Warm Jupiters are less lonely than hot Jupiters: close neighbours

Exploiting the Kepler transit data, we uncover a dramatic distinction in the prevalence of sub-Jovian companions, between systems that contain hot Jupiters (periods inward of 10 days) and those that host warm Jupiters (periods between 10 and 200 days). Hot Jupiters, with the singular exception of WASP-47b, do not have any detectable inner or outer planetary companions (with periods inward of 50 days and sizes down to $2 R_{\rm Earth}$). Restricting ourselves to inner companions, our limits reach down to $1 R_{\rm Earth}$. In stark contrast, half of the warm Jupiters are closely flanked by small companions. Statistically, the companion fractions for hot and warm Jupiters are mutually exclusive, particularly in regard to inner companions. The high companion fraction of warm Jupiters also yields clues to their formation. The warm Jupiters that have close-by siblings should have low orbital eccentricities and low mutual inclinations. The orbital configurations of these systems are reminiscent of those of the low-mass, close-in planetary systems abundantly discovered by the Kepler mission. This, and other arguments, lead us to propose that these warm Jupiters are formed in-situ. There are indications that there may be a second population of warm Jupiters with different characteristics. In this picture, WASP-47b could be regarded as the extending tail of the in-situ warm Jupiters into the hot Jupiter region, and does not represent the generic formation route for hot Jupiters.Comment: 12 pages, 7 figures, accepted by Ap

A Control-Centric Benchmark for Video Prediction

Video is a promising source of knowledge for embodied agents to learn models of the world's dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it remains unclear whether current metrics are accurate indicators of performance on downstream tasks. We find empirically that for planning robotic manipulation, existing metrics can be unreliable at predicting execution success. To address this, we propose a benchmark for action-conditioned video prediction in the form of a control benchmark that evaluates a given model for simulated robotic manipulation through sampling-based planning. Our benchmark, Video Prediction for Visual Planning ($VP^2$), includes simulated environments with 11 task categories and 310 task instance definitions, a full planning implementation, and training datasets containing scripted interaction trajectories for each task category. A central design goal of our benchmark is to expose a simple interface -- a single forward prediction call -- so it is straightforward to evaluate almost any action-conditioned video prediction model. We then leverage our benchmark to study the effects of scaling model size, quantity of training data, and model ensembling by analyzing five highly-performant video prediction models, finding that while scale can improve perceptual quality when modeling visually diverse settings, other attributes such as uncertainty awareness can also aid planning performance.Comment: ICLR 202

B cells are capable of independently eliciting rapid reactivation of encephalitogenic CD4 T cells in a murine model of multiple sclerosis

<div><p>Recent success with B cell depletion therapies has revitalized efforts to understand the pathogenic role of B cells in Multiple Sclerosis (MS). Using the adoptive transfer system of experimental autoimmune encephalomyelitis (EAE), a murine model of MS, we have previously shown that mice in which B cells are the only MHCII-expressing antigen presenting cell (APC) are susceptible to EAE. However, a reproducible delay in the day of onset of disease driven by exclusive B cell antigen presentation suggests that B cells require optimal conditions to function as APCs in EAE. In this study, we utilize an <i>in vivo</i> genetic system to conditionally and temporally regulate expression of MHCII to test the hypothesis that B cell APCs mediate attenuated and delayed neuroinflammatory T cell responses during EAE. Remarkably, induction of MHCII on B cells following the transfer of encephalitogenic CD4 T cells induced a rapid and robust form of EAE, while no change in the time to disease onset occurred for recipient mice in which MHCII is induced on a normal complement of APC subsets. Changes in CD4 T cell activation over time did not account for more rapid onset of EAE symptoms in this new B cell-mediated EAE model. Our system represents a novel model to study how the timing of pathogenic cognate interactions between lymphocytes facilitates the development of autoimmune attacks within the CNS.</p></div

Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks. Reinforcement learning (RL) is a promising approach for acquiring short-horizon skills autonomously. However, the focus of RL algorithms has largely been on the success of those individual skills, more so than learning and grounding a large repertoire of skills that can be sequenced to complete extended multi-stage tasks. The latter demands robustness and persistence, as errors in skills can compound over time, and may require the robot to have a number of primitive skills in its repertoire, rather than just one. To this end, we introduce EMBER, a model-based RL method for learning primitive skills that are suitable for completing long-horizon visuomotor tasks. EMBER learns and plans using a learned model, critic, and success classifier, where the success classifier serves both as a reward function for RL and as a grounding mechanism to continuously detect if the robot should retry a skill when unsuccessful or under perturbations. Further, the learned model is task-agnostic and trained using data from all skills, enabling the robot to efficiently learn a number of distinct primitives. These visuomotor primitive skills and their associated pre- and post-conditions can then be directly combined with off-the-shelf symbolic planners to complete long-horizon tasks. On a Franka Emika robot arm, we find that EMBER enables the robot to complete three long-horizon visuomotor tasks at 85% success rate, such as organizing an office desk, a file cabinet, and drawers, which require sequencing up to 12 skills, involve 14 unique learned primitives, and demand generalization to novel objects.Comment: Equal advising and contribution for last two author

Learning Needs Assessment and Preferred Instructional Methods among Nurses Participating in Continuous Professional Education.

