Tootfinder

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…

Where to watch Chiefs vs. Giants: TV channel, live stream, prediction, pick, odds, spread for 'SNF' matchup

https://www.cbssports.com/nfl/news/where-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 …

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…

Bold Predictions: Cowboys offense soldiers on in Chicago https://insidethestar.com/bold-predictions-cowboys-offense-soldiers-on-in-chicago

"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.

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…

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…

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…

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

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…

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 …

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…

"…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…

The joy of commercial big data. Thomson Reuters helps ICE to see in realtime which drivers are Democrats.
https://www.404media.co/this-app-lets-ice-track-vehicles-and-owners-across-the-country/

This App Lets ICE Track Vehicles and Owners Across the Country
Material viewed by 404 Media shows data giant Thomson Reuters enriches license plate data with marriage, voter, and ownership records. The tool can predict where a car may be in the future.

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 …

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.

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…

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…

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…

🎗️ Why people embrace conspiracy theories: It's about community, not gullibility
https://phys.org/news/2025-08-people-embrace-conspiracy-theories-community.html

Why people embrace conspiracy theories: It's about community, not gullibility
Psychologists have long considered how a tendency towards irrational thinking or particular personality traits might predict people's interest in conspiracies. Yet these individual factors do not explain the group processes through which conspiracy theorists are extending their influence and impact.

A future 'Hari Seldon' takes notes:
Mathematicians Unveil a Smarter Way to Predict the Future
https://scitechdaily.com/mathematicians-unveil-a-smarter-way-to-predict-the-future/

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…

@… @… 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%).

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…

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

Why trying to predict the end of the world won’t save it:
https://www.brichapman.com/p/the-case-for-possibility?r=1o5n19&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false

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 …

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…

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…

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

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…

This App Lets ICE Track Vehicles and Owners Across the Country https://www.404media.co/this-app-lets-ice-track-vehicles-and-owners-across-the-country/

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…

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…

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…

House Dems predict major GOP support on bill to release Epstein files (Hailey Fuchs/Politico)
https://www.politico.com/live-updates/2025/11/12/congress/dems-optimistic-about-epstein-vote-00648328
http://www.memeorandum.com/251112/p101#a251112p101

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…

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.

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…

"Specialist" and "generalist" are surprisingly slippery categories— biology abhors a binary— but quantifying the shared evolutionary history of plants eaten by insects introduced to North America let these authors predict their plant-damaging impacts https://doi.org/10.1111/ele.70083

Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
toXiv_bot_toot

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…

I saw Paul McCartney in concert last night & predict he has a very bright future ahead of him. 👍
#MarkMyWords
-- Mark Hamill
https://bsky.app/profile/markhamilloff

Mark Hamill (@markhamillofficial.bsky.social)
I saw Paul McCartney in concert last night & predict he has a very bright future ahead of him. 👍 #MarkMyWords

Is Google about to destroy the web?
#Internet

🧚 Sustainable energy: 'Fairy circles' can provide clues to depth of natural hydrogen sources
https://phys.org/news/2025-09-sustainable-energy-fairy-circles-clues.html

Sustainable energy: 'Fairy circles' can provide clues to depth of natural hydrogen sources
Natural hydrogen from deep underground could be an important building block for the sustainable energy system of the future, but it is currently still difficult to predict where and at what depth elevated concentrations are located. New study results from the Department of Geology at the University of Vienna could make such predictions easier in the future. So-called "fairy circles"—round patches with vegetation damage—could be helpful indicators. This is because these "fairy circles" subsi…

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

Obesity & diet
I wouldn't normally share a positive story about the new diet drugs, because I've seen someone get obsessed with them who was at a perfectly acceptable weight *by majority standards* (surprise: every weight is in fact perfectly acceptable by *objective* standards, because every "weight-associated" health risk is its own danger that should be assessed *in individuals*). I think two almost-contradictory things:
1. In a society shuddering under the burden of metastasized fatmisia, there's a very real danger in promoting the new diet drugs because lots of people who really don't need them will be psychologically bullied into using them and suffer from the cost and/or side effects.
2. For many individuals under the assault of our society's fatmisia, "just ignore it" is not a sufficient response, and also for specific people for whom decreasing their weight can address *specific* health risks/conditions that they *want* to address that way, these drugs can be a useful tool.
I know @… to be a trustworthy & considerate person, so I think it's responsible to share this:
#Fat #Diet #Obesity

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…

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.

We found that ChatGPT made executives significantly more optimistic in their forecasts while peer discussions tended to encourage caution. Additionally, we found that the executives armed with ChatGPT made worse predictions, based on actual stock figures, than they had before they consulted the tool.”
But they'll still keep pushing it down our throats, because nobody wants to be first to get out of the bubble.

Research: Executives Who Used Gen AI Made Worse Predictions
In a recent experiment, nearly 300 executives and managers were shown recent stock prices for the chip-maker Nvidia and then asked to predict the stock’s price in a month’s time. Then, half the group was given the opportunity to ask questions of ChatGPT while the other half were allowed to consult with their peers about Nvidia’s stock. The executives who used ChatGPT became significantly more optimistic, confident, and produced worse forecasts than the group who discussed with their peers…

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…

Multi-Granularity Sequence Denoising with Weakly Supervised Signal for Sequential Recommendation
Liang Li, Zhou Yang, Xiaofei Zhu
https://arxiv.org/abs/2510.10564 https://

Multi-Granularity Sequence Denoising with Weakly Supervised Signal for Sequential Recommendation
Sequential recommendation aims to predict the next item based on user interests in historical interaction sequences. Historical interaction sequences often contain irrelevant noisy items, which significantly hinders the performance of recommendation systems. Existing research employs unsupervised methods that indirectly identify item-granularity irrelevant noise by predicting the ground truth item. Since these methods lack explicit noise labels, they are prone to misidentify users' interested i…

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…

The emergence of globular clusters and globular-cluster-like dwarfs: #cosmological simulations in which both dark-matter-free GCs and dark-matter-rich dwarf galaxies naturally emerge in the Standard Cosmology. We show that these objects inhabit distinct locations in the size–luminosity plane and that they have similar ages, age spread, metallicity and metallicity spread to globulars and dwarfs in the nearby Universe. About half of our simulated globulars form by means of regular star formation near the centres of their host dwarf, with the rest forming further out, triggered by mergers. The latter are more tidally isolated and more likely to survive to the present day. Finally, our simulations predict the existence of a new class of object that we call ‘globular-cluster-like dwarfs’ (GCDs)."

Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction
Seon-Geun Jeong, Kyeong-Hwan Moon, Won-Joo Hwang
https://arxiv.org/abs/2509.11046 https:/…

Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction
Protein-ligand binding affinity is critical in drug discovery, but experimentally determining it is time-consuming and expensive. Artificial intelligence (AI) has been used to predict binding affinity, significantly accelerating this process. However, the high-performance requirements and vast datasets involved in affinity prediction demand increasingly large AI models, requiring substantial computational resources and training time. Quantum machine learning has emerged as a promising solution …

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…

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…

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

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…

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…

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/

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…

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

What Entropy Can Predict About the Future of Authoritarianism | Dame Magazine
https://www.damemagazine.com/2025/09/23/what-entropy-can-predict-about-the-future-of-authoritarianism/

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…

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 …

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

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…

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…

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 …

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…

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…

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…

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

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…

🚬 How frequently young adults use cannabis may predict their binge drinking
#drugs

How frequently young adults use cannabis may predict their binge drinking
Changing cannabis use frequency may predict changes in binge drinking frequency among young adults, according to a new study that illuminates shifts in substance use across early adulthood. The findings imply opportunities for targeted interventions to prevent and treat binge drinking, a behavior linked to a range of negative consequences, including alcohol use disorder (AUD) and high economic costs.

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…

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

Verifying Chain-of-Thought Reasoning via Its Computational Graph
Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang, Naila Murray, Nicola Cancedda
https://arxiv.org/abs/2510.09312 …

Verifying Chain-of-Thought Reasoning via Its Computational Graph
Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into why a computation fails. We introduce a white-box method: Circuit-based Reasoning Verification (CRV). We hypothesize that attribution graphs of correct CoT steps, viewed as execution traces of the model's latent reasoning circuits, possess distinct structural fingerprints from those of incorrect steps. By training a classifier o…

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…

🍔 Thermal, Mechanical, And Material Stresses Grow With Die Stacking
https://semiengineering.com/thermal-mechanical-and-material-stresses-grow-with-die-stacking/

Thermal, Mechanical, And Material Stresses Grow With Die Stacking
More analysis and more data are needed to predict how different dies will interact in the same package.

Pointing-Guided Target Estimation via Transformer-Based Attention
Luca M\"uller, Hassan Ali, Philipp Allgeuer, Luk\'a\v{s} Gajdo\v{s}ech, Stefan Wermter
https://arxiv.org/abs/2509.05031

Pointing-Guided Target Estimation via Transformer-Based Attention
Deictic gestures, like pointing, are a fundamental form of non-verbal communication, enabling humans to direct attention to specific objects or locations. This capability is essential in Human-Robot Interaction (HRI), where robots should be able to predict human intent and anticipate appropriate responses. In this work, we propose the Multi-Modality Inter-TransFormer (MM-ITF), a modular architecture to predict objects in a controlled tabletop scenario with the NICOL robot, where humans indicate…

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…

An improved perturbative QCD study of the decays $B_c^ \to \eta_c L^ $
Wen-Jing Zhang, Xin Liu
https://arxiv.org/abs/2510.12216 https://arxiv.org/pdf/2510…

An improved perturbative QCD study of the decays $B_c^+ \to η_c L^+$
We perform an improved perturbative QCD study of the decays $B_c^+ \to η_c L^+$ ($L$ denotes the light ground state pseudoscalar, vector mesons and the corresponding $p$-wave scalar, axial-vector, and tensor ones) and predict their branching ratios (BRs) associated with relative ratios at leading order in the strong coupling $α_s$. Our results ${\rm BR}(B_c^+ \to η_c π^+) =(2.03^{+0.53}_{-0.41}) \times 10^{-3}$ and ${\rm BR}(B_c^+ \to η_c π^+)/{\rm BR}(B_c^+ \to J/ψπ^+) = 1.74^{+0.66}_{…

Origin of pressure-flow non-linearity in two-phase intermittent flow in porous media
Paolo Botticini, Davide Picchi, Santanu Sinha, Alex Hansen
https://arxiv.org/abs/2510.12588 …

Origin of pressure-flow non-linearity in two-phase intermittent flow in porous media
This study presents a first-principles model to predict the two-phase pressure drop in gas-liquid intermittent flow through round capillaries, which serve as the simplest analogous of a porous medium. Building upon the classical capillary flow theory, we derive a model for a train of elongated bubbles, and rigorously quantify its validity in terms of the dimensionless parameters of the problem (capillary number, number of bubbles, gas volume fraction, and channel length-to-diameter ratio). The …

WUGNECTIVES: Novel Entity Inferences of Language Models from Discourse Connectives
Daniel Brubaker, William Sheffield, Junyi Jessy Li, Kanishka Misra
https://arxiv.org/abs/2510.09556

WUGNECTIVES: Novel Entity Inferences of Language Models from Discourse Connectives
The role of world knowledge has been particularly crucial to predict the discourse connective that marks the discourse relation between two arguments, with language models (LMs) being generally successful at this task. We flip this premise in our work, and instead study the inverse problem of understanding whether discourse connectives can inform LMs about the world. To this end, we present WUGNECTIVES, a dataset of 8,880 stimuli that evaluates LMs' inferences about novel entities in contexts w…

