Tootfinder

Grammarly debuts AI agents for writing and grading assistance, including an AI grader and a citation finder agent, available for Grammarly Free and Pro users (Jess Weatherbed/The Verge)
https://www.theverge.com/news/760508/grammarly-ai-agents-help-students-ed…

Grammarly says its AI agent can predict an A paper
Grammarly is launching several new AI agents for specific writing challenges for educators and students. It can catch plagiarism and even predict a grade.

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Wenkai Yu, Jianhang Tang, Yang Zhang, Shanjiang Tang, Kebing Jin, Hankz Hankui Zhuo
https://arxiv.org/abs/2508.11524

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and actions. Recently, researchers have explored the effectiveness of leveraging Large Language Models (LLMs) to generate helpful actions and states to prune the search space. However, prior works have largely overlooked integrating LLMs with domain-specific knowledge to ensure valid plans. In this paper, we propose a nove…

Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes
Iyadh Ben Cheikh Larbi, Ajay Madhavan Ravichandran, Aljoscha Burchardt, Roland Roller
https://arxiv.org/abs/2509.13696

Integrating Text and Time-Series into (Large) Language Models to Predict Medical Outcomes
Large language models (LLMs) excel at text generation, but their ability to handle clinical classification tasks involving structured data, such as time series, remains underexplored. In this work, we adapt instruction-tuned LLMs using DSPy-based prompt optimization to process clinical notes and structured EHR inputs jointly. Our results show that this approach achieves performance on par with specialized multimodal systems while requiring less complexity and offering greater adaptability acros…

NFL picks: Predicting every game, final 2025 record for each NFC team https://www.nfl.com/news/nfl-picks-predicting-every-game-final-2025-record-for-each-nfc-team

PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma
L. Zimmer, J. Weidner, M. Balcerak, F. Kofler, I. Ezhov, B. Menze, B. Wiestler
https://arxiv.org/abs/2509.13360

PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma
Glioblastoma is the most prevalent primary brain malignancy, distinguished by its highly invasive behavior and exceptionally high rates of recurrence. Conventional radiation therapy, which employs uniform treatment margins, fails to account for patient-specific anatomical and biological factors that critically influence tumor cell migration. To address this limitation, numerous computational models of glioblastoma growth have been developed, enabling generation of tumor cell distribution maps e…

Artificial neural networks ensemble methodology to predict significant wave height
Felipe Crivellaro Minuzzi, Leandro Farina
https://arxiv.org/abs/2509.14020 https://

Artificial neural networks ensemble methodology to predict significant wave height
The forecast of wave variables are important for several applications that depend on a better description of the ocean state. Due to the chaotic behaviour of the differential equations which model this problem, a well know strategy to overcome the difficulties is basically to run several simulations, by for instance, varying the initial condition, and averaging the result of each of these, creating an ensemble. Moreover, in the last few years, considering the amount of available data and the co…

"Researchers Use Al to Predict Beavers' Impact on Local Habitats-and Show How Humans Can Help"
#Beavers #Animals

Researchers Use AI to Predict Beavers’ Impact on Local Habitats—and Show How Humans Can Help - Inside Climate News
By linking pond size to dam length, vegetation and stream power, researchers give land managers more precise tools to anticipate water storage, biodiversity and climate resilience.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation
Jiyun Im, SuBeen Lee, Miso Lee, Jae-Pil Heo
https://arxiv.org/abs/2509.13907 https://

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation
Few-Shot 3D Point Cloud Segmentation (FS-PCS) aims to predict per-point labels for an unlabeled point cloud, given only a few labeled examples. To extract discriminative representations from the limited support set, existing methods have constructed prototypes using conventional algorithms such as farthest point sampling. However, we point out that its initial randomness significantly affects FS-PCS performance and that the prototype generation process remains underexplored despite its prevalen…

Minimizing Surrogate Losses for Decision-Focused Learning using Differentiable Optimization
Jayanta Mandi, Ali \.Irfan Mahmuto\u{g}ullar{\i}, Senne Berden, Tias Guns
https://arxiv.org/abs/2508.11365

Minimizing Surrogate Losses for Decision-Focused Learning using Differentiable Optimization
Decision-focused learning (DFL) trains a machine learning (ML) model to predict parameters of an optimization problem, to directly minimize decision regret, i.e., maximize decision quality. Gradient-based DFL requires computing the derivative of the solution to the optimization problem with respect to the predicted parameters. However, for many optimization problems, such as linear programs (LPs), the gradient of the regret with respect to the predicted parameters is zero almost everywhere. Exi…

More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognition
James Tavernor, Emily Mower Provost
https://arxiv.org/abs/2509.12295 https://

More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognition
Speech emotion recognition systems often predict a consensus value generated from the ratings of multiple annotators. However, these models have limited ability to predict the annotation of any one person. Alternatively, models can learn to predict the annotations of all annotators. Adapting such models to new annotators is difficult as new annotators must individually provide sufficient labeled training data. We propose to leverage inter-annotator similarity by using a model pre-trained on a l…

BaMANI: Bayesian Multi-Algorithm causal Network Inference
Habibolla Latifizadeh, Anika C. Pirkey, Alanna Gould, David J. Klinke II
https://arxiv.org/abs/2508.11741 https://

BaMANI: Bayesian Multi-Algorithm causal Network Inference
Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also bear an opaque imprint of the specific computational algorithm used. Following a ``wisdom of the crowds" strategy, we developed an ensemble learning approach …

Language Conditioning Improves Accuracy of Aircraft Goal Prediction in Untowered Airspace
Sundhar Vinodh Sangeetha, Chih-Yuan Chiu, Sarah H. Q. Li, Shreyas Kousik
https://arxiv.org/abs/2509.14063

Language Conditioning Improves Accuracy of Aircraft Goal Prediction in Untowered Airspace
Autonomous aircraft must safely operate in untowered airspace, where coordination relies on voice-based communication among human pilots. Safe operation requires an aircraft to predict the intent, and corresponding goal location, of other aircraft. This paper introduces a multimodal framework for aircraft goal prediction that integrates natural language understanding with spatial reasoning to improve autonomous decision-making in such environments. We leverage automatic speech recognition and l…

Human Digital Twin: Data, Models, Applications, and Challenges
Rong Pan, Hongyue Sun, Xiaoyu Chen, Giulia Pedrielli, Jiapeng Huang
https://arxiv.org/abs/2508.13138 https://

Human Digital Twin: Data, Models, Applications, and Challenges
Human digital twins (HDTs) are dynamic, data-driven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health trajectories. By integrating clinical, physiological, behavioral, and environmental inputs, HDTs enable personalized diagnostics, treatment planning, and anomaly detection. This paper reviews current approaches to HDT modeling, with a focus on statistical and machine learning techniques, including recent advances in anomal…

CARGO: A Framework for Confidence-Aware Routing of Large Language Models
Amine Barrak, Yosr Fourati, Michael Olchawa, Emna Ksontini, Khalil Zoghlami
https://arxiv.org/abs/2509.14899

CARGO: A Framework for Confidence-Aware Routing of Large Language Models
As large language models (LLMs) proliferate in scale, specialization, and latency profiles, the challenge of routing user prompts to the most appropriate model has become increasingly critical for balancing performance and cost. We introduce CARGO (Category-Aware Routing with Gap-based Optimization), a lightweight, confidence-aware framework for dynamic LLM selection. CARGO employs a single embedding-based regressor trained on LLM-judged pairwise comparisons to predict model performance, with a…

Starting up my rewatch of Minority Report. It's been a LOT of years.
#movies #scifi

Minority Report (2002)
John Anderton is a top 'Precrime' cop in the late-21st century, when technology can predict crimes before they're committed. But Anderton becomes the quarry when another investigator targets him for a murder charge.

