Knowledge catalogue
arXiv:2607.05443v1 Announce Type: cross Abstract: Scientists increasingly rely on open-source tools to support their research workflows, yet discovering relevant software among over 600 million GitHub
arXiv:2607.05734v1 Announce Type: cross Abstract: Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to thi
arXiv:2607.05798v1 Announce Type: cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have evolved from static perception to interleaved visual-language reasoning, often re
arXiv:2607.06132v1 Announce Type: cross Abstract: Multi-Pool Chemical Exchange Saturation Transfer (CEST) MRI provides valuable metabolic information but is clinically limited by long acquisition time
arXiv:2607.06048v1 Announce Type: cross Abstract: We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension. The networks we consid
arXiv:2601.01484v2 Announce Type: replace Abstract: Knowledge Distillation (KD) is a central paradigm for transferring knowledge from a large teacher network to a typically smaller student model, ofte
arXiv:2607.06442v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not nece
arXiv:2607.05952v1 Announce Type: cross Abstract: While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by str
arXiv:2607.05994v1 Announce Type: new Abstract: Human-Object Interaction (HOI) video generation aims to synthesize realistic videos of humans manipulating diverse objects, serving as a promising avenu
arXiv:2607.05988v1 Announce Type: new Abstract: Multispectral image(MSI) and hyperspectral image(HSI) object tracking object tracking exploits recorded band-wise observations to improve target--backgr
arXiv:2607.06124v1 Announce Type: cross Abstract: The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs. Reasoning on energy usage
arXiv:2607.05628v1 Announce Type: new Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment. However, the prese
arXiv:2607.06408v1 Announce Type: new Abstract: Robust remote verification of identity documents relies on analyzing faint, transparent security features like Optically Variable Devices (OVDs), or 'ho
arXiv:2607.05416v1 Announce Type: new Abstract: We present a new method for structural sequence analysis grounded in Algorithmic Information Theory (AIT). At its core is the Ladderpath approach, which
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Why worms (and microbes) are catching on as a manure pollution
The emoluments clause is supposed to prevent the President from receiving foreign bribes. That hasn’t stopped Trump’s family from receiving about $13 billion in “investments” from foreign governments,
arXiv:2607.06356v1 Announce Type: cross Abstract: Accurate quantification of lung disease severity from chest imaging is critical for clinical decision-making and resource allocation. We propose a tri
arXiv:2607.06309v1 Announce Type: cross Abstract: Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolat
arXiv:2607.06179v1 Announce Type: cross Abstract: There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenar
arXiv:2607.05689v1 Announce Type: new Abstract: We present our systems for SemEval-2026 Task 10 (PsyCoMark), addressing conspiracy marker extraction (Subtask 1) and document-level conspiracy detection
arXiv:2607.05964v1 Announce Type: new Abstract: Filled pauses (FPs) are a universal feature of spontaneous speech, yet most studies rely on small, single-language corpora, limiting the generalisabilit
arXiv:2607.05901v1 Announce Type: new Abstract: Automatic depression detection using audio-visual data faces significant challenges, particularly in disentangling overlapping feature distributions and
Under Trump, pardons have a price, and criminals know it. Trump has erased a growing number of criminal convictions – and $1.5 billion in penalties and restitution owed to taxpayers and victims – for
arXiv:2602.14761v2 Announce Type: replace-cross Abstract: Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability. Moreov
arXiv:2607.05996v1 Announce Type: new Abstract: Unlearnable examples keep publicly shared photos from being learned by unauthorized face-recognition models. An imperceptible perturbation, added before
arXiv:2607.06549v1 Announce Type: new Abstract: Deep learning-based computer-aided diagnosis (CAD) systems have shown strong performance in breast cancer diagnosis, particularly for classification tas
arXiv:2506.17182v3 Announce Type: replace Abstract: Disentangled representations separate factors that are shared across conditions from those that are condition-specific. Such separation is needed fo
arXiv:2607.06560v1 Announce Type: new Abstract: We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation sp
arXiv:2607.06295v1 Announce Type: new Abstract: Deep learning models have emerged in machine learning and related fields, demonstrating astonishing performance in various visual tasks. Despite their g
arXiv:2603.14033v2 Announce Type: replace-cross Abstract: Audio anti-spoofing systems are typically trained to assign one authenticity label to an entire speech utterance. This formulation becomes und
arXiv:2607.05420v1 Announce Type: cross Abstract: This study examines how alternative systems of scholarly representation identify and characterize broad public administration (PA) and artificial inte
