Tootfinder

A foundation model to predict and capture human cognition.
https://www.nature.com/articles/s41586-025-09215-4
And the reaction and criticism,

A foundation model to predict and capture human cognition - Nature
A computational model called Centaur, developed by fine-tuning a language model on a huge dataset called Psych-101, can predict and simulate human nature in experiments expressible in natural language, even in previously unseen situations.

Voxel, whose AI-powered logistics safety software integrates into existing security cameras to detect and predict workplace hazards, raised a $44M Series B (Colin Campbell/Axios)
https://www.axios.com/pro/supply-chain-deals/2025/06/03/lo…

Exclusive: Logistics safety startup Voxel raises $44M Series B
It gives San Franscisco-based Voxel three years of runway to reach breakeven or accelerate growth and raise again, its CEO says.

Heavy sudden rain storms are the only really thing that scares me about climate change; I live far enough in land that I don't need to worry directly about sea level rises, I'm assuming will manage with food crops somehow, but there's fuck all you can do when you suddenly get a torrential rain storm that decides to park itself over your city for an hour at full pelt. You might be able to predict heavy rain, but where's it going to land - no way to tell.

Can Internal Software Metrics Predict App Popularity at Launch? Yeas! and Nays!
Md Nahidul Islam Opu, Fatima Islam Mouri, Rick Kazman, Yuanfang Cai, Shaiful Chowdhury
https://arxiv.org/abs/2507.02110

Can Internal Software Metrics Predict App Popularity at Launch? Yeas! and Nays!
Predicting mobile app popularity before release can provide developers with a strategic advantage in a competitive marketplace, yet it remains a challenging problem. This study explores whether internal software metrics, measurable from source code before deployment, can predict an app's popularity, defined by user ratings (calculated from user reviews) and DownloadsPerYear (yearly downloads). Using a dataset of 446 open-source Android apps from F-Droid, we extract a wide array of features, inc…

Cowboys wise to keep low-cost acquisition on current deal before giving $25 million a year https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/07/cowboys-george-pickens-expiring-deal/84093396007/…

Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
Boshra Khajehpiri, Eric Granger, Massimiliano de Zambotti, Fiona C. Baker, Mohamad Forouzanfar
https://arxiv.org/abs/2506.00279

Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
Despite extensive research on the relationship between sleep and cognition, the connection between sleep microstructure and human performance across specific cognitive domains remains underexplored. This study investigates whether deep learning models can predict executive functions, particularly cognitive adaptability and conceptual reasoning from physiological processes during a night's sleep. To address this, we introduce CogPSGFormer, a multi-scale convolutional-transformer model designed t…

Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma, Zhizheng Wang, Liang Yang, Hongfei Lin
https://arxiv.org/abs/2506.05044

Rethinking Contrastive Learning in Session-based Recommendation
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consist…

Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning / First Observations of a Geomagnetic Superstorm With a Sub-L1 Monitor: #SolarStorms: https://science.nasa.gov/science-research/heliophysics/nasa-missions-help-explain-predict-severity-of-solar-storms/

Enhancing the Merger Simulation Toolkit with ML/AI
Harold D. Chiang, Jack Collison, Lorenzo Magnolfi, Christopher Sullivan
https://arxiv.org/abs/2506.05225

Enhancing the Merger Simulation Toolkit with ML/AI
This paper develops a flexible approach to predict the price effects of horizontal mergers using ML/AI methods. While standard merger simulation techniques rely on restrictive assumptions about firm conduct, we propose a data-driven framework that relaxes these constraints when rich market data are available. We develop and identify a flexible nonparametric model of supply that nests a broad range of conduct models and cost functions. To overcome the curse of dimensionality, we adapt the Variat…

This https://arxiv.org/abs/2505.14613 has been replaced.
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Virtual Cells: Predict, Explain, Discover
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researchers to generate and test large numbers of therapeutic hypotheses safely and economically before initiating costly clinical trials. Even a more specific model that predicts the functional response of cells to a wide range of perturbations would be tremendously valuable for disco…

CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering
Shuren Zhou, Zhao Chen, Yu Yu
https://arxiv.org/abs/2506.04671

CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering
Galaxy clustering is an important probe in the upcoming China Space Station Telescope (CSST) survey to understand the structure growth and reveal the nature of the dark sector. However, it is a long-term challenge to model this biased tracer and connect the observable to the underlying physics. In this work, we present a hybrid Lagrangian bias expansion emulator, combining the Lagrangian bias expansion and the accurate dynamical evolution from $N$-body simulation, to predict the power spectrum …

predict erik uden crashout (name reason as comment)

This https://arxiv.org/abs/2506.01993 has been replaced.
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A refinement of the Lorentz local field expression with impact on the Clausius-Mossotti and Lorentz-Lorenz models
In the 19th century Mossotti and Clausius formulated an expression that relates electrical permittivity of a dielectric to the product of molecular polarizability and number density. Lorenz and Lorentz extended the use of that expression to also cover the dielectric s refractive index. These expressions successfully predict the change of the dielectric s permittivity and refractive index as a function of a wide range of its number density, using the consideration that molecular polarizability s…

This https://arxiv.org/abs/2406.18080 has been replaced.
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Capacitance-based Fermion parity read-out and predicted Rabi oscillations in a Majorana nanowire
Recent experiments have measured flux dependent capacitance at radio frequencies leading to the potential for a fast parity read-out of a Majorana qubit. In this work we argue that the quantum dot used in the capacitance measurement can be reasonably approximated by a non-interacting weakly coupled orbital. We then predict the measured flux and parity dependent capacitance for several parameter regimes of the disordered Majorana nanowire model that are both topological and trivial. Following th…

This https://arxiv.org/abs/2407.00144 has been replaced.
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SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation
This article presents a family of Stochastic Cartographic Occupancy Prediction Engines (SCOPEs) that enable mobile robots to predict the future states of complex dynamic environments. They do this by accounting for the motion of the robot itself, the motion of dynamic objects, and the geometry of static objects in the scene, and they generate a range of possible future states of the environment. These prediction engines are software-optimized for real-time performance for navigation in crowded …

Bullshit or water, humanity, the choice is yours. Choose wisely. https://mastodon.green/@gerrymcgovern/114783392100794507

Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures
Heisam Moustafa, Alexander Kovacs, Johann Fischbacher, Markus Gusenbauer, Qais Ali, Leoni Breth, Thomas Schrefl, Harald Oezelt
https://arxiv.org/abs/2506.23615

