Asymptotic Optimality Theory For Decentralized Sequential Multihypothesis Testing Problems

The Bayesian formulation of sequentially testing $M \ge 3$ hypotheses is studied in the context of a decentralized sensor network system. In such a system, local sensors observe raw observations and send quantized sensor messages to a fusion center which makes a final decision when stopping taking observations. Asymptotically optimal decentralized sequential tests are developed from a class of "two-stage" tests that allows the sensor network system to make a preliminary decision in the first stage and then optimize each local sensor quantizer accordingly in the second stage. It is shown that the optimal local quantizer at each local sensor in the second stage can be defined as a maximin quantizer which turns out to be a randomization of at most $M-1$ unambiguous likelihood quantizers (ULQ). We first present in detail our results for the system with a single sensor and binary sensor messages, and then extend to more general cases involving any finite alphabet sensor messages, multiple sensors, or composite hypotheses.Comment: 14 pages, 1 figure, submitted to IEEE Trans. Inf. Theor

Understanding Polarization Correlation of Entangled Vector Meson Pairs

We propose an experimental test of local hidden variable theories against quantum mechanics by measuring the polarization correlation of entangled vector meson pairs. In our study, the form of the polarization correlation probability is reproduced in a natural way by interpreting the two-body decay of the meson as a measurement of its polarization vector within the framework of quantum mechanics. This provides more detailed information on the quantum entanglement, thus a new Monte Carlo method to simulate the quantum correlation is introduced. We discuss the feasibility of carrying out such a test at experiments in operation currently and expect that the measured correlated distribution may provide us with deeper insight into the fundamental question about locality and reality.Comment: 7 pages, 3 figures. v3: The version published in PR

Search for Evergreens in Science: A Functional Data Analysis

Evergreens in science are papers that display a continual rise in annual citations without decline, at least within a sufficiently long time period. Aiming to better understand evergreens in particular and patterns of citation trajectory in general, this paper develops a functional data analysis method to cluster citation trajectories of a sample of 1699 research papers published in 1980 in the American Physical Society (APS) journals. We propose a functional Poisson regression model for individual papers' citation trajectories, and fit the model to the observed 30-year citations of individual papers by functional principal component analysis and maximum likelihood estimation. Based on the estimated paper-specific coefficients, we apply the K-means clustering algorithm to cluster papers into different groups, for uncovering general types of citation trajectories. The result demonstrates the existence of an evergreen cluster of papers that do not exhibit any decline in annual citations over 30 years.Comment: 40 pages, 9 figure

Modern Power System Dynamic Performance Improvement through Big Data Analysis

Higher penetration of Renewable Energy (RE) is causing generation uncertainty and reduction of system inertia for the modern power system. This phenomenon brings more challenges on the power system dynamic behavior, especially the frequency oscillation and excursion, voltage and transient stability problems. This dissertation work extracts the most useful information from the power system features and improves the system dynamic behavior by big data analysis through three aspects: inertia distribution estimation, actuator placement, and operational studies.First of all, a pioneer work for finding the physical location of COI in the system and creating accurate and useful inertia distribution map is presented. Theoretical proof and dynamic simulation validation have been provided to support the proposed method for inertia distribution estimation based on measurement PMU data. Estimation results are obtained for a radial system, a meshed system, IEEE 39 bus-test system, the Chilean system, and a real utility system in the US. Then, this work provided two control actuator placement strategy using measurement data samples and machine learning algorithms. The first strategy is for the system with single oscillation mode. Control actuators should be placed at the bus that are far away from the COI bus. This rule increased damping ratio of eamples systems up to 14\% and hugely reduced the computational complexity from the simulation results of the Chilean system. The second rule is created for system with multiple dynamic problems. General and effective guidance for planners is obtained for IEEE 39-bus system and IEEE 118-bus system using machine learning algorithms by finding the relationship between system most significant features and system dynamic performance. Lastly, it studied the real-time voltage security assessment and key link identification in cascading failure analysis. A proposed deep-learning framework has Achieved the highest accuracy and lower computational time for real-time security analysis. In addition, key links are identified through distance matrix calculation and probability tree generation using 400,000 data samples from the Western Electricity Coordinating Council (WECC) system

The phenotypic expression of QTLs for partial resistance to barley leaf rust during plant development

Partial resistance is generally considered to be a durable form of resistance. In barley, Rphq2, Rphq3 and Rphq4 have been identified as consistent quantitative trait loci (QTLs) for partial resistance to the barley leaf rust pathogen Puccinia hordei. These QTLs have been incorporated separately into the susceptible L94 and the partially resistant Vada barley genetic backgrounds to obtain two sets of near isogenic lines (NILs). Previous studies have shown that these QTLs are not effective at conferring disease resistance in all stages of plant development. In the present study, the two sets of QTL–NILs and the two recurrent parents, L94 and Vada, were evaluated for resistance to P. hordei isolate 1.2.1 simultaneously under greenhouse conditions from the first leaf to the flag leaf stage. Effect of the QTLs on resistance was measured by development rate of the pathogen, expressed as latency period (LP). The data show that Rphq2 prolongs LP at the seedling stage (the first and second leaf stages) but has almost no effect on disease resistance in adult plants. Rphq4 showed no effect on LP until the third leaf stage, whereas Rphq3 is consistently effective at prolonging LP from the first leaf to the flag leaf. The changes in the effectiveness of Rphq2 and Rphq4 happen at the barley tillering stage (the third to fourth leaf stages). These results indicate that multiple disease evaluations of a single plant by repeated inoculations of the fourth leaf to the flag leaf should be conducted to precisely estimate the effect of Rphq4. The present study confirms and describes in detail the plant development-dependent effectiveness of partial resistance genes and, consequently, will enable a more precise evaluation of partial resistance regulation during barley developmen