Structured Cooperative Reinforcement Learning With Time-Varying Composite Action Space.

In recent years, reinforcement learning has achieved excellent results in low-dimensional static action spaces such as games and simple robotics. However, the action space is usually composite, composed of multiple sub-action with different functions, and time-varying for practical tasks. The existing sub-actions might be temporarily invalid due to the external environment, while unseen sub-actions can be added to the current system.

Hierarchical Multiagent Reinforcement Learning for Allocating Guaranteed Display Ads.

In this article, we study the problem of guaranteed display ads (GDAs) allocation, which requires proactively allocate display ads to different impressions to fulfill their impression demands indicated in the contracts. Existing methods for this problem either assume the impressions that are static or solely consider a specific ad's benefits. Thus, it is hard to generalize to the industrial production scenario where the impressions are dynamical and large-scale, and the overall allocation optimality of all the considered GDAs is required.

New Probabilistic Multi-Graph Decomposition Model to Identify Consistent Human Brain Network Modules.

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding large-scale brain networks that underlie higher-level cognition in human. However, suitable network analysis computational tools are still lacking in human brain connectivity research. To address this problem, we propose a novel probabilistic multi-graph decomposition model to identify consistent network modules from the brain connectivity networks of the studied subjects.

Combining knowledge and data driven insights for identifying risk factors using electronic health records.

Background: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost.

Integrative molecular profiling reveals asparagine synthetase is a target in castration-resistant prostate cancer.

The identification of new and effective therapeutic targets for the lethal, castration-resistant stage of prostate cancer (CRPC) has been challenging because of both the paucity of adequate frozen tissues and a lack of integrated molecular analysis. Therefore, in this study, we performed a genome-wide analysis of DNA copy number alterations from 34 unique surgical CRPC specimens and 5 xenografts, with matched transcriptomic profiling of 25 specimens. An integrated analysis of these data revealed that the asparagine synthetase (ASNS) gene showed a gain in copy number and was overexpressed at the transcript level.

ASICs aggravate acidosis-induced injuries during ischemic reperfusion.

Although acidosis is considered as a byproduct of brain ischemia, its effect on neurons during ischemia and reperfusion remains controversial, and the exact role of acid-sensing ion channels (ASICs) is unclear. Here we investigated the effect of acidosis on hippocampal neurons and the role of ASICs during both oxygen-glucose deprivation (OGD) and reperfusion. MTT assay and annexin V/PI staining suggested that although acidosis had a negative effect during OGD, it was more detrimental during reperfusion.