VL-1.6. We then investigate the mechanistic origin of these OCR heads by comparing them with independentlyYuanxiang Huangfu
  • 39
    Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification
    Multi-label few-shot learning (MLFSL) remains a significant challenge in medical image analysis (MIA). Current metric-based meta-learning methods face two critical limitations in MIA. First, conventional prototype generation often entangles irrelevant disease information, leading to contaminated prototypes and degraded performance. Second, prior studies typically enforce inter-class separability in embedding space, largely neglecting the inherent correlations among diseases. To overcome these chYing-Chih Lin
  • 40
    Refine Then Fusion: Training-Free 3D Point Cloud Adaptation with Priority Refinement and Multi-Modal Knowledge Fusion
    Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representations in few-shot scenarios is limited by two fundamental challenges: High-dimensional features often contain substantial channel redundancy and task-irrelevant noise, while the reliability of different modalities varies across samples. Consequently, direct aggregation of heterogeneous representations overlooks sample-dependent modality reliability anHang Cheng
  • 41
    VidOmni-Bench: A Benchmark for Fine-Grained Video Understanding via Spatio-Temporal Event Verification across Complexity and Duration
    While Video Large Language Models (Video-LLMs) have recently demonstrated strong performance, reliably evaluating their fine-grained video understanding remains challenging. Existing benchmarks often rely on question answering or ground-truth caption matching, where models may succeed through superficial cues and incomplete annotations. To this end, we introduce VidOmni-Bench, a benchmark that requires models to verify whether each event in dense video captions is supported by the video. VidOmniChangbeen Kim
  • 42
    2D GauSS-MI: Efficient Active Scene Reconstruction with Balanced Visual and Geometric Quality
    Active reconstruction requires efficient active view selection to achieve high-quality reconstruction within limited onboard computational resources. Existing methods face challenges in adequately balancing visual and geometric quality with the computational efficiency required for real-time operation. In this work, we present an active reconstruction framework based on 2D Gaussian Splatting (2DGS). We develop an efficient online 2DGS mapping pipeline for incremental RGB-D observations and introYuhan Xie
  • 43
    2nd Place Solution to the HANDS 2026 Workshop Challenge-Dexterous Grasp Motion Track: Single-Shot Trajectory Warping for Grasp Motion Generation
    This report describes our 2nd place solution to the HANDS 2026 workshop challenge (Dexterous Grasp Motion track) in conjunction with ECCV 2026. In this challenge, we address grasp motion generation for the 12-DoF LinkerHand O6, aiming to produce physically plausible reach-and-lift trajectories for unseen objects from randomized initial hand poses in simulation. This task is particularly challenging because each grasp requires a per-step policy to make approximately $70$ twelve-dimensional decisiMuneeb A. Khan
  • 44
    Adaptive World Memory 3D Foundation Model for Scalable 3D Mapping, Localization, and Rendering
    Recent 3D foundation models enable generalizable geometric reasoning from RGB images but remain limited in persistent memory, scalability, and renderable scene modeling. We present a memory-centric 3D foundation model for scalable robotic localization, reconstruction, and Gaussian rendering. Its core is an adaptive world memory mechanism that combines transformer-based gated updates with test-time temporal-spatial regulation. Learned gates control recurrent memory propagation, while temporal staTianchen Deng
  • 45
    VoxelTTO: Voxel-Aligned Feed-Forward 3D Gaussian Splatting with Test-Time Optimization
    Recent feed-forward 3D Gaussian Splatting (3DGS) methods typically regress pixel-aligned Gaussian primitives, often causing excessive overlap and artifacts, while inaccuracies in predicted camera poses can lead to misalignment in novel-view synthesis (NVS). We present VoxelTTO, a feed-forward framework for reconstructing geometrically accurate 3DGS scenes from an arbitrary number of images and optional camera parameters. VoxelTTO aggregates dense image features into a global voxel representationYibin Zhao
  • 46
    OpenSAL360: Open-Source Crowdsourcing Platform for Omnidirectional Video Saliency Collection
    Omnidirectional video saliency prediction plays an important role in many immersive multimedia applications, including viewport-adaptive streaming and compression, foveated rendering, mesh simplification, perceptual quality assessment. Yet progress in this area remains constrained by the cost and complexity of collecting eye-tracking data with VR headsets, which makes large-scale dataset creation difficult to extend. We present OpenSAL360, the first open-source platform for scalable, low-cost 36Alexey Bryncev
  • 47
    MT-WAM: Reorienting the One-Pass Predictive Representation Toward Action Generation
    Fast-WAM shows that video-action co-training improves control without generating future video at inference, making the representation from a single video diffusion Transformer forward central to action generation. However, future-observation prediction does not explicitly prioritize the future dynamics and visual structure needed for control. We present MT-WAM, which retains the original training objectives and adds complementary supervision for future two-dimensional point trajectories and visuYiguang Yang
  • 48
    SkillIR: Evolving Scene-Aware Skills for Agentic Image Restoration
    This paper studies agentic image restoration, in which multimodal agents coordinate specialized restoration tools to recover images affected by complex degradations. Existing restoration agents often derive complete tool-use plans from the original degraded image or retrieve previously successful trajectories, providing limited support for adapting individual actions to evolving intermediate restoration states. We find that accepted tool executions can change the residual degradation state and,Jie Shao
  • 49
    PSEE: Progressive Sensor Event Expansion for Point-Supervised Temporal Action Localization
    Temporal action localization (TAL) in wearable sensor streams identifies action classes and temporal boundaries, enabling finer-grained activity understanding than conventional action recognition. However, training typically requires costly start--end annotations for every action instance. To reduce this burden, we study point-supervised TAL, where each instance is labeled with only one timestamp and its class. We propose Progressive Sensor Event Expansion (PSEE), which combines semantic activatJiaxi Yin
  • 50
    CompAdapt: Adaptable Composite Motion Modeling for Physics-Consistent Text-to-Video Generation
    While diffusion-based text-to-video (T2V) models have demonstrated impressive capability in generating realistic and temporally coherent videos, they often fail to respect fundamental physical dynamics. Although recent physics-constrained methods incorporate explicit dynamics priors to improve physical plausibility, they remain limited to simple single-type motions, depend on manually specified parameters, and struggle to generalize to unseen physical laws. In this work, we propose CompAdapt, aHaoran Qin
  • 51
    ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling
    World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future visual states. Tactile sensing complements this foundation with direct measurements of physical interaction. Some existing methods use tactile features as conditioning inputs without jointly predicting future tactile states, visual observations, and actions. Our key insight is that tactile signals, like video, provide observations of the evolving worXuancheng Zhang
4小时前更新
  • 019053
    三体全集(全三册)
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    长安的荔枝(同名影视原著)
    真人有声书已上线,点上方“听”→“真人有声版”可收听。 大唐天宝十四年,长安城小吏李善德突然接到一个任务:要在贵妃诞日之前,从岭南运来新鲜荔枝。荔枝保鲜期只有三天,而岭南距长安五千余里,山水迢迢,这是个不可能完成的任务。为了家人,李善德只得放手一搏…… 古装版社畜求生记,帝国夹缝中的小人物史诗。马伯庸
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  • 042649
    活着
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    平凡的世界(全三册)
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  • 06973
    追风筝的人(珍藏纪念版)
    全球现象级畅销书,快乐大本营高圆圆感动推荐,窦靖童创作灵感的来源,奥巴马送给女儿的新年礼物。十周年珍藏纪念版,特别收入新版前言和阿富汗摄影别册。为你千千万万遍! “许多年过去了,人们说陈年旧事可以被埋葬,然而我终于明白这是错的,因为往事会自行爬上来。回首前尘,我意识到在过去二十六年里,自己始终在窥视着那荒芜的小径。” 12岁的阿富汗富家少爷阿米尔与仆人哈桑情同手足。然而,在一场风筝比赛后,发生了一件悲惨不堪的事,阿米尔为自己的懦弱感到自责和痛苦,逼走了哈桑,不久,自己也跟随父亲逃往美国。 成年后的阿米尔始终无法原谅自己当年对哈桑的背叛。为了赎罪,阿米尔再度踏上暌违二十多年的故乡,希望能为不幸的好友尽最后一点心力,却发现一个惊天谎言,儿时的噩梦再度重演,阿米尔该如何抉择? 小说如此残忍而又美丽,作者以温暖细腻的笔法勾勒人性的本质与救赎,读来令人荡气回肠。卡勒德·胡赛尼
  • 072760
    杀死一只知更鸟(同名电影原著)
    《杀死一只知更鸟》的故事发生在大萧条时期美国南方一个静谧的小镇,几桩离奇的疑案彻底打破了几个孩子平静的生活:事件的真凶,怪人的谜底,传言背后的真相……在父亲的指引下,他们在迷雾中寻找真知,在磨难中历练风度,在不公平中积累正气,经历了暴风骤雨般的成长,也感受了人间的温暖与真情。哈珀·李
  • 081884
    太白金星有点烦
