20,622 results on '"Huguet, A."'
Search Results
2. ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
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Orlando, Riccardo, Huguet-Cabot, Pere-Lluis, Barba, Edoardo, and Navigli, Roberto
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module undertakes the identification of candidate entities or relations that could potentially appear within the text. Subsequently, the Reader module is tasked to discern the pertinent retrieved entities or relations and establish their alignment with the corresponding textual spans. Notably, we put forward an innovative input representation that incorporates the candidate entities or relations alongside the text, making it possible to link entities or extract relations in a single forward pass and to fully leverage pre-trained language models contextualization capabilities, in contrast with previous Retriever-Reader-based methods, which require a forward pass for each candidate. Our formulation of EL and RE achieves state-of-the-art performance in both in-domain and out-of-domain benchmarks while using academic budget training and with up to 40x inference speed compared to competitors. Finally, we show how our architecture can be used seamlessly for Information Extraction (cIE), i.e. EL + RE, and setting a new state of the art by employing a shared Reader that simultaneously extracts entities and relations., Comment: To be presented at ACL 2024
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- 2024
3. ImageFlowNet: Forecasting Multiscale Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images
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Liu, Chen, Xu, Ke, Shen, Liangbo L., Huguet, Guillaume, Wang, Zilong, Tong, Alexander, Bzdok, Danilo, Stewart, Jay, Wang, Jay C., Del Priore, Lucian V., and Krishnaswamy, Smita
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
The forecasting of disease progression from images is a holy grail for clinical decision making. However, this task is complicated by the inherent high dimensionality, temporal sparsity and sampling irregularity in longitudinal image acquisitions. Existing methods often rely on extracting hand-crafted features and performing time-series analysis in this vector space, leading to a loss of rich spatial information within the images. To overcome these challenges, we introduce ImageFlowNet, a novel framework that learns latent-space flow fields that evolve multiscale representations in joint embedding spaces using neural ODEs and SDEs to model disease progression in the image domain. Notably, ImageFlowNet learns multiscale joint representation spaces by combining cohorts of patients together so that information can be transferred between the patient samples. The dynamics then provide plausible trajectories of progression, with the SDE providing alternative trajectories from the same starting point. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We then demonstrate ImageFlowNet's effectiveness through empirical evaluations on three longitudinal medical image datasets depicting progression in retinal geographic atrophy, multiple sclerosis, and glioblastoma., Comment: Fixed some typos. Merged multibib
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- 2024
4. Evaluating the local bandgap across InxGa1-xAs multiple quantum wells in a metamorphic laser via low-loss EELS
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Stephen, Nicholas, Pinto-Huguet, Ivan, Lawrence, Robert, Kepaptsoglou, Demie, Botifoll, Marc, Gocalinska, Agnieszka, Mura, Enrica, Pelucchi, Emanuele, Arbiol, Jordi, and Arredondo, Miryam
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Condensed Matter - Materials Science - Abstract
We investigate spatially resolved variations in the bandgap energy across multiple InxGa1-xAs quantum wells (QWs) on a GaAs substrate within a metamorphic laser structure. Using high resolution scanning transmission electron microscopy and low-loss electron energy loss spectroscopy, we present a detailed analysis of the local bandgap energy, indium concentration, and strain distribution within the QWs. Our findings reveal significant inhomogeneities, particularly near the interfaces, in both the strain and indium content, and a bandgap variability across QWs. These results are correlated with density functional theory simulations to further elucidate the interplay between strain, composition, and bandgap energy. This work underscores the importance of spatially resolved analysis in understanding, and optimising, the electronic and optical properties of semiconductor devices. The study suggests that the collective impact of individual QWs might affect the emission and performance of the final device, providing insights for the design of next-generation metamorphic lasers with multiple QWs as the active region.
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- 2024
5. Reconstruction of phase-amplitude dynamics from electrophysiological signals
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Yeldesbay, Azamat, Huguet, Gemma, and Daun, Silvia
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Mathematics - Dynamical Systems - Abstract
Signals from interacting brain regions display transient synchronization of phases and amplitudes in different frequencies. Commonly, the interaction between regions of the brain is quantitatively described by either analyzing the correlations of amplitudes of the measured signals or by calculating phase-synchronization measures. However, for a complete picture of the interactions it is important to analyze the dynamics of both the amplitude and the phase. In this work, we present a new method for finding the coupling between brain regions by reconstructing the phase-amplitude dynamics directly from the measured electrophysiological signals. For this purpose, we use the recent advances in the field of phase-amplitude reduction of oscillatory systems, which allow the representation of an uncoupled oscillatory system as a phase-amplitude oscillator in a unique form using transformations (parametrizations) related to the eigenfunctions of the Koopman operator. By combining the parametrization method and the Fourier-Laplace averaging method of finding the eigenfunctions of the Koopman operator, we developed a novel method of assessing the transformation functions from the signals of the interacting oscillatory systems. The resulting reconstructed dynamical system is a network of phase-amplitude oscillators with the interactions between them represented as coupling functions in phase and amplitude coordinates. Using synthetic signals generated from several models with known and unknown theoretical phase-amplitude reduced forms, we demonstrate that our method is capable of finding the proper unique dynamic form of these oscillatory systems in the reduced phase-amplitude space. Our method can be applied to describe any network of interacting oscillators as a dynamical system using signals of the network elements.
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- 2024
6. Around the codifferential of products of p-forms and generalized interior products
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Huguet, E., Queva, J., and Renaud, J.
