Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs
In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (see more)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Ali Imran
David St-Onge
In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. T… (see more)his paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
Yewon Lee
Philip Huang
Yizhou Huang
Krishna Murthy
Andrew Zou Li
Fabian Damken
Eric Heiden
Kevin A. Smith
Fabio Ramos
Florian Shkurti
Carnegie-mellon University
M. I. O. Technology
Technische Universitat Darmstadt
Nvidia
M. University
University of Sydney
Planning for many manipulation tasks, such as using tools or assembling parts, often requires both symbolic and geometric reasoning. Task an… (see more)d Motion Planning (TAMP) algorithms typically solve these problems by conducting a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. While performant, most existing algorithms are highly inefficient as their time complexity grows exponentially with the number of possible actions and objects. Additionally, they only find a single solution to problems in which many feasible plans may exist. To address these limitations, we propose a novel algorithm called Stein Task and Motion Planning (STAMP) that leverages parallelization and differentiable simulation to efficiently search for multiple diverse plans. STAMP relaxes discrete-and-continuous TAMP problems into continuous optimization problems that can be solved using variational inference. Our algorithm builds upon Stein Variational Gradient Descent, a gradient-based variational inference algorithm, and parallelized differentiable physics simulators on the GPU to efficiently obtain gradients for inference. Further, we employ imitation learning to introduce action abstractions that reduce the inference problem to lower dimensions. We demonstrate our method on two TAMP problems and empirically show that STAMP is able to: 1) produce multiple diverse plans in parallel; and 2) search for plans more efficiently compared to existing TAMP baselines.
Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
Ronan Legin
Maximiliano Isi
Kaze W. K. Wong
Artificial Neural Networks for Magnetoencephalography: A review of an emerging field
Arthur Dehgan
Hamza Abdelhedi
Vanessa Hadid
Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (see more)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.
Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Elliot Paquette
Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Elliot Paquette
We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by dat… (see more)a complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional stochastic gradient descent with momentum (SGD-M) yields identical scaling law exponents to SGD, dimension-adapted Nesterov acceleration (DANA) improves these exponents by scaling momentum hyperparameters based on model size and data complexity. This outscaling phenomenon, which also improves compute-optimal scaling behavior, is achieved by DANA across a broad range of data and target complexities, while traditional methods fall short. Extensive experiments on high-dimensional synthetic quadratics validate our theoretical predictions and large-scale text experiments with LSTMs show DANA's improved loss exponents over SGD hold in a practical setting.
Structure-Aligned Protein Language Model
Can Chen
David Heurtel-Depeiges
Robert M. Vernon
Christopher J. Langmead
Quentin Fournier
Structure-Aligned Protein Language Model
Can Chen
David Heurtel-Depeiges
Robert M. Vernon
Christopher J. Langmead
Quentin Fournier
Adaptive Cyclic Diffusion for Inference Scaling
Gyubin Lee
Truong Nhat Nguyen Bao
Jaesik Yoon
Dongwoo Lee
Minsu Kim
Sungjin Ahn
Adaptive Cyclic Diffusion for Inference Scaling
Gyubin Lee
Truong Nhat Nguyen Bao
Jaesik Yoon
Dongwoo Lee
Minsu Kim
Sungjin Ahn
Determinants of surgical approach to pediatric appendicitis in Brazil.
Ayla Gerk
Paulo Henrique Moreira Melo
Mohsen Amoei
Shreenik Kundu
Luiza Telles
Justina O. Seyi-Olajide
Dunya Moghul
Gabriel Schnitman
Cristina Camargo
David P. Mooney
Joaquim Bustorff-Silva