Dynamics of conductive nonmagnetic objects in presence of the Lenz effect
Alessandro Arduino, Oriano Bottauscio, Michael Steckner, Umberto Zanovello, Luca Zilberti
https://arxiv.org/abs/2509.14976

Dynamics of conductive nonmagnetic objects in presence of the Lenz effect
Purpose: To model and predict the dynamics of conductive nonmagnetic objects within the MRI room under the influence of Lenz effect. Methods: The dynamics are described by an ordinary differential equation and the Lenz effect approximated by recognizing that the skin effect is negligible. This separated Lenz effect dependency on the object position and velocity, leading to a simple numerical procedure for objects of any shape. Results: The model and numerical procedure were validated with exper…

"…Another attempt was thwarted last year when…Rampinis threatened to blow…house up, Zaia said."
"Tito said…eviction…carefully planned. '…reaction was so violent…it was hard to predict,'…prosecutor said."
Hard to predict people who threatened to blow up house might...do it?

3 Italian carabinieri killed in apparently deliberate farmhouse explosion
Authorities have reported that three carabinieri officers were killed and 13 others injured in an explosion near Verona, Italy, on Tuesday. The blast occurred during an eviction. Two brothers and a sister have been detained in connection with the incident. Verona’s carabinieri commander described the explosion as intentional, triggered by a gas tank. This was the second eviction attempt after a previous threat by the siblings to blow up the house. Italian Premier Giorgia Meloni and Defense Mi…

Study of Heavy Quarkonia Melting in QGP Using Deep Neural Networks
Mohammad Yousuf Jamal, Fu-Peng Li, Long-Gang Pang, Guang-You Qin
https://arxiv.org/abs/2509.14970 https://

Study of Heavy Quarkonia Melting in QGP Using Deep Neural Networks
Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework employing deep neural networks to systematically predict the temperature-dependent behavior of the screening mass $m_D(T)$ and the strong coupling constant $α_s(T)$ within a quark-gluon plasma medium. These medium-sensitive quantities are subsequently employed to compute the thermal widths $Γ_{\text{n}}(T)$ and bindi…

Coupled Infrared Imaging and Multiphysics Modeling to Predict Three-Dimensional Thermal Characteristics during Selective Laser Melting
Vijay Kumar, Kaitlyn M. Mullin, Hyunggon Park, Matthew Gerigk, Andrew Bresk, Tresa M. Pollock, Yangying Zhu
https://arxiv.org/abs/2509.12545

Coupled Infrared Imaging and Multiphysics Modeling to Predict Three-Dimensional Thermal Characteristics during Selective Laser Melting
Laser heating during additive manufacturing (AM) induces extreme and transient thermal conditions which critically influence the microstructure evolution and mechanical properties of the resulting component. However, accurately resolving these conditions with sufficient spatiotemporal accuracy remains a central challenge. We demonstrate a unique approach that couples high-speed infrared imaging, during selective laser melting of MAR-M247, with a transient three-dimensional (3D) multiphysics sim…

A finite element framework for simulating residential burglary in realistic urban geometries
Baoli Hao, Kamrun Mily, Annalisa Quaini, Ming Zhong
https://arxiv.org/abs/2508.11055

A finite element framework for simulating residential burglary in realistic urban geometries
We consider a partial differential equation (PDE) model to predict residential burglary derived from a probabilistic agent-based model through a mean-field limit operation. The PDE model is a nonlinear, coupled system of two equations in two variables (attractiveness of residential sites and density of criminals), similar to the Keller-Segel model for aggregation based on chemotaxis. Unlike previous works, which applied periodic boundary conditions, we enforce boundary conditions that arise nat…

Red giant asteroseismic binaries in the Kepler field
Francisca Espinoza-Rojas, Nathalie Theme{\ss}l, Saskia Hekker
https://arxiv.org/abs/2509.13412 https://

Red giant asteroseismic binaries in the Kepler field
Systems in which two oscillating stars are observed in the same light curve, so-called asteroseismic binaries (ABs), arise from either chance alignments or gravitationally bound stars. In the latter case, ABs offer a novel way to find binary systems and combine asteroseismology and orbital dynamics to determine precise stellar parameters for both stars. Such systems provide valuable tests to stellar models and scaling relations. While population synthesis studies predict approximately 200 ABs i…

ATLAS: A Self-Supervised and Cross-Stage Netlist Power Model for Fine-Grained Time-Based Layout Power Analysis
Wenkai Li, Yao Lu, Wenji Fang, Jing Wang, Qijun Zhang, Zhiyao Xie
https://arxiv.org/abs/2508.12433

ATLAS: A Self-Supervised and Cross-Stage Netlist Power Model for Fine-Grained Time-Based Layout Power Analysis
Accurate power prediction in VLSI design is crucial for effective power optimization, especially as designs get transformed from gate-level netlist to layout stages. However, traditional accurate power simulation requires time-consuming back-end processing and simulation steps, which significantly impede design optimization. To address this, we propose ATLAS, which can predict the ultimate time-based layout power for any new design in the gate-level netlist. To the best of our knowledge, ATLAS …

Molecular Hydrogen in High-redshift Damped Lyman-{\alpha} Absorbers
Alon Gurman, Amiel Sternberg, Shmuel Bialy, Rachel K. Cochrane, Jonathan Stern
https://arxiv.org/abs/2508.13080

Molecular Hydrogen in High-redshift Damped Lyman-α Absorbers
Simulations predict that circumgalactic hydrogen gas surrounding massive ($M_{\rm{halo}}^{z=1}=10^{12}-10^{13}\ M_{\odot}$) galaxies at $z\sim4$ may be predominantly neutral, and could produce damped Ly$α$ absorbers (DLAs) along sight-lines to background quasars \citep{Stern2021}. A circumgalactic medium (CGM) origin for DLAs naturally explains high redshift HI absorption-selected galaxy detections at physical separations much greater than the likely extents of the galaxy disks \citep{Neeleman…

