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He, Fei

Publications and source records attributed to He, Fei.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants

Abstract FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

ML Classifier Fusion for Three Data Streams with Quality Inversely Proportional to Time Resolution

We consider a monitoring scenario of phenomenon using three different streams of measurements whose quality is proportional to their constant inter-arrival times. Each measurement of a stream needs to be binary-classified to reflect the state of interest of the phenomenon. A set of classifiers is separately trained and fused for each stream at its time resolution using measurements collected under known states. We present a machine learning method to fuse the outputs of these fusers to provide a final classification at the finest time resolution. We show that this fused-fusers method provides decisions with likely superior classification probability compared to the best individual classifiers and fused-classifiers. We derive generalization equations that guarantee a superior classification probability of fused-fusers with a confidence probability specified by the classifiers’ generalization equations. We apply these results to study a practical problem of classifying Pu/Np target dissolution events at a radiochemical processing facility using gamma spectral measurements of effluent flows.

Rao, Nageswara↗
DOE OSTI2023

Game-Theoretic Strategies for Quantum-Conventional Network Infrastructures

Fundamentally and practically, quantum networks and conventional networks are inextricably tied, since the basic quantum protocols such as teleportation require both networks and the conventional network fiber is also used for the quantum network. A Recursive System of Systems (RSOS) model is developed for quantum-conventional (QC) networks by modeling the correlations at various levels based on the failure and attack modes of quantum, conventional and hybrid components and the propagative effects across QC boundaries. A game-theoretic formulation is developed to capture the cost-benefit trade-offs of the provider in defending against component attacks, using sum-form utility functions. By applying the Nash Equilibrium results, the conditions and sensitivity functions of the survivalprobabilities of a QC network at different levels are derived using the strong dependencies between quantum and conventional infrastructures. The results provide insights into the dependenciesbetween conventional and quantum networks, including cross QC boundary effects in terms of disruption impact of conventional networks on quantum networks, and vice versa.

Rao, Nageswara↗
DOE OSTI2022

Game-Theoretic Strategies for Cyber-Physical Infrastructures Under Component Disruptions

In this work, networked infrastructures of recursively defined systems composed of discrete cyber and physical components are considered. The components of basic systems at the finest levels can be disrupted by cyber or physical means, and can be reinforced to survive at certain costs. A problem of ensuring the infrastructure performance is formulated as a game between a provider and an attacker, who probabilistically choose components to reinforce and attack, respectively. The disruptions of this infrastructure are characterized using the aggregate failure correlation function that specifies the conditional failure probability of the infrastructure given the failure of an individual system at that level. The survival probabilities of basic systems satisfy simple product-form, first-order differential equations expressed in terms of the multiplier functions. The utility functions of the provider and attacker are composed of the reward and cost terms, both expressed in terms of the component reinforcement and attack probabilities. The Nash equilibrium of this game is characterized, along with the sensitivity functions of the survival probabilities of basic systems that highlight their dependence individually on the cost-benefit terms, the correlation functions, and the multiplier functions. These results are illustrated using simplified models of a distributed cloud servers infrastructure, a 5G data network infrastructure, a high performance computing federation, and a smart energy grid infrastructure.

42 ENGINEERING↗
DOE OSTI2022

Classification and Fusion of Two Disparate Data Streams and Nuclear Dissolutions Application

We consider two streams of data or measurements with disparate qualities and time resolutions that need to be classified. The first stream consists of higher quality data at a coarser time resolution, and the other consists of lower quality data at a finer time resolution. We present a fuser-switch method that fuses the set of classifiers of each stream separately and switches between them. We show that this method provides classification decisions at a finer time resolution with superior detection and false alarm probabilities compared to individual classifiers, under the statistical independence and time resolution ratio conditions. When classifiers are trained using machine learning methods, we show that this superior performance is guaranteed with a confidence probability specified by the classifiers' generalization equations. We use these results to provide analytical foundations for previous practical results that achieved significant performance improvements in classifying Pu/Np target dissolution events at a radiochemical processing facility.

Rao, Nageswara↗
DOE OSTI2021

Game-Theoretic Approach for Grace-Period Policy in Supercomputers

Job scheduling at supercomputing facilities is important for achieving high utilization of these valuable resources while ensuring effective execution of jobs submitted by users. The jobs are scheduled according to their specified resource demands such as expected job completion times, and the available resources based on allocations. Jobs that overrun their allocated times are terminated, for example, after a grace-period. It is non-trivial and often very complex for users to accurately estimate the completion times of their jobs, and consequently they face a dilemma: underestimate the job time to have a higher priority and risk job termination due to overrun, or overestimate it to ensure its completion and risk its delayed execution. In this paper, we investigate whether providing grace-period can benefit facility performance by developing a game- theoretic model between a facility provider and multiple users for a simplified scheduling scenario based on job execution times. We present closed-form expressions for the provider’s and user’s best-response strategies to maximize their respective utility functions. We describe conditions under which offering a grace-period is advantageous to both facility provider and users by deriving the Nash equilibrium of the game.

He, Fei↗
NASA NTRS2016

Determination of the Earth's Plasmapause Location from the CE-3 EUVC Images

The Moon-based Extreme Ultraviolet Camera (EUVC) aboard China's Chang'e-3 (CE-3) mission has successfully imaged the entire Earth's plasmasphere for the first time from the side views on lunar surface. An EUVC image on 21 April 2014 is used in this study to demonstrate the characteristics and configurations of the Moon-based EUV imaging and to illustrate the determination algorithm of the plasmapause locations on the magnetic equator. The plasmapause locations determined from all the available EUVC images with the Minimum L Algorithm are quantitatively compared with those extracted from insitu observations (Defense Meteorological Satellite Program, Time History of Events and Macroscale Interactions during Substorms, and Radiation Belt Storm Probes). Excellent agreement between the determined plasmapauses seen by EUVC and the extracted ones from other satellites indicates the reliability of the Moon-based EUVC images as well as the determination algorithm. This preliminary study provides an important basis for future investigation of the dynamics of the plasmasphere with the Moon-based EUVC imaging.

He, Fei↗
NASA NTRS2012

Longwave Band-by-band Cloud Radiative Effect and its Application in GCM Evaluation

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM fs radiation code is uniquely valuable for GCM evaluation because (1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and (2) the fractional contribution of each band to the LW broadband CRE (f(sub CRE)) is sensitive to cloud top height but largely insensitive to cloud fraction, presenting thus a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band ]by ]band quantities from collocated AIRS and CERES observations. We present here a study that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs (GFDL AM2, NASA GEOS-5, and CCCma CanAM4) forced by observed SST. The models agree with observation on the annual ]mean LW broadband CRE over the tropical oceans within +/-1W/sq m. However, the differences among these three GCMs in some bands can be as large as or even larger than +/-1W/sq m. Observed seasonal cycles of f(sub CRE) in major bands are shown to be consistent with the seasonal cycle of cloud top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of f(sub CRE) agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of f(sub CRE) highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands.

Huang, Xianglei↗