    天庭神仙皆社畜,西游路上打工人。 真人有声书已上线,点上方“听”→“真人有声版”可收听。 太白金星李长庚最近有点烦。天庭和西天联合推出了“西天取经”的重大项目,他受命策划九九八十一难,确保唐僧能安全走完流程,平稳取经。老神仙本以为一切尽在掌控中,谁知天大的麻烦才刚刚开始:费用报销、工作汇报、人事安排、各路大仙塞来的条子、各地妖怪暗藏的心思,捋不出的千头万缕,做不完的繁杂琐事……当大闹天宫的真相重新浮出水面,牵扯出无数因果,李长庚发觉自己成就金仙的道路越加渺茫。马伯庸
  • 09485
    南京大屠杀
    1937年12月,日军攻入中国古都南京。几周之内,30多万中国平民和士兵遭到有计划地强暴、折磨和屠杀——死亡人数超过广岛和长崎原子弹爆炸遇难人数的总和。 张纯如不仅在书中详述了日军疯狂暴行的细节,而且分析了在军国主义文化背景下成长起来的日本士兵对人类生命的漠视。张纯如对南京大屠杀的幸存者进行了大量采访,并首次发掘了许多重要文献。全书先是从日本士兵、军官为什么完全脱离了人类基本的行为规范,日本学校和教科书从心理层面向学生灌输对中国人民的仇恨和蔑视,以及高度军事化的教育体制等多个方面阐述了南京大屠杀发生的根源性原因。张纯如
  • 102643
    白鹿原
    同名电视剧由张嘉译、何冰、秦海璐、刘佩琦等人主演。当代文学里程碑之作,茅盾文学奖获奖作品。一部记录“民族的秘史”的长卷,一部渭河平原50年变迁的雄奇史诗,一轴中国农村斑斓多彩,触目惊心的长幅画卷。主人公白嘉轩六娶六丧,神秘的序曲预示着不祥。一个家族两代子孙,为争夺白鹿原的统治代代争斗不已,上演了一幕幕惊心动魄的活剧:巧取风水地,恶施美人计,孝子为匪,亲翁杀媳,兄弟相煎,情人反目……大革命、日寇入侵、三年内战,白鹿原翻云覆雨,王旗变幻,家仇国恨交错缠结,冤冤相报代代不已。陈忠实
  • 111292
    一个叫欧维的男人决定去死(同名电影原著)
    认识一下欧维。他59岁,脾气古怪,嫌东嫌西,带着坚不可摧的原则、每天恪守的常规以及随时发飙的脾性在社区晃来晃去,被背地里称为“地狱来的恶邻”。他每天一大早就四处巡视,搬动没停进格线的脚踏车,检查垃圾是否按规定分类,抱怨谁家的草坪还不修剪,诅咒那只掉了毛的流浪猫。没完没了。他想自杀。直到一个十一月的早晨,当一对话痨夫妇和他们的两个话痨女儿搬到隔壁,不小心撞坏了他的邮筒……看哭全北欧的瑞典票房冠军电影同名原著小说,韩国治愈系男神池昌旭温情推荐。弗雷德里克·巴克曼
  • 125075
    置身事内:中国政府与经济发展
    本书是复旦大学经济学院教授兰小欢多年教学与研究内容的凝练,将经济学原理与中国经济发展的实践有机融合,以地方政府投融资为主线,深入浅出地论述了中国经济的发展,广泛采纳各领域学者全新研究成果。全书分上下两篇。上篇解释微观机制,包括地方政府的基本事务、收支、土地融资和开发、投资和债务等;下篇解释这些微观行为与宏观现象的联系,包括城市化和工业化、房价、地区差异、债务风险、国内经济结构失衡、国际贸易冲突等。最后一章通过对中国政治经济体系的论述,作者简明地刻画了地方政府进行经济治理的基本方式,指出中国政府通过深度介入工业化和城市化的进程,在发展经济的同时逐步推动了市场机制的建立和完善。兰小欢
  • 132758
    白夜行
    1999年,东野圭吾41岁正值盛年,写作出道已14年,在笔力、技巧、体力和雄心上都炉火纯青,于是洋洋洒洒写出了这部鸿篇巨制《白夜行》。这种规模宏大的长篇作品在职业作家一生的创作中极为罕见,完成后基本都被视为生涯代表作。《白夜行》一经推出即成为东野圭吾的长篇小说代表作,被万千书迷视为东野圭吾作品中的无冕之·王,畅销至今。《白夜行》中文版发行量超600万册,和《嫌疑人X的献身》《恶意》《解忧杂货店》并称为东野圭吾四大杰作。东野圭吾
  • 14524
    我们仨
    1998年,钱钟书逝世,而他和杨绛唯一的女儿钱瑗已于此前(1997年)先他们而去。在人生的伴侣离去四年后,杨绛用心记述了他们这个特殊家庭63年的风风雨雨、点点滴滴,结成回忆录《我们仨》。杨绛
  • 151.0万
    认知觉醒:开启自我改变的原动力
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  • 16198
    小王子
    3D动画奇幻电影《小王子》中文配音版于2015年10月16日中国上映!这是一本足以让人永葆童心的不朽经典,被全球亿万读者誉为最值得收藏的书。翻开本书,您将看到遥远星球上的小王子,与美丽而骄傲的玫瑰吵架负气出走,在各星球漫游中,小王子遇到了傲慢的国王、酒鬼、惟利是图的商人,死守教条的地理学家,最后来到地球上,试图找到治愈孤独和痛苦的良方。这时,他遇到一只奇怪的狐狸,于是奇妙而令人惊叹的事情发生了…… 《小王子》犹如透亮的镜子,照出了荒唐的成人世界。她在提醒我们,只有爱,才是最高的哲学,才是我们活下去的唯一理由。[法]安托万·德·圣埃克苏佩里著
  • 174800
    我与地坛
    要是有些事我没说,地坛,你别以为是我忘了,我什么也没忘;但是有些事只适合收藏,不能说,不能想,却又不能忘。《我与地坛》是史铁生文学作品中,充满哲思又极为人性化的代表作之一。其前两段被纳入人民教育出版社的高一教材中。前两部分注重讲地坛和他与母亲的后悔,对中学生来说,这是一篇令人反思的优秀文章。史铁生
  • 181278
    绝叫(同名日剧原著)
    罗翔推荐。长达四十年的恶女编年史,被网友称为“被嫌弃的松子的黑化”、“更现实版的《都挺好》”。铃木阳子死了,死在独居的公寓里。正确说来,是铃木阳子几个月前死了。因为发现她时,遗体不但遭到屋内的十一只猫啃食殆尽,连猫也全数饿死了。铃木阳子显然是“孤独死”的最佳范例,但这名女子为何落到这步田地?她的亲人、朋友、同事在哪里?她的人生轨迹又是怎样的?本书以两起相对独立的死亡事件为线索,牵出了三起相互交织的案件,讲述了女主人公阳子在时代裹挟中的个人挣扎,从平庸一步步滑落到不可救药的深渊。故事发生在2014年的日本都市。本书分为三条线:第一条线是“中年女子孤独死”案,从发现女尸开始,叙述女警绫乃的调查过程,逐渐揭露女主人公阳子的人生。第二条线从“非营利组织头目死亡”案开始。看似无关的两个案件逐渐交会,交织出真相。第三条线以第二人称叙述阳子的人生经历,从阳子童年时期开始,到女尸被发现为止,细腻地描写了阳子的成长变化和被社会吞噬的过程。叶真中显
  • 191450
    献给阿尔吉侬的花束
    很多人都笑我。但他们是我的朋友我们都很快乐。 声称能改造智能的科学实验在白老鼠阿尔吉侬身上获得了突破性的进展,下一步急需进行人体实验。个性和善、学习态度积极的心智障碍者查理·高登成为最佳人选。 查理对实验只有模糊的了解,但他知道自己想变聪明,想要受重视,爱人和被爱。 手术成功后,查理的智商从68跃升为185,那些从未有过的情绪和记忆也逐渐浮现。丹尼尔·凯斯
  • 203853
    蛤蟆先生去看心理医生
    蛤蟆先生一向爱笑爱闹,如今却一反常态地郁郁寡欢。他一个人躲在屋里,连起床梳洗的力气都没有。朋友们非常担心他,建议他去做心理咨询。在10次心理咨询中,蛤蟆在咨询师苍鹭的带领下,勇敢地探索了自己的内心世界,也逐渐找回了信心与希望……为了向大众读者普及心理学知识,作者借用了英国文学经典《柳林风声》的故事主角,让蛤蟆先生和他的朋友们再次登场,演绎了这个关于心理咨询的故事。读者犹如亲临现场,体验心理咨询的每一个细节,见证疗愈和改变的发生。作者借由蛤蟆和心理咨询师苍鹭的互动,探索了蛤蟆自卑、软弱、爱炫耀的个性与抑郁的情绪究竟来源于何处,以及如何才能在心理上真正长大成人,独立、自信、充满希望地生活。罗伯特·戴博德
10天前更新
  • 01
    Leader-Follower Formation Control with Prescribed Convergence Rates under Bearing Persistence of Excitation
    This paper addresses leader-follower formation control using only relative bearing and velocity measurements. Bearing-based leader-follower control strategies commonly use fixed control gains, for which the guaranteed convergence rates explicitly depend on the persistence of excitation (PE) properties of the desired formations. We propose a time-varying matrix gain that evolves according to the bearing information available to each follower and decouples the convergence rate from the PE bound. WTarek Bouazza
  • 02
    Securing quantum error correction against misleading advice from AI agents
    Can an attacker turn influence over an artificial intelligence (AI) adviser into a harmful quantum error-correction update? We identify an ambiguity in passive syndrome records that obstructs recovery selection, then show how additional calibration measurements support certified recovery updates under uncertainty and drift. In an odd-distance square toric code with error-free preparation, syndrome measurements, and recovery operations, opposite coherent $X$ rotations produce identical passive syA. Barış Özgüler
  • 03
    Trajectory Manifolds for Nonlinear Data-Enabled Predictive Control
    This note establishes a geometric foundation for trajectory-manifold representations of deterministic nonlinear systems in a behavioral setting motivated by data-enabled predictive control. For a discrete-time system $x_{k+1}=f(x_k,u_k)$ with measured state and a $C^r$ transition map, $r\geq 1$, we consider the terminal-state-augmented finite-horizon behavior consisting of all admissible state-input trajectories over a prediction horizon $N$. We prove that this behavior is a $C^r$ embedded submaArda Bayer
  • 04
    On asymptotic stability of the time-varying Kalman filter for unstabilizable linear systems: an optimization perspective
    This paper establishes the necessary and sufficient conditions for asymptotic stability of the time-varying Kalman filter applied to a linear time invariant system with semidefinite initial state covariance and positive definite process and measurement noise. Rather than analyze the discrete Riccati equation as in the classic literature, the equivalent state smoothing optimization problem is stated and all results are established using properties of this optimization problem. A Lyapunov-like funJames B. Rawlings
  • 05
    Designing Grid-Aware Dynamic Specifications for Large Data Center Loads
    As data center (DC) loads increasingly penetrate the power grid, there is an urgent need for grid operators to provide clear dynamic specifications to DC owners to ensure safe grid operation. To this end, we study two salient behaviors of large language model (LLM) training loads: abrupt ramps at job initiation and termination, which induce transient frequency excursions, and sustained periodic oscillations during training, which result in oscillatory steady-state behavior. For ramping loads, weAshutossh Gupta
  • 06
    Preventing Model Collapse: A Fisher-Rao Perspective on the Dynamics of Training with Synthetic Data
    Large Language Models (LLMs) are now routinely trained using synthetic data, since high-quality human data has been exhausted by the ever increasing needs of larger and larger models. However, recursive training on synthetic data frequently induces model collapse, a degenerative feedback loop where models progressively forget the true underlying data distribution. Training on a mixture of synthetic and fresh human data is a logical countermeasure and can prevent model collapse. However, it is anMatteo Marchi
  • 07
    Time-Optimal Operation of a Load-Hoisting Gantry Crane
    This paper addresses the problem of designing time-optimal control profiles for point-to-point control of a gantry crane moving in a two dimensional plane. It is assumed that the hoisting motor completes the hoisting maneuver at a constant rate and completes its transition in the same time that it takes for the cart to reach its terminal position. This results in a linear time-varying model and a closed form solution to the time-optimal control problem is shown to be parameterized with Bessel fuEric Mountain
  • 08
    Optimizing Lyapunov Certificates via Stability-Preserving Quadratization for Polynomial Systems
    Region-of-attraction (ROA) certificates for polynomial systems become expensive as state dimension and degree grow: direct sum-of-squares (SOS) formulations require combinatorially growing monomial bases. Quadratization represents a polynomial vector field exactly on an invariant manifold of a quadratic system, allowing a quadratic Lyapunov function to certify the ROA. For a fixed lift, stabilizer gains shape the off-manifold extension and transverse dynamics, while representation gauges changeYubo Cai
  • 09
    Learning to Solve Two-Stage Stochastic Unit Commitment Problems with Quality Guarantees
    Two-stage stochastic Mixed-Integer Linear Programs are a canonical modeling tool to optimize power system operations under uncertainty, yet their extensive-form counterparts scale linearly with the number of scenarios and quickly become computationally prohibitive under day-ahead time constraints. We propose an Input Convex Neural Network architecture to learn a convex surrogate of the second-stage value function, enabling fast first-stage optimization while preserving convexity by construction.Andrea Fusco
  • 10
    Taming the Agentic RAN: Stability-Guaranteed Arbitration of Autonomous AI Agents in O-RAN
    The O-RAN control plane is becoming agentic: autonomous AI agents, deployed as rApps by different vendors, independently close control loops over shared radio resources. We demonstrate on a live O-RAN system that this independence is unsafe. Two agents with individually correct objectives, one protecting a latency SLA and one maximizing utilization for energy efficiency, jointly drive recurring opposing excursions of the shared resource partition that neither produces alone. Existing conflict-miSeyed Bagher Hashemi Natanzi
  • 11
    Towards Interaction Regulation from Human Feedback via Free Energy Minimization
    A central challenge across control and learning is the design of mechanisms regulating the interactions between humans and autonomous agents. Inspired by the free energy principle from computational neuroscience, we introduce a control-theoretical framework to integrate human preferences online into an agent policy. We turn the framework into an open control architecture and validate our approach using a human-in-the-loop experimental testbed involving a rover navigating via onboard sensing. TheMaria Paula Diaz Monfort
  • 12
    Quantum Computing in Next-Gen Smart Grid Operations: A Comprehensive Review
    The rapid proliferation of grid-edge distributed energy resources has significantly increased the operational complexity of modern power systems. Consequently, conventional computational techniques face growing scalability and computational-efficiency challenges in addressing large-scale optimization and control, uncertainty management, nonlinear dynamics, and combinatorial decision-making in smart grid operations. Quantum computing has therefore emerged as a promising computational paradigm thaMd Habib Ullah
  • 13
    Forgetting While Remembering, an Invariant Online Data-Driven Predictive Control Formulation
    Low signal-to-noise ratio (SNR) data is a core challenge of online Data-Driven Predictive Control (DPC) for linear, time-varying systems. This paper proposes a Bayesian, online DPC framework based on autoregressive models with exogenous inputs (ARX) that uses an externally-provided prior, which encodes inductive bias such as smooth system dynamics and stability, to safeguard performance when SNR is low. The posterior estimate of the ARX parameter is propagated forward in time using a Kalman filtAlessandro Chiuso
  • 14
    GNN-Accelerated Mixed-Integer Dual MPC for Interactive Driving
    In interactions with uncertain opponents, dual model predictive control (MPC) can improve performance through information-seeking actions that reduce uncertainty about opponents' behavior. Its recent applications to autonomous driving, however, are limited to scenarios involving a single opponent on a single lane. This paper presents a mixed-integer dual MPC for multiple reactive opponents on multi-lane roads, jointly optimizing integer-valued maneuver decisions (lane changes and safe-region selYidan Zhu
  • 15
    A Closed-Loop Model of an Anion Exchange Membrane Electrolyser Based on Operational Data
    The main contribution of this work is to identify model parameters for an Anion Exchange Membrane (AEM) system directly from measured operational data, enabling their use in power system studies. Due to the relatively low technology readiness level (TRL) of AEM electrolysers, only limited literature reports operational parameters supported by openly available experimental data. By capturing the realistic dynamic behaviour and power consumption of the AEM electrolyser, this model allows for moreMaiken Borud Omtveit
  • 16
    Optimization Design and Simulation Validation of a Variable Stiffness Actuator Based on a Crossed Four-Bar Mechanism
    This paper presents a bio-inspired antagonistic variable stiffness actuator (VSA) based on two crossed four-bar compliant transmission elastic units (CFB-CTEs). The design addresses the difficulty of combining nonlinear elastic shaping with low structural inertia in antagonistic VSA mechanisms. Inspired by the crossed constraint behavior of the anterior and posterior cruciate ligaments during knee flexion, the proposed actuator uses geometric transmission, elastic energy storage, and bilateral aYuanlong Ji
  • 17
    Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving
    Safe motion planning in automated driving requires anticipating evolving traffic risks and deciding when to revise the current planned trajectory. We introduce RiskWorld, a risk-aware world modeling framework for shared occupancy forecasting and selective trajectory replacement. Spatial risk fields and temporal actor context are fused with visual bird's-eye-view features. Flow-guided evolution transports occupancy and scene features, while signed residuals correct occupancy after transport. OneRongxiang Zeng
  • 18
    The Verifiable Action Card: Trustworthy Human-in-the-Loop Control for Secure Autonomous Agents
    Agentic browsers can execute security-sensitive actions under a user's authenticated session, making indirect prompt injection and deceptive confirmation interfaces a direct threat to action integrity. Existing human-in-the-loop (HITL) safeguards are insufficient when the approval prompt itself can be influenced by untrusted page content or model-generated text. We present the \emph{Verifiable Action Card} (VAC), an architectural defence that reconstructs approval information from the ground-truHasnain Irshad
  • 19
    Physically Consistent Modeling of Dispersive Time-Modulated Reconfigurable Intelligent Surfaces for Wideband OFDM
    The elements of a reconfigurable intelligent surface (RIS) are commonly modeled either as frequency-selective time-invariant reflectors or as instantaneous time-varying reflection coefficients. In practice, however, time-modulated metasurfaces exhibit both frequency selectivity and periodic time variation. We develop a physically consistent linear periodically time-varying (LPTV) model that jointly captures these effects and characterizes their impact on wideband orthogonal frequency-division muIvan Iudice
  • 20
    A Stochastic Mean-CVaR Framework for BESS Multi-Market Bidding Strategies
    Battery Energy Storage Systems (BESS) operators face significant challenges when participating in multiple electricity markets due to the complex coupling of price volatility and stochastic reserve activation. Traditional deterministic dispatch models neglect the "tail risks" associated with extreme market realizations, potentially leading to technical infeasibility or severe economic losses. This paper proposes a risk-aware stochastic optimization framework for the co-optimization of BESS partiYounes Zahraoui
  • 21
    Macroscopic Motion Patterns from Generalized Velocity Rigidity in Multi-Agent Networks
    Traditional graph rigidity theory enforces structural constraints in the position space, effectively restricting a multi-agent formation to a static geometric shape. In this paper, we introduce a fundamental paradigm shift by applying rigidity constraints directly to the agents' continuous-time velocity space. We propose the concept of generalized velocity rigidity, demonstrating that the macroscopic physical motion patterns of a multi-agent network are entirely dictated by the underlying staticRonghai He
  • 22
    A Recursive CBF Framework for Safety under State Uncertainty
    The practical implementation of Control Barrier Functions (CBFs) for safety-critical control is often hindered by uncertainty in the knowledge of the state. While existing robust CBF methods address state uncertainty, they often lack recursive feasibility guarantees or fail when uncertainty levels are high, allowing the system to enter regions where no safe control input exists. To resolve this, we propose a novel framework of enforcing recursive CBFs. Rather than merely ensuring the invarianceRahal Nanayakkara
  • 23
    Indicators of resilience for autonomous control systems
    As modern societies rely more on autonomous systems to facilitate daily life, assuring their safe operation is paramount. Naturally, there are many techniques available to predict and prevent system failures. However, the safety afforded by such schemes may become misaligned with the true system, which can change in unexpected ways - from partial faults to natural wear-and-tear - that subtly degrade its stability. The implications that such subtle changes have on autonomous system stability canJasper van Beers
  • 24
    Prior Evolution and Task Alignment for Aerial Grasping
    Aerial grasping is a remarkable capability exhibited by predatory birds, allowing them to capture prey through highly coordinated maneuvers in flight. Inspired by this capability, researchers have developed various formulations to reproduce such maneuvers through trajectory optimization. However, two limitations remain in practice. First, the resulting optimization problem is highly nonconvex and sensitive to initialization, making high-quality solutions difficult to obtain under a limited compuWeiliang Deng
  • 25
    Learning Fractional-Order Dynamics from a Single Trajectory
    Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone. This paper studies system identification for discrete-time fractional-order linear time-invariant systems from a single observed trajectory of length $t$, a setting that captures such non-Markovian dynamics through the Grünwald--Letnikov difference operator. Unlike Markovian systems, fractional-order systems couple estimatiXiaole Zhang
