Tootfinder

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Sezen Per\c{c}in, Xin Su, Qutub Sha Syed, Phillip Howard, Aleksei Kuvshinov, Leo Schwinn, Kay-Ulrich Scholl
https://arxiv.org/abs/2507.06956

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Large language models (LLMs) are very costly and inefficient to update with new information. To address this limitation, retrieval-augmented generation (RAG) has been proposed as a solution that dynamically incorporates external knowledge during inference, improving factual consistency and reducing hallucinations. Despite its promise, RAG systems face practical challenges-most notably, a strong dependence on the quality of the input query for accurate retrieval. In this paper, we investigate th…

Reply to "Comment(s) to "Consequences of the single-pair measurement of the Bell parameter""
Marco Genovese, Fabrizio Piacentini
https://arxiv.org/abs/2509.07922

Reply to "Comment(s) to "Consequences of the single-pair measurement of the Bell parameter""
We reply to the comments made by M. Kupczynski and J. P. Lambare on our recent paper titled ''Consequences of the single-pair measurement of the Bell parameter'' [Phys. Rev. A 111, 022204 (2025)], questioning our claims on the implications on Quantum Mechanics interpretations (and alternative theories) generated by the results of a recent experiment [Quantum Sci. Technol. 9, 045027 (2024)] demonstrating the possibility of measuring the entire Bell parameter on a single entangled pair.

Approaching Deadline: CFP: Conference: American Jewish Landscapes after October 7th
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Pirino on Suzuki, 'Humanitarian Internationalism Under Empire: The Global Evolution of the Japanese…
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Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
Alexander Nikitas Dimopoulos, Joseph Grasso
https://arxiv.org/abs/2508.00822

Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
This study analyzes semantic segmentation performance across heterogeneously labeled point-cloud datasets relevant to public safety applications, including pre-incident planning systems derived from lidar scans. Using NIST's Point Cloud City dataset (Enfield and Memphis collections), we investigate challenges in unifying differently labeled 3D data. Our methodology employs a graded schema with the KPConv architecture, evaluating performance through IoU metrics on safety-relevant features. Resul…

Laser-Synthesized Amorphous PdSe$_{\mathrm{2-x}}$ Nanoparticles: A Defect-Rich Platform for High-Efficiency SERS, Photocatalysis, and Photothermal Conversion
Andrei Ushkov, Nadezhda Belozerova, Dmitriy Dyubo, Ilya Martynov, Alexander Syuy, Daniil Tselikov, Georgy Ermolaev, Sergey V. Bazhenov, Roman I. Romanov, Ivan Kruglov, Anton A. Popov, Alexander Chernov, Alexey D. Bolshakov, Sergey Novikov, Andrey A. Vyshnevyy, Aleksey Arsenin, Andrei V. Kabashin, Gleb I. Tselikov, Valentyn Volkov

Laser-Synthesized Amorphous PdSe$_{\mathrm{2-x}}$ Nanoparticles: A Defect-Rich Platform for High-Efficiency SERS, Photocatalysis, and Photothermal Conversion
The control of material properties at the atomic scale remains a central challenge in materials science. Transition metal dichalcogenides (TMDCs) offer remarkable electronic and optical properties, but their functionality is largely dictated by their stable crystalline phases. Here we demonstrate a single-step, ligand-free strategy using femtosecond laser ablation in liquid to transform crystalline, stoichiometric palladium diselenide (PdSe$_{\mathrm{2}}$) into highly stable, amorphous, and non…