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新论文 · v1arXiv:2611.14136

HaiRuo: Forecasting Ocean Eddies as Fields and Objects with a Satellite-Altimetry Foundation Model

Xukun Qin, Haochen Sun, Yuyang Tian, Ge Chen

原文摘要

Mesoscale eddies carry most of the ocean's kinetic energy and move heat, salt and nutrients across basins, yet where an eddy will be two weeks ahead is still read off ocean models with post-hoc tracking heuristics. We introduce HaiRuo, which forecasts eddies as fields and as objects at once: from 30 days of satellite sea-level maps, one forward pass returns 15 days of sea level and geostrophic velocity, daily eddy masks and the trajectory of every eddy alive today. HaiRuo pairs two components with a clear division of labour: (1) HaiRuo-1B, an open-weight 1.05-billion-parameter foundation model pretrained without labels on 24 years of global satellite-altimetry observations, which supplies the 15-day physical fields and masks (+0.14 field skill and +1.1 mIoU points under the same decoder) and further lowers 15-day centre error by 1.2 km frozen (1.35 km over training from scratch); and (2) XuanJi, a Lagrangian object decoder that samples the evolving forecast in each eddy's co-moving polar frame and refines the 15-day path across three look-ahead rounds, removing 2.5 km on HaiRuo-1B (2.9 km with joint fine-tuning) and 2.5-3.3 km via its object attention across six published backbones. Under one pre-registered global evaluation over the whole ocean, HaiRuo improves on fourteen methods on every endpoint (25.2 vs. 30.6 km 15-day centre error, -18%), holding its margin outside the labelled regions and through both phases of the El Nino-Southern Oscillation (ENSO), while reproducing westward drift, 50-60 deg S eastward reversal and poleward cyclone-anticyclone deflection. Over ten days through one fixed tracker, HaiRuo's forecast field cuts the eddy-centre error of WenHai and XiHe by 37-43% (-2.8 km vs. day 0 while they degrade +4.3-8.7 km), and its object trajectories cut pipeline error by 77-78% globally. Open-weight checkpoints, code and project page: https://github.com/xkqin/HaiRuo

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12796

智能手机深度感知透射成像用于鸡胸肉肌病检测

Smartphone-Enabled Depth-Aware Transillumination Imaging for Detection of Chicken Breast Fillet Myopathies in Chicken Fillets

Chaitanya Pallerla, Ramesh Bahadur Bist, Yihong Feng, Yang Tian, Shiva Paudel, Casey M. Owens, Cengiz Koparan, Jeyamkondan Subbiah 等 9 位作者

AI 速读 基于摘要

该研究提出一种基于智能手机透射成像与3D点云的非破坏性方法，用于对鸡胸肉木质胸（WB）严重程度进行分级。

cs.LG

arXiv ↗PDF ↗
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Woody Breast (WB) myopathy is a major quality defect in broiler chickens characterized by fibrosis, increased tissue hardness, and altered structural properties that affect product quality. Conventional palpation is subjective and labor-intensive, while many non-destructive methods require specialized equipment or substantial computational resources. This study developed a smartphone-based transillumination and 3D point-cloud framework for non-destructive WB severity classification. A total of 267 chicken breast fillets, including normal breast (NB, n = 87), mild WB (MWB, n = 85), and severe WB (SWB, n = 95), were evaluated using RAW transillumination images and 3D point clouds acquired with an iPhone 16 Pro. A total of 91 features, comprising 14 image-derived, 76 geometric, and one physics-informed Beer-Lambert attenuation feature, were extracted. Partial correlation controlling for thickness revealed a significant association between image characteristics and WB severity (r = 0.413, p < 0.0001), indicating optical information independent of fillet geometry. SWB tissue exhibited approximately 26% greater light attenuation per unit thickness than NB. The 2D-only models achieved approximately 82% test accuracy, while the multimodal ensemble combining Fusion-DGCNN, XGBoost, and physics-guided logistic regression (PGLR) achieved approximately 90% accuracy. These findings demonstrate the complementary value of optical, geometric, and physics-informed features and establish the feasibility of smartphone-based, non-destructive WB severity assessment.

