Knowledge catalogue
arXiv:2610.07232v1 Announce Type: new Abstract: Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation
arXiv:2610.08533v1 Announce Type: new Abstract: Geometric alignment scores based on Gram determinants provide a compact way to model higher-order consistency among modalities, yet how such scores resp
arXiv:2610.07843v1 Announce Type: new Abstract: In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In hie
arXiv:2610.07177v1 Announce Type: cross Abstract: An open contrastive decision model is near chance as a judge on the hard public benchmarks: Contrastive-LM/CLM-v0.1-8B scores between 0.351 (best- of-
arXiv:2610.07059v1 Announce Type: new Abstract: Yield forecasts help planners and farmers decide on inputs, storage and imports, but small agricultural tables can make reported accuracy fail on a new
arXiv:2610.07075v1 Announce Type: new Abstract: High-quality datasets are essential for reliable machine learning, but dataset curation remains costly and hard to generalize across domains. Existing m
arXiv:2610.07048v1 Announce Type: cross Abstract: We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an externa
arXiv:2610.07067v1 Announce Type: new Abstract: Image denoising is a crucial task in image processing, focused on improving image quality by minimizing noise while maintaining essential structural ele
arXiv:2610.08618v1 Announce Type: cross Abstract: A robot that offloads its control loop to the network carries the receiver with it, so link quality is decided by where it goes. Simulating this requi
arXiv:2507.07426v4 Announce Type: replace Abstract: Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their e
arXiv:2610.07795v1 Announce Type: new Abstract: This paper presents a novel effective Gaussian Splatting (GS) sequence Compression method that utilizes the Video codec (GSCV). Existing video-based GS
arXiv:2610.07345v1 Announce Type: cross Abstract: IAM policy analysis typically emphasizes the authorization capabilities encoded in a policy, but security analyst review priority may also depend on t
arXiv:2610.08363v1 Announce Type: new Abstract: Maritime systems operate in highly dynamic environments where unexpected equipment failures can compromise safety, reliability, and operational efficien
arXiv:2509.07936v3 Announce Type: replace Abstract: This paper aims to analyse the feature space of a vision-related Deep Neural Network (DNN) by proposing a decoder that can generate an image whose f
arXiv:2610.07399v1 Announce Type: new Abstract: Federated deep clustering seeks to learn clustering-friendly representations from decentralized unlabeled data while preserving client privacy. However,
arXiv:2610.08574v1 Announce Type: cross Abstract: Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment
arXiv:2610.07550v1 Announce Type: cross Abstract: Random access (RA) is one of the most foundational medium access control (MAC) layer scheduling schemes for handling unpredictable data traffic from m
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, ex
arXiv:2610.08345v1 Announce Type: cross Abstract: Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample si
arXiv:2610.07243v1 Announce Type: new Abstract: Breast cancer is the leading cause of cancer-related mortality among women in Sub-Saharan Africa, where delayed diagnosis results from limited radiology
arXiv:2610.07791v1 Announce Type: new Abstract: Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tend
arXiv:2610.08414v1 Announce Type: new Abstract: Image Bitstream Fine-grained Understanding (IBFU) aims to directly perform fine-grained classification and semantic description generation from encoded
arXiv:2610.07788v1 Announce Type: new Abstract: Consumer-grade cameras in flat-port housings are widely used for underwater exploration and mapping of coral reefs and seafloor habitats due to their lo
arXiv:2610.08585v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failu
arXiv:2610.07032v1 Announce Type: cross Abstract: Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual
arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. E
arXiv:2610.08214v1 Announce Type: new Abstract: We report on an approach to teaching logic to mixed groups of computer science, mathematics, and philosophy students, based on the logico-pluralistic Lo
arXiv:2610.08669v1 Announce Type: cross Abstract: On-device learning is necessary when the model encounters user-,sensor-, or environment-specific shifts after deployment. Although parameter-efficient
Agentic AI governance helps midsized distributors decide which tasks software agents can handle and which require human oversight. For lean IT teams, automating more work also means checking the data
arXiv:2408.12549v4 Announce Type: replace-cross Abstract: This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. The
arXiv:2610.07110v1 Announce Type: new Abstract: High-quality 3D reconstruction of lunar terrain from sparse rover images is indispensable for autonomous lunar exploration, but remains challenging beca
arXiv:2610.07766v1 Announce Type: new Abstract: We present Open Telco (OTel), an open telecom AI resource that releases derived telecom datasets for retrieval, reranking, instruction tuning, and safet
arXiv:2605.06021v2 Announce Type: replace Abstract: Systematic reviews and meta-analyses often need numerical data reported only in figures, and extracting them with interactive digitisers usually req
arXiv:2610.06964v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in interactive environments, yet their ability to continually evolve from experi
arXiv:2610.08689v1 Announce Type: new Abstract: Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limiti
arXiv:2610.08640v1 Announce Type: new Abstract: Long-horizon urban navigation requires sequential local decisions whose errors can compound over time. Imitation learning (IL) rarely learns from failur
arXiv:2610.07532v1 Announce Type: cross Abstract: LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks incl
arXiv:2610.07114v1 Announce Type: cross Abstract: The Frechet Inception Distance (FID) is widely used to evaluate generative models, but its empirical plug-in estimator suffers from finite-sample bias
arXiv:2610.07093v1 Announce Type: new Abstract: The rapid proliferation of heterogeneous data sources within the Internet of Things (IoT) across domains such as smart cities, energy management, and en
arXiv:2610.07430v1 Announce Type: new Abstract: Reducing nonstationarity in a persistent time series entails deciding how much of its temporal dependence to remove. In finance, fractional differencing
arXiv:2610.08161v1 Announce Type: cross Abstract: Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized. We introduce MedCo
arXiv:2610.08155v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) have become a promising paradigm for complex information-seeking and reasoning tasks by ena
arXiv:2610.07320v1 Announce Type: new Abstract: Multi-robot missions in infrastructure-denied environments frequently rely on reliable communication links between spatially separated locations. We pro
arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and avera
arXiv:2610.07823v1 Announce Type: new Abstract: The AI CUP 2025 Precise Analysis of Table Tennis Smart Racket Data Competition introduced smart table tennis rackets that collect extensive player swing
arXiv:2610.06974v1 Announce Type: new Abstract: Knowledge graph embedding (KGE) methods represent entities and predicates in continuous vector spaces to infer missing knowledge. Despite strong benchma
arXiv:2610.04687v2 Announce Type: replace Abstract: Answering questions over imperfect tables requires handling errors that can affect the answer. We investigate two challenges for large language mode
arXiv:2610.08043v1 Announce Type: new Abstract: Ensuring safety in autonomous driving requires continuous map maintenance supported by reliable quality indicators. In this context, it is crucial to id
arXiv:2610.05281v2 Announce Type: replace Abstract: Modern agents increasingly ground their reasoning in observations returned by tools, such as file contents read from a workspace. However, the data
arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical
arXiv:2610.04343v1 Announce Type: cross Abstract: Reservoir computing (RC) is a machine learning technique for data-driven modeling of dynamics for forecasting and control, primarily studied in comput
arXiv:2610.05749v1 Announce Type: new Abstract: This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliabili
arXiv:2506.09368v2 Announce Type: replace Abstract: Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by i
arXiv:2610.06443v1 Announce Type: new Abstract: The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant ba
arXiv:2503.06340v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated remarkable generative capabilities in continuous data domains such as images and videos. Recently, discrete
arXiv:2610.04624v1 Announce Type: new Abstract: Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for which g
arXiv:2610.04381v1 Announce Type: new Abstract: Fine-grained image editing requires more than producing a visually plausible result: an editor must execute the requested attribute change precisely whi
arXiv:2511.19453v2 Announce Type: replace-cross Abstract: Autonomous vehicles continuously generate large volumes of heterogeneous sensor and state data streams. While this data supports real-time aut
arXiv:2610.05154v1 Announce Type: new Abstract: Geostationary satellite observations are important for wildfire detection and monitoring. The current study evaluates machine learning models for MTG FC
arXiv:2412.18539v3 Announce Type: replace-cross Abstract: Recent advances in statistical learning theory have revealed profound connections between mutual information (MI) bounds, PAC-Bayesian theory,