Crosslisted article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor...
L. Zimmer, J. Weidner, M. Balcerak, F. Kofler, I. Ezhov, B. Menze, B. Wiestler

Studying SNR-MC interactions as galactic PeVatrons in the era of CTAO and ASTRI Mini-Array
Alan Sunny, Martina Cardillo, Antonio Tutone
https://arxiv.org/abs/2509.14867 https://…

Studying SNR-MC interactions as galactic PeVatrons in the era of CTAO and ASTRI Mini-Array
Supernova remnants (SNRs) are widely recognized as key accelerators of Galactic cosmic rays (CRs), supported by the detection of the characteristic pion bump in the gamma-ray spectra of several SNRs. However, the recent observation of ultra-high-energy (UHE, greater than 100 TeV) gamma-rays by LHAASO from sources such as W51 region challenges standard models, which predict CR acceleration up to PeV energies only during the early (~ 100 year) phase of SNR evolution. Given the older age of known …

Large Wing Model
Howon Lee, Pranay Seshadri, Juergen Rauleder
https://arxiv.org/abs/2508.11827 https://arxiv.org/pdf/2508.11827

Large Wing Model
Developing a generalized aerodynamics prediction machine learning model for finite wings with different airfoil sections is challenging due to the vast parameter space and a relative scarcity of available data. This paper presents the Large Wing Model (LWM), a probabilistic machine learning model designed to predict pressure coefficient ($C_p$) distributions using a small, strictly experimental data set. From its uncertainty-aware $C_p$ predictions, the sectional and total wing lift coefficient…

Axial Hall Effect in Altermagnetic Lieb Lattices
Xilong Xu, Haonan Wang, Li Yang
https://arxiv.org/abs/2509.13554 https://arxiv.org/pdf/2509.13554

Axial Hall Effect in Altermagnetic Lieb Lattices
We predict a so-called axial Hall effect, a Berry-curvature-driven anomalous Hall response, in Lieb-lattice altermagnets. By constructing a tight-binding model, we identify the axial direction as a hidden topological degree of freedom. Breaking the double degeneracy of axial symmetry generates substantial Berry curvature and induces a pronounced anomalous Hall conductivity. First-principles calculations further confirm the emergence of this effect in strained altermagnets, particularly in terna…

Analytical models for coated plasmonic particles: effects of shape and size-corrected dielectric function
Nikolai G. Khlebtsov, Sergey V. Zarkov
https://arxiv.org/abs/2508.11248

Analytical models for coated plasmonic particles: effects of shape and size-corrected dielectric function
Recently, modal expansion methods (MEMs) were developed to accurately predict the extinction and scattering spectra of bare and coated plasmonic particles. However, it remains uncertain whether the accuracy of analytical models is preserved when incorporating size-corrected dielectric functions. We compare numerical (COMSOL) and analytical extinction and scattering spectra for various particle shapes - rods, disks, prisms, bicones, and bipyramids - using both bulk and size-corrected dielectric …

BConformeR: A Conformer Based on Mutual Sampling for Unified Prediction of Continuous and Discontinuous Antibody Binding Sites
Zhangyu You, Jiahao Ma, Hongzong Li, Ye-Fan Hu, Jian-Dong Huang
https://arxiv.org/abs/2508.12029

BConformeR: A Conformer Based on Mutual Sampling for Unified Prediction of Continuous and Discontinuous Antibody Binding Sites
Accurate prediction of antibody-binding sites (epitopes) on antigens is crucial for vaccine design, immunodiagnostics, therapeutic antibody development, antibody engineering, research into autoimmune and allergic diseases, and for advancing our understanding of immune responses. Despite in silico methods that have been proposed to predict both linear (continuous) and conformational (discontinuous) epitopes, they consistently underperform in predicting conformational epitopes. In this work, we p…

NFL Week 3 predictions, picks: Our experts face off on Rams-Eagles playoff rematch, Lions-Ravens showdown

https://www.cbssports.com/nfl/news/nfl-wee

Scientists detail Delphi-2M, an AI model trained on large-scale health records that can predict susceptibility to over 1,000 diseases decades into the future (Clive Cookson/Financial Times)
https://www.ft.com/content/598e07ec-954f-49b7-9bc5-ce77f9fff934

GazeDETR: Gaze Detection using Disentangled Head and Gaze Representations
Ryan Anthony Jalova de Belen, Gelareh Mohammadi, Arcot Sowmya
https://arxiv.org/abs/2508.12966 https://…

GazeDETR: Gaze Detection using Disentangled Head and Gaze Representations
Gaze communication plays a crucial role in daily social interactions. Quantifying this behavior can help in human-computer interaction and digital phenotyping. While end-to-end models exist for gaze target detection, they only utilize a single decoder to simultaneously localize human heads and predict their corresponding gaze (e.g., 2D points or heatmap) in a scene. This multitask learning approach generates a unified and entangled representation for human head localization and gaze location pr…

Long-context Reference-based MT Quality Estimation
Sami Ul Haq, Chinonso Cynthia Osuji, Sheila Castilho, Brian Davis
https://arxiv.org/abs/2509.13980 https://

Long-context Reference-based MT Quality Estimation
In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level Error Span Annotation (ESA) scores using augmented long-context data. To construct long-context training data, we concatenate in-domain, human-annotated sentences and compute a weighted average of their scores. We integrate multiple human judgment datasets…

High School Science Profile Predicts Adults' Views on the Future of AI and STS
Gyeonggeon Lee
https://arxiv.org/abs/2508.12083 https://arxiv.org/pdf/25…

High School Science Profile Predicts Adults' Views on the Future of AI and STS
This study investigates the long term influence of high school science education on adults' engagement with artificial intelligence (AI) and their views on science-technology-society (STS) issues. Drawing on longitudinal data from the Korea Employment Education Panel (KEEP) II (n = 2,348), which tracked general high school students from 2016 to 2023, we applied structural equation modeling (SEM) to examine how science interest and achievement in adolescence predict AI use and perceptions in adu…

Gut Feeling: Cowboys-Commanders staff predictions https://www.dallascowboys.com/news/gut-feeling-cowboys-commanders-staff-predictions

@… @… I am putting on the record: I predict that when I flip my light switch next time, it will have been flipped.
(There’s even a chance the light will come on - But you’re right that chance isn’t exactly 100%).