Graph Neural Networks to Predict Coercivity of Hard Magnetic Microstructures
Graph neural networks (GNN) are a promising tool to predict magnetic properties of large multi-grain structures, which can speed up the search for rare-earth free permanent magnets. In this paper, we use our magnetic simulation data to train a GNN to predict coercivity of hard magnetic microstructures. We evaluate the performance of the trained GNN and quantify its uncertainty. Subsequently, we reuse the GNN architecture for predicting the maximum energy product. Out-of-distribution predictions…

Developer Builds Tool That Scrapes YouTube Comments, Uses AI to Predict Where Users Live https://www.404media.co/developer-builds-tool-that-scrapes-youtube-comments-uses-ai-to-predict-where-users-live/

A Gaussian process approach for rapid evaluation of skin tension
Matt Nagle, Hannah Conroy Broderick, Christelle Vedel, Michel Destrade, Michael Fop, Aisling Ni Annaidh
https://arxiv.org/abs/2506.05118

A Gaussian process approach for rapid evaluation of skin tension
Skin tension plays a pivotal role in clinical settings, it affects scarring, wound healing and skin necrosis. Despite its importance, there is no widely accepted method for assessing in vivo skin tension or its natural pre-stretch. This study aims to utilise modern machine learning (ML) methods to develop a model that uses non-invasive measurements of surface wave speed to predict clinically useful skin properties such as stress and natural pre-stretch. A large dataset consisting of simulated w…

News Sentiment Embeddings for Stock Price Forecasting
Ayaan Qayyum
https://arxiv.org/abs/2507.01970 https://arxiv.org/pdf/2507.01970

News Sentiment Embeddings for Stock Price Forecasting
This paper will discuss how headline data can be used to predict stock prices. The stock price in question is the SPDR S&P 500 ETF Trust, also known as SPY that tracks the performance of the largest 500 publicly traded corporations in the United States. A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encodings of each headline with principal componen…

Learning to cluster neuronal function
Nina S. Nellen, Polina Turishcheva, Michaela Vystr\v{c}ilov\'a, Shashwat Sridhar, Tim Gollisch, Andreas S. Tolias, Alexander S. Ecker
https://arxiv.org/abs/2506.03293

Learning to cluster neuronal function
Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from these models could potentially be used to map the functional landscape or identify cell types. However, state-of-the-art predictive models of mouse V1 do not generate functional embeddings that exhibit clear clustering patterns which would correspond to cell types. This raises the question whether th…

This https://arxiv.org/abs/2503.00993 has been replaced.
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Exploring fully-heavy tetraquarks through the CGAN framework: Mass and width
Fully-heavy tetraquark states, $QQ\bar{Q}\bar{Q} (Q=c,b)$, have garnered significant attention both experimentally and theoretically, due to their unique properties and potential to provide new insights into Quantum Chromodynamics (QCD). In this study, we employ Conditional Generative Adversarial Networks (CGANs) to predict the masses and decay widths of fully-heavy tetraquarks. To deepen our understanding of heavy multiquark structures, we prepare datasets based on two distinct approaches and …

This https://arxiv.org/abs/2411.17931 has been replaced.
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Combining Threat Intelligence with IoT Scanning to Predict Cyber Attack
While the Web has become a global platform for communication, malicious actors, including hackers and hacktivist groups, often disseminate ideological content and coordinate activities through the "Dark Web", an obscure counterpart of the conventional web. Presently, challenges such as information overload and the fragmented nature of cyber threat data impede comprehensive profiling of these actors, thereby limiting the efficacy of predictive analyses of their online activities. Concurrently, t…

External Attention Transformer: A Robust AI Model for Identifying Initial Eccentricity Signatures in Binary Black Hole Events in Simulated Advanced LIGO Data
Elahe Khalouei, Cristiano G. Sabiu, Hyung Mok Lee, A. Gopakumar
https://arxiv.org/abs/2506.03634

External Attention Transformer: A Robust AI Model for Identifying Initial Eccentricity Signatures in Binary Black Hole Events in Simulated Advanced LIGO Data
Initial orbital eccentricities of gravitational wave (GW) events associated with merging binary black holes (BBHs) should provide clues to their formation scenarios, mainly because various BBH formation channels predict distinct eccentricity distributions. However, searching for inspiral GWs from eccentric BBHs is computationally challenging due to sophisticated approaches to model such GW events. This ensures that Bayesian parameter estimation methods to characterize such events are computatio…

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Tinghuan Li, Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
https://arxiv.org/abs/2506.03209

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in…

This https://arxiv.org/abs/2505.24434 has been replaced.
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Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields
Flow matching casts sample generation as learning a continuous-time velocity field that transports noise to data. Existing flow matching networks typically predict each point's velocity independently, considering only its location and time along its flow trajectory, and ignoring neighboring points. However, this pointwise approach may overlook correlations between points along the generation trajectory that could enhance velocity predictions, thereby improving downstream generation quality. To …

Multiband superconductivity in the topological Kramers nodal-line semimetals
Tian Shang, Jianzhou Zhao, Keqi Xia, Lun-Hui Hu, Yang Xu, Qingfeng Zhan, Dariusz Jakub Gawryluk, Toni Shiroka
https://arxiv.org/abs/2506.03509

Multiband superconductivity in the topological Kramers nodal-line semimetals
Recent band-structure calculations predict that the ruthenium-based ternary silicides are three-dimensional Kramers nodal line semimetals. Among them, NbRuSi and TaRuSi show bulk superconductivity (SC) below $T_c \sim 3$ K and 4 K, as well as spontaneous magnetic fields. The latter indicates the breaking of time-reversal symmetry and, thus, unconventional SC in both compounds. Previous temperature-dependent muon-spin spectroscopy studies failed to distinguish whether such compounds exhibit sing…

Machine learning prediction of a chemical reaction over 8 decades of energy
Daniel Julian, Jes\'us P\'erez-R\'ios
https://arxiv.org/abs/2507.01793

Machine learning prediction of a chemical reaction over 8 decades of energy
Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for any chemical reaction given reactants and physical conditions. In pursuit of ever more universal chemical predictors, machine learning models for atom-diatom and diatom-diatom reactions have been developed, yet no such models exist for termolecular reactions.…

The Basic Reproduction Number for Petri Net Models: A Next-Generation Matrix Approach
Trevor Reckell, Beckett Sterner, Petar Jevti\'c
https://arxiv.org/abs/2507.02344

The Basic Reproduction Number for Petri Net Models: A Next-Generation Matrix Approach
The basic reproduction number (R_0) is an epidemiological metric that represents the average number of new infections caused by a single infectious individual in a completely susceptible population. The methodology for calculating this metric is well-defined for numerous model types, including, most prominently, Ordinary Differential Equations (ODEs). The basic reproduction number is used in disease modeling to predict the potential of an outbreak and the transmissibility of a disease, as well …