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Mathematical Physics ,General Relativity and Quantum Cosmology ,Mathematics - Differential Geometry - Abstract
Identities pertaining to the de Rham codifferential {\delta} in differential geometry are scattered in the literature. This article gathers such formulas involving usual differential operators (Lie derivative, Schouten-Nijenhuis bracket, ...), while adding a few new ones using a natural extension of the interior product, to provide a compact handy summary., Comment: Revtex 8 pages, v2: typo corrected
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- 2024
7. Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
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Huguet, Guillaume, Vuckovic, James, Fatras, Kilian, Thibodeau-Laufer, Eric, Lemos, Pablo, Islam, Riashat, Liu, Cheng-Hao, Rector-Brooks, Jarrid, Akhound-Sadegh, Tara, Bronstein, Michael, Tong, Alexander, and Bose, Avishek Joey
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Computer Science - Machine Learning ,Quantitative Biology - Biomolecules - Abstract
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences. In this paper, we exploit the rich biological inductive bias of amino acid sequences and introduce FoldFlow-2, a novel sequence-conditioned SE(3)-equivariant flow matching model for protein structure generation. FoldFlow-2 presents substantial new architectural features over the previous FoldFlow family of models including a protein large language model to encode sequence, a new multi-modal fusion trunk that combines structure and sequence representations, and a geometric transformer based decoder. To increase diversity and novelty of generated samples -- crucial for de-novo drug design -- we train FoldFlow-2 at scale on a new dataset that is an order of magnitude larger than PDB datasets of prior works, containing both known proteins in PDB and high-quality synthetic structures achieved through filtering. We further demonstrate the ability to align FoldFlow-2 to arbitrary rewards, e.g. increasing secondary structures diversity, by introducing a Reinforced Finetuning (ReFT) objective. We empirically observe that FoldFlow-2 outperforms previous state-of-the-art protein structure-based generative models, improving over RFDiffusion in terms of unconditional generation across all metrics including designability, diversity, and novelty across all protein lengths, as well as exhibiting generalization on the task of equilibrium conformation sampling. Finally, we demonstrate that a fine-tuned FoldFlow-2 makes progress on challenging conditional design tasks such as designing scaffolds for the VHH nanobody., Comment: preprint
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- 2024
8. The Brenier-Schr\'odinger problem with respect to Feller semimartingales and non-local Hamilton-Jacobi-Bellman equations
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Herry, Ronan and Huguet, Baptiste
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Mathematics - Probability ,Mathematics - Analysis of PDEs ,60G53 (Primary), 49Q20 (Secondary) - Abstract
Motivated by a problem from incompressible fluid mechanics of Brenier (JAMS 1989), and its recent entropic relaxation by Arnaudo, Cruizero, L\'eonard & Zambrini (AIHP PS 2020), we study a problem of entropic minimization on the path space when the reference measure is a generic Feller semimartingale. We show that, under some regularity condition, our problem connects naturally with a, possibly non-local, version of the Hamilton-Jacobi-Bellman equation. Additionally, we study existence of minimizers when the reference measure in a Ornstein-Uhlenbeck process., Comment: 13 pages, comments welcome!
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- 2024
9. Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
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Ly, Timothée, Ferry, Julien, Huguet, Marie-José, Gambs, Sébastien, and Aivodji, Ulrich
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Cryptography and Security - Abstract
Differentially-private (DP) mechanisms can be embedded into the design of a machine learningalgorithm to protect the resulting model against privacy leakage, although this often comes with asignificant loss of accuracy. In this paper, we aim at improving this trade-off for rule lists modelsby establishing the smooth sensitivity of the Gini impurity and leveraging it to propose a DP greedyrule list algorithm. In particular, our theoretical analysis and experimental results demonstrate thatthe DP rule lists models integrating smooth sensitivity have higher accuracy that those using otherDP frameworks based on global sensitivity.
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- 2024
10. Using rare genetic mutations to revisit structural brain asymmetry.
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Kopal, Jakub, Kumar, Kuldeep, Shafighi, Kimia, Saltoun, Karin, Modenato, Claudia, Moreau, Clara, Huguet, Guillaume, Jean-Louis, Martineau, Martin, Charles-Olivier, Saci, Zohra, Younis, Nadine, Douard, Elise, Jizi, Khadije, Beauchamp-Chatel, Alexis, Kushan, Leila, Silva, Ana, van den Bree, Marianne, Linden, David, Owen, Michael, Hall, Jeremy, Lippé, Sarah, Draganski, Bogdan, Sønderby, Ida, Andreassen, Ole, Glahn, David, Thompson, Paul, Zatorre, Robert, Jacquemont, Sébastien, Bzdok, Danilo, and Bearden, Carrie
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Humans ,DNA Copy Number Variations ,Genome-Wide Association Study ,Functional Laterality ,Brain Mapping ,Brain ,Magnetic Resonance Imaging - Abstract
Asymmetry between the left and right hemisphere is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variants, which typically exert small effects on brain-related phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We designed a pattern-learning approach to dissect the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior data fusion highlights the consequences of genetically controlled brain lateralization on uniquely human cognitive capacities.
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- 2024
11. SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
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Ferry, Julien, Aïvodji, Ulrich, Gambs, Sébastien, Huguet, Marie-José, and Siala, Mohamed
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias, and do not leak sensitive information regarding their training data. Indeed, interpretability, fairness and privacy are key requirements for the development of responsible machine learning, and all three have been studied extensively during the last decade. However, they were mainly considered in isolation, while in practice they interplay with each other, either positively or negatively. In this Systematization of Knowledge (SoK) paper, we survey the literature on the interactions between these three desiderata. More precisely, for each pairwise interaction, we summarize the identified synergies and tensions. These findings highlight several fundamental theoretical and empirical conflicts, while also demonstrating that jointly considering these different requirements is challenging when one aims at preserving a high level of utility. To solve this issue, we also discuss possible conciliation mechanisms, showing that a careful design can enable to successfully handle these different concerns in practice.
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- 2023
12. SO$(2,n)$-compatible embeddings of conformally flat $n$-dimensional submanifolds in $\mathbb{R}^{n+2}$
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Huguet, E., Queva, J., and Renaud, J.
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Mathematical Physics ,General Relativity and Quantum Cosmology ,Mathematics - Differential Geometry ,53B25 - Abstract
We describe embeddings of $n$-dimensional Lorentzian manifolds, including Friedmann-Lema\^itre-Robertson-Walker spaces, in $\mathbb{R}^{n+2}$ such that the metrics of the submanifolds are inherited by a restriction from that of $\mathbb{R}^{n+2}$, and the action of the linear group SO$(2, n)$ of the ambient space reduces to conformal transformations on the submanifolds., Comment: 6 pages, no figures, v2: close match to published version
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- 2023
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13. Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
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Liao, Danqi, Liu, Chen, Christensen, Benjamin W., Tong, Alexander, Huguet, Guillaume, Wolf, Guy, Nickel, Maximilian, Adelstein, Ian, and Krishnaswamy, Smita
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Information Theory ,Computer Science - Machine Learning - Abstract
Entropy and mutual information in neural networks provide rich information on the learning process, but they have proven difficult to compute reliably in high dimensions. Indeed, in noisy and high-dimensional data, traditional estimates in ambient dimensions approach a fixed entropy and are prohibitively hard to compute. To address these issues, we leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. Specifically, we define diffusion spectral entropy (DSE) in neural representations of a dataset as well as diffusion spectral mutual information (DSMI) between different variables representing data. First, we show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data that outperform classic Shannon entropy, nonparametric estimation, and mutual information neural estimation (MINE). We then study the evolution of representations in classification networks with supervised learning, self-supervision, or overfitting. We observe that (1) DSE of neural representations increases during training; (2) DSMI with the class label increases during generalizable learning but stays stagnant during overfitting; (3) DSMI with the input signal shows differing trends: on MNIST it increases, while on CIFAR-10 and STL-10 it decreases. Finally, we show that DSE can be used to guide better network initialization and that DSMI can be used to predict downstream classification accuracy across 962 models on ImageNet. The official implementation is available at https://github.com/ChenLiu-1996/DiffusionSpectralEntropy.
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- 2023
14. Influence of Oxidizing Atmosphere on the Oxidation of Ni-based Superalloy Rene 65
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Huguet, M., Boissonnet, G., Bonnet, G., and Pedraza, F.
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- 2024
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15. Efficacy of a Virtual Reality Intervention for Reducing Anxiety, Depression, and Increasing Disease Coping in Patients with Breast Cancer Before Their First Chemotherapy Dose
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Torres García, Ariadna, Morcillo Serra, César, Argilés Huguet, Marta, González Gardó, Laura, Abad Esteve, Albert, and Ramos Quiroga, Josep Antoni
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- 2024
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16. Optimal control of oscillatory neuronal models with applications to communication through coherence
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Orieux, Michael, Guillamon, Antoni, and Huguet, Gemma
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Quantitative Biology - Neurons and Cognition ,Mathematics - Dynamical Systems ,Mathematics - Optimization and Control ,92B25, 37N25, 49M99 - Abstract
Macroscopic oscillations in the brain are involved in various cognitive and physiological processes, yet their precise function is not not completely understood. Communication Through Coherence (CTC) theory proposes that these rhythmic electrical patterns might serve to regulate the information flow between neural populations. Thus, to communicate effectively, neural populations must synchronize their oscillatory activity, ensuring that input volleys from the presynaptic population reach the postsynaptic one at its maximum phase of excitability. We consider an Excitatory-Inhibitory (E-I) network whose macroscopic activity is described by an exact mean-field model. The E-I network receives periodic inputs from either one or two external sources, for which effective communication will not be achieved in the absence of control. We explore strategies based on optimal control theory for phase-amplitude dynamics to design a control that sets the target population in the optimal phase to synchronize its activity with a specific presynaptic input signal and establish communication. The control mechanism resembles the role of a higher cortical area in the context of selective attention. To design the control, we use the phase-amplitude reduction of a limit cycle and leverage recent developments in this field in order to find the most effective control strategy regarding a defined cost function. Furthermore, we present results that guarantee the local controllability of the system close to the limit cycle.
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- 2023
17. SE(3)-Stochastic Flow Matching for Protein Backbone Generation
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Bose, Avishek Joey, Akhound-Sadegh, Tara, Huguet, Guillaume, Fatras, Kilian, Rector-Brooks, Jarrid, Liu, Cheng-Hao, Nica, Andrei Cristian, Korablyov, Maksym, Bronstein, Michael, and Tong, Alexander
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions -- i.e. the group $\text{SE}(3)$ -- enabling accurate modeling of protein backbones. We first introduce FoldFlow-Base, a simulation-free approach to learning deterministic continuous-time dynamics and matching invariant target distributions on $\text{SE}(3)$. We next accelerate training by incorporating Riemannian optimal transport to create FoldFlow-OT, leading to the construction of both more simple and stable flows. Finally, we design FoldFlow-SFM, coupling both Riemannian OT and simulation-free training to learn stochastic continuous-time dynamics over $\text{SE}(3)$. Our family of FoldFlow, generative models offers several key advantages over previous approaches to the generative modeling of proteins: they are more stable and faster to train than diffusion-based approaches, and our models enjoy the ability to map any invariant source distribution to any invariant target distribution over $\text{SE}(3)$. Empirically, we validate FoldFlow, on protein backbone generation of up to $300$ amino acids leading to high-quality designable, diverse, and novel samples., Comment: ICLR 2024 Spotlight
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- 2023
18. Linguistic Acculturation Preferences of Autochthonous Students toward Their Latin American Peers in Western Catalonia
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Ursula Hinostroza-Castillo, Ángel Huguet, Judit Janés, and Cecilio Lapresta-Rey
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Located in the province of Lleida (Catalonia, Spain), this study aims to identify and analyze the predictors of linguistic acculturation preferences of autochthonous high-school students toward their peers of Latin American descent. Autochthonous high-school students (N = 349) filled a questionnaire measuring linguistic acculturation and a series of linguistic and social-psychological variables (i.e. multicultural ideology, ethnic tolerance, attitudes toward minority languages, identification with Catalan culture and identification with Spanish culture). A k-means cluster analysis identified that autochthonous students endorse two linguistic acculturation preferences toward their Latin American peers: assimilation and multilingual preferences. Meanwhile, a logistic regression model found that participants with higher scores on attitudes toward minority languages have more likelihood to endorse a multilingual preference. The results highlight the importance and need to further work for a genuine intercultural educational model that allows the integration of Latin American students as well as of other minority groups. Particularly, this study found the importance of boosting the use of minority languages through educational approaches such as translanguaging and language architecture.
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- 2024
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19. Assessing the Relationship between L1 Knowledge and Fluid Intelligence in Second Language Acquisition: The Case of Immigrant Students in Catalonia
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Fernando Senar, Judit Janés, Elisabet Serrat, and Ángel Huguet
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The linguistic interdependence hypothesis posits the existence of language features common to different languages. This set of characteristics, known as Common Underlying Proficiency (CUP), is a powerful facilitating agent in second language acquisition. Fluid intelligence (Gf), on the other hand, is the construct that encompasses those cognitive resources devoted to general learning, and its involvement in second language acquisition is unproven. The aim of this study is to determine the direct and interactive effect of L1 knowledge and Gf on second language acquisition in language immersion learners across different linguistic domains. The study analyzed the proficiency of 131 Romanian students in Spanish and Catalan, the official languages of Catalonia. Mixed-effects regression models were used to analyze lexical, morphosyntactic, and orthographic aspects. The results were obtained using mixed-effects regression models, revealing a particularly noticeable interdependence effect in lexical, morphosyntactic, and orthographic aspects, with differences between Catalan and Spanish. Furthermore, Gf had an impact on the morphosyntactic component with similar intensity for both languages but did not moderate the interdependence effect. The study discusses the possible causes of these effects, as well as their psycho-pedagogical consequences.
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- 2024
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20. Intracranial self-stimulation reverses impaired spatial learning and regulates serum microRNA levels in a streptozotocin-induced rat model of Alzheimer disease
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Riberas-Sanchez, Andrea, Puig-Parnau, Irene, Vila-Soles, Laia, Garcia-Brito, Soleil, Aldavert-Vera, Laura, Segura- Torres, Pilar, Huguet, Gemma, and Kadar, Elisabet
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Neurons -- Health aspects ,Streptozocin -- Health aspects ,MicroRNA -- Health aspects ,Advertising executives -- Health aspects ,Proteins -- Health aspects ,Alzheimer's disease -- Health aspects ,Health ,Psychology and mental health - Abstract
Background: The assessment of deep brain stimulation (DBS) as a therapeutic alternative for treating Alzheimer disease (AD) is ongoing. We aimed to determine the effects of intracranial self-stimulation at the medial forebrain bundle (MFB-ICSS) on spatial memory, neurodegeneration, and serum expression of microRNAs (miRNAs) in a rat model of sporadic AD created by injection of streptozotocin. We hypothesized that MFB-ICSS would reverse the behavioural effects of streptozotocin and modulate hippocampal neuronal density and serum levels of the miRNAs. Methods: We performed Morris water maze and light-dark transition tests. Levels of various proteins, specifically amyloid-[beta] precurser protein (APP), phosphorylated tau protein (pTAU), and sirtuin 1 (SIRT1), and neurodegeneration were analyzed by Western blot and Nissl staining, respectively. Serum miRNA expression was measured by reverse transcription polymerase chain reaction. Results: Male rats that received streptozotocin had increased hippocampal levels of pTAU S202/T205, APP, and SIRT1 proteins; increased neurodegeneration in the CA1, dentate gyrus (DG), and dorsal tenia tecta; and worse performance in the Morris water maze task. No differences were observed in miRNAs, except for miR-181c and miR-let-7b. After MFB-ICSS, neuronal density in the CA1 and DG regions and levels of miR-181c in streptozotocin-treated and control rats were similar. Rats that received streptozotocin and underwent MFB-ICSS also showed lower levels of miR-let-7b and better spatial learning than rats that received streptozotocin without MFB-ICSS. Limitations: The reversal by MFB-ICSS of deficits induced by streptozotocin was fairly modest. Conclusion: Spatial memory performance, hippocampal neurodegeneration, and serum levels of miR-let-7b and miR-181c were affected by MFB-ICSS under AD-like conditions. Our results validate the MFB as a potential target for DBS and lend support to the use of specific miRNAs as promising biomarkers of the effectiveness of DBS in combatting AD-associated cognitive deficits., Introduction Alzheimer disease (AD), the most prevalent aging-related neurodegenerative disease, is characterized by progressive memory loss and cognitive dysfunction in older adults. (1) Although the underlying molecular mechanisms for AD [...]
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- 2024
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21. Cardiac magnetic resonance ventricular parameters correlate with cardiopulmonary fitness in patients with functional single ventricle
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Bredy, Charlene, Werner, Oscar, Helena, Huguet, Picot, Marie-Christine, Amedro, Pascal, and Adda, Jerome
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- 2024
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22. IBD-PODCAST Spain: A Close Look at Current Daily Clinical Practice in IBD Management
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Vega, P., Huguet, J. M., Gómez, E., Rubio, S., Suarez, P., Vera, M. I., Paredes, J. M., Hernández-Camba, A., Plaza, R., Mañosa, M., Pajares, R., Sicilia, B., Madero, L., Kolterer, S., Leitner, C., Heatta-Speicher, T., Michelena, N., Santos de Lamadrid, R., Dignass, A., and Gomollón, F.
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- 2024
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23. Iatrogenic Shapiro syndrome: a case report
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Ferrer Tarrés, Rosa, Garcia Huguet, Marina, Vera Cáceres, Carla, Boix Lago, Almudena, Ramió Torrentà, LLuís, and Álvarez-Bravo, Gary
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- 2024
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24. Probabilistic Dataset Reconstruction from Interpretable Models
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Ferry, Julien, Aïvodji, Ulrich, Gambs, Sébastien, Huguet, Marie-José, and Siala, Mohamed
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Computer Science - Artificial Intelligence ,Computer Science - Information Theory - Abstract
Interpretability is often pointed out as a key requirement for trustworthy machine learning. However, learning and releasing models that are inherently interpretable leaks information regarding the underlying training data. As such disclosure may directly conflict with privacy, a precise quantification of the privacy impact of such breach is a fundamental problem. For instance, previous work have shown that the structure of a decision tree can be leveraged to build a probabilistic reconstruction of its training dataset, with the uncertainty of the reconstruction being a relevant metric for the information leak. In this paper, we propose of a novel framework generalizing these probabilistic reconstructions in the sense that it can handle other forms of interpretable models and more generic types of knowledge. In addition, we demonstrate that under realistic assumptions regarding the interpretable models' structure, the uncertainty of the reconstruction can be computed efficiently. Finally, we illustrate the applicability of our approach on both decision trees and rule lists, by comparing the theoretical information leak associated to either exact or heuristic learning algorithms. Our results suggest that optimal interpretable models are often more compact and leak less information regarding their training data than greedily-built ones, for a given accuracy level.
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- 2023
25. Simulation-free Schr\'odinger bridges via score and flow matching
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Tong, Alexander, Malkin, Nikolay, Fatras, Kilian, Atanackovic, Lazar, Zhang, Yanlei, Huguet, Guillaume, Wolf, Guy, and Bengio, Yoshua
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Computer Science - Machine Learning - Abstract
We present simulation-free score and flow matching ([SF]$^2$M), a simulation-free objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary source and target distributions. Our method generalizes both the score-matching loss used in the training of diffusion models and the recently proposed flow matching loss used in the training of continuous normalizing flows. [SF]$^2$M interprets continuous-time stochastic generative modeling as a Schr\"odinger bridge problem. It relies on static entropy-regularized optimal transport, or a minibatch approximation, to efficiently learn the SB without simulating the learned stochastic process. We find that [SF]$^2$M is more efficient and gives more accurate solutions to the SB problem than simulation-based methods from prior work. Finally, we apply [SF]$^2$M to the problem of learning cell dynamics from snapshot data. Notably, [SF]$^2$M is the first method to accurately model cell dynamics in high dimensions and can recover known gene regulatory networks from simulated data. Our code is available in the TorchCFM package at https://github.com/atong01/conditional-flow-matching., Comment: AISTATS 2024. Code: https://github.com/atong01/conditional-flow-matching
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- 2023
26. Incorporating Graph Information in Transformer-based AMR Parsing
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Vasylenko, Pavlo, Cabot, Pere-Lluís Huguet, Lorenzo, Abelardo Carlos Martínez, and Navigli, Roberto
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on autoregressive language models such as BART or T5, fine-tuned through Teacher Forcing to obtain a linearized version of the AMR graph from a sentence. In this paper, we present LeakDistill, a model and method that explores a modification to the Transformer architecture, using structural adapters to explicitly incorporate graph information into the learned representations and improve AMR parsing performance. Our experiments show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing through self-knowledge distillation, even without the use of additional data. We release the code at \url{http://www.github.com/sapienzanlp/LeakDistill}., Comment: ACL 2023. Please cite authors correctly using both lastnames ("Mart\'inez Lorenzo", "Huguet Cabot")
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- 2023
27. AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing
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Lorenzo, Abelardo Carlos Martínez, Cabot, Pere-Lluís Huguet, and Navigli, Roberto
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions. Our analysis reveals that the present models often violate AMR structural constraints. To address this issue, we develop a validation method, and show how ensemble models can exploit SMATCH metric weaknesses to obtain higher scores, but sometimes result in corrupted graphs. Additionally, we highlight the demanding need to compute the SMATCH score among all possible predictions. To overcome these challenges, we propose two novel ensemble strategies based on Transformer models, improving robustness to structural constraints, while also reducing the computational time. Our methods provide new insights for enhancing AMR parsers and metrics. Our code is available at \href{https://www.github.com/babelscape/AMRs-Assemble}{github.com/babelscape/AMRs-Assemble}., Comment: ACL 2023. Please cite authors correctly using both lastnames ("Mart\'inez Lorenzo", "Huguet Cabot")
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- 2023
28. RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset
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Cabot, Pere-Lluís Huguet, Tedeschi, Simone, Ngomo, Axel-Cyrille Ngonga, and Navigli, Roberto
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Computer Science - Computation and Language - Abstract
Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English. In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems. First, we present SRED$^{\rm FM}$, an automatically annotated dataset covering 18 languages, 400 relation types, 13 entity types, totaling more than 40 million triplet instances. Second, we propose RED$^{\rm FM}$, a smaller, human-revised dataset for seven languages that allows for the evaluation of multilingual RE systems. To demonstrate the utility of these novel datasets, we experiment with the first end-to-end multilingual RE model, mREBEL, that extracts triplets, including entity types, in multiple languages. We release our resources and model checkpoints at https://www.github.com/babelscape/rebel, Comment: ACL 2023. Please cite authors correctly using both lastnames ("Huguet Cabot", "Ngonga Ngomo")
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- 2023
29. Traveling waves in a model for cortical spreading depolarization with slow-fast dynamics
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Reyner-Parra, David, Bonet, Carles, Seara, Teresa M., and Huguet, Gemma
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Mathematics - Dynamical Systems ,92C20 (Primary) 37M21, 35C07 (Secondary) - Abstract
Cortical spreading depression and spreading depolarization (CSD) are waves of neuronal depolarization that spread across the cortex, leading to a temporary saturation of brain activity. They are associated to various brain disorders such as migraine and ischemia. We consider a reduced version of a biophysical model of a neuron-astrocyte network for the initiation and propagation of CSD waves (Huguet et al., Biophys. J. , 2016), consisting of reaction-diffusion equations. The reduced model considers only the dynamics of the neuronal and astrocytic membrane potentials and the extracellular potassium concentration, capturing the instigation process implicated in such waves. We present a computational and mathematical framework based on the parameterization method and singular perturbation theory to provide semi-analytical results on the existence of a wave solution and to compute it jointly with its velocity of propagation. The traveling wave solution can be seen as an heteroclinic connection of an associated system of ordinary differential equations with a slow-fast dynamics. The presence of distinct time scales within the system introduces numerical instabilities, which we successfully address through the identification of significant invariant manifolds and the implementation of the parameterization method. Our results provide a methodology that allows to identify efficiently and accurately the mechanisms responsible for the initiation of these waves and the wave propagation velocity., Comment: 36 pages, 13 figures
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- 2023
30. Graph Fourier MMD for Signals on Graphs
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Leone, Samuel, Venkat, Aarthi, Huguet, Guillaume, Tong, Alexander, Wolf, Guy, and Krishnaswamy, Smita
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Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called "gene localization score" which helps select genes for cellular state space characterization.
