To address these challenges, we propose a density-based probabilistic graphical model (DB-PGM) to achieve adaptive multi-target encirclement. DB-PGM models the cooperative encirclement of multiple ...

In a paper published in National Science Review, the team of Pro. Liu present an innovative computational framework, the sample-perturbed Gaussian graphical model (sPGGM), designed to analyse ...

In the very near future, victory will belong to the savvy blackhat hacker who uses AI to generate code at scale.

In addition, model heterogeneity has been a significant issue in integration efforts. Bayesian Networks are well suited for multi-model calibration tasks as they can be used to formulate a ...

AI weather models can accurately predict everyday weather but often fail to forecast rare, unprecedented events like Category 5 hurricanes. Integrating physics-principles into AI models could improve ...

Fig. 1. The workflow of Virtual Brain Inference (VBI). This probabilistic approach is designed to estimate the posterior distribution of control parameters in virtual brain models from whole-brain ...

Developers can now use Pydantic's mcp-run-python server, distributed via JSR, to allow AI agents to execute Python code with automatic dependency handling in isolation.

Cybersecurity researchers are warning of a new type of supply chain attack, Slopsquatting, induced by a hallucinating generative AI model recommending non-existent dependencies. According to ...

run main.py from esm.models.esmc import ESMC from esm.sdk.api import ESMProtein, LogitsConfig in order to use esm.models, i install the esm package via pip. The issue im facing is that the available ...

There are numerous ways to run large language models such as DeepSeek, Claude or Meta's Llama locally on your laptop, including Ollama and Modular's Max platform. But if you want to fully control the ...