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VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Introducing PyCross: PyCloudy Rendering Of Shape Software...
K. Fitzgerald, E. J Harvey, N. Keaveney, M. Redman · 2020-05-06 · via cs.SE updates on arXiv.org

Research into the processes of photoionised nebulae plays a significant part in our understanding of stellar evolution. It is extremely difficult to visually represent or model ionised nebula, requiring astronomers to employ sophisticated modelling code to derive temperature, density and chemical composition. Existing codes are available that often require steep learning curves and produce models derived from mathematical functions. In this article we will introduce PyCross: PyCloudy Rendering Of Shape Software. This is a pseudo 3D modelling application that generates photoionisation models of optically thin nebulae, created using the Shape software. Currently PyCross has been used for novae and planetary nebulae, and it can be extended to Active Galactic Nuclei or any other type of photoionised axisymmetric nebulae. Functionality, an operational overview, and a scientific pipeline will be described with scenarios where PyCross has been adopted for novae (V5668 Sagittarii (2015) & V4362 Sagittarii (1994)) and a planetary nebula (LoTr1). Unlike the aforementioned photoionised codes this application does not require any coding experience, nor the need to derive complex mathematical models, instead utilising the select features from Cloudy/PyCloudy and Shape. The software was developed using a formal software development lifecycle, written in Python and will work without the need to install any development environments or additional python packages. This application, Shape models and PyCross archive examples are freely available to students, academics and research community on GitHub for download (https://github.com/karolfitzgerald/PyCross_OSX_App).