Perceptual Ratings Predict Speech Inversion Articulatory Kinematics in Childhood Speech Sound Disorders
Nina R. Benway, Saba Tabatabaee, Dongliang Wang, Benjamin Munson, Jonathan L. Preston, Carol Espy-Wilson
https://arxiv.org/abs/2507.01888

Perceptual Ratings Predict Speech Inversion Articulatory Kinematics in Childhood Speech Sound Disorders
Purpose: This study evaluated whether articulatory kinematics, inferred by Articulatory Phonology speech inversion neural networks, aligned with perceptual ratings of /r/ and /s/ in the speech of children with speech sound disorders. Methods: Articulatory Phonology vocal tract variables were inferred for 5,961 utterances from 118 children and 3 adults, aged 2.25-45 years. Perceptual ratings were standardized using the novel 5-point PERCEPT Rating Scale and training protocol. Two research ques…

Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
Michael A. Kraus, Helen Bartsch
https://arxiv.org/abs/2507.02005

Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
This research introduces a unified approach combining Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to predict fatigue strength in welded transverse stiffener details. It integrates expert-driven feature engineering with algorithmic feature creation to enhance accuracy and explainability. Based on the extensive fatigue test database regression models - gradient boosting, random forests, and neural networks - were trained using AutoML under three feature sc…

This https://arxiv.org/abs/2502.14708 has been replaced.
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Human Misperception of Generative-AI Alignment: A Laboratory Experiment
We conduct an incentivized laboratory experiment to study people's perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. We find that people overestimate the degree of alignment between GenAI'…

2025 Packers game-by-game predictions: Can Green Bay improve vs. division rivals? https://www.foxsports.com/stories/nfl/predicting-packers-2025-season-game-by-game

Sorcha - a Solar System Survey Simulator for the Legacy Survey of Space and Time: #Rubin Observatory will discover millions of solar system objects: https://www.geekwire.com/2025/sorcha-software-rubin-observatory-millions/

Beyond Sociodemographic Prompting: Using Supervision to Align LLMs with Human Response Distributions
Gauri Kambhatla, Sanjana Gautam, Angela Zhang, Alex Liu, Ravi Srinivasan, Junyi Jessy Li, Matthew Lease
https://arxiv.org/abs/2507.00439

Beyond Sociodemographic Prompting: Using Supervision to Align LLMs with Human Response Distributions
The ability to accurately predict how different population groups would answer subjective questions would have great value. In this work, we show that use of relatively simple supervision can greatly improve language model alignment with diverse population groups, as measured over three datasets spanning various topics. Beyond evaluating average performance, we also report how alignment varies across specific groups. The simplicity and generality of our approach promotes easy adoption, while ou…

When Brooke Nichols plugged in the data, she couldn’t comprehend what she was seeing.
The mathematician and professor of infectious diseases at Boston University had created a model to predict the human cost of the Department of Government Efficiency’s (Doge) USAid funding cuts.
As Elon Musk, the head of Doge, arrived at the White House on Friday afternoon
to be congratulated by President Trump for his work slashing the federal budget,
the number of deaths on Nichols’…

Doge cuts to USAid blamed for 300,000 deaths — most of them children
Elon Musk’s tenure running Trump’s Department of Government Efficiency has, according to the academic Brooke Nichols, caused thousands of ‘preventable deaths’

Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts
Yuxuan Yang
https://arxiv.org/abs/2507.01776

Human-Machine Collaboration-Guided Space Design: Combination of Machine Learning Models and Humanistic Design Concepts
The integration of machine learning (ML) into spatial design holds immense potential for optimizing space utilization, enhancing functionality, and streamlining design processes. ML can automate tasks, predict performance outcomes, and tailor spaces to user preferences. However, the emotional, cultural, and aesthetic dimensions of design remain crucial for creating spaces that truly resonate with users-elements that ML alone cannot address. The key challenge lies in harmonizing data-driven effi…

Post-glitch Recovery and the Neutron Star Structure: The Vela Pulsar
Himanshu Grover, Erbil G\"ugercino\u{g}lu, Bhal Chandra Joshi, M. A. Krishnakumar, Shantanu Desai, P. Arumugam, Debades Bandyopadhyay
https://arxiv.org/abs/2506.02100

Post-glitch Recovery and the Neutron Star Structure: The Vela Pulsar
Pulsar glitches, sudden changes in a neutron star's rotation, offer a unique window into the extreme physics governing these celestial bodies. The post-glitch recovery phase, characterized by a gradual recovery, reveals insights into the superfluid dynamics within the star. The post-glitch recovery phase probes the coupling mechanisms between different stellar components, sheds light on the properties of neutron star matter at supra-nuclear densities and even allows us to predict the time to th…

This https://arxiv.org/abs/2409.12829 has been replaced.
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Altermagnetic Weyl node-network semimetals protected by spin symmetry
Symmetry protected topology has been studied extensively in the past twenty years, but the topology protected by spin symmetry has just begun to be studied. In this work, based on spin symmetry analysis, we propose that a class of Weyl nodal line semimetals is protected by the spin symmetry. Then, by the first-principles electronic structure calculations, we predict that both altermagnetic $\rm Nb_2FeB_2$ and $\rm Ta_2FeB_2$ are node-network semimetals protected by the spin symmetry. Moreover, …

Classification based deep learning models for lung cancer and disease using medical images
Ahmad Chaddad, Jihao Peng, Yihang Wu
https://arxiv.org/abs/2507.01279

Classification based deep learning models for lung cancer and disease using medical images
The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the established ResNet framework. This model is specifically designed to improve the prediction of lung cancer and diseases using the images. To address the challenge of missing feature information that occurs during the downsampling process in CNNs, we integrate …

A finite element implementation of a large deformation gradient-damage theory for fracture with Abaqus user material subroutines
Keven Alkhoury, Shawn A. Chester, Vikas Srivastava
https://arxiv.org/abs/2507.02702

A finite element implementation of a large deformation gradient-damage theory for fracture with Abaqus user material subroutines
Recent advancements in computations have enabled the application of various modeling approaches to predict fracture and failure, such as the gradient-damage (phasefield) method. Several existing studies have leveraged the heat equation solver in Abaqus to model gradient-damage, due to its mathematical resemblance to the heat equation. Particular care is required when extending the approach to large deformation scenarios due to differences in the referential and spatial configurations, especiall…

This https://arxiv.org/abs/2505.16865 has been replaced.
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LARES: Latent Reasoning for Sequential Recommendation
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequential recommendation methods predominantly rely on non-reasoning paradigms, which may limit the model's computational capacity and result in suboptimal recommendation performance. To address these limitations, we present LARES, a novel and scalable LAtent REasoning framework for Sequential recommendation that enhance…

