Publications

Concurrent product layout design optimization and dependency management using a modified NSGA-III approach
Yann-Seing Law-Kam Cio
Giovanni Beltrame
Aurelian Vadean
Abolfazl Mohebbi
Sofiane Achiche
The complexity of mechatronic systems has increased with the significant advancements of technology in their components which makes their de… (voir plus)sign more challenging. This is due to the need for incorporating expertise from different domains as well as the increased number and complexity of components integrated into the product. To alleviate the burden of designing such products, many industries and researchers are attracted to the concept of modularization which is to identify a subset of system components that can form a module. To achieve this, a novel product-related dependency management approach is proposed in this paper with the support of an augmented design structure matrix. This approach makes it possible to model positive and negative dependencies and to compute the combination potency between components to form modules. This approach is then integrated into a modified non-dominated sorting genetic algorithm III to concurrently optimize the design and identify the modules. The methodology is exemplified through the case study of a layout design of an automatic greenhouse. By applying the proposed methodology to the case study, it was possible to generate concepts that decreased the number of modules from 9 down to 4 while ensuring the optimization of the design performance.
2024-08-20
Frontiers in Mechanical Engineering (publié)
doi.org
BlueCelluLab: Biologically Detailed Neural NetworkExperimentation API
Anıl Tuncel
Werner Van Geit
Mike Gevaert
Benjamin Torben-Nielsen
Darshan Mandge
İlkan Kılıç
Aurélien T. Jaquier
Eilif Muller
Lida Kanari
Henry Markram
The NEURON simulator, established in 1984 and continuously developed since, stands as\nthe preeminent tool for neuron simulation within comp… (voir plus)utational neuroscience. Its widespread\nadoption and compatibility with computational clusters and supercomputers underscore its\npivotal role in large-scale neuronal research. However, its integration with the Python pro-\ngramming language has introduced complexities, particularly concerning memory management\nand object lifecycle. To conceal these challenges from the user and seamlessly interface\nwith community standards for neural network representation data formats such as SONATA,\nwe introduce BlueCelluLab. The high-level Python API simplifies the execution of neural\nsimulations, ranging from single neurons to intricate networks, by managing complexities\nrelated to memory management and object lifecycle, thus providing a streamlined experience\nfor users. Today, BlueCelluLab is powering various Python packages, command line interfaces,\nweb applications, and data analysis workflows.
2024-08-16
Journal of Open Source Software (publié)
doi.org
Zero-Shot Object-Centric Representation Learning
Aniket Rajiv Didolkar
Andrii Zadaianchuk
Anirudh Goyal
Michael Curtis Mozer
Yoshua Bengio
Georg Martius
Maximilian Seitzer
The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (voir plus). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.
2024-08-16
ArXiv (prépublication)
doi.org
arxiv.org
What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models
Ahmed Imtiaz Humayun
Ibtihel Amara
Candice Schumann
Golnoosh Farnadi
Negar Rostamzadeh
Mohammad Havaei
2024-08-14
ArXiv (prépublication)
doi.org
openreview.net
<scp>RF</scp> shimming in the cervical spinal cord at <scp>7 T</scp>
Daniel Papp
Kyle M. Gilbert
Gaspard Cereza
Alexandre D'Astous
Nibardo Lopez‐Rios
Mathieu Boudreau
Marcus J. Couch
Pedram Yazdanbakhsh
Robert L. Barry
Eva Alonso‐Ortiz
Julien Cohen‐Adad
The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enab… (voir plus)ling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification.
2024-08-12
Magnetic Resonance in Medicine (publié)
doi.org
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
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
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