Transition from Tonks-Girardeau gas to super-Tonks-Girardeau gas as an exact many-body dynamics problem

We investigate transition of a one-dimensional interacting Bose gas from a strongly repulsive regime to a strongly attractive regime, where a stable highly excited state known as the super Tonks-Girardeau gas was experimentally realized very recently. By solving exact dynamics of the integrable Lieb-Liniger Bose gas, we demonstrate that such an excited gas state can be a very stable dynamic state. Furthermore we calculate the breathing mode of the super Tonks-Girardeau gas which is found to be in good agreement with experimental observation. Our results show that the highly excited super Tonks-Girardeau gas phase can be well understood from the fundamental theory of the solvable Bose gas.Comment: 4 pages, 4 figures, version to appear in Phys. Rev. A as a Rapid Communicatio

Federated Learning for Medical Image Analysis: A Survey

Machine learning in medical imaging often faces a fundamental dilemma, namely the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/datasets to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We first introduce the background and motivation of federated learning for dealing with privacy protection and collaborative learning issues in medical imaging. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges and potential research opportunities in this promising research field.Comment: 19 pages, 6 figure

Non-ergodic Convergence Analysis of Heavy-Ball Algorithms

In this paper, we revisit the convergence of the Heavy-ball method, and present improved convergence complexity results in the convex setting. We provide the first non-ergodic O(1/k) rate result of the Heavy-ball algorithm with constant step size for coercive objective functions. For objective functions satisfying a relaxed strongly convex condition, the linear convergence is established under weaker assumptions on the step size and inertial parameter than made in the existing literature. We extend our results to multi-block version of the algorithm with both the cyclic and stochastic update rules. In addition, our results can also be extended to decentralized optimization, where the ergodic analysis is not applicable

MobiCacher: Mobility-Aware Content Caching in Small-Cell Networks

Small-cell networks have been proposed to meet the demand of ever growing mobile data traffic. One of the prominent challenges faced by small-cell networks is the lack of sufficient backhaul capacity to connect small-cell base stations (small-BSs) to the core network. We exploit the effective application layer semantics of both spatial and temporal locality to reduce the backhaul traffic. Specifically, small-BSs are equipped with storage facility to cache contents requested by users. As the {\em cache hit ratio} increases, most of the users' requests can be satisfied locally without incurring traffic over the backhaul. To make informed caching decisions, the mobility patterns of users must be carefully considered as users might frequently migrate from one small cell to another. We study the issue of mobility-aware content caching, which is formulated into an optimization problem with the objective to maximize the caching utility. As the problem is NP-complete, we develop a polynomial-time heuristic solution termed {\em MobiCacher} with bounded approximation ratio. We also conduct trace-based simulations to evaluate the performance of {\em MobiCacher}, which show that {\em MobiCacher} yields better caching utility than existing solutions.Comment: Accepted by Globecom 201