Tootfinder

Performance of the image persistence model for Euclid infrared detectors
B. Kubik, R. Barbier, G. Smadja, S. Ferriol, Y. Conseil, Y. Copin, W. Gillard, S. Dusini, K. Jahnke, E. Prieto, N. Auricchio, E. Balbi, A. Balestra, P. Battaglia, V. Capobianco, R. Chary, L. Corcione, F. Cogato, G. Delucchi, E. Franceschi, L. Gabarra, F. Gianotti, F. Grupp, E. Lentini, S. Ligori, E. Medinaceli, G. Morgante, K. Paterson, E. Romelli, L. Sauniere, M. Schirmer, C. Sirignano G. Testera, M. Trifoglio, A. Troja, L. Valenziano, M. Frailis, M. Scodeggio, J. -C. Barriere, M. Berthe, C. Bodendorf, A. Caillat, M. Carle, R. Casas, H. Cho, A. Costille, F. Ducret, B. Garilli, W. Holmes, F. Hormuth, A. Hornstrup, M. Jhabvala, R. Kohley, D. Le Mignant, P. B. Lilje, I. Lloro, C. Padilla, G. Polenta, J. -C. Salvignol, G. Seidel, B. Serra, A. Secroun, L. Stanco, R. Toledo-Moreo, S. Anselmi, E. Borsato, L. Caillat, C. Colodro-Conde, V. Conforti, J. E. Davies, A. Renzi, F. Dal Corso, S. Davini, A. Derosa, J. J. Diaz, S. Di Domizio, D. Di Ferdinando, R. Farinelli, A. G. Ferrari, F. Fornari, F. Giacomini, O. Krause, F. Laudisio, J. Macias-Perez, J. Marpaud, N. Mauri, R. da Silva, M. Niclas, F. Passalacqua, I. Risso, P. Lagier, A. N. Sorensen, P. Stassi, J. Steinwagner, M. Tenti, C. Thizy, S. Tosi, R. Travaglini, O. Tubio, C. Valieri, S. Ventura, C. Vescovi, J. Zoubian
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From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
Jacob de Nobel, Jeroen J. Briaire, Thomas H. W. Baeck, Anna V. Kononova, Johan H. M. Frijns
https://arxiv.org/abs/2506.03959

From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using an inverse Fourier transform. The system applies straightforward signal processing to neurogram representations, time-frequency binned outputs from auditory nerve fiber models. Crucially, the model architecture is modular, allowing for easy substitution or modification of the underlying auditory models. This flexibility eliminates the need for speec…

I Replaced #SpringBoot with #Quarkus — Here’s What Happened (And Why I’m Never Going Back)

A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
Beomjoon Lee, Changjoo Nam
https://arxiv.org/abs/2506.01628

A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
We address the bin packing problem (BPP), which aims to maximize bin utilization when packing a variety of items. The offline problem, where the complete information about the item set and their sizes is known in advance, is proven to be NP-hard. The semi-online and online variants are even more challenging, as full information about incoming items is unavailable. While existing methods have tackled both 2D and 3D BPPs, the 2D BPP remains underexplored in terms of fully maximizing utilization. …

New around here, hi!
For those who'd like a big old dose of "yes we can" energy,
please check out
"Words to Win By "
where every episode details a campaign we've won somewhere
and delves into the research, messaging & strategies used to do it.
https://wordstowinby-pod.com/

Words to Win By
Words to Win By, formerly Brave New Words, takes listeners on a journey around the globe with renowned communications researcher and campaign advisor Anat Shenker-Osorio as she unpacks real-world narrative shifts that led to real-world victories.

Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
Arjun Rao, Hanieh Alipour, Nick Pendar
https://arxiv.org/abs/2506.00049

Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
This paper presents a comparison of embedding models in tri-modal hybrid retrieval for Retrieval-Augmented Generation (RAG) systems. We investigate the fusion of dense semantic, sparse lexical, and graph-based embeddings, focusing on the performance of the MiniLM-v6 and BGE-Large architectures. Contrary to conventional assumptions, our results show that the compact MiniLM-v6 outperforms the larger BGE-Large when integrated with LLM-based re-ranking within our tri-modal hybrid framework. Experim…

On the regularization property of Levenberg-Marquardt method with Singular Scaling for nonlinear inverse problems
Rafaela Filippozzi, Everton Boos, Douglas S. Gon\c{c}alves, Fermin S. V. Baz\'an
https://arxiv.org/abs/2506.00190

On the regularization property of Levenberg-Marquardt method with Singular Scaling for nonlinear inverse problems
Recently, in Applied Mathematics and Computation 474 (2024) 128688, a Levenberg-Marquardt method (LMM) with Singular Scaling was analyzed and successfully applied in parameter estimation problems in heat conduction where the use of a particular singular scaling matrix (semi-norm regularizer) provided approximate solutions of better quality than those of the classic LMM. Here we propose a regularization framework for the Levenberg-Marquardt method with Singular Scaling (LMMSS) applied to nonline…

Convex Approximations of Random Constrained Markov Decision Processes
V Varagapriya, Vikas Vikram Singh, Abdel Lisser
https://arxiv.org/abs/2505.24815 http…

Convex Approximations of Random Constrained Markov Decision Processes
Constrained Markov decision processes (CMDPs) are used as a decision-making framework to study the long-run performance of a stochastic system. It is well-known that a stationary optimal policy of a CMDP problem under discounted cost criterion can be obtained by solving a linear programming problem when running costs and transition probabilities are exactly known. In this paper, we consider a discounted cost CMDP problem where the running costs and transition probabilities are defined using ran…

HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
Priya Sundaresan, Rhea Malhotra, Phillip Miao, Jingyun Yang, Jimmy Wu, Hengyuan Hu, Rika Antonova, Francis Engelmann, Dorsa Sadigh, Jeannette Bohg
https://arxiv.org/abs/2506.01185

HoMeR: Learning In-the-Wild Mobile Manipulation via Hybrid Imitation and Whole-Body Control
We introduce HoMeR, an imitation learning framework for mobile manipulation that combines whole-body control with hybrid action modes that handle both long-range and fine-grained motion, enabling effective performance on realistic in-the-wild tasks. At its core is a fast, kinematics-based whole-body controller that maps desired end-effector poses to coordinated motion across the mobile base and arm. Within this reduced end-effector action space, HoMeR learns to switch between absolute pose pred…

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Shunyao Wang, Ming Cheng, Christina Dan Wang
https://arxiv.org/abs/2505.06864

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Stochastic Discount Factor (SDF) models provide a unified framework for asset pricing and risk assessment, yet traditional formulations struggle to incorporate unstructured textual information. We introduce NewsNet-SDF, a novel deep learning framework that seamlessly integrates pretrained language model embeddings with financial time series through adversarial networks. Our multimodal architecture processes financial news using GTE-multilingual models, extracts temporal patterns from macroecono…