Tootfinder

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/

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…

Is Google about to destroy the web?
#Internet

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…

Imagine ChatGPT but instead of predicting text it just linked you to the to 3 documents most-influential on the probabilities that would have been used to predict that text.
Could even generate some info about which parts of each would have been combined how.
There would still be issues with how training data is sourced and filtered, but these could be solved by crawling normally respecting robots.txt and by paying filterers a fair wage with a more relaxed work schedule and mental health support.
The energy issues are mainly about wild future investment and wasteful query spam, not optimized present-day per-query usage.
Is this "just search?"
Yes, but it would have some advantages for a lot of use cases, mainly in synthesizing results across multiple documents and in leveraging a language model more fully to find relevant stuff.
When we talk about the harms of current corporate LLMs, the opportunity cost of NOT building things like this is part of that.
The equivalent for art would have been so amazing too! "Here are some artists that can do what you want, with examples pulled from their portfolios."
It would be a really cool coding assistant that I'd actually encourage my students to use (with some guidelines).
#AI #GenAI #LLMs

After today's capitulation by the D-party Fox News is forecasting a total sweep by the R-party in November 2026.
Given that the D-party can't organize themselves out of a wet paper bag, and then sinks their own boat by elevating identity politics over pretty much everything else, I can see the logic in such a move by Fox News.
(And, no, Fox News did not really predict that total sweep.)

📷 Two-stage framework reconstructs sharp 4D scenes from blurry handheld videos
#imaging

Two-stage framework reconstructs sharp 4D scenes from blurry handheld videos
Neural Radiance Fields (NeRF) is a fascinating technique that creates three-dimensional (3D) representations of a scene from a set of two-dimensional (2D) images, captured from different angles. It works by training a deep neural network to predict the color and density at any point in 3D space.

@… two precautions that may help, in lossy situations:
pkg prime-origins | sort -u > /var/tmp/pkg-prime-origins.txt
/usr/local/etc/periodic/daily/411.pkg-backup
If – following an issue – you predict the need to revert to the backup, you can:
service cron stop

Post-hoc Stochastic Concept Bottleneck Models
Wiktor Jan Hoffmann, Sonia Laguna, Moritz Vandenhirtz, Emanuele Palumbo, Julia E. Vogt
https://arxiv.org/abs/2510.08219 https://

Post-hoc Stochastic Concept Bottleneck Models
Concept Bottleneck Models (CBMs) are interpretable models that predict the target variable through high-level human-understandable concepts, allowing users to intervene on mispredicted concepts to adjust the final output. While recent work has shown that modeling dependencies between concepts can improve CBM performance, especially under interventions, such approaches typically require retraining the entire model, which may be infeasible when access to the original data or compute is limited. I…

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
Zhiyu Zheng, Shaoyu Chen, Haoran Yin, Xinbang Zhang, Jialv Zou, Xinggang Wang, Qian Zhang, Lefei Zhang
https://arxiv.org/abs/2510.08562

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
End-to-end autonomous driving (E2EAD) systems, which learn to predict future trajectories directly from sensor data, are fundamentally challenged by the inherent spatio-temporal imbalance of trajectory data. This imbalance creates a significant optimization burden, causing models to learn spurious correlations instead of causal inference, while also prioritizing uncertain, distant predictions, thereby compromising immediate safety. To address these issues, we propose ResAD, a novel Normalized R…

Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
Alvaro Becerra, Ruth Cobos, Charles Lang
https://arxiv.org/abs/2509.07742

Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
In modern online learning, understanding and predicting student behavior is crucial for enhancing engagement and optimizing educational outcomes. This systematic review explores the integration of biosensors and Multimodal Learning Analytics (MmLA) to analyze and predict student behavior during computer-based learning sessions. We examine key challenges, including emotion and attention detection, behavioral analysis, experimental design, and demographic considerations in data collection. Our st…

The Future of Sci-Fi and Fantasy Panel Highlights | DSNX25 – Dragonsteel Books https://share.google/siJXiuu0PyZOEb3lW

Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
Robert Scholz, Kunal Bagga, Christine Ahrends, Carlo Alberto Barbano
https://arxiv.org/abs/2510.06235

Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
We present our submission to the Algonauts 2025 Challenge, where the goal is to predict fMRI brain responses to movie stimuli. Our approach integrates multimodal representations from large language models, video encoders, audio models, and vision-language models, combining both off-the-shelf and fine-tuned variants. To improve performance, we enhanced textual inputs with detailed transcripts and summaries, and we explored stimulus-tuning and fine-tuning strategies for language and vision models…

Farmers' Almanac, first printed in 1818, says its 2026 edition will be its last, citing financial challenges; access to the online version will end in December (Associated Press)
https://apnews.com/article/farmers-almanac-ceases-publication-…

Farmers' Almanac to cease publication after 2 centuries of predicting weather
A 208-year-old publication that farmers, gardeners and others keen to predict the weather have relied on for guidance will be publishing for the final time. Farmers’ Almanac said Thursday that its 2026 edition will be its last, citing the growing financial challenges of producing and distributing the book in today’s “chaotic media environment.” Access to the online version will cease next month. The Maine-based publication was first printed in 1818. It's distinct from the Old Farmer’s…

🚨 New SciFi story out by the excellent Pippa Goldschmidt, free and sponsored by the *National Academy of Sciences*
https://issues.org/futuretensefiction/the-way-out-pippa-goldschmidt/

The Way Out | Future Tense Fiction
Can animal behavior predict extreme weather? Pippa Goldschmidt’s story imagines a future in which analysts anticipate climate catastrophes by monitoring bats.

Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding
Haomiao Chen, Keith W Jamison, Mert R. Sabuncu, Amy Kuceyeski
https://arxiv.org/abs/2510.07342

Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding
Neural encoding models aim to predict fMRI-measured brain responses to natural images. fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain. However, conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent outputs. This removes spatial context, discards anatomical information, and ties each model to a subject-specific voxel grid. We introduce the Neural Response Function (NRF), a fra…

Wind farm layout optimization using a novel machine learning approach
Mehrshad Gholami Anjiraki, Christian Santoni, Samin Shapourmiandouab, Hossein Seyedzadeh, Jonathan Craig, Fotis Sotiropoulos, Ali Khosronejad
https://arxiv.org/abs/2509.07868

Wind farm layout optimization using a novel machine learning approach
We present a novel approach to optimize wind farm layouts for maximum annual energy production (AEP). The optimization effort requires efficient wake models to predict the wake flow and, subsequently, the power generation of wind farms with reasonable accuracy and low computational cost. Wake flow predictions using large-eddy simulation (LES) ensure high fidelity, while reduced-order models, e.g., the Gaussian-curl hybrid (GCH), provide computational efficiency. We integrate LES results and the…

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.

SViM3D: Stable Video Material Diffusion for Single Image 3D Generation
Andreas Engelhardt, Mark Boss, Vikram Voletti, Chun-Han Yao, Hendrik P. A. Lensch, Varun Jampani
https://arxiv.org/abs/2510.08271 …

SViM3D: Stable Video Material Diffusion for Single Image 3D Generation
We present Stable Video Materials 3D (SViM3D), a framework to predict multi-view consistent physically based rendering (PBR) materials, given a single image. Recently, video diffusion models have been successfully used to reconstruct 3D objects from a single image efficiently. However, reflectance is still represented by simple material models or needs to be estimated in additional steps to enable relighting and controlled appearance edits. We extend a latent video diffusion model to output spa…

Learning What's Missing: Attention Dispersion and EMA Stabilization in Length Generalization
P\'al Zs\'amboki, Benjamin Levi, David Ansel Josef Smith, Mitansh Kagalwala, Arlington Kell, Samuel Liechty, Cong Wang
https://arxiv.org/abs/2510.08341

Learning What's Missing: Attention Dispersion and EMA Stabilization in Length Generalization
We study length generalization in transformers through the set complement task, where a model must predict a uniform distribution over tokens absent from an input sequence -- an ability central to board-game style reasoning. Our main theoretical result establishes two statements. First, we prove tight bounds on embedding and value dimensions for single-layer attention-only transformers. Second, we show that if such a model achieves balanced logit displacement at lengths 1 and 2, then it must ge…

Bayesian Portfolio Optimization by Predictive Synthesis
Masahiro Kato, Kentaro Baba, Hibiki Kaibuchi, Ryo Inokuchi
https://arxiv.org/abs/2510.07180 https://

Bayesian Portfolio Optimization by Predictive Synthesis
Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up the portfolio. However, such distribution information is usually unknown to investors. Various methods have been proposed to estimate distribution information, but their accuracy greatly depends on the uncertainty of the financial markets. Due to this uncertainty, a model that could well predict the distribution infor…

Exploring the nature of the emergent gauge field in composite-fermion metals: A large-scale microscopic study
Amogh Anakru, Mytraya Gattu, Ajit C. Balram, Xiao-Chuan Wu, Prashant Kumar, Zhen Bi, J. K. Jain
https://arxiv.org/abs/2509.07151

Exploring the nature of the emergent gauge field in composite-fermion metals: A large-scale microscopic study
Field theories of the composite-fermion (CF) metal model it as a Fermi sea of composite fermions coupled to an emergent gauge field. Within a random phase approximation, these theories predict that the Landau damping of the gauge field resulting from its coupling to the low-energy, long-wavelength CF particle-hole excitations modifies the electrons' density-density correlation function related to the static structure factor $S(q)$ at wave vector $q$. This produces a non-analytic correction $\pr…

The Safety Challenge of World Models for Embodied AI Agents: A Review
Lorenzo Baraldi, Zifan Zeng, Chongzhe Zhang, Aradhana Nayak, Hongbo Zhu, Feng Liu, Qunli Zhang, Peng Wang, Shiming Liu, Zheng Hu, Angelo Cangelosi, Lorenzo Baraldi
https://arxiv.org/abs/2510.05865

The Safety Challenge of World Models for Embodied AI Agents: A Review
The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental dynamics. In this context, World Models (WMs) have been introduced to provide embodied agents with the abilities to anticipate future environmental states and fill in knowledge gaps, thereby enhancing agents' ability to plan and execute actions. However, when dealing with embodied agents it is fundamental to ensure t…

Proposal for Forward Brillouin Inter-Modal Scattering in Non-suspended Lithium Niobate Waveguides at Visible Wavelengths
Jia-Lin Chen, Yuan-Hao Yang, Zheng-Xu Zhu, Jia-Qi Wang, Xin-Biao Xu, Ming Li, Zheng-Fu Han, Guang-Can Guo, Wei Chen, Chang-Ling Zou
https://arxiv.org/abs/2510.08170

