Tootfinder

This https://arxiv.org/abs/2506.00593 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Look mom, no experimental data! Learning to score protein-ligand interactions from simulations
Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for novel protein targets dissimilar from the training set. In contrast, physics-based binding free energy calculations offer high accuracy across chemical space but are computationally prohibitive for large-scale screening. We propose a hybrid approach that approximates the accuracy of physics-based methods by training targ…

This https://arxiv.org/abs/2505.07802 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Improving Trajectory Stitching with Flow Models
Generative models have shown great promise as trajectory planners, given their affinity to modeling complex distributions and guidable inference process. Previous works have successfully applied these in the context of robotic manipulation but perform poorly when the required solution does not exist as a complete trajectory within the training set. We identify that this is a result of being unable to plan via stitching, and subsequently address the architectural and dataset choices needed to re…

Cowboys Headlines: Oxnard votes on camp's future; who are most important Cowboys in 2025? https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/07/02/news-headlines…

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
Jing Xue, Zhishen Sun, Haishan Ye, Luo Luo, Xiangyu Chang, Ivor Tsang, Guang Dai
https://arxiv.org/abs/2506.02711

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
Membership inference attack (MIA) has become one of the most widely used and effective methods for evaluating the privacy risks of machine learning models. These attacks aim to determine whether a specific sample is part of the model's training set by analyzing the model's output. While traditional membership inference attacks focus on leveraging the model's posterior output, such as confidence on the target sample, we propose IMIA, a novel attack strategy that utilizes the process of generatin…

Sound Field Reconstruction Using Physics-Informed Boundary Integral Networks
Stefano Damiano, Toon van Waterschoot
https://arxiv.org/abs/2506.03917 https:/…

Sound Field Reconstruction Using Physics-Informed Boundary Integral Networks
Sound field reconstruction refers to the problem of estimating the acoustic pressure field over an arbitrary region of space, using only a limited set of measurements. Physics-informed neural networks have been adopted to solve the problem by incorporating in the training loss function the governing partial differential equation, either the Helmholtz or the wave equation. In this work, we introduce a boundary integral network for sound field reconstruction. Relying on the Kirchhoff-Helmholtz bo…

Liquid Glass will set back CUA (computer use agent) models on macOS & iOS by months to years. The UI has changed, and drastically increased in its visual complexity.
How long will it take to train in the visual differences with enough training data?
Strategic play for Apple? Or Long-term blunder?

This https://arxiv.org/abs/2112.13738 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evaluation of binary classifiers for asymptotically dependent and independent extremes
Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this paper, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic…

Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
Meng Zhang, Maosheng Xiang, Yuan-Sen Ting, Anish Maynur Amarsi, Hua-Wei Zhang, Jianrong Shi, Haibo Yuan, Haining Li, Jiahui Wang, Yaqian Wu, Tianmin Wu, Lanya Mou, Hong-liang Yan, Jifeng Liu
https://

Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated catalog of stellar labels derived from LAMOST low-resolution spectra in a physics-sensible and rigorous manner with DD-Payne, taking labels from high-resolution spectroscopy as training set. The catalog contains atmospheric parameters for 6.4 million stars re…

Inverse design for robust inference in integrated computational spectrometry
Wenchao Ma, Rapha\"el Pestourie, Zin Lin, Steven G. Johnson
https://arxiv.org/abs/2506.02194

Inverse design for robust inference in integrated computational spectrometry
For computational spectrometers, we propose an inverse-design approach in which the scattering media are topology-optimized to achieve better performance in inference, without the need of a training set of spectra and a distribution of detector noise. Our approach also allows the selection of the inference algorithm to be decoupled from that of the scatterer. For smooth spectra, we additionally devise a regularized reconstruction algorithm based on Chebyshev interpolation, which yields higher a…

2025 Dallas Cowboys training camp schedule: Full list of official dates https://www.si.com/nfl/cowboys/news/2025-dallas-cowboys-training-camp-schedule-full-list-of-official-dates

MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
Nikolai Lund K\"uhne, Jesper Jensen, Jan {\O}stergaard, Zheng-Hua Tan
https://arxiv.org/abs/2507.00966

MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
With the advent of new sequence models like Mamba and xLSTM, several studies have shown that these models match or outperform state-of-the-art models in single-channel speech enhancement, automatic speech recognition, and self-supervised audio representation learning. However, prior research has demonstrated that sequence models like LSTM and Mamba tend to overfit to the training set. To address this issue, previous works have shown that adding self-attention to LSTMs substantially improves gen…

Look mom, no experimental data! Learning to score protein-ligand interactions from simulations
Michael Brocidiacono, James Wellnitz, Konstantin I. Popov, Alexander Tropsha
https://arxiv.org/abs/2506.00593

Look mom, no experimental data! Learning to score protein-ligand interactions from simulations
Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for novel protein targets dissimilar from the training set. In contrast, physics-based binding free energy calculations offer high accuracy across chemical space but are computationally prohibitive for large-scale screening. We propose a hybrid approach that approximates the accuracy of physics-based methods by training targ…

This https://arxiv.org/abs/2406.10065 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An Extended Validity Domain for Constraint Learning
We consider embedding a predictive machine-learning model within a prescriptive optimization problem. In this setting, called constraint learning, we study the concept of a validity domain, i.e., a constraint added to the feasible set, which keeps the optimization close to the training data, thus helping to ensure that the computed optimal solution exhibits less prediction error. In particular, we propose a new validity domain which uses a standard convex-hull idea but in an extended space. We …

A Practical and Secure Byzantine Robust Aggregator
De Zhang Lee, Aashish Kolluri, Prateek Saxena, Ee-Chien Chang
https://arxiv.org/abs/2506.23183 https://

A Practical and Secure Byzantine Robust Aggregator
In machine learning security, one is often faced with the problem of removing outliers from a given set of high-dimensional vectors when computing their average. For example, many variants of data poisoning attacks produce gradient vectors during training that are outliers in the distribution of clean gradients, which bias the computed average used to derive the ML model. Filtering them out before averaging serves as a generic defense strategy. Byzantine robust aggregation is an algorithmic pri…

2025 Dallas Cowboys training camp schedule: Full list of official dates https://www.si.com/nfl/cowboys/news/2025-dallas-cowboys-training-camp-schedule-full-list-of-official-dates

Maximally-Informative Retrieval for State Space Model Generation
Evan Becker, Benjamin Bowman, Matthew Trager, Tian Yu Liu, Luca Zancato, Wei Xia, Stefano Soatto
https://arxiv.org/abs/2506.12149

Maximally-Informative Retrieval for State Space Model Generation
Given a query and dataset, the optimal way of answering the query is to make use all the information available. Modern LLMs exhibit impressive ability to memorize training data, but data not deemed important during training is forgotten, and information outside that training set cannot be made use of. Processing an entire dataset at inference time is infeasible due to the bounded nature of model resources (e.g. context size in transformers or states in state space models), meaning we must resor…

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet, Lucien Walewski, Manus McAuliffe, Valentin Heyraud, Solal Attias, Martin Maarand, Yessine Khanfir, Edan Toledo, Fabio Falcioni, Marie Bluntzer, Silvia Acosta-Guti\'errez, Jules Tilly

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT). In this white paper, we present our MLIP library which was created with two core aims: (1) provide to industry experts without machine learning background a user-friendly and computationally efficient set of tools to experiment with MLIP models, (2) provide m…

Quantum Workshop for IT-Professionals
Bettina Just, J\"org Hettel, Gerhard Hellstern
https://arxiv.org/abs/2506.22525 https://ar…

Quantum Workshop for IT-Professionals
Quantum computing is gaining strategic relevance beyond research-driven industries. However, many companies lack the expertise to evaluate its potential for real-world applications. Traditional training formats often focus on physical principles without demonstrating practical relevance, which limits success. This paper presents a user-centered workshop concept tailored to IT professionals without prior quantum knowledge. Using a business game set in a fictitious company, participants explore q…

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study
Tingrui Zhang, Honglin Wu, Zekun Jiang, Yingying Wang, Rui Ye, Huiming Ni, Chang Liu, Jin Cao, Xuan Sun, Rong Shao, Xiaorong Wei, Yingchun Sun
https://arxiv.org/abs/2506.18106

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study
Aimed to develop and validate a CT radiomics-based explainable machine learning model for diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pr…

Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks
Ehsan Roohi, Ahmad Shoja-sani
https://arxiv.org/abs/2506.22453 https://

Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks
This study presents a deep neural network (DNN) framework that accelerates Direct Simulation Monte Carlo (DSMC) computations for rarefied-gas flows, while maintaining high physical fidelity. First, a fully connected deep neural network is trained on high-quality DSMC data for seven temperatures (200-650 K) to reproduce the Maxwell-Boltzmann speed distribution of argon. Injecting the physical boundary point into the training set enforces the correct low-speed limit. It reduces the mean-squared e…

Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos, Antonio A. Gentile, Osvaldo Simeone
https://arxiv.org/abs/2506.19714 …

Conservative quantum offline model-based optimization
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), which leverages the expressive power of variational quantum circuits to learn accurate surrogate functions by training on a few data points. However, as widely studied in the classical machine learning literature, predictive models may incorrectly e…

Physics. Tasks With Solutions
Lidiia L. Chinarova, Ivan L. Andronov, Nina V. Savchuk, Serhii I. Iovchev, Hanna M. Akopian
https://arxiv.org/abs/2507.00064 …

Physics. Tasks With Solutions
The study guide (textbook) is part of a set of materials designed to support high-quality practical training in physics. It includes a collection of tasks for organizing both in-class and independent work. The guide serves as a foundation for further study in physics-related disciplines and aligns with current educational programs. This textbook presents a curated set of 120 physics problems with detailed solutions, structured according to the first-year bachelor's curriculum. Each section ad…

ZeroSep: Separate Anything in Audio with Zero Training
Chao Huang, Yuesheng Ma, Junxuan Huang, Susan Liang, Yunlong Tang, Jing Bi, Wenqiang Liu, Nima Mesgarani, Chenliang Xu
https://arxiv.org/abs/2505.23625

ZeroSep: Separate Anything in Audio with Zero Training
Audio source separation is fundamental for machines to understand complex acoustic environments and underpins numerous audio applications. Current supervised deep learning approaches, while powerful, are limited by the need for extensive, task-specific labeled data and struggle to generalize to the immense variability and open-set nature of real-world acoustic scenes. Inspired by the success of generative foundation models, we investigate whether pre-trained text-guided audio diffusion models c…

I just hit a #calisthenics goal: 100 decline push-ups in one set. I started training this level of difficulty—30-cm elevation at the feet, (ersatz) parallettes to facilitate full range of motion—last September after reaching previous targets. Two sets of 100 are coming!

This https://arxiv.org/abs/2406.10065 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An Extended Validity Domain for Constraint Learning
We consider embedding a predictive machine-learning model within a prescriptive optimization problem. In this setting, called constraint learning, we study the concept of a validity domain, i.e., a constraint added to the feasible set, which keeps the optimization close to the training data, thus helping to ensure that the computed optimal solution exhibits less prediction error. In particular, we propose a new validity domain which uses a standard convex-hull idea but in an extended space. We …

What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li, Bradley A. Malin, Kassem Fawaz, Somesh Jha, Atul Prakash, Amrita Roy Chowdhury
https://arxiv.org/abs/2506.06003

What Really is a Member? Discrediting Membership Inference via Poisoning
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often fail under the strict definition of membership based on exact matching, and have suggested relaxing this definition to include semantic neighbors as members as well. In this work, we show that membership inference tests are still unreliable under this relaxation - it is possible to poison the training dataset in a way…

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Xiaotian Chen, Hongyun Liu, Seyed Sahand Mohammadi Ziabari
https://arxiv.org/abs/2506.09204

