Publications

Seeing SDG 6 from space: Local-scale monitoring of piped water and sewage systems across Africa using satellite imagery and self-supervised learning
Othmane Echchabi
Aya Lahlou
Nizar Talty
Josh Malcolm Manto
Tongshu Zheng
Ka Leung Lam
Access to drinking water and sanitation services is essential. Sustainable Development Goal 6 (SDG 6) aims to achieve universal access, but … (see more)progress monitoring remains constrained by costly, infrequent, and spatially uneven household surveys and censuses, particularly in data-scarce regions. To address this gap, this study develops a scalable remote-sensing framework for estimating area-level presence of piped water and sewage systems at 2.56 km spatial resolution across Africa. The framework integrates Sentinel-2 imagery, enumerator-observed system-presence records from Afrobarometer enumeration areas, 30 m population data, and Vision Transformer representations learned with DINO self-supervised learning. The best-performing models achieve held-out AUROC values of 91.54% for piped water and 93.24% for sewage across enumeration areas. Under leave-one-region-out cross-validation, this falls to 75.5% and 78.7% respectively, reflecting transfer difficulty to unsampled regions. Applied across 50 African countries, population-weighted piped water estimates closely track WHO/UNICEF JMP piped water access ( R 2 = 0.92 ), while sewage estimates show meaningful agreement with the broader JMP safely managed sanitation benchmark ( R 2 = 0.72 ). In countries without Afrobarometer survey coverage, the model achieves population-weighted mean absolute errors of 9.5% for piped water and 10.7% for sewage. A Nigeria application across 767 Local Government Areas shows how our framework’s fine-scale predictions reveal substantial subnational inequality, with the largest populations living where no piped water system is present reaching 1.187 million, and no sewage system 1.577 million. These findings show that DINO-based self-supervised learning using freely available satellite imagery can complement traditional household surveys, supporting SDG 6 monitoring, infrastructure planning, and environmental equity assessment.
2026-08-20
Water Research X (published)
doi.org
Who, how, and where: navigating towards a micro-foundations perspective on university technology transfer ecosystems
Thomas Crispeels
Kevin De Moortel
Marie Gruber
Maribel Guerrero
2026-08-20
Journal of Technology Transfer (published)
doi.org
Social interactions between people of same and different generations shape longitudinal changes in interpersonal neural synchrony, loneliness, and social connection
Ryssa Moffat
Guillaume Dumas
Emily S. Cross
Loneliness is globally acknowledged as a severe and burgeoning health risk, fueling interest in helping people of all ages form meaningful s… (see more)ocial connections. One promising approach consists of intergenerational social programs. While behavioral and qualitative evidence derived from such programs promise health and wellbeing benefits, the physiological consequences of repeated intergenerational encounters remain unknown. Insight into physiological changes will shed light on the mechanisms of social connection. We charted longitudinal changes in interpersonal neural synchrony (INS) in 31 intergenerational (older/younger adult) and 30 same-generation (younger adult) dyads across a six-session creative drawing program. At each session, dyads completed self-report measures, drew together and alone, and had their cortical activation recorded with fNIRS. In both groups, INS was greater while dyads drew together than alone. Across sessions, intergenerational dyads' INS decreased and same-generation dyads' INS increased. INS in RIFG~RTPJ and RIFG~RIFG were predictive of loneliness levels and feelings of social closeness, respectively. This exploratory longitudinal research reinforces the multi-faceted nature of INS dynamics as social connections are forged.
2026-08-19
PLOS Biology (published)
doi.org
SingularClip: Preventing Spectral Collapse to Maintain Plasticity in Continual and Reinforcement Learning
Tyler Kastner
Nimrod De La Vega
Amir-massoud Farahmand
Neural networks trained on nonstationary tasks frequently lose the ability to fit new targets, a phenomenon referred to as loss of plasticit… (see more)y. We identify a novel source of plasticity loss due to the growing anisotropy of weight matrices'singular values during training, and analyze this phenomenon both empirically and theoretically. To mitigate this issue, we introduce SingularClip, a procedure that periodically clips the singular values of all weight matrices. We show that SingularClip performs strongly against baselines across a range of tasks in both continual supervised learning and deep reinforcement learning.