arXiv:2607.06166v1 Announce Type: new Abstract: Prediction markets aggregate dispersed beliefs into prices that act as probabilistic forecasts of uncertain events. Classical theory establishes a clean
arXiv:2607.05691v1 Announce Type: new Abstract: Every chemical language model reading SMILES begins with a tokenizer, yet the field has inherited byte-pair encoding (BPE) from natural language with li
1,200 former DOJ employees have just sent a letter urging Senators not to confirm Todd Blanche as AG: 'In the coming weeks, many will rightly underscore the corruption and abuses that have defined the
Serial Experiments Lain is a landmark 1998 anime series that premiered on TV Tokyo, known for its experimental narrative structure and exploration of consciousness, technology, and virtual reality. Th
arXiv:2607.04898v1 Announce Type: new Abstract: We study 3D point cloud reconstruction from multiple partially observed 2D projections. Given two or more projections of an unknown 3D point cloud, toge
arXiv:2607.04184v1 Announce Type: new Abstract: Market manipulation is the dubious practice of manipulating stock prices in order to make a quick profit, which truly degrades confidence on trading pla
arXiv:2607.03256v1 Announce Type: new Abstract: Few-step distilled diffusion students cut text-to-image inference from ~50 to 1-8 network evaluations, but the quality gap is usually summarised by a si
arXiv:2607.02670v1 Announce Type: new Abstract: Electroencephalography (EEG) offers a noninvasive approach for examining neurophysiological correlates of dimensional psychopathology, yet systematic ev
arXiv:2607.04518v1 Announce Type: new Abstract: Gait is a distinctive behavioral characteristic that enables non-invasive individual identification without requiring physical interaction with an anima
arXiv:2607.04680v1 Announce Type: new Abstract: Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws. Developing data-driven surrog
arXiv:2607.02782v1 Announce Type: cross Abstract: Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible e
arXiv:2602.02908v2 Announce Type: replace-cross Abstract: Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise
arXiv:2603.00819v2 Announce Type: replace-cross Abstract: This paper surveys recent developments at the intersection of operator learning, statistical learning theory, and approximation theory. First,
arXiv:2607.03815v1 Announce Type: cross Abstract: For compact convex sets L,K subset R^n, denote by lambda_K(L) the smallest size of a homothet of K that contains L. We define a measure of symmetry ba
arXiv:2408.05124v2 Announce Type: replace-cross Abstract: Image sensors are fundamental to many intelligent systems, allowing visual perception and AI-driven decision-making. However, their integrity
arXiv:2607.02753v1 Announce Type: cross Abstract: Neuromorphic controllers for size, weight, and power-constrained systems require neural architectures that are both energy-efficient and interpretable
arXiv:2607.04028v1 Announce Type: cross Abstract: We propose a unified algebraic framework for classification performance evaluation that encompasses binary, multiclass, multilabel, ordinal, hierarchi
arXiv:2607.04081v1 Announce Type: new Abstract: In-context learning (ICL) has emerged as a central capability of pretrained language models, yet its theoretical analysis has focused primarily on causa
arXiv:2607.03860v1 Announce Type: new Abstract: The Strong Lottery Ticket Hypothesis (SLTH) asserts that sufficiently overparameterized, randomly initialized neural networks contain sparse subnetworks
arXiv:2607.03621v1 Announce Type: new Abstract: This paper presents the development and evaluation of a collaborative system for real-time reconstruction of fragmented paper documents in the context o
arXiv:2510.01038v2 Announce Type: replace Abstract: Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality
arXiv:2607.03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets. We extend Bir
arXiv:2507.23033v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient computing paradigm for neural rendering, but existing spike-based Neural Radiance Field (
arXiv:2607.04256v1 Announce Type: new Abstract: Current feed-forward 3D reconstruction methods predict pixel aligned Gaussian primitives, resulting in highly redundant representations. A natural solut
arXiv:2607.04719v1 Announce Type: new Abstract: Aerial robots are increasingly moving from remote observation toward physical interaction with objects, surfaces, structures, loads, and surrounding flo
arXiv:2607.04224v1 Announce Type: cross Abstract: AI-RAN aims to unify artificial intelligence and radio access network workloads on a shared compute substrate. While this paradigm has so far been dem
arXiv:2607.05100v1 Announce Type: cross Abstract: Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent
arXiv:2607.04254v1 Announce Type: cross Abstract: Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devic
arXiv:2506.16898v2 Announce Type: replace Abstract: Diffusion-based text-to-image models are increasingly used for urban analysis and scenario generation, but their geographic knowledge and representa