Tootfinder

RELATE-Sim: Leveraging Turning Point Theory and LLM Agents to Predict and Understand Long-Term Relationship Dynamics through Interactive Narrative Simulations
Matthew Yue, Zhikun Xu, Vivek Gupta, Thao Ha, Liesal Sharabi, Ben Zhou
https://arxiv.org/abs/2510.00414

RELATE-Sim: Leveraging Turning Point Theory and LLM Agents to Predict and Understand Long-Term Relationship Dynamics through Interactive Narrative Simulations
Most dating technologies optimize for getting together, not staying together. We present RELATE-Sim, a theory-grounded simulator that models how couples behave at consequential turning points-exclusivity talks, conflict-and-repair episodes, relocations-rather than static traits. Two persona-aligned LLM agents (one per partner) interact under a centralized Scene Master that frames each turning point as a compact set of realistic options, advances the narrative, and infers interpretable state cha…

Rams vs. 49ers prediction: High-octane L.A. offense looking to keep rolling against banged-up San Francisco

https://www.cbssports.com/nfl/news/where-to-watch-rams-vs…

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Fan Yang, Zhiyang Chen, Yousong Zhu, Xin Li, Jinqiao Wang
https://arxiv.org/abs/2510.00806

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Current video generation models produce physically inconsistent motion that violates real-world dynamics. We propose TrajVLM-Gen, a two-stage framework for physics-aware image-to-video generation. First, we employ a Vision Language Model to predict coarse-grained motion trajectories that maintain consistency with real-world physics. Second, these trajectories guide video generation through attention-based mechanisms for fine-grained motion refinement. We build a trajectory prediction dataset ba…

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
Tavor Z. Baharav, Spyros Dragazis, Aldo Pacchiano
https://arxiv.org/abs/2510.01020 https:/…

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
We study the problem of sequentially testing individuals for a binary disease outcome whose true risk is governed by an unknown logistic model. At each round, a patient arrives with feature vector $x_t$, and the decision maker may either pay to administer a (noiseless) diagnostic test--revealing the true label--or skip testing and predict the patient's disease status based on their feature vector and prior history. Our goal is to minimize the total number of costly tests required while guarante…

Regression Language Models for Code
Yash Akhauri, Xingyou Song, Arissa Wongpanich, Bryan Lewandowski, Mohamed S. Abdelfattah
https://arxiv.org/abs/2509.26476 https://

Regression Language Models for Code
We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the …

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

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.

Understanding Collective Social Behavior in OSS Communities: A Co-editing Network Analysis of Activity Cascades
Lisi Qarkaxhija, Maximilian Carparo, Stefan Menzel, Bernhard Sendhoff, Ingo Scholtes
https://arxiv.org/abs/2509.26173

Understanding Collective Social Behavior in OSS Communities: A Co-editing Network Analysis of Activity Cascades
Understanding the collective social behavior of software developers is crucial to model and predict the long-term dynamics and sustainability of Open Source Software (OSS) communities. To this end, we analyze temporal activity patterns of developers, revealing an inherently ``bursty'' nature of commit contributions. To investigate the social mechanisms behind this phenomenon, we adopt a network-based modelling framework that captures developer interactions through co-editing networks. Our frame…

Quantum speed limit as a sensitive probe of Planck-scale effects
Salman Sajad Wani, Saif Al-Kuwari
https://arxiv.org/abs/2510.00057 https://arxiv.org/pdf/2…

Quantum speed limit as a sensitive probe of Planck-scale effects
Many quantum-gravity scenarios predict a minute modification of the canonical commutator, known as the generalized uncertainty principle (GUP), whose low-energy signatures are, in principle, accessible to state-of-the-art laboratory tests. We compute first-order minimal-length corrections to the quantum speed limit (QSL) for three cases: uniform superpositions in an infinite square well, coherent harmonic-oscillator states, and squeezed-oscillator states. We identify a universal amplification l…

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

Radiative hydrodynamic simulations of FIP fractionation in solar flares
Jeffrey W. Reep, Luke Fushimi Benavitz, Andy S. H. To, David H. Brooks, J. Martin Laming, Patrick Antolin, David M. Long, Deborah Baker
https://arxiv.org/abs/2509.25695

Radiative hydrodynamic simulations of FIP fractionation in solar flares
Elemental abundances in solar flares are observed to vary both spatially and temporally, but the underlying mechanisms remain poorly understood. There is an interplay between advection and the preferential acceleration of low first ionization potential (FIP) elements that likely shapes the observed abundance distributions. Models of the FIP effect predict enhancements near loop footpoints that diffuse upward over time. We simulate strong evaporation events that advect this low-FIP enhancement i…

Dereverberation Using Binary Residual Masking with Time-Domain Consistency
Daniel G. Williams
https://arxiv.org/abs/2510.00356 https://arxiv.org/pdf/2510.0…

Dereverberation Using Binary Residual Masking with Time-Domain Consistency
Vocal dereverberation remains a challenging task in audio processing, particularly for real-time applications where both accuracy and efficiency are crucial. Traditional deep learning approaches often struggle to suppress reverberation without degrading vocal clarity, while recent methods that jointly predict magnitude and phase have significant computational cost. We propose a real-time dereverberation framework based on residual mask prediction in the short-time Fourier transform (STFT) domai…