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 …

Ego-Vision World Model for Humanoid Contact Planning
Hang Liu, Yuman Gao, Sangli Teng, Yufeng Chi, Yakun Sophia Shao, Zhongyu Li, Maani Ghaffari, Koushil Sreenath
https://arxiv.org/abs/2510.11682

Ego-Vision World Model for Humanoid Contact Planning
Enabling humanoid robots to exploit physical contact, rather than simply avoid collisions, is crucial for autonomy in unstructured environments. Traditional optimization-based planners struggle with contact complexity, while on-policy reinforcement learning (RL) is sample-inefficient and has limited multi-task ability. We propose a framework combining a learned world model with sampling-based Model Predictive Control (MPC), trained on a demonstration-free offline dataset to predict future outco…

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…

Nonlinear optimal perturbation growth in pulsatile pipe flow
Patrick Keuchel, Marc Avila
https://arxiv.org/abs/2509.10103 https://arxiv.org/pdf/2509.10103

Nonlinear optimal perturbation growth in pulsatile pipe flow
Pulsatile fluid flows through straight pipes undergo a sudden transition to turbulence that is extremely difficult to predict. The difficulty stems here from the linear Floquet stability of the laminar flow up to large Reynolds numbers, well above experimental observations of turbulent flow. This makes the instability problem fully nonlinear and thus dependent on the shape and amplitude of the flow perturbation, in addition to the Reynolds and Womersley numbers and the pulsation amplitude. This…

Spectroscopy of charmonium-like mesons, heavy-light mesons with charm, AdS/QCD, and configurational entropy
A. E. Bernardini, W. de Paula, R. da Rocha
https://arxiv.org/abs/2510.11885

Spectroscopy of charmonium-like mesons, heavy-light mesons with charm, AdS/QCD, and configurational entropy
Heavy-light-flavor meson resonances with charm, in the $D^0$ and $D^*$ families, and charmonium-like states, in the $η_c$ and $χ_{c1}$ families, are explored and discussed in the AdS/QCD model with four quark flavors. The differential configurational entropy is computed and analyzed for these four charmed meson families, also combining 4-flavor AdS/QCD to experimental data for the $D^0$, $D^*$, $η_c$, and $χ_{c1}$ meson families. It makes it possible to predict the mass spectrum of unexplor…

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…

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
David Nguyen, Zulfiqar Zaidi, Kevin Karol, Jessica Hodgins, Zhaoming Xie
https://arxiv.org/abs/2510.08754

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and pred…

NFL Week 2 picks: Our experts face off on Bears-Lions, Eagles-Chiefs and Seahawks-Steelers

https://www.cbssports.com/nfl/news/nfl-week…

Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing
Zhiyue Liu, Fanrong Ma, Xin Ling
https://arxiv.org/abs/2509.09160 https://

Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing
Target-oriented multimodal sentiment classification seeks to predict sentiment polarity for specific targets from image-text pairs. While existing works achieve competitive performance, they often over-rely on textual content and fail to consider dataset biases, in particular word-level contextual biases. This leads to spurious correlations between text features and output labels, impairing classification accuracy. In this paper, we introduce a novel counterfactual-enhanced debiasing framework …

Bold Predictions: Expect a big day from the Cowboys' offense vs the Giants https://insidethestar.com/bold-predictions-expect-a-big-day-from-the-cowboys-offense-vs-the-giants

GeoArena: An Open Platform for Benchmarking Large Vision-language Models on WorldWide Image Geolocalization
Pengyue Jia, Yingyi Zhang, Xiangyu Zhao, Yixuan Li
https://arxiv.org/abs/2509.04334

GeoArena: An Open Platform for Benchmarking Large Vision-language Models on WorldWide Image Geolocalization
Image geolocalization aims to predict the geographic location of images captured anywhere on Earth, but its global nature presents significant challenges. Current evaluation methodologies suffer from two major limitations. First, data leakage: advanced approaches often rely on large vision-language models (LVLMs) to predict image locations, yet these models are frequently pretrained on the test datasets, compromising the accuracy of evaluating a model's actual geolocalization capability. Second…