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Deep Neural Networks (DNNs) have been proven to be exceptionally effective and have been applied across diverse domains within deep learning. However, as DNN models increase in complexity, the demand for reduced computational costs and memory overheads has become increasingly urgent. Sparsity has emerged as a leading approach in this area. The robustness of sparse Multi-layer Perceptrons (MLPs) for supervised feature selection, along with the application of Sparse Evolutionary Training (SET), i…

Tyler Booker grateful for guidance, all set for Cowboys' training camp: 'I'm not gonna hold the offense back' https://www.dallascowboys.com/news/tyler-booker-grateful-for-guidance…

Bridging Offline and Online Reinforcement Learning for LLMs
Jack Lanchantin, Angelica Chen, Janice Lan, Xian Li, Swarnadeep Saha, Tianlu Wang, Jing Xu, Ping Yu, Weizhe Yuan, Jason E Weston, Sainbayar Sukhbaatar, Ilia Kulikov
https://arxiv.org/abs/2506.21495 https://arxiv.org/pdf/2506.21495 https://arxiv.org/html/2506.21495
arXiv:2506.21495v1 Announce Type: new
Abstract: We investigate the effectiveness of reinforcement learning methods for finetuning large language models when transitioning from offline to semi-online to fully online regimes for both verifiable and non-verifiable tasks. Our experiments cover training on verifiable math as well as non-verifiable instruction following with a set of benchmark evaluations for both. Across these settings, we extensively compare online and semi-online Direct Preference Optimization and Group Reward Policy Optimization objectives, and surprisingly find similar performance and convergence between these variants, which all strongly outperform offline methods. We provide a detailed analysis of the training dynamics and hyperparameter selection strategies to achieve optimal results. Finally, we show that multi-tasking with verifiable and non-verifiable rewards jointly yields improved performance across both task types.
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This https://arxiv.org/abs/2505.21527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining
Automatic speech recognition (ASR) has made remarkable progress but heavily relies on large-scale labeled data, which is scarce for low-resource languages like Vietnamese. While existing systems such as Whisper, USM, and MMS achieve promising performance, their efficacy remains inadequate in terms of training costs, latency, and accessibility. To address these issues, we propose VietASR, a novel ASR training pipeline that leverages vast amounts of unlabeled data and a small set of labeled data.…

Mathematical Modelling and Optimisation of Athletic Performance: Tapering and Periodisation
David Ceddia, Howard Bondell, Peter Taylor
https://arxiv.org/abs/2505.20859

Mathematical Modelling and Optimisation of Athletic Performance: Tapering and Periodisation
We conduct a mathematical optimisation of the training load to maximise performance for two seminal athletic performance models: the Banister et al. 1975 Fitness-Fatigue Impulse Response Model and the Busso 2003 Variable Dose-Response Model. We discuss discrepancies in the general trends of the optimised training loads compared to common training practices recommended in the sports science literature, such as tapering and periodisation. We then propose a set of interpretable nonlinear modificat…

Leave No One Undermined: Policy Targeting with Regret Aversion
Toru Kitagawa, Sokbae Lee, Chen Qiu
https://arxiv.org/abs/2506.16430 https://

Leave No One Undermined: Policy Targeting with Regret Aversion
While the importance of personalized policymaking is widely recognized, fully personalized implementation remains rare in practice. We study the problem of policy targeting for a regret-averse planner when training data gives a rich set of observable characteristics while the assignment rules can only depend on its subset. Grounded in decision theory, our regret-averse criterion reflects a planner's concern about regret inequality across the population, which generally leads to a fractional opt…

Tyler Booker grateful for guidance, all set for Cowboys' training camp: 'I'm not gonna hold the offense back' https://www.dallascowboys.com/news/tyler-booker-grateful-for-guidance…

Counterexample-Guided Synthesis of Robust Discrete-Time Control Barrier Functions
Erfan Shakhesi, Alexander Katriniok, W. P. M. H. Heemels
https://arxiv.org/abs/2506.13011

