Clean GitHub repo tricks AI coding agents into running malware

An agentic coding tool tasked with cloning and setting up a seemingly benign GitHub repository could execute a malicious ...

Frontline Systems Launches RASON® Desktop: Brings Simulation, Optimization, Decision Intelligence Directly into Power BI

Business users can now determine the best course of action under real-world constraints and uncertainty, with input ...
Frontiers

NEURONpyxl: fast, flexible, Python-integrated simulation of biophysical neural networks with complex plastic synapses

NEURON has been widely used as an empirically-based simulation tool, especially for multi-compartment conductance-based neuronal modeling. The network mediating feeding in Aplysia californica has been ...
Scientific American

Human neurons on a chip learned to play Doom

The Australian biotech company Cortical Labs recently posted a video in which 200,000 living human neurons grown on a silicon chip played the 1993 first-person shooter Doom. The neuron-controlled main ...
IEEE

Causality-Informed Neural Networks for Regularized Learning in Regression Problems

Abstract: Neural networks that overlook the underlying causal relationships among observed variables pose significant risks in high-stakes decision-making contexts due to concerns about the robustness ...
Nature

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability

This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent representations ...
LinkedIn

Coding a more complex Convolutional Neural Network in Python

The goal of this tutorial is to learn to design and code a more complex Convolutional Neural Network (CNN) in Python. In previous edition https://www.linkedin.com ...
GitHub

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence

Dharmesh Tailor, Alvaro H.C. Correia, Eric Nalisnick and Christos Louizos. "Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence." [ICLR2025] Qualcomm AI ...
Nature

Uncertainty quantification for neural network potential foundation models

For neural network potentials (NNPs) to gain widespread use, researchers must be able to trust model outputs. However, the blackbox nature of neural networks and their inherent stochasticity are often ...
GitHub

Physics-Informed Neural Network (PINN) Project

physics_informed_neural_network/ ├── app/ # FastAPI application │ ├── __init__.py │ ├── api/ # API endpoints │ │ ├── __init__.py ...
C&EN

Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-Scale Simulations

Fujitsu Research, Fujitsu Limited, 4-1-1, Kamiodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan ...
Frontiers

TBESO-BP: an improved regression model for predicting subclinical mastitis

Subclinical mastitis in dairy cows carries substantial economic, animal welfare, and biosecurity implications. The identification of subclinical forms of the disease is routinely performed through the ...