Proposal for Forward Brillouin Inter-Modal Scattering in Non-suspended Lithium Niobate Waveguides at Visible Wavelengths
Thin-film lithium niobate on sapphire provides an excellent platform for simultaneously confining acoustic and optical modes without suspended structures, enabling efficient acousto-optic modulation through strong piezoelectric coupling. Here, we identify the challenges in realizing the forward Brillouin interaction at visible wavelengths, and overcome the limitation by introducing a quasi-phase-matching scheme through periodic waveguide width modulation. We predict a complete inter-modal optic…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predict...
Robert Scholz, Kunal Bagga, Christine Ahrends, Carlo Alberto Barbano

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Wencong Yang, Haoyu Ji, Leo Lonzarich, Yalan Song, Chaopeng Shen
https://arxiv.org/abs/2510.08488

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Hourly predictions are critical for issuing flood warnings as the flood peaks on the hourly scale can be distinctly higher than the corresponding daily ones. Currently a popular hourly data-driven prediction scheme is multi-time-scale long short-term memory (MTS-LSTM), yet such models face challenges in probabilistic forecasts or integrating observations when available. Diffusion artificial intelligence (AI) models represent a promising method to predict high-resolution information, e.g., hourl…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Harshit Goyal
https://arxiv.org/abs/2510.05127 https://arxiv.org/pdf/2510.…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Efficient resource allocation is a key challenge in modern cloud computing. Over-provisioning leads to unnecessary costs, while under-provisioning risks performance degradation and SLA violations. This work presents an artificial intelligence approach to predict resource utilization in big data pipelines using Random Forest regression. We preprocess the Google Borg cluster traces to clean, transform, and extract relevant features (CPU, memory, usage distributions). The model achieves high predi…

On Information Transfer and Symmetry Breaking in Dynamical Systems
Subhrajit Sinha, Parvathi Kooloth
https://arxiv.org/abs/2510.03572 https://arxiv.org/pdf…

On Information Transfer and Symmetry Breaking in Dynamical Systems
In this work, we propose a novel approach to investigate symmetry breaking in dynamical systems. In particular, we use the notion of information transfer, which provides a measure of the entropy transfer between any pair of observables in a system, to predict symmetry breaking. We show that as a system loses symmetry, its trajectories \emph{slow down} and its Shannon entropy increases. This connection enables us to use information transfer to predict the onset of symmetry breaking. Furthermore,…

PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk
Diego Fajardo-Rojas, Levente Baljer, Jordina Aviles Verdera, Megan Hall, Daniel Cromb, Mary A. Rutherford, Lisa Story, Emma C. Robinson, Jana Hutter
https://arxiv.org/abs/2509.07042

PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk
Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and impede optimal care. In this study, a dual-branch deep learning architecture (PUUMA) was developed to predict gestational age (GA) at birth using T2* fetal MRI data from 295 pregnancies, encompassing a heterogeneous and imbalanced population. The model integrates both global whole-uterus and local placental features. I…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
Jonathan Teagan
https://arxiv.org/abs/2509.07813 https://arxiv.org/pdf/2509…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning …

After tonight's election results, I predict a lot of GOP retirement announcements as the mid-terms approach.

Photoluminescence excitation spectroscopy of quantum wire-like dislocation states in ZnS
Alexander Blackston, Alexandra Fonseca Montenegro, Sevim Polat Genlik, Maryam Ghazisaeidi, Roberto C. Myers
https://arxiv.org/abs/2510.05357

Photoluminescence excitation spectroscopy of quantum wire-like dislocation states in ZnS
Recent \textit{ab initio} calculations predict 1D dispersive electronic bands confined to the atomic scale cores of dislocations in the wide bandgap (3.84 eV) semiconductor ZnS. We test these predictions by correlating sub-bandgap optical transitions with the density of dislocations formed during strain relaxation in epitaxial ZnS grown on GaP. The densities for four predicted partial dislocations are quantified using scanning electron microscopy-based electron channeling contrast imaging. Room…

Heptapod: Language Modeling on Visual Signals
Yongxin Zhu, Jiawei Chen, Yuanzhe Chen, Zhuo Chen, Dongya Jia, Jian Cong, Xiaobin Zhuang, Yuping Wang, Yuxuan Wang
https://arxiv.org/abs/2510.06673

Heptapod: Language Modeling on Visual Signals
We introduce Heptapod, an image autoregressive model that adheres to the foundational principles of language modeling. Heptapod employs \textbf{causal attention}, \textbf{eliminates reliance on CFG}, and \textbf{eschews the trend of semantic tokenizers}. Our key innovation is \textit{next 2D distribution prediction}: a causal Transformer with reconstruction-focused visual tokenizer, learns to predict the distribution over the entire 2D spatial grid of images at each timestep. This learning obje…

Emergent Directedness in Social Contagion
Fabian Tschofenig, Douglas Guilbeault
https://arxiv.org/abs/2510.06012 https://arxiv.org/pdf/2510.06012

Emergent Directedness in Social Contagion
An enduring challenge in contagion theory is that the pathways contagions follow through social networks exhibit emergent complexities that are difficult to predict using network structure. Here, we address this challenge by developing a causal modeling framework that (i) simulates the possible network pathways that emerge as contagions spread and (ii) identifies which edges and nodes are most impactful on diffusion across these possible pathways. This yields a surprising discovery. If people r…

Modulation Discovery with Differentiable Digital Signal Processing
Christopher Mitcheltree, Hao Hao Tan, Joshua D. Reiss
https://arxiv.org/abs/2510.06204 https://

Modulation Discovery with Differentiable Digital Signal Processing
Modulations are a critical part of sound design and music production, enabling the creation of complex and evolving audio. Modern synthesizers provide envelopes, low frequency oscillators (LFOs), and more parameter automation tools that allow users to modulate the output with ease. However, determining the modulation signals used to create a sound is difficult, and existing sound-matching / parameter estimation systems are often uninterpretable black boxes or predict high-dimensional framewise …

A finite element model for thermomechanical stress-strain fields in transversely isotropic strain-limiting materials
Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah
https://arxiv.org/abs/2510.05704

A finite element model for thermomechanical stress-strain fields in transversely isotropic strain-limiting materials
This paper presents a comprehensive computational framework for investigating thermo-elastic fracture in transversely isotropic materials, where classical linear elasticity fails to predict physically realistic behavior near stress concentrations. We address the challenge of unphysical strain singularities at crack tips by employing a strain-limiting theory of elasticity. This theory is characterized by an algebraically nonlinear constitutive relationship between stress and strain, which intrin…

Crosslisted article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predict...
Robert Scholz, Kunal Bagga, Christine Ahrends, Carlo Alberto Barbano

A Data-Guided Coalescence Model for Light Nuclei and Hypernuclei: Validation and Predictions
Yue Hang Leung, Yingjie Zhou, Norbert Herrmann
https://arxiv.org/abs/2510.06758 http…

A Data-Guided Coalescence Model for Light Nuclei and Hypernuclei: Validation and Predictions
The production of light hypernuclei in relativistic heavy-ion collisions provides a unique opportunity to probe hyperon--nucleon interactions and possible three-body forces, which are central to the resolution of the hyperon puzzle in neutron star matter. In this work, we develop a data-guided coalescence framework in which the source size is extracted from proton and deuteron yields and used, together with measured proton and $Λ$ spectra, to predict the production of $A=3$ nuclei $(t,{}^{3}\r…

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

Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN
Farhad Rezazadeh, Hatim Chergui, Merouane Debbah, Houbing Song, Dusit Niyato, Lingjia Liu
https://arxiv.org/abs/2510.05255

Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN
In sixth-generation (6G) Open Radio Access Networks (O-RAN), proactive control is preferable. A key open challenge is delivering control-grade predictions within Near-Real-Time (Near-RT) latency and computational constraints under multi-timescale dynamics. We therefore cast RAN Intelligent Controller (RIC) analytics as an agentic perceive-predict xApp that turns noisy, multivariate RAN telemetry into short-horizon per-User Equipment (UE) key performance indicator (KPI) forecasts to drive antici…

Self-Speculative Masked Diffusions
Andrew Campbell, Valentin De Bortoli, Jiaxin Shi, Arnaud Doucet
https://arxiv.org/abs/2510.03929 https://arxiv.org/pdf/2…

Self-Speculative Masked Diffusions
We present self-speculative masked diffusions, a new class of masked diffusion generative models for discrete data that require significantly fewer function evaluations to generate samples. Standard masked diffusion models predict factorized logits over currently masked positions. A number of masked positions are then sampled, however, the factorization approximation means that sampling too many positions in one go leads to poor sample quality. As a result, many simulation steps and therefore n…

Resolved Profiles of Stellar Mass, Star Formation Rate, and Predicted CO-to-H$_2$ Conversion Factor Across Thousands of Local Galaxies
Jiayi Sun, Yu-Hsuan Teng, I-Da Chiang, Adam K. Leroy, Karin Sandstrom, Jakob den Brok, Alberto D. Bolatto, Jeremy Chastenet, Ryan Chown, Annie Hughes, Eric W. Koch, Thomas G. Williams
https://arxiv.org/abs/…

Resolved Profiles of Stellar Mass, Star Formation Rate, and Predicted CO-to-H$_2$ Conversion Factor Across Thousands of Local Galaxies
We present radial profiles of surface brightness in UV and IR bands, estimate stellar mass surface density ($Σ_\star$) and star formation rate surface density ($Σ_\mathrm{SFR}$), and predict the CO-to-H$_2$ conversion factor ($α_\mathrm{CO}$) for over 5,000 local galaxies with stellar mass $M_\star\,{\geq}\,10^{9.3}\rm\,M_\odot$. We build these profiles and measure galaxy half-light radii using GALEX and WISE images from the $z$0MGS program, with special care given to highly inclined galaxie…

Overlap-aware segmentation for topological reconstruction of obscured objects
J. Schueler, H. M. Ara\'ujo, S. N. Balashov, J. E. Borg, C. Brew, F. M. Brunbauer, C. Cazzaniga, A. Cottle, D. Edgeman, C. D. Frost, F. Garcia, D. Hunt, M. Kastriotou, P. Knights, H. Kraus, A. Lindote, M. Lisowska, D. Loomba, E. Lopez Asamar, P. A. Majewski, T. Marley, C. McCabe, L. Millins, R. Nandakumar, T. Neep, F. Neves, K. Nikolopoulos, E. Oliveri, A. Roy, T. J. Sumner, E. Tilly, W. Thompson, M. A. V…

Overlap-aware segmentation for topological reconstruction of obscured objects
The separation of overlapping objects presents a significant challenge in scientific imaging. While deep learning segmentation-regression algorithms can predict pixel-wise intensities, they typically treat all regions equally rather than prioritizing overlap regions where attribution is most ambiguous. Recent advances in instance segmentation show that weighting regions of pixel overlap in training can improve segmentation boundary predictions in regions of overlap, but this idea has not yet be…