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
Jiahao Yang, Ran Fang, Ming Zhang, Jun Zhou
https://arxiv.org/abs/2505.22678

An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes. However, this task is challenging due to the high-dimensional and volatile nature of the original data. Even recent deep learning models often struggle to capture price movement patterns effectively, particularly without well-designed features. We observed that raw LOB data exhibits inherent symmetry between the ask and bid sides, and the bid-ask diffe…

Resolving Turbulent Magnetohydrodynamics: A Hybrid Operator-Diffusion Framework
Semih Kacmaz, E. A. Huerta, Roland Haas
https://arxiv.org/abs/2507.02106 ht…

Resolving Turbulent Magnetohydrodynamics: A Hybrid Operator-Diffusion Framework
We present a hybrid machine learning framework that combines Physics-Informed Neural Operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive magnetohydrodynamic (MHD) turbulence across a broad range of Reynolds numbers ($\mathrm{Re}$). The framework leverages the equation-constrained generalization capabilities of PINOs to predict coherent, low-frequency dynamics, while a conditional diffusion mo…

This https://arxiv.org/abs/2412.07223 has been replaced.
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A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.

AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam MIRI Colors and Photometry
Kurt Hamblin, Allison Kirkpatrick, Bren E. Backhaus, Gregory Troiani, Fabio Pacucci, Jonathan R. Trump, Alexander de la Vega, L. Y. Aaron Yung, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne, Guang Yang, Pablo Arrabal Haro, Micaela B. Bagley, Mark Dickinson, Steven L. Finkelstein, Nor Pirzkal

AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry
We present AGNBoost, a machine learning framework utilizing XGBoostLSS to identify AGN and estimate redshifts from JWST NIRCam and MIRI photometry. AGNBoost constructs 121 input features from 7 NIRCam and 4 MIRI bands-including magnitudes, colors, and squared color terms-to simultaneously predict the fraction of mid-IR $3-30\,μ$m emission attributable to an AGN power law (frac$_\text{AGN}$) and photometric redshift. Each model is trained on a sample of $10^6$ simulated galaxies from CIGALE pro…

This https://arxiv.org/abs/2408.08294 has been replaced.
link: https://scholar.google.com/scholar?q=a

eGAD! double descent is explained by Generalized Aliasing Decomposition
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, over-parameterized models exhibit counterintuitive behaviors, such as "double descent" in which models of increasing complexity exhibit decreasing generalization error. Others may e…

IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
The IntFold Team, Leon Qiao, Wayne Bai, He Yan, Gary Liu, Nova Xi, Xiang Zhang
https://arxiv.org/abs/2507.02025

IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
We introduce IntFold, a controllable foundation model for both general and specialized biomolecular structure prediction. IntFold demonstrates predictive accuracy comparable to the state-of-the-art AlphaFold3, while utilizing a superior customized attention kernel. Beyond standard structure prediction, IntFold can be adapted to predict allosteric states, constrained structures, and binding affinity through the use of individual adapters. Furthermore, we introduce a novel confidence head to esti…

This https://arxiv.org/abs/2411.16967 has been replaced.
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A Review of Design Concerns in Superconducting Quantum Circuits
In this short review we describe the process of designing a superconducting circuit device for quantum information applications. We discuss the factors that must be considered to implement a desired effective Hamiltonian on a device. We describe the translation between a device's physical layout, the circuit graph, and the effective Hamiltonian. We go over the process of electromagnetic simulation of a device layout to predict its behavior. We also discuss concerns such as connectivity, crossta…

Universal Preference-Score-based Pairwise Speech Quality Assessment
Yu-Fei Shi, Yang Ai, Zhen-Hua Ling
https://arxiv.org/abs/2506.01455 https://

Universal Preference-Score-based Pairwise Speech Quality Assessment
To compare the performance of two speech generation sys- tems, one of the most effective approaches is estimating the preference score between their generated speech. This pa- per proposes a novel universal preference-score-based pairwise speech quality assessment (UPPSQA) model, aimed at predict- ing the preference score between paired speech samples to de- termine which one has better quality. The model first predicts the absolute mean opinion score (MOS) for the two speech sam- ples separate…

Can We Reliably Predict the Fed's Next Move? A Multi-Modal Approach to U.S. Monetary Policy Forecasting
Fiona Xiao Jingyi, Lili Liu
https://arxiv.org/abs/2506.22763

Can We Reliably Predict the Fed's Next Move? A Multi-Modal Approach to U.S. Monetary Policy Forecasting
Forecasting central bank policy decisions remains a persistent challenge for investors, financial institutions, and policymakers due to the wide-reaching impact of monetary actions. In particular, anticipating shifts in the U.S. federal funds rate is vital for risk management and trading strategies. Traditional methods relying only on structured macroeconomic indicators often fall short in capturing the forward-looking cues embedded in central bank communications. This study examines whether …

This https://arxiv.org/abs/2401.03276 has been replaced.
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Robust Binary and Multinomial Logit Models for Classification with Data Uncertainties
Binary logit (BNL) and multinomial logit (MNL) models are the two most widely used discrete choice models for travel behavior modeling and prediction. However, in many scenarios, the collected data for those models are subject to measurement errors. Previous studies on measurement errors mostly focus on "better estimating model parameters" with training data. In this study, we focus on using BNL and MNL for classification problems, that is, to ``better predict the behavior of new samples'' when…

This https://arxiv.org/abs/2408.10868 has been replaced.
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Inconsistency Removal of Reduced Bases in Parametric Model Order Reduction by Matrix Interpolation using Adaptive Sampling and Clustering
Parametric model order reduction by matrix interpolation allows for efficient prediction of the behavior of dynamic systems without requiring knowledge about the underlying parametric dependency. Within this approach, reduced models are first sampled and then made consistent with each other by transforming the underlying reduced bases. Finally, the transformed reduced operators can be interpolated to predict reduced models for queried parameter points. However, the accuracy of the predicted red…

