Tootfinder

In the next #ag2025goerlitz talk the Co-PI of the #DST2000 radio survey telescope array to be built in #Nevada - https://www.deepsynoptic.org - starting next year introduced the current plan with 1650 6.15-meter telescopes, to be operational in 2029. A German antenna maker is involved - https://schauenburg-international.com/mtex-antenna-technology-entwirft-prototypen-des-dsa-2000-projekts-der-caltech/ - as are German astronomers. The DST-2000 will be the radio counterpart to SPHEREx, Rubin and eROSITA.

Chat-Driven Text Generation and Interaction for Person Retrieval
Zequn Xie, Chuxin Wang, Sihang Cai, Yeqiang Wang, Shulei Wang, Tao Jin
https://arxiv.org/abs/2509.12662 https://…

Chat-Driven Text Generation and Interaction for Person Retrieval
Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich p…

Quantum Control of Thermal Emission from Photonic Crystals with Two-Level Atoms
Chih-Wei Wang, Jhih-Sheng Wu
https://arxiv.org/abs/2508.11191 https://arxiv…

Quantum Control of Thermal Emission from Photonic Crystals with Two-Level Atoms
Thermal light engineering is a field of considerable interest and potential. We study quantum light-matter interactions in a one-dimensional photonic crystal with two-level atoms as the active medium, replacing classical oscillators in traditional blackbody models. In a thermal bath with pumping, these atoms modulate thermal emission via interactions with photonic modes. The model with quantum two-level systems enables the processes of spontaneous emission, stimulated absorption, and stimulated…

Randomized Feasibility-Update Algorithms for Stochastic Variational Inequality Problems
Abhishek Chakraborty, Angelia Nedi\'c
https://arxiv.org/abs/2509.12613 https://

Randomized Feasibility-Update Algorithms for Stochastic Variational Inequality Problems
This paper considers stochastic monotone variational inequalities whose feasible region is the intersection of a (possibly infinite) number of convex functional level sets. A projection-based approach or direct Lagrangian-based techniques for such problems can be computationally expensive if not impossible to implement. To deal with the problem, we consider randomized methods that avoid the projection step on the whole constraint set by employing random feasibility updates. In particular, we pr…

Parton Distribution Functions and their Generalizations
C\'edric Lorc\'e (Ecole Polytechnique, CPHT), A. Metz (Temple U.), B. Pasquini (Pavia U.,INFN, Pavia), P. Schweitzer (Connecticut U.)
https://arxiv.org/abs/2507.12664

Parton Distribution Functions and their Generalizations
This article is an introduction to parton distribution functions and their generalizations which describe the quark and gluon structure of hadrons, and can be measured in various high-energy scattering processes. We provide the theoretical background, highlight both historical and recent developments, explain the connections between the different functions, and expose in which processes these functions can be accessed and what we can learn from them about hadron structure.

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Nathan He, Cody Chen
https://arxiv.org/abs/2509.12287 https://arxi…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaChe…

Distributed Coherent Beamforming at 60 GHz Enabled by Optically-Established Coherence
Drake Silbernagel, Yu Rong, Isabella Lenz, Prithvi Hemanth, Carl Morgenstern, Owen Ma, Nolan Matthews, Nader Zaki, Kyle W. Martin, John D. Elgin, Jacob Holtom, Daniel W. Bliss, Kimberly Frey
https://arxiv.org/abs/2509.13984

Distributed Coherent Beamforming at 60 GHz Enabled by Optically-Established Coherence
We implement and experimentally demonstrate a 60 GHz distributed system leveraging an optical time synchronization system that provides precise time and frequency alignment between independent elements of the distributed mesh. Utilizing such accurate coherence, we perform receive beamforming with interference rejection and transmit nulling. In these configurations, the system achieves a coherent gain over an incoherent network of N nodes, significantly improving the relevant signal power ratios…

MMORE: Massive Multimodal Open RAG & Extraction
Alexandre Sallinen, Stefan Krsteski, Paul Teiletche, Marc-Antoine Allard, Baptiste Lecoeur, Michael Zhang, Fabrice Nemo, David Kalajdzic, Matthias Meyer, Mary-Anne Hartley
https://arxiv.org/abs/2509.11937

MMORE: Massive Multimodal Open RAG & Extraction
We introduce MMORE, an open-source pipeline for Massive Multimodal Open RetrievalAugmented Generation and Extraction, designed to ingest, transform, and retrieve knowledge from heterogeneous document formats at scale. MMORE supports more than fifteen file types, including text, tables, images, emails, audio, and video, and processes them into a unified format to enable downstream applications for LLMs. The architecture offers modular, distributed processing, enabling scalable parallelization ac…

When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
Mahdi Dhaini, Stephen Meisenbacher, Ege Erdogan, Florian Matthes, Gjergji Kasneci
https://arxiv.org/abs/2508.10482

When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
In the study of trustworthy Natural Language Processing (NLP), a number of important research fields have emerged, including that of \textit{explainability} and \textit{privacy}. While research interest in both explainable and privacy-preserving NLP has increased considerably in recent years, there remains a lack of investigation at the intersection of the two. This leaves a considerable gap in understanding of whether achieving \textit{both} explainability and privacy is possible, or whether t…

Using Integer Programming to Solve Games, Puzzles, and Ciphers
Elizabeth Bouzarth, John Harris, Kevin Hutson, Christian Millichap
https://arxiv.org/abs/2509.12174 https://

Using Integer Programming to Solve Games, Puzzles, and Ciphers
In this paper, we introduce three different classes of undergraduate research projects that implement model building and integer programming. These research projects focus on determining and analyzing solutions to the game The Genius Square, optimizing allocation of trains to maximize points in the game Ticket to Ride, and (code)breaking monoalphabetic substitution ciphers. Initial models and analyses for these scenarios that came from previous undergraduate research projects are shared along w…