Publications

Seeing the world as animals do: How to leverage generative AI for ecological neuroscience
Shahab Bakhtiari
2024-12-31
The Transmitter (published)
doi.org
Self-Refined Generative Foundation Models for Wireless Traffic Prediction
Chengming Hu
Hao Zhou
Di Wu
Xi Chen
Jun Yan
Xue Liu
With a broad range of emerging applications in 6G networks, wireless traffic prediction has become a critical component of network managemen… (see more)t. However, the dynamically shifting distribution of wireless traffic in non-stationary 6G networks presents significant challenges to achieving accurate and stable predictions. Motivated by recent advancements in Generative AI (GenAI)-enabled 6G networks, this paper proposes a novel self-refined Large Language Model (LLM) for wireless traffic prediction, namely TrafficLLM, through in-context learning without parameter fine-tuning or model training. The proposed TrafficLLM harnesses the powerful few-shot learning abilities of LLMs to enhance the scalability of traffic prediction in dynamically changing wireless environments. Specifically, our proposed TrafficLLM embraces an LLM to iteratively refine its predictions through a three-step process: traffic prediction, feedback generation, and prediction refinement. Initially, the proposed TrafficLLM conducts traffic predictions using task-specific demonstration prompts. Recognizing that LLMs may generate incorrect predictions on the first attempt, this paper designs feedback demonstration prompts to provide multifaceted and valuable feedback related to these initial predictions. The validation scheme is further incorporated to systematically enhance the accuracy of mathematical calculations during the feedback generation process. Following this comprehensive feedback, our proposed TrafficLLM introduces refinement demonstration prompts, enabling the same LLM to further refine its predictions and thereby enhance prediction performance. Evaluations on two realistic datasets demonstrate that the proposed TrafficLLM outperforms LLM-based in-context learning methods, achieving performance improvements of 23.17% and 17.09%, respectively.
2024-12-31
IEEE Transactions on Vehicular Technology (published)
doi.org
arxiv.org
Sensory multi-brain stimulation enhances dyadic cooperative behavior
Ivo Leiva-Cisterna
Paulo Barraza
Eugenio Rodríguez
Guillaume Dumas
Hyperscanning research suggests that interbrain synchronization supports the regulation of social behavior. However, the evidence is predomi… (see more)nantly correlational, leaving a gap for epiphenomenal accounts, where synchrony merely represents concurrent stimulus processing rather than a mechanism relevant to interpersonal interactions. Here, we demonstrate that interbrain synchrony causally drives cooperative success, as evidenced by non-invasive stimulation enhancing coupling and subsequently improving performance in a concurrent interdependent cooperation task. We applied dual-sensory entrainment at 16 Hz and 40 Hz to dyads and compared their performance with non-entrained control dyads performing the same cooperation task. We found that dual stimulation improved interbrain synchrony at the targeted frequencies relative to controls, with 16 Hz entrainment producing the most prominent effect. Strikingly, sensory entrainment facilitated sustained behavioral coupling, allowing partners to maintain coordination over extended periods. Notably, these effects are contingent on improved response coordination, indicating the importance of interbrain coupling for facilitating coordination and demonstrating causally that partner neural attunement is necessary to produce effective joint behavior. Thus, our study supports the concept that interbrain synchrony represents a neural mechanism with functional specificity in social interactions.
2024-12-31
Social Cognitive and Affective Neuroscience (published)
doi.org
Seth H. Holmes, Fruits frais, corps brisés. Les ouvriers agricoles migrants aux États-Unis
Nicolas Roux
2024-12-31
La Nouvelle Revue du Travail (published)
doi.org
Is sharing always caring? Entropy, boundaries and the plurality of psychotherapeutic process.
Lena Adel
Ana Gómez-Carrillo
Jonas Mago
Michael Lifshitz
Guillaume Dumas
2024-12-31
Physics of Life Reviews (published)
doi.org
Single-Cell Multi-Omics Profiling of Immune Cells Isolated from Atherosclerotic Plaques in Male ApoE Knockout Mice Exposed to Arsenic
Kiran Makhani
Xiuhui Yang
France Dierick
Nivetha Subramaniam
Natascha Gagnon
Talin Ebrahimian
Stephanie Lehoux
Hao Wu
Jun Ding
Koren K. Mann
Millions worldwide are exposed to elevated levels of arsenic that significantly increase their risk of developing atherosclerosis, a patholo… (see more)gy primarily driven by immune cells. While the impact of arsenic on immune cell populations in atherosclerotic plaques has been broadly characterized, cellular heterogeneity is a substantial barrier to in-depth examinations of the cellular dynamics for varying immune cell populations. This study aimed to conduct single-cell multi-omics profiling of atherosclerotic plaques in apolipoprotein E knockout (ApoE–/–) mice to elucidate transcriptomic and epigenetic changes in immune cells induced by arsenic exposure. The ApoE–/– mice were fed a high-fat diet and were exposed to either 200 ppb arsenic in drinking water or a tap water control, and single-cell multi-omics profiling was performed on atherosclerotic plaque-resident immune cells. Transcriptomic and epigenetic changes in immune cells were analyzed within the same cell to understand the effects of arsenic exposure. Our data revealed that the transcriptional profile of macrophages from arsenic-exposed mice were significantly different from that of control mice and that differences were subtype specific and associated with cell–cell interaction and cell fates. Additionally, our data suggest that differences in arsenic-mediated changes in chromosome accessibility in arsenic-exposed mice were statistically more likely to be due to factors other than random variation compared to their effects on the transcriptome, revealing markers of arsenic exposure and potential targets for intervention. These findings in mice provide insights into how arsenic exposure impacts immune cell types in atherosclerosis, highlighting the importance of considering cellular heterogeneity in studying such effects. The identification of subtype-specific differences and potential intervention targets underscores the significance of understanding the molecular mechanisms underlying arsenic-induced atherosclerosis. Further research is warranted to validate these findings and explore therapeutic interventions targeting immune cell dysfunction in arsenic-exposed individuals. https://doi.org/10.1289/EHP14285
2024-12-31
Environmental Health Perspectives (published)
doi.org
Software and Data for "Capacity Planning in Stable Matching"
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Ignacio Rios
Alfredo Torrico
2024-12-31
Operational Research (published)
doi.org
Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSO
Junyi Song
Shadi Zabad
Archer Yang
Simon Gravel
Yuemei Li
2024-12-31
Bioinform. (published)
doi.org
Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy
Abdul Al-Shawwa
Michael Craig
Kalum Ost
David Anderson
Steve Casha
W Bradley Jacobs
Nathan Evaniew
Saswati Tripathy
Jacques Bouchard
Peter Lewkonia
Fred Nicholls
Alex Soroceanu
Ganesh Swamy
Kenneth C Thomas
Stephan duPlessis
Michael MH Yang
Julien Cohen-Adad
Nicholas Dea
Jefferson R Wilson
David W Cadotte
Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Clinical guidelines regarding surg… (see more)ery for patients with mild DCM and minimal symptoms remain uncertain. This study aims to identify imaging and clinical predictors of neurological deterioration in mild DCM and explore pathophysiological correlates to guide clinical decision-making. Patients with mild DCM underwent advanced MRI scans that included T2-weighted, diffusion tensor imaging and magnetisation transfer (MT) sequences, along with clinical outcome measures at baseline and 6-month intervals after enrolment. Quantitative MRI (qMRI) metrics were derived above and below maximally compressed cervical levels (MCCLs). Various machine learning (ML) models were trained to predict 6 month neurological deterioration, followed by global and local model interpretation to assess feature importance. A total of 49 patients were followed for a maximum of 2 years, contributing 110 6-month data entries. Neurological deterioration occurred in 38% of cases. The best-performing ML model, combining clinical and qMRI metrics, achieved a balanced accuracy of 83%, and an area under curve-receiver operating characteristic of 0.87. Key predictors included MT ratio (demyelination) above the MCCL in the dorsal and ventral funiculi and moderate tingling in the arm, shoulder or hand. qMRI metrics significantly improved predictive performance compared to models using only clinical (bal. acc=68.1%) or imaging data (bal. acc=57.4%). Reduced myelin content in the dorsal and ventral funiculi above the site of compression, combined with sensory deficits in the hands and gait/balance disturbances, predicts 6-month neurological deterioration in mild DCM and may warrant early surgical intervention.
2024-12-31
BMJ Neurology Open (published)
doi.org
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2024-12-31
Int. Trans. Oper. Res. (published)
doi.org
arxiv.org
Structure–function coupling and decoupling during movie watching and resting state: Novel insights bridging EEG and structural imaging
Venkatesh Subramani
Giulia Lioi
Karim Jerbi
Nicolas Farrugia
The intricate structural and functional architecture of the brain enables a wide range of cognitive processes ranging from perception and ac… (see more)tion to higher-order abstract thinking. Despite important progress, the relationship between the brain’s structural and functional properties is not yet fully established. In particular, the way the brain’s anatomy shapes its electrophysiological dynamics remains elusive. The electroencephalography (EEG) activity recorded during naturalistic tasks is thought to exhibit patterns of coupling with the underlying brain structure that vary as a function of behavior. Yet these patterns have not yet been sufficiently quantified. We address this gap by jointly examining individual Diffusion-Weighted Imaging (DWI) scans and continuous EEG recorded during video-watching and resting state, using a Graph Signal Processing (GSP) framework. By decomposing the structural graph into Eigenmodes and expressing the EEG activity as an extension of anatomy, GSP provides a way to quantify the structure-function coupling. We elucidate how the structure shapes function during naturalistic tasks such as movie-watching and how this association is modulated by tasks. We quantify the coupling relationship in a region-, time-, frequency-resolved manner. First of all, our findings indicate that the EEG activity in the sensorimotor cortex is strongly coupled with brain structure, while the activity in higher-order systems is less constrained by anatomy, i.e., shows more flexibility. In addition, we found that watching videos was associated with stronger structure-function coupling in the sensorimotor cortex, as compared to resting-state data. Second, time-resolved analysis revealed that the unimodal systems undergo minimal temporal fluctuation in structure-function association, and the transmodal system displays highest temporal fluctuations, with the exception of PCC seeing low fluctuations. Lastly, our frequency-resolved analysis revealed a consistent topography across different EEG rhythms, suggesting a similar relationship with the anatomical structure across frequency bands. Together, this unprecedented characterization of the link between structure and function using continuous EEG during naturalistic behavior underscores the role of anatomy in shaping ongoing cognitive processes. Taken together, by combining the temporal and spectral resolution of EEG and the methodological advantages of GSP, our work sheds new light onto the anatomo-functional organization of the brain.
2024-12-31
Imaging Neuroscience (published)
doi.org
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Yoshua Bengio
Michael Cohen
Damiano Fornasiere
Joumana Ghosn
Pietro Greiner
Matt MacDermott
Sören Mindermann
Adam Oberman
Jesse Richardson
Oliver Richardson
Marc-Antoine Rondeau
Pierre-Luc St-Charles
David Williams-King
The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue go… (see more)als across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.
2024-12-31
arXiv (preprint)
doi.org
arxiv.org