Grounding Actions in Camera Space: Observation-Centric Vision-Language-Action Policy
Tianyi Zhang, Haonan Duan, Haoran Hao, Yu Qiao, Jifeng Dai, Zhi Hou
https://arxiv.org/abs/2508.13103

Grounding Actions in Camera Space: Observation-Centric Vision-Language-Action Policy
Vision-Language-Action (VLA) models frequently encounter challenges in generalizing to real-world environments due to inherent discrepancies between observation and action spaces. Although training data are collected from diverse camera perspectives, the models typically predict end-effector poses within the robot base coordinate frame, resulting in spatial inconsistencies. To mitigate this limitation, we introduce the Observation-Centric VLA (OC-VLA) framework, which grounds action predictions…

A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder
Diwakara Reddy, Christian Herglotz, Andr\'e Kaup
https://arxiv.org/abs/2510.12754 https…

A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder
In today's society, live video streaming and user generated content streamed from battery powered devices are ubiquitous. Live streaming requires real-time video encoding, and hardware video encoders are well suited for such an encoding task. In this paper, we introduce a high-level feature model using Gaussian process regression that can predict the encoding energy of a hardware video encoder. In an evaluation setup restricted to only P-frames and a single keyframe, the model can predict the e…

PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction
Ranga Baminiwatte, Kazi Jewel Rana, Aaron J. Masino
https://arxiv.org/abs/2509.14037 ht…

PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction
Understanding disease similarity is critical for advancing diagnostics, drug discovery, and personalized treatment strategies. We present PhenoGnet, a novel graph-based contrastive learning framework designed to predict disease similarity by integrating gene functional interaction networks with the Human Phenotype Ontology (HPO). PhenoGnet comprises two key components: an intra-view model that separately encodes gene and phenotype graphs using Graph Convolutional Networks (GCNs) and Graph Atten…

Revisiting the systematics of Brevipalpus flat mites (Tenuipalpidae): phylogeny, species groups and cryptic diversity
Renata Santos de Mendon\c{c}a (PNPD/CAPES, UnB, CENARGEN), Francisco Ferragut (UPV, IAM), Isis Carolina S. de Oliveira (UnB, CENARGEN), Aline Daniele Tassi (ESALQ, UF), Felipe Fileni (UMR CBGP), Ronald Ochoa (UMR CBGP), Denise Navia (UMR CBGP)
h…

Revisiting the systematics of Brevipalpus flat mites (Tenuipalpidae): phylogeny, species groups and cryptic diversity
Phytophagous mites in the genus Brevipalpus (Tenuipalpidae) can be major pests, causing direct damage to their host plants or vectoring viruses. However, a phylogeny-based classification to understand their evolution and predict their bioecological aspects is lacking. Accurate species identification is crucial for studying pathosystem interactions, implementing quarantine measures, and developing control strategies. This study revisited the classification of Brevipalpus based on phylogenetic re…

Understanding large local CP violation in $B^\pm\to K^\pm\pi^ \pi^-$ using dispersive methods
L. A. Heuser, A. Reyes-Torrecilla, C. Hanhart, B. Kubis, P. C. Magalh\~aes, T. Mannel, J. R. Pel\'aez
https://arxiv.org/abs/2508.10989

Understanding large local CP violation in $B^\pm\to K^\pmπ^+π^-$ using dispersive methods
We utilize the universality of pion--pion ($ππ$) final-state interactions at small invariant masses to understand their enhanced local CP violation in $B^\pm\to K^\pmπ^+π^-$, using a dispersive approach. From a fit to the integrated CP-asymmetry data, we successfully predict the Dalitz-plot kinematic distribution of the asymmetry in the low-energy $ππ$ region, including the large local CP violation recently observed by LHCb. An essential role is played by the contributions of isospin 2. T…

RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation
Yuming Jiang, Siteng Huang, Shengke Xue, Yaxi Zhao, Jun Cen, Sicong Leng, Kehan Li, Jiayan Guo, Kexiang Wang, Mingxiu Chen, Fan Wang, Deli Zhao, Xin Li
https://arxiv.org/abs/2509.15212

RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation
This paper presents RynnVLA-001, a vision-language-action(VLA) model built upon large-scale video generative pretraining from human demonstrations. We propose a novel two-stage pretraining methodology. The first stage, Ego-Centric Video Generative Pretraining, trains an Image-to-Video model on 12M ego-centric manipulation videos to predict future frames conditioned on an initial frame and a language instruction. The second stage, Human-Centric Trajectory-Aware Modeling, extends this by jointly …

Prediction of Spotify Chart Success Using Audio and Streaming Features
Ian Jacob Cabansag, Paul Ntegeka
https://arxiv.org/abs/2508.11632 https://arxiv.org/…

Prediction of Spotify Chart Success Using Audio and Streaming Features
Spotify's streaming charts offer a real-time lens into music popularity, driving discovery, playlists, and even revenue potential. Understanding what influences a song's rise in ranks on these charts-especially early on-can guide marketing efforts, investment decisions, and even artistic direction. In this project, we developed a classification pipeline to predict a song's chart success based on its musical characteristics and early engagement data. Using all 2024 U.S. Top 200 Spotify Daily Cha…

Predicting the winner of each NFL quarterback battle, plus winners and losers from Week 2 of preseason

https://www.cbssports.com/nfl/news/predict

Mathematical Validation of a Cancer Model
Marcela V. Reale, Gustavo Paccosi, David H. Margarit, Lilia Romanelli
https://arxiv.org/abs/2508.10910 https://ar…

Mathematical Validation of a Cancer Model
Understanding cancer cell differentiation is essential for advancing its detection, diagnosis, and treatment. Mathematical models significantly contribute to this by providing a theoretical framework to understand the complex interactions between cancer stem cells, differentiated cancer cells, and immune system components. Such models depend on experimental data and computational simulations to predict tumor dynamics, offering insights into how different cell populations evolve over time. Howev…

Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
Alva West, Yixuan Weng, Minjun Zhu, Zhen Lin, Yue Zhang
https://arxiv.org/abs/2509.10401

Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
Failure attribution in multi-agent systems -- pinpointing the exact step where a decisive error occurs -- is a critical yet unsolved challenge. Current methods treat this as a pattern recognition task over long conversation logs, leading to critically low step-level accuracy (below 17\%), which renders them impractical for debugging complex systems. Their core weakness is a fundamental inability to perform robust counterfactual reasoning: to determine if correcting a single action would have ac…

Modeling wind farm noise emission and propagation: effects of flow layout
J. Colas, A. Emmanuelli, D. Dragna, R. J. A. M. Stevens
https://arxiv.org/abs/2508.13128 https://

Modeling wind farm noise emission and propagation: effects of flow layout
This study demonstrates how wind farm flow physics influence noise generation and downstream propagation through numerical simulations. The flow field is modeled using large-eddy simulations (LES), and the time-averaged output serves as input to acoustic models that predict wind turbine noise. In the first turbine row, turbulence-induced noise (TIN) and trailing edge noise (TEN) contribute equally, with TIN dominating at low frequencies and TEN at higher frequencies. Downstream, TEN decreases d…