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- 2023
31. Neural FIM for learning Fisher Information Metrics from point cloud data
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Fasina, Oluwadamilola, Huguet, Guillaume, Tong, Alexander, Zhang, Yanlei, Wolf, Guy, Nickel, Maximilian, Adelstein, Ian, and Krishnaswamy, Smita
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells)., Comment: 13 pages, 11 figures, 1 table
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- 2023
32. Auriculotherapy and acupuncture treatments for chemotherapy-induced nausea and vomiting: a multicenter clinical trial
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Michel-Cherqui, Mireille, Ma, Sabrina, Bacrie, Joy, Huguet, Sophie, Lemaire, Nicolas, Le Guen, Morgan, and Fischler, Marc
- Published
- 2024
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33. Multitemporal monitoring of paramos as critical water sources in Central Colombia
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Cesar Augusto Murad, Jillian Pearse, and Carme Huguet
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Paramo ,Land cover change ,Landsat ,Sentinel-2 ,Ecosystem services ,Coking ,Medicine ,Science - Abstract
Abstract Paramos, unique and biodiverse ecosystems found solely in the high mountain regions of the tropics, are under threat. Despite their crucial role as primary water sources and significant carbon repositories in Colombia, they are deteriorating rapidly and garner less attention than other vulnerable ecosystems like the Amazon rainforest. Their fertile soil and unique climate make them prime locations for agriculture and cattle grazing, often coinciding with economically critical deposits such as coal which has led to a steady decline in paramo area. Anthropic impact was evaluated using multispectral images from Landsat and Sentinel over 37 years, on the Guerrero and Rabanal paramos in central Colombia which have experienced rapid expansion of mining and agriculture. Our analysis revealed that since 1984, the Rabanal and Guerrero paramos have lost 47.96% and 59.96% of their native vegetation respectively, replaced primarily by crops, pastures, and planted forests. We detected alterations in the spectral signatures of native vegetation near coal coking ovens, indicating a deterioration of paramo health and potential impact on ecosystem services. Consequently, human activity is reducing the extent of paramos and their efficiency as water sources and carbon sinks, potentially leading to severe regional and even global consequences.
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- 2024
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34. Effectiveness of pulmonary rehabilitation programmes and/or respiratory muscle training in patients with post-COVID conditions: a systematic review
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Lucía Ortiz-Ortigosa, Paula Gálvez-Álvarez, María Jesús Viñolo-Gil, Manuel Rodriguez-Huguet, Jorge Góngora-Rodríguez, and Rocío Martín-Valero
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post-COVID-19 condition ,Sequelae ,Pulmonary rehabilitation program ,Respiratory muscle training ,Diseases of the respiratory system ,RC705-779 - Abstract
Abstract Background The term “post-COVID-19 condition” refers to the symptomatology that appears between four to twelve weeks after Covid-19 infection. These symptoms can persist for weeks or even months, significantly diminishing the quality of life for affected individuals. The primary objective of this study was to assess the effectiveness of pulmonary rehabilitation programs and/or respiratory muscle training on respiratory sequelae in patients with post-COVID condition. Methods The literature search was conducted in the following databases: PubMed, PEDro, Embase, Cochrane, Scopus, and Web of Science. Randomized clinical trials were included in which participants were aged 18 years or older. Articles were excluded if at least one of the therapies did not involve pulmonary rehabilitation or respiratory muscle training, if the participants were COVID positive, if studies lacked results, and finally, if interventions were conducted without supervision or at home. This review only encompasses supervised non-virtual interventions. This study adheres to the PRISMA statement and has been registered in the PROSPERO database (CRD42023433843). Results The outcomes obtained in the included studies are assessed across the following variables: Exercise capacity using the 6-minute walk test, Dyspnea, fatigue, Pulmonary function, Maximum inspiratory pressure, and Quality of life. Conclusion Despite the absence of a specific treatment at present, it was evident from this review that a well-structured pulmonary rehabilitation program that incorporates both aerobic and muscular strength exercises along with techniques and inspiratory muscle exercises was the most effective form of treatment.
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- 2024
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35. A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
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Huguet, Guillaume, Tong, Alexander, De Brouwer, Edward, Zhang, Yanlei, Wolf, Guy, Adelstein, Ian, and Krishnaswamy, Smita
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Computer Science - Machine Learning ,Quantitative Biology - Genomics ,Quantitative Biology - Quantitative Methods ,Statistics - Machine Learning - Abstract
Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE)., Comment: 31 pages, 13 figures, 10 tables
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- 2023
36. Molecular acclimation of Halobacterium salinarum to halite brine inclusions
- Author
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Favreau, C., Tribondeau, A., Marugan, M., Guyot, F., Alpha-Bazin, B., Marie, A., Puppo, R., Dufour, T., Huguet, A., Zirah, S., and Kish, A.
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Physics - Biological Physics ,Physics - Plasma Physics - Abstract
Halophilic microorganisms have long been known to survive within the brine inclusions of salt crystals, as evidenced by the change in color for salt crystals containing pigmented halophiles. However, the molecular mechanisms allowing this survival has remained an open question for decades. While protocols for the surface sterilization of halite (NaCl) have enabled isolation of cells and DNA from within halite brine inclusions, "-omics" based approaches have faced two main technical challenges: (1) removal of all contaminating organic biomolecules (including proteins) from halite surfaces, and (2) performing selective biomolecule extractions directly from cells contained within halite brine inclusions with sufficient speed to avoid modifications in gene expression during extraction. In this study, we tested different methods to resolve these two technical challenges. Following this method development, we then applied the optimized methods to perform the first examination of the early acclimation of a model haloarchaeon (Halobacterium salinarum NRC-1) to halite brine inclusions. Examinations of the proteome of Halobacterium cells two months post-evaporation revealed a high degree of similarity with stationary phase liquid cultures, but with a sharp down-regulation of ribosomal proteins. While proteins for central metabolism were part of the shared proteome between liquid cultures and halite brine inclusions, proteins involved in cell mobility (archaellum, gas vesicles) were either absent or less abundant in halite samples. Proteins unique to cells within brine inclusions included transporters, suggesting modified interactions between cells and the surrounding brine inclusion microenvironment. The methods and hypotheses presented here enable future studies of the survival of halophiles in both culture model and natural halite systems.
