Tootfinder

Non-Existent Outcomes in Research on Inequality: A Causal Approach
Ian Lundberg, Soonhong Cho
https://arxiv.org/abs/2508.14770 https://arxiv.org/pdf/2508.1…

Non-Existent Outcomes in Research on Inequality: A Causal Approach
Scholars of social stratification often study exposures that shape life outcomes. But some outcomes (such as wage) only exist for some people (such as those who are employed). We show how a common practice -- dropping cases with non-existent outcomes -- can obscure causal effects when a treatment affects both outcome existence and outcome values. The effects of both beneficial and harmful treatments can be underestimated. Drawing on existing approaches for principal stratification, we show how …

Does the ‘hard on crime’ approach to law and order actually work?
https://thehill.com/opinion/criminal-justice/5512574-hard-on-crime-outcomes-based-model/

chess: Kaggle chess players (2010)
A network among chess players (nodes) giving the chess match outcomes (edges), for game-by-game results among the world’s top chess players. The direction of edge (i,j) denotes white player (i) and black player (j). Each edge is timestamped (approximate). Edge sign is 1 for a win by white, 0 for draw, and -1 for a win by black.
This network has 7301 nodes and 65053 edges.
Tags: Social, Offline, Signed, Timestamps

Overly academic/distanced ethical discussions
Had a weird interaction with @/brainwane@social.coop just now. I misinterpreted one of their posts quoting someone else and I think the combination of that plus an interaction pattern where I'd assume their stance on something and respond critically to that ended up with me getting blocked. I don't have hard feelings exactly, and this post is only partly about this particular person, but I noticed something interesting by the end of the conversation that had been bothering me. They repeatedly criticized me for assuming what their position was, but never actually stated their position. They didn't say: "I'm bothered you assumed my position was X, it's actually Y." They just said "I'm bothered you assumed my position was X, please don't assume my position!" I get that it's annoying to have people respond to a straw man version of your argument, but when I in response asked some direct questions about what their position was, they gave some non-answers and then blocked me. It's entirely possible it's a coincidence, and they just happened to run out of patience on that iteration, but it makes me take their critique of my interactions a bit less seriously. I suspect that they just didn't want to hear what I was saying, while at the same time they wanted to feel as if they were someone who values public critique and open discussion of tricky issues (if anyone reading this post also followed our interaction and has a different opinion of my behavior, I'd be glad to hear it; it's possible In effectively being an asshole here and it would be useful to hear that if so).
In any case, the fact that at the end of the entire discussion, I'm realizing I still don't actually know their position on whether they think the AI use case in question is worthwhile feels odd. They praised the system on several occasions, albeit noting some drawbacks while doing so. They said that the system was possibly changing their anti-AI stance, but then got mad at me for assuming this meant that they thought this use-case was justified. Maybe they just haven't made up their mind yet but didn't want to say that?
Interestingly, in one of their own blog posts that got linked in the discussion, they discuss a different AI system, and despite listing a bunch of concrete harms, conclude that it's okay to use it. That's fine; I don't think *every* use of AI is wrong on balance, but what bothered me was that their post dismissed a number of real ethical issues by saying essentially "I haven't seen calls for a boycott over this issue, so it's not a reason to stop use." That's an extremely socially conformist version of ethics that doesn't sit well with me. The discussion also ended up linking this post: https://chelseatroy.com/2024/08/28/does-ai-benefit-the-world/ which bothered me in a related way. In it, Troy describes classroom teaching techniques for introducing and helping students explore the ethics of AI, and they seem mostly great. They avoid prescribing any particular correct stance, which is important when teaching given the power relationship, and they help students understand the limitations of their perspectives regarding global impacts, which is great. But the overall conclusion of the post is that "nobody is qualified to really judge global impacts, so we should focus on ways to improve outcomes instead of trying to judge them." This bothers me because we actually do have a responsibility to make decisive ethical judgments despite limitations of our perspectives. If we never commit to any ethical judgment against a technology because we think our perspective is too limited to know the true impacts (which I'll concede it invariably is) then we'll have to accept every technology without objection, limiting ourselves to trying to improve their impacts without opposing them. Given who currently controls most of the resources that go into exploration for new technologies, this stance is too permissive. Perhaps if our objection to a technology was absolute and instantly effective, I'd buy the argument that objecting without a deep global view of the long-term risks is dangerous. As things stand, I think that objecting to the development/use of certain technologies in certain contexts is necessary, and although there's a lot of uncertainly, I expect strongly enough that the overall outcomes of objection will be positive that I think it's a good thing to do.
The deeper point here I guess is that this kind of "things are too complicated, let's have a nuanced discussion where we don't come to any conclusions because we see a lot of unknowns along with definite harms" really bothers me.

Green schoolyard investments influence local-level economic and equity outcomes through spatial-statistical modeling and geospatial analysis in urban contexts
Mahshid Gorjian
https://arxiv.org/abs/2507.14232

Green schoolyard investments influence local-level economic and equity outcomes through spatial-statistical modeling and geospatial analysis in urban contexts
Investing in urban green schoolyards is becoming more popular around the world because they could enhance health, education, and community outcomes. There is still considerable debate regarding the impact of urban green schoolyards on economic growth, equity, and community stability, particularly when analyzed using spatial-statistical and geospatial methods. The current study focuses mostly on health or individual case results and avoids looking at broader economic and equality concerns. It al…

Enumerating Vector Parking Functions and their Outcomes Based on Specified Lucky Cars
Melanie Ferreri, Pamela E. Harris, Lucy Martinez, Eric Swartz
https://arxiv.org/abs/2508.13917

Enumerating Vector Parking Functions and their Outcomes Based on Specified Lucky Cars
In a parking function, a car is considered lucky if it is able to park in its preferred spot. Extending work of Harris and Martinez, we enumerate outcomes of parking functions with a fixed set of lucky cars. We then consider a generalization of parking functions known as vector parking functions or $\boldsymbol{u}$-parking functions, in which a nonnegative integer capacity is given to each parking spot in the street. With certain restrictions on $\boldsymbol{u}$, we enumerate outcomes of $\bold…

A distressing story less told about drug overdoses- and I can't help but wonder if wider adoption of the Zurich "Four Pillars" approach might help prevent these outcomes.
It's pretty clear that current policies related to #drugs and #addiction are not doing such a great job.
…

Doubly Robust Estimation of Continuous Outcomes under Multiple Treatment Levels via GPS, CBPS, and Penalized Empirical Likelihood
Byeonghee Lee, Joonsung Kang
https://arxiv.org/abs/2509.15846

Doubly Robust Estimation of Continuous Outcomes under Multiple Treatment Levels via GPS, CBPS, and Penalized Empirical Likelihood
This paper develops a unified framework for estimating continuous outcomes under multiple treatment levels in observational studies. We integrate the Generalized Propensity Score (GPS), Covariate Balancing Propensity Score (CBPS), and outcome regression into a Penalized Empirical Likelihood (PEL) formulation. The GPS is parameterized by $\boldsymbolβ$ and denoted $π_{\boldsymbolβ}(\mathbf{X})$, while CBPS imposes moment conditions to ensure covariate balance. Outcome regression flexibly mode…

Explainable AI for Infection Prevention and Control: Modeling CPE Acquisition and Patient Outcomes in an Irish Hospital with Transformers
Minh-Khoi Pham, Tai Tan Mai, Martin Crane, Rob Brennan, Marie E. Ward, Una Geary, Declan Byrne, Brian O Connell, Colm Bergin, Donncha Creagh, Nick McDonald, Marija Bezbradica
https://arxiv.org/abs/2509.1…

Explainable AI for Infection Prevention and Control: Modeling CPE Acquisition and Patient Outcomes in an Irish Hospital with Transformers
Carbapenemase-Producing Enterobacteriace poses a critical concern for infection prevention and control in hospitals. However, predictive modeling of previously highlighted CPE-associated risks such as readmission, mortality, and extended length of stay (LOS) remains underexplored, particularly with modern deep learning approaches. This study introduces an eXplainable AI modeling framework to investigate CPE impact on patient outcomes from Electronic Medical Records data of an Irish hospital. We…

