Knowledge catalogue
arXiv:2606.22835v1 Announce Type: new Abstract: Controllable video generation demands independent command of the camera and the subject, yet 2D conditioning entangles them: camera- and object-induced
arXiv:2606.21655v1 Announce Type: cross Abstract: Implicit Neural Representations (INRs) have recently emerged as a promising paradigm for image compression, offering a fundamentally different approac
arXiv:2606.18627v2 Announce Type: replace Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a s
arXiv:2606.20623v1 Announce Type: cross Abstract: How do people build abstract, reusable knowledge from sequential experience under bounded cognitive resources? To answer this question, we integrate r
arXiv:2606.23570v1 Announce Type: new Abstract: Contrastive representation learning struggles on physiological signals when each subject contributes a distinct baseline pattern. If class differences o
arXiv:2606.23086v1 Announce Type: new Abstract: Latent diffusion models achieve strong generative performance by operating in a compressed latent space produced by a variational autoencoder (VAE). How
arXiv:2606.22491v1 Announce Type: new Abstract: This paper presents 'PenduMorph', a wireless reconfigurable rolling spherical robot designed as a modular platform for enclosed locomotion and inter-mod
arXiv:2606.23665v1 Announce Type: cross Abstract: We introduce PHAST-Net, an attention-guided, physics-informed network for unified estimation of Ideal Time-Frequency Representations (ITFRs), spanning
arXiv:2606.20983v1 Announce Type: new Abstract: Understanding and predicting microstructure evolution is central to materials design, yet purely data-driven spatiotemporal learning models often suffer
arXiv:2606.23028v1 Announce Type: new Abstract: Real-time weld-pool perception is critical for closed-loop control in laser wire-feed welding, where sensing, computation, and actuator response introdu
arXiv:2606.21781v1 Announce Type: cross Abstract: High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the
arXiv:2606.21162v1 Announce Type: cross Abstract: 3D human avatars have shown impressive visual fidelity driven by pose-conditioned models, yet they still lack the physical ability required for intera
MIT engineers have found the first direct evidence that plant seeds can sense sounds in nature: Rice submerged in shallow water germinated 30% to 40% more quickly when exposed to vibrations from water
arXiv:2606.23373v1 Announce Type: new Abstract: Medical image segmentation is a fundamental task for medical image processing and computer-assisted intervention, yet data imbalance and small lesion de
arXiv:2606.21917v1 Announce Type: cross Abstract: Detecting hallucination risk before generation enables abstention, retrieval augmentation, and routing decisions without incurring the cost of decodin
arXiv:2408.06350v2 Announce Type: replace-cross Abstract: One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving.
arXiv:2606.21492v1 Announce Type: new Abstract: Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whet
arXiv:2606.18123v2 Announce Type: replace Abstract: Predicting immune biomarkers associated with the tumor immune microenvironment (TIME) is critical for advancing precision oncology, yet existing app
arXiv:2510.04646v2 Announce Type: replace Abstract: Flow matching models generate high-fidelity molecular geometries but incur significant computational costs during inference, requiring hundreds of n
arXiv:2606.23044v1 Announce Type: new Abstract: Numbers have algebraic structure that standard neural embeddings often fail to expose. We introduce Prime Fourier Embeddings (PFE), which encode integer
arXiv:2606.23230v1 Announce Type: new Abstract: Person re-identification (Re-ID) is a crucial task in surveillance and human behavior analysis, often used in public spaces such as transport hubs. Trad
arXiv:2409.18905v3 Announce Type: replace-cross Abstract: We consider the effect of Gaussian perturbations on least-squares residuals, orthogonal projections, and QR-type algorithms. The problem that
arXiv:2603.01949v2 Announce Type: replace Abstract: Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are
arXiv:2606.22525v1 Announce Type: new Abstract: Sparse-view computed tomography is a severely ill-posed inverse problem, where recent 3D Gaussian Splatting methods offer an efficient explicit represen
arXiv:2606.21863v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) in remote sensing images aims to segment categories beyond a fixed label space. Recent SAM 3-based methods
arXiv:2606.20587v1 Announce Type: cross Abstract: What matters more for building foundation models for wireless packet traces: the tokenizer or the architecture or both? To answer this question, we bu
arXiv:2606.20817v1 Announce Type: cross Abstract: NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for con
arXiv:2606.23591v1 Announce Type: new Abstract: One way to understand LLM behavior is to trace its output back to the training data. Two types of measures are commonly used for output tracing: data-si
arXiv:2606.21749v1 Announce Type: new Abstract: Deep neural networks often produce poorly calibrated confidence estimates, overstating their certainty even when predictions are incorrect. Temperature
arXiv:2606.20326v2 Announce Type: replace Abstract: We develop QCPIKAN, the first quantum-classical physics-informed Kolmogorov-Arnold network designed to solve partial differential equations (PDEs).
arXiv:2606.23411v1 Announce Type: cross Abstract: Quantum machine learning methods are increasingly explored for modeling complex environmental systems, including groundwater heat plume dynamics. In t
arXiv:2508.10900v2 Announce Type: replace Abstract: Quantum Implicit Neural Representations (QINRs) have emerged as a promising paradigm that leverages parametrised quantum circuits to encode and proc
arXiv:2606.21333v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning on graph-structured data by iteratively propagating and aggregating inform
arXiv:2606.21889v1 Announce Type: new Abstract: We study fixed-precision ranking-and-selection in structured settings where the answer may be non-unique and where noisy estimates may temporarily admit
arXiv:2606.22749v1 Announce Type: new Abstract: Pre-trained Vision Foundation Models (VFMs) have become central to modern computer vision due to their powerful semantic representations and strong gene
arXiv:2602.06937v2 Announce Type: replace-cross Abstract: Realistic sound propagation is essential for immersion in a virtual scene, yet physically accurate wave-based simulations remain computational
arXiv:2603.19925v2 Announce Type: replace-cross Abstract: Whole slide image (WSI) analysis heavily relies on multiple instance learning (MIL). While recent methods benefit from large-scale foundation
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