Tootfinder

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

🚬 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.

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

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/

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 …

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.

💣 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.

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…

A survey of 700 Hollywood insiders: 52% think Netflix will buy WBD, 80% say they use AI in their work, 30% predict Josh D'Amaro as Disney's next CEO, and more (Lucas Shaw/Bloomberg)
https://www.bloomberg.com/news/newsletters/2025-12-21/…

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…

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…

"…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…

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/

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 <…

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-

Former Cowboys Predict Eagles vs Cowboys | Players Lounge https://www.youtube.com/watch?v=RiQ2VoGdruo

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.

NFL Week 17 picks: Our experts face off on Cowboys vs. Commanders, Lions at Vikings and more

https://www.cbssports.com/nfl/news/nfl-week-17-…

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

Series A, Episode 13 - Orac
ORAC: The words future, present, past are meaningless.
AVON: Define 'meaningless'.
ORAC: I have the capacity to predict events that have not yet taken place.
AVON: That is not what I asked.
https://blake.torpidity.net/m/113/456 B7B3

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- Optimizing Text Search: A Novel Pattern Matching Algorithm Based on Ukkonen's Approach
Xinyu Guan, Shaohua Zhang
https://arxiv.org/abs/2512.16927 https://mastoxiv.page/@arXiv_csDS_bot/115762062326187898
- SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
Shravan Chaudhari, Rahul Thomas Jacob, Mononito Goswami, Jiajun Cao, Shihab Rashid, Christian Bock
https://arxiv.org/abs/2512.16956 https://mastoxiv.page/@arXiv_csSE_bot/115762248476963893
- MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
Saksham Sahai Srivastava, Haoyu He
https://arxiv.org/abs/2512.16962 https://mastoxiv.page/@arXiv_csCR_bot/115762140339109012
- Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Clas...
Faisal Ahmed
https://arxiv.org/abs/2512.16964 https://mastoxiv.page/@arXiv_eessIV_bot/115762196702065869
- Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
Wanghan Xu, et al.
https://arxiv.org/abs/2512.16969 https://mastoxiv.page/@arXiv_csAI_bot/115762050529328276
- PAACE: A Plan-Aware Automated Agent Context Engineering Framework
Kamer Ali Yuksel
https://arxiv.org/abs/2512.16970 https://mastoxiv.page/@arXiv_csAI_bot/115762054461584205
- A Women's Health Benchmark for Large Language Models
Elisabeth Gruber, et al.
https://arxiv.org/abs/2512.17028 https://mastoxiv.page/@arXiv_csCL_bot/115762049873946945
- Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Pranav Shetty, Mirazul Haque, Petr Babkin, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
https://arxiv.org/abs/2512.17075 https://mastoxiv.page/@arXiv_csCL_bot/115762077400293945
- Disentangled representations via score-based variational autoencoders
Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin
https://arxiv.org/abs/2512.17127 https://mastoxiv.page/@arXiv_statML_bot/115762251753966702
- Biosecurity-Aware AI: Agentic Risk Auditing of Soft Prompt Attacks on ESM-Based Variant Predictors
Huixin Zhan
https://arxiv.org/abs/2512.17146 https://mastoxiv.page/@arXiv_csCR_bot/115762318582013305
- Application of machine learning to predict food processing level using Open Food Facts
Arora, Chauhan, Rana, Aditya, Bhagat, Kumar, Kumar, Semar, Singh, Bagler
https://arxiv.org/abs/2512.17169 https://mastoxiv.page/@arXiv_qbioBM_bot/115762302873829397
- Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Warning
Sandeep Neela
https://arxiv.org/abs/2512.17185 https://mastoxiv.page/@arXiv_qfinRM_bot/115762275982224870
- Do Foundational Audio Encoders Understand Music Structure?
Keisuke Toyama, Zhi Zhong, Akira Takahashi, Shusuke Takahashi, Yuki Mitsufuji
https://arxiv.org/abs/2512.17209 https://mastoxiv.page/@arXiv_csSD_bot/115762341541572505
- CheXPO-v2: Preference Optimization for Chest X-ray VLMs with Knowledge Graph Consistency
Xiao Liang, Yuxuan An, Di Wang, Jiawei Hu, Zhicheng Jiao, Bin Jing, Quan Wang
https://arxiv.org/abs/2512.17213 https://mastoxiv.page/@arXiv_csCV_bot/115762574180736975
- Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Fun...
Deriyan Senjaya, Stephen Ekaputra Limantoro
https://arxiv.org/abs/2512.17245 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115762447037143855
- AlignDP: Hybrid Differential Privacy with Rarity-Aware Protection for LLMs
Madhava Gaikwad
https://arxiv.org/abs/2512.17251 https://mastoxiv.page/@arXiv_csCR_bot/115762396593872943
- Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Baolei Zhang, Minghong Fang, Zhuqing Liu, Biao Yi, Peizhao Zhou, Yuan Wang, Tong Li, Zheli Liu
https://arxiv.org/abs/2512.17254 https://mastoxiv.page/@arXiv_csCR_bot/115762402470985707
- Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling A...
Abhivansh Gupta
https://arxiv.org/abs/2512.17259 https://mastoxiv.page/@arXiv_csMA_bot/115762225538364939
- Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest
Saeed Ebrahimi, Weijie Jiang, Jaewon Yang, Olafur Gudmundsson, Yucheng Tu, Huizhong Duan
https://arxiv.org/abs/2512.17277 https://mastoxiv.page/@arXiv_csIR_bot/115762214396869930
- LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection
Ioannis Stylianou, Achintya kr. Sarkar, Nauman Dawalatabad, James Glass, Zheng-Hua Tan
https://arxiv.org/abs/2512.17281 https://mastoxiv.page/@arXiv_csSD_bot/115762361858560703
- Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose
https://arxiv.org/abs/2512.17340 https://mastoxiv.page/@arXiv_statME_bot/115762446402738033
toXiv_bot_toot

AI can be considered successful once it can accurately predict the revolution that ends the hegemony of billionairedom.

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

🇦🇹 Austria: In Vienna, the police is trialing an automated software to predict crime. Every day, the system outputs hotspots to patrol [Kurier, 3 Dec 🔒, in German]. via Algorithm Watch
#ai #privacy

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

Power Rankings: Ordering 10 remaining games on the schedule https://www.dallascowboys.com/news/power-rankings-ordering-10-remaining-games-on-the-schedule

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

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

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…

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

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

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

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

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

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

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

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

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

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

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.)

🚨 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.

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

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…

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

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

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…

Power Rankings: Ordering 10 remaining games on the schedule https://www.dallascowboys.com/news/power-rankings-ordering-10-remaining-games-on-the-schedule

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

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

"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.

@… 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

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

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

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

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

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
Maral Doctorarastoo, Katherine A. Flanigan, Mario Berg\'es, Christopher McComb
https://arxiv.org/abs/2510.10954

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which ne…

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,…

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

🧬 Largest RNA language model to date offers new way to predict behavior and boost drug discovery
#rna

Largest RNA language model to date offers new way to predict behavior and boost drug discovery
RNA plays a vital role in how our genes are expressed and how diseases develop. Yet, because RNA molecules constantly change shape, understanding how they work has long been a major scientific challenge.

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…

Temperature and conditions for thermalization after canonical quenches
Lennart Dabelow
https://arxiv.org/abs/2510.12696 https://arxiv.org/pdf/2510.12696

Temperature and conditions for thermalization after canonical quenches
We consider quenches of a quantum system that is prepared in a canonical equilibrium state of one Hamiltonian and then evolves unitarily in time under a different Hamiltonian. Technically, our main result is a systematic expansion of the pre- and post-quench canonical ensembles in the quench strength. We first demonstrate how this can be used to predict the system's temperature after the quench from equilibrium properties at the pre-quench temperature. For a thermalizing post-quench system, it …

A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?
Graham Tierney, Srikar Katta, Christopher Bail, Sunshine Hillygus, Alexander Volfovsky
https://arxiv.org/abs/2510.08758

A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?
Many social science questions ask how linguistic properties causally affect an audience's attitudes and behaviors. Because text properties are often interlinked (e.g., angry reviews use profane language), we must control for possible latent confounding to isolate causal effects. Recent literature proposes adapting large language models (LLMs) to learn latent representations of text that successfully predict both treatment and the outcome. However, because the treatment is a component of the tex…

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

A Hybrid Agent-Based and System Dynamics Framework for Modelling Project Execution and Technology Maturity in Early-Stage R&D
R. W. S. Pessoa, M. H. N{\ae}ss, J. C. Bijos, C. M. Rebello, D. Colombo, L. Schnitman, I. B. R. Nogueira
https://arxiv.org/abs/2510.09688

A Hybrid Agent-Based and System Dynamics Framework for Modelling Project Execution and Technology Maturity in Early-Stage R&D
This paper presents a hybrid approach to predict the evolution of technological maturity in R and D projects, using the oil and gas sector as an example. Integrating System Dynamics (SD) and Agent Based Modelling (ABM) allows the proposed multi level framework to capture uncertainties in work effort, team size, and project duration, which influence technological progress. While AB SD hybrid models are established in other fields, their use in R and D remains limited. The model combines system l…

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…

deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
Sankalp Gilda
https://arxiv.org/abs/2510.09362 https://…

deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning framework that utilizes a regularized, multi-task approach to predict stellar atmospheric parameters from observed spectra. We train a deep convolutional neural network on the PHOENIX synthetic spectral library and use transfer learning to fine-tune the model on a small subset of observed FGK dwarf spectra from the MARV…

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

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

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

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

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

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

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

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

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…

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

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

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

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…

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

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

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

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

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…

NFL Week 16 picks: Our experts face off on Steelers against Lions, Patriots at Ravens and more

https://www.cbssports.com/nfl/news/nfl-week-16-expert-picks-head-t…

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

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

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

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…

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…

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.

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

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

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…

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

NFL Week 16 picks: Our experts face off on Rams vs. Seahawks, Steelers at Lions, Patriots against Ravens, more

https://www.cbssports.com/nfl/news/nfl-week-16-exp…

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…

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...

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

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

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…

📷 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.

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

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

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…

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…

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

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

2026 Dallas Cowboys Draft Board: Rebuilding This Defense https://insidethestar.com/2026-dallas-cowboys-draft-board-rebuilding-this-defense

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…

Chiefs vs. Lions prediction, where to watch: Can Patrick Mahomes outduel Detroit's high-octane attack?

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

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…

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…

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…

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

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

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…