INTRODUCTION: Globally, the concept of continuing professional education (CPE) has been acknowledged by all professionals as a primary method for regular enhancement of basic professional education. In the clinical sector, when providing in service programs, learning needs assessment provides the basis for the design of effective educational programs. The purpose of the study was to examine the learning needs and preferred instructional method among nurses. Also, the significant difference of professional development learning needs, clinical skills learning needs and instructional method was measured in relations to sex and years of clinical experience. METHOD: The study utilized descriptive research design. Convenient sampling was used to sample 120 nurses from selected hospitals in Laguna. A self constructed questionnaires were used as the instruments of the study. The statistical treatment used were mean, standard deviation, t-test, and ANOVA. RESULTS: The study showed that highest priority of learning needs in terms of professional development was stress management. Emergency management was the highest priority perceived by the nurses in terms of clinical skills. The learning method most preferred by the nurses was the use of lectures. There was no significant difference in terms of professional development learning needs, clinical skills learning needs and instructional method when considering sex and years of clinical experience. DISCUSSIONS AND RECOMMENDATION: The study recommends the nurse educator and managers of the selected hospitals to utilize learning needs assessment results to implement educational programs. It is further recommended that learning needs assessment should be an ongoing process involving other professional and clinical topics to promote better quality service

Factors Associated with Immunization Opinion Leadership among Men Who Have Sex with Men in Los Angeles, California

We sought to identify the characteristics of men who have sex with men (MSM) who are opinion leaders on immunization issues and to identify potential opportunities to leverage their influence for vaccine promotion within MSM communities. Using venue-based sampling, we recruited and enrolled MSM living in Los Angeles (N = 520) from December 2016 to February 2017 and evaluated characteristic differences in sociodemographic characteristics, health behaviors, and technology use among those classified as opinion leaders versus those who were not. We also asked respondents about their past receipt of meningococcal serogroups A, C, W, and Y (MenACWY) and meningococcal B (MenB) vaccines, as well as their opinions on the importance of 13 additional vaccines. Multivariable results revealed that non-Hispanic black (aOR = 2.64; 95% CI: 1.17–5.95) and other race/ethnicity (aOR = 2.98; 95% CI: 1.41–6.29) respondents, as well as those with a history of an STI other than HIV (aOR = 1.95; 95% CI: 1.10–3.48), were more likely to be opinion leaders. MenACWY (aOR = 1.92; 95% CI: 1.13–3.25) and MenB (aOR = 3.09; 95% CI: 1.77–5.41) vaccine uptake, and perceived importance for these and seven additional vaccines, were also associated with being an opinion leader. The results suggest that the co-promotion of vaccination and other health promotion initiatives via opinion leaders could be a useful strategy for increasing vaccination among MSM

Disentanglement via Latent Quantization

In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another. Since the model is provided with no ground truth information about these sources, inductive biases take a paramount role in enabling disentanglement. In this work, we construct an inductive bias towards compositionally encoding and decoding data by enforcing a harsh communication bottleneck. Concretely, we do this by (i) quantizing the latent space into learnable discrete codes with a separate scalar codebook per dimension and (ii) applying strong model regularization via an unusually high weight decay. Intuitively, the quantization forces the encoder to use a small number of latent values across many datapoints, which in turn enables the decoder to assign a consistent meaning to each value. Regularization then serves to drive the model towards this parsimonious strategy. We demonstrate the broad applicability of this approach by adding it to both basic data-reconstructing (vanilla autoencoder) and latent-reconstructing (InfoGAN) generative models. In order to reliably assess these models, we also propose InfoMEC, new metrics for disentanglement that are cohesively grounded in information theory and fix well-established shortcomings in previous metrics. Together with regularization, latent quantization dramatically improves the modularity and explicitness of learned representations on a representative suite of benchmark datasets. In particular, our quantized-latent autoencoder (QLAE) consistently outperforms strong methods from prior work in these key disentanglement properties without compromising data reconstruction.Comment: 20 pages, 8 figures, code available at https://github.com/kylehkhsu/disentangl