Publications

Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection
Alex Koran
Hadi Hojjati
Narges Armanfard
Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade. Despite the reported improveme… (voir plus)nts in several papers, the practical application of these models remains limited. Recent studies have cast doubt on these models, attributing their results to flawed evaluation techniques. However, the impact of initialization has largely been overlooked. This paper provides a critical analysis of the initialization effects on TSAD model performance. Our extensive experiments reveal that TSAD models are highly sensitive to hyperparameters such as window size, seed number, and normalization. This sensitivity often leads to significant variability in performance, which can be exploited to artificially inflate the reported efficacy of these models. We demonstrate that even minor changes in initialization parameters can result in performance variations that overshadow the claimed improvements from novel model architectures. Our findings highlight the need for rigorous evaluation protocols and transparent reporting of preprocessing steps to ensure the reliability and fairness of anomaly detection methods. This paper calls for a more cautious interpretation of TSAD advancements and encourages the development of more robust and transparent evaluation practices to advance the field and its practical applications.
2024-08-12
ArXiv (prépublication)
doi.org
arxiv.org
Feasibility and safety of endoscopic ultrasound-guided diffusing alpha emitter radiation therapy for advanced pancreatic cancer: Preliminary data
Corey S Miller
Magali Lecavalier-Barsoum
Kim Ma
Miriam Santos Dutra
Youri Kaitoukov
Boris Bahoric
Nada Tomic
Francine Dinelle
Shirin Enger
Gerald Batist
Stephen Yang
Donald Laporta
Petr Kavan
Anand Sahai
David Roberge
David Donath
Abstract Background and study aims Pancreatic cancer is a devastating disease with limited locoregional treatment options. Diffusing alpha-e… (voir plus)mitter radiation therapy (Alpha DaRT), a novel cancer treatment using alpha-particle interstitial radiotherapy, may help address this challenge. The aim of this study was to evaluate the feasibility and safety of endoscopic ultrasound (EUS)-guided Alpha DaRT for advanced pancreatic cancer. Patients and methods Patients with inoperable locally advanced or metastatic pancreatic adenocarcinoma were treated with EUS-guided Alpha DaRT insertion. The Alpha DaRT sources were delivered into pancreatic tumors using a standard EUS needle with a novel proprietary applicator. Adverse events (AEs) were assessed based on the Common Terminology Criteria for Adverse Events version 5.0. Tumor response was evaluated by imaging 4 to 6 weeks post treatment. Results The first five patients were treated between March and September 2023. The procedure was technically successful in all cases, with Alpha DaRT sources inserted into the target tumor. Estimated gross tumor volume coverage ranged from 8% to 44%. Fourteen AEs were reported among three patients. Four were serious AEs, none of which was associated with the treatment, but rather, with disease progression or medical assistance in dying. Only two AEs (mild) were deemed possibly related to the study device. At the 35-day visit, two patients had progressive disease and three had stable disease, with one of the latter showing partial response 2 months post procedure. Conclusions Preliminary results from this first-in-human trial indicate that EUS-guided Alpha DaRT treatment for unresectable pancreatic cancer is feasible and safe, with no device-associated serious AEs. Further investigation of this promising novel modality is underway.
2024-08-11
Endoscopy International Open (publié)
doi.org
Oxygen thresholds in critically ill patients: need for personalized targets. Author's reply.
Guillaume Dumas
Laveena Munshi
2024-08-11
Intensive Care Medicine (publié)
doi.org
Path-based reasoning for biomedical knowledge graphs with BioPathNet
Yue Hu
Svitlana Oleshko
Samuele Firmani
Zhaocheng Zhu
Hui Cheng
Maria Ulmer
Matthias Arnold
Maria Colomé-Tatché
Jian Tang
Sophie Xhonneux
Annalisa Marsico
Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) m… (voir plus)ethods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer’s, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet’s interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.
2024-08-09
bioRxiv (prépublication)
doi.org
Revisiting Feature Prediction for Learning Visual Representations from Video
Adrien Bardes
Quentin Garrido
Jean Ponce
Xinlei Chen
Michael G. Rabbat
Yann Lecun
Mahmoud Assran
Nicolas Ballas
2024-08-08
TMLR (accepté)
doi.org
openreview.net
Cardinality Minimization, Constraints, and Regularization: A Survey
Andreas M. Tillmann
Daniel Bienstock
Andrea Lodi
Alexandra Schwartz
We survey optimization problems that involve the cardinality of variable vectors in constraints or the objective function. We provide a unif… (voir plus)ied viewpoint on the general problem classes and models, and give concrete examples from diverse application fields such as signal and image processing, portfolio selection, or machine learning. The paper discusses general-purpose modeling techniques and broadly applicable as well as problem-specific exact and heuristic solution approaches. While our perspective is that of mathematical optimization, a main goal of this work is to reach out to and build bridges between the different communities in which cardinality optimization problems are frequently encountered. In particular, we highlight that modern mixed-integer programming, which is often regarded as impractical due to commonly unsatisfactory behavior of black-box solvers applied to generic problem formulations, can in fact produce provably high-quality or even optimal solutions for cardinality optimization problems, even in large-scale real-world settings. Achieving such performance typically draws on the merits of problem-specific knowledge that may stem from different fields of application and, e.g., shed light on structural properties of a model or its solutions, or lead to the development of efficient heuristics; we also provide some illustrative examples.
2024-08-07
SIAM Review (publié)
doi.org
arxiv.org
Large language models auto-profile conscious awareness changes under psychedelic drug effects
Danilo Bzdok
Robin Carhart-Harris
Chloé Savignac
Gregory Bell
Steven Laureys
Abstract