新论文 · v1arXiv:2611.12695

TACTUS: A Robotic Palpation System for Spatially Heterogeneous Hardness Mapping of Woody Breast Myopathy

Chaitanya Pallerla, Yihong Feng, Avisekh Tripathy, Yang Tian, Pouya Sohrabipour, Amirreza Davar, Casey M. Owens, Cengiz Koparan 等 9 位作者

原文摘要

Woody breast (WB) myopathy is a spatially heterogeneous fibro-degenerative condition of the broiler Pectoralis major muscle. Existing mechanical reference methods rely on single-point or sequential compression measurements, limiting characterization of spatial hardness variability. This study presents TACTUS (Tactile Adaptive Conformal 3D Uniform Sensing), an automated tactile array that generates thickness-normalized, three-dimensional hardness maps at 20 anatomically distributed locations within approximately 30 s. TACTUS integrates 20 independently motorized tactuators with force-sensing resistors and encoder-driven linear actuators, alongside a LiDAR depth camera for surface profiling and adaptive compression. The system was evaluated using 100 broiler breast fillets across four WB severity classes (Normal, Mild, Moderate, Severe). TACTUS identified significant force differences among severity classes (ANOVA, p < 0.001), with the strongest differentiation in the cranial region and a significant caudal effect not detected by texture-analyzer measurements. Fillet-level TACTUS force showed moderate but significant correlation with texture-analyzer measurements (Pearson r = 0.441, p < 0.001). Semivariogram, local change entropy, and heterogeneity analyses revealed increasing and anatomically structured hardness variability with WB severity. These findings demonstrate TACTUS as an automated, operator-independent platform for spatially resolved mechanical characterization and ground-truth generation to support hyperspectral and near-infrared spectroscopy-based WB classification systems.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.11884

Sobolev Regularized MMD Gradient Flow

Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton, Zonghao Chen

原文摘要

We analyze Sobolev descent, a regularized variant of maximum mean discrepancy (MMD) gradient flow based on a gradient penalty on the MMD witness function. This regularization mitigates the non-convexity of the MMD objective, and yields provable \emph{global} convergence guarantees in MMD, in both continuous and discrete time. A surprising appeal is that our convergence analysis does not rely on isoperimetric assumptions on the target distribution. Instead, it is based on a regularity condition on the difference between kernel mean embeddings. We extend Sobolev descent such that it is applicable in both sampling (from an unnormalized target distribution)---using Stein kernels---and generative modeling settings, unlike previous works, where a gradient flow is suitable for only generative modeling or sampling but not both. We empirically validate the method on a broad range of tasks across both settings.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39287HF 社区推荐

OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation

Junli Liu, Zhongrui You, Chenhui Li, Zhen Li, Yuyang Tian, Qizhen Chen, Delin Qu, Haoming Song 等 12 位作者

原文摘要

Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referenced entities, reason about spatial relationships, and act. Endowing aerial robots with an analogous functional organization remains challenging, as existing systems are often specialized for individual capabilities, dependent on offboard computation, or unable to sustain closed-loop operation on resource-constrained platforms. We present OpenFlyV2, a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous aerial control within a single model. A compact action expert converts the shared multimodal context into short-horizon flight waypoints, while motion-aware temporal memory and action priors reduce the computational cost of closed-loop execution. We construct \dataset{}, a simulation-based dataset comprising approximately 70,000 navigation trajectories and 60,000 vision-language instruction--response pairs. OpenFlyV2 generates action chunks, each comprising 12 waypoints, entirely onboard at 10 Hz using an NVIDIA Jetson Orin NX. It transfers zero-shot from simulation to physical quadrotor flight, achieving a 48\% navigation success rate across four real-world environments without real-world aerial training data. Extensive results demonstrate that broad vision-language competence and continuous closed-loop flight can coexist within a single model on a resource-constrained aerial platform. Code will be made publicly available at https://github.com/Ideal-ljl/AeroBrain.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35563

High-resolution tactile observation makes hidden racket-control mechanisms learnable

