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The Vertebrate Breed Ontology: Toward Effective Breed data Standardization

Journal of veterinary internal medicine 2025年第4期39卷 e70133页

作者： Kathleen R Mullen Imke Tammen Nicolas A Matentzoglu Marius Mather James P Balhoff Elizabeth Esdaile Gregoire Leroy Carissa A Park Halie M Rando Nadia T Saklou Tracy L Webb Nicole A Vasilevsky Christopher J Mungall Melissa A Haendel Frank W Nicholas Sabrina Toro Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Sydney School of Veterinary Science The University of Sydney Sydney New South Wales Australia. Semanticly Ltd Athens Greece. Sydney Informatics Hub The University of Sydney Sydney New South Wales Australia. Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Veterinary Genetics Laboratory School of Veterinary Medicine University of California Davis California USA. Animal Production and Health Division Food and Agriculture Organization of the United Rome Italy. Department of Animal Science Iowa State University Ames Iowa USA. Department of Computer Science Smith College Northampton Massachusetts USA. Department of Clinical Sciences Colorado State University Fort Collins Colorado USA. Data Collaboration Center Critical Path Institute Tucson Arizona USA. Lawrence Berkeley National Laboratory Berkeley California USA.

BACKGROUND:Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. HYPOTHESIS/OBJECTIVES:A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. ANIMALS:No live animals were used. METHODS:The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. RESULTS:VBO is an open, community-driven ontology representing over 19 500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. CONCLUSION AND CLINICAL IMPORTANCE:The adoption of VBO as a standard for breed names in databases and veterinary electronic health records enhances veterinary data interoperability and computability, supporting precision medicine.

关键词： animal population groups biological ontologies breeding genetics precision medicine theriogenology

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SCIENTIFIC MACHINE LEARNING FOR CLOSURE MODELS IN MULTISCALE PROBLEMS: A REVIEW

arXiv

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arXiv 2024年

作者： Sanderse, Benjamin Stinis, Panos Maulik, Romit Ahmed, Shady E. Scientific Computing Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA99352 United States College of Information Sciences and Technology The Pennsylvania State University Westgate Building University ParkPA16802 United States

Closure problems are omnipresent when simulating multiscale systems, where some quantities and processes cannot be fully prescribed despite their effects on the simulation’s accuracy. Recently, scientific machine learning approaches have been proposed as a way to tackle the closure problem, combining traditional (physics-based) modeling with data-driven (machine-learned) techniques, typically through enriching differential equations with neural networks. This paper reviews the different reduced model forms, distinguished by the degree to which they include known physics, and the different objectives of a priori and a posteriori learning. The importance of adhering to physical laws (such as symmetries and conservation laws) in choosing the reduced model form and choosing the learning method is discussed. The effect of spatial and temporal discretization and recent trends toward discretization-invariant models are reviewed. In addition, we make the connections between closure problems and several other research disciplines: inverse problems, Mori-Zwanzig theory, and multi-fidelity methods. In conclusion, much progress has been made with scientific machine learning approaches for solving closure problems, but many challenges remain. In particular, the generalizability and interpretability of learned models is a major issue that needs to be addressed *** Codes 65MXX, 76MXX, 68T07 © 2024, CC BY-NC-SA.

关键词： Inverse problems

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Radiation-coupled front-tracking simulations for laser-driven shock experiments

Radiation-coupled front-tracking simulations for laser-drive...

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作者： Zhang, Yongmin Drake, R. Paul Glimm, James Grove, John W. Sharp, David H. Department of Applied Mathematics and Statistics University at Stony Brook Stony Brook NY 11974-3600 United States University of Michigan 2455 Hayward St. Ann Arbor MI 48105 United States Center for Data Intensive Computing Brookhaven National Laboratory Upton NY 11793-6000 United States Continuum Dynamics Group Computer and Computational Science Division Los Alamos National Laboratory Los Alamos NM 87545 United States Complex Systems Group Theoretical Division Los Alamos National Laboratory Los Alamos NM 87545 United States

The purpose of this paper is to develop a numerical algorithm to track the preheat interface motion driven by radiation transfer in high-intensity laser experiments. Our front-tracking algorithm is coupled to a radiation process through an inter-package coupling by connecting the output from a radhydro code HYADES to the input of FronTier code of front tracking. Our coupled algorithm is validated by comparing simulation results from both codes in both low and high radiation cases. Significant interface motion and deformation of the harmonic perturbation due to radiation preheat are observed in high radiation heat case. © 2005 Elsevier Ltd. All rights reserved.