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Ruihan Zhang, Jun Sun
https://arxiv.org/abs/2508.15642 https://arxiv…

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Fairness in machine learning is more important than ever as ethical concerns continue to grow. Individual fairness demands that individuals differing only in sensitive attributes receive the same outcomes. However, commonly used machine learning algorithms often fail to achieve such fairness. To improve individual fairness, various training methods have been developed, such as incorporating fairness constraints as optimisation objectives. While these methods have demonstrated empirical effectiv…

DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
Sikai Bai, Haoxi Li, Jie Zhang, Zicong Hong, Song Guo
https://arxiv.org/abs/2509.16105 https:…

DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
Despite the significant breakthrough of Mixture-of-Experts (MoE), the increasing scale of these MoE models presents huge memory and storage challenges. Existing MoE pruning methods, which involve reducing parameter size with a uniform sparsity across all layers, often lead to suboptimal outcomes and performance degradation due to varying expert redundancy in different MoE layers. To address this, we propose a non-uniform pruning strategy, dubbed \textbf{Di}fferentiable \textbf{E}xpert \textbf{P…

The C-index Multiverse
Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Catalina A. Vallejos
https://arxiv.org/abs/2508.14821 https://arxiv.org/pdf/2508.14…

The C-index Multiverse
Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between proposed C-index estimators (e.g. Harrell's, Uno's and Antolini's), we demonstrate the existence of a C-index multiverse among available R and pyth…

LLM-Driven Self-Refinement for Embodied Drone Task Planning
Deyu Zhang, Xicheng Zhang, Jiahao Li, Tingting Long, Xunhua Dai, Yongjian Fu, Jinrui Zhang, Ju Ren, Yaoxue Zhang
https://arxiv.org/abs/2508.15501

LLM-Driven Self-Refinement for Embodied Drone Task Planning
We introduce SRDrone, a novel system designed for self-refinement task planning in industrial-grade embodied drones. SRDrone incorporates two key technical contributions: First, it employs a continuous state evaluation methodology to robustly and accurately determine task outcomes and provide explanatory feedback. This approach supersedes conventional reliance on single-frame final-state assessment for continuous, dynamic drone operations. Second, SRDrone implements a hierarchical Behavior Tree…

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents
Hengyu An, Jinghuai Zhang, Tianyu Du, Chunyi Zhou, Qingming Li, Tao Lin, Shouling Ji
https://arxiv.org/abs/2508.15310

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents
Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks. However, when interacting with untrusted data sources (e.g., fetching information from public websites), tool responses may contain injected instructions that covertly influence agent behaviors and lead to malicious outcomes, a threat referred to as Indirect Prompt Injection (IPI). Existing defenses typically rely on advanced pro…

Eagles deal Rams bettors ‘worst NFL beat ever’ on wild Week 3 https://www.reviewjournal.com/sports/sports-columns/todd-dewey/eagles-deal-rams-bettors-worst-nfl-beat-ever-on-wild-week-3-3464099/

Auto-grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges
Adam Zhang, Heather Burte, Jaromir Savelka, Christopher Bogart, Majd Sakr
https://arxiv.org/abs/2507.14235

Auto-grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges
Automated grading systems, or auto-graders, have become ubiquitous in programming education, and the way they generate feedback has become increasingly automated as well. However, there is insufficient evidence regarding auto-grader feedback's effectiveness in improving student learning outcomes, in a way that differentiates students who utilized the feedback and students who did not. In this study, we fill this critical gap. Specifically, we analyze students' interactions with auto-graders in …

Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform
Jinliang Yu, Mingduo Xie, Yue Wang, Tianfan Fu, Xianglai Xu, Jiajun Wang
https://arxiv.org/abs/2508.15379

Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform
Clinical cystoscopy, the current standard for bladder cancer diagnosis, suffers from significant reliance on physician expertise, leading to variability and subjectivity in diagnostic outcomes. There is an urgent need for objective, accurate, and efficient computational approaches to improve bladder cancer diagnostics. Leveraging recent advancements in deep learning, this study proposes an integrated multi-task deep learning framework specifically designed for bladder cancer diagnosis from cy…

Artificial Intelligence-derived Cardiotocography Age as a Digital Biomarker for Predicting Future Adverse Pregnancy Outcomes
Jinshuai Gu, Zenghui Lin, Jingying Ma, Jingyu Wang, Linyan Zhang, Rui Bai, Zelin Tu, Youyou Jiang, Donglin Xie, Yuxi Zhou, Guoli Liu, Shenda Hong
https://arxiv.org/abs/2509.14242…

Artificial Intelligence-derived Cardiotocography Age as a Digital Biomarker for Predicting Future Adverse Pregnancy Outcomes
Cardiotocography (CTG) is a low-cost, non-invasive fetal health assessment technique used globally, especially in underdeveloped countries. However, it is currently mainly used to identify the fetus's current status (e.g., fetal acidosis or hypoxia), and the potential of CTG in predicting future adverse pregnancy outcomes has not been fully explored. We aim to develop an AI-based model that predicts biological age from CTG time series (named CTGage), then calculate the age gap between CTGage an…

A Spectroscopic Hunt for Post-Red Supergiants in the Large Magellanic Cloud II: Turbulent Line Broadening in the Spectra of LMC Yellow Supergiants
Trevor Z. Dorn-Wallenstein, Kaitlyn M. Chen, Samantha C. Wu, Jared A. Goldberg, Anna J. G. O'Grady, Ayanna T. Mann, Poderosa I. Don-Wallanchez
https://arxiv.org/abs/2508.14971

A Spectroscopic Hunt for Post-Red Supergiants in the Large Magellanic Cloud II: Turbulent Line Broadening in the Spectra of LMC Yellow Supergiants
Massive stars in the Hertzsprung gap are a mixed population of objects in short-lived evolutionary phases: yellow supergiants (YSGs) evolving towards the red supergiant (RSG) phase, partially-stripped post-RSGs, and other, rarer outcomes of stellar evolution. Studies of sufficiently large samples of these objects can constrain massive star structure and evolution during these poorly-understood phases. As part of our ongoing program searching for post-RSGs, we characterized the spectral line pro…

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
Aqsa Ashraf Makhdomi, Iqra Altaf Gillani
https://arxiv.org/abs/2507.15282

Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery
The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimization objective. This study proposes a novel eco-friendly food delivery optimization framework that integrates demand prediction, delivery person routing, and order allocation to m…

General Matching Games
Felipe Garrido-Lucero, Rida Laraki
https://arxiv.org/abs/2507.15737 https://arxiv.org/pdf/2507.15737

General Matching Games
Matching games is a one-to-one two sided market model introduced by Garrido-Lucero and Laraki, in which coupled agents' utilities are endogenously determined as the outcome of a strategic game. They refine the classical pairwise stability by requiring robustness to renegotiation and provide general conditions under which pairwise stable and renegotiation-proof outcomes exist as the limit of a deferred acceptance with competitions algorithm together with a renegotiation process. In this article,…

The day-long, repeating GRB 250702BDE / EP250702a: A unique extragalactic transient
Andrew J. Levan, Antonio Martin-Carrillo, Tanmoy Laskar, Rob A. J. Eyles-Ferris, Albert Sneppen, Maria Edvige Ravasio, Jillian C. Rastinejad, Joe S. Bright, Francesco Carotenuto, Ashley A. Chrimes, Gregory Corcoran, Benjamin P. Gompertz, Peter G. Jonker, Gavin P. Lamb, Daniele B. Malesani, Andrea Saccardi, Javier Sanchez Sierras, Benjamin Schneider, Steve Schulze, Nial R. Tanvir, Susana D. Vergani, Dara…

The day-long, repeating GRB 250702BDE / EP250702a: A unique extragalactic transient
Gamma-ray bursts (GRBs) are singular outbursts of high-energy radiation with durations typically lasting from milliseconds to minutes and, in extreme cases, a few hours. They are attributed to the catastrophic outcomes of stellar-scale events and, as such, are not expected to recur. Here, we present observations of an exceptional GRB\,250702BDE which triggered the {\em Fermi} gamma-ray burst monitor on three occasions over several hours, and which was detected in soft X-rays by the \textit{Eins…

Which NFL teams could change QBs next year? 12 situations to watch -- and possible options for each https://www.espn.com/nfl/story/_/id/46018436/2026-nfl-quarterback-market-predictions-12-teams-starter-questions

On the Illusion of Success: An Empirical Study of Build Reruns and Silent Failures in Industrial CI
Henri A\"idasso, Francis Bordeleau, Ali Tizghadam
https://arxiv.org/abs/2509.14347

On the Illusion of Success: An Empirical Study of Build Reruns and Silent Failures in Industrial CI
Reliability of build outcomes is a cornerstone of effective Continuous Integration (CI). Yet in practice, developers often struggle with non-deterministic issues in the code or CI infrastructure, which undermine trust in build results. When faced with such unexpected outcomes, developers often repeatedly rerun jobs hoping for true success, but this practice is known to increase CI costs and reduce productivity. While recent studies have focused on intermittent job failures, no prior work has in…

Forecasting Faculty Placement from Patterns in Co-authorship Networks
Samantha Dies, David Liu, Tina Eliassi-Rad
https://arxiv.org/abs/2507.14696 https://

Forecasting Faculty Placement from Patterns in Co-authorship Networks
Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies have found strong correlations between faculty hiring and attributes such as doctoral department prestige and publication record, they rarely assess whether these associations generalize to individual hiring outcomes, particularly for future candidates outside …

Day 26: Emily Short
If you know who Short is, you know exactly why she's on this list. If you don't, you're probably in the majority. She's an absolutely legendary author within the interactive fiction (IF) community, which gets somewhat pigeonholed by stuff like Zork when there's actually a huge range of stuff in the medium some of which isn't even puzzle-focused, and Short has been writing & coding on the bleeding edge of things for decades.
I was lucky enough to be introduced to Short's work in graduate school, where we played "Galatea" as part of an interactive fiction class. Short uses a lot of clever parser tricks to make your conversation with a statue feel very fluid and conversational, giving to contemporary audiences a great example of how vibrant interaction with a well-designed agent can be in contrast to an LLM, if you're willing to put in some work on bespoke parsing & responses (although the user does need to know basic IF conventions). While I didn't explore the full range of Galatea's many possible outcomes, it left a strong impression on me as a vision for what IF could be besides dorky puzzles, and I think that "visionary" is a great term to describe Short.
If you'd like you get a feel for her (very early) work, you can play Galatea here: #30AuthorsNoMen

CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration
Ziyi Wang, Ziwen Zeng, Yuan Li, Zijian Ding
https://arxiv.org/abs/2509.11461

CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration
Career exploration is uncertain, requiring decisions with limited information and unpredictable outcomes. While generative AI offers new opportunities for career guidance, most systems rely on linear chat interfaces that produce overly comprehensive and idealized suggestions, overlooking the non-linear and effortful nature of real-world trajectories. We present CareerPooler, a generative AI-powered system that employs a pool-table metaphor to simulate career development as a spatial and narrati…

Classification errors distort findings in automated speech processing: examples and solutions from child-development research
Lucas Gautheron, Evan Kidd, Anton Malko, Marvin Lavechin, Alejandrina Cristia
https://arxiv.org/abs/2508.15637

Classification errors distort findings in automated speech processing: examples and solutions from child-development research
With the advent of wearable recorders, scientists are increasingly turning to automated methods of analysis of audio and video data in order to measure children's experience, behavior, and outcomes, with a sizable literature employing long-form audio-recordings to study language acquisition. While numerous articles report on the accuracy and reliability of the most popular automated classifiers, less has been written on the downstream effects of classification errors on measurements and statist…

Q&A with researcher Petter Törnberg on his pre-print study showing how social media's structural architecture creates problematic outcomes like echo chambers (Jennifer Ouellette/Ars Technica)
https://arstechnica.com/science/2025/08/study-social-medi…

Study: Social media probably can’t be fixed
“The [structural] mechanism producing these problematic outcomes is really robust and hard to resolve.”…

Matrix-Game 2.0: An Open-Source, Real-Time, and Streaming Interactive World Model
Xianglong He, Chunli Peng, Zexiang Liu, Boyang Wang, Yifan Zhang, Qi Cui, Fei Kang, Biao Jiang, Mengyin An, Yangyang Ren, Baixin Xu, Hao-Xiang Guo, Kaixiong Gong, Cyrus Wu, Wei Li, Xuchen Song, Yang Liu, Eric Li, Yahui Zhou
https://arxiv.org/abs/2508.13009

Matrix-Game 2.0: An Open-Source, Real-Time, and Streaming Interactive World Model
Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynamics and interactive behaviors. However, existing interactive world models depend on bidirectional attention and lengthy inference steps, severely limiting real-time performance. Consequently, they are hard to simulate real-world dynamics, where outcomes must update instantaneously based on historical context and current actions. To address this, we pr…

Policy relevance of causal quantities in networks
Sahil Loomba, Dean Eckles
https://arxiv.org/abs/2507.14391 https://arxiv.org/pdf/25…

Policy relevance of causal quantities in networks
In settings where units' outcomes are affected by others' treatments, there has been a proliferation of ways to quantify effects of treatments on outcomes. Here we describe how many proposed estimands can be represented as involving one of two ways of averaging over units and treatment assignments. The more common representation often results in quantities that are irrelevant, or at least insufficient, for optimal choice of policies governing treatment assignment. The other representation often…

A novel imaging solution, called
"prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scanning",
can more effectively detect the recurrence of prostate cancer
compared to standard imaging methods,
and is associated with improved survival outcomes.

New imaging technique boosts survival in prostate cancer patients
A multicenter study led by London Health Sciences Centre Research Institute (LHSCRI), Lawson Research Institute of St. Joseph's Health Care London (Lawson), and University Health Network (UHN) has found a novel imaging solution, called prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scanning, can more effectively detect the recurrence of prostate cancer compared to standard imaging methods, and is associated with improved survival outcomes.

Beyond Copenhagen: Following the Trail of Decoherence in Feynman's Light Microscope
Brian C. Odom
https://arxiv.org/abs/2508.13385 https://arxiv.org/pd…

Beyond Copenhagen: Following the Trail of Decoherence in Feynman's Light Microscope
Feynman's light microscope invites us to reconsider what we thought we knew about quantum reality. Rather than invoking wavefunction collapse to predict the loss of fringes in a monitored interferometer, Feynman analyzes the problem in terms of a disturbance. This approach raises the question of whether the classical world, including its localized particles and definite measurement outcomes, might emerge as the universe evolves smoothly according to ß. Treating the particle and its environment…

US officials provide contradictory statements on security guarantees, fueling uncertainty ahead of Trump-Zelensky meeting: https://benborges.xyz/2025/08/17/us-officials-provide-contradictory-statements.html

Predicting stellar collision outcomes of main sequence stars
Pau Amaro Seoane
https://arxiv.org/abs/2509.12352 https://arxiv.org/pdf/2509.12352

Predicting stellar collision outcomes of main sequence stars
Stellar collisions in dense galactic nuclei might play an important role in fueling supermassive black holes (SMBHs) and shaping their environments. The gas released during these collisions can contribute to SMBH accretion, influencing phenomena such as active galactic nuclei and tidal disruption events of the remnants. We address the challenge of rapidly and accurately predicting the outcomes of stellar collisionsincluding remnant masses and unbound gasacross a broad parameter space of initial…

Identifying treatment effects on categorical outcomes in IV models
Onil Boussim
https://arxiv.org/abs/2510.10946 https://arxiv.org/pdf/2510.10946

Identifying treatment effects on categorical outcomes in IV models
This paper provides a nonparametric framework for causal inference with categorical outcomes under binary treatment and binary instrument settings. We decompose the observed joint probability of outcomes and treatment into marginal probabilities of potential outcomes and treatment, and association parameters that capture selection bias due to unobserved heterogeneity. Under a novel identifying assumption, association similarity, which requires the dependence between unobserved factors and poten…

Click, Watch, Learn: The Impact of Student Self-Study Materials on Physics E&M Course Outcomes
James K. Hirons, Jonathan D. Perry, Dawson T. Nodurft, Scott Crawford, William Bassichis, Tatiana L. Erukhimova
https://arxiv.org/abs/2508.12143

Click, Watch, Learn: The Impact of Student Self-Study Materials on Physics E&M Course Outcomes
Performance in introductory courses, particularly physics, is often crucial for student success in STEM majors and can impact an individual's tendency to persist in their chosen field. To enhance students' individual learning experiences, faculty at many universities have worked to develop open-access, self-study materials to help build conceptual understanding and problem-solving skills. Faculty at Texas A&M University have contributed to these efforts, creating more than 200 online video reso…

Distributed Kaplan-Meier Analysis via the Influence Function with Application to COVID-19 and COVID-19 Vaccine Adverse Events
Malcolm Risk, Xu Shi, Lili Zhao
https://arxiv.org/abs/2507.14351

Distributed Kaplan-Meier Analysis via the Influence Function with Application to COVID-19 and COVID-19 Vaccine Adverse Events
During the COVID-19 pandemic, regulatory decision-making was hampered by a lack of timely and high-quality data on rare outcomes. Studying rare outcomes following infection and vaccination requires conducting multi-center observational studies, where sharing individual-level data is a privacy concern. In this paper, we conduct a multi-center observational study of thromboembolic events following COVID-19 and COVID-19 vaccination without sharing individual-level data. We accomplish this by devel…

Utilizing the RAIN method and Graph SAGE Model to Identify Effective Drug Combinations for Gastric Neoplasm Treatment
S. Z. Pirasteh, Ali A. Kiaei, Mahnaz Bush, Sabra Moghadam, Raha Aghaei, Behnaz Sadeghigol
https://arxiv.org/abs/2508.13207

Utilizing the RAIN method and Graph SAGE Model to Identify Effective Drug Combinations for Gastric Neoplasm Treatment
Background: Gastric neoplasm, primarily adenocarcinoma, is an aggressive cancer with high mortality, often diagnosed late, leading to complications like metastasis. Effective drug combinations are vital to address disease heterogeneity, enhance efficacy, reduce resistance, and improve patient outcomes. Methods: The RAIN method integrated Graph SAGE to propose drug combinations, using a graph model with p-value-weighted edges connecting drugs, genes, and proteins. NLP and systematic literature r…

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
Valentin Robu, Mark Klein
https://arxiv.org/abs/2509.10885 https://

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
This paper studies how utility graphs decomposition algorithms can be used to effectively search for Pareto-efficient outcomes in complex automated negotiation. We propose a number of algorithms that can efficiently handle high-dimensional utility graphs, and test them on a variety of utility graph topologies, generated based on state of the art methods for analysing complex graphs. We show that we can achieve exponential speed-up, for many structures, even for very large utility graphs. To our…

The Australian Vote: Transferable Voting, Its Limitations and Strengths
Anthony B. Morton
https://arxiv.org/abs/2507.15383 https://ar…

The Australian Vote: Transferable Voting, Its Limitations and Strengths
The voting systems known as Alternative Vote (AV) and Single Transferable Vote (STV) are extensively used for elections in Australia, possibly more than in any other jurisdiction. Often proposed as superior alternatives to Plurality and other common systems, they are also criticised by theoreticians for their vulnerability to paradoxical outcomes. It is argued these 'transferable voting' systems in fact operate to balance competing desiderata that attempt to distil the common notions of 'majori…

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

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

Conditionally specified graphical modeling of stationary multivariate time series
Anirban Bhattacharya, Jan Johannes, Suhasini Subba Rao
https://arxiv.org/abs/2508.13572 https:/…

Conditionally specified graphical modeling of stationary multivariate time series
Graphical models are ubiquitous for summarizing conditional relations in multivariate data. In many applications involving multivariate time series, it is of interest to learn an interaction graph that treats each individual time series as nodes of the graph, with the presence of an edge between two nodes signifying conditional dependence given the others. Typically, the partial covariance is used as a measure of conditional dependence. However, in many applications, the outcomes may not be Gau…

Reinforcement Learning with Rubric Anchors
Zenan Huang, Yihong Zhuang, Guoshan Lu, Zeyu Qin, Haokai Xu, Tianyu Zhao, Ru Peng, Jiaqi Hu, Zhanming Shen, Xiaomeng Hu, Xijun Gu, Peiyi Tu, Jiaxin Liu, Wenyu Chen, Yuzhuo Fu, Zhiting Fan, Yanmei Gu, Yuanyuan Wang, Zhengkai Yang, Jianguo Li, Junbo Zhao
https://arxiv.org/abs/2508.12790

Reinforcement Learning with Rubric Anchors
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs), exemplified by the success of OpenAI's o-series. In RLVR, rewards are derived from verifiable signals-such as passing unit tests in code generation or matching correct answers in mathematical reasoning. While effective, this requirement largely confines RLVR to domains with automatically checkable outcomes. To overcome this, we extend the RLVR paradigm to open-end…

Attribution Locus and the Timeliness of Long-lived Asset Write-downs
Yao-Lin Chang, Chun-Yang Lin, Chi-Chun Liu, Stephen G. Ryan
https://arxiv.org/abs/2509.10810 https://…

Attribution Locus and the Timeliness of Long-lived Asset Write-downs
We examine the relative timeliness with which write-downs of long-lived assets incorporate adverse macroeconomic and industry outcomes versus adverse firm-specific outcomes. We posit that users of financial reports are more likely to attribute adverse firm-specific outcomes to suboptimal managerial actions, which provide managers with more incentive to delay write downs. We provide evidence that, controlling for other incentives to manage earnings, firms record write-downs in the current year t…

Co-Investment with Payoff-Sharing Mechanism for Cooperative Decision-Making in Network Design Games
Mingjia He, Andrea Censi, Emilio Frazzoli, Gioele Zardini
https://arxiv.org/abs/2508.12059

Co-Investment with Payoff-Sharing Mechanism for Cooperative Decision-Making in Network Design Games
Network-based systems are inherently interconnected, with the design and performance of subnetworks being interdependent. However, the decisions of self-interested operators may lead to suboptimal outcomes for users and the overall system. This paper explores cooperative mechanisms that can simultaneously benefit both operators and users. We address this challenge using a game-theoretical framework that integrates both non-cooperative and cooperative game theory. In the non-cooperative stage, w…

The Varieties of Schelling Model Experience
Marlyn Boke, Timothy Sorochkin, Jesse Anttila-Hughes, Alan O. Jamison
https://arxiv.org/abs/2509.14462 https://…

The Varieties of Schelling Model Experience
The Schelling model is a prototype for agent-based modeling in social systems. A comprehensive analysis of Schelling model rule variants is achieved by classification of the space of macroscopic outcomes via phase diagrams. Among 54 rule variants, only 3 phase diagram classes are found, characterized by the number of phase transitions. This classification scheme is found to be robust to the use of sociological and percolation-inspired measures of segregation. The statistical and dynamic drivers…

Beyond Test Scores: How Academic Rank Shapes Long-Term Outcomes
Emilia Del Bono, Angus Holford, Tommaso Sartori
https://arxiv.org/abs/2510.11973 https://ar…

Beyond Test Scores: How Academic Rank Shapes Long-Term Outcomes
We study the effects of academic rank using data on the entire population of children enrolled in primary schools in Aberdeen, Scotland, in 1962. Exploiting quasi-random variation in peer group composition, we estimate the causal impact of rank on academic performance, noncognitive development, parental investment, and long-term outcomes. Higher rank improves achievement on the high-stakes eleven-plus examination and strengthens internalizing skills (traits related to self-concept and confidenc…

A national cohort study in France shows no association between mRNA COVID-19 vaccine received in the first trimester & congenital defects in babies.
This adds to previous national studies that reported similar outcomes.
Kennedy is wrong, as usual.
🧪 https://jamanetwork.com/journals/ja…

Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models
Zhaokun Chen, Chaopeng Zhang, Xiaohan Li, Wenshuo Wang, Gentiane Venture, Junqiang Xi
https://arxiv.org/abs/2508.13881

Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models
Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning capability inherent to human experts. This discrepancy results in a fundamental misalignment between algorithmic classifications and expert judgments. To bridge this gap, we propose a novel framework that integrates Semantic Privileged Information (SPI) derived from large language models (LLMs) to align recognition outcomes with human-interpretable …

Inverse regression for causal inference with multiple outcomes
Wei Zhang, Qizhai Li, Peng Ding
https://arxiv.org/abs/2509.12587 https://arxiv.org/pdf/2509.…

Inverse regression for causal inference with multiple outcomes
With multiple outcomes in empirical research, a common strategy is to define a composite outcome as a weighted average of the original outcomes. However, the choices of weights are often subjective and can be controversial. We propose an inverse regression strategy for causal inference with multiple outcomes. The key idea is to regress the treatment on the outcomes, which is the inverse of the standard regression of the outcomes on the treatment. Although this strategy is simple and even counte…

Quantum State Recovery via Direct Sum Formalism Without Measurement Outcomes
Taiga Suzuki, Masayuki Ohzeki
https://arxiv.org/abs/2509.11066 https://arxiv.o…

Quantum State Recovery via Direct Sum Formalism Without Measurement Outcomes
This study proposes a new approach to quantum state recovery following measurement. Specifically, we introduce a special operation that transfers the probability amplitude of the quantum state into its orthogonal complement. This operation is followed by a measurement performed on this orthogonal subspace, enabling the undisturbed original quantum state to be regained. Remarkably, this recovery is achieved without dependence of the post-measurement operation on the measurement outcome, thus all…

Colon Polyps Detection from Colonoscopy Images Using Deep Learning
Md Al Amin, Bikash Kumar Paul
https://arxiv.org/abs/2508.13188 https://arxiv.org/pdf/250…

Colon Polyps Detection from Colonoscopy Images Using Deep Learning
Colon polyps are precursors to colorectal cancer, a leading cause of cancer-related mortality worldwide. Early detection is critical for improving patient outcomes. This study investigates the application of deep learning-based object detection for early polyp identification using colonoscopy images. We utilize the Kvasir-SEG dataset, applying extensive data augmentation and splitting the data into training (80\%), validation (20\% of training), and testing (20\%) sets. Three variants of the YO…

Digital-GenAI-Enhanced HCI in DevOps as a Driver of Sustainable Innovation: An Empirical Framework
Jun Cui
https://arxiv.org/abs/2508.13185 https://arxiv.o…

Digital-GenAI-Enhanced HCI in DevOps as a Driver of Sustainable Innovation: An Empirical Framework
This study examines the impact of Digital-GenAI-Enhanced Human-Computer Interaction (HCI) in DevOps on sustainable innovation performance among Chinese A-share internet technology firms. Using panel data from 2018-2024, we analyze 5,560 firm-year observations from CNRDS and CSMAR databases. Our empirical framework reveals significant positive associations between AI-enhanced HCI implementation and sustainable innovation outcomes. Results demonstrate that firms adopting advanced HCI technologies…

Transporting Predictions via Double Machine Learning: Predicting Partially Unobserved Students' Outcomes
Falco J. Bargagli-Stoffi, Emma Landry, Kevin P. Josey, Kenneth De Beckker, Joana E. Maldonado, Kristof De Witte
https://arxiv.org/abs/2509.12533

Transporting Predictions via Double Machine Learning: Predicting Partially Unobserved Students' Outcomes
Educational policymakers often lack data on student outcomes in regions where standardized tests were not administered. Machine learning techniques can be used to predict unobserved outcomes in target populations by training models on data from a source population. However, differences between the source and target populations, particularly in covariate distributions, can reduce the transportability of these models, potentially reducing predictive accuracy and introducing bias. We propose using…

Indirect CW for teen pregnancy, rape, death.
Just finished "Girls Like Us" by Randi Pink. Pink has a knack for telling stories that capture the grim but also vibrant nuances of African-American history. I previously read "Under the Heron's Light" which has more elements of magical realism and connects more directly to the history of enslavement; "Girls Like Us" is more historical fiction, with a bridge at the end to contemporary times (circa 2019, when the book was published). It tells the story of a disparate group of mostly-Black teens who are pregnant in 1972, and shows a range of different outcomes as varied as the backstories of the different girls. Rather than just separate vignettes, the girls' stories are women together into a single plot, and Pink is a expert at pulling us in to deeply contemplate all the complexities of these girls' lives, showing rather than telling us truths about the politics of teen pregnancy and abortion, and how even though the choices involved don't have simple answers, taking those choices out of the hands of the people they most intimately affect is cruel and deadly.
#AmReading #ReadingNow

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- The Role of Radiographic Knee Alignment in Knee Replacement Outcomes and Opportunities for Artifi...
Zhisen Hu, David S. Johnson, Aleksei Tiulpin, Timothy F. Cootes, Claudia Lindner

Translating the Force Concept Inventory in the age of AI
Marina Babayeva, Justin Dunlap, Marie Sn\v{e}tinov\'a, Ralf Widenhorn
https://arxiv.org/abs/2508.13908 https://

Translating the Force Concept Inventory in the age of AI
We present a study that translates the Force Concept Inventory (FCI) using OpenAI GPT-4o and assess the specific difficulties of translating a scientific-focused topic using Large Language Models (LLMs). The FCI is a physics exam meant to evaluate outcomes of a student cohort before and after instruction in Newtonian physics. We examine the problem-solving ability of the LLM in both the translated document and the translation back into English, detailing the language-dependent issues that compl…

KoMbine: Propagating Statistical and Systematic Errors to Kaplan--Meier Curves
Jeffrey Roskes
https://arxiv.org/abs/2509.15371 https://arxiv.org/pdf/2509.1…

KoMbine: Propagating Statistical and Systematic Errors to Kaplan--Meier Curves
Kaplan--Meier curves are widely used in medical research to evaluate the performance of biomarkers and predict patient outcomes. These curves are often shown without error bands, and even when error bands are provided, they typically only account for the statistical uncertainty resulting from the finite number of patients in the study. In reality, other sources of uncertainty affect the measurements as well. As datasets grow, the statistical uncertainty on the number of patients no longer domin…

Compositional difference-in-differences for categorical outcomes
Onil Boussim
https://arxiv.org/abs/2510.11659 https://arxiv.org/pdf/2510.11659

Compositional difference-in-differences for categorical outcomes
In difference-in-differences (DiD) settings with categorical outcomes, treatment effects often operate on both total quantities (e.g., voter turnout) and category shares (e.g., vote distribution across parties). In this context, linear DiD models can be problematic: they suffer from scale dependence, may produce negative counterfactual quantities, and are inconsistent with discrete choice theory. We propose compositional DiD (CoDiD), a new method that identifies counterfactual categorical quant…

Data-driven Smile Design: Personalized Dental Aesthetics Outcomes Using Deep Learning
Marcus Lin, Jennifer Lai
https://arxiv.org/abs/2509.12001 https://arx…

Data-driven Smile Design: Personalized Dental Aesthetics Outcomes Using Deep Learning
A healthy smile plays a significant role in functional as well as esthetic considerations, improving confidence. It is difficult for dental professionals to strike a balance between esthetic requirements and functional requirements. Traditional smile design has had heavy reliance on dentist expertise and used plaster models and hand drawings, raising questions about the outcome for patients. Digital technology, led by Dr. Christian Coachman in 2007, allows photographic and videographic assessme…

VCBench: Benchmarking LLMs in Venture Capital
Rick Chen, Joseph Ternasky, Afriyie Samuel Kwesi, Ben Griffin, Aaron Ontoyin Yin, Zakari Salifu, Kelvin Amoaba, Xianling Mu, Fuat Alican, Yigit Ihlamur
https://arxiv.org/abs/2509.14448

VCBench: Benchmarking LLMs in Venture Capital
Benchmarks such as SWE-bench and ARC-AGI demonstrate how shared datasets accelerate progress toward artificial general intelligence (AGI). We introduce VCBench, the first benchmark for predicting founder success in venture capital (VC), a domain where signals are sparse, outcomes are uncertain, and even top investors perform modestly. At inception, the market index achieves a precision of 1.9%. Y Combinator outperforms the index by a factor of 1.7x, while tier-1 firms are 2.9x better. VCBench p…

Mind the Ethics! The Overlooked Ethical Dimensions of GenAI in Software Modeling Education
Shalini Chakraborty, Lola Burgue\~no, Nathalie Moreno, Javier Troya, Paula Mu\~noz
https://arxiv.org/abs/2509.13896

Mind the Ethics! The Overlooked Ethical Dimensions of GenAI in Software Modeling Education
Generative Artificial Intelligence (GenAI) is rapidly gaining momentum in software modeling education, embraced by both students and educators. As GenAI assists with interpreting requirements, formalizing models, and translating students' mental models into structured notations, it increasingly shapes core learning outcomes such as domain comprehension, diagrammatic thinking, and modeling fluency without clear ethical oversight or pedagogical guidelines. Yet, the ethical implications of this in…

FACET:Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets
Jana Gonnermann-M\"uller, Jennifer Haase, Konstantin Fackeldey, Sebastian Pokutta
https://arxiv.org/abs/2508.11401

FACET:Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets
The increasing heterogeneity of student populations poses significant challenges for teachers, particularly in mathematics education, where cognitive, motivational, and emotional differences strongly influence learning outcomes. While AI-driven personalization tools have emerged, most remain performance-focused, offering limited support for teachers and neglecting broader pedagogical needs. This paper presents the FACET framework, a teacher-facing, large language model (LLM)-based multi-agent s…

A hastily organised summit with unclear outcomes
https://www.bbc.com/news/live/c2kzn1nw1d4t?post=asset:7766b68f-32c0-4212-8b1e-c214a098d74b#post

Live updates: Trump meets Putin in Alaska for Ukraine talks
The US and Russian leaders exchanged two handshakes before travelling together in Trump's limo to the summit.

Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
Adam Howes, Alex Stringer, Seth R. Flaxman, Jeffrey W. Imai-Eaton
https://arxiv.org/abs/2508.15665

Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment coverage are jointly modelled using both household survey data and routinely reported health system data. The model is provided as a tool for countries to input their data to and generate estimates with during a yearly process supported by UNAIDS. Previously, i…

Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes
Briana Lee, Samantha Limon, Alyssia Chen, Kenny Ka'aiakamanu-Quibilan, Anthony Peruma
https://arxiv.org/abs/2509.10717

Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes
The technology industry offers exciting and diverse career opportunities, ranging from traditional software development to emerging fields such as artificial intelligence, cybersecurity, and data science. Career fairs play a crucial role in helping Computer Science (CS) students understand the various career pathways available to them in the industry. However, limited research exists on how CS students experience and benefit from these events. Through a survey of 86 students, we investigate the…

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Dennis Frauen, Stefan Feuerriegel
https://arxiv.org/abs/2509.26429

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to give to a cancer patient). However, predicting potential outcomes over long horizons is notoriously difficult. Existing methods that break the curse of the horizon typically lack strong theoretical guarantees such as orthogonality and quasi-oracle efficiency. In this paper, we revisit the problem of predicting individ…

chess: Kaggle chess players (2010)
A network among chess players (nodes) giving the chess match outcomes (edges), for game-by-game results among the world’s top chess players. The direction of edge (i,j) denotes white player (i) and black player (j). Each edge is timestamped (approximate). Edge sign is 1 for a win by white, 0 for draw, and -1 for a win by black.
This network has 7301 nodes and 65053 edges.
Tags: Social, Offline, Signed, Timestamps

Homogeneity Test of Proportions for Combined Unilateral and Bilateral Data via GEE and MLE Approaches
Jia Zhou, Chang-Xing Ma
https://arxiv.org/abs/2508.12008 https://

Homogeneity Test of Proportions for Combined Unilateral and Bilateral Data via GEE and MLE Approaches
In clinical trials involving paired organs such as eyes, ears, and kidneys, binary outcomes may be collected bilaterally or unilaterally. In such combined datasets, bilateral outcomes exhibit intra-subject correlation, while unilateral outcomes are assumed independent. We investigate the generalized Estimating Equations (GEE) approach for testing homogeneity of proportions across multiple groups for the combined unilateral and bilateral data, and compare it with three likelihood-based statistic…

Internalizing Self-Consistency in Language Models: Multi-Agent Consensus Alignment
Ankur Samanta, Akshayaa Magesh, Youliang Yu, Runzhe Wu, Ayush Jain, Daniel Jiang, Boris Vidolov, Paul Sajda, Yonathan Efroni, Kaveh Hassani
https://arxiv.org/abs/2509.15172

Internalizing Self-Consistency in Language Models: Multi-Agent Consensus Alignment
Language Models (LMs) are inconsistent reasoners, often generating contradictory responses to identical prompts. While inference-time methods can mitigate these inconsistencies, they fail to address the core problem: LMs struggle to reliably select reasoning pathways leading to consistent outcomes under exploratory sampling. To address this, we formalize self-consistency as an intrinsic property of well-aligned reasoning models and introduce Multi-Agent Consensus Alignment (MACA), a reinforceme…

PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Suhang You, Carla Pitarch-Abaigar, Sanket Kachole, Sumedh Sonawane, Juhyung Ha, Anish Sudarshan Gada, David Crandall, Rakesh Shiradkar, Spyridon Bakas
https://arxiv.org/abs/2509.14051

PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Almost 30% of prostate cancer (PCa) patients undergoing radical prostatectomy (RP) experience biochemical recurrence (BCR), characterized by increased prostate specific antigen (PSA) and associated with increased mortality. Accurate early prediction of BCR, at the time of RP, would contribute to prompt adaptive clinical decision-making and improved patient outcomes. In this work, we propose prostate cancer BCR prediction via fused multi-modal embeddings (PROFUSEme), which learns cross-modal int…

Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy
Kit T. Rodolfa, Erika Salomon, Jin Yao, Steve Yoder, Robert Sullivan, Kevin McGuire, Allie Dickinson, Rob MacDougall, Brian Seidler, Christina Sung, Claire Herdeman, Rayid Ghani
https://arxiv.org/abs/2509.14129

Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy
Many incarcerated individuals face significant and complex challenges, including mental illness, substance dependence, and homelessness, yet jails and prisons are often poorly equipped to address these needs. With little support from the existing criminal justice system, these needs can remain untreated and worsen, often leading to further offenses and a cycle of incarceration with adverse outcomes both for the individual and for public safety, with particularly large impacts on communities of …

A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation
Xingran Chen, Tyler McCormick, Bhramar Mukherjee, Zhenke Wu
https://arxiv.org/abs/2508.15162

A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation
Pre-trained machine learning (ML) predictions have been increasingly used to complement incomplete data to enable downstream scientific inquiries, but their naive integration risks biased inferences. Recently, multiple methods have been developed to provide valid inference with ML imputations regardless of prediction quality and to enhance efficiency relative to complete-case analyses. However, existing approaches are often limited to missing outcomes under a missing-completely-at-random (MCAR)…

Predicting ChatGPT Use in Assignments: Implications for AI-Aware Assessment Design
Surajit Das, Aleksei Eliseev
https://arxiv.org/abs/2508.12013 https://ar…

Predicting ChatGPT Use in Assignments: Implications for AI-Aware Assessment Design
The rise of generative AI tools like ChatGPT has significantly reshaped education, sparking debates about their impact on learning outcomes and academic integrity. While prior research highlights opportunities and risks, there remains a lack of quantitative analysis of student behavior when completing assignments. Understanding how these tools influence real-world academic practices, particularly assignment preparation, is a pressing and timely research priority. This study addresses this gap…

Mutually equi-biased bases
Seyed Javad Akhtarshenas, Saman Karimi, Mahdi Salehi
https://arxiv.org/abs/2508.08969 https://arxiv.org/pdf/2508.08969

Mutually equi-biased bases
In the framework of mutually unbiased bases (MUBs), a measurement in one basis gives \emph{no information} about the outcomes of measurements in another basis. Here, we relax the no-information condition by allowing the $d$ outcomes to be predicted according to a predefined probability distribution $q=(q_0,\cdots,q_{d-1})$. The notion of mutual unbiasedness, however, is preserved by requiring that the extracted information is the same for any preparation and any measurement; regardless of which…

Mantis: A Simulation-Grounded Foundation Model for Disease Forecasting
Carson Dudley, Reiden Magdaleno, Christopher Harding, Ananya Sharma, Emily Martin, Marisa Eisenberg
https://arxiv.org/abs/2508.12260

Mantis: A Simulation-Grounded Foundation Model for Disease Forecasting
Infectious disease forecasting in novel outbreaks or low resource settings has been limited by the need for disease-specific data, bespoke training, and expert tuning. We introduce Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. Mantis is built on over 400 million simulated days of outbreak dynamics spanning diverse pathogens, transmission modes…

Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts
Vishnu Menon, Andy Cherney, Elizabeth B. Cloude, Li Zhang, Tiffany D. Do
https://arxiv.org/abs/2508.04787

Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts
This study examined whether embedding LLM-guided reflection prompts in an interactive AI-generated podcast improved learning and user experience compared to a version without prompts. Thirty-six undergraduates participated, and while learning outcomes were similar across conditions, reflection prompts reduced perceived attractiveness, highlighting a call for more research on reflective interactivity design.

Bivariate Distribution Regression; Theory, Estimation and an Application to Intergenerational Mobility
Victor Chernozhukov, Iv\'an Fern\'andez-Val, Jonas Meier, Aico van Vuuren, Francis Vella
https://arxiv.org/abs/2508.12716

Bivariate Distribution Regression; Theory, Estimation and an Application to Intergenerational Mobility
We employ distribution regression (DR) to estimate the joint distribution of two outcome variables conditional on chosen covariates. While Bivariate Distribution Regression (BDR) is useful in a variety of settings, it is particularly valuable when some dependence between the outcomes persists after accounting for the impact of the covariates. Our analysis relies on a result from Chernozhukov et al. (2018) which shows that any conditional joint distribution has a local Gaussian representation. W…

Index Date Imputation For Survival Outcomes for Externally Controlled Trials
Q. Le Coent, G. L. Rosner, M-C. Wang, C. Hu
https://arxiv.org/abs/2509.14183 https://

Index Date Imputation For Survival Outcomes for Externally Controlled Trials
Externally controlled trials (ECTs) compare outcomes between a single-arm trial and external controls drawn from sources such as historical trials, registries, or observational studies. In survival analysis, a major challenge arises when the time origin (index date) differs across groups, for example, when treatment initiation occurs after a delay in the single-arm trial but is undefined in the external controls. This misalignment can bias treatment effect estimates and distort causal interpret…

GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
Jizhuo Chen, Diwen Liu, Jiaming Wang, Harold Soh
https://arxiv.org/abs/2509.11594 https://

GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refines the model to match true grasp outcomes. A PointNet++ style point cloud encoder with an MLP scores dense grids of candidate poses, enabling rapid online selection without full task-and-motion optimization.…

Durbin to Bondi
in this morning's Senate hearing:
"Although you've been a registered foreign agent for Qatar,
you didn't recuse yourself from signing off on Trump's solicitation of a free jet from the royal family,
even though this gift was clearly illegal.
Connections to Bondi seem to yield more favorable outcomes for defendants."

Aaron Rupar (@atrupar.com)
Durbin to Bondi: "Although you've been a registered foreign agent for Qatar, you didn't recuse yourself from signing off on Trump's solicitation of a free jet from the royal family, even though this gift was clearly illegal. Connections to Bondi seem to yield more favorable outcomes for defendants."

Optimizing Peer Grading: A Systematic Literature Review of Reviewer Assignment Strategies and Quantity of Reviewers
Uchswas Paul, Shail Shah, Sri Vaishnavi Mylavarapu, M. Parvez Rashid, Edward Gehringer
https://arxiv.org/abs/2508.11678

Optimizing Peer Grading: A Systematic Literature Review of Reviewer Assignment Strategies and Quantity of Reviewers
Peer assessment has established itself as a critical pedagogical tool in academic settings, offering students timely, high-quality feedback to enhance learning outcomes. However, the efficacy of this approach depends on two factors: (1) the strategic allocation of reviewers and (2) the number of reviews per artifact. This paper presents a systematic literature review of 87 studies (2010--2024) to investigate how reviewer-assignment strategies and the number of reviews per submission impact the …

RailSafeNet: Visual Scene Understanding for Tram Safety
Ing. Ondrej Valach, Ing. Ivan Gruber
https://arxiv.org/abs/2509.12125 https://arxiv.org/pdf/2509.12…

RailSafeNet: Visual Scene Understanding for Tram Safety
Tram-human interaction safety is an important challenge, given that trams frequently operate in densely populated areas, where collisions can range from minor injuries to fatal outcomes. This paper addresses the issue from the perspective of designing a solution leveraging digital image processing, deep learning, and artificial intelligence to improve the safety of pedestrians, drivers, cyclists, pets, and tram passengers. We present RailSafeNet, a real-time framework that fuses semantic segmen…

A comparison of approaches to incorporate patient-selected and patient-ranked outcomes in clinical trials
David S. Robertson, Thomas Jaki
https://arxiv.org/abs/2510.11578 https:…

A comparison of approaches to incorporate patient-selected and patient-ranked outcomes in clinical trials
A key aspect of patient-focused drug development is identifying and measuring outcomes that are important to patients in clinical trials. Many medical conditions affect multiple symptom domains, and a consensus approach to determine the relative importance of the associated multiple outcomes ignores the heterogeneity in individual patient preferences. Patient-selected outcomes offer one way to incorporate individual patient preferences, as proposed in recent regulatory guidance for the treatmen…

Are AI Machines Making Humans Obsolete?
Matthias Scheutz
https://arxiv.org/abs/2508.11719 https://arxiv.org/pdf/2508.11719…

Are AI Machines Making Humans Obsolete?
This chapter starts with a sketch of how we got to "generative AI" (GenAI) and a brief summary of the various impacts it had so far. It then discusses some of the opportunities of GenAI, followed by the challenges and dangers, including dystopian outcomes resulting from using uncontrolled machine learning and our failures to understand the results. It concludes with some suggestions for how to control GenAI and address its dangers.

Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery
Yinzheng Zhao, Zhihao Zhao, Rundong Jiang, Louisa Sackewitz, Quanmin Liang, Mathias Maier, Daniel Zapp, Peter Charbel Issa, Mohammad Ali Nasseri
https://arxiv.org/abs/2509.09227

Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery
Purpose: To introduce novel dynamic structural parameters and evaluate their integration within a multimodal deep learning (DL) framework for predicting postoperative visual recovery in idiopathic full-thickness macular hole (iFTMH) patients. Methods: We utilized a publicly available longitudinal OCT dataset at five stages (preoperative, 2 weeks, 3 months, 6 months, and 12 months). A stage specific segmentation model delineated related structures, and an automated pipeline extracted quantitativ…

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
He Zhang, Yueyan Liu, Xin Guan, Jie Cai, John M. Carroll
https://arxiv.org/abs/2509.12709

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
Semi-structured interviews highly rely on the quality of follow-up questions, yet interviewers' knowledge and skills may limit their depth and potentially affect outcomes. While many studies have shown the usefulness of large language models (LLMs) for qualitative analysis, their possibility in the data collection process remains underexplored. We adopt an AI-driven "Wizard-of-Oz" setup to investigate how real-time LLM support in generating follow-up questions shapes semi-structured interviews.…

From Individual to Multi-Agent Algorithmic Recourse: Minimizing the Welfare Gap via Capacitated Bipartite Matching
Zahra Khotanlou, Kate Larson, Amir-Hossein Karimi
https://arxiv.org/abs/2508.11070

From Individual to Multi-Agent Algorithmic Recourse: Minimizing the Welfare Gap via Capacitated Bipartite Matching
Decision makers are increasingly relying on machine learning in sensitive situations. In such settings, algorithmic recourse aims to provide individuals with actionable and minimally costly steps to reverse unfavorable AI-driven decisions. While existing research predominantly focuses on single-individual (i.e., seeker) and single-model (i.e., provider) scenarios, real-world applications often involve multiple interacting stakeholders. Optimizing outcomes for seekers under an individual welfare…

Semiparametric Causal Inference for Right-Censored Outcomes with Many Weak Invalid Instruments
Qiushi Bu, Wen Su, Xingqiu Zhao, Zhonghua Liu
https://arxiv.org/abs/2509.13176 htt…

Semiparametric Causal Inference for Right-Censored Outcomes with Many Weak Invalid Instruments
We propose a semiparametric framework for causal inference with right-censored survival outcomes and many weak invalid instruments, motivated by Mendelian randomization in biobank studies where classical methods may fail. We adopt an accelerated failure time model and construct a moment condition based on augmented inverse probability of censoring weighting, incorporating both uncensored and censored observations. Under a heteroscedasticity-based condition on the treatment model, we establish p…

Singing Syllabi with Virtual Avatars: Enhancing Student Engagement Through AI-Generated Music and Digital Embodiment
Xinxing Wu
https://arxiv.org/abs/2508.11872 https://

Singing Syllabi with Virtual Avatars: Enhancing Student Engagement Through AI-Generated Music and Digital Embodiment
In practical teaching, we observe that few students thoroughly read or fully comprehend the information provided in traditional, text-based course syllabi. As a result, essential details, such as course policies and learning outcomes, are frequently overlooked. To address this challenge, in this paper, we propose a novel approach leveraging AI-generated singing and virtual avatars to present syllabi in a format that is more visually appealing, engaging, and memorable. Especially, we leveraged t…

Network Meta-Analysis of survival outcomes with non-proportional hazards using flexible M-splines
David M. Phillippo (University of Bristol, Bristol, UK), Ayman Sadek (University of Bristol, Bristol, UK), Hugo Pedder (University of Bristol, Bristol, UK), Nicky J. Welton (University of Bristol, Bristol, UK)
https://arxiv.org/abs/2509.10383

Network Meta-Analysis of survival outcomes with non-proportional hazards using flexible M-splines
Network meta-analysis (NMA) is widely used in healthcare decision-making, where estimates of the effect of multiple treatments on outcomes are required. For time-to-event outcomes such as survival or disease progression the most common approach is to model log hazard ratios; however, this relies on the proportional hazards assumption. Novel treatments such as immunotherapies are expected to display complex hazard functions that cannot be captured by standard parametric models, which results in …

Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death
Sean McGrath, Takuya Kawahara, Joshua Petimar, Sheryl L. Rifas-Shiman, Iv\'an D\'iaz, Jason P. Block, Jessica G. Young
https://arxiv.org/abs/2509.13971

Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death
Researchers are often interested in estimating effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. For example, the Medications and Weight Gain in PCORnet (MedWeight) study aimed to estimate effects of adhering to flexible medication regimes on future weight change using electronic health records (EHR) data. This problem presents several methodological challenges that have not been jointly addressed in the prior…

Partial Identification of Causal Effects for Endogenous Continuous Treatments
Abhinandan Dalal, Eric J. Tchetgen Tchetgen
https://arxiv.org/abs/2508.13946 https://

Partial Identification of Causal Effects for Endogenous Continuous Treatments
No unmeasured confounding is a common assumption when reasoning about counterfactual outcomes, but such an assumption may not be plausible in observational studies. Sensitivity analysis is often employed to assess the robustness of causal conclusions to unmeasured confounding, but existing methods are predominantly designed for binary treatments. In this paper, we provide natural extensions of two extensively used sensitivity frameworks -- the Rosenbaum and Marginal sensitivity models -- to the…

A Latent Class Bayesian Model for Multivariate Longitudinal Outcomes with Excess Zeros
Chitradipa Chakraborty, Kiranmoy Das
https://arxiv.org/abs/2509.04804 https://

A Latent Class Bayesian Model for Multivariate Longitudinal Outcomes with Excess Zeros
Latent class models have been successfully used to handle complex datasets in different disciplines. For longitudinal outcomes, we often get a trajectory of the outcome for each individual, and on that basis, we cluster them for a powerful statistical inference. Latent class models have been used to handle multivariate longitudinal outcomes coming from biology, health sciences, and economics. In this paper, we propose a Bayesian latent class model for multivariate outcomes with excess zeros. We…

Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes
Xi Fang, Bingkai Wang, Liangyuan Hu, Fan Li
https://arxiv.org/abs/2510.08438 https://…

Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes
Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to intervention groups. Analyzing CRT requires distinguishing between treatment effects at the cluster level and the individual level, which requires a clear definition of the estimands under the potential outcomes framework. For analyzing survival outcomes, it is common to assess the treatment effect by comparing survival functio…

Efficient Inference for Time-to-Event Outcomes by Integrating Right-Censored and Current Status Data
Xiudi Li, Sijia Li
https://arxiv.org/abs/2508.10357 https://

Efficient Inference for Time-to-Event Outcomes by Integrating Right-Censored and Current Status Data
We propose a semiparametric data fusion framework for efficient inference on survival probabilities by integrating right-censored and current status data. Existing data fusion methods focus largely on fusing right-censored data only, while standard meta-analysis approaches are inadequate for combining right-censored and current status data, as estimators based on current status data alone typically converge at slower rates and have non-normal limiting distributions. In this work, we consider a …

A New Integrative Learning Framework for Integrating Multiple Secondary Outcomes into Primary Outcome Analysis: A Case Study on Liver Health
Daxuan Deng, Peisong Han, Shuo Chen, Ming Wang, Chixiang Chen
https://arxiv.org/abs/2507.18865

A New Integrative Learning Framework for Integrating Multiple Secondary Outcomes into Primary Outcome Analysis: A Case Study on Liver Health
In the era of big data, secondary outcomes have become increasingly important alongside primary outcomes. These secondary outcomes, which can be derived from traditional endpoints in clinical trials, compound measures, or risk prediction scores, hold the potential to enhance the analysis of primary outcomes. Our method is motivated by the challenge of utilizing multiple secondary outcomes, such as blood biochemistry markers and urine assays, to improve the analysis of the primary outcome relate…

Doubly Robust Estimation with Stabilized Weights for Binary Proximal Outcomes in Micro-Randomized Trials
Jinho Cha, Eunchan Cha
https://arxiv.org/abs/2510.08359 https://

Doubly Robust Estimation with Stabilized Weights for Binary Proximal Outcomes in Micro-Randomized Trials
Micro-randomized trials (MRTs) are increasingly used to evaluate mobile health interventions with binary proximal outcomes. Standard inverse probability weighting (IPW) estimators are unbiased but unstable in small samples or under extreme randomization. Estimated mean excursion effect (EMEE) improves efficiency but lacks double robustness. We propose a doubly robust EMEE (DR-EMEE) with stabilized and truncated weights, combining per-decision IPW and outcome regression. We prove double robustne…

Distribution-Free Prediction Sets for Regression under Target Shift
Menghan Yi, Yanlin Tang, Huixia Judy Wang
https://arxiv.org/abs/2510.10985 https://arxi…

Distribution-Free Prediction Sets for Regression under Target Shift
In real-world applications, the limited availability of labeled outcomes presents significant challenges for statistical inference due to high collection costs, technical barriers, and other constraints. In this work, we propose a method to construct efficient conformal prediction sets for new target outcomes by leveraging a source distribution that is distinct from the target but related through a distributional shift assumption and provides abundant labeled data. When the target data are full…