Showing papers by "Hong Kong University of Science and Technology published in 2013"

Bioprobes Based on AIE Fluorogens

Abstract: Fluorescent bioprobes are powerful tools for analytical sensing and optical imaging, which allow direct visualization of biological analytes at the molecular level and offer useful insights into complex biological structures and processes. The sensing and imaging sensitivity of a bioprobe is determined by the brightness and contrast of its fluorescence before and after analyte binding. Emission from a fluorophore is often quenched at high concentration or in aggregate state, which is notoriously known as concentration quenching or aggregation-caused quenching (ACQ). The ACQ effect limits the label-to-analyte ratio and forces researchers to use very dilute solutions of fluorophores. It compels many probes to operate in a fluorescence “turn-off” mode with a narrow scope of practical applications.The unique aggregation-induced emission (AIE) process offers a straightforward solution to the ACQ problem. Typical AIE fluorogens are characterized by their propeller-shaped rotorlike structures, which undergo low-...

Learning a Deep Compact Image Representation for Visual Tracking

Abstract: In this paper, we study the challenging problem of tracking the trajectory of a moving object in a video with possibly very complex background. In contrast to most existing trackers which only learn the appearance of the tracked object online, we take a different approach, inspired by recent advances in deep learning architectures, by putting more emphasis on the (unsupervised) feature learning problem. Specifically, by using auxiliary natural images, we train a stacked de-noising autoencoder offline to learn generic image features that are more robust against variations. This is then followed by knowledge transfer from offline training to the online tracking process. Online tracking involves a classification neural network which is constructed from the encoder part of the trained autoencoder as a feature extractor and an additional classification layer. Both the feature extractor and the classifier can be further tuned to adapt to appearance changes of the moving object. Comparison with the state-of-the-art trackers on some challenging benchmark video sequences shows that our deep learning tracker is more accurate while maintaining low computational cost with real-time performance when our MATLAB implementation of the tracker is used with a modest graphics processing unit (GPU).

Review of Recent Research on Data-Based Process Monitoring

Abstract: Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.

From RSSI to CSI: Indoor localization via channel response

Abstract: The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics.Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

Materials for Microfluidic Chip Fabrication

Abstract: Through manipulating fluids using microfabricated channeland chamber structures, microfluidics is a powerful tool to realize high sensitive, high speed, high throughput, and low cost analysis. In addition, the method can establish a well-controlled microenivroment for manipulating fluids and particles. It also has rapid growing implementations in both sophisticated chemical/biological analysis and low-cost point-of-care assays. Some unique phenomena emerge at the micrometer scale. For example, reactions are completed in a shorter amount of time as the travel distances of mass and heat are relatively small; the flows are usually laminar; and the capillary effect becomes dominant owing to large surface-to-volume ratios. In the meantime, the surface properties of the device material are greatly amplified, which can lead to either unique functions or problems that we would not encounter at the macroscale. Also, each material inherently corresponds with specific microfabrication strategies and certain native p...

A photostable AIE luminogen for specific mitochondrial imaging and tracking.

Abstract: Tracking the dynamics of mitochondrial morphology has attracted much research interest because of its involvement in early stage apoptosis and degenerative conditions. To follow this process, highly specific and photostable fluorescent probes are in demand. Commercially available mitochondria trackers, however, suffer from poor photostability. To overcome this limitation, we have designed and synthesized a fluorescent agent, tetraphenylethene-triphenylphosphonium (TPE-TPP), for mitochondrial imaging. Inherent from the mitochondrial-targeting ability of TPP groups and the aggregation-induced emission (AIE) characteristics of the TPE core, TPE-TPP possesses high specificity to mitochondria, superior photostability, and appreciable tolerance to environmental change, allowing imaging and tracking of the mitochondrial morphological changes in a long period of time.

Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

Abstract: Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that the above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.

Automatic patch generation learned from human-written patches

Abstract: Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by Weimer et al., has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations. To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (Par), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically. We experimentally evaluated Par on 119 real bugs. In addition, a user study involving 89 students and 164 developers confirmed that patches generated by our approach are more acceptable than those generated by GenProg. Par successfully generated patches for 27 out of 119 bugs, while GenProg was successful for only 16 bugs.

Product Variety Management

Abstract: A great challenge facing industry today is managing variety throughout the entire products life cycle. Drivers of products variety, its benefits, pre-requisites and associated complexity and cost are presented. Enhancing consumers’ value through variety and approaches for achieving it efficiently including modularity, commonality and differentiation are discussed. Variant-oriented manufacturing systems paradigms, as enablers of product variety, and the effective co-development of variants and their manufacturing systems to ensure economic sustainability are reviewed. Industrial applications and guidelines to achieve economy of scope with advantages of economy of scale are discussed. Perspectives and insights on future research in this field are offered.

Demand-Side Management via Distributed Energy Generation and Storage Optimization

Abstract: Demand-side management, together with the integration of distributed energy generation and storage, are considered increasingly essential elements for implementing the smart grid concept and balancing massive energy production from renewable sources. We focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy sources and/or energy storage devices. By means of a day-ahead optimization process regulated by an independent central unit, the latter users intend to reduce their monetary energy expense by producing or storing energy rather than just purchasing their energy needs from the grid. In this paper, we formulate the resulting grid optimization problem as a noncooperative game and analyze the existence of optimal strategies. Furthermore, we present a distributed algorithm to be run on the users' smart meters, which provides the optimal production and/or storage strategies, while preserving the privacy of the users and minimizing the required signaling with the central unit. Finally, the proposed day-ahead optimization is tested in a realistic situation.

New Perspectives on How to Discover Drugs from Herbal Medicines: CAM's Outstanding Contribution to Modern Therapeutics.

Abstract: With tens of thousands of plant species on earth, we are endowed with an enormous wealth of medicinal remedies from Mother Nature. Natural products and their derivatives represent more than 50% of all the drugs in modern therapeutics. Because of the low success rate and huge capital investment need, the research and development of conventional drugs are very costly and difficult. Over the past few decades, researchers have focused on drug discovery from herbal medicines or botanical sources, an important group of complementary and alternative medicine (CAM) therapy. With a long history of herbal usage for the clinical management of a variety of diseases in indigenous cultures, the success rate of developing a new drug from herbal medicinal preparations should, in theory, be higher than that from chemical synthesis. While the endeavor for drug discovery from herbal medicines is “experience driven,” the search for a therapeutically useful synthetic drug, like “looking for a needle in a haystack,” is a daunting task. In this paper, we first illustrated various approaches of drug discovery from herbal medicines. Typical examples of successful drug discovery from botanical sources were given. In addition, problems in drug discovery from herbal medicines were described and possible solutions were proposed. The prospect of drug discovery from herbal medicines in the postgenomic era was made with the provision of future directions in this area of drug development.

Graphene-based nanocomposites: Preparation, functionalization, and energy and environmental applications

Abstract: Graphene, a two-dimensional carbon sheet with one atom thickness and one of the thinnest materials in universe, has inspired huge interest in physics, materials science, chemistry and biology. However, pure graphene sheets are limited for many applications despite their excellent characteristics and scientists face challenges to induce more and controlled functionality. Therefore graphene nanocomposites or hybrids are attracting increasing efforts for real applications in energy and environmental areas by introducing controlled functional building blocks to graphene. In this Review, we first give a brief introduction of graphene's unique physical and chemical properties followed by various preparation and functionalization methods for graphene nanocomposites in the second section. We focus on recent energy-related progress of graphene nanocomposites in solar energy conversion (e.g., photovoltaic and photoelectrochemical devices, artificial photosynthesis) and electrochemical energy devices (e.g., lithium ion battery, supercapacitor, fuel cell) in the third section. We then review the advances in environmental applications of functionalized graphene nanocomposites for the detection and removal of heavy metal ions, organic pollutants, gas and bacteria in the fourth section. Finally a conclusion and perspective is given to discuss the remaining challenges for graphene nanocomposites in energy and environmental science.

Trinity: a distributed graph engine on a memory cloud

Abstract: Computations performed by graph algorithms are data driven, and require a high degree of random data access. Despite the great progresses made in disk technology, it still cannot provide the level of efficient random access required by graph computation. On the other hand, memory-based approaches usually do not scale due to the capacity limit of single machines. In this paper, we introduce Trinity, a general purpose graph engine over a distributed memory cloud. Through optimized memory management and network communication, Trinity supports fast graph exploration as well as efficient parallel computing. In particular, Trinity leverages graph access patterns in both online and offline computation to optimize memory and communication for best performance. These enable Trinity to support efficient online query processing and offline analytics on large graphs with just a few commodity machines. Furthermore, Trinity provides a high level specification language called TSL for users to declare data schema and communication protocols, which brings great ease-of-use for general purpose graph management and computing. Our experiments show Trinity's performance in both low latency graph queries as well as high throughput graph analytics on web-scale, billion-node graphs.

Event-Based Sensor Data Scheduling: Trade-Off Between Communication Rate and Estimation Quality

Abstract: We consider sensor data scheduling for remote state estimation. Due to constrained communication energy and bandwidth, a sensor needs to decide whether it should send the measurement to a remote estimator for further processing. We propose an event-based sensor data scheduler for linear systems and derive the corresponding minimum squared error estimator. By selecting an appropriate event-triggering threshold, we illustrate how to achieve a desired balance between the sensor-to-estimator communication rate and the estimation quality. Simulation examples are provided to demonstrate the theory.

KNN Matting

Abstract: This paper proposes to apply the nonlocal principle to general alpha matting for the simultaneous extraction of multiple image layers; each layer may have disjoint as well as coherent segments typical of foreground mattes in natural image matting. The estimated alphas also satisfy the summation constraint. As in nonlocal matting, our approach does not assume the local color-line model and does not require sophisticated sampling or learning strategies. On the other hand, our matting method generalizes well to any color or feature space in any dimension, any number of alphas and layers at a pixel beyond two, and comes with an arguably simpler implementation, which we have made publicly available. Our matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by using K nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm that produces competitive results with sparse user markups. KNN matting has a closed-form solution that can leverage the preconditioned conjugate gradient method to produce an efficient implementation. Experimental evaluation on benchmark datasets indicates that our matting results are comparable to or of higher quality than state-of-the-art methods requiring more involved implementation. In this paper, we take the nonlocal principle beyond alpha estimation and extract overlapping image layers using the same Laplacian framework. Given the alpha value, our closed form solution can be elegantly generalized to solve the multilayer extraction problem. We perform qualitative and quantitative comparisons to demonstrate the accuracy of the extracted image layers.

CSI-Based Indoor Localization

Abstract: Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in orthogonal frequency division multiplexing systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.

Comparative Developmental Toxicity of New Aromatic Halogenated DBPs in a Chlorinated Saline Sewage Effluent to the Marine Polychaete Platynereis dumerilii

Abstract: Using seawater for toilet flushing may introduce high levels of bromide and iodide into a city’s sewage treatment works, and result in the formation of brominated and iodinated disinfection byproducts (DBPs) during chlorination to disinfect sewage effluents. In a previous study, the authors’ group has detected the presence of many brominated DBPs and identified five new aromatic brominated DBPs in chlorinated saline sewage effluents. The presence of brominated DBPs in chlorinated saline effluents may pose adverse implications for marine ecology. In this study, besides the detection and identification of another seven new aromatic halogenated DBPs in a chlorinated saline sewage effluent, their developmental toxicity was evaluated using the marine polychaete Platynereis dumerilii. For comparison, the developmental toxicity of some commonly known halogenated DBPs was also examined. The rank order of the developmental toxicity of 20 halogenated DBPs was 2,5-dibromohydroquinone > 2,6-diiodo-4-nitrophenol ≥ 2,4...

Transfer defect learning

Abstract: Many software defect prediction approaches have been proposed and most are effective in within-project prediction settings. However, for new projects or projects with limited training data, it is desirable to learn a prediction model by using sufficient training data from existing source projects and then apply the model to some target projects (cross-project defect prediction). Unfortunately, the performance of cross-project defect prediction is generally poor, largely because of feature distribution differences between the source and target projects. In this paper, we apply a state-of-the-art transfer learning approach, TCA, to make feature distributions in source and target projects similar. In addition, we propose a novel transfer defect learning approach, TCA+, by extending TCA. Our experimental results for eight open-source projects show that TCA+ significantly improves cross-project prediction performance.

Synergy between Twisted Conformation and Effective Intermolecular Interactions: Strategy for Efficient Mechanochromic Luminogens with High Contrast

Abstract: A strategy towards efficient mechanochromic luminogens with high contrast is developed. The twisted propeller-like conformations and effective intermolecular interactions not only endow the luminogens with AIE characteristics and high efficiency in the crystalline state, but also render them to undergo conformational planarization and disruption in intermolecular interactions upon mechanical stimuli, resulting in remarkable changes in emission wavelength and efficiency.

All-solid-state hybrid solar cells based on a new organometal halide perovskite sensitizer and one-dimensional TiO2 nanowire arrays

Abstract: A novel organometal halide perovskite (CH3NH3PbI2Br) is synthesized and used as a visible light absorber to sensitize one-dimensional (1D) TiO2 nanowire arrays (NWAs) for all-solid-state hybrid solar cells. It achieved a power conversion efficiency (PCE) of 4.87% and an open circuit voltage (Voc) of 0.82 V, both higher than those of its analogue CH3NH3PbI3.

Full-Range Intracellular pH Sensing by an Aggregation-Induced Emission-Active Two-Channel Ratiometric Fluorogen

Abstract: Intracellular pH (pHi) is an important parameter associated with cellular behaviors and pathological conditions. Sensing pHi and monitoring its changes in live cells are essential but challenging due to the lack of effective probes. We herein report a pH-sensitive fluorogen for pHi sensing and tracking. The dye is a tetraphenylethene–cyanine adduct (TPE-Cy). It is biocompatible and cell-permeable. Upon diffusing into cells, it responds sensitively to pHi in the entire physiological range, visualizing the acidic and basic compartments with intense red and blue emissions, respectively. The ratiometric signal of the red and blue channels can thus serve as an indicator for local proton concentration. The utility of TPE-Cy in pHi imaging and monitoring is demonstrated with the use of confocal microscopy, ratiometric analysis, and flow cytometry.

A Multilevel Model of Team Goal Orientation, Information Exchange, and Creativity

Abstract: Adopting a multilevel approach, we examined how team goal orientation may relate to team creativity and individual creativity. We also theorized and examined the bottom- up process linking individual creativity and team creativity. Multisource data were collected from 485 members and their leaders within 100 R&D teams. The results indicated that a team learning goal and team performance approach goal were positively related-whereas a team performance avoidance goal was negatively related- to both team creativity and individual creativity through team information exchange. Furthermore, a trust relationship with a team leader played a moderating role: when the trust was stronger, the indirect positive relationship with team creativity and individual creativity was stronger for the team learning goal but weaker for the team performance approach goal. We also found that average individual creativity within a team was positively related to team creativity (going above and beyond the effect of team information exchange) through a supportive climate for creativity. [ABSTRACT FROM AUTHOR]

Indoor aerosols: from personal exposure to risk assessment

Abstract: Motivated by growing considerations of the scale, severity, and risks associated with human exposure to indoor particulate matter, this work reviewed existing literature to: (i) identify state-of-the-art experimental techniques used for personal exposure assessment; (ii) compare exposure levels reported for domestic/school settings in different countries (excluding exposure to environmental tobacco smoke and particulate matter from biomass cooking in developing countries); (iii) assess the contribution of outdoor background vs indoor sources to personal exposure; and (iv) examine scientific understanding of the risks posed by personal exposure to indoor aerosols. Limited studies assessing integrated daily residential exposure to just one particle size fraction, ultrafine particles, show that the contribution of indoor sources ranged from 19% to 76%. This indicates a strong dependence on resident activities, source events and site specificity, and highlights the importance of indoor sources for total personal exposure. Further, it was assessed that 10-30% of the total burden of disease from particulate matter exposure was due to indoor-generated particles, signifying that indoor environments are likely to be a dominant environmental factor affecting human health. However, due to challenges associated with conducting epidemiological assessments, the role of indoor-generated particles has not been fully acknowledged, and improved exposure/risk assessment methods are still needed, together with a serious focus on exposure control.