2026-08-17
arXiv (preprint)
doi.org
arxiv.org
Tuning in together: LSD enhances inter-brain synchrony and felt connectedness in romantic couples
Natasha L. Mason
Artur Czeszumski
Iva Totomanova
Filip Trbusek
Mauro Cavarra
Steph Ashton
Stefan Toennes
Eef Theunissen
Johannes Reckweg
P. L. Lockwood
Marieke DeWitte
Katrin Preller
Kim Kuypers
Guillaume Dumas
Pablo Mallaroni
Jan Ramaekers
Abstract Social connection is fundamental to human wellbeing. Serotonergic psychedelics such as lysergic acid diethylamide (LSD) acutely hei… (see more)ghten subjective connectedness, yet their effects on real-time social connection remain poorly understood. Using EEG hyperscanning in a randomized, double-blind, placebo-controlled crossover study, we recorded neural activity simultaneously from both members of healthy romantic couples (N=25) who received LSD (50 μg) or placebo together, across resting and interactive states. LSD increased subjective connectedness, including feelings of love, closeness, trust, and being “in sync,” while reducing loneliness, compared to placebo. This affiliative shift dissociated from the drug’s pharmacokinetic time-course, remaining elevated as subjective intensity and plasma concentration declined. In parallel, LSD increased inter-brain synchrony during shared rest, carried specifically by theta-band amplitude-envelope coupling. Importantly, this effect survived two complementary controls. First, it exceeded coupling between unrelated individuals and second the effects depended on contemporaneous neural alignment rather than shared drug-induced dynamics. Exploratory analyses showed that romantic partners with greater resting synchrony reported greater feelings of connectedness. These findings provide the first evidence that a psychedelic enhances brain-to-brain coupling between people, linking a pharmacologically induced state of felt connection to a measurable signature shared across interacting brains.
2026-08-17
bioRxiv (preprint)
doi.org
GeCo: Fine-Grained Cross-View UAV Geo-Localization via Geometric Consistency
Tianqi Ying
Jing Ren
Kaikai Pan
Qidi Zhong
Wenyuan Xu
This technical report presents GeCo, our solution for the challenge at the 4th Workshop on UAVs in Multimedia (UAVM 2026), ACM Multimedia 20… (see more)26. We use a partially fine-tuned DINOv2 to extract features and fuse via cross-attention. The relative pose (heading and translation) is then regressed and regularized by geometric consistency loss. GeCo achieves a 0.00583 final score on the PairUAV dataset. Code is available at https://github.com/Link-ying/GeCo.
2026-08-15
UAVM @ ACM International Conference on Multimedia (poster)
openreview.net
openreview.net
Confidence Intervals for the Return Process in Markov Decision Processes
Reza Alvandi
Borna Sayedana
Aditya Mahajan
In this work, we derive confidence intervals for the return process in discounted reward Markov Decision Processes with continuous state and… (see more) action spaces. These confidence bounds depend only on the statistics of the value function, which may be derived using dynamic programming. In the special case of MDPs with uniformly bounded value functions, simpler confidence intervals are provided for the return process. Finally, we study the effect of epistemic uncertainty on the derived confidence intervals. Numerical examples are provided to show how these bounds may be used in practice.
2026-08-14
Finding the Frame @ Reinforcement Learning Conference (published)
openreview.net
openreview.net
TEA: Text Encoder Alignment for Robust Concept Erasure in Text-to-Image Models
Alireza Farashah
Zhuan Shi
Negar Rostamzadeh
Golnoosh Farnadi
Text-to-image diffusion models can be misused to generate harmful content through adversarial or paraphrased prompts that bypass built-in sa… (see more)fety mechanisms. Existing concept erasure methods often suffer from limited robustness against adversarial prompts, degradation of benign generation quality, or reliance on inference-time interventions that introduce persistent computational overhead. To address these limitations, we formulate concept erasure as a domain alignment problem in the text representation space. We propose a lightweight Text Encoder Alignment framework (TEA) that fine-tunes only the text encoder while keeping the generative backbone fully frozen. Given concept--anchor prompt pairs, our method trains a discriminator to distinguish token-level representations of concept-containing prompts from those of safe anchor prompts, while updating the text encoder to make these representations indistinguishable. TEA introduces zero inference-time overhead and requires only a small number of fine-tuning steps, making it highly efficient to deploy at scale. Despite this efficiency, TEA achieves state-of-the-art erasure robustness against black-box and white-box adversarial attacks on Stable Diffusion v1.4, while preserving generation quality on benign prompts. Furthermore, TEA is model-agnostic and achieves the lowest attack success rate on Stable Diffusion v3.5, extending concept erasure to a Rectified Flow Transformer architecture with T5 conditioning where prior methods remain largely unexplored. Code is available at \href{https://github.com/alirezafarashah/TEA.git}{https://github.com/alirezafarashah/TEA.git}
2026-08-14
arXiv (preprint)
doi.org
arxiv.org
Three Winning AI Strategies
Maxime C. Cohen
Eddy Hage-Youssef
Daniel McCarthy
D. Daniel Sokol
AI competition is widely framed as a winner-take-all race. Mobile consumer data for the AI assistant category from 2023 through 2025 suggest… (see more)s otherwise: three strategies coexist, and all are growing. Scale (ChatGPT), adjacency (Gemini), and premium specialization (Claude) capture value in strategically distinct ways. Major launches reward the launcher without measurably harming rivals. These archetypes are not unique to AI; they recur whenever a disruptive technology opens a market before competition settles. For managers, the most important question is which strategy their distribution, economics, and customer base can sustain.
2026-08-14
California Management Review (published)
doi.org
What are we measuring? A critical examination of MO-MuJoCo and evaluation consistency in continuous multi-objective reinforcement learning
Baptiste Bonin
Caro Strickland
Audrey Durand
Although Multi-Objective Reinforcement Learning (MORL) research relies heavily on MO-MuJoCo as its go-to continuous control benchmark, the v… (see more)alidity of the conclusions drawn from it remain underexamined. In this paper, we first discuss three structural limitations of MO-MuJoCo; 1) its objectives are decomposed from pre-existing scalar rewards rather than independently motivated goals; 2) environments repeat the same underlying trade-off structure across varied locomotion morphologies, providing \textit{surface variety} without genuine \textit{problem diversity}; and 3) empirically approximated Pareto fronts appear broadly convex across research, potentially failing to stress-test the limitations of scalarization-based techniques. Setting these concerns aside, we further demonstrate that algorithmic rankings under MO-MuJoCo are highly sensitive to often undocumented evaluation choices in research papers. Across five evaluation axes, including reference point selection, weight distribution, normalization, return type, and front extraction method, pairwise algorithm rankings reverse in up to 47\% of configurations. Variance decomposition reveals that normalization alone accounts for nearly 69\% of hypervolume variance, suppressing the algorithm impact. Ultimately, we argue that progress in MORL research requires not only increased scrutiny of the benchmarks we rely upon, but also greater clarity in how results obtained within them are reported.
2026-08-14
Finding the Frame @ Reinforcement Learning Conference (published)
openreview.net
openreview.net
Artificial intelligence in implementation science and practice: a living scoping review
Guillaume Fontaine
Olivia Di Lalla
Rachael Laritz
Jeremiah Durran
Chi Zhang
Jeffery Chan
Laura Crump
Alenda Dwiadila Matra Putra
Samira Abbasgholizadeh-Rahimi
Ruopeng An
Rinad S. Beidas
Christine Fahim
Elvin Geng
Ian D. Graham
Janna Hastings
Sylvie D. Lambert
France Légaré
Susan Michie
Byron J. Powell
Justin Presseau … (see 6 more)
Joseph Elias
Thomas Rudge
Sharon E. Straus
James Thomas
Vivian Welch
Natalie Taylor
Artificial intelligence (AI) encompasses computational systems that perform tasks typically requiring human intelligence, including machine … (see more)learning, generative AI, and agentic applications. The use of AI to support implementation activities is growing, but its applications and evaluation remain poorly characterised. We conducted the first cycle of a living scoping review to identify how AI is being used and evaluated across implementation science and practice. We followed JBI and Cochrane guidance and reported findings per PRISMA-ScR and PRISMA-LSR. We searched six databases through April 6, 2026, and included sources describing or evaluating AI to support implementation research or practice activities. We excluded adjacent uses such as knowledge synthesis automation. We extracted study characteristics, AI approaches, implementation tasks, evaluation methods, outcomes, and risks, and synthesised findings descriptively. We identified 7,203 records and included 40 sources, 34 (85%) of which were published since 2021. Thirty-one sources described, developed, or evaluated a specific AI system, comprising 22 primary research articles, three protocols, three conference abstracts, and three other sources. The remaining nine conceptual, methodological, framework or review articles discussed potential applications of AI in implementation science. Across all 40 sources, AI was most often used or proposed to support evaluation (25/40), implementation strategy selection and tailoring (23/40), barrier and facilitator assessment (21/40), and implementation monitoring (20/40). Among the 31 sources involving a specific AI system, the use of conventional machine learning was most common (10/31), followed by generative AI (7/31), multicomponent AI systems or studies comparing AI approaches (5/31), and non-generative deep learning (3/31). Implementation-related outcomes were reported or prospectively specified in 25/31 sources, technical performance in 19/31, human-centred outcomes in 12/31, time or efficiency in 10/31, clinical or health system outcomes in 9/31, equity in 3/31, and cost or resource outcomes in 2/31. Risks were discussed in about half of sources, although assessment of harms and environmental or societal consequences was rare. AI is in a nascent stage of supporting implementation science and practice. Reported use is narrow and methodologically underdeveloped, and much routine use is likely unpublished. The field needs rigorous, comparative, prospective, and equity-attentive research to establish whether and how AI improves implementation methods, processes, and outcomes. Open Science Framework, May 2025: https://doi.org/10.17605/OSF.IO/2Q5DV
2026-08-13
Implementation Science (published)
doi.org
Determinants of functional burden pleiotropy and gene dosage responses across human traits
Sayeh Kazem
Kuldeep Kumar
Jane Yang
Florian Benitiere
Guillaume Huguet
Josephine Mollon
Thomas Renne
Laura M. Schultz
Emma E.M. Knowles
Worrawat Engchuan
Omar Shanta
Bhooma Thiruvahindrapuram
Jeffrey R. MacDonald
Celia M. T. Greenwood
Stephen W. Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Guillaume Dumas
Sébastien Jacquemont
Pleiotropic and monotonic effects of gene dosage are central to understanding comorbidities in developmental pediatric and psychiatric disor… (see more)ders, yet the underlying biological processes are not well characterized. Here we develop a functional burden analysis to investigate the association of all protein-coding copy-number variants, genome-wide, with 43 complex traits in approximately 500,000 UK Biobank participants. We test variant associations disrupting 172 tissue or cell-type gene sets, finding associations for all traits, which we replicate in the All of Us cohort. Functional burden pleiotropy, defined as the number of traits significantly associated with a gene set, correlates with genetic constraint and is higher for brain than non-brain functions, even after normalizing for genetic constraint. Levels of pleiotropy, measured by burden correlation, are similar in deletions and loss-of-function single-nucleotide variants, and higher than in common variants and duplications. Most gene dosage responses are non-monotonic, with deletions and duplications showing same-direction effects, and monotonic responses decrease with genetic constraint. We observe associations between functional gene sets and traits for either deletions or duplications, but rarely both, with negatively correlated effect sizes. Together, these results link genetic constraint and brain-specific mechanisms to the whole-body multimorbidity of neurodevelopmental and psychiatric conditions. Gene dosage can help explain comorbidities in developmental pediatric and psychiatric disorders. Here, the authors map how rare copy-number variants disrupting tissue and cell-type gene sets shape 43 human traits.
2026-08-13
Nature Communications (published)
doi.org