Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids

Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures.Comment: 33 pages, 6 figure

Two stage Robust Nash Bargaining based Benefit Sharing between Electric and HCNG Distribution Networks Bridged with SOFC

Hydrogen-enriched compressed natural gas (HCNG) networks have potentized sustainability and efficiency of integrated electricity and natural gas systems. However, paucity of benefit sharing risks the IENGS's development in multiple entities and bottlenecks its efficacy. To fill the gap, a robust Nash bargaining-based benefit sharing mechanism for HCNG-enabled IENGS is proposed

Vasopressors in septic shock: a systematic review and network meta-analysis

Objective: Vasopressor agents are often prescribed in septic shock. However, their effects remain controversial. We conducted a systematic review and Bayesian network meta-analysis to compare the effects among different types of vasopressor agents. Data sources We searched for relevant studies in PubMed, Embase, and the Cochrane Library databases from database inception until December 2014. Study selection Randomized controlled trials in adults with septic shock that evaluated different vasopressor agents were selected. Data extraction Two authors independently selected studies and extracted data on study characteristics, methods, and outcomes. Data synthesis Twenty-one trials (n=3,819) met inclusion criteria, which compared eleven vasopressor agents or vasopressor combinations (norepinephrine [NE], dopamine [DA], vasopressin [VP], epinephrine [EN], terlipressin [TP], phenylephrine [PE], TP+NE, TP + dobutamine [DB], NE+DB, NE+EN, and NE + dopexamine [DX]). Except for the superiority of NE over DA, the mortality of patients treated with any vasopressor agent or vasopressor combination was not significantly different. Compared to DA, NE was found to be associated with decreased cardiac adverse events, heart rate (standardized mean difference [SMD]: −2.10; 95% confidence interval [CI]: −3.95, −0.25; P=0.03), and cardiac index (SMD: −0.73; 95% CI: −1.14, −0.03; P=0.004) and increased systemic vascular resistance index (SVRI) (SMD: 1.03; 95% CI: 0.61, 1.45; P<0.0001). This Bayesian meta-analysis revealed a possible rank of probability of mortality among the eleven vasopressor agents or vasopressor combinations; from lowest to highest, they are NE+DB, EN, TP, NE+EN, TP+NE, VP, TP+DB, NE, PE, NE+DX, and DA. Conclusion: In terms of survival, NE may be superior to DA. Otherwise, there is insufficient evidence to suggest that any other vasopressor agent or vasopressor combination is superior to another. When compared to DA, NE is associated with decreased heart rate, cardiac index, and cardiovascular adverse events, as well as increased SVRI. The effects of vasopressor agents or vasopressor combinations on mortality in patients with septic shock require further investigation

The Nuclear Spectroscopic Telescope Array (NuSTAR)

The Nuclear Spectroscopic Telescope Array (NuSTAR) is a NASA Small Explorer mission that will carry the first focusing hard X-ray (5 -- 80 keV) telescope to orbit. NuSTAR will offer a factor 50 -- 100 sensitivity improvement compared to previous collimated or coded mask imagers that have operated in this energy band. In addition, NuSTAR provides sub-arcminute imaging with good spectral resolution over a 12-arcminute field of view. After launch, NuSTAR will carry out a two-year primary science mission that focuses on four key programs: studying the evolution of massive black holes through surveys carried out in fields with excellent multiwavelength coverage, understanding the population of compact objects and the nature of the massive black hole in the center of the Milky Way, constraining explosion dynamics and nucleosynthesis in supernovae, and probing the nature of particle acceleration in relativistic jets in active galactic nuclei. A number of additional observations will be included in the primary mission, and a guest observer program will be proposed for an extended mission to expand the range of scientific targets. The payload consists of two co-aligned depth-graded multilayer coated grazing incidence optics focused onto solid state CdZnTe pixel detectors. To be launched in early 2012 on a Pegasus rocket into a low-inclination Earth orbit. Data will be publicly available at GSFC's High Energy Astrophysics Science Archive Research Center (HEASARC) following validation at the science operations center located at Caltech.Comment: 9 pages, 5 figures, to appear in Proceedings of the SPIE, Space Telescopes and Instrumentation 2010: Ultraviolet to Gamma Ra

PEELER: Learning to Effectively Predict Flakiness without Running Tests

—Regression testing is a widely adopted approach to expose change-induced bugs as well as to verify the correctness/robustness of code in modern software development settings. Unfortunately, the occurrence of flaky tests leads to a significant increase in the cost of regression testing and eventually reduces the productivity of developers (i.e., their ability to find and fix real problems). State-of-the-art approaches leverage dynamic test information obtained through expensive re-execution of test cases to effectively identify flaky tests. Towards accounting for scalability constraints, some recent approaches have built on static test case features, but fall short on effectiveness. In this paper, we introduce PEELER, a new fully static approach for predicting flaky tests through exploring a representation of test cases based on the data dependency relations. The predictor is then trained as a neural network based model, which achieves at the same time scalability (because it does not require any test execution), effectiveness (because it exploits relevant test dependency features), and practicality (because it can be applied in the wild to find new flaky tests). Experimental validation on 17,532 test cases from 21 Java projects shows that PEELER outperforms the state-of-the-art FlakeFlagger by around 20 percentage points: we catch 22% more flaky tests while yielding 51% less false positives. Finally, in a live study with projects in-the-wild, we reported to developers 21 flakiness cases, among which 12 have already been confirmed by developers as being indeed flaky