The Bohemian Club: An Empirical Investigation of the Power Elite

Articlehttp://deepblue.lib.umich.edu/bitstream/2027.42/97015/1/UMURF-Issue02_2005-BKamal.pd

Atomistic Line Graph Neural Network for Improved Materials Property Predictions

Graph neural networks (GNN) have been shown to provide substantial performance improvements for representing and modeling atomistic materials compared with descriptor-based machine-learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles. We present an Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles. We demonstrate that angle information can be explicitly and efficiently included, leading to improved performance on multiple atomistic prediction tasks. We use ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT, Materials project, and QM9 databases. ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks by up to 85 % in accuracy with better or comparable model training speed

Graphics Performance Benchmarks: Summary Report

Report on study of seven different benchmark software systems viewing the parameters and configurations of graphics systems

Immunomodulation of Autoimmune Arthritis by Herbal CAM

Rheumatoid arthritis (RA) is a debilitating autoimmune disease of global prevalence. The disease is characterized by synovial inflammation leading to cartilage and bone damage. Most of the conventional drugs used for the treatment of RA have severe adverse reactions and are quite expensive. Over the years, increasing proportion of patients with RA and other immune disorders are resorting to complementary and alternative medicine (CAM) for their health needs. Natural plant products comprise one of the most popular CAM for inflammatory and immune disorders. These herbal CAM belong to diverse traditional systems of medicine, including traditional Chinese medicine, Kampo, and Ayurvedic medicine. In this paper, we have outlined the major immunological pathways involved in the induction and regulation of autoimmune arthritis and described various herbal CAM that can effectively modulate these immune pathways. Most of the information about the mechanisms of action of herbal products in the experimental models of RA is relevant to arthritis patients as well. The study of immunological pathways coupled with the emerging application of genomics and proteomics in CAM research is likely to provide novel insights into the mechanisms of action of different CAM modalities

Performance assessment of lower VHF band for short‐range communication and geolocation applications

The focus of this paper is to characterize near‐ground wave propagation in the lower very high frequency (VHF) band and to assess advantages that this frequency band offers for reliable short‐range low‐data rate communications and geolocation applications in highly cluttered environments as compared to conventional systems in the microwave range. With the advent of palm‐sized miniaturized VHF antennas, interest in low‐power and low‐frequency communication links is increasing because (1) channel complexity is far less in this frequency band compared to higher frequencies and (2) significant signal penetration through/over obstacles is possible at this frequency. In this paper, we quantify the excess path loss and small‐scale fading at the lower VHF and the 2.4 GHz bands based on short‐range measurements in various environments. We consider indoor‐to‐indoor, outdoor‐to‐indoor, and non‐line‐of‐sight outdoor measurements and compare the results with measurements at higher frequencies which are used in conventional systems (i.e., 2.4 GHz). Propagation measurements at the lower VHF band are carried out by using an electrically small antenna to assess the possibility of achieving a miniaturized, mobile system for near‐ground communication. For each measurement scenario considered, path loss and small‐scale fading are characterized after calibrating the differences in the systems used for measurements at different frequencies, including variations in antenna performance.Key PointsLow VHF has favorable short‐range characteristics and low signal distortionPenetration through many layers of building walls is possible at low VHFNovel miniaturized VHF antennas with reasonable performance have been designedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111943/1/rds20240.pd

On the redundancy in large material datasets: efficient and robust learning with less data

Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95 % of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the "bigger is better" mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume.Comment: Main text: 10 pages, 2 tables, 5 figures. Supplemental information: 29 pages, 1 table, 23 figure

Computational Approaches for Modeling Power Consumption on an Underwater Flapping Fin Propulsion System

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations. To optimize for different gait performance metrics, we develop a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, that is able to evaluate different fin designs and kinematics, and allow for comparison with other bio-inspired platforms. We create and train computational models using experimental data, and use these models to predict thrust and power under different fin operating states, providing efficiency profiles. We then use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. These comparisons provide a better understanding of how fin materials affect our thrust generation and propulsive efficiency, allowing us to inform control systems and weight for efficiency on an inverse gait-selector model.Comment: 9 pages, 8 figures, conferenc

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at the National Institute of Standards and Technology (NIST) is a large-scale collection of curated datasets and tools with more than 80000 materials and millions of properties. JARVIS uses a combination of electronic structure, artificial intelligence (AI), advanced computation and experimental methods to accelerate materials design. Here we report some of the new features that were recently included in the infrastructure such as: 1) doubling the number of materials in the database since its first release, 2) including more accurate electronic structure methods such as Quantum Monte Carlo, 3) including graph neural network-based materials design, 4) development of unified force-field, 5) development of a universal tight-binding model, 6) addition of computer-vision tools for advanced microscopy applications, 7) development of a natural language processing tool for text-generation and analysis, 8) debuting a large-scale benchmarking endeavor, 9) including quantum computing algorithms for solids, 10) integrating several experimental datasets and 11) staging several community engagement and outreach events. New classes of materials, properties, and workflows added to the database include superconductors, two-dimensional (2D) magnets, magnetic topological materials, metal-organic frameworks, defects, and interface systems. The rich and reliable datasets, tools, documentation, and tutorials make JARVIS a unique platform for modern materials design. JARVIS ensures openness of data and tools to enhance reproducibility and transparency and to promote a healthy and collaborative scientific environment

Mediators of Inflammation-Induced Bone Damage in Arthritis and Their Control by Herbal Products

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the synovial joints leading to bone and cartilage damage. Untreated inflammatory arthritis can result in severe deformities and disability. The use of anti-inflammatory agents and biologics has been the mainstay of treatment of RA. However, the prolonged use of such agents may lead to severe adverse reactions. In addition, many of these drugs are quite expensive. These limitations have necessitated the search for newer therapeutic agents for RA. Natural plant products offer a promising resource for potential antiarthritic agents. We describe here the cellular and soluble mediators of inflammation-induced bone damage (osteoimmunology) in arthritis. We also elaborate upon various herbal products that possess antiarthritic activity, particularly mentioning the specific target molecules. As the use of natural product supplements by RA patients is increasing, this paper presents timely and useful information about the mechanism of action of promising herbal products that can inhibit the progression of inflammation and bone damage in the course of arthritis