Guarantees on Robot System Performance Using Stochastic Simulation Rollouts

We provide finite-sample performance guarantees for control policies executed on stochastic robotic systems. Given an open- or closed-loop policy and a finite set of trajectory rollouts under the policy, we bound the expected value, value-at-risk, and conditional-value-at-risk of the trajectory cost, and the probability of failure in a sparse rewards setting. The bounds hold, with user-specified probability, for any policy synthesis technique and can be seen as a post-design safety certification. Generating the bounds only requires sampling simulation rollouts, without assumptions on the distribution or complexity of the underlying stochastic system. We adapt these bounds to also give a constraint satisfaction test to verify safety of the robot system. Furthermore, we extend our method to apply when selecting the best policy from a set of candidates, requiring a multi-hypothesis correction. We show the statistical validity of our bounds in the Ant, Half-cheetah, and Swimmer MuJoCo environments and demonstrate our constraint satisfaction test with the Ant. Finally, using the 20 degree-of-freedom MuJoCo Shadow Hand, we show the necessity of the multi-hypothesis correction.Comment: Submitted to IEEE-TR

Note on the preparation of thin vanadium targets

http://www.archive.org/details/noteonpreparatio00sch

Multivariable association discovery in population-scale meta-omics studies.

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2\u27s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles

Analysis with respect to instrumental variables for the exploration of microarray data structures

BACKGROUND: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various factors structuring the data which should be taken into account. The objective of these experiments is the exploration of some given factors while controlling other factors. RESULTS: We present here a family of methods, the analyses with respect to instrumental variables, which can be easily applied to the particular case of microarray data. An illustrative example of analysis with instrumental variables is given in the case of microarray data investigating the effect of beverage intake on peripheral blood gene expression. This approach is compared to an ANOVA-based gene-by-gene statistical method. CONCLUSION: Instrumental variables analyses provide a simple way to control several sources of variation in a multivariate analysis of microarray data. Due to their flexibility, these methods can be associated with a large range of ordination techniques combined with one or several qualitative and/or quantitative descriptive variables

Tomato Aqueous Extract Modulates the Inflammatory Profile of Immune Cells and Endothelial Cells

Nutrients transiently or chronically modulate functional and biochemical characteristics of cells and tissues both in vivo and in vitro. The influence of tomato aqueous extract (TAE) on the in vitro inflammatory response of activated human peripheral blood leukocytes (PBLs) and macrophages was investigated. Its effect on endothelial dysfunction (ED) was analyzed in human umbilical vein endothelial cells (HUVECs). Murine macrophages (RAW264.7 cells), PBLs and HUVECs were incubated with TAE. They were activated with LPS or TNF-α in order to induce inflammatory processes and ED, respectively. Inflammatory mediators and adhesion molecules were measured by immune assay-based multiplex analysis. Gene expression was quantified by RT-PCR. TAE altered the production of interleukins (IL-1β, IL-6, IL-10, IL-12) and chemokines (CCL2/MCP-1, CCL3/MIP-1α, CCL5/RANTES, CXCL8/IL-8, CXCL10/IP-10) in PBLs. TAE reduced ED-associated expression of adhesion molecules (ICAM-1, VCAM-1) in endothelial cell. In macrophages, the production of nitric oxide, PGE2, cytokines and ILs (TNF-α, IL-1β, IL-6, IL-12), which reflects chronic inflammatory processes, was reduced. Adenosine was identified as the main bioactive of TAE. Thus, TAE had cell-specific and context-dependent effects. We infer from these in vitro data, that during acute inflammation TAE enhances cellular alertness and therefore the sensing of disturbed immune homeostasis in the vascular-endothelial compartment. Conversely, it blunts inflammatory mediators in macrophages during chronic inflammation. A novel concept of immune regulation by this extract is proposed