Psychedelic experiences open a colorful view into drug-induced changes in conscious awareness. Small-samp… (voir plus)le studies on psychedelic drug action have gained traction in recent years. Yet, today’s means for measuring changes in subjective experience are mostly limited to legacy questionnaires of pre-assumed relevance, which could be complemented by bottom-up explorations of semantic facets that underlie experience reports. Here, we show how to harness large language models (LLMs) to i) design from scratch, ii) annotate at scale, and iii) evaluate with rigor a vast portfolio of experience dimensions during psychoactive drug influence, yielding > 2 million automatic dimension ratings that would otherwise have been done by hand. Investigator-independent LLM scoring of these drug effects on the human mind alone allowed to robustly discriminate the unique mental effects of 30 psychoactive substances. Successful knowledge integration of how psychedelics mediate shifts in subjective awareness will be an unavoidable milestone towards charting the full drug design space.

2024-08-07
Research Square (prépublication)
doi.org
Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps
Mariam Zabihi
Seyed Mostafa Kia
Thomas Wolfers
Stijn de Boer
Charlotte Fraza
Richard Dinga
Alberto Llera Arenas
Danilo Bzdok
Christian F. Beckmann
Andre Marquand
Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapp… (voir plus)ing and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.
2024-08-07
Public Library of Science ONE (publié)
doi.org
Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability
Divyansha Lachi
Ann Huang
Augustine N. Mavor-Parker
Arna Ghosh
Blake Richards
Anthony Zador
The development of neural connectivity is a crucial biological process that gives rise to diverse brain circuits and behaviors. Neural devel… (voir plus)opment is a stochastic process, but this stochasticity is often treated as a nuisance to overcome rather than as a functional advantage. Here we use a computational model, in which connection probabilities between discrete cell types are genetically specified, to investigate the benefits of stochasticity in the development of neural wiring. We show that this model can be viewed as a generalization of a powerful class of artificial neural networks—Bayesian neural networks—where each network parameter is a sample from a distribution. Our results reveal that stochasticity confers a greater benefit in large networks and variable environments, which may explain its role in organisms with larger brains. Surprisingly, we find that the average fitness over a population of agents is higher than a single agent defined by the average connection probability. Our model reveals how developmental stochasticity, by inducing a form of non-heritable phenotypic variability, can increase the probability that at least some individuals will survive in rapidly changing, unpredictable environments. Our results suggest how stochasticity may be an important feature rather than a bug in neural development.
2024-08-07
bioRxiv (prépublication)
doi.org
Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies
Andrea I. Luppi
S. Parker Singleton
Justine Y. Hansen
Keith W. Jamison
Danilo Bzdok
Amy Kuceyeski
Richard F. Betzel
Bratislav Misic
The mechanisms linking the brain’s network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveragi… (voir plus)ng principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.
2024-08-04
Nature Biomedical Engineering (publié)
doi.org
Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity
Charlotte Maschke
Jordan O'Byrne
Michele Angelo Colombo
Melanie Boly
Olivia Gosseries
Steven Laureys
Mario Rosanova
Karim Jerbi
Stefanie Blain-Moraes
Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits ad… (voir plus)aptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects’ PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness.
2024-08-04
Communications Biology (publié)
doi.org
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Julien Ferry
Gabriel Laberge
Ulrich Matchi Aïvodji
A hybrid model involves the cooperation of an interpretable model and a complex black box. At inference, any input of the hybrid model is as… (voir plus)signed to either its interpretable or complex component based on a gating mechanism. The advantages of such models over classical ones are two-fold: 1) They grant users precise control over the level of transparency of the system and 2) They can potentially perform better than a standalone black box since redirecting some of the inputs to an interpretable model implicitly acts as regularization. Still, despite their high potential, hybrid models remain under-studied in the interpretability/explainability literature. In this paper, we remedy this fact by presenting a thorough investigation of such models from three perspectives: Theory, Taxonomy, and Methods. First, we explore the theory behind the generalization of hybrid models from the Probably-Approximately-Correct (PAC) perspective. A consequence of our PAC guarantee is the existence of a sweet spot for the optimal transparency of the system. When such a sweet spot is attained, a hybrid model can potentially perform better than a standalone black box. Secondly, we provide a general taxonomy for the different ways of training hybrid models: the Post-Black-Box and Pre-Black-Box paradigms. These approaches differ in the order in which the interpretable and complex components are trained. We show where the state-of-the-art hybrid models Hybrid-Rule-Set and Companion-Rule-List fall in this taxonomy. Thirdly, we implement the two paradigms in a single method: HybridCORELS, which extends the CORELS algorithm to hybrid modeling. By leveraging CORELS, HybridCORELS provides a certificate of optimality of its interpretable component and precise control over transparency. We finally show empirically that HybridCORELS is competitive with existing hybrid models, and performs just as well as a standalone black box (or even better) while being partly transparent.
2024-08-04
TMLR (accepté)
doi.org
openreview.net