Counterexample-Guided Synthesis of Robust Discrete-Time Control Barrier Functions
Learning-based methods have gained popularity for training candidate Control Barrier Functions (CBFs) to satisfy the CBF conditions on a finite set of sampled states. However, since the CBF is unknown a priori, it is unclear which sampled states belong to its zero-superlevel set and must satisfy the CBF conditions, and which ones lie outside it. Existing approaches define a set in which all sampled states are required to satisfy the CBF conditions, thus introducing conservatism. In this paper, …

Enforcing tail calibration when training probabilistic forecast models
Jakob Benjamin Wessel, Maybritt Schillinger, Frank Kwasniok, Sam Allen
https://arxiv.org/abs/2506.13687

Enforcing tail calibration when training probabilistic forecast models
Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated by optimizing a proper scoring rule over a set of training data. If the model class is not correctly specified, then the learned model will not necessarily issue forecasts that are calibrated. Calibrated forecasts allow users to appropriately balance risks in decision making, and it is particularly important that forecast models issue calibrated predicti…

This https://arxiv.org/abs/2411.14608 has been replaced.
initial toot: https://mastoxiv.page/@a…

Toward machine learning interatomic potentials for modeling uranium mononitride
Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce …

Improved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of Gradient Trajectory Errors
Jonathan B. Martin, Hannah E. Alderson, John C. Gore, Mark D. Does, Kevin D. Harkins
https://arxiv.org/abs/2506.14995

Improved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of Gradient Trajectory Errors
Summary: Errors in gradient trajectories introduce significant artifacts and distortions in magnetic resonance images, particularly in non-Cartesian imaging sequences, where imperfect gradient waveforms can greatly reduce image quality. Purpose: Our objective is to develop a general, nonlinear gradient system model that can accurately predict gradient distortions using convolutional networks. Methods: A set of training gradient waveforms were measured on a small animal imaging system, and used …

A Synthetic Pseudo-Autoencoder Invites Examination of Tacit Assumptions in Neural Network Design
Assaf Marron
https://arxiv.org/abs/2506.12076 https://

A Synthetic Pseudo-Autoencoder Invites Examination of Tacit Assumptions in Neural Network Design
We present a handcrafted neural network that, without training, solves the seemingly difficult problem of encoding an arbitrary set of integers into a single numerical variable, and then recovering the original elements. While using only standard neural network operations -- weighted sums with biases and identity activation -- we make design choices that challenge common notions in this area around representation, continuity of domains, computation, learnability and more. For example, our const…

Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches
Andrea Moglia (Politecnico di Milano), Matteo Leccardi (Politecnico di Milano), Matteo Cavicchioli (Politecnico di Milano), Alice Maccarini (Universit\`a di Pavia), Marco Marcon (Politecnico di Milano), Luca Mainardi (Politecnico di Milano), Pietro Cerveri (Politecnico di Milano, Universit\`a di Pavia)

Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches
Following the successful paradigm shift of large language models, leveraging pre-training on a massive corpus of data and fine-tuning on different downstream tasks, generalist models have made their foray into computer vision. The introduction of Segment Anything Model (SAM) set a milestone on segmentation of natural images, inspiring the design of a multitude of architectures for medical image segmentation. In this survey we offer a comprehensive and in-depth investigation on generalist models…

This https://arxiv.org/abs/2506.02763 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated catalog of stellar labels derived from LAMOST low-resolution spectra in a physics-sensible and rigorous manner with DD-Payne, taking labels from high-resolution spectroscopy as training set. The catalog contains atmospheric parameters for 6.4 million stars re…

Double Jointed: Cowboys to host Rams at practice prior to rolling to SoFi for exhibition https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/11/cowboys-rams-joint-practice-training-camp-p…

Double Jointed: Cowboys to host Rams at practice prior to rolling to SoFi for exhibition https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/11/cowboys-rams-joint-practice-training-camp-p…

Comparing the Cowboys’ revamped defensive line against the NFC East https://insidethestar.com/comparing-the-cowboys-revamped-defensive-line-against-the-nfc-east