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Related Articles from SNS
Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation
new Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundation models like the Segment Anything Model (SAM) show promise, they often struggle with medical images without specific adaptation. Moreover, point prompts, despite being the most natural form of user interaction, provide insufficient spatial context for reliable segmentation, particularly when target structures are irregular or...
I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models
Announce Type: replace Abstract: Deep learning models are increasingly used in scientific prediction tasks where strong benchmark performance is often interpreted as evidence of scientifically meaningful behavior. This interpretation is fragile, as models may exploit shortcut features, dataset-specific regularities, or distributional biases that are predictive on held-out data but not aligned with domain-relevant structure. To address this limitation, we introduce the \textsc{I-SAFE}...
Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging
Announce Type: new Abstract: Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterative update rules. Although these updates are principled and interpretable, their dynamics cannot be adapted from data. We propose to learn the update mechanism itself while preserving the underlying Bayesian structure by unfolding a classical joint hyperparameter-learning solver into a trainable neural...
What Can Verifiable Decapsulation Tests Certify? Pass Bounds and Fault-Recognition Limits for FO-Based KEMs
arXiv:2606.04443v1 Announce Type: new Abstract: Black-box tests for Fujisaki-Okamoto decapsulation observe the sampled execution seen by the harness, whereas the reencryption computation itself is visible only through the values that reach final key derivation. We study confirmation-code-augmented KEM variants under an honest-reference harness in which the reference encapsulation fixes a hidden final-key point $\langle good,B,W\rangle$, with $W$ the confirmation witness. For a $q$-localized...
Interpreting Brain Responses to Language with Sparse Features from Language Models
arXiv:2606.06857v1 Announce Type: new Abstract: A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs) have emerged as a powerful tool to address this challenge, but studies relating biological and artificial representations are often criticized as relating one black box to another. The present work introduces Augmented Sparse Encoding Models, an encoding framework that replaces dense LM hidden...
TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions
arXiv:2606.01540v1 Announce Type: new Abstract: Shapley values are a widely used tool for attributing importance and interactions among input variables in black-box models, but their computation involves a function defined over an exponentially large space of subsets. We propose TN-SHAP-G, a framework that exploits structure in graph-structured inputs to compute Shapley values and higher-order interaction indices efficiently. Given a predictor and a fixed masking scheme, TN-SHAP-G learns a...
Health-related ballot measures more likely to pass
Health-related ballot measures more likely to pass Sadie Harley Scientific Editor Andrew Zinin Lead Editor As voters are increasingly asked to decide complex health policy questions at the ballot box, new research from the Brown School at Washington University in St. Louis finds that health care-related ballot measures draw more voters to the polls and are more likely to pass than other initiatives—but they're also especially sensitive to opposition spending by special-interest groups. The...
Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset
Announce Type: new Abstract: Background: Alzheimer's disease (AD) affects over 55 million people worldwide. Accurate, interpretable detection of normal cognition (NC), mild cognitive impairment (MCI), and AD from routine clinical assessments remains a critical unmet need. Methods: An XGBoost classifier was developed for three-class detection using eight clinical features from the Alzheimer's Disease Neuroimaging Initiative (ADNI): MMSE, CDR Global, CDR Sum of Boxes (CDR-SB), MoCA, FAQ, age,...
A prognostic human brain network for diffuse midline glioma
Abstract Diffuse midline gliomas (DMGs) are near-universally lethal tumours of the childhood central nervous system1,2. In animal models, DMGs form brain-wide integrated networks through neuron-to-glioma synapses3,4,5,6 and glioma-to-glioma gap junctional coupling3. This extensive connectivity robustly promotes the growth and invasion of DMG3,4,5,6,7,8,9 and other glial malignancies10,11,12 through paracrine mechanisms and direct neuron-to-glioma synapses.
World Cup Power Rankings: Who are the front-runners with 48 hours to go?
It's official: Tuesday marks just 48 hours until the 2026 World Cup kicks off in earnest, when Mexico host South Africa in Mexico City on June 11. As we did at the 100-day and 30-day mark, we're looking at how our global reporters feel about the tournament from a contenders vs. pretenders perspective with just two days until kickoff. We asked our panel of 20 reporters to rank their top 15 favorites from No. 1 (meaning "the trophy is theirs") to No. 15 ("the cool outsider's pick for a...