Publications

Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread
Nicola Neophytou
Afaf Taïk
Golnoosh Farnadi
The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such dispa… (voir plus)rities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking
Pratheeksha Nair
Javin Liu
Catalina Vajiac
Andreas Olligschlaeger
Duen Horng Chau
Mirela T. Cazzolato
Cara Jones
Christos Faloutsos
Reihaneh Rabbany
Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions o… (voir plus)f people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically contain hundreds of thousands of ads including those posted by independent escorts, massage parlor agencies and spammers (fake ads). Detecting suspicious activity in these ads is difficult and developing data-driven methods is challenging due to the hard-to-label, complex and sensitive nature of the data. In this paper, we propose T-Net, which unlike previous solutions, formulates this problem as weakly supervised classification. Since it takes several months to years to investigate a case and obtain a single definitive label, we design domain-specific signals or indicators that provide weak labels. T-Net also looks into connections between ads and models the problem as a graph learning task instead of classifying ads independently. We show that T-Net outperforms all baselines on a real-world dataset of ads by 7% average weighted F1 score. Given that this data contains personally identifiable information, we also present a realistic data generator and provide the first publicly available dataset in this domain which may be leveraged by the wider research community.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Abstract 6324: Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity
Taisuke Kondo
François X. P. Bourassa
Sooraj R. Achar
Justyn DuSold
Pablo Cespedes
Madison Wahlsten
Audun Kvalvaag
Guillaume Gaud
Paul E. Love
Michael Dustin
Grégoire Altan-Bonnet
Paul François
Naomi Taylor
Chimeric Antigen Receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies. However, the … (voir plus)rarity of cell surface protein targets that are specific to cancerous but not vital healthy tissue has hindered its broad application to solid tumor treatment. While new logic-gated CAR designs have shown reduced toxicity against healthy tissues, the generalizability of such approaches across tumors remains unclear. Here, we harness a universal characteristic of endogenous T cell receptors (TCRs), their ability to discriminate between self and non-self ligands through inhibition of response against self (weak) antigens, to develop a broadly applicable method of enhancing immunotherapeutic precision. We hypothesized that this discriminatory mechanism, known as antagonism, would apply across receptors, allowing for a transfer of specificity from TCRs onto CARs. We therefore systematically mapped out the responses of CAR T cells to joint TCR and CAR stimulations. We first engineered murine T cells with an ovalbumin-specific TCR to express a CAR targeting murine CD19 and discovered that the expression of a strong TCR antigen on CD19+ leukemia enhanced CAR T killing. Importantly though, the presence of a weak TCR antigen antagonized CAR T responses, assessed by in vitro multiplexed dynamic profiling as well as in vivo cytotoxicity. We developed a mathematical model based on cross-receptor inhibitory coupling that accurately predicted the extent of TCR/CAR antagonism across a wide range of immunological settings. This model was validated in a CD19+ B16 mouse melanoma model showing that TCR/CAR antagonism decreased the infiltration of a tumor-reactive T cell cluster, while TCR/CAR agonism enhanced infiltration of this T cell cluster. We then applied our quantitative knowledge of TCR/CAR crosstalk to design an Antagonism-Enforced Braking System (AEBS) for CAR T cell therapy. This was assessed in a model system using a CAR targeting the tyrosine-protein kinase erbB-2 (HER2) together with a hedgehog acyltransferase (HHAT) peptide-specific TCR that binds strongly to mutated tumor neoantigen while retaining weak affinity for the wild-type self-antigen on healthy tissue. We established a humanized in vivo model of CAR T function and found that AEBS CAR T cells maintained high anti-tumor activity against a human lung adenocarcinoma (PC9) but notably, their anti-tissue cytotoxicity against human bronchial epithelial cells (BEAS-2B) was minimized. AEBS CAR T cells therefore sharpen the discriminatory power of synthetic anti-tumor lymphocytes. Our work highlights a novel mechanism by which TCRs can enforce CAR T cell specificity, with practical implications for the rational design of future anti-leukemia immunotherapies. Citation Format: Taisuke Kondo, François X. Bourassa, Sooraj Achar, Justyn DuSold, Pablo Cespedes, Madison Wahlsten, Audun Kvalvaag, Guillaume Gaud, Paul Love, Michael Dustin, Gregoire Altan-Bonnet, Paul François, Naomi Taylor. Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6324.
2024-03-22
Cancer Research (publié)
doi.org
Essential surgery delivery in the Northern Kivu Province of the Democratic Republic of the Congo
Luc Kalisya Malemo
Ava Yap
Boniface Mitume
Christian Salmon
Kambale Karafuli
Dan Poenaru
Rosebella Onyango
2024-03-22
BMC Surgery (publié)
doi.org
Vulnerability of terrestrial vertebrate food webs to anthropogenic threats in Europe
Louise M. J. O'Connor
Francesca Cosentino
Michael B. J. Harfoot
Luigi Maiorano
Chiara Mancino
Laura J. Pollock
Wilfried Thuiller
2024-03-22
Global Change Biology (publié)
doi.org
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
Jonathan Lebensold
Maziar Sanjabi
Pietro Astolfi
Adriana Romero Soriano
Kamalika Chaudhuri
Mike Rabbat
Chuan Guo
2024-03-21
ArXiv (prépublication)
doi.org
arxiv.org
Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman
Martin Weiss
Manuel Wüthrich
Yoshua Bengio
Erran L. Li
Chris Pal
Bernhard Schölkopf
2024-03-21
ArXiv (prépublication)
doi.org
arxiv.org
Multi-Resolution Continuous Normalizing Flows
Vikram Voleti
Chris Finlay
Adam M. Oberman
Chris Pal
2024-03-21
Annals of Mathematics and Artificial Intelligence (publié)
doi.org
openreview.net
Assistive sensory-motor perturbations influence learned neural representations
Pavithra Rajeswaran
Alexandre Payeur
Guillaume Lajoie
Amy L. Orsborn
Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using B… (voir plus)rain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.
2024-03-20
bioRxiv (prépublication)
doi.org
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Khaoula Chehbouni
Megha Roshan
Emmanuel Ma
Futian Andrew Wei
Afaf Taïk
Jackie CK Cheung
Golnoosh Farnadi
2024-03-20
ArXiv (prépublication)
doi.org
arxiv.org
HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
Daniel Duenias
Brennan Nichyporuk
Tal Arbel
Tammy Riklin-Raviv
The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Reco… (voir plus)rds (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.
2024-03-20
ArXiv (prépublication)
doi.org
arxiv.org
Unravelling the neural dynamics of hypnotic susceptibility: Aperiodic neural activity as a central feature of hypnosis
Mathieu Landry
Jason da Silva Castanheira
Catherine Boisvert
Floriane Rousseaux
Jérôme Sackur
Amir Raz
Philippe Richebé
David Ogez
Pierre Rainville
Karim Jerbi
2024-03-20
bioRxiv (prépublication)
doi.org