  • 26
    Sliding Mode Control of Cardiac Rhythms in the Sinoatrial Node using Gaussian Process Regression
    The Sinoatrial node (SA), also called natural pacemaker, is responsible to initiate the heart electrical activity, usually represented by electrocardiograms (ECGs). Abnormalities at the SA node can produce disordered heart rhythms or, in other words, cardiac arrhythmia that are visualized in the ECGs. The development of control strategies to stabilize the cardiac rhythm at the natural pacemaker can provide efficient ways to deal with and avoid some heart pathology. This paper investigates the usGabriel da Silva Lima
  • 27
    PESTO: Formally Correct Registration of LiDAR Point Clouds with Limited Overlap
    In this paper we tackle the problem of aligning LiDAR point clouds also known as the point cloud registration problem. We propose a new algorithm, PESTO, that exploits tetrahedra as "universal features" for LiDAR data, i.e., features that are agnostic to the environment where the LiDAR sensors are deployed. We show empirically that PESTO is competitive with existing solutions for aligning LiDAR point clouds, especially in environments with occlusions. Moreover, we establish PESTO's formal correcValen Yamamoto
  • 28
    Agents in the Scene: An Agentic Framework for Resource-Efficient Site-Specific Base Station Deployment
    An agentic framework is proposed for autonomous site-specific base station (BS) deployment in wireless network planning. In contrast to conventional approaches that rely on manual site surveys or extensive ray-tracing (RT) simulations with significant human intervention, the proposed framework autonomously explores and optimizes BS deployment under a limited RT evaluation budget, enabling resource-efficient network planning. To this end, a continuous, geometry-grounded deployment action space isZihao Zhou
  • 29
    Chaotically Paced Transit of an Embedded-Leader Swarm with Local Spring-Damper Formation Control
    This paper treats planar swarm transit along a fixed route when no ground station streams the reference in flight. One embedded leader stores the deployment point and the destination, generates the reference onboard, and drives its progress rate with a saturated coordinate of a Chua oscillator; the saturation keeps the rate inside a prescribed positive band, which gives explicit bounds on the reference arrival time. This leader broadcasts the one scalar rate to the other leaders; every leader adBo Tu
  • 30
    The Attention Within: Consensus Dynamics in Selective State Space Models
    Selective state space models (SSMs) have recently emerged as a compelling alternative to transformers, combining competitive performance with substantially improved inference efficiency. At each SSM layer, a sequence of hidden states are propagated by a recurrence, mixing information of different tokens. Despite using a different mechanism, this mixing plays a role analogous to attention in transformers. In fact, recent works have shown that the two architectures may be closer than they first apJoão Pedro Silvestre
  • 31
    On the Optimal Co-location of Data Centers with Renewable Energy Sources in SIDS
    The rapid expansion of data centers presents both economic opportunities and systemic risks for Caribbean Small Island Developing States~(SIDS). This paper develops a PyPSA-based capacity expansion framework for optimal co-location of data centers with renewable energy infrastructure, incorporating behind-the-meter battery storage, workload flexibility (70\%/30\% inflexible/flexible split), and multi-bus transmission topology. Two case studies are examined: Trinidad~\&~Tobago (single-bus, 92\,MWShankar Ramharack
  • 32
    The BAR-SOT Method: Long-term Average Cost Control as Stochastic Optimal Self-Transport
    We reformulate average-cost (ergodic) control of a Markov jump process as a finite-horizon stochastic optimal transport (SOT) problem that jointly optimizes over the controlled evolution and the marginal law from which it starts and returns to (i.e., a self-transport). The constraint relating the marginal flow to the controlled generator is the basic adjoint relationship (BAR), so we call the resulting problem BAR-SOT. For any time horizon T>0, its optimal value is a constant scaling of the longSharan Srinivasan
  • 33
    Mixed-Integer Nonlinear Differentiable Predictive Control for Underground Pumped Hydro Energy Storage Systems
    This paper extends Mixed-Integer Differentiable Predictive Control (MI-DPC) to multi-modal discrete decisions and nonconvex polynomial dynamics arising in Underground Pumped Hydro Energy Storage Systems (UPHES). A neural policy mapping problem parameters to continuous setpoints and integer mode selections via a Gumbel-Softmax layer is trained in a self-supervised manner by differentiating the expectation of the finite horizon control objective through the nonlinear dynamics model. Three methodolHonghui Zheng
  • 34
    Day-ahead Coordination of Virtual Power Plants within Active Distribution Networks using Deterministic Bi-Level Optimization
    This paper proposes a deterministic bilevel optimization framework for the coordinated operation of Virtual Power Plants (VPPs) embedded in an active distribution network. The Distribution System Operator (DSO) acts as the upper-level leader, minimizing a weighted combination of expenditure, active losses and voltage deviation subject to nonlinear AC power flow constraints, while each VPP operates as a lower-level follower that maximizes its profit under the uniform price signal issued by the DSLaura M. Barajas-Arguello
  • 35
    Predicting Viral Evolution from a Single Early Measurement Using the Target Cell Limited Model
    Recent advances in diagnostic techniques have enabled the accurate quantification of early-stage viral loads. A key problem of interest is translating these measurements into predictive clinical insights, such as forecasting a patient's onset of infectiousness and peak infection severity. In this work, we address this problem using the Target Cell Limited (TCL) model. Because the host's internal biological states are practically unobservable, predicting the viral trajectory from a single noisy vRahal Nanayakkara
  • 36
    Hybrid Sequential Feedback Optimization for Wind Farm Power Maximization
    This paper considers feedback optimization for optimal steady-state operation of nonlinear discrete-time systems when the steady-state input-output map and its sensitivity are expensive to compute. We propose a hybrid extension of sequential feedback optimization (SFO) that augments the model-based SFO gradient with correction terms through a convex combination with summable diminishing weights. Two variants are studied: one based on recursive least-squares (RLS) sensitivity estimation, and anotShijie Huang
  • 37
    Multi-Scale Datacenter Power Modulation
    Cloud datacenters must increasingly modulate power in response to time-varying grid and infrastructure constraints. We study this problem as finite-horizon control of a networked hybrid dynamical system, where datacenter power and service capacity depend on interactions between servers, workers, and hosted services. Power can be reduced through fast continuous worker throttling, which acts immediately but degrades service capacity, and slow discrete server transitions, which provide deeper savinAkshay Sreekumar
  • 38
    Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder
    The increasing adoption of electric vehicles (EVs) is expected to place significant additional demand on residential distribution networks, creating a need for realistic charging datasets for planning and simulation studies. However, access to real-world EV charging data is often limited due to privacy constraints, incomplete records, and restricted availability. This paper proposes a conditional variational autoencoder (CVAE) for the generation of synthetic EV charging sessions from real transaGraeme Kelly
  • 39
    SPROUT: The Open-Source Soft Growing Robot for Search and Rescue
    Soft robotic systems have long been theorized as ideal candidates for use in search and rescue operations; however, there have been significant barriers to entry in graduating soft robotic systems from the laboratory to the field. To address this gap in replicable, reliable soft robot systems, we present the designs for SPROUT, the Soft Pathfinding Robotic Observation Unit. The system has matured over years of interaction with professional urban search and rescue communities, with the goal of opAntonio Alvarez Valdivia
  • 40
    Barrier Functions Against Safety Drift in Shared Control
    Shared-control arbitration mechanisms can fail structurally when they are driven by quantities that evolve on the same automation-assisted trajectory that unsafe automation can corrupt. Under unsafe assistance, arising from faults or flawed design, the arbitration logic can adapt to the corrupted trajectory instead of resisting it. Focusing on workload regulation, this paper proposes a framework that prevents this problem by anchoring the safety constraints to an unassisted pilot trajectory. A bM. Yusuf Uzun