A Physics-Informed Multi-Source Domain Adaptation Framework for Label-Free Post-Earthquake Damage Assessment
Yifeng Zhang, Xiao Liang
https://arxiv.org/abs/2509.26356 https://…

A Physics-Informed Multi-Source Domain Adaptation Framework for Label-Free Post-Earthquake Damage Assessment
Efficient and intelligent assessment of post-earthquake structural damage is critical for rapid disaster response. While data-driven approaches have shown promise, traditional supervised learning methods rely on extensive labeled datasets, which are often impractical to obtain for damaged structures. To address this limitation, we propose a physics-informed multi-source domain adaptation framework to predict post-earthquake structural damage for a target building without requiring damage labels…

Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs
Ankush Kumar Mishra, Jacob P. Mauthe, Nicholas Luke, Aram Amassian, Baskar Ganapathysubramanian
https://arxiv.org/abs/2509.21330

Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs
Self-driving labs (SDLs) promise faster materials discovery by coupling automation with machine learning, but a central challenge is predicting costly, slow-to-measure properties from inexpensive, automatable readouts. We address this for doped conjugated polymers by learning interpretable spectral fingerprints from optical spectroscopy to predict electrical conductivity. Optical spectra are fast, non-destructive, and sensitive to aggregation and charge generation; we automate their featurizati…

CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization
Prasad Rajesh Posture, Yashrajsinh Mahida, Suman Majumdar, Leon Noble
https://arxiv.org/abs/2510.01121

CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization
The observation of the redshifted 21-cm signal from the intergalactic medium will probe the epoch of reionization (EoR) with unprecedented detail. Various simulations are being developed and used to predict and understand the nature and morphology of this signal. However, these simulations are computationally very expensive and time-consuming to produce in large numbers. To overcome this problem, an efficient field-level emulator of this signal is required. However, the EoR 21-cm signal is high…

AI-assisted Advanced Propellant Development for Electric Propulsion
Angel Pan Du, Miguel Arana-Catania, Enric Grustan Guti\'errez
https://arxiv.org/abs/2509.26567 https://…

AI-assisted Advanced Propellant Development for Electric Propulsion
Artificial Intelligence algorithms are introduced in this work as a tool to predict the performance of new chemical compounds as alternative propellants for electric propulsion, focusing on predicting their ionisation characteristics and fragmentation patterns. The chemical properties and structure of the compounds are encoded using a chemical fingerprint, and the training datasets are extracted from the NIST WebBook. The AI-predicted ionisation energy and minimum appearance energy have a mean …

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Nishant Doshi
https://arxiv.org/abs/2509.21873 https://arxiv.org/pdf/2509.21873

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Conventional maneuver prediction methods use some sort of classification model on temporal trajectory data to predict behavior of agents over a set time horizon. Despite of having the best precision and recall, these models cannot predict a lane change accurately unless they incorporate information about the entire scene. Level-k game theory can leverage the human-like hierarchical reasoning to come up with the most rational decisions each agent can make in a group. This can be leveraged to mod…

Architecture of planetary systems with and without outer giant planets I. Inner planet detections around HD 23079, HD 196067, and HD 86226
J. -B. Delisle, J. P. Faria, D. S\'egransan, E. Fontanet, W. Ceva, D. Barbato, S. G. Sousa, N. Unger, A. Leleu, F. Bouchy, M. Cretignier, R. F. D\'iaz, X. Dumusque, Y. G. C. Frensch, N. C. Hara, G. Laughlin, G. Lo Curto, C. Lovis, M. Marmier, M. Mayor, L. Mignon, C. Mordasini, F. Pepe, N. C. Santos, S. Udry

Architecture of planetary systems with and without outer giant planets I. Inner planet detections around HD 23079, HD 196067, and HD 86226
Understanding the link between outer giant planets (OGPs) and inner light planets (ILPs) is key to understanding planetary system formation and architecture. The correlation between these two populations of planets is debated both theoretically -- different formation models predict either a correlation or an anticorrelation -- and observationally. Several recent attempts to constrain this correlation have yielded contradictory results, due to small-number statistics and heterogeneous samples. W…

The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
Eirini Angeloudi, Marc Huertas-Company, Jes\'us Falc\'on-Barroso, Laurence Perreault-Levasseur, Alexandre Adam, Alina Boecker
https://arxiv.org/abs/2509.25340

The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
Understanding the origin of stars within a galaxy - whether formed in-situ or accreted from other galaxies (ex-situ) - is key to constraining its evolution. Spatially resolving these components provides crucial insights into a galaxy's mass assembly history. We aim to predict the spatial distribution of ex-situ stellar mass fraction in MaNGA galaxies, and to identify distinct assembly histories based on the radial gradients of these predictions in the central regions. We employ a diffusion mode…

NFL Week 5 predictions, picks: Our experts face off on Eagles vs. Broncos, Patriots against Bills and more

https://www.cbssports.com/nfl/news/nfl-week-5-betting-picks-predictions-odds/

Mathematical Theory of Collinearity Effects on Machine Learning Variable Importance Measures
Kelvyn K. Bladen, D. Richard Cutler, Alan Wisler
https://arxiv.org/abs/2510.00557 ht…

