Ranking Median Regression: Learning to Order through Local Consensus

This article is devoted to the problem of predicting the value taken by a random permutation $\Sigma$, describing the preferences of an individual over a set of numbered items $\{1,\; \ldots,\; n\}$ say, based on the observation of an input/explanatory r.v. $X$ e.g. characteristics of the individual), when error is measured by the Kendall $\tau$ distance. In the probabilistic formulation of the 'Learning to Order' problem we propose, which extends the framework for statistical Kemeny ranking aggregation developped in \citet{CKS17}, this boils down to recovering conditional Kemeny medians of $\Sigma$ given $X$ from i.i.d. training examples $(X_1, \Sigma_1),\; \ldots,\; (X_N, \Sigma_N)$. For this reason, this statistical learning problem is referred to as \textit{ranking median regression} here. Our contribution is twofold. We first propose a probabilistic theory of ranking median regression: the set of optimal elements is characterized, the performance of empirical risk minimizers is investigated in this context and situations where fast learning rates can be achieved are also exhibited. Next we introduce the concept of local consensus/median, in order to derive efficient methods for ranking median regression. The major advantage of this local learning approach lies in its close connection with the widely studied Kemeny aggregation problem. From an algorithmic perspective, this permits to build predictive rules for ranking median regression by implementing efficient techniques for (approximate) Kemeny median computations at a local level in a tractable manner. In particular, versions of $k$-nearest neighbor and tree-based methods, tailored to ranking median regression, are investigated. Accuracy of piecewise constant ranking median regression rules is studied under a specific smoothness assumption for $\Sigma$'s conditional distribution given $X$

Persistence of extrahepatic hepatitis B virus DNA in the absence of detectable hepatic replication in patients with baboon liver transplants

The presence of hepatitis B virus (HBV) DNA in extrahepatic tissues has been well documented. Whether HBV DNA can persist in extrahepatic tissues for long periods of time in the absence of replication in the liver has not been determined previously. Recently, two patients with end‐stage liver disease secondary to chronic active HBV were treated with baboon liver xenotransplants as these animals are felt to be resistant to HBV infection. Multiple tissues from these two patients were examined for HBV DNA using polymerase chain reaction (PCR). HBV DNA was not detectable in four of five samples of the liver xenografts. A positive signal was observed in a single assay for one sample, but this sample was not positive in subsequent assays. HBV DNA was detected in peripheral blood lymphocytes, spleen, kidney, bone marrow, pancreas, lymph node, heart and small intestine. The level of HBV DNA in these tissues was too low for the detection of HBV DNA replicative intermediates by Southern hybridization; thus, it could not be determined whether the HBV DNA in these tissues represented actively replicating HBV in extrahepatic sites, integrated HBV sequences, HBV in infiltrating lymphocytes, or deposition of HBV immune complexes originating from the plasma. However, it is clear from this study that HBV DNA persisted in multiple tissues for 70 days after replication in the liver had ceased or at least was below the level of detection by PCR. © 1995 Wiley‐Liss, Inc. Copyright © 1995 Wiley‐Liss, Inc., A Wiley Compan

A study on LQG/LTR control for damping inter-area oscillations in power systems

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Construction of a heterologous vaccine against Bordetella avium and Campylobacter jejuni utilizing the B. avium transporter, Baa1

A Gram negative species of bacteria, Campylobacter jejuni, is the leading cause of food poisoning worldwide. Humans often contract food poisoning after ingesting contaminated poultry. Detecting the presence of C. jejuni in poultry is difficult because it is part of the natural flora and does not cause symptomatic infection. In a related manner, Bordetella avium is a Gram negative species of bacteria that causes bordetellosis in poultry. This disease is similar to whooping cough caused by the related pathogen of humans, B. pertussis. Though the mortality rate for bordetellosis is low, it weakens the birds’ immune systems, often leading to secondary infections. The aim of this project was to construct a vaccine platform capable of immunizing poultry against both pathogens—B. avium and C. jejuni—thus reducing disease in birds and humans. A heterologous construct can be made utilizing the B. avium autotransporter Baa1 that plays a role in host cell attachment. Autotransporters are comprised of three genetic regions: promoters to drive expression, and encoded passenger and transporter domains. The transporter is a beta barrel anchored in the outer membrane. The passenger domain is translocated through the transporter and secreted to the outer surface of the bacterial cell. An antigenic Campylobacter gene was cloned into the encoded passenger domain of the Baa1 autotransporter in the context of a suicide plasmid. Tri-parental mating was done to promote homologous recombination of the construct into the B. avium chromosome. After concluding the construct was not in the chromosome most likely due to instability, a plasmid was synthesized and the codons of the Campylobacter region were optimized. Tri-parental mating was completed again. All steps were verified with PCR and gel electrophoresis. A B. avium transconjugant containing the chimeric cja::baa1construct was isolated. Future work will involve gene expression and challenge studies

A semidefinite relaxation procedure for fault-tolerant observer design

A fault-tolerant observer design methodology is proposed. The aim is to guarantee a minimum level of closed-loop performance under all possible sensor fault combinations while optimizing performance under the nominal, fault-free condition. A novel approach is proposed to tackle the combinatorial nature of the problem, which is computationally intractable even for a moderate number of sensors, by recasting the problem as a robust performance problem, where the uncertainty set is composed of all combinations of a set of binary variables. A procedure based on an elimination lemma and an extension of a semidefinite relaxation procedure for binary variables is then used to derive sufficient conditions (necessary and sufficient in the case of one binary variable) for the solution of the problem which significantly reduces the number of matrix inequalities needed to solve the problem. The procedure is illustrated by considering a fault-tolerant observer switching scheme in which the observer outputs track the actual sensor fault condition. A numerical example from an electric power application is presented to illustrate the effectiveness of the design

Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature

Centralized wide area damping controller for power system oscillation problems

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, three different centralized control designs that vary on complexity are presented to damp inter-area oscillations in large power systems. All the controls are based on phasor measurements. The first two proposed architectures use simple proportional gains that consider availability of measurements from different areas of the system and fulfill different optimization functions. The third controller is based on a more sophisticated Linear Quadratic Gaussian approach which requires access to the state space model of the system under investigation. The novelty of the proposed scheme resides in designing a single control to command the most influence group of machines in the system. To illustrate the effectiveness of the proposed algorithms, simulations results in the IEEE New England model are presented