Knowledge catalogue
arXiv:2610.05607v1 Announce Type: new Abstract: Emergency obstacle avoidance requires a mobile robot to brake or change its heading within the available distance. This paper investigates the dependenc
arXiv:2610.04726v1 Announce Type: new Abstract: Full actuation is commonly valued for independent position and attitude tracking. This work establishes a complementary principle: when only a position
arXiv:2610.05235v1 Announce Type: new Abstract: Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine w
arXiv:2610.06545v1 Announce Type: new Abstract: We present Empirical Variational Autoencoder, a general generative framework for continuous-valued (i.e., non-vector-quantized) sequences. EVA is based
arXiv:2510.23087v2 Announce Type: replace Abstract: In robot-assisted minimally invasive surgery, accurate 3D reconstruction from endoscopic video is vital for downstream tasks and improved outcomes.
arXiv:2610.05996v1 Announce Type: new Abstract: Latent world models based on Joint-Embedding Predictive Architecture (JEPA) are deterministic by design. While successful in fully observable scenarios,
arXiv:2610.04066v1 Announce Type: new Abstract: Automatic calving-front delineation from synthetic aperture radar imagery is challenging because the front is a thin and often ambiguous boundary betwee
arXiv:2312.00113v4 Announce Type: replace Abstract: We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image and an event camera stream. Conventional ca
arXiv:2604.26654v1 Announce Type: cross Abstract: This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model
arXiv:2610.04746v1 Announce Type: cross Abstract: Simulation is a fundamental computational primitive in reinforcement learning (RL), yet conventional simulation explicitly generates individual trajec
arXiv:2610.04085v1 Announce Type: cross Abstract: Balanced discrete optimal transport between n sources and n targets of unit mass is exactly the minimum-cost assignment problem-a bipartite perfect ma
arXiv:2610.05207v1 Announce Type: new Abstract: Feed-forward 3D models provide strong multi-view geometric priors, while on- line simultaneous localization and mapping (SLAM) relies mainly on local me
arXiv:2610.05279v1 Announce Type: new Abstract: In federated learning, mitigating class imbalance is essential to improve minority-class performance. A common approach to address this problem is to au
arXiv:2610.04538v1 Announce Type: new Abstract: Reward-guided image editing at test time seeks to improve a specified reward while preserving source content and visual plausibility. Many existing appr
arXiv:2610.06810v1 Announce Type: cross Abstract: We study agnostic tomography of pure bosonic Gaussian states: given copies of an arbitrary n-mode bosonic state rho, the goal is to output a pure Gaus
arXiv:2610.05296v1 Announce Type: new Abstract: Most deep learning weather models assign a fixed set of variables and pressure levels to predefined channels, limiting transfer across atmospheric field
arXiv:2610.04258v1 Announce Type: cross Abstract: Biomedical imaging modalities often require a trade-off among resolution, field of view (FOV), and acquisition speed. Video mosaicking offers a strate
arXiv:2610.05880v1 Announce Type: new Abstract: Resting-state fMRI is important in the classification of brain disorders, but highly multimodal and exhibits strong multisite heterogeneity. Existing me
arXiv:2603.08433v2 Announce Type: replace Abstract: The Foret Montmorency (FoMo) dataset is a comprehensive multi-season data collection, recorded over the span of one year in a boreal forest. Featuri
arXiv:2610.03755v1 Announce Type: new Abstract: The magnitude of the generalized Euclidean cross product is a Gram volume whose degree under common scaling is fixed by the integer dimension of the spa
arXiv:2610.06575v1 Announce Type: new Abstract: FrontVeg V2 is an open-source, training-free software framework for foregroundaware zero-shot segmentation of plant traits in high-resolution images of
arXiv:2011.09192v1 Announce Type: cross Abstract: The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individua
arXiv:2610.05801v1 Announce Type: new Abstract: We propose a Gauss-map variation (GMV) model for image denoising that measures the spatial variation of the tangent-plane projectors of the scaled image
arXiv:2610.03757v1 Announce Type: cross Abstract: Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth
arXiv:2610.05026v1 Announce Type: new Abstract: Vision-language-action (VLA) models encode semantic information from vision-language pretraining, but manipulation also requires precise spatial reasoni
arXiv:2406.07579v2 Announce Type: replace-cross Abstract: 2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas
arXiv:2610.05874v1 Announce Type: new Abstract: Scalable Graph Transformers are commonly trained and evaluated on static large graphs in a transductive setup. Many scalable Graph Transformer component
arXiv:2610.06042v1 Announce Type: new Abstract: Discovery of causal relationships in high-dimensional Gene Regulatory Networks (GRN) is computationally challenging and often difficult to interpret due
arXiv:2508.11880v2 Announce Type: replace Abstract: Convolutional Neural Networks (CNNs) are an effective approach for classification tasks, particularly when the training dataset is large. Although C
arXiv:2511.16976v3 Announce Type: replace Abstract: Deep equilibrium models (DEQs) have recently emerged as a powerful paradigm for training infinitely deep weight-tied neural networks that achieve st
arXiv:2610.04568v1 Announce Type: cross Abstract: We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model. Replacing the drift
arXiv:2610.06688v1 Announce Type: new Abstract: Factories, museums and surveyors photograph the same space months apart and need to know which objects changed. When each visit is reconstructed with 3D
arXiv:2610.06805v1 Announce Type: new Abstract: Long-horizon planning with latent world models requires reasoning across timescales and levels of abstraction. Existing task-agnostic JEPA world models
arXiv:2409.18778v2 Announce Type: replace Abstract: Efficiently determining the satisfiability of a boolean equation -- known as the SAT problem for brevity -- is crucial in various industrial problem
arXiv:2610.05139v1 Announce Type: new Abstract: Code large language model (Code LLM) assistants generate code from heterogeneous development contexts, including open files, imported modules, pasted sn
arXiv:2603.26954v2 Announce Type: replace Abstract: The trend towards larger training setups has brought a renewed interest in partially asynchronous two-phase optimizers which optimize locally and th
arXiv:2610.05739v1 Announce Type: new Abstract: Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full gener
arXiv:2610.05135v1 Announce Type: new Abstract: Under a fixed physical law, the visible geometry of a scene determines how motion must change. We ask how video generators realize this relationship. We
arXiv:2610.04988v1 Announce Type: new Abstract: Latent world models plan by rolling a frozen predictor forward under candidate action sequences and ranking the candidates by the latent distance betwee
arXiv:2610.06034v1 Announce Type: cross Abstract: We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB)
arXiv:2610.05757v1 Announce Type: new Abstract: This paper introduces Hybrid DeepSEE (HDS), a Human-in-the-Loop (HITL) neuro-symbolic framework for proactive drift anticipation in Visual SLAM (V-SLAM)
arXiv:2610.05463v1 Announce Type: cross Abstract: Visual search is a fundamental cognitive ability. This study investigates whether Multimodal Large Language Models (MLLMs) exhibit human-like difficul
arXiv:2610.06778v1 Announce Type: cross Abstract: While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the i
arXiv:2610.06146v1 Announce Type: cross Abstract: Accurately quantifying the predictive uncertainty or improving model calibration plays an important role in medical image classification, in particula
arXiv:2610.06675v1 Announce Type: new Abstract: We study gradient descent (GD) with a large constant stepsize for logistic regression on linearly separable data. Existing analysis shows an accelerated
arXiv:2511.18199v2 Announce Type: replace-cross Abstract: Ecological Momentary Assessment (EMA) studies provide real-time data on suicidal thoughts and behaviors, but forecasting suicide attempts rema
arXiv:2610.06505v1 Announce Type: cross Abstract: Proactive AI assistants continuously observe a user's activity and decide whether to provide new guidance or remain silent. They should provide approp
arXiv:2610.05997v1 Announce Type: new Abstract: Early development unfolds in caregiver-infant dyads, where infants' sensorimotor streams are shaped by physical contact and face-to-face interaction. Ye
arXiv:2610.04273v1 Announce Type: new Abstract: We study supervised classification problems with missing feature values. Existing approaches often decouple imputation from classification, producing im
arXiv:2610.06128v1 Announce Type: new Abstract: Predictive maintenance requires reliable remaining useful life (RUL) estimation. Existing methods mainly follow two paradigms: wear-based aging models t
arXiv:2610.06630v1 Announce Type: cross Abstract: CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators direc
arXiv:2610.06018v1 Announce Type: new Abstract: Spatio-temporal video grounding (STVG) aims to localize objects or events described by natural language queries in both space and time. Existing STVG mo
arXiv:2610.04727v1 Announce Type: new Abstract: In worker-equipment proximity monitoring, video transformers are widely used for collision anticipation and have demonstrated strong performance. Howeve
arXiv:2610.04396v1 Announce Type: cross Abstract: Split Computing (SC) enables efficient deployment of Deep Neural Networks (DNNs) by partitioning inference between edge devices and cloud servers. How
arXiv:2610.05967v1 Announce Type: new Abstract: Image compression, restoration, and generation all require a way to measure how different two images look to a person. Pixel error ignores how people se
arXiv:2610.06234v1 Announce Type: new Abstract: Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classifi
arXiv:2610.05549v1 Announce Type: new Abstract: We formulate joint estimation of common-slope decay rates and amplitudes from room impulse responses (RIRs) as parameterized nonnegative matrix factoriz
arXiv:2610.04786v1 Announce Type: cross Abstract: Time-series foundation models buy zero-shot forecasting with large temporal corpora; a vision model needs none, since a natural image implicitly embed
arXiv:2610.06588v1 Announce Type: new Abstract: Long-horizon camera-controlled video generation relies on persistent memory to maintain scene consistency. Existing systems follow two strategies to ach
arXiv:2610.06349v1 Announce Type: new Abstract: Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often ado