Mathematical Theory of Collinearity Effects on Machine Learning Variable Importance Measures
In many machine learning problems, understanding variable importance is a central concern. Two common approaches are Permute-and-Predict (PaP), which randomly permutes a feature in a validation set, and Leave-One-Covariate-Out (LOCO), which retrains models after permuting a training feature. Both methods deem a variable important if predictions with the original data substantially outperform those with permutations. In linear regression, empirical studies have linked PaP to regression coefficie…

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/

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
Ali Abu-Nada, Subhashish Banerjee
https://arxiv.org/abs/2509.22758

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
We present a \emph{novel} and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory using only local ancilla measurements. A system qubit is coherently coupled to an ancilla via a symmetric XY Hamiltonian; the ancilla interacts with a noisy environment and is the only qubit we measure. A feedforward neural network, trained on short sliding windows of supplementary data from the past, forecasts the observable system $\langle Z_{(S)…

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

Efficient spectra from atomistic simulation: a generalized master equation study of the air-water interface
Thomas Sayer
https://arxiv.org/abs/2510.00867 https://

Efficient spectra from atomistic simulation: a generalized master equation study of the air-water interface
Computing condensed phase spectra from atomistic simulations requires calculating correlation functions from molecular dynamics and can be very expensive. A totally general, data-driven method to reduce cost is to employ an exact rewriting to a generalized master equation characterized by a memory kernel. The decay time of the kernel can be less than the original function, reducing the amount of data required. In this paper we construct the minimal projection operator to predict vibrational sum…

🚬 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.

Parametric modeling of shear wave velocity profiles for the conterminous U.S
Morgan D. Sanger, Brett W. Maurer
https://arxiv.org/abs/2510.00372 https://arx…

Parametric modeling of shear wave velocity profiles for the conterminous U.S
Earthquake ground motions and the related damage can be significantly impacted by near-surface soils. Accurate predictions of seismic hazard require depth-continuous models of soil stiffness, commonly described in terms of shear-wave velocity (VS). For regional-scale studies, efforts to predict VS remotely, such as the U.S. Geological Survey's National Crustal Model, tend to emphasize deeper lithologic velocity structures, thus simplifying important near-surface soil velocity variations, and te…

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
Lingyu Zhang, Guobin Wu, Yan Wang, Pengfei Xu, Jian Liang, Xuan Song, Yunhai Wang
https://arxiv.org/abs/2509.22661

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
The next Point-of-interest (POI) recommendation is mainly based on sequential traffic information to predict the user's next boarding point location. This is a highly regarded and widely applied research task in the field of intelligent transportation, and there have been many research results to date. Traditional POI prediction models primarily rely on short-term traffic sequence information, often neglecting both long-term and short-term preference data, as well as crucial spatiotemporal cont…

Applications of Machine Learning in Constraining Multi-Scalar Models
Darius Jur\v{c}iukonis
https://arxiv.org/abs/2509.24092 https://arxiv.org/pdf/2509.240…

Applications of Machine Learning in Constraining Multi-Scalar Models
Machine learning techniques are used to predict theoretical constraints such as unitarity and boundedness from below in extensions of the Standard Model. This approach has proven effective for models incorporating additional SU(2) scalar multiplets, in particular the quadruplet and sixplet cases. High predictive performance is achieved through the use of suitable neural network architectures and well-prepared training datasets. Moreover, machine learning provides a substantial computational adv…

Causal Theories of Relativistic Hydrodynamics
Raphael E. Hoult
https://arxiv.org/abs/2509.22862 https://arxiv.org/pdf/2509.22862

Causal Theories of Relativistic Hydrodynamics
The first-order textbook formulations of relativistic viscous hydrodynamics are unstable and acausal. These shortcomings may be rectified by using effective theories which maintain stability and causality. In this dissertation, which is intended to also serve as an introduction to the field, causal theories of relativistic hydrodynamics are developed and explored. Conditions are obtained for a linearized analysis to predict the non-linear causality of a theory, and constraints are found on shor…

Consistency Models as Plug-and-Play Priors for Inverse Problems
Merve G\"ulle, Junno Yun, Ya\c{s}ar Utku Al\c{c}alar, Mehmet Ak\c{c}akaya
https://arxiv.org/abs/2509.22736 h…

Consistency Models as Plug-and-Play Priors for Inverse Problems
Diffusion models have found extensive use in solving numerous inverse problems. Such diffusion inverse problem solvers aim to sample from the posterior distribution of data given the measurements, using a combination of the unconditional score function and an approximation of the posterior related to the forward process. Recently, consistency models (CMs) have been proposed to directly predict the final output from any point on the diffusion ODE trajectory, enabling high-quality sampling in jus…

Load Transfer along Continuous Collagen Fibers Reduces the Importance of Wall Thickness Variations
Yamnesh Agrawal (Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, PA, USA), Masoud Zamani (Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, PA, USA), James R. Thunes (ANSYS Canada Ltd, Waterloo, ON, Canada), Spandan Maiti (Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, PA, USA,…

Load Transfer along Continuous Collagen Fibers Reduces the Importance of Wall Thickness Variations
The mechanical response of biological soft tissues is influenced by wall heterogeneity, including spatial variations in wall thickness. Traditional models for homogeneous soft tissues under uniaxial loading predict higher stretch and stress in thinner regions. In fact, large gradients in stretch and stress are predicted to be induced by spatial variations in wall thickness. In prior studies, the role of collagen fibers in regions of thickness transition has been largely neglected or only consid…

Kinetic Monte Carlo prediction of the morphology of pentaerythritol tetranitrate
Jacob Jeffries, Himanshu Singh, Romain Perriot, Christian Negre, Antonio Redondo, Enrique Martinez
https://arxiv.org/abs/2509.25490

Kinetic Monte Carlo prediction of the morphology of pentaerythritol tetranitrate
In this work, we develop an atomistic, graph-based kinetic Monte Carlo (KMC) simulation routine to predict crystal morphology. Within this routine, we encode the state of the supercell in a binary occupation vector and the topology of the supercell in a simple nearest-neighbor graph. From this encoding, we efficiently compute the interaction energy of the system as a quadratic form of the binary occupation vector, representing pairwise interactions. This encoding, coupled with a simple diffusio…

A classification of mode-1 internal solitary waves in a three-layer fluid
Ricardo Barros, Alex Doak, Wooyoung Choi, Paul Milewski
https://arxiv.org/abs/2509.24669 https://

A classification of mode-1 internal solitary waves in a three-layer fluid
We explore the bifurcation structure of mode-1 solitary waves in a three-layer fluid confined between two rigid boundaries. A recent study (Lamb, J. Fluid Mech. 2023, 962, A17) proposed a method to predict the coexistence of solitary waves with opposite polarity in a continuously stratified fluid with a double pycnocline by examining the conjugate states for the Euler equations. We extend this line of inquiry to a piecewise-constant three-layer stratification, taking advantage of the fact that …

Semianalytical Accretion-Tracer Emission: Forming Planets Are Intrinsically Faint
Gabriel-Dominique Marleau
https://arxiv.org/abs/2510.00138 https://arxiv.…

Semianalytical Accretion-Tracer Emission: Forming Planets Are Intrinsically Faint
Direct-imaging surveys have looked for accreting planets through their accretion tracers such as H alpha but have been less fruitful than expected. However, up to now, hydrogen-line emission at accreting planets has been estimated primarily with extrapolations of stellar-scaling relationships or with theoretical spherically-symmetric computations. To predict the line emission intensity during the formation phase, we wish to follow the consequences of angular momentum conservation of the materia…

Here’s a good analysis by Gartner and The Next Platform that tries to make sense of spending on AI predictions over the next few years. “if you add devices to datacenter stuff, then AI drove 19.6 percent of IT revenues in 2024 and will drive 27.2 percent of IT revenues in 2025 and 34.1 percent in 2026.” h…

Dicing, Slicing, And Augmenting Gartner’s AI Spending Forecast
When we try to predict the weather, we use ensembles of the initial conditions on the ground, in the oceans, and throughout the air to create a kind of

Sonnet 014 - XIV
Not from the stars do I my judgement pluck;
And yet methinks I have Astronomy,
But not to tell of good or evil luck,
Of plagues, of dearths, or seasons' quality;
Nor can I fortune to brief minutes tell,
Pointing to each his thunder, rain and wind,
Or say with princes if it shall go well
By oft predict that I in heaven find:
But from thine eyes my knowledge I derive,
And, constant stars, in them I read such art

Spectral Control of a Cavity-Based X-ray Free-Electron Laser via Active Mode Locking
Nanshun Huang, Hanxiang Yang, Haixiao Deng
https://arxiv.org/abs/2510.00640 https://

Spectral Control of a Cavity-Based X-ray Free-Electron Laser via Active Mode Locking
Precise spectral control in the hard X-ray regime remains a long-standing challenge that limits applications in atomic-scale science and ultrafast spectroscopy. We present an actively mode-locked cavity-based X-ray free-electron laser that achieves deterministic spectral programmability with phase-locked pulse trains and comb-like spectra, by coherently modulating the electron-beam energy. Three-dimensional time-dependent simulations predict \SI{700}{\micro\joule} total energy, \SI{30}{\giga\wa…

Situational Awareness for Safe and Robust Multi-Agent Interactions Under Uncertainty
Benjamin Alcorn, Eman Hammad
https://arxiv.org/abs/2509.23425 https://…

Situational Awareness for Safe and Robust Multi-Agent Interactions Under Uncertainty
Multi-agent systems are prevalent in a wide range of domains including power systems, vehicular networks, and robotics. Two important problems to solve in these types of systems are how the intentions of non-coordinating agents can be determined to predict future behavior and how the agents can achieve their objectives under resource constraints without significantly sacrificing performance. To study this, we develop a model where an autonomous agent observes the environment within a safety rad…

TopoSPAM: Topology grounded Simulation Platform for morphogenesis and biological Active Matter
Abhinav Singh (Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany, Center for Systems Biology Dresden, Dresden, Germany, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA), Abhijeet Krishna (Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany, Center for Systems Biology Dresden, Dresden, Germany), Aboutaleb …

TopoSPAM: Topology grounded Simulation Platform for morphogenesis and biological Active Matter
We present a topology grounded, multiscale simulation platform for morphogenesis and biological active matter. Morphogenesis and biological active matter represent keystone problems in biology with additional, far-reaching implications across the biomedical sciences. Addressing these problems will require flexible, cross-scale models of tissue shape, development, and dysfunction that can be tuned to understand, model, and predict relevant individual cases. Current approaches to simulating anato…

AI is transforming how we predict hurricanes. NOAA is now using machine learning to analyze billions of data points, making forecasts more accurate than ever.
The results? Better rapid intensification predictions and stronger decision support for coastal communities at risk. As hurricane season wraps, this tech could be a game-changer for saving lives.

