HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity

The BioAssay network and its implications to future therapeutic discovery

Background: Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem. Results: In this paper we construct a network from bioassay data in PubChem, apply network biology concepts to characterize this bioassay network, integrate information from multiple biological databases (e.g. DrugBank, OMIM, and UniHI), and systematically analyze the potential of bioassay targets being new drug targets in the context of complex biological networks. We propose a model to quantitatively prioritize this druggability of bioassay targets, and literature evidence was found to confirm our prioritization of bioassay targets at a roughly 70% accuracy. Conclusions: Our analysis provide some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery

The BioAssay network and its implications to future therapeutic discovery

Background: Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem. Results: In this paper we construct a network from bioassay data in PubChem, apply network biology concepts to characterize this bioassay network, integrate information from multiple biological databases (e.g. DrugBank, OMIM, and UniHI), and systematically analyze the potential of bioassay targets being new drug targets in the context of complex biological networks. We propose a model to quantitatively prioritize this druggability of bioassay targets, and literature evidence was found to confirm our prioritization of bioassay targets at a roughly 70% accuracy. Conclusions: Our analysis provide some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery

Noise Expands the Response Range of the Bacillus subtilis Competence Circuit

Gene regulatory circuits must contend with intrinsic noise that arises due to finite numbers of proteins. While some circuits act to reduce this noise, others appear to exploit it. A striking example is the competence circuit in Bacillus subtilis, which exhibits much larger noise in the duration of its competence events than a synthetically constructed analog that performs the same function. Here, using stochastic modeling and fluorescence microscopy, we show that this larger noise allows cells to exit terminal phenotypic states, which expands the range of stress levels to which cells are responsive and leads to phenotypic heterogeneity at the population level. This is an important example of how noise confers a functional benefit in a genetic decision-making circuit

Theory of the Fano Resonance in the STM Tunneling Density of States due to a Single Kondo Impurity

The conduction electron density of states nearby single magnetic impurities, as measured recently by scanning tunneling microscopy (STM), is calculated, taking into account tunneling into conduction electron states only. The Kondo effect induces a narrow Fano resonance in the conduction electron density of states, while scattering off the d-level generates a weakly energy dependent Friedel oscillation. The line shape varies with the distance between STM tip and impurity, in qualitative agreement with experiments, but is very sensitive to details of the band structure. For a Co impurity the experimentally observed width and shift of the Kondo resonance are in accordance with those obtained from a combination of band structure and strongly correlated calculations.Comment: 4 pages, ReVTeX + 4 figures (Encapsulated Postscript), submitted to PR

SOML: Sparse Online Metric Learning with Application to Image Retrieval

Ministry of Education, Singapore under its Academic Research Funding Tier

Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling

Battery health assessment and recuperation play a crucial role in the utilization of second-life Li-ion batteries. However, due to ambiguous aging mechanisms and lack of correlations between the recovery effects and operational states, it is challenging to accurately estimate battery health and devise a clear strategy for cell rejuvenation. This paper presents aging and reconditioning experiments of 62 commercial high-energy type lithium iron phosphate (LFP) cells, which supplement existing datasets of high-power LFP cells. The relatively large-scale data allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity. Considering cell-to-cell inconsistencies, an average test error of $16.84\% \pm 1.87\%$ (mean absolute percentage error) for cycle life prediction is achieved by gradient boosting regressor given information from the first 80 cycles. In addition, it is found that some of the recoverable lost capacity is attributed to the lateral lithium non-uniformity within the electrodes. An equivalent circuit model is built and experimentally validated to demonstrate how such non-uniformity can be accumulated, and how it can give rise to recoverable capacity loss. SHapley Additive exPlanations (SHAP) analysis also reveals that battery operation history significantly affects the capacity recovery.Comment: 20 pages, 5 figures, 1 tabl

Deep learning for content-based image retrieval: A comprehensive study

Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR sys-tems. The key challenge has been attributed to the well-known “se-mantic gap ” issue that exists between low-level image pixels cap-tured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been ac-tively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning tech-niques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements i

Ideology in the Era of Xi Jinping

Publisher Copyright: © 2018, The Author(s).After 1978, Maoism as a living mass ideological and social force in the People’s Republic of China largely died away. The Party state’s legitimacy since that time has been based on a new pillar of economic competence and the delivery of tangible economic gains. But China is still a place where, at least within the political elite, there is an identifiable ideology and associated language that links the aims of a political force, the Communist Party of China, with national prosperity, historic rejuvenation, and the delivery of the political goals promised when the Communist Party was founded almost a century ago – modernity in Chinese society. Ideology has not disappeared in this interpretation. It has just become more concealed, more nuanced, and in some spaces more flexible. For Chinese contemporary leaders, ideology is partly a body of practices, beliefs, and language which have been bequeathed to them by previous leaders, and which show that they are part of the same historic movement that runs from 1921 to 1949, and through 1978 until today. This body of practices is aimed at maintaining a sustainable system of one party rule, as well as an assertion of discipline and control in the core tactical spaces of political power. Under Xi, a group of twelve keywords maps out the discursive space that matters to the CPC today. These terms exemplify the ways in which the contemporary CPC is willing to use ideas from diverse sources, either from its own past, or from classical Chinese thinking, as a means of achieving emotional as well as intellectual impact, and to assist in the delivery of the major Party goal of the twenty-first century – the creation of a great nation with the CPC at the heart of its governance. Underlying the keywords and the ideological space they define is the larger notion of the Party, not just attending to material but also spiritual needs – and creating not just a wealthy country, but also a spiritual socialist civilization.publishersversionPeer reviewe