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Additional file 8: Supplementary figures Fig. S1-S42.

Additional file 8: Supplementary figures Fig. S1-S42.

We propose a novel hierarchy construction algorithm for 3D scenes with repeated elements, such as classrooms with multiple desk--chair pairs. Most existing algorithms focus on scenes such as bedrooms or living rooms, which rarely contain repeated patterns. Consequently, such methods may not recognize repeated patterns, which are vital for understanding the structure and context of scenes such as classrooms. Therefore, we propose a new global optimization algorithm for recognizing repeated patterns and building hierarchical structures based on repeated patterns. First, we find a repeated template by calculating the coverage ratios and frequencies of many substructures in a scene. Once the repeated template has been determined, a minimum cost maximum flow problem can be solved to find all instances (repetitions) of it in the scene and then group objects accordingly. Second, we group objects in the region outside the repeated elements according to their adjacency. Finally, based on these two sets of results, we build the hierarchy of the entire scene. We test this hierarchy construction algorithm on the Princeton and SceneNN databases and show that our algorithm can correctly find repeated patterns and construct a hierarchy that is more similar to the ground truth than the results of previous methods.

Mobile network that millions of people use every day is one of the most complex systems in the world. Optimization of mobile network to meet exploding customer demand and reduce capital/operation expenditures poses great challenges. Despite recent progress, application of deep reinforcement learning (DRL) to complex real world problem still remains unsolved, given data scarcity, partial observability, risk and complex rules/dynamics in real world, as well as the huge reality gap between simulation and real world. To bridge the reality gap, we introduce a Sim-to-Real framework to directly transfer learning from simulation to real world via graph convolutional neural network (CNN) - by abstracting partially observable mobile network into graph, then distilling domain-variant irregular graph into domain-invariant tensor in locally Euclidean space as input to CNN -, domain randomization and multi-task learning. We use a novel self-play mechanism to encourage competition among DRL agents for best record on multiple tasks via simulated annealing, just like athletes compete for world record in decathlon. We also propose a decentralized multi-agent, competitive and cooperative DRL method to coordinate the actions of multi-cells to maximize global reward and minimize negative impact to neighbor cells. Using 6 field trials on commercial mobile networks, we demonstrate for the first time that a DRL agent can successfully transfer learning from simulation to complex real world problem with imperfect information, complex rules/dynamics, huge state/action space, and multi-agent interactions, without any training in the real world., Comment: Accepted by NIPS 2018 Workshop

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is a database for postmarketing drug safety monitoring and influences changes in FDA safety guidance documents such as drug labels. The number of cases in the FAERS has rapidly increased with the improvement of submission methods and data standards and thus has become an important resource for regulatory science. Although the FAERS has been predominantly used for safety signal detection, this study explored its utility for disease characteristics.

Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. Conclusion We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity. Published version