NOAA chief plugs AI in forecasting as hurricane season wraps
While no hurricanes struck the U.S., Hurricane Melissa left behind devastation in the Caribbean.

Self-driving cars: Are we there yet?
Merve Atasever, Zhuochen Liu, Qingpei Li, Akshay Hitendra Shah, Hans Walker, Jyotirmoy V. Deshmukh, Rahul Jain
https://arxiv.org/abs/2509.22754

Self-driving cars: Are we there yet?
Autonomous driving remains a highly active research domain that seeks to enable vehicles to perceive dynamic environments, predict the future trajectories of traffic agents such as vehicles, pedestrians, and cyclists and plan safe and efficient future motions. To advance the field, several competitive platforms and benchmarks have been established to provide standardized datasets and evaluation protocols. Among these, leaderboards by the CARLA organization and nuPlan and the Waymo Open Dataset …

The design and reconstructible history of the Mayan eclipse table of the Dresden Codex: #Maya eclipse tables: #eclipse tables in the #DresdenCodex were based on lunar tables and adjusted for slippage over time.

Generative multi-fidelity modeling and downscaling via spatial autoregressive transport maps
Alejandro Calle-Saldarriaga, Paul F. V. Wiemann, Matthias Katzfuss
https://arxiv.org/abs/2509.22474

Generative multi-fidelity modeling and downscaling via spatial autoregressive transport maps
Spatial fields are often available at multiple fidelities or resolutions, where high-fidelity data is typically more costly to obtain than low-fidelity data. Statistical surrogates or emulators can predict high-fidelity fields from cheap low-fidelity output. We propose a highly scalable Bayesian approach that can learn the joint non-Gaussian distribution and nonlinear dependence structure of nonstationary spatial fields at multiple fidelities from a small number of training samples. Our method …

Truth Social launches Truth Predict, a prediction market platform in partnership with Crypto.com to let users trade on major current events like elections (Financial Times)
https://www.ft.com/content/485594b3-5a72-4ae1-aa93-8ee987be1569

NeuroScalar: A Deep Learning Framework for Fast, Accurate, and In-the-Wild Cycle-Level Performance Prediction
Shayne Wadle, Yanxin Zhang, Vikas Singh, Karthikeyan Sankaralingam
https://arxiv.org/abs/2509.22410

NeuroScalar: A Deep Learning Framework for Fast, Accurate, and In-the-Wild Cycle-Level Performance Prediction
The evaluation of new microprocessor designs is constrained by slow, cycle-accurate simulators that rely on unrepresentative benchmark traces. This paper introduces a novel deep learning framework for high-fidelity, ``in-the-wild'' simulation on production hardware. Our core contribution is a DL model trained on microarchitecture-independent features to predict cycle-level performance for hypothetical processor designs. This unique approach allows the model to be deployed on existing silicon to…

SAGA-SR: Semantically and Acoustically Guided Audio Super-Resolution
Jaekwon Im, Juhan Nam
https://arxiv.org/abs/2509.24924 https://arxiv.org/pdf/2509.2492…

SAGA-SR: Semantically and Acoustically Guided Audio Super-Resolution
Versatile audio super-resolution (SR) aims to predict high-frequency components from low-resolution audio across diverse domains such as speech, music, and sound effects. Existing diffusion-based SR methods often fail to produce semantically aligned outputs and struggle with consistent high-frequency reconstruction. In this paper, we propose SAGA-SR, a versatile audio SR model that combines semantic and acoustic guidance. Based on a DiT backbone trained with a flow matching objective, SAGA-SR i…

"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.

Modelling spin-wave interference with electromagnetic leakage in micron-scaled spin-wave transducers
Felix Kohl, Bj\"orn Heinz, Matthias Wagner, Christoph Adelmann, Florin Ciubotaru, Philipp Pirro
https://arxiv.org/abs/2509.24647

Modelling spin-wave interference with electromagnetic leakage in micron-scaled spin-wave transducers
Utilization of spin-wave transducers for radio-frequency signal processing provides significant potential due to intrinsic tunability, scalability and nonlinearity. However, such components can exhibit passband ripples diminishing their operation and functionality. Here, we experimentally identify the electromagnetic crosstalk (EM) as a major source of passband ripples and provide a simple analytic model to predict the impact on device operation. The results are in good agreement with the exper…

Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
Changde Du, Yizhuo Lu, Zhongyu Huang, Yi Sun, Zisen Zhou, Shaozheng Qin, Huiguang He
https://arxiv.org/abs/2509.24298

Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
The ability to represent emotion plays a significant role in human cognition and social interaction, yet the high-dimensional geometry of this affective space and its neural underpinnings remain debated. A key challenge, the `behavior-neural gap,' is the limited ability of human self-reports to predict brain activity. Here we test the hypothesis that this gap arises from the constraints of traditional rating scales and that large-scale similarity judgments can more faithfully capture the brain'…

Execs predict the NFL season's second half and awards https://www.espn.com/nfl/story/_/id/46780885/2025-nfl-season-midseason-playoffs-mvp-super-bowl-breakout-players-divisions

Crosslisted article(s) found for cond-mat.soft. https://arxiv.org/list/cond-mat.soft/new
[1/1]:
- Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs
Ankush Kumar Mishra, Jacob P. Mauthe, Nicholas Luke, Aram Amassian, Baskar Gan…

"…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…

Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks
Hai Siong Tan
https://arxiv.org/abs/2509.24327 https://arxiv.org/pdf/250…

Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks
We examine the use of a novel variant of Physics-Informed Neural Networks to predict cosmological parameters from recent supernovae and baryon acoustic oscillations (BAO) datasets. Our machine learning framework generates uncertainty estimates for target variables and the inferred unknown parameters of the underlying PDE descriptions. Built upon a hybrid of the principles of Evidential Deep Learning, Physics-Informed Neural Networks, Bayesian Neural Networks and Gaussian Processes, our model en…

MeanFlowSE: One-Step Generative Speech Enhancement via MeanFlow
Yike Zhu (Audio, Speech and Language Processing Group), Boyi Kang (The Hong Kong University of Science and Technology, Hong Kong, China, Audio, Speech and Language Processing Group), Ziqian Wang (Audio, Speech and Language Processing Group), Xingchen Li (Audio, Speech and Language Processing Group, Huawei Technologies, China), Zihan Zhang (Huawei Technologies, China), Wenjie Li (Huawei Technologies, China), Longshuai Xiao …

MeanFlowSE: One-Step Generative Speech Enhancement via MeanFlow
Speech enhancement (SE) recovers clean speech from noisy signals and is vital for applications such as telecommunications and automatic speech recognition (ASR). While generative approaches achieve strong perceptual quality, they often rely on multi-step sampling (diffusion/flow-matching) or large language models, limiting real-time deployment. To mitigate these constraints, we present MeanFlowSE, a one-step generative SE framework. It adopts MeanFlow to predict an average-velocity field for on…

California’s Governor should sign S.B. 7,
a common-sense bill to end some of the harshest consequences of automated abuseat work.
EFF is proud to join dozens of labor, digital rights, and other advocates in support of the
“No Robo Bosses Act.”
Algorithmic decision-making is a growing threat to workers.
Bosses are using AI to assess the body language and voice tone of job candidates.
They’re using algorithms to predict when employees are organizing a union o…

Yes to California’s “No Robo Bosses Act”
California’s Governor should sign S.B. 7, a common-sense bill to end some of the harshest consequences of automated abuse at work. EFF is proud to join dozens of labor, digital rights, and other advocates in support of the “No Robo Bosses Act.”Algorithmic decision-making is a growing threat to...

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…

Clover Security, whose AI agents plug into developer platforms like GitHub to predict and detect security flaws, raised $36M led by Notable Capital and Team8 (Sam Sabin/Axios)
https://www.axios.com/2025/11/25/clover-security-funding-wiz-crowdstrike-no…

Exclusive: Wiz founders back AI security agent startup
Clover Security provides agentic tools that help companies embed security throughout their products and ensure that their products remain secure during their lifecycle.

Observing compact Pop III star clusters and the presence of cosmic streaming
Claire E. Williams, Smadar Naoz, Naoki Yoshida, William Lake, Blakesley Burkhart, Federico Marinacci, Mark Vogelsberger, Shyam H. Menon, Avi Chen, Kyle Kremer
https://arxiv.org/abs/2509.22776

Observing compact Pop III star clusters and the presence of cosmic streaming
The formation of the Universe's first luminous stellar structures depends on the unique conditions at "Cosmic Dawn," which are set by the underlying cosmological model and early baryonic physics. Observations suggest that high-$z$ star clusters reached stellar surface densities above $10^5 M_\odot$ pc$^{-2}$, suggesting scenarios where models predict that the ability of stellar feedback to counter gravitational collapse is severely limited. We investigate the first star clusters in a suite of A…

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.

Event Generator Tuning as a Robustness Test
Jean Wolfs, Chris M. Marshall
https://arxiv.org/abs/2509.22526 https://arxiv.org/pdf/2509.22526

Event Generator Tuning as a Robustness Test
Neutrino oscillation experiments use Monte Carlo event generators to predict neutrino-nucleus interactions. Cross section uncertainties are typically implemented by varying the parameters of the model(s) used in the generator. We study the performance of two commonly-used model configurations of the GENIE generator (G18_10a_02_11a and AR23_0i_00_000) and their uncertainties by tuning parameters to cross section data, and then comparing the resulting tuned prediction to a suite of other measurem…

Predicting when each winless NFL team will get its first win: Saints' breakthrough coming soon?

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

Predicting the distribution of yield-stress fluids in branched pipe manifolds
Elliott Sutton, Waldo Rosales Trujillo, Adam Kowalski, Cl\'audio P. Fonte, Anne Juel
https://arxiv.org/abs/2509.22192

Predicting the distribution of yield-stress fluids in branched pipe manifolds
We develop a one-dimensional network model to predict the steady-state distribution of yield-stress fluids in branched pipe manifolds under wall-slip conditions. The model accounts for major friction losses between junctions and incorporates wall slip through a power-law relation calibrated independently via capillary rheometry. Predictions from the model are validated against both bench-scale experiments and fully resolved computational fluid dynamics simulations, showing excellent agreement a…

"Plastic pollution could linger at ocean surfaces for over a century, new research finds"
#Plastic #Plastics #Oceans

Plastic pollution could linger at ocean surfaces for over a century, new research finds
Scientists from the Department of Geography and Environmental Science at Queen Mary University of London have developed a simple model to show how buoyant plastic can settle through the water column and they predict it could take over 100 years to remove plastic waste from the ocean's surface.

