Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning

We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments, behavioral descriptors, and fitness. We specify different benchmarks based on the complexity of both the task and the agent controlled by a deep neural network. The benchmark uses standard Quality-Diversity metrics, including coverage, QD-score, maximum fitness, and an archive profile metric to quantify the relation between coverage and fitness. We also present how to quantify the robustness of the solutions with respect to environmental stochasticity by introducing corrected versions of the same metrics. We believe that our benchmark is a valuable tool for the community to compare and improve their findings. The source code is available online: https://github.com/adaptive-intelligent-robotics/QDaxComment: Accepted at GECCO Workshop on Quality Diversity Algorithm Benchmark

mpiJava 1.2: API Specification

This document defines the API of mpiJava, a Java language binding for MPI 1.1. The document is not a standalone specification of the behaviour of MPI--it is meant to be read in conjunction with the MPI standard document [2]. Subsections are laid out in the same way as in the standard document, to allow cross-referencing. Where the mpiJava binding makes no significant change to a particular section of the standard document, we will just note here that there are no special issues for the Java binding. This does not mean that the corresponding section of the standard is irrelevant to the Java binding--it may mean it is 100% relevant! Where practical the API is modelled on the MPI C++ interface defined in the MPI standard version 2.0 [3]. Changes to the mpiJava 1.1 interface: -The MPI.OBJECT basic type has been added. –The interface for MPI.Buffer_detach has been corrected. – The API of User_funcion has been changed. – Attributes cached in communicators are now assumed to have integer values. Attr_put and Attr_delete have been removed. –The interface to Cartcomm.dimsCreate has been corrected. –Errorhandler_set, Errorhandler_get have changed from static members of MPI to instance methods on Comm. –The method Is_null has been added to class Comm. –The Initialization method MPI.Init now returns the command line arguments set by MPI. –MPI exception classes are now specified to be subclasses of MPIException rather than I0Exception. Methods are now declared to throw MPIException (see section 8). The current API is viewed as an interim measure. Further significant changes are likely to result from the efforts of the Message-passing working group of the Java Grande Forum

Mapping interactions with the chaperone network reveals factors that protect against tau aggregation.

A network of molecular chaperones is known to bind proteins ('clients') and balance their folding, function and turnover. However, it is often unclear which chaperones are critical for selective recognition of individual clients. It is also not clear why these key chaperones might fail in protein-aggregation diseases. Here, we utilized human microtubule-associated protein tau (MAPT or tau) as a model client to survey interactions between ~30 purified chaperones and ~20 disease-associated tau variants (~600 combinations). From this large-scale analysis, we identified human DnaJA2 as an unexpected, but potent, inhibitor of tau aggregation. DnaJA2 levels were correlated with tau pathology in human brains, supporting the idea that it is an important regulator of tau homeostasis. Of note, we found that some disease-associated tau variants were relatively immune to interactions with chaperones, suggesting a model in which avoiding physical recognition by chaperone networks may contribute to disease

Overcoming the fear of lethal injury: Evaluating suicidal behavior in the military through the lens of the Interpersonal–Psychological Theory of Suicide

Suicide rates have been increasing in military personnel since the start of Operation Enduring Freedom and Operation Iraqi Freedom, and it is vital that efforts be made to advance suicide risk assessment techniques and treatment for members of the military who may be experiencing suicidal symptoms. One potential way to advance the understanding of suicide in the military is through the use of the Interpersonal-Psychological Theory of Suicide. This theory proposes that three necessary factors are needed to complete suicide: feelings that one does not belong with other people, feelings that one is a burden on others or society, and an acquired capability to overcome the fear and pain associated with suicide. This review analyzes the various ways that military service may influence suicidal behavior and integrates these findings into an overall framework with relevant practical implications. Findings suggest that although there are many important factors in military suicide, the acquired capability may be the most impacted by military experience because combat exposure and training may cause habituation to fear of painful experiences, including suicide. Future research directions, ways to enhance risk assessment, and treatment implications are also discussed.Psycholog

GMC Collisions as Triggers of Star Formation. III. Density and Magnetically Regulated Star Formation

We study giant molecular cloud (GMC) collisions and their ability to trigger star cluster formation. We further develop our three dimensional magnetized, turbulent, colliding GMC simulations by implementing star formation sub-grid models. Two such models are explored: (1) Density-Regulated, i.e., fixed efficiency per free-fall time above a set density threshold; (2) Magnetically- Regulated, i.e., fixed efficiency per free-fall time in regions that are magnetically supercritical. Variations of parameters associated with these models are also explored. In the non-colliding simulations, the overall level of star formation is sensitive to model parameter choices that relate to effective density thresholds. In the GMC collision simulations, the final star formation rates and efficiencies are relatively independent of these parameters. Between non-colliding and colliding cases, we compare the morphologies of the resulting star clusters, properties of star-forming gas, time evolution of the star formation rate (SFR), spatial clustering of the stars, and resulting kinematics of the stars in comparison to the natal gas. We find that typical collisions, by creating larger amounts of dense gas, trigger earlier and enhanced star formation, resulting in 10 times higher SFRs and efficiencies. The star clusters formed from GMC collisions show greater spatial sub-structure and more disturbed kinematics

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations