Hong Kong University of Science and Technology

About: Hong Kong University of Science and Technology is a education organization based out in Hong Kong, Hong Kong, China. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 20126 authors who have published 52428 publications receiving 1965915 citations. The organization is also known as: HKUST & The Hong Kong University of Science and Technology.

Gassing in Li(4)Ti(5)O(12)-based batteries and its remedy.

Abstract: Destructive gas generation with associated swelling has been a major challenge to the large-scale application of lithium ion batteries (LIBs) made from Li4Ti5O12 (LTO) anodes. Here we report root causes of the gassing behavior and suggest remedy to suppress it. The generated gases mainly contain H2, CO2 and CO, which originate from interfacial reactions between LTO and surrounding alkyl carbonate solvents. The reactions occur at the very thin outermost surface of LTO (111) plane, which result in transformation from (111) to (222) plane and formation of (101) plane of anatase TiO2. A nanoscale carbon coating along with a stable solid electrolyte interface (SEI) film around LTO is seen most effective as a barrier layer in suppressing the interfacial reaction and resulting gassing from the LTO surface. Such an ability to tune the interface nanostructure of electrodes has practical implications in the design of next-generation high power LIBs.

Aggregation-induced Emission of Silole Molecules and Polymers: Fundamental and Applications

Abstract: Aggregation generally quenches the light emissions of chromophoric molecules. In this review, we demonstrate that 1,1-disubstituted 2,3,4,5-tetraphenyl siloles and 2,5-difunctionalized siloles as well as their polymers exhibit the opposite behaviors. Instead of quenching, aggregation has greatly boosted their photoluminescence quantum yields by up to two orders of magnitude, turning them from faint fluorophores into strong emitters. Such “abnormal” phenomenon of “aggregation-induced emission (AIE)” is attributed to restricted intramolecular rotations of the peripheral phenyl rings against the central silole core, which block the nonradiative channel via the rotational energy relaxation processes and effectively populate the radiative decay of the excitons. Utilizing such a novel effect, siloles and their polymers find an array of applications as: sensors for chemicals, explosives, pH, and biomacromolecules (proteins, DNAs and RNAs), indicators for determining CMC and monitoring layer-by-layer self-assembling, biocompatible fluorogens for cell imaging, visualizing agent for DNA gel electrophoresis, biolabels for immunoassay, stimuli-responsive organic nanomaterials, magnetic fluorescent nanoparticles for potential bio-imaging and -separation, and outstanding materials for efficient OLEDs and PV cells.

Managing network mobility with tradable credits

Abstract: A system of tradable travel credits is explored in a general network with homogeneous travelers. A social planner is assumed to initially distribute a certain number of travel credits to all eligible travelers, and then there are link-specific charges to travelers using that link. Free trading of credits among travelers is assumed. For a given credit distribution and credit charging scheme, the existence of a unique equilibrium link flow pattern is demonstrated with either fixed or elastic demand. It can be obtained by solving a standard traffic equilibrium model subject to a total credit consumption constraint. The credit price at equilibrium in the trading market is also conditionally unique. The appropriate distribution of credits among travelers and correct selection of link-specific rates is shown to lead to the most desirable network flow patterns in a revenue-neutral manner. Social optimum, Pareto-improving and revenue-neutral, and side-constrained traffic flow patterns are investigated.

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

Abstract: Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.

A simple and efficient search algorithm for block-matching motion estimation

Abstract: The three-step search (TSS) algorithm for block-matching motion estimation, due to its simplicity, significant computational reduction, and good performance, has been widely used in real-time video applications. A new search algorithm is proposed for further reduction of computational complexity for motion estimation. It is shown that the proposed algorithm is simple and efficient and requires about one half of the computation for the TSS while keeping the same regularity and good performance.