Haoyang Tian, Zhengwei Li, Houcheng Li, Xu Yang, Peigeng Gu, Xinyue Wang, Tairan Zhu, Shuo Jiang 等 9 位作者

原文摘要

Experts can master fine hand-object manipulation skills, yet limited conscious perception of rapid tactile interactions hinders their ability to articulate the underlying control mechanisms. Badminton racket control requires rapid, fine hand-force coordination, providing a demanding testbed for uncovering these mechanisms. An 864-node tactile-inertial glove with synchronized 100-Hz acquisition recorded over 14,000 strokes from more than 70 participants across five skills. Inspired by sensorimotor control, our mechanics-grounded two-layer framework inferred object-driving and grip-stabilizing control from tactile history, while resolving their spatiotemporal force realization. We found that higher proficiency was associated with selective rather than extensive control: greater skill specificity, anticipatory regulation, focal distal modulation and lower geometric torque cancellation, while spatiotemporal coordination showed a non-monotonic acquire-refine pattern. We translated these mechanisms into personalized guidance targeting force control and force-motion coordination. In a randomized cohort, mechanism-guided learners gained 0.72 expert-rated skill levels versus 0.05 with level-only feedback, making control mechanisms actionable.

cs.LG · cs.HC · cs.RO · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34843

When Respondents Are Scarce: LLM-Derived Semantic Priors for Bayesian Confirmatory Factor Analysis

Shaohuai Feng, Mohd Wira Mohd Shafiei, Mohd Sukri Shafie, Siyang Tian

原文摘要

Across social and behavioral research, measuring latent constructs can be challenging when target populations are small or difficult to reach. Recruiting additional respondents may be costly or infeasible, leaving researchers with limited data for estimating questionnaire factor structures. Yet item wording contains structural expectations before data collection, and whether these can improve small-sample estimation without replacing empirical validation remains unclear. We introduce the Semantic-Empirical Updating Framework (SEUF), which converts large language model paraphrases and embeddings, aligned with hypothesized item-factor assignments, into prespecified priors for Bayesian confirmatory factor analysis. We evaluated SEUF using a 38-item survey of 617 industrial-park stakeholders and two public surveys, FEVS and RIASEC. Semantic structure persisted after wording controls across all three instruments. With 50 respondents, SEUF achieved loading precision comparable to conventional CFA with 70-82 respondents, equivalent to 29-39% fewer respondents at the specified precision benchmark. Results showed lower loading errors and improved credible-interval coverage relative to conventional CFA in both public surveys. Gains were greatest when respondent data were limited, highlighting the value of semantic priors under information scarcity. By harnessing structural information already embedded in questionnaires, SEUF advances a more resource-efficient approach to measurement and opens new directions for study design and empirical inference in hard-to-reach populations.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2505.12864Google Research

LEXam: Benchmarking Legal Reasoning on 340 Law Exams

Yu Fan, Jingwei Ni, Jakob Merane, Yang Tian, Yoan Hermstrüwer, Yinya Huang, Mubashara Akhtar, Etienne Salimbeni 等 17 位作者

原文摘要

Long-form legal reasoning remains a key challenge for large language models (LLMs) in spite of recent advances in test-time scaling. To address this, we introduce LEXam, a novel benchmark derived from 340 law exams spanning 116 law school courses across a range of subjects and degree levels. The dataset comprises 7,537 law exam questions in English and German. It includes both long-form, open-ended questions and multiple-choice questions with varying numbers of options. Besides reference answers, the open questions are also accompanied by explicit guidance outlining the expected legal reasoning approach such as issue spotting, rule recall, or rule application. Our evaluation on both open-ended and multiple-choice questions present significant challenges for current LLMs; in particular, they notably struggle with open questions that require structured, multi-step legal reasoning. Moreover, our results underscore the effectiveness of the dataset in differentiating between models with varying capabilities. Deploying an ensemble LLM-as-a-Judge paradigm with rigorous human expert validation, we demonstrate how model-generated reasoning steps can be evaluated consistently and accurately, closely aligning with human expert assessments. Our evaluation setup provides a scalable method to assess legal reasoning quality beyond simple accuracy metrics. Project page: https://lexam-benchmark.github.io/.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