Tootfinder

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
Mohammad Baqar
https://arxiv.org/abs/2510.08667 https://

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA tickets, developer discussions, and GitHub pull requests (PRs). To address this challenge, we propose a Retrieval-Augmented Generation (RAG) framework that integrates Sentence-Transformers for semantic embeddings with FAISS-based vector search to deliver context-aware ticket resolution recommendations. The approach embeds historical JIRA tickets, user comm…

I've spent most of today -- while I really should have been working on other things -- building a little search engine for @yogthos' Cryogen blogging engine, which is what I use for my blog.
I can now generate an index very fast; the index I'm currently generating is too big, and doesn't have enough metadata, and consequently the search side can't return that metadata, but I'm very pleased with progress for one day.

elboob
A site search engine for Cryogen with search on the client side

(Not a #30DayMapChallenge map)
I was so busy the past couple of days that I missed the news that there’s a category-1 typhoon (#Kalmaegi / #TinoPH) that’s about to make landfall in a few hours in th…

Enhancing the development of Cherenkov Telescope Array control software with Large Language Models
Dmitriy Kostunin, Elisa Jones, Vladimir Sotnikov, Valery Sotnikov, Sergo Golovachev, Alexandre Strube
https://arxiv.org/abs/2510.01299

Enhancing the development of Cherenkov Telescope Array control software with Large Language Models
We develop AI agents based on instruction-finetuned large language models (LLMs) to assist in the engineering and operation of the Cherenkov Telescope Array Observatory (CTAO) Control and Data Acquisition Software (ACADA). These agents align with project-specific documentation and codebases, understand contextual information, interact with external APIs, and communicate with users in natural language. We present our progress in integrating these features into CTAO pipelines for operations and o…

Ensembled Direct Multi Step forecasting methodology with comparison on macroeconomic and financial data
Tomasz M. {\L}api\'nski, Krzysztof Zi\'o{\l}kowski
https://arxiv.org/abs/2509.13945

Ensembled Direct Multi Step forecasting methodology with comparison on macroeconomic and financial data
Accurate forecasts of macroeconomic and financial data, such as GDP, CPI, unemployment rates, and stock indices, are crucial for the success of countries, businesses, and investors, resulting in a constant demand for reliable forecasting models. This research introduces a novel methodology for time series forecasting that combines Ensemble technique with a Direct Multi-Step (DMS) forecasting procedure. This Ensembled Direct Multi-Step (EDMS) approach not only leverages the strengths of both tec…

Does Adoption of Zero Tillage Reduce Crop Residue Burning? Evidence from Satellite Remote Sensing and Household Survey Data in India
Dominik Naeher, Virginia Ziulu
https://arxiv.org/abs/2510.01351

Does Adoption of Zero Tillage Reduce Crop Residue Burning? Evidence from Satellite Remote Sensing and Household Survey Data in India
Previous research indicates that zero tillage technology offers a profitable alternative to crop residue burning, with significant potential to reduce agricultural emissions and contribute to improvements in air quality and public health. Yet, empirical evidence on the link between zero tillage adoption and residue burning remains scarce, adding to the difficulties policy makers face in this context. This study addresses this gap by integrating high-resolution satellite imagery with household s…

Noise Modeling and Calibration of a Two-Stage Cryogenic Charge Amplifier for the SPLENDOR Experiment
Jadyn Anczarski, Owen Andrews, Taylor Aralis, Caleb Fink, Noah Kurinsky, Arran Phipps, Aditi Pradeep, Betty A. Young
https://arxiv.org/abs/2510.01463

Noise Modeling and Calibration of a Two-Stage Cryogenic Charge Amplifier for the SPLENDOR Experiment
The SPLENDOR Collaboration studies novel narrow-gap semiconductors and engineered a substrate agnostic detector platform to achieve $\mathcal{O}$(meV) energy sensitivity designed for low mass dark matter searches. This was achieved using low-capacitance and low-noise commercial CryoHEMTs in a split-stage topology integrated throughout a dilution refrigerator. Designed with a source-follower HEMT at the base temperature stage and a voltage amplifier at 4\,K, this amplifier has input-limited volt…