So #Gentoo #Python eclasses are pretty modern, in the sense that they tend to follow the best practices and standards, and eventually deal with deprecations. Nevertheless, they have a long history and carry quite some historical burden, particularly regarding to naming.
The key point is that the eclasses were conceived as a replacement for the old eclasses: "distutils" and "python". Hence, much like we revision ebuilds, I've named the matching eclasses "distutils-r1" and "python-r1". For consistency, I've also used the "-r1" suffix for the remaining eclasses introduced at the time: "python-any-r1", "python-single-r1" and "python-utils-r1" — even though there were never "r0"s.
It didn't take long to realize my first mistake. I've made the multi-impl eclass effectively the "main" eclass, probably largely inspired by the previous Gentoo recommendations. However, in the end I've found out that for the most use cases (i.e. where "distutils-r1" is not involved), there is no real need for multi-impl, and it makes things much harder. So if I were naming them today, I would have named it "python-multi", to indicate the specific use case — and either avoid designating a default at all, or made "python-single" the default.
What aged even worse is the "distutils-r1" eclass. Admittedly, back when it was conceived, distutils was still largely a thing — and there were people (like me) who avoided unnecessary dependency on setuptools. Of course, nowadays it has been entirely devoured by setuptools, and with #PEP517 even "setuptools" wouldn't be a good name anymore. Nowadays, people are getting confused why they are supposed to use "distutils-r1" for, say, Hatchling.
Admittedly, this is something I could have done differently — PEP517 support was a major migration, and involved an explicit switch. Instead of adding DISTUTILS_USE_PEP517 (what a self-contradictory name) variable, I could have forked the eclass. Why didn't I do that? Because there used to be a lot of code shared between the two paths. Of course, over time they diverged more, and eventually I've dropped the legacy support — but the opportunity to rename was lost.
In fact, as a semi-related fact, I've recognized another design problem with the eclass earlier — I should have gone for two eclasses rather than one: a "python-phase" eclass with generic sub-phase support, and a "distutils" (or later "python-pep517") implementing default sub-phases for the common backends. And again, this is precisely how I could have solved the code reuse problem when I introduced PEP517 support.
But then, I didn't anticipate how the eclasses would end up looking like in the end — and I can't really predict what new challenges the Python ecosystem is going to bring us. And I think it's too late to rename or split stuff — too much busywork on everyone.

Predicting and Explaining Customer Data Sharing in the Open Banking
Jo\~ao B. G. de Brito, Rodrigo Heldt, Cleo S. Silveira, Matthias Bogaert, Guilherme B. Bucco, Fernando B. Luce, Jo\~ao L. Becker, Filipe J. Zabala, Michel J. Anzanello
https://arxiv.org/abs/2507.01987

Predicting and Explaining Customer Data Sharing in the Open Banking
The emergence of Open Banking represents a significant shift in financial data management, influencing financial institutions' market dynamics and marketing strategies. This increased competition creates opportunities and challenges, as institutions manage data inflow to improve products and services while mitigating data outflow that could aid competitors. This study introduces a framework to predict customers' propensity to share data via Open Banking and interprets this behavior through Expl…

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang
https://arxiv.org/abs/2506.00708

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information. Recently, generative large language models (LLMs) have been increasingly employed for graph tasks. However, current approaches typically encode graph context in textual form, which fails to fully exploit the potential of LLMs for perceiving and reasoning about graph structures. To address this limitation, we propose DrKGC (Dynamic Subgraph Retrieval-…

This week, we were discussing the central question Can we "predict" a word? as the basis for statistical language models in our #ISE2025 lecture. Of course, I wasx trying Shakespeare quotes to motivate the (international) students to complement the quotes with "predicted" missing words ;-)
"All the world's a stage, and all the men and women merely...."

Modification of a Numerical Method Using FIR Filters in a Time-dependent SIR Model for COVID-19
Felipe Rog\'erio Pimentel, Rafael Gustavo Alves
https://arxiv.org/abs/2506.21739

Modification of a Numerical Method Using FIR Filters in a Time-dependent SIR Model for COVID-19
Authors Yi-Cheng Chen, Ping-En Lu, Cheng-Shang Chang, and Tzu-Hsuan Liu use the Finite Impulse Response (FIR) linear system filtering method to track and predict the number of people infected and recovered from COVID-19, in a pandemic context in which there was still no vaccine and the only way to avoid contagion was isolation. To estimate the coefficients of these FIR filters, Chen et al. used machine learning methods through a classical optimization problem with regularization (ridge regressi…

Probing Top Quark - Electron Interactions at Future Colliders
Luigi Bellafronte, Sally Dawson, Pier Paolo Giardino, Hongkai Liu
https://arxiv.org/abs/2507.02039

Probing Top Quark - Electron Interactions at Future Colliders
Top quark interactions offer a window into possible new high scale physics and many models of new physics predict that the top quark interactions will deviate significantly from those predicted by the Standard Model (SM). We present an analysis of the experimental restrictions on anomalous 4-fermion $e^+e^- t {\overline{t}}$ operators that is accurate to next-to-leading order (NLO) in both the electroweak and QCD interactions within the Standard Model Effective Field Theory framework. At NLO, t…

Whole-Body Conditioned Egocentric Video Prediction
Yutong Bai, Danny Tran, Amir Bar, Yann LeCun, Trevor Darrell, Jitendra Malik
https://arxiv.org/abs/2506.21552

Whole-Body Conditioned Egocentric Video Prediction
We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, our model learns to simulate how physical human actions shape the environment from a first-person point of view. We train an auto-regressive conditional diffusion transformer on Nymeria, a large-scale dataset of real-world egocentric video and body pose c…

Delirium often goes underreported, making it hard for #AI to predict accurately. Our review of 120 studies found common data issues that hurt model reliability. We created a step-by-step guide to help build fairer, more accurate models—so we eventually can detect #delirium earlier and im…

Integrating nano- and micrometer-scale energy deposition models for mechanistic prediction of radiation-induced DNA damage and cell survival
Giulio Bordieri, Marta Missiggia, Gianluca Lattanzi, Carmen Villagrasa, Yann Perrot, Francesco G. Cordoni
https://arxiv.org/abs/2507.00929

Integrating nano- and micrometer-scale energy deposition models for mechanistic prediction of radiation-induced DNA damage and cell survival
We present an integrated modeling framework that combines the Generalized Stochastic Microdosimetric Model (GSM2), used to predict cell survival fractions, with MINAS-TIRITH, a fast and efficient Geant4 DNA-based tool for simulating radiation-induced DNA damage in cell populations. This approach enables the generation of spatially and structurally resolved double-strand break (DSB) distributions, capturing key features such as damage complexity and chromosome specificity. A novel application of…

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…

Machine-learning Growth at Risk
Tobias Adrian, Hongqi Chen, Max-Sebastian Dov\`i, Ji Hyung Lee
https://arxiv.org/abs/2506.00572 https://

Machine-learning Growth at Risk
We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors…

🧠 A new tool can help predict brain-damaging seizures and free up continuous EEG machines for high-risk patients
https://medicalxpress.com/news/2025-05-tool-brain-seizures-free-eeg.html

A new tool can help predict brain-damaging seizures and free up continuous EEG machines for high-risk patients
In hospital intensive care units, neurologists often use a simple scorecard to quickly evaluate a critically ill patient's likelihood of having a brain-damaging seizure so they can prevent it. The scorecard saves lives, lowers costs and helps doctors make far more efficient use of important, expensive medical equipment.

