Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution
Zehao Jin
Mario Pasquato
Benjamin L. Davis
Tristan Deleu
Yu Luo
Changhyun Cho
Pablo Lemos
Xi Kang
Andrea Maccio
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure… (voir plus) (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)--galaxy interaction has long been constrained by observed scaling relations, that is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date dataset, reveals a causal link between galaxy properties and dynamically-measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.
Challenges for impact evaluation of WHO’s normative output
Gaelle Foucault
Jean-Louis Denis
Pierre Larouche
Miriam Cohen
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
Yuanqi Du
Michael Plainer
Rob Brekelmans
Chenru Duan
Frank No'e
Carla P. Gomes
Alan Aspuru-Guzik
Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational cha… (voir plus)llenges due to an exponentially large space of trajectories. For settings where the dynamical system of interest follows a Brownian motion with known drift, the question of conditioning the process to reach a given endpoint or desired rare event is definitively answered by Doob's h-transform. However, the naive estimation of this transform is infeasible, as it requires simulating sufficiently many forward trajectories to estimate rare event probabilities. In this work, we propose a variational formulation of Doob's h-transform as an optimization problem over trajectories between a given initial point and the desired ending point. To solve this optimization, we propose a simulation-free training objective with a model parameterization that imposes the desired boundary conditions by design. Our approach significantly reduces the search space over trajectories and avoids expensive trajectory simulation and inefficient importance sampling estimators which are required in existing methods. We demonstrate the ability of our method to find feasible transition paths on real-world molecular simulation and protein folding tasks.
Efficient line search for optimizing Area Under the ROC Curve in gradient descent
Jadon Fowler
Receiver Operating Characteristic (ROC) curves are useful for evaluation in binary classification and changepoint detection, but difficult t… (voir plus)o use for learning since the Area Under the Curve (AUC) is piecewise constant (gradient zero almost everywhere). Recently the Area Under Min (AUM) of false positive and false negative rates has been proposed as a differentiable surrogate for AUC. In this paper we study the piecewise linear/constant nature of the AUM/AUC, and propose new efficient path-following algorithms for choosing the learning rate which is optimal for each step of gradient descent (line search), when optimizing a linear model. Remarkably, our proposed line search algorithm has the same log-linear asymptotic time complexity as gradient descent with constant step size, but it computes a complete representation of the AUM/AUC as a function of step size. In our empirical study of binary classification problems, we verify that our proposed algorithm is fast and exact; in changepoint detection problems we show that the proposed algorithm is just as accurate as grid search, but faster.
Finite Sample Complexity Analysis of Binary Segmentation
Binary segmentation is the classic greedy algorithm which recursively splits a sequential data set by optimizing some loss or likelihood fun… (voir plus)ction. Binary segmentation is widely used for changepoint detection in data sets measured over space or time, and as a sub-routine for decision tree learning. In theory it should be extremely fast for
Inferring electric vehicle charging patterns from smart meter data for impact studies
Feng Li
Élodie Campeau
Ilhan Kocar
Inferring electric vehicle charging patterns from smart meter data for impact studies
Feng Li
Élodie Campeau
Ilhan Kocar
Inferring electric vehicle charging patterns from smart meter data for impact studies
Feng Li
Élodie Campeau
Ilhan Kocar
Innovative transfusion strategies for blood deserts in disaster settings
Shreenik Kundu
Ayla Gerk
Robert Glatter
Long-term outcomes of critically ill patients with hematological malignancies: what is the impact of the coronavirus disease 2019 pandemic? Author's reply
Laveena Munshi
Sangeeta Mehta
MAP: Model Merging with Amortized Pareto Front Using Limited Computation
Lu Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
Siddharth Viswanath
Dhananjay Bhaskar
David R. Johnson
João Felipe Rocha
Egbert Castro
Jackson Grady
Alex T. Grigas
Michael Perlmutter
Corey S. O'Hern
Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress… (voir plus) has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.