Tootfinder

Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?
Yuqian Huo, Jinbiao Wei, Christopher Kverne, Mayur Akewar, Janki Bhimani, Tirthak Patel
https://arxiv.org/abs/2507.01195

Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?
Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The common wisdom is that each circuit should be transpiled using up-to-date noise calibration data to optimize fidelity. In this work, we revisit the necessity of frequent noise-adaptive transpilation, conducting an in-depth empirical study across five IBM 127-qubit quantum computers and 16 diverse quantum algorithms. Our…

A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysis
Zhiwei Lin, Bonan Ruan, Jiahao Liu, Weibo Zhao
https://arxiv.org/abs/2506.23474

A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysis
The Model Context Protocol (MCP) has recently emerged as a standardized interface for connecting language models with external tools and data. As the ecosystem rapidly expands, the lack of a structured, comprehensive view of existing MCP artifacts presents challenges for research. To bridge this gap, we introduce MCPCorpus, a large-scale dataset containing around 14K MCP servers and 300 MCP clients. Each artifact is annotated with 20+ normalized attributes capturing its identity, interface conf…

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Shamse Tasnim Cynthia, Nuri Almarimi, Banani Roy
https://arxiv.org/abs/2506.15884

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely underexamined. In this study, we investigated the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. First, we ex…

Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations
Elyar Tourani, Brian J. Edwards, Bamin Khomami
https://arxiv.org/abs/2507.17980

Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations
Currently, identification of crystallization pathways in polymers is being carried out using molecular simulation-based data on a preset cut-off point on a single order parameter (OP) to define nucleated or crystallized regions. Aside from sensitivity to cut-off, each of these OPs introduces its own systematic biases. In this study, an integrated machine learning workflow is presented to accurately quantify crystallinity in polymeric systems using atomistic molecular dynamics data. Each atom is…

Ooh die eeuwige gekmakende kortzichtigheid
https://www.volkskrant.nl/kijkverder/v/2025/namibie-waterstof-grondstoffen-reportage~v1449782/

High-Dimensional Model Averaging via Cross-Validation
Zhengyan Wan, Fang Fang, Binyan Jiang
https://arxiv.org/abs/2506.08451 https://…

High-Dimensional Model Averaging via Cross-Validation
Model averaging is an important alternative to model selection with attractive prediction accuracy. However, its application to high-dimensional data remains under-explored. We propose a high-dimensional model averaging method via cross-validation under a general framework and systematically establish its theoretical properties. Each candidate model is fitted using a flexible loss function paired with a general regularizer, and the optimal weights are determined by minimizing a cross-validation…