This https://arxiv.org/abs/2406.05298 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Spectral Codecs: Improving Non-Autoregressive Speech Synthesis with Spectrogram-Based Audio Codecs
Historically, most speech models in machine-learning have used the mel-spectrogram as a speech representation. Recently, discrete audio tokens produced by neural audio codecs have become a popular alternate speech representation for speech synthesis tasks such as text-to-speech (TTS). However, the data distribution produced by such codecs is too complex for some TTS models to predict, typically requiring large autoregressive models to get good quality. Most existing audio codecs use Residual Ve…

A refinement of the Lorentz local field expression with impact on the Clausius-Mossotti and Lorentz-Lorenz models
Jeroen van Duivenbode, Anne-Jans Faber
https://arxiv.org/abs/2506.01993

A refinement of the Lorentz local field expression with impact on the Clausius-Mossotti and Lorentz-Lorenz models
In the 19th century Mossotti and Clausius formulated an expression that relates electrical permittivity of a dielectric to the product of molecular polarizability and number density. Lorenz and Lorentz extended the use of that expression to also cover the dielectric s refractive index. These expressions successfully predict the change of the dielectric s permittivity and refractive index as a function of a wide range of its number density, using the consideration that molecular polarizability s…

Google DeepMind unveils AlphaGenome, an AI tool to predict the effects of DNA changes on molecular processes, available via an API for non-commercial research (Antonio Regalado/MIT Technology Review)
https://www.technologyreview.com/2025/06/25/1…

Google’s new AI will help researchers understand how our genes work
First came AlphaFold. Now comes AlphaGenome for DNA.

Theory of terahertz pulse transmission through ferroelectric nanomembranes
Yujie Zhu, Aiden Ross, Xiangwei Guo, Venkatraman Gopalan, Long-Qing Chen, Jia-Mian Hu
https://arxiv.org/abs/2506.00370

Theory of terahertz pulse transmission through ferroelectric nanomembranes
An analytical model is developed to predict the temporal evolution of the lattice polarization in ferroelectric nanomembranes upon the excitation by a terahertz (THz) electromagnetic pulse of an arbitrary waveform, and the concurrent transmission of the THz pulse in both the linear and the nonlinear regimes. It involves the use of the perturbation method to solve the equation of motion for the lattice polarization in both unclamped and strained ferroelectric nanomembranes within the framework o…

Higher-order homogenised riblet boundary conditions
Paolo Luchini, Daniel Chung
https://arxiv.org/abs/2506.22239 https://arxiv.org/pd…

Higher-order homogenised riblet boundary conditions
The description of riblets and other drag-reducing devices has long used the concept of longitudinal and transverse protrusion heights, both as a means to predict the drag reduction itself and as equivalent boundary conditions to simplify numerical simulations by transferring the effect of riblets onto a flat virtual boundary. The limitation of this idea is that it stems from a first-order approximation in the riblet-size parameter $s^+$, and as a consequence it cannot predict other than a line…

This https://arxiv.org/abs/2412.05718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

RLZero: Direct Policy Inference from Language Without In-Domain Supervision
The reward hypothesis states that all goals and purposes can be understood as the maximization of a received scalar reward signal. However, in practice, defining such a reward signal is notoriously difficult, as humans are often unable to predict the optimal behavior corresponding to a reward function. Natural language offers an intuitive alternative for instructing reinforcement learning (RL) agents, yet previous language-conditioned approaches either require costly supervision or test-time tr…

Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
Adebola K. Ojo, Ifechukwude Jude Okafor
https://arxiv.org/abs/2507.01964 h…

Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. Th…

Masses of hidden-charm pentaquark states with $J^P = \frac{3}{2}^-$
Tao Li, Hao-Song Li
https://arxiv.org/abs/2507.02502 https://arxi…

Masses of hidden-charm pentaquark states with $J^P = \frac{3}{2}^-$
Within the heavy pentaquark chiral perturbation theory, we calculate the chiral corrections for $J^P=\frac{3}{2}^-$ octet hidden-charm pentaquark masses up to next-to-leading order. Taking the LHCb-reported $P_ψ^N(4440)$ and $P_{ψs}^Λ(4459)$ (with $J^P=\frac{3}{2}^-$) as inputs, we predict the other two octet hidden-charm pentaquark states $P_{ψs}^Σ(4483)$ and $P_{ψss}^{N}(4490)$ with $J^P=\frac{3}{2}^-$. The results provide theoretical guidance for the further search of $P_{ψs}^Σ$ and …

Kinematic Distortions of the High-Redshift Universe as Seen from Quasar Proper Motions
Valeri V. Makarov
https://arxiv.org/abs/2507.01798 https://

Kinematic Distortions of the High-Redshift Universe as Seen from Quasar Proper Motions
Advances in optical astrometry allow us to infer the non-radial kinematic structure of the Universe directly from observations. Here I use a supervised machine learning neural network method to predict 1.57 million redshifts based on several photometric and metadata classifier parameters from the unWISE mid-infrared database and from Gaia. These estimates are used to divide the sample into three redshift bins: 1-2, 2-3, and $>3$. For each subset, all available Gaia proper motions are used in a …

Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
https://arxiv.org/abs/2506.22801

Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Quantum imaginary time evolution (QITE) is a powerful method to derive the ground states of the systems. Only the damping of quantum states leads it; hence, reaching the ground state is guaranteed by nature without any external manipulation. Numerous QITE methods by many groups are used to improve speed and accuracy, derive excited states, and solve combined optimization problems. However, the QITE methods have not been used for quantum machine learning to predict the ideal values for multiple …

Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping
Yifei Sun, Marshall A. Dalton, Robert D. Sanders, Yixuan Yuan, Xiang Li, Sharon L. Naismith, Fernando Calamante, Jinglei Lv
https://arxiv.org/abs/2507.01411

Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping
Grey matter loss in the hippocampus is a hallmark of neurobiological aging, yet understanding the corresponding changes in its functional connectivity remains limited. Seed-based functional connectivity (FC) analysis enables voxel-wise mapping of the hippocampus's synchronous activity with cortical regions, offering a window into functional reorganization during aging. In this study, we develop an interpretable deep learning framework to predict brain age from hippocampal FC using a three-dimen…

Identifying a Circuit for Verb Conjugation in GPT-2
David Demitri Africa
https://arxiv.org/abs/2506.22105 https://arxiv.org/pdf/2506.…

Identifying a Circuit for Verb Conjugation in GPT-2
I implement a procedure to isolate and interpret the sub-network (or "circuit") responsible for subject-verb agreement in GPT-2 Small. In this study, the model is given prompts where the subject is either singular (e.g. "Alice") or plural (e.g. "Alice and Bob"), and the task is to correctly predict the appropriate verb form ("walks" for singular subjects, "walk" for plural subjects). Using a series of techniques-including performance verification automatic circuit discovery via direct path patc…

Generative Next POI Recommendation with Semantic ID
Dongsheng Wang, Yuxi Huang, Shen Gao, Yifan Wang, Chengrui Huang, Shuo Shang
https://arxiv.org/abs/2506.01375

Generative Next POI Recommendation with Semantic ID
Point-of-interest (POI) recommendation systems aim to predict the next destinations of user based on their preferences and historical check-ins. Existing generative POI recommendation methods usually employ random numeric IDs for POIs, limiting the ability to model semantic relationships between similar locations. In this paper, we propose Generative Next POI Recommendation with Semantic ID (GNPR-SID), an LLM-based POI recommendation model with a novel semantic POI ID (SID) representation metho…

A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis
Christian Marius Lillelund, Sanjay Kalra, Russell Greiner
https://arxiv.org/abs/2506.02076

A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis
Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of motor neurons that causes progressive paralysis in patients. Current treatment options aim to prolong survival and improve quality of life; however, due to the heterogeneity of the disease, it is often difficult to determine the optimal time for potential therapies or medical interventions. In this study, we propose a novel method to predict the time until a patient with ALS experiences significant functional impairment (ALSFRS-R…

Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
Nurbanu Bursa
https://arxiv.org/abs/2506.01945

Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
Emerging economies, particularly the MINT countries (Mexico, Indonesia, Nigeria, and Türkiye), are gaining influence in global stock markets, although they remain susceptible to the economic conditions of developed countries like the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This interconnectedness and sensitivity of financial markets make understanding these relationships crucial for investors and policymakers to predict stock price movements accur…

Constraining the magnetic field in the galaxy cluster Abell 2142 using MeerKAT L-band polarisation data
A. Pagliotta, C. J. Riseley, A. Bonafede, C. Stuardi, F. Loi
https://arxiv.org/abs/2507.00114

Constraining the magnetic field in the galaxy cluster Abell 2142 using MeerKAT L-band polarisation data
Magnetic fields permeate the Universe, including galaxy clusters, and affect the thermodynamical properties of the intra-cluster medium (ICM). Cosmological simulations predict that seed magnetic fields are amplified up to the $μ$G-level in the ICM, but the magnetic field strength and structure have been studied in only a few clusters. Abell 2142 is a local massive cluster that shows evidence of a post-merger dynamical state. In this work, we aim to constrain the magnetic field intensity, radia…

2025 Bills game-by-game predictions: Is a perfect season in play? https://www.foxsports.com/stories/nfl/predicting-bills-2025-schedule-game-by-game-perfect-season

Predicting Empirical AI Research Outcomes with Language Models
Jiaxin Wen, Chenglei Si, Yueh-han Chen, He He, Shi Feng
https://arxiv.org/abs/2506.00794 htt…

Predicting Empirical AI Research Outcomes with Language Models
Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial for accelerating empirical AI research, a skill that even expert researchers can only acquire through substantial experience. We build the first benchmark for this task and compare LMs with human experts. Concretely, given two research ideas (e.g., two jailbreaking methods), we aim to predict which will perform better…

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data
Bowen Han, Yongqiang Cheng
https://arxiv.org/abs/2506.01860

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data
The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely used, are computationally expensive and demand substantial expertise. Emerging universal machine learning interatomic potentials (uMLIPs) offer a transformative alternative by employing pre-trained neural network surrogates to predict interatomic forc…

Electronic Temperature-Driven Phase Stability and Structural Evolution of Iron at High Pressure
S. Azadi, S. M. Vinko
https://arxiv.org/abs/2506.01809 http…

Electronic Temperature-Driven Phase Stability and Structural Evolution of Iron at High Pressure
We present Gibbs free-energy phase diagrams for compressed iron within a pressure range of 20 to 300 GPa and electronic temperature up to 3 eV obtained using finite-temperature density functional and density functional perturbation theories. Our results for bcc, fcc, and hcp phases predict solid-solid phase transitions in iron driven purely by electronic entropy and temperature. We found a phase transition from hcp to bcc at pressures above 200 GPa, which depends on the electronic temperature. …

The Effect of Weak Cosmic Ray Heating Events on the Desorption of $\rm H_2$
O. Sipil\"a, K. Silsbee, N. Carbajal, P. Caselli, M. Padovani
https://arxiv.org/abs/2507.01612

The Effect of Weak Cosmic Ray Heating Events on the Desorption of $\rm H_2$
The typical amount of molecular hydrogen (${\rm H_2}$) in interstellar ices is not known, but significant freeze-out of ${\rm H_2}$ on dust grains is not expected. However, chemical models ubiquitously predict large amounts of $\rm H_2$ freeze-out in dense cloud conditions, and specialized treatments are needed to control the $\rm H_2$ population on grains. Here we present a numerical desorption model where the effect of weak heating events induced by cosmic rays (CRs) that heat grains to tempe…

Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients
Abdi Reza, Takufumi Yanagisawa, Naoki Tani, Ryohei Fukuma, Takuto Emura, Satoru Oshino, Ben Seymour, Haruhiko Kishima
https://arxiv.org/abs/2506.21439

Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients
Parkinson disease (PD) patients experience pain fluctuations that significantly reduce their quality of life. Despite the vast knowledge of the subthalamic nucleus (STN) role in PD, the STN biomarkers for pain fluctuations and the relationship between bilateral subthalamic nucleus (STN) activities and pain occurrence are still less understood. This observational study used data-driven methods by collecting annotated pain followed by a series of corresponding binary pain ratings and wirelessly t…

Electron Excitation Probability in Dielectrics under Two-color Intense Laser Fields
Mizuki Tani, Kenichi L. Ishikawa, Tomohito Otobe
https://arxiv.org/abs/2507.01023

Electron Excitation Probability in Dielectrics under Two-color Intense Laser Fields
Two-color laser fields offer significantly enhanced control over electron excitation dynamics under ultrashort intense laser pulses compared to monochromatic fields. However, their strong nonlinearity necessitates computationally expensive first-principles calculations to accurately predict ionization dynamics. To overcome this challenge, we derive an analytical expression for the ionization rate in dielectrics subjected to intense two-color laser fields, refining the theoretical framework intr…