  • 41
    Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control
    Integrated deep reinforcement learning (DRL) and model predictive control (MPC) methods are increasingly used to control autonomous systems by combining their complementary capabilities. DRL learns control policies through interaction with the environment. MPC uses a system model to optimize control inputs while accounting for constraints. In DRL-MPC frameworks with shared control authority, both the DRL agent and the MPC controller each determine part of the control inputs. However, common learGiray Önür
  • 42
    Geometric Hybrid Dynamical Systems: Part I - Modeling and Stability
    We present a framework for the modeling and analysis of geometric hybrid dynamical systems as hybrid inclusions on $C^1$-manifolds. Using tools from nonsmooth and set-valued analysis on manifolds, we derive coordinate-independent sufficient conditions for the existence of nontrivial solutions to this type of systems. We present geometric notions of uniform stability and attractivity of compact sets, and establish their equivalence to metric-based stability notions when the manifold is endowed wiPiyush P. Jirwankar
  • 43
    Regularized Least Squares Training of Quadratic Neural Networks with Applications to System Identification
    This paper proposes a least squares approach for the training of quadratic neural networks with regularization. The proposed methodology yields a lower bound on the solution of the training optimization problem for the case where the regularization coefficient is positive. Moreover, it yields closed-form expressions for the approximate solution and its sensitivity The lower bound is tight and the approximate solution is the optimal solution when the regularization coefficient is zero. Having a cLuis Rodrigues
  • 44
    Hamilton-Jacobi Reachability for Hybrid Systems: Unified Goal-Driven Control with Safety Guarantees
    Hybrid dynamical systems provide a powerful modeling framework for robotic systems, particularly in contact-rich environments. However, ensuring safety and performance in such systems remains challenging due to the intricate coupling between continuous dynamics and discrete mode transitions. In this work, we extend classical Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for continuous-time nonlinear systems, to hybrid dynamical systems. Our framework characterizes safeJavier Borquez
  • 45
    RobResilience: Implementing and Evaluating a Resilience Framework for Cyber-Physical Embodied Systems
    In embodied cyber-physical systems, active cyberattacks pose an immediate threat not just to data, but to physical integrity and human safety. While existing security approaches excel at detection, they lack the runtime mechanisms to determine whether a disruption is tolerable or if performance degradation remains within safe operational bounds. This gap leaves autonomous systems vulnerable to graceful failure paralysis, where they cannot distinguish between a safe, degraded state and a catastroGysella Imrell
  • 46
    A Time-to-Collision Barrier Function Approach to Collision Avoidance for Stochastic Systems
    Collision avoidance constraints for autonomous systems are typically formulated in position or velocity space, implicitly reacting to geometric proximity. We propose an alternative paradigm based on the adversarial time-to-collision (aTTC): the minimum time in which an adversary could achieve a collision given its dynamical constraints. By defining a control barrier function (CBF) directly in the time domain, the resulting controller is inherently anticipatory. The evading agent responds not onlBenedikt Barthel Sorensen
  • 47
    Backstepping Design of Dynamic State Feedback Controllers for Parabolic Systems
    Recently, dynamic state feedback controllers that are based on dynamic extensions have been presented for heterodirectional hyperbolic systems. In this paper, a similar concept for the control of coupled diffusion-reaction systems is suggested. The introduction of a specific controller dynamics leads to homogenized diffusion coefficients for the extended system. Then, a backstepping-based static state feedback for the dynamically extended system is designed, which, overall, results in a dynamicNicole Gehring
  • 48
    Port-Hamiltonian Koopman Operator Synthesis for Mechanical Systems
    Finite-dimensional Koopman models enable efficient linear prediction and control of nonlinear robotic systems. However, models learned purely from trajectory data may violate the energetic structure of the underlying mechanics, producing predictions that exhibit artificial energy growth and diverge under recursive propagation. This work presents a structure-preserving Koopman framework for Euler-Lagrange systems built on generalized-momentum coordinates. The momentum transformation exposes the mRajpal Singh
  • 49
    Data-Driven Policy Iteration Without an Initial Stabilizing Policy: A Finite-Horizon Bootstrap Method
    This article investigates data-driven policy iteration (PI) for continuous-time linear systems without requiring an initially stabilizing policy. Standard infinite-horizon PI is not self-starting because its policy-evaluation step is well posed only when the feedback gain is stabilizing. However, verifying this property is difficult when the system matrices are unknown. To remove this requirement, we develop a finite-horizon bootstrap method. The key idea is to perform policy evaluation over a cJiacheng Wu
  • 50
    Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand
    A walking robotic hand must use the same fingers to move its body, support its weight, and interact with the environment. We show how an anthropomorphic hand can learn these skills while retaining its finger design and position controller. Onboard power and computation make the platform self-contained. Our reinforcement learning approach accounts for the hand's unequal fingers, with training in a simulator calibrated from hardware measurements. In simulation, the hand moves faster with our rewarAmirhossein Kazemipour
  • 51
    Intervention problems in the Linear Threshold Model: A general formulation and new results
    We study an optimal intervention problem for linear threshold models. This is a popular class of dynamical network systems whereby a number of agents, identified with the nodes of a graph, strategically change their binary action (0 or 1) according to a threshold rule. Specifically, an agent adopts action 1 if and only if the fraction of its neighbors in the interaction graph that do so is greater than or equal to a prescribed threshold. Assuming that a planner can modify the agents' thresholdsGiacomo Como
10天前更新
  • 01
    Neural noise enables accurate internal simulation of rare events
    The brain needs an accurate internal model of the world to generate predictions and guide behavior. However, it must estimate the statistical structure of the environment from limited experience. This is particularly difficult for rare events, whose observed frequencies in a limited sample may substantially under- or overestimate their true frequencies. How the brain constructs an accurate internal model despite this sampling problem remains unclear. We address this problem using a Bayesian ConfHeng Zhang
  • 02
    Learning Options for Compositional Motor Control with Adapter Banks
    Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of a shared recurrent network, but leaves open how such a system is learned. We translate this principle into a novel architecture for learning motor skills end-to-end: a shared recurrent core modulated by a bank of residual adapters, each selected by a discrete latent code. Trained on closed-loop biomechanical control,Sreejan Kumar
  • 03
    Predictor Construction Can Reverse Multimodal Neural Contrasts
    Foundation-model features are increasingly used to ask what information neural activity represents, often by comparing prediction gains between nested encoding models. We show that such multimodal contrasts can change sign when only the conditioning predictor is reconstructed. Using fMRI from the Natural Scenes Dataset, DINOv2 visual features, and MPNet embeddings of MS COCO captions and Localized Narratives, a caption-narrative contrast in the additional predictive contribution of vision favorsLucas Nadolskis
  • 04
    A neural-astrocyte architecture implements a hybrid automaton for evidence accumulation
    Astrocytes are non-neuronal glial cells that are receiving widespread attention due to their emerging role in neural computation. In this paper, we propose and study dynamical mechanisms by which astrocytes may augment the ability of neural networks to infer context in reinforcement learning (RL) settings. We construct a biologically inspired, two-level dynamical neural-astrocyte network with distinct spatial and temporal organization. We train this model on a hierarchical multi-context task thaGiacomo Vedovati