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- 2023
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37. Learning Optimal Fair Scoring Systems for Multi-Class Classification
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Rouzot, Julien, Ferry, Julien, and Huguet, Marie-José
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Computer Science - Machine Learning ,Computer Science - Computers and Society ,Mathematics - Optimization and Control - Abstract
Machine Learning models are increasingly used for decision making, in particular in high-stakes applications such as credit scoring, medicine or recidivism prediction. However, there are growing concerns about these models with respect to their lack of interpretability and the undesirable biases they can generate or reproduce. While the concepts of interpretability and fairness have been extensively studied by the scientific community in recent years, few works have tackled the general multi-class classification problem under fairness constraints, and none of them proposes to generate fair and interpretable models for multi-class classification. In this paper, we use Mixed-Integer Linear Programming (MILP) techniques to produce inherently interpretable scoring systems under sparsity and fairness constraints, for the general multi-class classification setup. Our work generalizes the SLIM (Supersparse Linear Integer Models) framework that was proposed by Rudin and Ustun to learn optimal scoring systems for binary classification. The use of MILP techniques allows for an easy integration of diverse operational constraints (such as, but not restricted to, fairness or sparsity), but also for the building of certifiably optimal models (or sub-optimal models with bounded optimality gap).
- Published
- 2023
38. Asymptotic states and $S$-matrix operator in de Sitter ambient space formalism
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Takook, M. V., Gazeau, J. P., and Huguet, E.
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High Energy Physics - Theory ,General Relativity and Quantum Cosmology - Abstract
Within the de Sitter ambient space framework, the two different bases of the one-particle Hilbert space of the de Sitter group algebra are presented for the scalar case. Using field operator algebra and its Fock space construction in this formalism, we discuss the existence of asymptotic states in de Sitter QFT under an extension of the adiabatic hypothesis and prove the Fock space completeness theorem for the massive scalar field. We define the quantum state in the limit of future and past infinity on the Sitter hyperboloid in an observer-independent way. These results allow us to examine the existence of the $S$-matrix operator for de Sitter QFT in ambient space formalism, a question usually obscure in spacetime with a cosmological event horizon for a specific observer. Some similarities and differences between QFT in Minkowski and de Sitter spaces are discussed., Comment: 21 pages
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- 2023
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39. Application of Percutaneous Electrolysis, Percutaneous Neuromodulation and Eccentric Exercise (MRH-EPTE)
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Jorge Manuel Góngora Rodriguez, Manuel Rodriguez Huguet, Pablo Rodriguez Huguet, Rocío Martín Valero, and Manuel Rodriguez Huguet, Degree Physiotherapy
- Published
- 2023
40. Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence.
- Author
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Kopal, Jakub, Kumar, Kuldeep, Saltoun, Karin, Modenato, Claudia, Moreau, Clara, Martin-Brevet, Sandra, Huguet, Guillaume, Jean-Louis, Martineau, Martin, Charles-Olivier, Saci, Zohra, Younis, Nadine, Tamer, Petra, Douard, Elise, Maillard, Anne, Rodriguez-Herreros, Borja, Pain, Aurèlie, Richetin, Sonia, Kushan, Leila, Silva, Ana, van den Bree, Marianne, Linden, David, Owen, Michael, Hall, Jeremy, Lippé, Sarah, Draganski, Bogdan, Sønderby, Ida, Andreassen, Ole, Glahn, David, Thompson, Paul, Jacquemont, Sébastien, Bzdok, Danilo, and Bearden, Carrie
- Subjects
Humans ,DNA Copy Number Variations ,Brain - Abstract
Copy number variations (CNVs) are rare genomic deletions and duplications that can affect brain and behaviour. Previous reports of CNV pleiotropy imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, existing studies have primarily examined single CNV loci in small clinical cohorts. It remains unknown, for example, how distinct CNVs escalate vulnerability for the same developmental and psychiatric disorders. Here we quantitatively dissect the associations between brain organization and behavioural differentiation across 8 key CNVs. In 534 CNV carriers, we explored CNV-specific brain morphology patterns. CNVs were characteristic of disparate morphological changes involving multiple large-scale networks. We extensively annotated these CNV-associated patterns with ~1,000 lifestyle indicators through the UK Biobank resource. The resulting phenotypic profiles largely overlap and have body-wide implications, including the cardiovascular, endocrine, skeletal and nervous systems. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.
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- 2023
41. Economic Development IN Historical Political Economy
- Author
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Morales-Arilla, Jose, Ricart-Huguet, Joan, Wantchekon, Leonard, Jenkins, Jeffery A., book editor, and Rubin, Jared, book editor
- Published
- 2024
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42. Topology optimization for magnetic circuits with continuous adjoint method in 3D
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Houta, Zakaria, Messine, Frederic, and Huguet, Thomas
- Published
- 2024
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43. Probabilistic Dataset Reconstruction from Interpretable Models.
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Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, and Mohamed Siala 0002
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- 2024
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44. Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy.
- Author
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Danqi Liao, Chen Liu, Benjamin W. Christensen, Alexander Tong 0001, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, and Smita Krishnaswamy
- Published
- 2024
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45. Simulation-Free Schrödinger Bridges via Score and Flow Matching.