Giant and robust Josephson diode effect in multiband topological nanowires
Bao-Zong Wang, Zi-Kai Li, Zhong-Da Li, Xiong-Jun Liu
https://arxiv.org/abs/2510.05772 https://

Giant and robust Josephson diode effect in multiband topological nanowires
We theoretically predict the giant and robust Josephson diode effect in quasi-one-dimensional topological Majorana nanowires in the regime with multiple subbands, which is expected to be relevant for the real experiment. In the multiband regime, the Majorana bound states and conventional Andreev bound states can naturally coexist, and respectively contribute to the fractional and conventional parts in the Josephson effect, with the former/latter having 4$π$/2$π$-periodicity. We show that the …

"Sunlight worsens wildfire smoke pollution, study finds"
#Climate #ClimateChange #Wildfires

Sunlight worsens wildfire smoke pollution, study finds
Wildfire smoke causes more air pollution than current atmospheric models can predict. A new study by researchers at King Abdullah University of Science and Technology (KAUST) and the Chinese Academy of Sciences explains why by revealing that, under sunlight, wildfire smoke particles act like tiny chemical factories, producing harmful oxidants such as peroxides, a group of highly reactive pollutants contributing to smog and haze.

Analytic timing calculations and timing limits with prompt photons, high-aspect-ratio crystals, and complex TOF-kernels in TOF-PET
Nicolaus Kratochwil, Emilie Roncali, Gerard Arino-Estrada
https://arxiv.org/abs/2510.05461

Analytic timing calculations and timing limits with prompt photons, high-aspect-ratio crystals, and complex TOF-kernels in TOF-PET
Modeling the timing performance of light-based radiation detectors accurately is essential for optimizing time-of-flight positron emission tomography (TOF-PET). We present an analytic framework that combines existing models to predict the timing behavior of high-aspect ratio crystals, including contributions from prompt photons such as Cherenkov radiation. This framework is built on a closed-form solution for optical light transport, convolved with the photodetector response and photon producti…

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Mohammad Zana Majidi, Sajjad Karimi, Teng Wang, Robert Kluger, Reginald Souleyrette
https://arxiv.org/abs/2509.07845

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Predicting injuries and fatalities in traffic crashes plays a critical role in enhancing road safety, improving emergency response, and guiding public health interventions. This study investigates the added value of unstructured crash narratives (written by police officers at the scene) when combined with structured crash data to predict injury severity. Two widely used Natural Language Processing (NLP) techniques, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, were employed t…

LLM-Enhanced, Data-Driven Personalized and Equitable Clinician Scheduling: A Predict-then-Optimize Approach
Anjali Jha, Wanqing Chen, Maxim Eckmann, Ian Stockwell, Jianwu Wang, Kai Sun
https://arxiv.org/abs/2510.02047

LLM-Enhanced, Data-Driven Personalized and Equitable Clinician Scheduling: A Predict-then-Optimize Approach
Clinician scheduling remains a persistent challenge due to limited clinical resources and fluctuating demands. This complexity is especially acute in large academic anesthesiology departments as physicians balance responsibilities across multiple clinical sites with conflicting priorities. Further, scheduling must account for individual clinical and lifestyle preferences to ensure job satisfaction and well-being. Traditional approaches, often based on statistical or rule-based optimization mode…

Signatures of superconducting Higgs mode in irradiated Josephson junctions
Aritra Lahiri, Juan Carlos Cuevas, Bj\"orn Trauzettel
https://arxiv.org/abs/2510.05730 https://…

Signatures of superconducting Higgs mode in irradiated Josephson junctions
The Higgs mode, originally proposed in the context of superconductivity, corresponds to oscillations of the amplitude of the superconducting order parameter. Recent THz-domain optical studies have found signatures consistent with the Higgs mode, but its unambiguous detection is still challenging. We predict that the existence of the Higgs mode can be unambiguously revealed by standard measurements of the transport characteristics in microwave-irradiated asymmetric and transparent Josephson junc…

Statistical properties of homoclinic bursting: an approach using infinite-modal maps toward predicting extreme events
Masaki Nakagawa
https://arxiv.org/abs/2509.07487 https://…

Statistical properties of homoclinic bursting: an approach using infinite-modal maps toward predicting extreme events
Predicting extreme events in nonlinear dynamical systems is challenging due to a limited understanding of their statistical properties. This study numerically and theoretically investigates the statistical properties of infinite-modal maps arising from homoclinic bursting to predict extreme events. The numerical investigation presents bifurcation diagrams, Lyapunov exponents, height probability distributions, and interevent interval probability distributions for infinite-modal maps. The theoret…

Strengthening legal protection against discrimination by algorithms and artificial intelligence
Frederik J. Zuiderveen Borgesius
https://arxiv.org/abs/2510.02859 https://…

Strengthening legal protection against discrimination by algorithms and artificial intelligence
Algorithmic decision-making and other types of artificial intelligence (AI) can be used to predict who will commit crime, who will be a good employee, who will default on a loan, etc. However, algorithmic decision-making can also threaten human rights, such as the right to non-discrimination. The paper evaluates current legal protection in Europe against discriminatory algorithmic decisions. The paper shows that non-discrimination law, in particular through the concept of indirect discriminatio…

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
Faduo Liang, Yunfeng Yang, Shi-Lu Dai
https://arxiv.org/abs/2510.02885

