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Related Articles from SNS
The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench
arXiv:2605.24782v2 Announce Type: replace Abstract: While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invariants, making even perception-based out-of-distribution accuracy a poor proxy for scientific utility. As a result, models may look correct without reasoning correctly, a discrepancy we term the Perception-Physics Paradox. To address this gap, we introduce scientific...
Graph Traversal on Tensor Cores: A BFS Framework for Modern GPUs
arXiv:2606.05081v1 Announce Type: new Abstract: Modern GPUs have Tensor Cores (TCs) capable of extremely high-throughput matrix operations, yet graph algorithms remain difficult to accelerate because of their irregular and data-dependent execution patterns. This work presents BLEST, a TC-accelerated framework that reformulates Breadth-First Search (BFS) as a bit-level sparse matrix-vector computation while addressing the load imbalance, memory inefficiency, and synchronization overheads that...
From Forecast to Action: A Deep Learning Model for Predicting Power Outages During Tropical Cyclones
arXiv:2512.06644v2 Announce Type: replace Abstract: Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution outage forecasting is essential for enabling both proactive mitigation planning and real-time emergency response. This study introduces the SpatioTemporal Outage ForeCAST (STO-CAST) model, a deep learning framework developed for real-time, regional-scale outage prediction during TC events...
MODIS Thermal Infrared Sounding (MOTIS): Estimating Tropical Cyclone Central Pressure from Warm-Core Anomalies
Announce Type: new Abstract: This study presents a novel framework for estimating the central sea-level pressure ($P_{\rm c}$) of tropical cyclones (TCs) using infrared radiometers. We leverage the long-overlooked combination of high spatial resolution and sounding capability of the Moderate Resolution Imaging Spectroradiometer (MODIS) to measure warm-core anomalies in TC eyes. We develop the MODIS Thermal Infrared Sounding (MOTIS) framework, which performs instrument-specific preprocessing...
Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
arXiv:2605.01129v2 Announce Type: replace Abstract: Machine unlearning (MU) has emerged as a key mechanism for ensuring data privacy and regulatory compliance by enabling models to forget specific training samples. However, recent studies have shown that the removal of data can inadvertently introduce privacy leakages to the retain set,i.e., data that remain in the model after unlearning. In this paper, we extend the scope of privacy analysis in unlearning to the often-overlooked retained data.
A first-in-class pulsatile FXR agonist for bile-acid-related liver diseases
Abstract Nuclear receptors are central regulators of metabolism1, yet therapeutic strategies that enforce continuous receptor activation frequently lead to reduced efficacy and unacceptable toxicity. Here we report a first-principles drug design strategy that aligns pharmacokinetics with physiological signalling cycles. We developed linafexor, a potent non-bile-acid agonist of the farnesoid X receptor (FXR)2; it is engineered for rapid systemic clearance, which enables pulsatile receptor...
Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
arXiv:2605.30523v1 Announce Type: new Abstract: Recent work describes what transformers can and cannot compute through connections to boolean circuits, but existing results lack exact characterizations and are sensitive to modeling choices. Padded transformers -- to whose input filler symbols such as ``...'' are appended -- emerge as a useful gadget for establishing equivalences to circuit classes by providing polynomial space for adaptive parallel computation.
FAF-CD: Frequency-Aware Fusion for Change Detection under Imperfect Multimodal Remote Sensing
arXiv:2606.03114v1 Announce Type: new Abstract: Remote sensing change detection for real-world monitoring often relies on imperfect heterogeneous observations, where pre- and post-event images may be asynchronous, cross-sensor, or affected by illumination, seasonal, and modality shifts. This setting is especially challenging for EO-SAR disaster mapping, where nuisance variation can resemble structural damage. We propose FAF-CD, a frequency-aware hybrid framework with a DINOv3-pretrained...
FrontierCode
Introducing FrontierCode Raising the bar from correctness to quality Today’s coding benchmarks have established that models can write correct code. But as AI-generated code becomes the dominant path to production, correctness is now table stakes. The question that we should be asking is: can models actually write good code?