- Author
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Alexander Tong 0001, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Guy Wolf, and Yoshua Bengio
- Published
- 2024
46. The generalized E-DVA method: a new approach for multi-modal pushover analysis under multi-component earthquakes with local variables maximization
- Author
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Lherminier, Olivier, Erlicher, Silvano, Huguet, Miquel, Civera, Marco, Ceravolo, Rosario, and Barakat, Maxime
- Published
- 2024
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47. Intraspecific differences in leaf chemical traits from five common evergreen species in contrasting climate conditions (temperature and precipitation) from northern Patagonian rainforest (42–44°S)
- Author
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Cerda-Peña, Carol, Contreras, Sergio, and Huguet, A.
- Published
- 2023
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48. Poincar{\'e} inequalities and integrated curvature-dimension criterion for generalised Cauchy and convex measures
- Author
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Huguet, Baptiste Nicolas
- Subjects
Mathematics - Functional Analysis ,Mathematics - Probability - Abstract
We obtain new sharp weighted Poincar{\'e} inequalities on Riemannian manifolds for a general class of measures. When specialised to generalised Cauchy measures, this gives a unified and simple proof of the weighted Poincar{\'e} inequality for the whole range of parameters, with the optimal spectral gap, the error term and the extremal functions.
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- 2023
49. Improving and generalizing flow-based generative models with minibatch optimal transport
- Author
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Tong, Alexander, Fatras, Kilian, Malkin, Nikolay, Huguet, Guillaume, Zhang, Yanlei, Rector-Brooks, Jarrid, Wolf, Guy, and Bengio, Yoshua
- Subjects
Computer Science - Machine Learning - Abstract
Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their simulation-based maximum likelihood training. We introduce the generalized conditional flow matching (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, we show that when the true OT plan is available, our OT-CFM method approximates dynamic OT. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schr\"odinger bridge inference., Comment: TMLR. Code: https://github.com/atong01/conditional-flow-matching
- Published
- 2023
50. The James Webb Space Telescope Mission: Optical Telescope Element Design, Development, and Performance
- Author
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McElwain, Michael W., Feinberg, Lee D., Perrin, Marshall D., Clampin, Mark, Mountain, C. Matt, Lallo, Matthew D., Lajoie, Charles-Philippe, Kimble, Randy A., Bowers, Charles W., Stark, Christopher C., Acton, D. Scott, Aiello, Ken, Atkinson, Charles, Barinek, Beth, Barto, Allison, Basinger, Scott, Beck, Tracy, Bergkoetter, Matthew D., Bluth, Marcel, Boucarut, Rene A., Brady, Gregory R., Brooks, Keira J., Brown, Bob, Byard, John, Carey, Larkin, Carrasquilla, Maria, Celeste, Sid, Chae, Dan, Chaney, David, Chayer, Pierre, Chonis, Taylor, Cohen, Lester, Cole, Helen J., Comeau, Thomas M., Coon, Matthew, Coppock, Eric, Coyle, Laura, Davis, Rick, Dean, Bruce H., Dziak, Kenneth J., Eisenhower, Michael, Flagey, Nicolas, Franck, Randy, Gallagher, Benjamin, Gilman, Larry, Glassman, Tiffany, Golnik, Gary, Green, Joseph J., Grieco, John, Haase, Shari, Hadjimichael, Theodore J., Hagopian, John G., Hahn, Walter G., Hartig, George F., Havey, Keith A., Hayden, William L., Hellekson, Robert, Hicks, Brian, Holfeltz, Sherie T., Howard, Joseph M., Huguet, Jesse A., Jahne, Brian, Johnson, Leslie A., Johnston, John D., Jurling, Alden S., Kegley, Jeffrey R., Kennard, Scott, Keski-Kuha, Ritva A., Knight, J. Scott, Kulp, Bernard A., Levi, Joshua S., Levine, Marie B., Lightsey, Paul, Luetgens, Robert A., Mather, John C., Matthews, Gary W., McKay, Andrew G., Mehalick, Kimberly I., Meléndez, Marcio, Messer, Ted, Mosier, Gary E., Murphy, Jess, Nelan, Edmund P., Niedner, Malcolm B., Noël, Darin M., Ohara, Catherine M., Ohl, Raymond G., Olczak, Eugene, Osborne, Shannon B., Park, Sang, Patton, Kevin, Perrygo, Charles, Pueyo, Laurent, Quesnel, Lisbeth, Ranck, Dale, Redding, David C., Regan, Michael W., Reynolds, Paul, Rifelli, Rich, Rigby, Jane R., Sabatke, Derek, Saif, Babak N., Scorse, Thomas R., Seo, Byoung-Joon, Shi, Fang, Sigrist, Norbert, Smith, Koby, Smith, J. Scott, Smith, Erin C., Sohn, Sangmo Tony, Spina, John, Stahl, H. Philip, Telfer, Randal, Terlecki, Todd, Texter, Scott C., Van Buren, David, Van Campen, Julie M., Vila, Begoña, Voyton, Mark F., Waldman, Mark, Walker, Chanda B., Weiser, Nick, Wells, Conrad, West, Garrett, Whitman, Tony L., Wick, Eric, Wolf, Erin, Young, Greg, and Zielinski, Thomas P.
- Subjects
Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The James Webb Space Telescope (JWST) is a large, infrared space telescope that has recently started its science program which will enable breakthroughs in astrophysics and planetary science. Notably, JWST will provide the very first observations of the earliest luminous objects in the Universe and start a new era of exoplanet atmospheric characterization. This transformative science is enabled by a 6.6 m telescope that is passively cooled with a 5-layer sunshield. The primary mirror is comprised of 18 controllable, low areal density hexagonal segments, that were aligned and phased relative to each other in orbit using innovative image-based wavefront sensing and control algorithms. This revolutionary telescope took more than two decades to develop with a widely distributed team across engineering disciplines. We present an overview of the telescope requirements, architecture, development, superb on-orbit performance, and lessons learned. JWST successfully demonstrates a segmented aperture space telescope and establishes a path to building even larger space telescopes., Comment: accepted by PASP for JWST Overview Special Issue; 34 pages, 25 figures
- Published
- 2023
- Full Text
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