关键词： Algorithms

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Truthful Generalized Linear Models

arXiv

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arXiv 2022年

作者： Qiu, Yuan Liu, Jinyan Wang, Di College of Computing Georgia Institute of Technology United States School of Computer Science and Technology Beijing Institute of Technology China Division of CEMSE King Abdullah University of Science and Technology Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia Computational Bioscience Research Center Saudi Arabia

In this paper we study estimating Generalized Linear Models (GLMs) in the case where the agents (individuals) are strategic or self-interested and they concern about their privacy when reporting data. Compared with the classical setting, here we aim to design mechanisms that can both incentivize most agents to truthfully report their data and preserve the privacy of individuals’ reports, while their outputs should also close to the underlying parameter. In the first part of the paper, we consider the case where the covariates are sub-Gaussian and the responses are heavy-tailed where they only have the finite fourth moments. First, motivated by the stationary condition of the maximizer of the likelihood function, we derive a novel private and closed form estimator. Based on the estimator, we propose a mechanism which has the following properties via some appropriate design of the computation and payment scheme for several canonical models such as linear regression, logistic regression and Poisson regression: (1) the mechanism is o(1)-jointly differentially private (with probability at least 1 − o(1));(2) it is an o(n1 )-approximate Bayes Nash equilibrium for a (1 − o(1))-fraction of agents to truthfully report their data, where n is the number of agents;(3) the output could achieve an error of o(1) to the underlying parameter;(4) it is individually rational for a (1 − o(1)) fraction of agents in the mechanism;(5) the payment budget required from the analyst to run the mechanism is o(1). In the second part, we consider the linear regression model under more general setting where both covariates and responses are heavy-tailed and only have finite fourth moments. By using an 4-norm shrinkage operator, we propose a private estimator and payment scheme which have similar properties as in the sub-Gaussian case. © 2022, CC BY.

关键词： Linear regression

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System

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Procedia Computer science 2023年 217卷 1266-1279页

作者： M.O. Okwu L.K. Tartibu E.O. Ojo S. Adume J.O. Gidiagba J. Fadeyi Department of Mechanical and Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling UK Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo USA

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach.

关键词： Adaptive Neuro-Fuzzy Inference System Dual Source Multi-Echelon Multi-Destinations

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Dynamic placement of rapidly deployable mobile sensor robots using machine learning and expected value of information

arXiv

引用

arXiv 2021年

作者： Agogino, Alice Jang, Hae Young Rao, Vivek Batra, Ritik Liao, Felicity Sood, Rohan Fang, Irving Hu, R. Lily Shoichet-Bartus, Emerson Matranga, John Dept. of Mechanical Engineering University of California BerkeleyCA94720 United States Dept. of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Division of Computing Data Science and Society Dept. of Mathematics University of California BerkeleyCA94720 United States Google Research Google Mountain ViewCA94043 United States Division of Computing Data Science and Society University of California BerkeleyCA94720 United States Business Incubation and Acceleration AVEVA San LeandroCA94577 United States

Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable to provide. Despite the relatively low cost of these assets, the choice of which robotic sensing systems to deploy to which part of an industrial process in a complex plant environment during emergency response remains challenging. This paper describes a framework for optimizing the deployment of emergency sensors as a preliminary step towards realizing the responsiveness of robots in disaster circumstances. AI techniques (Long short-term memory, 1-dimensional convolutional neural network, logistic regression, and random forest) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. In the case study described, the cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include implementing repairs or shutting down the plant. The Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios. © 2021, CC BY-NC-ND.

关键词： Decision trees

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SpaceCube GHOST: A Resilient Processor for Low-Power, High-Reliability Space computing

SpaceCube GHOST: A Resilient Processor for Low-Power, High-R...

引用

IEEE Conference on Aerospace

作者： Noah Perryman Nicholas Franconi Gary Crum Christopher Wilson Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA Science Data Processing Branch Code 587 Software Engineering Division NASA Goddard Space Flight Center Greenbelt MD

There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the SpaceCube GR740 Host for Onboard science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard Space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.

