We introduce CLAPP (CLASS LLM Agent for Pair Programming), an interactive AI assistant designed to support researchers working with the Einstein-Boltzmann solver CLASS. CLAPP leverages large language models (LLMs) and domain-specific retrieval to provide conversational coding support for CLASS-answering questions, generating code, debugging errors, and producing plots. Its architecture combines multi-agent LLM orchestration, semantic search across CLASS documentation, and a live Python execution environment. Deployed as a user-friendly web application, CLAPP lowers the entry barrier for scientists unfamiliar with AI tools and enables more productive human-AI collaboration in computational and numerical cosmology. The app is available at https://classclapp.streamlit.app

Message oriented and robotics middleware play an important role in facilitating robot control, abstracting complex functionality, and unifying communication patterns between sensors and devices. However, using multiple middleware frameworks presents a challenge in integrating different robots within a single system. To address this challenge, we present Wrapyfi, a Python wrapper supporting multiple message oriented and robotics middleware, including ZeroMQ, YARP, ROS, and ROS 2. Wrapyfi also provides plugins for exchanging deep learning framework data, without additional encoding or preprocessing steps. Using Wrapyfi eases the development of scripts that run on multiple machines, thereby enabling cross-platform communication and workload distribution. We finally present the three communication schemes that form the cornerstone of Wrapyfi's communication model, along with examples that demonstrate their applicability.

If you googled'How to use Stanford CoreNLP in Python?' and landed on this post then you already know what it is. For those who don't know, Stanford CoreNLP is an open source software developed by Stanford that provides various Natural Language Processing tools such as: Stemming, Lemmatization, Part-Of-Speech Tagging, Dependency Parsing, Sentiment Analysis, and Entity Extraction. Stanford CoreNLP is written in Java. If your application is in Java you can simply download and import all the needed jars or setup it with maven. However, I find Python to be more flexible in terms of processing text than Java.

New Research Group The IUPR Research Group at the University of Kaiserslautern has been was succeeded by the Pattern Recognition (MADM) group, headed by Vertr.- Since April 2015 it became absorbed by Prof. Dr. Andreas Dengel, Knowledge Management Department. This is the home page of the Image Understanding and Pattern Recognition group at the University of Kaiserslautern. The group was headed from 2004-2014 by Prof. Dr. Thomas Breuel. Prof. Breuel started working at Google in 2014, but still supervising several students in the department.