  • 05
    Decision-Related Cognitive Signatures from Fast-Slow Dynamics: A Low-Dimensional Observation-Operator Framework
    Repeated decisions exhibit temporal structures such as persistence, direction-dependent switching, recurrent alternation, and abrupt transitions. We examine the generative sufficiency of a two-dimensional fast-slow dynamical system. The system combines a cubic fast equation with linear slow feedback and is analyzed through its equilibrium geometry, trace-determinant structure, equilibrium-fold loci, candidate Hopf boundaries, and singular critical manifold. An explicit observation operator projeFurkan Emre Isik
  • 06
    When Teachers Smile or Frown: A Profile-Based Analysis of Achievement Emotions
    Achievement emotions shape how students engage with and learn from academic tasks, yet most studies examine individual emotions rather than co-occurring affective profiles and their dynamics. We examined latent achievement-emotion profiles and their transitions following exposure to different instructor facial expressions during a video lecture. Self-reported data from 78 Grade VII and VIII students revealed three profiles: enthusiastic, demotivated, and vulnerable. Profile transitions differedRudra Mukhopadhyay
  • 07
    Nonlinear dynamics of random neural networks with second-order synaptic motifs
    Classical theories of random neural networks typically assume independent connectivity, overlooking the local motif structures prevalent in biological circuits. Here, we investigate how four second-order synaptic motifs (chain, reciprocal, convergent, and divergent) shape the dynamics of nonlinear firing-rate networks. While previous studies have established that chain correlations generate outlier eigenvalues, we demonstrate that these motifs also jointly reshape the Jacobian eigenvalue bulk. UJun Yang
  • 08
    URCHIN: A Horizontal Spiking Language Model for Data-Constrained Pretraining
    The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora, yet prior language models forgo the biological constraints of the neural circuitry that acquires human language: spiking neurons separated into excitatory and inhibitory populations wired by a recurrent lateral connectome. This paper presents URCHIN (Unified Recurrent Connectome with Horizontal Integrate-and-fire Neurons), which applies the ParallPo-Han Chiang
  • 09
    Hierarchical emergence of network bursting in a four-cell central pattern generator model
    How can a neural circuit rhythmically burst when none of its constituent neurons can endogenously do so? We address this question through a bottom-up reconstruction of a 4-cell neural circuit modeled after the swim central pattern generator (CPG) of the sea slug \textit{Dendronotus iris}. We first map the intrinsic regimes of a swim interneuron (SiN) model neuron and show that slow mutual inhibition can generate anti-phase bursting in a half-center oscillator (HCO) assembled from tonic-spiking oKrishna Pusuluri
  • 10
    Pretraining for Sample-Efficient Neural Interfaces
    Brain-computer interfaces (BCIs) decode neural activity to restore lost function. Typically, training a high-performance neural decoder requires a large labeled dataset to be collected from every new subject. One way to reduce the labeled data cost is self-supervised pretraining, which learns general neural representations from unlabeled recordings that accumulate across subjects. However, for intracranial electroencephalography (iEEG) recordings, self-supervised learning has been challenging duBen Tang
  • 11
    Stability and Wandering of Bumps in Neural Fields with Interneuron Subtypes
    The maintenance of continuous variable information in working memory is thought to rely on persistent patterns of cortical activity. In delayed-estimation tasks, neural activity can form localized activity peaks, or ``bumps,'' whose positions track the remembered variable. Such activity is well described by continuous-attractor neural field models, but most existing models collapse cortical inhibition into a single homogeneous population. Here, we introduce a stochastic neural field model with dBilal Ahmed
  • 12
    pyAvalanches: A Python Package for Analyzing Spatiotemporal Propagation in Neuronal Avalanches
    The analysis of neuronal avalanches offers insights into brain dynamics utilizing the framework of criticality, but the reproducibility and comparability of studies are limited by the use of fragmented, lab-specific scripts. To address this issue, we introduce pyAvalanches, an open-source Python package providing a standardized, end-to-end pipeline for avalanche analysis from electrophysiological recordings (e.g., electroencephalography-EEG). Starting from the detection of neuronal avalanches thM. Marzulli
  • 13
    Degeneracy along the sensorimotor hierarchy: motor control within a framework larger than redundancy
    Motor control has described the surplus of solutions available to the nervous system as redundancy, a term that names duplication: interchangeable elements, robust to loss but incapable of differential adaptation. Biology has had a second term for twenty-five years. Degeneracy names elements that are not interchangeable and are nonetheless isofunctional with respect to a given output, and it supports adaptability, since non-identical elements necessarily diverge in some context. Circuit neurosciFlorent Paclet
  • 14
    The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language models
    Multimodal large language models predict brain activity, but brain alignment has been a measurement, not a design tool. We propose the Platonic brain bridge hypothesis: omni models, multimodal large language models that process video, audio and text jointly, converge on brain-like representations usable in both directions. From model to brain, brain-likeness of seven omni models is stable across participants, rises with every input channel in three bases, and our encoders lead the Algonauts 2025Pengfei Zhang
  • 15
    Discovering Subtypes of Neurodegenerative Progression with a Scalable Connectome-Constrained Dynamic Model
    Parkinson's disease is clinically and biologically heterogeneous, yet its spatiotemporal progression remains poorly characterized. We present a connectome-constrained disease progression model that jointly estimates subject-specific disease time and data-driven subtypes from longitudinal morphometry. Applied to 85 imaging and clinical biomarkers from the Parkinson's Progressive Markers Initiative (PPMI) cohort, the model recovers four morphologically distinct progression subtypes. We validate thDaniel Semchin
  • 16
    Cortical information transfer reveals conserved hemispherical network dynamics across human handedness
    Whether human motor and brain lateralization arises from fundamentally distinct neural architectures or emerges from conserved network dynamics remains a central question at the intersection of network science and neurobiology. Conventional measures of cortical activation often fail to resolve how directed information exchange adapts to manual preference during complex motor tasks. This ambiguity leaves it unclear whether left-handed individuals possess atypical neural organization or follow shaYago Emanoel Ramos
  • 17
    BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models
    fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stages without modifying the backbone. During pretraining, a lightweight Brain-DiT proxy estimates difficulty and directed facilitation across ten fMRI domains, yielding a priority-guided cumulative domainJunfeng Xia
  • 18
    A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception
    Locust visual systems have long served as an important biological paradigm for studying looming perception and collision avoidance. Numerous computational models have successfully reproduced the selective responses of Lobula Giant Movement Detector (LGMD) neurons to approaching objects, thereby emulating the fundamental functionality of the biological system. However, existing models remain limited in biological plausibility and robustness when operating in complex and dynamic visual environmentQinbing Fu
  • 19
    EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding
    Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform activity is likely to arise. Beyond detecting whether an IED is present, this setting requires assigning IED-positive activity to clinically meaningful brain-region categories. This setting is challenging because source-region evidence in short electroencephalography (EEG) windows can be subtle, partial, and affected by sMuchen Li
  • 20
    The Computational Primitives of Adaptation
    Research on adaptive systems has traditionally focused on behavior (what organisms do) and mechanism (how their machinery works). This paper focuses on a third level, computation, which considers what adaptive systems must compute to survive and reproduce. It is proposed that adaptation has its own computational structure, comprising a small set of primitive operations common to all adaptive systems, regardless of their physical form. Six primitives, Arouse, Orient, Valence, Position, Boundary,Jonathan W. Page