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/

U-SWIFT: A Unified Surface Wave Inversion Framework with Transformer via Normalization of Dispersion Curves
Tianjian Cheng, Hongrui Xu, Jiayu Feng, Xiongyu Hu, Chaofan Yao
https://arxiv.org/abs/2509.24872

U-SWIFT: A Unified Surface Wave Inversion Framework with Transformer via Normalization of Dispersion Curves
Deep learning is an increasingly popular approach for inverting surface wave dispersion curves to obtain Vs profiles. However, its generalizability is constrained by the depth and velocity scales of training data. We propose a unified deep learning framework that overcomes this limitation via normalization of dispersion curves. By leveraging the scaling properties of dispersion curves, our approach enables a single, pre-trained model to predict Vs profiles across diverse scales, from shallow su…

Selective nonthermal melting in phlogopite under ultrafast energy deposition
Nikita Medvedev
https://arxiv.org/abs/2509.23211 https://arxiv.org/pdf/2509.23…

Selective nonthermal melting in phlogopite under ultrafast energy deposition
Phlogopite is a complex magnesium-rich mineral from the dark mica group, KMg$_3$(AlSi$_3$O$_{10}$)(OH)$_2$. Its response to ultrafast excitation of its electronic system is studied using a hybrid model that combines tight-binding molecular dynamics with transport Monte Carlo and the Boltzmann equation. Simulations predict that at the deposited dose of ~0.17 eV/atom (electronic temperature $T_e$~11,000 K), the first hydrogens start to migrate in the otherwise preserved lattice, transiently turni…

Motional representation; the ability to predict odor characters using molecular vibrations
Yuki Harada, Shuichi Maeda, Junwei Shen, Taku Misonou, Hirokazu Hori, Shinichiro Nakamura
https://arxiv.org/abs/2509.16245

Motional representation; the ability to predict odor characters using molecular vibrations
The prediction of odor characters is still impossible based on the odorant molecular structure. We designed a CNN-based regressor for computed parameters in molecular vibrations (CNN\_vib), in order to investigate the ability to predict odor characters of molecular vibrations. In this study, we explored following three approaches for the predictability; (i) CNN with molecular vibrational parameters, (ii) logistic regression based on vibrational spectra, and (iii) logistic regression with molecu…

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…

LAVA: Explainability for Unsupervised Latent Embeddings
Ivan Stresec, Joana P. Gon\c{c}alves
https://arxiv.org/abs/2509.21149 https://arxiv.org/pdf/2509.21…

LAVA: Explainability for Unsupervised Latent Embeddings
Unsupervised black-box models can be drivers of scientific discovery, but remain difficult to interpret. Crucially, discovery hinges on understanding the model output, which is often a multi-dimensional latent embedding rather than a well-defined target. While explainability for supervised learning usually seeks to uncover how input features are used to predict a target, its unsupervised counterpart should relate input features to the structure of the learned latent space. Adaptations of superv…

Noise-to-Notes: Diffusion-based Generation and Refinement for Automatic Drum Transcription
Michael Yeung, Keisuke Toyama, Toya Teramoto, Shusuke Takahashi, Tamaki Kojima
https://arxiv.org/abs/2509.21739

Noise-to-Notes: Diffusion-based Generation and Refinement for Automatic Drum Transcription
Automatic drum transcription (ADT) is traditionally formulated as a discriminative task to predict drum events from audio spectrograms. In this work, we redefine ADT as a conditional generative task and introduce Noise-to-Notes (N2N), a framework leveraging diffusion modeling to transform audio-conditioned Gaussian noise into drum events with associated velocities. This generative diffusion approach offers distinct advantages, including a flexible speed-accuracy trade-off and strong inpainting …

Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network
Pin-Jui Huang, Yu-Hsuan Liao, SooHeon Kim, NoSeong Park, JongBae Park, DongMyung Shin
https://arxiv.org/abs/2509.19896

Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network
Computational models that predict cellular phenotypic responses to chemical and genetic perturbations can accelerate drug discovery by prioritizing therapeutic hypotheses and reducing costly wet-lab iteration. However, extracting biologically meaningful and batch-robust cell painting representations remains challenging. Conventional self-supervised and contrastive learning approaches often require a large-scale model and/or a huge amount of carefully curated data, still struggling with batch ef…

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…

Dolphins vs. Jets prediction: Which of these AFC East teams can avoid an 0-4 start in prime time?

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

A couple of months ago Bali experienced its worst floods in more than a decade.
-- They came at the peak of the dry season.
Instead of the empty wells farmers faced last year,
this time their crops were washed away;
roads turned into brown, swirling rivers;
houses collapsed in the torrents;
and 17 people were killed.
⭐️Indonesia, the world’s fourth-most populous country, faces a paradox.
Sometimes described as a sleeping giant, it is one of the …

Researchers detail popEVE, an AI model to predict the disease-causing potential of unknown human genetic mutations, and says it beats Google's AlphaMissense (Michael Peel/Financial Times)
https://www.ft.com/content/bc49e334-776b-41d0-a9be-fb0c29c54853

💣 Software supports safe, controlled detonations of aerial bombs
#europe

Software supports safe, controlled detonations of aerial bombs
Time and again, unexploded aerial bombs from the Second World War need to be defused or detonated in controlled explosions. Researchers at the Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI are working together with partners to develop models that predict fragmentation flight and simulate the underground spread of shock waves. This can help to reduce evacuation zones as well as improve forecasting of the risks to underground structures.