A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
\'Angel Ladr\'on, Miguel S\'anchez-Dom\'inguez, Javier Rozal\'en, Fernando R. S\'anchez, Javier de Vicente, Lucas Lacasa, Eusebio Valero, Gonzalo Rubio
https://arxiv.org/abs/2509.10227…

A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatigue cracks to prevent in-flight failures. Robust and precise fatigue life predictors are thus essential to ensure safety. Traditional engineering methods, while reliable, are time consuming and involve complex workflows, including steps such as conducting several Finite Element Method (FEM) simulations, deriving the ex…

Quantum reservoir computing for predicting and characterizing chaotic maps
Qingyu Li, Chiranjib Mukhopadhyay, Ludovico Minati, Abolfazl Bayat
https://arxiv.org/abs/2509.12071 ht…

Quantum reservoir computing for predicting and characterizing chaotic maps
Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently by bypassing the costly training of gradient-based learning methods. Here, we demonstrate the capability of this approach to predict and characterize chaotic dynamics in discrete nonlinear maps, exemplified through the logistic and Hénon maps. While achieving excellent predictive accuracy, we also demonstrate the optimization of training hyperparameters of the qua…

Towards a deeper fundamental understanding of (Al,Sc)N ferroelectric nitrides
Peng Chen, Dawei Wang, Alejandro Mercado Tejerina, Keisuke Yazawa, Andriy Zakutayev, Charles Paillard, Laurent Bellaiche
https://arxiv.org/abs/2509.15050

Towards a deeper fundamental understanding of (Al,Sc)N ferroelectric nitrides
Density Functional Theory (DFT) calculations, within the virtual crystal alloy approximation, are performed, along with the development of a Landau-type model employing a symmetry-allowed analytical expression of the internal energy and having parameters being determined from first principles, to investigate properties and energetics of Al1-xScxN ferroelectric nitrides in their hexagonal forms. These DFT computations and this model predict the existence of two different types of minima, namely …

Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
Benjamin Shaffer, Victoria Edwards, Brooks Kinch, Nathaniel Trask, M. Ani Hsieh
https://arxiv.org/abs/2509.14228

Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill. The flow dynamics can be time-varying and chaotic, resulting in sporadic and intermittent sensor readings, and complex environmental geometries further complicate a team's ability to model and predict the dispersion. To accurately account for the physical processes that drive the dispersion dynamics, robots must…

Implementing the finite-volume three-pion scattering formalism across all non-maximal isospins
Athari Alotaibi, Maxwell T. Hansen, Ra\'ul A. Brice\~no
https://arxiv.org/abs/2508.11627

Implementing the finite-volume three-pion scattering formalism across all non-maximal isospins
We present a numerical exploration of the relativistic-field-theory (RFT) formalism for three pions with all possible values of non-maximal isospin, $I_{πππ} = 2$, $1$ and $0$. Using the generic-isospin extension of the RFT formalism and applying our open-source Python library to implement the framework, we predict a range of three-pion energies for illustrative values of the two-to-two scattering amplitudes for various finite-volume irreps also with non-zero total momentum $\boldsymbol P$ i…

Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
Ruofan Lu, Yintong Huo, Meng Zhang, Yichen Li, Michael R. Lyu
https://arxiv.org/abs/2508.10074

Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
The rapid advancement of large language models (LLMs) has led to the widespread adoption of AI-powered coding assistants integrated into a development environment. On one hand, low-latency code completion offers completion suggestions but is fundamentally constrained to the cursor's current position. On the other hand, chat-based editing can perform complex modifications, yet forces developers to stop their work, describe the intent in natural language, which causes a context-switch away from t…

Inside Knowledge: Graph-based Path Generation with Explainable Data Augmentation and Curriculum Learning for Visual Indoor Navigation
Daniel Airinei, Elena Burceanu, Marius Leordeanu
https://arxiv.org/abs/2508.11446

Inside Knowledge: Graph-based Path Generation with Explainable Data Augmentation and Curriculum Learning for Visual Indoor Navigation
Indoor navigation is a difficult task, as it generally comes with poor GPS access, forcing solutions to rely on other sources of information. While significant progress continues to be made in this area, deployment to production applications is still lacking, given the complexity and additional requirements of current solutions. Here, we introduce an efficient, real-time and easily deployable deep learning approach, based on visual input only, that can predict the direction towards a target fro…

Gut Feeling: Cowboys-Commanders staff predictions https://www.dallascowboys.com/news/gut-feeling-cowboys-commanders-staff-predictions

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, Markku Juntti
https://arxiv.org/abs/2509.11725

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context, infrastructure-mounted cameras can capture rich environmental information that can facilitate beam tracking design. In this work, we develop an efficient attention-enhanced machine learning model for long-term beam tracking built upon convolutional neural networks and gated recurrent units to predict both current and …

Revisiting radiative transitions of charmonium states in covariant confined quark model
Aidos Issadykov, Mikhail A. Ivanov, Dang-Khoa N. Nguyen, Chien-Thang Tran, Akmaral Tyulemissova, Zhomart Tyulemissov
https://arxiv.org/abs/2509.13657

Revisiting radiative transitions of charmonium states in covariant confined quark model
We calculate the amplitudes and branching fractions of $χ_{cJ}$, $h_c$, $J/ψ$, and $ψ(2S)$ radiative decays in the framework of the covariant confined quark model. First, we explicitly show that all calculated amplitudes are gauge invariant. Next, we use experimental data for three decay channels $J/ψ\to η_cγ$, $χ_{c0}\to J/ψγ$, and $ψ(2S)\to χ_{c0}γ$ to fit the model parameters including the charm quark mass and the meson size parameter. Finally, we use the obtained parameters to p…

Explainable AI in Healthcare: to Explain, to Predict, or to Describe?
Alex Carriero, Anne de Hond, Bram Cappers, Fernando Paulovich, Sanne Abeln, Karel GM Moons, Maarten van Smeden
https://arxiv.org/abs/2508.05753

Explainable AI in Healthcare: to Explain, to Predict, or to Describe?
Explainable Artificial Intelligence (AI) methods are designed to provide information about how AI-based models make predictions. In healthcare, there is a widespread expectation that these methods will provide relevant and accurate information about a model's inner-workings to different stakeholders (ranging from patients and healthcare providers to AI and medical guideline developers). This is a challenging endeavour since what qualifies as relevant information may differ greatly depending on …

A Machine Learning Closure for Polymer Integral Equation Theory
Zhihao Feng, Christian T. Randolph, Tyler B. Martin, Thomas E. Gartner III
https://arxiv.org/abs/2509.11030 https…

A Machine Learning Closure for Polymer Integral Equation Theory
Polymer reference interaction site model (PRISM) theory, a descendent of Ornstein-Zernike liquid state theory, is a powerful tool to predict the structure and thermodynamics of equilibrium polymer systems, but its accuracy and applicability can be limited in some important cases. Typically, these shortcomings are traced to the analytical closure relationships used to solve the integral equations. Here, we propose a machine learning (ML)-based closure relation trained on a dataset of coarse-grai…

