Knowledge catalogue
arXiv:2606.21179v1 Announce Type: new Abstract: Soil properties relevant to agricultural and environmental applications are conventionally measured using elaborate laboratory methods involving physica
arXiv:2606.20832v1 Announce Type: cross Abstract: Astrophysical simulations frequently address multi-scale, multi-physics problems through subsystem decomposition, problem-tailored integration schemes
arXiv:2606.22351v1 Announce Type: cross Abstract: Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, wher
arXiv:2505.17338v2 Announce Type: replace Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and
arXiv:2606.20737v1 Announce Type: cross Abstract: We study factual edit propagation in a controlled synthetic knowledge-graph QA setting, comparing four architectures that cross loop recurrence with s
arXiv:2606.22195v1 Announce Type: new Abstract: Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems commonly extract target parameters by peak-s
arXiv:2606.23361v1 Announce Type: new Abstract: Backdoor attacks on molecular graph neural networks (GNNs) are typically evaluated as abstract graph edits, but real molecular learning pipelines do not
arXiv:2606.21913v1 Announce Type: new Abstract: Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remar
arXiv:2603.08305v2 Announce Type: replace Abstract: Text-conditioned generative models for volumetric medical imaging provide semantic control but lack explicit anatomical guidance, often resulting in
arXiv:2606.22823v1 Announce Type: new Abstract: Enzyme substrate interaction (ESI) prediction is a fundamental computational task for biocatalyst discovery and reaction screening in large biochemical
arXiv:2606.21271v1 Announce Type: new Abstract: Sparse rewards pose a central challenge in reinforcement learning, since agents receive no informative signal until they reach their goal. Intrinsic-rew
arXiv:2507.15232v2 Announce Type: replace-cross Abstract: Recent advances have sparked significant interest in the development of privacy-preserving Principal Component Analysis (PCA). However, many e
arXiv:2606.20666v1 Announce Type: cross Abstract: Associative memory models play a fundamental role in pattern retrieval, but their performance often degrades under adversarial perturbations and sever
arXiv:2606.22521v1 Announce Type: cross Abstract: We show that replacing the standard MSE denoising loss in diffusion models with a nonlinear transformation induced by an f-divergence yields a simple
arXiv:2606.20703v1 Announce Type: new Abstract: Label-free, image-based cellular mechanophenotyping in microfluidic devices provides a high-throughput method for single-cell profiling. However, while
arXiv:2606.21488v1 Announce Type: new Abstract: The vulnerability of ML models to adversarial examples has recently emerged as a major concern. While adversarial training is one of the most effective
arXiv:2606.21786v1 Announce Type: new Abstract: We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-c
arXiv:2508.08048v2 Announce Type: replace Abstract: While video generation models excel at producing high-quality monocular videos, generating 3D stereoscopic and spatial videos for immersive applicat
arXiv:2606.22630v1 Announce Type: new Abstract: Diffusion policies have recently emerged as a powerful paradigm for representing complex action distributions in reinforcement learning (RL). However, t
arXiv:2606.22471v1 Announce Type: new Abstract: A key bottleneck in training generalist policies for bimanual dexterous manipulation is the lack of large-scale, high-quality datasets. Synthetic data g
arXiv:2606.21947v1 Announce Type: new Abstract: Vision Transformers have achieved remarkable success in many fields, yet their deployment on edge devices remains challenging due to their substantial c
arXiv:2606.23524v1 Announce Type: new Abstract: End-to-end OCR increasingly relies on autoregressive sequence models, where the quadratic cost of Transformer attention limits efficient transcription o
arXiv:2606.21756v1 Announce Type: cross Abstract: In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the d
arXiv:2606.21326v1 Announce Type: cross Abstract: We present an ML-based vessel detection and localization system, trained with weak supervision from imperfect AIS labels, that achieves a 97.8% detect
arXiv:2606.23448v1 Announce Type: new Abstract: Deep learning methods have achieved state-of-the-art in time series forecasting, yet their accuracy varies considerably across samples, as some instance
arXiv:2606.22449v1 Announce Type: cross Abstract: Current embodied world models are primarily optimized for predictive objectives, limiting their ability to generalize under distribution shifts and re
arXiv:2606.21027v1 Announce Type: new Abstract: Single-shot fringe projection profilometry (FPP) has been actively studied for real-time measurement, dynamic object reconstruction, and motion-sensitiv
arXiv:2503.16000v2 Announce Type: replace Abstract: We present extbf{SenseExpo}, a lightweight single-robot exploration framework that integrates a compact map prediction network into a frontier-based
arXiv:2606.21716v1 Announce Type: new Abstract: The capability to encode spatially distinct magnetization patterns within soft materials enables remote control over complex deformations. This characte
arXiv:2606.20684v1 Announce Type: new Abstract: Viewport-adaptive 360-degree imaging seeks to allocate a fixed sampling budget to the region a viewer is likely to observe. Existing view-biased project
arXiv:2606.20918v1 Announce Type: new Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimizat
arXiv:2606.21099v1 Announce Type: new Abstract: Variational inference (VI) is a powerful method for principled posterior inference for scientific inverse imaging. VI learns the posterior distribution,
arXiv:2312.07865v4 Announce Type: replace Abstract: Despite the success of diffusion-based customization methods on visual content creation, increasing concerns have been raised about such techniques
arXiv:2606.23536v1 Announce Type: new Abstract: We propose a method for estimating time-varying traffic flow patterns from sparse aggregated vehicle counts. The method partitions the study area into s
arXiv:2606.21096v1 Announce Type: new Abstract: Semi-supervised learning (SSL) on data streams is challenging due to the continuous evolution of high-volume data and the scarcity of labels. Existing m
arXiv:2606.23357v1 Announce Type: new Abstract: Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show tha
arXiv:2510.04180v2 Announce Type: replace Abstract: Deep neural networks can achieve high accuracy while relying on evidence that is hard to inspect or misaligned with the intended task. Concept Bottl
arXiv:2606.22308v1 Announce Type: cross Abstract: Lesion segmentation in breast ultrasound involves two related challenges. In images with lesions, speckle noise, low tissue contrast, and posterior ac
arXiv:2606.21828v1 Announce Type: cross Abstract: Neural operators are increasingly used to warm-start Newton solvers for nonlinear PDEs, on the premise that a low test error places the initial guess
arXiv:2606.22874v1 Announce Type: new Abstract: Long contexts have become standard in pretrained LLMs, yet they remain expensive to run: prefill compute grows quadratically with sequence length, and e
arXiv:2606.23188v1 Announce Type: new Abstract: Black-box optimization (BBO) deals with problems where objective functions lack explicit analytical forms and are expensive to evaluate. Factorization m
arXiv:2606.18697v2 Announce Type: replace Abstract: Model-based learning agents use learned world models to predict future states, plan actions, and adapt to new environments. However, the process of
arXiv:2606.20856v1 Announce Type: new Abstract: Multi-view surface reconstruction is a core problem in computer vision. One prominent line of work represents the surface implicitly as a signed distanc
arXiv:2606.23186v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) estimates the blood volume pulse (BVP) signal from facial videos, enabling contact-free health monitoring. Convention
arXiv:2603.07307v2 Announce Type: replace Abstract: Recent token merging techniques for Vision Transformers (ViTs) provide substantial speedups by reducing the number of tokens processed by self-atten
arXiv:2507.03318v3 Announce Type: replace Abstract: Explainable artificial intelligence approaches accelerate drug discovery by improving molecular representation learning, identifying key molecular s
arXiv:2510.10779v5 Announce Type: replace Abstract: With the growing volume of CT examinations, there is an increasing demand for automated tools such as organ segmentation, abnormality detection, and
arXiv:2606.23477v1 Announce Type: cross Abstract: Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations
Here’s a problem you probably didn’t solve in school: You’re an ambitious young plumber from Brooklyn in a world inhabited by violent human-size mushrooms called Goombas. The love of your life has bee
arXiv:2606.22124v1 Announce Type: new Abstract: Accurate recognition of anatomical structures is essential for safe and effective minimally invasive surgery (MIS), yet it remains underexplored in surg
arXiv:2606.20864v1 Announce Type: cross Abstract: Developing robust intrusion detection systems (IDS) for IoT environments requires large, labeled datasets capturing realistic traffic distributions ac
arXiv:2606.21607v1 Announce Type: new Abstract: Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing mode
arXiv:2606.17055v2 Announce Type: replace Abstract: The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based
arXiv:2606.23132v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time ada
arXiv:2509.21606v3 Announce Type: replace Abstract: Federated continual learning (FCL) enables collaborative model training across distributed clients on sequentially arriving tasks without revisiting
arXiv:2606.22867v1 Announce Type: cross Abstract: Three-dimensional full waveform inversion (3DFWI) is a powerful technique for reconstructing high-resolution subsurface velocity models. However, its
arXiv:2606.21189v1 Announce Type: new Abstract: Non-stationary partial differential equations (PDEs) arise throughout scientific computing, where the dominant frequency content and energy distribution
Jos Benschop is climbing a ladder to get to the top of his newest machine. It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled al
arXiv:2606.23306v1 Announce Type: cross Abstract: We study the limits of CTC-internal scoring for N-best hypothesis selection and locate the information bottleneck separating acoustic confidence from
arXiv:2606.21585v1 Announce Type: new Abstract: We equip the space of beliefs with a cost geometry (what it costs to pass from one belief to another): optimal transport in Wasserstein space, reweighte