Knowledge catalogue
arXiv:2610.17312v1 Announce Type: new Abstract: This paper proposes a method for constructing a neural network that achieves perfect classification of all samples in a given training set, without taki
arXiv:2610.14680v1 Announce Type: cross Abstract: Sign Language Recognition (SLR) presents complex challenges due to variable signing speeds and spatial intricacies. Specifically, Indian Sign Language
arXiv:2610.17392v1 Announce Type: cross Abstract: We present a method for enforcing exactly Dirichlet, Neumann, and Robin conditions on general triangular domains with arbitrarily curved boundaries. T
arXiv:2508.18564v2 Announce Type: replace Abstract: A recent paper, ``A Graphon-Signal Analysis of Graph Neural Networks'', by Levie, analyzed message passing graph neural networks (MPNNs) by embeddin
arXiv:2510.16044v2 Announce Type: replace-cross Abstract: Identification of anomalous events within system logs constitutes a pivotal element within the frame- work of cybersecurity defense strategies
arXiv:2610.16996v1 Announce Type: new Abstract: The penetration-resistant design of concrete structures requires the spatiotemporal evolution of the stress field within the target, whereas in design o
arXiv:2610.18739v1 Announce Type: new Abstract: We develop a rate-transfer method that converts finite-time Lyapunov estimates for an associated stochastic differential equation (SDE) into finite-time
arXiv:2610.18695v1 Announce Type: new Abstract: Kronecker-preconditioned optimizers recompute a cached curvature eigendecomposition only periodically, and implementations and adaptive methods control
arXiv:2610.14133v1 Announce Type: cross Abstract: Generative modelling involves two complementary tasks: learning a probabilistic approximation of an unknown data distribution from observations, and d
arXiv:2610.16456v1 Announce Type: cross Abstract: Comparing temporal networks is a challenging problem in network analysis. Commonly adopted methods compare structural properties of temporal graphs an
arXiv:2610.17490v1 Announce Type: new Abstract: Retrospective evaluation of early-warning models may not reflect information available when predictions are issued and outcomes become observable. We de
arXiv:2610.14688v1 Announce Type: new Abstract: LLM-augmented data workflows often involve multiple heterogeneous models operating over substantial shared context, such as schemas, tool specifications
arXiv:2610.17333v1 Announce Type: cross Abstract: Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, w
arXiv:2610.16635v1 Announce Type: new Abstract: Natural-language self-consistency and executable programs provide complementary test-time evidence for mathematical reasoning, yet either channel may su
arXiv:2610.14549v1 Announce Type: new Abstract: Physical systems and distributed AI workflows can retain past information, receive delayed feedback and combine conflicting signals. These features pose
arXiv:2610.14963v1 Announce Type: new Abstract: Sparse-view Cone-Beam Computed Tomography (CBCT) reconstruction is essential for reducing radiation dose and scan effort, but remains underconstrained d
arXiv:2610.14252v1 Announce Type: new Abstract: Looped transformers reuse the same layer multiple times, allowing inference compute to be adjusted without changing the model. Recent adaptive-depth met
arXiv:2610.16624v1 Announce Type: new Abstract: Human motion prediction during human-robot collaboration is critical for achieving safe and efficient interactions in shared environments. Unlike previo
arXiv:2610.15810v1 Announce Type: new Abstract: The introduction of long chain-of-thought (CoT) reasoning and external tools has substantially improved the performance of multimodal large language mod
arXiv:2610.15723v1 Announce Type: cross Abstract: We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination
arXiv:2610.16532v1 Announce Type: cross Abstract: This work develops a geometric theory of aerodynamic promptness for multirotor platforms whose propeller directions depend on a smooth orientation-mec
arXiv:2603.17712v1 Announce Type: cross Abstract: Zero-Shot Object Navigation (ZSON) in unknown multi-floor environments presents a significant challenge. Recent methods, mostly based on semantic valu
arXiv:2610.14210v1 Announce Type: cross Abstract: ScanRefer Multiple queries require spatial disambiguation: several instances of the target class are present, and the referencer must pick one. When p
arXiv:2610.16891v1 Announce Type: new Abstract: Inferring tumor-growth laws from sparse, noisy observations requires more than accurate curve fitting. We study an AI-assisted workflow involving symbol
arXiv:2610.17944v1 Announce Type: new Abstract: Current image-guided neuronavigation techniques, such as intraoperative MRI and ultrasound, are limited by high costs, limited sensitivity and operation
arXiv:2604.02706v2 Announce Type: replace Abstract: Odometry estimation using light detection and ranging (LiDAR) and an inertial measurement unit (IMU), known as LiDAR-inertial odometry (LIO), often
arXiv:2508.06313v2 Announce Type: replace Abstract: This paper presents a unified system-level modeling and control framework for an all-electric heavy-duty robotic manipulator (HDRM) driven by electr
arXiv:2610.17975v1 Announce Type: cross Abstract: We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems. Electrostatics are evaluated anal
arXiv:2610.16669v1 Announce Type: cross Abstract: Bayesian inference for latent point process intensities often relies on per-dataset sampling or optimization, making repeated inference computationall
arXiv:2610.14932v1 Announce Type: cross Abstract: Clustering is typically performed using only object features, although additional information about relationships between objects may be available and
arXiv:2610.15328v1 Announce Type: new Abstract: We introduce Anchor-and-Harvest Curriculum (AHC), a domain-informed, rule-based training system for Craftax-Full. AHC combines achievement-oriented task
arXiv:2610.15030v1 Announce Type: new Abstract: Can a volume that reproduces every external RGB view still have an incorrect density? We study this question in emission-absorption rendering with an in
arXiv:2610.15388v1 Announce Type: new Abstract: Contrastive representation learning (CRL) is a fundamental paradigm for learning transferable representations. Recent work identifies its population tar
arXiv:2610.17227v1 Announce Type: new Abstract: Generating images beyond the training resolution of Diffusion Transformers (DiTs) remains challenging, as the number of tokens grows rapidly with resolu
arXiv:2610.15287v1 Announce Type: new Abstract: Category-level 6D pose and shape estimation requires recovering an object's pose and geometry from partial observations without an exact instance model.