Droplet at the Corner of a V-Shaped Fiber
Yi Zhang, Apurav Tambe, Zhao Pan
https://arxiv.org/abs/2509.13632 https://arxiv.org/pdf/2509.13632

Droplet at the Corner of a V-Shaped Fiber
A fundamental question in the physics of droplet--fiber interactions is: What is the maximum droplet volume a fiber can retain? While this problem has been studied for horizontal fibers and at the apex $Λ$-shaped bent fibers, it remains less explored for V-shaped bent fibers, despite their demonstrated advantages in engineering applications such as fog harvesting. This work investigates the capability of V-shaped fibers in retaining droplets against gravity. An analytical model to predict the …

Reaction-diffusion models of invasive tree pest spread: quantifying the spread of oak processionary moth in the UK
Jamie P. McKeown, Laura E. Wadkin, Nick G. Parker, Andrew Golightly, Andrew W. Baggaley
https://arxiv.org/abs/2509.14166

Reaction-diffusion models of invasive tree pest spread: quantifying the spread of oak processionary moth in the UK
UK woodlands, forests, and urban treescapes are under threat from invasive species, exacerbated by climate change, trade, and transport. Invasive tree pests debilitate their host and disrupt forest ecosystems, thus it is imperative to quantitatively model and predict their spread. Addressing this, we represent the spatial distribution of the pest as a population density field which evolves according to a spatiotemporal reaction-diffusion equation. We solve this intractable system of equations n…

Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Saeideh Sekhavat, Mostafa Jamshidian, Adam Wittek, Karol Miller
https://arxiv.org/abs/2509.12550

Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by permanent enlargement of the aorta, often detected incidentally during imaging for unrelated conditions. Current management relies primarily on aneurysm diameter and growth rate, which may not reliably predict patient-specific rupture risk. Computation of AAA wall stress and strain has the potential to improve individualized risk assessment, but these analyses depend on image-derived geometry, which is subject to s…

Giants Hangout | Prep for the Home Opener https://www.giants.com/video/giants-hangout-prep-for-the-home-opener

Predicting when each NFL team will lose its first game: Steelers outlast almost everyone, Eagles lose early

https://www.cbssports.com/nfl/news/predict

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
Elaheh Kazemi-Khasragh, Roc\'io Mercado, Carlos Gonzalez, Maciej Haranczyk
https://arxiv.org/abs/2509.11874

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
Copolymers are highly versatile materials with a vast range of possible chemical compositions. By using computational methods for property prediction, the design of copolymers can be accelerated, allowing for the prioritization of candidates with favorable properties. In this study, we utilized two distinct representations of molecular ensembles to predict the seven different physical polymer properties copolymers using machine learning: we used a random forest (RF) model to predict polymer pro…

UK-based startup Mantic ranked #8 in the Metaculus forecasting cup that asks entrants to predict 60 geopolitical events, the first time an AI made the top 10 (Nikita Ostrovsky/Time)
https://time.com/7318577/ai-model-forecasting-predict-future-metaculus/

AI Is Learning to Predict the Future—And Beating Humans at It
An AI model just cracked the top 10 in a global prediction contest, signaling how machines are reshaping the future of forecasting.

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Tim R\"az
https://arxiv.org/abs/2509.11847 https://arxiv.org/pdf/2509…

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of p…

Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors
Trung Kien La, Eric Guiffo Kaigom
https://arxiv.org/abs/2509.12739 https://…

Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors
In this work, deep neural networks made up of multiple hidden Long Short-Term Memory (LSTM) and Feedforward layers are trained to predict the thermal behavior of the joint motors of robot manipulators. A model-free and scalable approach is adopted. It accommodates complexity and uncertainty challenges stemming from the derivation, identification, and validation of a large number of parameters of an approximation model that is hardly available. To this end, sensed joint torques are collected and…

Mo' Memory, Mo' Problems: Stream-Native Machine Unlearning
Kennon Stewart
https://arxiv.org/abs/2508.10193 https://arxiv.org/pdf/2508.10193

Mo' Memory, Mo' Problems: Stream-Native Machine Unlearning
Machine unlearning work assumes a static, i.i.d training environment that doesn't truly exist. Modern ML pipelines need to learn, unlearn, and predict continuously on production streams of data. We translate the notion of the batch unlearning scenario to the online setting using notions of regret, sample complexity, and deletion capacity. We further tighten regret bounds to a logarithmic $\mathcal{O}(\ln{T})$, a first for a machine unlearning algorithm. And we swap out an expensive Hessian inve…

One-dimensional tunneling of the two-body bound state
N. Shypka, O. Hryhorchak, V. Pastukhov
https://arxiv.org/abs/2510.11932 https://arxiv.org/pdf/2510.11…

One-dimensional tunneling of the two-body bound state
We consider bound and scattering states of the one-dimensional dimer formed by two coupled non-identical atoms when one of them also interacts with the zero-range potential located at the origin. By calculating the dimer localized and scattering wave functions, we identify properties of the system without the two-body bound-state collapse. In general, we predict an enhancement of the dimer reflection compared to a single atom, except for a narrow region on the attractive side of the external po…

Intelligent Vacuum Thermoforming Process
Andi Kuswoyo, Christos Margadji, Sebastian W. Pattinson
https://arxiv.org/abs/2509.13250 https://arxiv.org/pdf/250…

Intelligent Vacuum Thermoforming Process
Ensuring consistent quality in vacuum thermoforming presents challenges due to variations in material properties and tooling configurations. This research introduces a vision-based quality control system to predict and optimise process parameters, thereby enhancing part quality with minimal data requirements. A comprehensive dataset was developed using visual data from vacuum-formed samples subjected to various process parameters, supplemented by image augmentation techniques to improve model t…

Bold Predictions: Cowboys will take "command" vs Washington https://insidethestar.com/bold-predictions-cowboys-will-take-command-vs-washington

Deep learning-driven adaptive optics for laser wavefront correction
Jikai Wang, Sven Burckhard, Sonam Smitha Ravi, Dominik Bauer, Volker Rominger, Stefan Nolte, Daniel Flamm
https://arxiv.org/abs/2509.10662

Deep learning-driven adaptive optics for laser wavefront correction
{We report on an intensity-only and deep-learning based method for laser beam characterization that allows to predict the underlying optical field within milliseconds. A simple near-field / far-field camera setup enables online control of an adaptive optics to optimize beam quality. The robustness and precision of the method is enhanced by applying the concept of phase diversity based on spiral phase plates.

Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training
Nutthawara Buatthaisong, Inigo Val Slijepcevic, Anna M. M. Scaife, Micah Bowles, Andrew Hopkins, Devina Mohan, Stanislav S Shabala, O. Ivy Wong
https://arxiv.org/abs/2509.11988

Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training
In this study, we examine over 14,000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5,900 FRIs and 8,100 FRIIs. We present an analysis of these predicted radio galaxy morphologies for the RGZ catalogue, classified using a pre-trained radio galaxy foundation model that has been fine-tuned to predict Fanaroff-Riley (FR) morphology. As seen in previous studies, our results show overlap between morphologically classified FRI and FRI…

Predicting Structural Relaxation in Supercooled Small Molecules via Molecular Dynamics Simulations and Microscopic Theory
Anh D. Phan, Ngo T. Que, Nguyen T. T. Duyen
https://arxiv.org/abs/2509.12092

Predicting Structural Relaxation in Supercooled Small Molecules via Molecular Dynamics Simulations and Microscopic Theory
Understanding and predicting the glassy dynamics of small organic molecules is critical for applications ranging from pharmaceuticals to energy and food preservation. In this work, we present a theoretical framework that combines molecular dynamics simulations and Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to predict the structural relaxation behavior of small organic glass-formers. By using propanol, glucose, fructose, and trehalose as model systems, we estimate the glas…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/7]:
- Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
Ruofan Lu, Yintong Huo, Meng Zhang, Yichen Li, Michael R. Lyu

NASA and IBM release Surya, an open-source AI model trained on over a decade's worth of NASA solar data to predict solar flares and winds (Peter Hall/MIT Technology Review)
https://www.technologyreview.com/2025/08/20/1122163/nasa-ibm-ai-predict-solar-…

NASA’s new AI model can predict when a solar storm may strike
Surya, an AI model developed by NASA and IBM, tries to find hidden patterns in solar data to detect solar flares and winds.

Not in Sync: Unveiling Temporal Bias in Audio Chat Models
Jiayu Yao, Shenghua Liu, Yiwei Wang, Rundong Cheng, Lingrui Mei, Baolong Bi, Zhen Xiong, Xueqi Cheng
https://arxiv.org/abs/2510.12185

Not in Sync: Unveiling Temporal Bias in Audio Chat Models
Large Audio Language Models (LALMs) are increasingly applied to audio understanding and multimodal reasoning, yet their ability to locate when events occur remains underexplored. We present the first systematic study of temporal bias in LALMs, revealing a key limitation in their timestamp prediction. For example, when asked "At which second does the lecturer introduce the key formula?", models often predict timestamps that are consistently earlier or later than the ground truth. Through control…

Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response
Morgan D. Sanger, Mertcan Geyin, Brett W. Maurer
https://arxiv.org/abs/2509.10962

Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response
Using machine learning (ML), high performance computing, and a large body of geospatial information, we develop surrogate models to predict soil liquefaction across regional scales. Two sets of models - one global and one specific to New Zealand - are trained by learning to mimic geotechnical models at the sites of in-situ tests. Our geospatial approach has conceptual advantages in that predictions: (i) are anchored to mechanics, which encourages more sensible response and scaling across the do…

NFL Week 7 picks: Our experts face off on Steelers vs. Bengals, Jaxson Dart's Giants vs. Broncos and more

https://www.cbssports.com/nfl/news/nfl-week-7-betting-picks-predictions-odds/

Semiclassical analytical solutions of the eigenstate thermalization hypothesis in a quantum billiard
Yaoqi Ye, Chengkai Lin, Xiao Wang
https://arxiv.org/abs/2510.12517 https://

Semiclassical analytical solutions of the eigenstate thermalization hypothesis in a quantum billiard
We derive semiclassical analytical solutions for both the diagonal and off-diagonal functions in the eigenstate thermalization hypothesis (ETH) in a quarter-stadium quantum billiard. For a representative observable, we obtain an explicit expression and an asymptotic closed-form solution that naturally separate into a local contribution and a phase-space correlation term. These analytical results predict the band structure of the observable matrix, including its bandwidth and scaling behavior. W…

Diffractive vector meson photo-production in Oxygen--Oxygen and Neon--Neon ultra-peripheral collisions at the LHC
J. Cepila, J. G. Contreras, M. Matas, A. Ridzikova
https://arxiv.org/abs/2509.11359

Diffractive vector meson photo-production in Oxygen--Oxygen and Neon--Neon ultra-peripheral collisions at the LHC
The energy-dependent hotspot model is used to predict cross sections for vector-meson diffractive photo-nuclear production off oxygen ($γ$O) and neon ($γ$Ne) that can be extracted from ultra-peripheral O--O and Ne--Ne collisions, recently recorded at the LHC. In both cases, two models are used to describe the nuclear shapes. Woods-Saxon prescriptions for O and Ne as well as an alpha-cluster description of O and a bowling-pin-like shape for Ne, according to the PGCM formalism. Predictions are …

Using Large Language Models to Measure Symptom Severity in Patients At Risk for Schizophrenia
Andrew X. Chen, Guillermo Horga, Sean Escola
https://arxiv.org/abs/2508.10226 https…

Using Large Language Models to Measure Symptom Severity in Patients At Risk for Schizophrenia
Patients who are at clinical high risk (CHR) for schizophrenia need close monitoring of their symptoms to inform appropriate treatments. The Brief Psychiatric Rating Scale (BPRS) is a validated, commonly used research tool for measuring symptoms in patients with schizophrenia and other psychotic disorders; however, it is not commonly used in clinical practice as it requires a lengthy structured interview. Here, we utilize large language models (LLMs) to predict BPRS scores from clinical intervi…

Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
Daria Laslo, Efthymios Georgiou, Marius George Linguraru, Andreas Rauschecker, Sabine Muller, Catherine R. Jutzeler, Sarah Bruningk
https://arxiv.org/abs/2509.09610

Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and es…

Reasons to Worry: Why the Cowboys-Giants matchup may not be the easy win many predict https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/09/12/two-reasons-the…

Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
Amandip Sangha
https://arxiv.org/abs/2509.09991 https://

Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
This paper presents a machine learning-based approach to estimate the energy consumption of virtual servers without access to physical power measurement interfaces. Using resource utilization metrics collected from guest virtual machines, we train a Gradient Boosting Regressor to predict energy consumption measured via RAPL on the host. We demonstrate, for the first time, guest-only resource-based energy estimation without privileged host access with experiments across diverse workloads, achiev…

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning
Lichang Zhu, Laura Schaefer, Leitao Chen, Ben Xu
https://arxiv.org/abs/2509.11863

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning
Predicting and interpreting thermal performance under oscillating flow in porous structures remains a critical challenge due to the complex coupling between fluid dynamics and geometric features. This study introduces a data-driven wGAN-LBM-Nested_CV framework that integrates generative deep learning, numerical simulation based on the lattice Boltzmann method (LBM), and interpretable machine learning to predict and explain the thermal behavior in such systems. A wide range of porous structures …