Universal Quantum Computational Spectroscopy on a Quantum Chip
Chonghao Zhai, Jinzhao Sun, Jieshan Huang, Jun Mao, Hongchang Bao, Siyuan Zhang, Qihuang Gong, Vlatko Vedral, Xiao Yuan, Jianwei Wang
https://arxiv.org/abs/2506.22418

Universal Quantum Computational Spectroscopy on a Quantum Chip
Spectroscopy underpins modern scientific discovery across diverse disciplines. While experimental spectroscopy probes material properties through scattering or radiation measurements, computational spectroscopy combines theoretical models with experimental data to predict spectral properties, essential for advancements in physics, chemistry, and materials science. However, quantum systems present unique challenges for computational spectroscopy due to their inherent complexity, and current quan…

No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction
Haoshuai Zhou, Changgeng Mo, Boxuan Cao, Linkai Li, Shan Xiang Wang
https://arxiv.org/abs/2506.02039

No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction
Personalized speech intelligibility prediction is challenging. Previous approaches have mainly relied on audiograms, which are inherently limited in accuracy as they only capture a listener's hearing threshold for pure tones. Rather than incorporating additional listener features, we propose a novel approach that leverages an individual's existing intelligibility data to predict their performance on new audio. We introduce the Support Sample-Based Intelligibility Prediction Network (SSIPNet), a…

Modeling the Cosmological Lyman-$\alpha$ Forest at the Field Level
Roger de Belsunce, Mikhail M. Ivanov, James M. Sullivan, Kazuyuki Akitsu, Shi-Fan Chen
https://arxiv.org/abs/2507.00284

Modeling the Cosmological Lyman-$α$ Forest at the Field Level
The distribution of absorption lines in the spectra of distant quasars, called the Lyman-$α$ (Ly-$α$) forest, is a unique probe of cosmology and the intergalactic medium at high redshifts and small scales. The statistical power of ongoing redshift surveys demands precise theoretical tools to model the Ly-$α$ forest. We address this challenge by developing an analytic, perturbative forward model to predict the Ly-$α$ forest at the field level for a given set of cosmological initial condition…

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…

Prompt-Guided Turn-Taking Prediction
Koji Inoue, Mikey Elmers, Yahui Fu, Zi Haur Pang, Divesh Lala, Keiko Ochi, Tatsuya Kawahara
https://arxiv.org/abs/2506.21191

Prompt-Guided Turn-Taking Prediction
Turn-taking prediction models are essential components in spoken dialogue systems and conversational robots. Recent approaches leverage transformer-based architectures to predict speech activity continuously and in real-time. In this study, we propose a novel model that enables turn-taking prediction to be dynamically controlled via textual prompts. This approach allows intuitive and explicit control through instructions such as "faster" or "calmer" adapting dynamically to conversational partne…

Molecular states with bottom mesons and multistrange baryons systems
Jing Song, Yi-Yao Li, Eulogio Oset
https://arxiv.org/abs/2507.00840 https://

Molecular states with bottom mesons and multistrange baryons systems
We investigate molecular states formed by bottom mesons and multistrange baryons from both octet and decuplet flavor representations, using the local hidden gauge approach combined with coupled-channel Bethe-Salpeter equations. Focusing on strangeness sectors \(S = -1\) to \(-4\), we predict several bound states in the \(S=-1, I=1/2,~3/2\) and \(S=-2, I=0\) sectors. No bound states are found in other isospin channels, where the interaction is repulsive, nor in higher strangeness sectors. The bi…

This https://arxiv.org/abs/2412.17820 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Hyperbolic embedding of brain networks can predict the surgery outcome in temporal lobe epilepsy
Epilepsy surgery, particularly for temporal lobe epilepsy (TLE), remains a vital treatment option for patients with drug-resistant seizures. However, accurately predicting surgical outcomes remains a significant challenge. This study introduces a novel biomarker derived from brain connectivity, analyzed using non-Euclidean network geometry, to predict the surgery outcome in TLE. Using structural and diffusion magnetic resonance imaging (MRI) data from 51 patients, we examined differences in str…

pySTARBURST99: The Next Generation of STARBURST99
Calum Hawcroft, Claus Leitherer, Oskar Arangure, John Chisholm, Sylvia Ekstrom, Sebastien Martinet, Lucimara Martins, Georges Meynet, Christophe Morisset, Andreas Sander, Aida Wofford
https://arxiv.org/abs/2505.24841

pySTARBURST99: The Next Generation of STARBURST99
STARBURST99 is a population synthesis code tailored to predict the integrated properties or observational characteristics of star-forming galaxies. Here we present an update to STARBURST99 where we port the code to python, include new evolutionary tracks both rotating and non-rotating at a range of low metallicity environments. We complement these tracks with a corresponding grid of new synthetic SEDs. Additionally we include both evolutionary and spectral models of stars up to 300-500Msol. Syn…

Symmetry-Breaking Magneto-Optical Effects in Altermagnets
Jiuyu Sun, Yongping Du, Erjun Kan
https://arxiv.org/abs/2505.24124 https://…

Symmetry-Breaking Magneto-Optical Effects in Altermagnets
The recently discovered altermagnets (AMs), hosting momentum-dependent spin splitting and vanishing net magnetization, have attracted intensive attention for their promising application in novel spintronics. However, limited by facility and material constraints, experimentally distinguishing them from conventional antiferromagnets (AFMs) remains a challenge, which hinders the high-throughput screening for AM candidates. Here, we predict strain-mediated magneto-optical responses in AMs, which ca…

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat-Seng Chua
https://arxiv.org/abs/2506.21579

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have mo…

This https://arxiv.org/abs/2505.22688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Investigating the effectiveness of multimodal data in forecasting SARS-COV-2 case surges
The COVID-19 pandemic response relied heavily on statistical and machine learning models to predict key outcomes such as case prevalence and fatality rates. These predictions were instrumental in enabling timely public health interventions that helped break transmission cycles. While most existing models are grounded in traditional epidemiological data, the potential of alternative datasets, such as those derived from genomic information and human behavior, remains underexplored. In the current…

Alter-Piezoresponse in Two-Dimensional Lieb-Lattice Altermagnets
Xilong Xu, Li Yang
https://arxiv.org/abs/2506.22663 https://arxiv.or…

Alter-Piezoresponse in Two-Dimensional Lieb-Lattice Altermagnets
Altermagnetism, featuring alternating spin structures in reciprocal space, has sparked growing interest. Here, we predict novel real-space alternative piezomagnetic and piezoelectric responses in an emerging altermagnetic family of Lieb lattices, specifically transition-metal chalcogenides M2WS4 (M = Mn, Fe, Co). The unique S4T crystal-spin symmetry leads to distinct magnetic and electric responses depending on the direction of applied stress. When subjected to axial stress, they exhibit a gian…