Real-Time Adaptive Multi-Classifier Multi-Resolution Visual Tracking Framework for Unmanned Aerial Vehicles

This paper presents a novel robust visual tracking framework, based on discriminative method, for Unmanned Aerial Vehicles (UAVs) to track an arbitrary 2D/3D target at real-time frame rates, that is called the Adaptive Multi-Classifier Multi-Resolution (AMCMR) framework. In this framework, adaptive Multiple Classifiers (MC) are updated in the (k-1)th frame-based Multiple Resolutions (MR) structure with compressed positive and negative samples, and then applied them in the kth frame-based Multiple Resolutions (MR) structure to detect the current target. The sample importance has been integrated into this framework to improve the tracking stability and accuracy. The performance of this framework was evaluated with the Ground Truth (GT) in different types of public image databases and real flight-based aerial image datasets firstly, then the framework has been applied in the UAV to inspect the Offshore Floating Platform (OFP). The evaluation and application results show that this framework is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant illumination, partial/full target occlusion, blur motion, rapid pose variation and onboard mechanical vibration, among others. To our best knowledge, this is the first work to present this framework for solving the online learning and tracking freewill 2D/3D target problems, and applied it in the UAVs.

Chaperone-supervised conversion of prion protein to its protease-resistant form

Transmissible spongiform encephalopathies (TSEs) are lethal, infectious disorders of the mammalian nervous system. A TSE hallmark is the conversion of the cellular protein PrPC to disease-associated PrPSc (named for scrapie, the first known TSE). PrPC is protease-sensitive, monomeric, detergent soluble, and primarily α-helical; PrPSc is protease-resistant, polymerized, detergent insoluble, and rich in β-sheet. The “protein-only” hypothesis posits that PrPSc is the infectious TSE agent that directly converts host-encoded PrPC to fresh PrPSc, harming neurons and creating new agents of infection. To gain insight on the conformational transitions of PrP, we tested the ability of several protein chaperones, which supervise the conformational transitions of proteins in diverse ways, to affect conversion of PrPC to its protease-resistant state. None affected conversion in the absence of pre-existing PrPSc. In its presence, only two, GroEL and Hsp104 (heat shock protein 104), significantly affected conversion. Both promoted it, but the reaction characteristics of conversions with the two chaperones were distinct. In contrast, chemical chaperones inhibited conversion. Our findings provide new mechanistic insights into nature of PrP conversions, and provide a new set of tools for studying the process underlying TSE pathogenesis.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Adaptive linear classifier by linear programming,

Mode of access: Internet.

Costs as an aid to management as operated in plants supervised by S.K.F. Industries, Inc.

Mode of access: Internet.

Supervised study in mathematics and science,

Bibliography: p. 229-231.

Postoperative Analgesia Following Sciatic Nerve Blockade Administered by Nurse Anesthetists Supervised by Regional Anesthesia Faculty in an Academic Hospital

Introduction: Assessment of expertise in regional anesthesia techniques is traditionally based upon quota fulfillment of procedures during training. Validation of practitioner proficiency in performing procedures in surgical specialties has moved from simple measurement of technical skills to evaluation of global patient outcomes. Complete absence of pain as a result of nerve blockade is the most important clinical endpoint but patient, technical and procedural factors influence results. The purpose of this study was to measure the postoperative pain scores and associated analgesic medication requirements for patients administered sciatic nerve blockade by nurse anesthetists and determine patient or procedural factors that influenced this outcome. Methods: Either nerve stimulator or ultrasound guided sciatic nerve blockade was administered by nurse anesthetists under the supervision of regional anesthesia faculty. Patient demographic data that was collected included gender, body mass index, surgical procedure, and pre-existing chronic pain with associated opioid use. Patient self-reported pain scores and opioid analgesic dosages in the preoperative, intraoperative, immediate postoperative and 24 hour post procedure intervals were recorded. Results: 22 nurse anesthetists administered sciatic nerve blockade to 48 patients during a 36 month interval. Transition from a nerve stimulator to ultrasound guided sciatic nerve block technique resulted in lower mean pain scores. Patients reporting chronic opioid use were observed to have elevated perioperative opioid analgesic requirements and pain scores compared to opioid naïve patients. Conclusion: Effective analgesia is a prime measure for assessing expertise in regional anesthesia and continuous evaluation of this outcome in everyday practice is proposed.

Parent Engagement in Supported Supervised Visitation in Child Welfare

Thesis (Master's)--University of Washington, 2016-06

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06

Indefinite determiner introducing definite referent: a special use of 'yi 'one' + classifier' in Chinese

The indefinite determiner yi 'one'+ classifier' is the most approximate to an indefinite article, like the English a, in Chinese. It serves all the functions characteristic of representative stages of grammaticalization from a numeral to a generalized indefinite determiner as elaborated in the literature. It is established in this paper that the Chinese indefinite determiner has developed a special use with definite expressions, serving as a backgrounding device marking entities as of low thematic importance and unlikely to receive subsequent mentions in ensuing discourse. 'yi+ classifier' in the special use with definite expressions displays striking similarities in terms of semantic bleaching and phonological reduction with the same determiner at the advanced stage of grammaticalization characterized by uses with generics, nonspecifics and nonreferentials. An explanation is offered in terms of an implicational relation between nonreferentiality and low thematic importance which characterize the two uses of the indefinite determiner. While providing another piece of evidence in support of the claim that semantically nonreferentials and entities of low thematic importance tend to be encoded in terms of same linguistic devices in language, findings in this paper have shown how an indefinite determiner can undergo a higher degree of grammaticalization than has been reported in the literature-it expands its scope to mark not only indefinite but also definite expressions as semantically nonreferential and/or thematically unimportant. (C) 2003 Elsevier B.V. All rights reserved.

Gene-expression profiling reveals distinct expression patterns for classic versus variant Merkel cell phenotypes and new classifier genes to distinguish Merkel cell from small-cell lung carcinoma

Merkel cell carcinoma (MCC) is a rare aggressive skin tumor which shares histopathological and genetic features with small-cell lung carcinoma (SCLC), both are of neuroendocrine origin. Comparable to SCLC, MCC cell lines are classified into two different biochemical subgroups designated as 'Classic' and 'Variant'. With the aim to identify typical gene-expression signatures associated with these phenotypically different MCC cell lines subgroups and to search for differentially expressed genes between MCC and SCLC, we used cDNA arrays to pro. le 10 MCC cell lines and four SCLC cell lines. Using significance analysis of microarrays, we defined a set of 76 differentially expressed genes that allowed unequivocal identification of Classic and Variant MCC subgroups. We assume that the differential expression levels of some of these genes reflect, analogous to SCLC, the different biological and clinical properties of Classic and Variant MCC phenotypes. Therefore, they may serve as useful prognostic markers and potential targets for the development of new therapeutic interventions specific for each subgroup. Moreover, our analysis identified 17 powerful classifier genes capable of discriminating MCC from SCLC. Real-time quantitative RT-PCR analysis of these genes on 26 additional MCC and SCLC samples confirmed their diagnostic classification potential, opening opportunities for new investigations into these aggressive cancers.