Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network
arXiv:2603.02273v1 Announce Type: new Abstract: Prioritizing disease-associated genes is central to understanding the molecular mechanisms of complex disorders such as Alzheimer's disease (AD). Traditional network-based approaches rely on static centrality measures and often fail to capture cross-modal biological heterogeneity. We...
A Comparative Study of UMAP and Other Dimensionality Reduction Methods
arXiv:2603.02275v1 Announce Type: new Abstract: Uniform Manifold Approximation and Projection (UMAP) is a widely used manifold learning technique for dimensionality reduction. This paper studies UMAP, supervised UMAP, and several competing dimensionality reduction methods, including Principal Component Analysis (PCA), Kernel PCA,...
Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning
arXiv:2603.02280v1 Announce Type: new Abstract: With the widespread adoption of deep learning in visual tasks, Class-Incremental Learning (CIL) has become an important paradigm for handling dynamically evolving data distributions. However, CIL faces the core challenge of catastrophic forgetting, often manifested...
The Malignant Tail: Spectral Segregation of Label Noise in Over-Parameterized Networks
arXiv:2603.02293v1 Announce Type: new Abstract: While implicit regularization facilitates benign overfitting in low-noise regimes, recent theoretical work predicts a sharp phase transition to harmful overfitting as the noise-to-signal ratio increases. We experimentally isolate the geometric mechanism of this transition: the...
Preconditioned Score and Flow Matching
arXiv:2603.02337v1 Announce Type: new Abstract: Flow matching and score-based diffusion train vector fields under intermediate distributions $p_t$, whose geometry can strongly affect their optimization. We show that the covariance $\Sigma_t$ of $p_t$ governs optimization bias: when $\Sigma_t$ is ill-conditioned, and...
Learning graph topology from metapopulation epidemic encoder-decoder
arXiv:2603.02349v1 Announce Type: new Abstract: Metapopulation epidemic models are a valuable tool for studying large-scale outbreaks. With the limited availability of epidemic tracing data, it is challenging to infer the essential constituents of these models, namely, the epidemic parameters and...
Learning Optimal Search Strategies
arXiv:2603.02356v1 Announce Type: new Abstract: We explore the question of how to learn an optimal search strategy within the example of a parking problem where parking opportunities arrive according to an unknown inhomogeneous Poisson process. The optimal policy is a...
Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles
arXiv:2603.02406v1 Announce Type: new Abstract: Generative models have recently advanced $\textit{de novo}$ protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks,...
Personalized Multi-Agent Average Reward TD-Learning via Joint Linear Approximation
arXiv:2603.02426v1 Announce Type: new Abstract: We study personalized multi-agent average reward TD learning, in which a collection of agents interacts with different environments and jointly learns their respective value functions. We focus on the setting where there exists a shared...
A Unified Revisit of Temperature in Classification-Based Knowledge Distillation
arXiv:2603.02430v1 Announce Type: new Abstract: A central idea of knowledge distillation is to expose relational structure embedded in the teacher's weights for the student to learn, which is often facilitated using a temperature parameter. Despite its widespread use, there remains...
Manifold Aware Denoising Score Matching (MAD)
arXiv:2603.02452v1 Announce Type: new Abstract: A major focus in designing methods for learning distributions defined on manifolds is to alleviate the need to implicitly learn the manifold so that learning can concentrate on the data distribution within the manifold. However,...
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
arXiv:2603.02462v1 Announce Type: new Abstract: A key challenge in deriving unified neural solvers for combinatorial optimization (CO) is efficient generalization of models between a given set of tasks to new tasks not used during the initial training process. To address...
ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
arXiv:2603.02510v1 Announce Type: new Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. This challenge is magnified for irregular data structures (such as sparse...
Thermodynamic Regulation of Finite-Time Gibbs Training in Energy-Based Models: A Restricted Boltzmann Machine Study
arXiv:2603.02525v1 Announce Type: new Abstract: Restricted Boltzmann Machines (RBMs) are typically trained using finite-length Gibbs chains under a fixed sampling temperature. This practice implicitly assumes that the stochastic regime remains valid as the energy landscape evolves during learning. We argue...
Bridging Diffusion Guidance and Anderson Acceleration via Hopfield Dynamics
arXiv:2603.02531v1 Announce Type: new Abstract: Classifier-Free Guidance (CFG) has significantly enhanced the generative quality of diffusion models by extrapolating between conditional and unconditional outputs. However, its high inference cost and limited applicability to distilled or single-step models have shifted research...
EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networks
arXiv:2603.02562v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. However, FL systems face significant communication bottlenecks due to inevitable client-server data...
Court unanimously sides with government in immigration dispute
The Supreme Court unanimously sided with the federal government on Wednesday in Urias-Orellana v. Bondi, holding in an opinion by Justice Ketanji Brown Jackson that federal courts of appeals must […]The postCourt unanimously sides with government in immigration disputeappeared first...
Opinions for Wednesday, March 4
We were live as the court released its opinions in Urias-Orellana v. Bondi and Galette v. New Jersey Transit Corp..The postOpinions for Wednesday, March 4appeared first onSCOTUSblog.
Birthright citizenship: an empirical analysis of supposedly originalist briefs
Brothers in Law is a recurring series by brothers Akhil and Vikram Amar, with special emphasis on measuring what the Supreme Court says against what the Constitution itself says. For more content from […]The postBirthright citizenship: an empirical analysis of...
The SCOTUS attorney switcheroo
Empirical SCOTUS is a recurring series by Adam Feldman that looks at Supreme Court data, primarily in the form of opinions and oral arguments, to provide insights into the justices’ decision making and […]The postThe SCOTUS attorney switcherooappeared first onSCOTUSblog.
SCOTUStoday for Wednesday, March 4
Good morning, and welcome to the court’s fourth opinion day in less than two weeks. We will be live blogging beginning at 9:30 a.m. EST.The postSCOTUStoday for Wednesday, March 4appeared first onSCOTUSblog.
Anthropic CEO Dario Amodei calls OpenAI’s messaging around military deal ‘straight up lies,’ report says
Anthropic gave up its contract with the Pentagon over AI safety disagreements -- then, OpenAI swooped in.
Apple Music to add Transparency Tags to distinguish AI music, says report
The label or distributor has to opt in to tagging their music as AI, so it's unclear how effective this intervention will be.
Google Search rolls out Gemini’s Canvas in AI Mode to all US users
Canvas in AI Mode is available to U.S. users in English for creating plans, projects, apps, and more.
The US military is still using Claude — but defense-tech clients are fleeing
As the U.S. continues its aerial attack on Iran, Anthropic models are being used for many targeting decisions.
Who needs data centers in space when they can float offshore?
Offshore wind developer Aikido will deploy a small data center beneath a floating offshore wind turbine later this year.
How Large Language Models Get Stuck: Early structure with persistent errors
arXiv:2603.00359v1 Announce Type: new Abstract: Linguistic insights may help make Large Language Model (LLM) training more efficient. We trained Meta's OPT model on the 100M word BabyLM dataset, and evaluated it on the BLiMP benchmark, which consists of 67 classes,...
A Typologically Grounded Evaluation Framework for Word Order and Morphology Sensitivity in Multilingual Masked LMs
arXiv:2603.00432v1 Announce Type: new Abstract: We introduce a typology-aware diagnostic for multilingual masked language models that tests reliance on word order versus inflectional form. Using Universal Dependencies, we apply inference-time perturbations: full token scrambling, content-word scrambling with function words fixed,...