  • 21
    Emergence of criticality in models of real neurons
    Critical systems sit near boundaries between qualitatively distinct behaviors. When inferring models of neural activity, this proximity to criticality is thought to require the precise tuning of parameters. Here, we show that as the number of neurons increases, criticality can emerge naturally without fine-tuning. When computing observable statistics from parameters (the forward problem), some small regions in parameter space map to large regions in statistics space. These special parameters areDavid P. Carcamo
  • 22
    XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction
    Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reproducible or neurobiologically supported mechanisms. Post-hoc explanation methods can expose these mechanisms, but existing workflows typically stop at diagnosis or require correction targets to be specified before model analysis. We propose XAI-Refine, an automated explanation-knowledge loop for brain-age prediction from resting-state functional coYang Qiao
  • 23
    Hi-M imaging of chromatin architecture in adult Drosophila brain cryosections
    Hi-M combines fluorescence in situ hybridization (FISH), automated microfluidics, sequential imaging, and computational chromatin tracing to measure the three-dimensional organization of selected genomic regions in single cells. This chapter describes a Hi-M workflow adapted for cryosections of adult Drosophila melanogaster brains, enabling chromatin tracing while preserving tissue architecture and cell identity. The protocol covers Oligopaint library design and amplification, fixation, brain diChristel Elkhoury Youhanna
  • 24
    Why shared attention vectors fail: a case for outcome-indexed tuning
    Dimensional attention in learning is often implemented as a globally shared attention vector, where each stimulus dimension corresponds to a single scalar. These scalars are learned by models through gradient-descent on error, where predictive features acquire more salience. We show that under multi-outcome learning, where models predict more than one outcome, this shared vector becomes unstable; it collapses to its bounds and prevents the models from learning meaningful attentional tunings forLenard Dome
  • 25
    An Evidence-Aware Framework for EEG Microstate Analysis: Improved Sensitivity to Alzheimer's Disease and Ageing
    Electroencephalography (EEG) microstate analysis commonly converts each scalp topography into a winner-take-all hard label and summarises the resulting sequence using duration, occurrence, coverage, transitions, and symbolic complexity. Although interpretable, this readout discards evidence strength, assignment ambiguity, and low-confidence periods. We introduce a template evidence trajectory framework that retains, at each sampled Global Field Power (GFP) peak, the evidence for all templates orKaidong Wu
  • 26
    A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
    The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrogate gradients, decoupling learning from biological spike timing. Here, we reformulate temporal credit assignment as a state separation problem: extracting task-required components induced by historicaXiangnan Zhang
  • 27
    Fisher-Rao Distance Detects Shifts in Kinematic Profiles under Cognitive Load
    Motor control research involves the study of movement kinematics derived from the positional trajectories that complex motions describe. In natural, unconstrained motions requiring cognitive and memory processes in real time, the temporal speed profiles are not bell-shaped, may have multiple maxima and the peaks distribution is best fit by the continuous gamma family with two parameters, the shape and the scale. As the stochastic processes described by complex motion trajectories are non-stationJoseph Vero
  • 28
    Fisher Information Metric as a model-free measure of proximity to criticality in neural systems
    Critical phenomena are widespread across many disciplines and have recently become a topic of deep interest in the study of biological and artificial neural networks. A distinct signature of criticality is the emergence of avalanches with power-law-distributed sizes and durations. However, empirically estimating the critical exponents remains challenging, and their interpretation is often model-dependent. In this work, we demonstrate how the Fisher Information Metric (FIM), a measure of generaliYuewei Du
  • 29
    Homeostasis Revisited and Reformulated Through Hidden Markov Model Control
    A common formalization of homeostasis is the free energy principle, a framework that defines a set of desired observation values, or critical states, that the agent should reach or remain close to. Under the free energy principle, an agent should act to maximize the probability of receiving the desired observations. Here we revisit the common approach of solving the problem of maximizing the log probability of the desired observations by maximizing a variational lower bound, the so-called negatiRubén Moreno-Bote
  • 30
    Revisiting the Aerts-Broekaert-Smets quantum model of the liar paradox
    The quantum model of the two-sentence liar paradox proposed by Aerts, Broekaert, and Smets is an early example of the use of quantum formalism to describe cognitive dynamics. Our reconstruction is primarily pedagogical in intent, but it also leads to a number of clarifications, and to some new observations, concerning the structure of the model. Rewriting the model in Dirac notation, we make explicit the distinction between truth values originating from a decision and from semantic inference, anMassimiliano Sassoli de Bianchi
  • 31
    Determinants of hyperparameter robustness in connectome reservoir computing
    Reservoir computing provides a controlled setting for studying how recurrent network architectureshapes computation: input signals are projected into a high-dimensional state space by a fixed nonlinear dynamical system, and only the readout is trained. However, reservoir performance can be dependent on hyperparameters; this paper asks which recurrent network features support robustness to those parameter changes. We characterize computational performance using memory capacity (MC), truncated sinMiles Walter Churchland
  • 32
    Adaptive Entangled Game Modules in Artificial General Intelligence
    We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. This framework captures a broad range of human intelligence behaviors with analytical mechanisms and offers an indirect method to examine the Liu-Chen-Ao (LCA) hypothesis of nonlocal entangled nerve fibers in the brain through collective trader behaviors. Our empirical anHaochen Li
  • 33
    Formation of structural attractors in neuromorphic systems
    This paper examines the theory of Invariant Structural Learning (ISL), which proposes a non-optimization approach to concept formation. Learning is interpreted as convergence to structural attractors in a hypergraph space, rather than as the minimization of a global loss function. The paper presents the ISL model, including its mathematical formalization, computational verification, and a hypothetical neurobiological interpretation. The mathematical section introduces the formal apparatus of theYurii Parzhyn
  • 34
    A Roadmap for MEG Foundation Models
    Foundation models are beginning to reshape brain-signal analysis by moving the field beyond task-specific decoding pipelines toward reusable models pretrained on broad neural datasets. Magnetoencephalography (MEG) is a compelling but still underdeveloped target for this shift: it captures human cortical dynamics at millisecond resolution while offering stronger spatial interpretability than EEG, making it especially valuable for source-resolved studies of perception, language, cognition, and cliPhilipp Thölke
  • 35
    Axonal delay dispersion decides whether a neuron detects an event or a sequence, and predicts cortical column diameter
    Cortical neurons fire sparsely -- often fewer than one spike per sensory window -- making rate coding insufficient and temporal coding a necessity. That conduction delays convert firing order into synchrony is long established. What governs which class of temporal feature a neuron detects -- one volley of coincident input, or two in a particular order -- has not been examined. We propose a delay-signature framework in which the axonal conduction delays converging on a dendritic branch constituteCheng Bi
  • 36
    Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks
    Temporal integration gives continuous-time recurrent networks memory, but in deep stacks it also delays bottom-up signals and attenuates top-down errors. We develop Recursive Quadrature Filters (RQFs), biologically motivated complex-valued temporal filters that are a special case of diagonal state-space models (SSMs), and ask whether this failure mode can be addressed by making each layer's bottom-up input prospective. Starting from an energy model, we derive the RQF dynamics and show that eachShivang Rawat