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Boltzmann machine learning with a variational quantum algorithm

引用

Physical Review A 2021年第3期104卷 032413-032413页

作者： Yuta Shingu Yuya Seki Shohei Watabe Suguru Endo Yuichiro Matsuzaki Shiro Kawabata Tetsuro Nikuni Hideaki Hakoshima Department of Physics Faculty of Science Division I Tokyo University of Science Shinjuku Tokyo 162-8601 Japan Research Center for Emerging Computing Technologies (RCECT) National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan Department of Materials University of Oxford Parks Road Oxford OX1 3PH United Kingdom NTT Computer and Data Science laboratories NTT corporation Musashino Tokyo 180-8585 Japan

A Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state is typically used for the Boltzmann machine learning to obtain a suitable probability distribution. The Boltzmann machine learning consists of calculating the gradient of the loss function given in terms of the thermal average, which is the most time-consuming procedure. Here, we propose a method to implement the Boltzmann machine learning by using noisy intermediate-scale quantum devices. We prepare an initial pure state that contains all possible computational basis states with the same amplitude, and we apply a variational imaginary time simulation. Readout of the state after the evolution in the computational basis approximates the probability distribution of the thermal equilibrium state that is used for the Boltzmann machine learning. We perform the numerical simulations of our scheme and confirm that the Boltzmann machine learning works well. Our scheme leads to a significant step toward an efficient machine learning using quantum hardware.

关键词： Machine learning Quantum algorithms Quantum gates

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Green Synthesis and Scale-Up of MOFs for Water Harvesting from Air

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Molecular Frontiers Journal 2023年第1N02期7卷 20-39页

作者： Zhiling Zheng Ali H. Alawadhi Omar M. Yaghi https://***/0000-0001-6090-2258 Department of Chemistry University of California Berkeley California USA Kavli Energy Nanoscience Institute University of California Berkeley California USA Bakar Institute of Digital Materials for the Planet Division of Computing Data Science and Society University of California Berkeley California USA https://***/0000-0002-5611-3325

The synthetic scalability of water harvesting metal–organic frameworks (MOFs) is crucial for making these promising materials accessible and widely available for use in household devices. Herein, we present a facile, sustainable, and high-yield synthesis method to produce a series of water-harvesting MOFs, including MOF-303, CAU-23, MIL-160, MOF-313, CAU-10, and Al-fumarate. Using readily available reactants and water as the only solvent, we were able to synthesize these materials at the kilogram scale in a 200 L batch reactor with yields of 84–96% and space-time yields of 238–305 kg/day/m 3 under optimized reaction conditions. We also show that our procedure preserves framework crystallinity, porosity, and water-harvesting performance of the MOFs synthesized at scale.

关键词： Metal–Organic Frameworks Materials Water Sorption Green Synthesis Atmospheric Water Harvesting

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Dynamics of surface electrons in a topological insulator: cyclotron resonance at room temperature

arXiv

引用

arXiv 2025年

作者： Mohelsky, I. Le Mardelé, F. Dzian, J. Wyzula, J. Sun, X.D. Cho, C.W. Piot, B.A. Shankar, M. Sankar, R. Ferguson, A. Santos-Cottin, D. Maršík, P. Bernhard, C. Akrap, A. Potemski, M. Orlita, M. Laboratoire National des Champs Magnétiques Intenses EMFL CNRS UPR3228 Univ. Grenoble Alpes Univ. Toulouse Univ. Toulouse 3 INSA-T Grenoble France Department of Physics Université de Fribourg Chemin du Musée 3 Fribourg1700 Switzerland Faculty of Mathematics and Physics Charles University Ke Karlovu 5 Prague121 16 Czech Republic Scientific Computing Theory and Data Division Paul Scherrer Institute Switzerland Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics Faculty of Science University of Zagreb Zagreb10000 Croatia Institute of High Pressure Physics PAS WarsawPL-01-142 Poland CENTERA CEZAMAT Warsaw University of Technology WarsawPL-02-822 Poland

The ability to manipulate the surface states of topological insulators using electric or magnetic fields under ambient conditions is a key step toward their integration into future electronic and optoelectronic devices. Here, we demonstrate – using cyclotron resonance measurements on a tin-doped BiSbTe2S topological insulator – that moderate magnetic fields can quantize massless surface electrons into Landau levels even at room temperature. This finding suggests that surface-state electrons can behave as long-lived quasiparticles at unexpectedly high temperatures. Copyright © 2025, The Authors. All rights reserved.

关键词： Electron cyclotron resonance

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