arXiv:2610.14075v1 Announce Type: new Abstract: Multi-view perception is often treated as a safe design choice for robot manipulation: if one camera is useful, more cameras should be better. We revisi
arXiv:2610.17469v1 Announce Type: new Abstract: Social media platforms generate vast volumes of user-generated content that often obscure meaningful discourse, a phenomenon conceptualized as social no
arXiv:2610.18169v1 Announce Type: cross Abstract: A scenario-based development approach has become state of the art for automated driving systems. Once a set of scenario classes covering the operation
arXiv:2610.16518v1 Announce Type: new Abstract: We study the last iterate of stochastic gradient descent (SGD) on streaming least squares with discretely distributed features (Slock's model), which de
arXiv:2610.17743v1 Announce Type: new Abstract: Image retargeting aims to resize images while preserving salient content free from distortion and stretching. Recent learning-based solutions reduce rel
arXiv:2610.18422v1 Announce Type: cross Abstract: We present a real-time, hybrid framework for occlusion handling and depth-aware tracking in minimally invasive surgeries (MIS). Occlusions caused by s
arXiv:2610.15245v1 Announce Type: cross Abstract: Objective. To develop an automated method for segmentation and uptake quantification of carotid atherosclerotic inflammation on 64Cu-CANF-comb PET whi
arXiv:2610.14733v1 Announce Type: new Abstract: B-Human won multiple competitions in the Middle Division of the new Humanoid Soccer League in 2026. In addition to regular matches with three or five pl
arXiv:2610.17615v1 Announce Type: cross Abstract: High-dimensional microscopy captures features across space, time, and spectrum, but complex backgrounds obscure signals. We present Background2Backgro
arXiv:2610.14017v1 Announce Type: cross Abstract: We propose Barycentric Anchored Search, a post-hoc weight-space procedure for improving fine-tuned vision models under distribution shift without addi
arXiv:2610.16892v1 Announce Type: new Abstract: Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space. Existing VA-based image editors are typically train
arXiv:2610.16330v1 Announce Type: new Abstract: Feed-forward transformer models have driven remarkable progress in 3D vision,yet their quadratic complexity renders them impractical for long video sequ
arXiv:1808.02932v5 Announce Type: replace-cross Abstract: In stochastic multi-armed bandits, rewards are generated by an unknown distribution; the world's reward uncertainty induces the exploration-ex
arXiv:2610.18656v1 Announce Type: new Abstract: Language model systems repeatedly make test-time decisions by comparing similarity: they retrieve exemplars, rank candidates, and revise responses. Yet
arXiv:2610.16745v1 Announce Type: new Abstract: Attention sinks are usually studied after they have formed. We ask whether a measurable hidden-state change appears beforehand and whether changing that
arXiv:2610.16696v1 Announce Type: new Abstract: Signal Temporal Logic (STL) specifies robot tasks with spatio-temporal requirements, and its robustness semantics allow planning by trajectory optimizat
arXiv:2610.15543v1 Announce Type: cross Abstract: We study federated learning from continuously arriving data: each client samples from its own distribution, these distributions drift over time, and t
arXiv:2610.14151v1 Announce Type: cross Abstract: Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for r
arXiv:2607.01416v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) provide an intrinsically interpretable alternative to post-hoc explanations. However, existing CBMs often rely on p
arXiv:2610.18390v1 Announce Type: cross Abstract: We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own h
arXiv:2610.16074v1 Announce Type: cross Abstract: Co-circulating influenza, COVID-19, and respiratory syncytial virus (RSV) create a shared hospital-admission burden, because beds, staff, testing capa
arXiv:2610.18375v1 Announce Type: new Abstract: A learned representation is useful beyond its source task only if later tasks can make use of what the encoder has already learned. Source-task performa
arXiv:2610.18543v1 Announce Type: new Abstract: Foundation models are increasingly used as feature extractors for downstream tasks with limited data, typically through representations obtained from th
arXiv:2610.18399v1 Announce Type: new Abstract: Visual token pruning for multimodal large language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned perform
arXiv:2610.14261v1 Announce Type: new Abstract: Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histo