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1301 storiesPublic health and medicine worldwide — sourced from WHO News.
TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space
arXiv:2509.03242v3 Announce Type: replace Abstract: Testing Deep Learning (DL)-based systems is an open challenge. Although it is relatively easy to find inputs that cause a DL model to misbehave, the grouping of such in…
Fast Multipole Method with Complex Coordinates
arXiv:2509.05458v3 Announce Type: replace Abstract: When solving problems of potential theory in unbounded layered media, recently developed integral equation methods rely on coordinate complexification to permit truncat…
Connecting Strategy Improvement, the Simplex Algorithm and Lopsidedness
arXiv:2509.16075v2 Announce Type: replace Abstract: The strategy improvement algorithm for mean payoff games and parity games is a local improvement algorithm, just like the simplex algorithm for linear programs. Their s…
SAM-DCE: Decoupled Context Enhancement for Prompt-Free Medical Image Segmentation
arXiv:2509.16886v3 Announce Type: replace Abstract: The Segment Anything Model (SAM) is a promising foundation model for medical image segmentation, but its prompt-driven design is not directly suitable for fully automat…
BrainPro: Process-conditioned Pretraining with Topographic Retrieval for Large EEG Models
arXiv:2509.22050v3 Announce Type: replace Abstract: EEG foundation models are pretrained on heterogeneous corpora, but two challenges remain underexplored: (1) experimental protocols provide mental-process cues that are …
$\mathbf{R}^3$: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain
arXiv:2509.24022v3 Announce Type: replace Abstract: Image reconstruction from corrupted images is crucial across many domains. Most reconstruction networks are trained on post-ISP sRGB images, even though the image-signa…
SDPose: Exploiting Diffusion Priors for Out-of-Domain and Robust Pose Estimation
arXiv:2509.24980v4 Announce Type: replace Abstract: Pre-trained diffusion models provide rich latent features across U-Net levels and are emerging as powerful vision backbones. While prior works such as Marigold and Lotu…
Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
arXiv:2509.25045v4 Announce Type: replace Abstract: Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations. Current interpretability methods ei…
AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?
arXiv:2510.01433v3 Announce Type: replace Abstract: Vision-based manipulation policies are commonly learned from rich visual observations, typically using dense visual features extracted from full resolution images. It r…
HEHA: Bounded Suboptimal Planning for Constrained Routing and Its Use in Heterogeneous Multi-Robot Exploration of Unknown Environments
arXiv:2510.04161v2 Announce Type: replace Abstract: This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and leg…
LARA-Gen: Enabling Continuous Emotion Control for Music Generation Models via Latent Affective Representation Alignment
arXiv:2510.05875v3 Announce Type: replace Abstract: Recent advances in text-to-music models have enabled coherent music generation from text prompts, yet fine-grained emotional control remains unresolved. We introduce LA…
The Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction
arXiv:2510.06198v4 Announce Type: replace Abstract: Despite the remarkable reasoning capabilities of large language models, they still struggle with one-shot relation extraction without predefined relation labels. We ide…
Protecting De-identified Documents from Search-based Linkage Attacks
arXiv:2510.06383v3 Announce Type: replace Abstract: While de-identification models can help conceal the identity of the individuals mentioned in a document, they fail to address linkage risks, defined as the potential to…
The Feature Understandability Scale for Human-Centred Explainable AI: Assessing Tabular Feature Importance
arXiv:2510.07050v3 Announce Type: replace Abstract: As artificial intelligence becomes increasingly pervasive and powerful, the ability to audit AI-based systems is growing in importance. However, explainability for arti…
Deterministic algorithms for inhomogeneous Bernoulli trials: Shapley value of network devices
arXiv:2510.07572v2 Announce Type: replace Abstract: Suppose that $n$ computer devices are to be connected to a network via inhomogeneous Bernoulli trials. The Shapley value of a device quantifies how much the network's v…
Sculpting the Latent Space: Disentanglement with WAE-MMD and Programmable Priors
arXiv:2510.11953v2 Announce Type: replace Abstract: Learning disentangled representations, where semantic features are independently captured in a latent space, is dominated by Variational Auto Encoders (VAE) and Adversa…
Adversarially Robust Few-Shot Anomaly Detection with Vision Foundation Models
arXiv:2510.13643v3 Announce Type: replace Abstract: Vision foundation models such as DINOv2 enable strong few-shot anomaly detection (FSAD) through simple non-parametric k-nearest-neighbor (k-NN) scoring over frozen patc…
Grid-Supervised Flow Refinement for Wireless Localization
arXiv:2510.14111v2 Announce Type: replace Abstract: This paper introduces FlowLoc, a framework for high-accuracy user equipment positioning from channel state information observed at multiple base stations. The input is …
Memorizing Long-tail Data Can Help Generalization Through Composition
arXiv:2510.16322v2 Announce Type: replace Abstract: Deep learning has led researchers to rethink the relationship between memorization and generalization. In many settings, memorization does not hurt generalization due t…
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
arXiv:2510.16511v2 Announce Type: replace Abstract: Real-world multivariate time series anomalies are rare and often unlabeled. Additionally, prevailing methods rely on increasingly complex architectures tuned to benchma…