NFL execs predict the biggest award winners: Picks for MVP, best rookies, top comeback player, breakouts https://www.espn.com/nfl/story/_/id/46208174/nfl-executives-pick-award-winners-2025-pr…

Neural Bandit Based Optimal LLM Selection for a Pipeline of Tasks
Baran Atalar, Eddie Zhang, Carlee Joe-Wong
https://arxiv.org/abs/2508.09958 https://arxiv…

Neural Bandit Based Optimal LLM Selection for a Pipeline of Tasks
With the increasing popularity of large language models (LLMs) for a variety of tasks, there has been a growing interest in strategies that can predict which out of a set of LLMs will yield a successful answer at low cost. This problem promises to become more and more relevant as providers like Microsoft allow users to easily create custom LLM "assistants" specialized to particular types of queries. However, some tasks (i.e., queries) may be too specialized and difficult for a single LLM to han…

High-Fidelity Simulations of Two Miscible Fluids in Small Scale Turbulent Mixers Using a Variational Multiscale Finite Element Method
Dongjie Jia, Mohammad Majidi, Kurt D. Ristroph, Arezoo Ardekani
https://arxiv.org/abs/2509.12029

High-Fidelity Simulations of Two Miscible Fluids in Small Scale Turbulent Mixers Using a Variational Multiscale Finite Element Method
Turbulent mixers have been widely used in industrial settings for chemical production and increasingly for therapeutic nanoparticle formulation by antisolvent precipitation. The quality of the product is closely related to the fluid and mixing dynamics inside the mixers. Due to the rapid time scales and small sizes of many turbulent mixing geometries, computational fluid dynamics simulations have been the primary tool used to predict and understand fluid behavior within these mixers. In this st…

Doubly heavy spin-$\frac {3}{2} $ baryons spectrum in the ground and excited states
M. Shekari Tousi, K. Azizi
https://arxiv.org/abs/2508.10328 https://arx…

Doubly heavy spin-$\frac {3}{2} $ baryons spectrum in the ground and excited states
This study employs QCD sum rules to predict the masses and residues of spin-$\frac {3}{2} $ doubly heavy baryons including two heavy quarks (c and/or b) and one light quark, specifically focusing on $ Ξ_{cc}^*$, $ Ξ_{bc}^*$, $ Ξ_{bb}^*$, $ Ω_{cc}^*$, $ Ω_{bc}^*$ and $ Ω_{bb}^*$. Our study provides results for the ground state ($1S$), first orbital excitation ($1P$), and the first radial excitation ($2S$), within a consistent theoretical framework. In addition to mass spectra, we provide r…

How Reinforcement Learning After Next-Token Prediction Facilitates Learning
Nikolaos Tsilivis, Eran Malach, Karen Ullrich, Julia Kempe
https://arxiv.org/abs/2510.11495 https://

How Reinforcement Learning After Next-Token Prediction Facilitates Learning
Recent advances in reasoning domains with neural networks have primarily been enabled by a training recipe that optimizes Large Language Models, previously trained to predict the next-token in a sequence, with reinforcement learning algorithms. We introduce a framework to study the success of this paradigm, and we theoretically expose the optimization mechanisms by which reinforcement learning improves over next-token prediction in this setting. We study learning from mixture distributions of s…

DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
Yingyan Li, Shuyao Shang, Weisong Liu, Bing Zhan, Haochen Wang, Yuqi Wang, Yuntao Chen, Xiaoman Wang, Yasong An, Chufeng Tang, Lu Hou, Lue Fan, Zhaoxiang Zhang
https://arxiv.org/abs/2510.12796

DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence. However, VLA models are limited by a ``supervision deficit'': the vast model capacity is supervised by sparse, low-dimensional actions, leaving much of their representational power underutilized. To remedy this, we propose \textbf{DriveVLA-W0}, a training paradigm that employs world modeling to predict future images. This task generates a dense, self-supe…

Predicting overreactions for NFL Week 7: Trey Hendrickson done with Bengals? Commanders miss playoffs?

https://www.cbssports.com/nfl/news/predict

Prime Implicant Explanations for Reaction Feasibility Prediction
Klaus Weinbauer, Tieu-Long Phan, Peter F. Stadler, Thomas G\"artner, Sagar Malhotra
https://arxiv.org/abs/2510.09226

Prime Implicant Explanations for Reaction Feasibility Prediction
Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often lack transparency and interpretability. We introduce a novel formulation of prime implicant explanations--also known as minimally sufficient reasons--tailored to this domain, and propose an algorithm for computing such explanations in small-scale reaction prediction tasks. Preliminary experiments demonstrate that our…

Harnessing Input-Adaptive Inference for Efficient VLN
Dongwoo Kang, Akhil Perincherry, Zachary Coalson, Aiden Gabriel, Stefan Lee, Sanghyun Hong
https://arxiv.org/abs/2508.09262

Harnessing Input-Adaptive Inference for Efficient VLN
An emerging paradigm in vision-and-language navigation (VLN) is the use of history-aware multi-modal transformer models. Given a language instruction, these models process observation and navigation history to predict the most appropriate action for an agent. While they have significantly improved performance, the scale of these models can be a bottleneck in practical settings with limited computational resources. In this work, we propose a novel input-adaptive navigation method to enhance VLN …

How many samples to label for an application given a foundation model? Chest X-ray classification study
Nikolay Nechaev, Evgenia Przhezdzetskaya, Viktor Gombolevskiy, Dmitry Umerenkov, Dmitry Dylov
https://arxiv.org/abs/2510.11553

How many samples to label for an application given a foundation model? Chest X-ray classification study
Chest X-ray classification is vital yet resource-intensive, typically demanding extensive annotated data for accurate diagnosis. Foundation models mitigate this reliance, but how many labeled samples are required remains unclear. We systematically evaluate the use of power-law fits to predict the training size necessary for specific ROC-AUC thresholds. Testing multiple pathologies and foundation models, we find XrayCLIP and XraySigLIP achieve strong performance with significantly fewer labeled …

SpiderNets: Estimating Fear Ratings of Spider-Related Images with Vision Models
Dominik Pegler, David Steyrl, Mengfan Zhang, Alexander Karner, Jozsef Arato, Frank Scharnowski, Filip Melinscak
https://arxiv.org/abs/2509.04889

SpiderNets: Estimating Fear Ratings of Spider-Related Images with Vision Models
Advances in computer vision have opened new avenues for clinical applications, particularly in computerized exposure therapy where visual stimuli can be dynamically adjusted based on patient responses. As a critical step toward such adaptive systems, we investigated whether pretrained computer vision models can accurately predict fear levels from spider-related images. We adapted three diverse models using transfer learning to predict human fear ratings (on a 0-100 scale) from a standardized da…