Understanding the gravitational and magnetic environment of a very long baseline atom interferometer

By utilizing the quadratic dependency of the interferometry phase on time, the Hannover Very Long Baseline Atom Interferometer facility (VLBAI) aims for sub nm/s$^2$ gravity measurement sensitivity. With its 10 m vertical baseline, VLBAI offers promising prospects in testing fundamental physics at the interface between quantum mechanics and general relativity. Here we discuss the challenges imposed on controlling VLBAI's magnetic and gravitational environment and report on their effect on the device's accuracy. Within the inner 8 m of the magnetic shield, residual magnetic field gradients expect to cause a bias acceleration of only 6$\times$10$^{-14}$ m/s$^2$ while we evaluate the bias shift due to the facility's non-linear gravity gradient to 2.6 nm/s$^2$. The model allows the VLBAI facility to be a reference to other mobile devices for calibration purposes with an uncertainty below the 10 nm/s$^2$ level.Comment: Presented at the Ninth Meeting on CPT and Lorentz Symmetry, Bloomington, Indiana, May 17-26, 202

Stochastic Modeling of Intrusion-Tolerant Server Architectures for Dependability and Performance Evaluation

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryDARPA / F30602-00-C-017

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard

Intrusion-Tolerant State Transfer for Group Communication Systems

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryDARPA / F30602-00-C-0172U of I OnlyRestricted to UIUC communit

Knowledge engineering for modern information systems: methods, models and tools De Gruyter series on smart computing applications ;, v. 3./ edited by Anand Sharma, Sandeep Kautish, Prateek Agrawal, Vishu Madaan, Charu Gupta, Saurav Nanda.

Includes bibliographical references and index.Knowledge Engineering (KE) is a field within artificial intelligence that develops knowledge based systems. KE is the process of imitating how a human expert in a specific domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain. Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artificial Intelligence, Cognitive Neuroscience, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.P. Megaladevi -- Saikat Samanta, Achyuth Sarkar, Charu Gupta, Aditi Sharma -- Kudirat Abiola Adegoke, Akor P. Usman, Mohamed Bitagi -- Anagha Shenoy R, Bhoomika M, Annaiah H -- Neeraj Bhanot, Parth Padalkar -- Shubhika Gaur, Vibha Maheshwari -- Sanjive Saxena -- Ria Rawal, Kartik Goel, Akshay Gulati, Shivang Sharma, Palak Girdhar, Charu Gupta, Prateek Agrawal -- A. Ilmudeen -- Andualem Walelign Lale -- Hazik Mohamed -- Priyanka Jain, Ram Bhavsar, B.V. Pawar, N.K. Jain, Hemant Darbari, Virendrakumar C. Bhavsar. Knowledge engineering for industrial expert systems / Machine learning integrated blockchain model for Industry 4.0 smart applications / Prototyping the expectancy disconfirmation theory model for quality service delivery in federal university libraries in Nigeria / Design of chatbot using natural language processing / Algorithm development based on an integrated approach for identifying cause and effect relationships between different factors / Risk analysis and management in projects / Assessing and managing risks in smart computing applications / COVID-19 visualization and exploratory data analysis / Business intelligence and decision support systems: business applications in the modern information system era / Business intelligence implementation in different organizational setup evidence from reviewed literatures / Conceptualization of a modern digital-driven health-care management information system (HMIS) / Knowledge engine for a Hindi text-to-scene generation system /1 online resource (vi, 232 pages)

Improving deep learning model performance under parametric constraints for materials informatics applications

Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties