Discriminative learning with application to interactive facial image retrieval

The amount of digital images is growing drastically and advanced tools for searching in large image collections are therefore becoming urgently needed. Content-based image retrieval is advantageous for such a task in terms of automatic feature extraction and indexing without human labor and subjectivity in image annotations. The semantic gap between high-level semantics and low-level visual features can be reduced by the relevance feedback technique. However, most existing interactive content-based image retrieval (ICBIR) systems require a substantial amount of human evaluation labor, which leads to the evaluation fatigue problem that heavily restricts the application of ICBIR. In this thesis a solution based on discriminative learning is presented. It extends an existing ICBIR system, PicSOM, towards practical applications. The enhanced ICBIR system allows users to input partial relevance which includes not only relevance extent but also relevance reason. A multi-phase retrieval with partial relevance can adapt to the user's searching intention in a from-coarse-to-fine manner. The retrieval performance can be improved by employing supervised learning as a preprocessing step before unsupervised content-based indexing. In this work, Parzen Discriminant Analysis (PDA) is proposed to extract discriminative components from images. PDA regularizes the Informative Discriminant Analysis (IDA) objective with a greatly accelerated optimization algorithm. Moreover, discriminative Self-Organizing Maps trained with resulting features can easily handle fuzzy categorizations. The proposed techniques have been applied to interactive facial image retrieval. Both a query example and a benchmark simulation study are presented, which indicate that the first image depicting the target subject can be retrieved in a small number of rounds

A Universal Attenuation Model of Terahertz Wave in Space-Air-Ground Channel Medium

Providing continuous bandwidth over several tens of GHz, the Terahertz (THz) band (0.1-10 THz) supports space-air-ground integrated network (SAGIN) in 6G and beyond wireless networks. However, it is still mystery how THz waves interact with the channel medium in SAGIN. In this paper, a universal space-air-ground attenuation model is proposed for THz waves, which incorporates the attenuation effects induced by particles including condensed particles, molecules, and free electrons. The proposed model is developed from the insight into the attenuation effects, namely, the physical picture that attenuation is the result of collision between photons that are the essence of THz waves and particles in the environment. Based on the attenuation model, the propagation loss of THz waves in the atmosphere and the outer space are numerically assessed. The results indicate that the attenuation effects except free space loss are all negligible at the altitude higher than 50 km while they need to be considered in the atmosphere lower than 50 km. Furthermore, the capacities of THz SAGIN are evaluated in space-ground, space-sea, ground-sea, and sea-sea scenarios, respectively

Structural analysis and insight into novel MMP-13 inhibitors from natural chemiome as disease-modifying osteoarthritis drugs

Purpose: To identify natural chemiome that inhibits matrix-metalloproteinases (MMPs) with a view to discovering novel disease-modifying osteoarthritis drugs (DMOADs).Methods: Computer-aided drug design (CADD) with virtual screening, ADME/Tox, molecular docking, molecular dynamics simulation, and MM-PBSA calculations were used in search of novel natural compounds that inhibit MMPs.Results: From more than fifty thousand compounds, a single lead compound (IBS ID: 77312) was shortlisted using bias based on binding energy and drug-likeness. This lead compound synergistically bound to the S1 domain of MMP-13 protein through five hydrogen bonds. The interactions became stable within 100-nanosecond molecular dynamics simulation run. The in vitro data for the lead compound showed that its minimal non-lethal dose increased collagen content but decreased aggrecan level in chondrocytes.Conclusion: This study has identified a natural lead compound that may pave the way for a novel DMOAD of natural origin against OA.Keywords: Osteoarthritis, MMP-13, Natural chemiome, Disease-modifying osteoarthritis drug, Molecular dockin

Bethe states of the trigonometric SU(3) spin chain with generic open boundaries

By combining the algebraic Bethe ansatz and the off-diagonal Bethe ansatz, we investigate the trigonometric SU(3) model with generic open boundaries. The eigenvalues of the transfer matrix are given in terms of an inhomogeneous T-Q relation, and the corresponding eigenstates are expressed in terms of nested Bethe-type eigenstates which have well-defined homogeneous limit. This exact solution provides a basis for further analyzing the thermodynamic properties and correlation functions of the anisotropic models associated with higher rank algebras.Comment: 17 pages, 3 tables. arXiv admin note: text overlap with arXiv:1705.0947

Association of Prior Atherosclerotic Cardiovascular Disease with Dementia After Stroke: A Retrospective Cohort Study.

BACKGROUND: Prior atherosclerotic cardiovascular disease (ASCVD), including coronary heart disease (CHD) and peripheral artery disease (PAD), are common among patients with stroke, a known risk factor for dementia. However, whether these conditions further increase the risk of post-stroke dementia remains uncertain. OBJECTIVE: To examine whether prior ASCVD is associated with increased risk of dementia among stroke patients. METHODS: A retrospective cohort study was conducted using the Clinical Practice Research Datalink with linkage to hospital data. Patients with first-ever stroke between 2006 and 2017 were followed up to 10 years. We used multi-variable Cox regression models to examine the associations of prior ASCVD with dementia and the impact of prior ASCVD onset and duration. RESULTS: Among 63,959 patients, 7,265 cases (11.4%) developed post-stroke dementia during a median of 3.6-year follow-up. The hazard ratio (HR) of dementia adjusted for demographics and lifestyle was 1.18 (95% CI: 1.12-1.25) for ASCVD, 1.16 (1.10-1.23) for CHD, and 1.25 (1.13-1.37) for PAD. The HRs additionally adjusted for multimorbidity and medications were 1.07 (1.00-1.13), 1.04 (0.98-1.11), and 1.11 (1.00-1.22), respectively. Based on the fully adjusted estimates, there was no linear relationship between the age of ASCVD onset and post-stroke dementia (all p-trend >0.05). The adjusted risk of dementia was not increased with the duration of pre-stroke ASCVD (all p-trend >0.05). CONCLUSION: Stroke patients with prior ASCVD are more likely to develop subsequent dementia. After full adjustment for confounding, however, the risk of post-stroke dementia is attenuated, with only a slight increase with prior ASCVD.We thank the CPRD@Cambridge team for developing the code lists used in this study. This work was supported by an independent grant from the National Institute for Health Research (NIHR) School of Primary Care Research [SPCR-2014-10043, reference number 340]. Mant and Brayne are NIHR Senior Investigators. Yang is supported by the Cambridge Commonwealth, European and International Trust. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health in the United Kingdom

Mandrake : visualizing microbial population structure by embedding millions of genomes into a low-dimensional representation

In less than a decade, population genomics of microbes has progressed from the effort of sequencing dozens of strains to thousands, or even tens of thousands of strains in a single study. There are now hundreds of thousands of genomes available even for a single bacterial species, and the number of genomes is expected to continue to increase at an accelerated pace given the advances in sequencing technology and widespread genomic surveillance initiatives. This explosion of data calls for innovative methods to enable rapid exploration of the structure of a population based on different data modalities, such as multiple sequence alignments, assemblies and estimates of gene content across different genomes. Here, we present Mandrake, an efficient implementation of a dimensional reduction method tailored for the needs of large-scale population genomics. Mandrake is capable of visualizing population structure from millions of whole genomes, and we illustrate its usefulness with several datasets representing major pathogens. Our method is freely available both as an analysis pipeline (https://github.com/johnlees/mandrake) and as a browser-based interactive application (https://gtonkinhill.github.io/mandrake-web/).This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.Peer reviewe