Publications

Posthoc Interpretation via Quantization
Yusuf Cem Sübakan
Francesco Paissan
Mirco Ravanaelli
In this paper, we introduce a new approach, called Posthoc Interpretation via Quantization (PIQ), for interpreting decisions made by trained… (voir plus) classifiers. Our method utilizes vector quantization to transform the representations of a classifier into a discrete, class-specific latent space. The class-specific codebooks act as a bottleneck that forces the interpreter to focus on the parts of the input data deemed relevant by the classifier for making a prediction. Our model formulation also enables learning concepts by incorporating the supervision of pretrained annotation models such as state-of-the-art image segmentation models. We evaluated our method through quantitative and qualitative studies involving black-and-white images, color images, and audio. As a result of these studies we found that PIQ generates interpretations that are more easily understood by participants to our user studies when compared to several other interpretation methods in the literature.
2023-03-21
ArXiv (prépublication)
doi.org
arxiv.org
Electromagnetic interference shielding in lightweight carbon xerogels
Biporjoy Sarkar
Floriane Miquet-Westphal
Sanyasi Bobbara
Ben George
Giovanni Beltrame
David Dousset
Ke Wu
Fabio Cicoira
With the increasing use of high-frequency electronic and wireless devices, electromagnetic interference (EMI) has become a growing concern d… (voir plus)ue to its potential impact on both electronic devices and human health. In this study, we demonstrated the performance of lightweight, electrically conducting 3D resorcinol-formaldehyde carbon xerogels, of 2.4 mm thickness, as an EMI shieldin the frequency range of 10–15 GHz (X-Ku band). The brittle carbon xerogels revealed complex porous structures with irregularly shaped pores that were randomly distributed. Electrochemical characterization revealed that the material behaved as an electrical double-layer capacitor. The carbon xerogels displayed reflection-dominated (∼ 84%) shielding behavior, with a total EMI shielding effectiveness (SE) value of ∼ 61 dB. The absorption process also contributed (∼ 16%) to the total SE. This behavior is attributed to the carbon xerogels' complex porous network, which effectively suppresses EM waves.
2023-03-20
Materials Research Express (publié)
doi.org
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT
Vincent Andrearczyk
Valentin Oreiller
Moamen A. Abobakr
Azadeh Akhavanallaf
Panagiotis Balermpas
Sarah Boughdad
Leo Capriotti
Joel Castelli
Catherine Cheze Le Rest
Pierre Decazes
Ricardo Correia
D. El-Habashy
Hesham M. Elhalawani
C. Fuller
Mario Jreige
Yomna Khamis
Agustina La Greca Saint-Esteven
Aliae Ar Hussein Mohamed
M. Naser
John O. Prior … (voir 11 de plus)
Su Ruan
Stephanie Tanadini-Lang
Olena Tankyevych
Yazdan Salimi
Martin Vallières
Pierre Véra
Dimitris Visvikis
K. Wahid
Habib Zaidi
Mathieu Hatt
Adrien Depeursinge
2023-03-17
Lecture Notes in Computer Science (publié)
doi.org
Behavioral Cloning for Crystal Design
Prashant Govindarajan
Santiago Miret
Jarrid Rector-Brooks
Mariano Phielipp
Janarthanan Rajendran
A. Chandar
Solid-state materials, which are made up of periodic 3D crystal structures, are particularly useful for a variety of real-world applications… (voir plus) such as batteries, fuel cells and catalytic materials. Designing solid-state materials, especially in a robust and automated fashion, remains an ongoing challenge. To further the automated design of crystalline materials, we propose a method to learn to design valid crystal structures given a crystal skeleton. By incorporating Euclidean equivariance into a policy network, we portray the problem of designing new crystals as a sequential prediction task suited for imitation learning. At each step, given an incomplete graph of a crystal skeleton, an agent assigns an element to a specific node. We adopt a behavioral cloning strategy to train the policy network on data consisting of curated trajectories generated from known crystals.
