Large genome model: Open source AI trained on trillions of bases

Late in 2025, we covered the development of an AI system called Evo that was trained on massive numbers of bacterial genomes. So many that, when prompted with sequences from a cluster of related genes ...
eLife

Boosting the signal: Expectation-driven gain modulation of preparatory spatial attention

The evidence is solid but not definitive, as the conclusions rely on the absence of changes in spatial breadth and would benefit from clearer statistical justification and a more cautious ...
Frontiers

AI for the Next Generation of Particle Physics Experiments

The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...
IEEE

A Novel Clustering Method for Extracting Representative Photovoltaic Scenarios Considering Power, Energy, and Variability

Abstract: Due to the significant uncertainty in photovoltaic (PV) power generation, grid operation scenarios with a high proportion of PV integration are complex and varied. To accurately extract ...
GitHub

NodeSentry: Effective Node-Level Anomaly Detection in HPC Systems via Coarse-Grained Clustering and Fine-Grained Model Sharing

The framework encompasses two principal phases: the offline model training phase and the online anomaly detection phase. During the offline model training phase, we first preprocess the raw MTS. We ...
GitHub

bethington/cheat-engine-server-python

This server operates in READ-ONLY mode for safety. It can read and analyze memory but cannot modify it. All operations are logged for security auditing.
IEEE

Contrastive Learning-Based Deep Embedded Clustering and the TCN-DMAttention Model for Traffic Congestion Prediction

Abstract: With the increasing number of motor vehicles per capita, the problem of road traffic congestion is becoming increasingly prominent. Intelligent transportation systems are playing an ...