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently me…

Neural Forward Filtering for Speaker-Image Separation
Jingqi Sun, Shulin He, Ruizhe Pang, Zhong-Qiu Wang
https://arxiv.org/abs/2510.05757 https://arxiv.org…

Neural Forward Filtering for Speaker-Image Separation
We address monaural multi-speaker-image separation in reverberant conditions, aiming at separating mixed speakers but preserving the reverberation of each speaker. A straightforward approach for this task is to directly train end-to-end DNN systems to predict the reverberant speech of each speaker based on the input mixture. Although effective, this approach does not explicitly exploit the physical constraint that reverberant speech can be reproduced by convolving the direct-path signal with a …

NFL Week 10 picks: Our experts face off on Steelers vs. Chargers, Patriots vs. Buccaneers and more

https://www.cbssports.com/nfl/news/nfl-week-10-bet…

Can LLMs Hit Moving Targets? Tracking Evolving Signals in Corporate Disclosures
Chanyeol Choi, Jihoon Kwon, Minjae Kim
https://arxiv.org/abs/2510.03195 https://

Can LLMs Hit Moving Targets? Tracking Evolving Signals in Corporate Disclosures
Moving targets -- managers' strategic shifting of key performance metrics when the original targets become difficult to achieve -- have been shown to predict subsequent stock underperformance. However, our work reveals that the method employed in that study exhibits two key limitations that hinder the accuracy -- noise in the extracted targets and loss of contextual information -- both of which stem primarily from the use of a named entity recognition (NER). To address these two limitations, we…

What are you going to do when the regime falls? After calling all your friends, after the great memes, after the parties, what are you going to do to make sure it never happens again? What world should we create?
Taxing billionaires is great and all, but we could build systems where billionaires are impossible. Is hoarding wealth and using it to control people even something we should consider part of a functional and humane system? Any system where one group of people doesn't have rights means that anyone can be stripped of their rights, like has happened with all the US citizens who've been illegally detained and deported by ICE. Does the concept of "rights" that must be defended with violence, that can be stripped away by people who can exercise more violence, even make sense? Or should the bedrock of a functional system be the obligations that we have to each other and to society, that cannot be severed or taken from us, that tell us we *must* defend regardless of whether systemic oppression will impact us or not?
Americans have been so restricted by the limitations of the two party system, only able to choose between options acceptable to different sections of the capitalist class. Would we even be able to imagine what we could do if those restrictions went away?
The fall of the Berlin wall was a surprise. The fall of Assad was faster than anyone expected. One day the government of Nepal was an unrepentant oligarchy, the next it was on fire. Everything can change in an instant, faster than anyone expects. No one can predict revolutionary change. Will you be ready if the opportunity presents itself?
The US cannot be fixed. The economic system is a ponzi scheme that has been patched again and again, but has finally run out of options. Racism, sexism, and Christian nationalism are baked into the system at every level. Trump gutted the system of soft power that held the US economy together, now there is only a slow decline. Even after he's gone, the damage is done. Once we let go of how to fix something that cannot be fixed, we can start to imagine something that cannot be achieved within the current system.
This is a time of opportunity. Do not burrow so deep in terror that you miss your chance to dream.
#USPol

The challenging first direct measurement of the 65 keV resonance strength in the $^{17}$O(p,$\gamma$)$^{18}$F reaction at LUNA
D. Piatti, LUNA Collaboration
https://arxiv.org/abs/2510.04597

The challenging first direct measurement of the 65 keV resonance strength in the $^{17}$O(p,$γ$)$^{18}$F reaction at LUNA
A precise determination of the proton capture rates on oxygen is mandatory to predict the abundance ratios of oxygen isotopes in a stellar environment where the hydrogen burning is active. The 17O(p,γ)18F reaction, in particular, plays a crucial role in AGB nucleosynthesis as well as in explosive hydrogen burning occurring in novae and type I supernovae. At the temperature of interest for the former scenario (from 20 MK to 80 MK) the main contribution to the astrophysical reaction rate comes f…

Cation vacancies mediate thermochemical water splitting with iron aluminates
Nathan J. Szymanski, Kent J. Warren, Alan W. Weimer, Christopher J. Bartel
https://arxiv.org/abs/2510.05328

Cation vacancies mediate thermochemical water splitting with iron aluminates
Solar thermochemical water splitting enables hydrogen production by cycling metal oxides between reduced and oxidized states, typically through an oxygen vacancy mechanism. However, recent experimental work suggests that cation vacancies have a greater influence on the redox behavior of iron aluminate spinels used in water splitting. This remains debated, as calculations predict that such cation vacancies are thermodynamically unfavorable. In the current work, we show that Fe vacancies in (Fe$�…

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/

Door Dasher Life Fun Facts: This might be useful to think of when you're rating your Dasher.... Who's at fault in this situation?
Let's say I accept an offer for McDonalds.
I head straight there... pull up to the store, and as I walk in the app buzzes again with a 2nd order for the same McDs.
Naturally, I accept. (not accepting reduces my Acceptance rate and potentially my ability to get new offers).
Now I have two orders at the same place! Great! Efficient!
The first order comes up quickly, I put it in my bag, 'pick it up' in the app, and wait for the 2nd order.
Now the fun begins. The restaurant says the 2nd order will be 10 minutes beyond the pickup time. I notify the app, and both customers of the wait... and I wait... and wait... if I remove the 2nd order from my app it affects my completion rate *and* means the customer has to wait for another dasher to come along.
Eventually the 2nd order is ready and I head out to make the deliveries. Both of course now late.. the first potentially cold (even in an insulated bag) *and* late.
In addition, sometimes, often a day or two later, the dasher will receive a “Contract Violation” for the lateness. 3 Contract Violations in a short period and you can be deactivated.
So... there ya go. It happens. I'm never sure what the best course of action is... unless both orders are immediately ready, which you can never predict, someone will end up losing; the store, customer, dasher, or a mix of all three.
#DasherLife #GigWork #DoorDash #Uber
P.S. It's worth noting that Door Dash is the only service I know of that is this strict. Uber Eats does not have mechanisms like this (at least in my experience). Never used Skip, so can't say on that.

Constraints on Radial Gas Flows in the Milky Way Disk Revealed by Large Stellar Age Catalogs
James W. Johnson
https://arxiv.org/abs/2510.05223 https://arxi…

Constraints on Radial Gas Flows in the Milky Way Disk Revealed by Large Stellar Age Catalogs
Disk galaxies like the Milky Way are expected to experience gas flows carrying matter toward their centers. This paper investigates the role of these radial gas flows in models of Galactic chemical evolution (GCE). We follow five different parameterizations of the Galactocentric radial velocity, $v_{r,g}$, of the interstellar medium (ISM). Relative to the $v_{r,g}=0$ limit, all models predict stellar metallicity to decline less significantly with age in the outer disk and more significantly in …

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.

"…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…

Taking a SEAT: Predicting Value Interpretations from Sentiment, Emotion, Argument, and Topic Annotations
Adina Nicola Dobrinoiu, Ana Cristiana Marcu, Amir Homayounirad, Luciano Cavalcante Siebert, Enrico Liscio
https://arxiv.org/abs/2510.01976

Taking a SEAT: Predicting Value Interpretations from Sentiment, Emotion, Argument, and Topic Annotations
Our interpretation of value concepts is shaped by our sociocultural background and lived experiences, and is thus subjective. Recognizing individual value interpretations is important for developing AI systems that can align with diverse human perspectives and avoid bias toward majority viewpoints. To this end, we investigate whether a language model can predict individual value interpretations by leveraging multi-dimensional subjective annotations as a proxy for their interpretive lens. That i…

HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
Junggu Choi, Junho Lee, Kyle L. Jung, Jae U. Jung
https://arxiv.org/abs/2510.01993 …

HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
In this study, we propose a quantum convolutional neural network (QCNN)-based framework with the neural quantum embedding (NQE) to predict HIV-1 protease cleavage sites in amino acid sequences from viral and human proteins. To assess the effectiveness and robustness of our framework, we compared the classification performance against classical neural networks under both noiseless and noisy simulations. Among experimental conditions, the QCNN with the angle and amplitude encoding NQE conditions …

Authentication Security of PRF GNSS Ranging
Jason Anderson
https://arxiv.org/abs/2510.02196 https://arxiv.org/pdf/2510.02196…

Authentication Security of PRF GNSS Ranging
This work derives the authentication security of pseudorandom function (PRF) GNSS ranging under multiple GNSS spoofing models, including the Security Code Estimation and Replay (SCER) spoofer. When GNSS ranging codes derive from a PRF utilizing a secret known only to the broadcaster, the spoofer cannot predict the ranging code before broadcast. Therefore, PRF ranging can be used to establish trust in the GNSS pseudoranges and the resulting receiver position, navigation, and timing (PNT) solutio…

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

🚬 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.

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…

Scarce Data, Noisy Inferences, and Overfitting: The Hidden Flaws in Ecological Dynamics Modelling
Mario Castro, Rafael Vida, Javier Galeano, Jos\'e A. Cuesta
https://arxiv.org/abs/2510.03718

Scarce Data, Noisy Inferences, and Overfitting: The Hidden Flaws in Ecological Dynamics Modelling
Metagenomic data has significantly advanced microbiome research by employing ecological models, particularly in personalised medicine. The generalised Lotka-Volterra (gLV) model is commonly used to understand microbial interactions and predict ecosystem dynamics. However, gLV models often fail to capture complex interactions, especially when data is limited or noisy. This study critically assesses the effectiveness of gLV and similar models using Bayesian inference and a model reduction method …

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

Predicting every NFL team's remaining schedule: Chiefs edge Broncos for AFC West crown; Packers win NFC North

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

The evidence that Sect'y of Defense Hogsbreath has committed war crimes and murder is accumulating rapidly.
He will, of course, try to put the blame on an admiral - who will give evidence that he had been ordered to do the dirty deeds.
And we can predict with almost certainty that El Cheato will issue a pardon.
But that still leaves the Congressional power to impeach Hogsbreath. And it leaves open the door for civil actions - such as wrongful death actions by the famil…

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

MEGATRON: Reproducing the Diversity of High-Redshift Galaxy Spectra with Cosmological Radiation Hydrodynamics Simulations
Harley Katz, Martin P. Rey, Corentin Cadiou, Oscar Agertz, Jeremy Blaizot, Alex J. Cameron, Nicholas Choustikov, Julien Devriendt, Uliana Hauk, Gareth C. Jones, Taysun Kimm, Isaac Laseter, Sergio Martin-Alvarez, Kosei Matsumoto, Autumn Pearce, Francisco Rodr\'iguez Montero, Joki Rosdahl, Mahsa Sanati, Aayush Saxena, Adrianne Slyz, Richard Stiskalek, Anatole Stor…

MEGATRON: Reproducing the Diversity of High-Redshift Galaxy Spectra with Cosmological Radiation Hydrodynamics Simulations
We present the MEGATRON suite of cosmological radiation hydrodynamics simulations following the formation of Milky Way-mass galaxies from the earliest cosmic epochs when Population III stars form to Cosmic Noon. The suite represents the first set of cosmological simulations that couples a vast non-equilibrium thermochemistry network of primordial species, metals, and molecules to multifrequency, on-the-fly radiation transport, allowing us to directly predict the spectral properties of early gal…

Self-Image Multiplicity in a Concave Cylindrical Mirror
Thach A. Nguyen, Kaitlyn S. Yasumura, Duy V. Tran, Trung V. Phan
https://arxiv.org/abs/2510.04370 https://

Self-Image Multiplicity in a Concave Cylindrical Mirror
Concave mirrors are fundamental optical elements, yet some easily observed behaviors are rarely addressed in standard textbooks, such as the formation of multiple reflected images. Here we investigate self-imaging -- where the observer is also the observed object -- using a concave cylindrical mirror. We predict the number of self-images visible from different observation points and classify space into regions by image count. We then test these predictions with an inexpensive stainless-steel co…

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…

Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
Jayanta Mandi, Marianne Defresne, Senne Berden, Tias Guns
https://arxiv.org/abs/2510.04951

Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages -- the prediction of the unknown parameters from contextual information and the subsequent optimization using those predicted parameters. Decision-focused learning (DFL) implements the first stage by training a machine learning (ML) model to optimize the quality of the decisions made using the predicted parameters. When parameters in the…

"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.

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...

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…

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

MICROTRIPS: MICRO-geography TRavel Intelligence and Pattern Synthesis
Yangyang Wang, Tayo Fabusuyi
https://arxiv.org/abs/2510.05080 https://arxiv.org/pdf/2…

MICROTRIPS: MICRO-geography TRavel Intelligence and Pattern Synthesis
This study presents a novel small-area estimation framework to enhance urban transportation planning through detailed characterization of travel behavior. Our approach improves on the four-step travel model by employing publicly available microdata files and machine learning methods to predict travel behavior for a representative, synthetic population at small geographic areas. This approach enables high-resolution estimation of trip generation, trip distribution, mode choice, and route assignm…

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.

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 …

Signatures of Galactic Expansion in Gaia DR3: Implications for the JWST Early-Galaxy Puzzle
G. S. Karapetian, A. P. Mahtessian, L. E. Byzalov, M. A. Hovhannisyan, L. A. Mahtessian
https://arxiv.org/abs/2510.03688

Signatures of Galactic Expansion in Gaia DR3: Implications for the JWST Early-Galaxy Puzzle
Recent observations with the James Webb Space Telescope (JWST) of massive galaxies at ages below 1 Gyr pose a challenge to standard models of galaxy formation, which predict significantly longer assembly timescales. One possible explanation is that active galactic nuclei (AGN) drive large scale outflows that accelerate galaxy growth. To test this scenario in the local Universe, we analyzed Gaia DR3 data for stars within 5 kpc of the Galactic center, computing galactocentric radial velocities (v…

NFL Week 14 picks: Our experts face off on Cowboys at Lions, Joe Burrow vs. Josh Allen, other crucial matchups

https://www.cbssports.com/nfl/news/nfl-week-14-expert-picks-predictions-a…

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…

INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models
Ulas Berk Karli, Ziyao Shangguan, Tesca FItzgerald
https://arxiv.org/abs/2510.01389

INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models
Recent Vision-Language-Action (VLA) models show strong generalization capabilities, yet they lack introspective mechanisms for anticipating failures and requesting help from a human supervisor. We present \textbf{INSIGHT}, a learning framework for leveraging token-level uncertainty signals to predict when a VLA should request help. Using $π_0$-FAST as the underlying model, we extract per-token \emph{entropy}, \emph{log-probability}, and Dirichlet-based estimates of \emph{aleatoric and epistemi…

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 …

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Muhammad Usama, Haris Koutsopoulos
https://arxiv.org/abs/2510.03121

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway…

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Sarvesh Chaudhari, Cristobal Mendez, Rushil Choudhary, Tathagata Banerjee, Maciej Olszewski, Jadrien Paustian, Jaehong Choi, Zhaslan Baraissov, Raul Hernandez, David Muller, Britton Plourde, Gregory Fuchs, Valla Fatemi, Tomas Arias

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide forma…

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

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…

Self-lensing binaries in globular clusters - predictions for ELT
Grzegorz Wiktorowicz, Matthew Middleton, Mirek Giersz, Adam Ingram, Adam McMaster, Abbas Askar, Lucas Hellstr\"om
https://arxiv.org/abs/2510.02751

Self-lensing binaries in globular clusters - predictions for ELT
Self-lensing (SL) represents a powerful technique for detecting compact objects in binary systems through gravitational microlensing effects, when a compact companion transits in front of its luminous partner. We present the first comprehensive study of SL probability within globular cluster (GC) environments, utilizing synthetic stellar populations from MOCCA simulations to predict detection rates for the Extremely Large Telescope (ELT). Our analysis incorporates finite-size lens effects for w…

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…

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/

Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Ethan Baron, Boris Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova
https://arxiv.org/abs/2510.02224

Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step lookahead predictions to improve the quality and efficiency of multi-step forecasts. However, these models only predict independent marginal distributions for each time step, rather than a full joint predictive distribution. To generate forecast sample paths with realistic correlation structures, one typically resorts to …

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

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…