2023-03-16
ICLR.cc/2023/Workshop/ML4Materials (poster)
openreview.net
openreview.net
Spatial Hard Attention Modeling via Deep Reinforcement Learning for Skeleton-Based Human Activity Recognition
Bahareh Nikpour
Narges Armanfard
Deep learning-based algorithms have been very successful in skeleton-based human activity recognition. Skeleton data contains 2-D or 3-D coo… (voir plus)rdinates of human body joints. The main focus of most of the existing skeleton-based activity recognition methods is on designing new deep architectures to learn discriminative features, where all body joints are considered equally important in recognition. However, the importance of joints varies as an activity proceeds within a video and across different activities. In this work, we hypothesize that selecting relevant joints, prior to recognition, can enhance performance of the existing deep learning-based recognition models. We propose a spatial hard attention finding method that aims to remove the uninformative and/or misleading joints at each frame. We formulate the joint selection problem as a Markov decision process and employ deep reinforcement learning to train the proposed spatial-attention-aware agent. No extra labels are needed for the agent’s training. The agent takes a sequence of features extracted from skeleton video as input and outputs a sequence of probabilities for joints. The proposed method can be considered as a general framework that can be integrated with the existing skeleton-based activity recognition methods for performance improvement purposes. We obtain very competitive activity recognition results on three commonly used human activity recognition datasets.
2023-03-16
IEEE Transactions on Systems, Man, and Cybernetics: Systems (inconnu)
doi.org
Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease
Andrea I. Luppi
S. Parker Singleton
Justine Y. Hansen
Danilo Bzdok
Amy Kuceyeski
Richard F. Betzel
Bratislav Misic
Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain’s network architect… (voir plus)ure. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies.
2023-03-16
bioRxiv (prépublication)
doi.org
Instance-Conditioned GAN Data Augmentation for Representation Learning
Pietro Astolfi
Arantxa Casanova
Jakob Verbeek
P Vincent
Adriana Romero
Michal Drozdzal
2023-03-15
ArXiv (prépublication)
doi.org
arxiv.org
Analysis of gene expression and use of connectivity mapping to identify drugs for treatment of human glomerulopathies
Chen-Fang Chung
Joan Papillon
José R. Navarro-Betancourt
Julie Guillemette
Ameya Bhope
Amin Emad
Andrey V. Cybulsky
2023-03-12
Frontiers in Medicine (publié)
doi.org
Applying the column generation method to the intensity modulated high dose rate brachytherapy inverse planning problem
Majd Antaki
Marc-André Renaud
Marc Morcos
Jan Seuntjens
S. Enger
2023-03-12
Physics in Medicine & Biology (publié)
doi.org
Investigating the neural correlates of affective mentalizing and their association with general intelligence in patients with schizophrenia
Wladimir Tantchik
Melissa J. Green
Yann Quidé
Susanne Erk
Sebastian Mohnke
Carolin Wackerhagen
Nina Romanczuk-Seiferth
Heike Tost
Kristina Schwarz
Carolin Moessnang
Danilo Bzdok
Andreas Meyer-Lindenberg
Andreas Heinz
Henrik Walter
2023-03-12
Schizophrenia Research (inconnu)
doi.org
Optimization of the location and design of urban green spaces
Caroline Leboeuf
Margarida Carvalho
Yan Kestens
Benoit Thierry
2023-03-12
ArXiv (prépublication)
doi.org
arxiv.org
Neural manifolds and learning regimes in neural-interface tasks
Alexandre Payeur
Amy L. Orsborn
Guillaume Lajoie
Neural activity tends to reside on manifolds whose dimension is lower than the dimension of the whole neural state space. Experiments using … (voir plus)brain-computer interfaces (BCIs) with microelectrode arrays implanted in the motor cortex of nonhuman primates have provided ways to test whether neural manifolds influence learning-related neural computations. Starting from a learned BCI-controlled motor task, these experiments explored the effect of changing the BCI decoder to implement perturbations that were either “aligned” or not with the pre-existing neural manifold. In a series of studies, researchers found that within-manifold perturbations (WMPs) evoked fast reassociations of existing neural patterns for rapid adaptation, while outside-manifold perturbations (OMPs) triggered a slower adaptation process that led to the emergence of new neural patterns. Together, these findings have been interpreted as suggesting that these different rates of adaptation might be associated with distinct learning mechanisms. Here, we investigated whether gradient-descent learning could alone explain these differences. Using an idealized model that captures the fixed-point dynamics of recurrent neural networks, we uncovered gradient-based learning dynamics consistent with experimental findings. Crucially, this experimental match arose only when the network was initialized in a lazier learning regime, a concept inherited from deep learning theory. A lazy learning regime—in contrast with a rich regime—implies small changes on synaptic strengths throughout learning. For OMPs, these small changes were less effective at increasing performance and could lead to unstable adaptation with a heightened sensitivity to learning rates. For WMPs, they helped reproduce the reassociation mechanism on short adaptation time scales, especially with large input variances. Since gradient descent has many biologically plausible variants, our findings establish lazy gradient-based learning as a plausible mechanism for adaptation under network-level constraints and unify several experimental results from the literature.
2023-03-11
bioRxiv (prépublication)
doi.org