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Publications
Adaptive Experiments Under High-Dimensional and Data Sparse Settings: Applications for Educational Platforms
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
En plein essor, la recherche empirique en droit participe à la création de nouvelles connaissances et ouvre aux juristes d’autres voies … (voir plus)pour étudier une question, un phénomène. Oser l’empirisme n’est pas chose aisée, mais les auteurs du présent article ont pris ce virage et proposent d’en exposer le récit. En construisant deux méthodes distinctes (pour deux projets), ils ont pu tester les possibilités qu’offre la recherche empirique pour appréhender l’enjeu du leadership normatif de l’Organisation mondiale de la Santé (OMS). Destiné à aiguiller à partir d’une expérience celles et ceux qui voudraient s’aventurer dans l’empirisme, cet article met en lumière les défis rencontrés, mais surtout les atouts d’une telle recherche. La richesse des informations obtenues a en effet grandement bonifié la compréhension de la trajectoire des normes de l’OMS et de leurs impacts sur les États.
Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (voir plus) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes
Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (voir plus) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes
Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain,… (voir plus) especially during sleep. We analyzed sleep EEG in 40 subjects, contrasting 200mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during NREM sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20-27 years) compared to middle-aged participants (41-58 years) during REM sleep, while no significant age effects were observed during NREM. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance suggests that caffeine promotes a shift in brain dynamics towards increased neural excitation and closer proximity to a critical regime, particularly during NREM sleep.
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.