  • 37
    High-Order Triadic Functional Connectivity in the Brain and Beyond
    Here, we report high-order functional network connectivity as a promising way for studying the brain connectome. Traditional functional connectivity approaches capture only pairwise relationships between brain regions, overlooking complex multivariate dependencies that underlie cognition and behavior. First, we demonstrated that high-order interactions capture more information and can distinguish between resting-state and task-state brain activity. Second, we introduce a matrix-based entropy-funQiang Li
  • 38
    Inferring Affective Consciousness in an Artificial Agent: A Case Study
    Creatures that display 'hedonic place preference behaviour' are thought by many scientists to experience feelings, on the assumption that their attraction to pleasure-producing substances which lack nutritional value (e.g. cocaine, morphine) cannot easily be attributed to unconscious instinctual behaviour. In this paper, we discuss how a simple artificial agent that instantiates attributes of an affective system engaging in felt uncertainty about its intrinsic needs in relation to environmentalMark Solms
  • 39
    Tensor-based Brain Surface Modeling and Analysis
    We present a unified computational approach to tensor-based morphometry in detecting the brain surface shape differences between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis in a coherent unified mathematical framework. The cerebral cortex has the topology of a 2D highly convoluted sheet. Between two different clinical groups, the local surface area and curvature of theMoo K. Chung
  • 40
    A Common Measure of Communication for Speech Brain-Computer Interfaces
    Speech brain-computer interfaces (speech BCIs) translate neural activity into language, offering a path towards restoring speech for people with paralysis and, more broadly, enabling new forms of natural human-computer interaction. Despite this promise, the field lacks a common measure of progress because systems use different datasets, recording methods, types of speech, and vocabularies, so their reported scores are rarely comparable. Underlying this measurement problem are two unresolved quesDulhan Jayalath
  • 41
    Prediction emerges in RNNs trained for perception
    The brain is highly proficient at making sense of noisy and ambiguous sensory inputs. Predictive processing hypothesises that this ability relies on prediction. However, it is unclear why the brain would have evolved to predict the sensory world, a computationally expensive process, in order to aid perception. Here we use simulations to argue that prediction naturally emerges in systems optimised for perception. We train recurrent neural networks (RNNs) to denoise a tokenised version of Bach's cAkanksha Gupta
  • 42
    Fungal Memory and Minimal Cognition
    This paper argues that fungal mycelial networks exhibit minimal cognition through memory-integrated adaptive regulation. Drawing on cybernetic and enactivist frameworks, I develop a non-representational account of memory as the organism's capacity to modulate behavior based on temporally extended environmental coupling. I propose four operational criteria for minimal cognition: feedback-guided regulation of behavior, maintenance of internal viability conditions, structural modulation based on paKristina Šekrst
  • 43
    Mus siliconus: A Neuro-Musculoskeletal Digital Twin of the Mouse Integrating Neural Dynamics, Biomechanics, and Tactile Sensing
    Digital twin technologies could transform neuroscience and biomedicine by creating predictive computational representations of living organisms. However, most animal digital twins model neural circuits, anatomy, or biomechanics separately rather than integrating the processes that generate behavior. We argue that animal digital twins should instead be conceived as embodied dynamical systems that unify neural activity, body mechanics, sensory feedback, and environmental interactions. We propose aSatoshi Oota
  • 44
    Memory as an Energy Landscape---Hopfield
    This chapter reconstructs the Hopfield network as a physical theory of memory rather than merely an early neural-network algorithm. It begins with the problem as it stood before 1982-threshold logic, Hebbian association, correlation memories, and recurrent binary networks-and isolates what Hopfield's synthesis added: a dynamical definition of content-addressable memory, a symmetric recurrent architecture with a Lyapunov function, a Hebbian embedding of patterns in its couplings, and a physical aNima Dehghani
  • 45
    Neural Logic, Invariance, and the Retina---McCulloch and Pitts
    This chapter reconstructs the McCulloch-Pitts program as a physics of neural computation rather than the familiar cartoon of a binary neuron. The 1943 logical calculus is developed in both directions: given a net, characterize the propositions realized by its activity; given an admissible logical expression, construct a net that realizes it. We recover the original distinction between thresholded excitatory summation and absolute inhibitory veto-one the weighted-threshold form cannot preserve foNima Dehghani
  • 46
    Adversarial Vulnerabilities of Neural Biomarker Identification Systems
    There is growing interest in the proposed use of EEG signals as biometric credentials, but thus far there has been little research on the reliability and security of such biometrics. Prior adversarial tests have focused on deep-learning classifiers and assumed attackers have full access to the classifier model. This has left unexamined other, more popular categories of neural signature methods as well as the more realistic case of an adversary having only black-box access to a classifier. In thiPolina Tapal
  • 47
    Interpretable Symptom Vectors for Depression in a Large Language Model
    Patients with depression present with diverse symptom profiles, yet clinical practice routinely reduces this variation to a single severity score. Large language models (LLMs) can potentially capture various symptoms and their severity from patient speech. However, how depressive symptoms are represented inside LLMs remains poorly understood, limiting clinical trust. To examine whether internal model activations match clinician judgment, we analyzed the residual stream of Gemma-3-27B-PT using meFangyi Zhu
  • 48
    Slow-Fast Brain-Computer Interfaces: Preventing Neuroadaptive Overfitting in AI-Mediated Neural Interfaces
    Artificial intelligence (AI) is transforming brain-computer interfaces (BCIs) from task-specific neural decoders into adaptive systems that complete language, smooth movement, regulate rehabilitation support and adjust stimulation. These capabilities can increase speed, fluency, usability and clinical reach, yet conventional performance metrics may overlook losses in intent fidelity, authorship, agency, therapeutic challenge and durable clinical benefit. I define neuroadaptive overfitting as a cAarthy Nagarajan
  • 49
    Active Visual Semantics: A large-scale MEG and eye-tracking dataset for understanding visual intelligence in action
    Here we present the Active Visual Semantics (AVS) dataset, a large-scale collection of magnetoencephalography (MEG) and eye-tracking data recorded while five participants freely explored 4,080 natural scenes over 10 sessions each, yielding more than 200,000 fixation epochs in total. Unlike existing neuroimaging datasets that rely on passive viewing with enforced central fixation, AVS captures brain activity during active scene exploration, including self-generated saccades and fixations. A semanPhilip Sulewski
  • 50
    Pulling Illusion in Individuals with Neurological Disorders
    The pulling illusion induced by asymmetric vibration stimuli has attracted attention for its potential applications in rehabilitation and sensory assessment. However, the underlying mechanism of the pulling illusion remains unclear. This study addressed the central question of whether peripheral vibrotactile sensitivity alone is sufficient for the illusion to emerge or whether processing beyond basic vibration detection is also required. Neurological disorders can involve impairments at differenTakeshi Tanabe
  • 51
    Temporally constraining source imaging estimates in an underdetermined neural system with eigenmodes of cortical geometry
    Geometric eigenmodes provide a compact and biologically grounded representation of large-scale neural activity. Previous work demonstrated that they can mitigate the underdetermined nature of electroencephalographic (EEG) and magnetoencephalographic (MEG) source localisation, an ill-posed inverse problem in which neural activity is reconstructed from non-invasive recordings. Beyond their spatial structure, neural field theory predicts the temporal evolution of eigenmodes through analytically derPok Him Siu
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