The AI Race Is Pressuring Utilities to Squeeze More From Europe’s Power Grids
As data center developers queue up to connect to power grids across Europe, network operators are experimenting with novel ways of clearing room for them.
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The AI Race Is Pressuring Utilities to Squeeze More From Europe’s Power Grids
As data center developers queue up to connect to power grids across Europe, network operators are experimenting with novel ways of clearing room for them.
The AI Race Is Pressuring Utilities to Squeeze More From Europe’s Power Grids
As data center developers queue up to connect to power grids across Europe, network operators are experimenting with novel ways of clearing room for them.
A Visualization for Comparative Analysis of Regression Models
arXiv:2603.19291v1 Announce Type: new Abstract: As regression is a widely studied problem, many methods have been proposed to solve it, each of them often requiring setting different hyper-parameters. Therefore, selecting the proper method for a given application may be very…
Interpretable Early Warnings using Machine Learning in an Online Game-experiment
arXiv:2502.09880v2 Announce Type: replace-cross Abstract: Stemming from physics and later applied to other fields such as ecology, the theory of critical transitions suggests that some regime shifts are preceded by statistical early warning signals. Reddit’s r/place experiment, a large-scale social…
Unsupervised Feature Selection via Robust Autoencoder and Adaptive Graph Learning
arXiv:2512.18720v2 Announce Type: replace-cross Abstract: Effective feature selection is essential for high-dimensional data analysis and machine learning. Unsupervised feature selection (UFS) aims to simultaneously cluster data and identify the most discriminative features. Most existing UFS methods linearly project features into…
ReLaX: Reasoning with Latent Exploration for Large Reasoning Models
arXiv:2512.07558v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated remarkable potential in enhancing the reasoning capability of Large Reasoning Models (LRMs). However, RLVR often drives the policy toward over-determinism, resulting in ineffective exploration and premature…
FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
arXiv:2603.16513v2 Announce Type: replace Abstract: Structured data is foundational to healthcare, finance, e-commerce, and scientific data management. Large structured-data models (LDMs) extend the foundation model paradigm to unify heterogeneous datasets for tasks such as classification, regression, and decision support. However,…
Evaluating Test-Time Adaptation For Facial Expression Recognition Under Natural Cross-Dataset Distribution Shifts
arXiv:2603.19994v1 Announce Type: cross Abstract: Deep learning models often struggle under natural distribution shifts, a common challenge in real-world deployments. Test-Time Adaptation (TTA) addresses this by adapting models during inference without labeled source data. We present the first evaluation of…
PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling
arXiv:2603.19299v1 Announce Type: new Abstract: In recent years, progress in medical informatics and machine learning has been accelerated by the availability of openly accessible benchmark datasets. However, patient-level electronic medical record (EMR) data are rarely available for teaching or methodological…