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/8]:
- Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?
Ayan Sar, Sampurna Roy, Tanupriya Choudhury, Ajith Abraham

SHMoAReg: Spark Deformable Image Registration via Spatial Heterogeneous Mixture of Experts and Attention Heads
Yuxi Zheng, Jianhui Feng, Tianran Li, Marius Staring, Yuchuan Qiao
https://arxiv.org/abs/2509.20073

SHMoAReg: Spark Deformable Image Registration via Spatial Heterogeneous Mixture of Experts and Attention Heads
Encoder-Decoder architectures are widely used in deep learning-based Deformable Image Registration (DIR), where the encoder extracts multi-scale features and the decoder predicts deformation fields by recovering spatial locations. However, current methods lack specialized extraction of features (that are useful for registration) and predict deformation jointly and homogeneously in all three directions. In this paper, we propose a novel expert-guided DIR network with Mixture of Experts (MoE) mec…

PGCLODA: Prompt-Guided Graph Contrastive Learning for Oligopeptide-Infectious Disease Association Prediction
Dayu Tan, Jing Chen, Xiaoping Zhou, Yansen Su, Chunhou Zheng
https://arxiv.org/abs/2509.20290

PGCLODA: Prompt-Guided Graph Contrastive Learning for Oligopeptide-Infectious Disease Association Prediction
Infectious diseases continue to pose a serious threat to public health, underscoring the urgent need for effective computational approaches to screen novel anti-infective agents. Oligopeptides have emerged as promising candidates in antimicrobial research due to their structural simplicity, high bioavailability, and low susceptibility to resistance. Despite their potential, computational models specifically designed to predict associations between oligopeptides and infectious diseases remain sc…

Bold Predictions: Can the Cowboys send Green Bay packing? https://insidethestar.com/bold-predictions-can-the-cowboys-send-green-bay-packing

Eagles vs. Bears prediction: Can Caleb Williams upset the reigning champions on Black Friday?

https://www.cbssports.com/nfl/news/eagles-vs-bears-where-to-watch-pick-…

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…

NFL Week 13 picks: Our experts face off on Shedeur Sanders' second start, Bills against Steelers and more

https://www.cbssports.com/nfl/news/nfl-week-13-picks-dueling-expert-predict…

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…

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…

Predictive Quality Assessment for Mobile Secure Graphics
Cas Steigstra, Sergey Milyaev, Shaodi You
https://arxiv.org/abs/2509.20028 https://arxiv.org/pdf/2…

Predictive Quality Assessment for Mobile Secure Graphics
The reliability of secure graphic verification, a key anti-counterfeiting tool, is undermined by poor image acquisition on smartphones. Uncontrolled user captures of these high-entropy patterns cause high false rejection rates, creating a significant 'reliability gap'. To bridge this gap, we depart from traditional perceptual IQA and introduce a framework that predictively estimates a frame's utility for the downstream verification task. We propose a lightweight model to predict a quality score…

NFL Week 4 predictions, picks: Our experts go head-to-head on Packers-Cowboys, Ravens-Chiefs, more

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

Bold Predictions: Dallas looks to change history in Denver https://insidethestar.com/bold-predictions-dallas-looks-to-change-history-in-denver

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

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…

StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions
Nicholas Kraabel, Jiangtao Liu, Yuchen Bian, Daniel Kifer, Chaopeng Shen
https://arxiv.org/abs/2509.17942

StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions
Stewarding natural resources, mitigating floods, droughts, wildfires, and landslides, and meeting growing demands require models that can predict climate-driven land-surface responses and human feedback with high accuracy. Traditional impact models, whether process-based, statistical, or machine learning, struggle with spatial generalization due to limited observations and concept drift. Recently proposed vision foundation models trained on satellite imagery demand massive compute and are ill-s…

Packers vs. Lions prediction: Can Detroit defend home turf or will Green Bay pull off the Thanksgiving upset?

https://www.cbssports.com/nfl/news/packers-vs-lions-wh…

NFL Week 4 predictions, picks: Our experts go head-to-head on Packers vs. Cowboys, Ravens vs. Chiefs, more

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

Ravens vs. Bengals prediction: Can Joe Burrow save Cincinnati on Thanksgiving?

https://www.cbssports.com/nfl/news/ravens-vs-bengals-prediction-pick-odds-thanksgiving-game/

Eagles vs. Giants prediction: Can Jalen Hurts exact revenge in NFC East rivalry after Jaxson Dart's upset?

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

NFL Week 4 predictions, picks: Our experts go head-to-head on Packers vs. Cowboys, Ravens vs. Chiefs, more

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

NFL Week 8 picks: Our experts face off on Giants vs. Eagles, Cowboys at Broncos and more

https://www.cbssports.com/nfl/news/nfl-week-8-betting-picks-predic…

Eagles vs. Giants prediction: Can Jalen Hurts exact revenge in NFC East rivalry after Jaxson Dart's upset?

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