Showing papers by "Hong Kong University of Science and Technology published in 2008"

An overview of limited feedback in wireless communication systems

Abstract: It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

One-Class Collaborative Filtering

Abstract: Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering (OCCF) problems. In these problems, the training data usually consist simply of binary data reflecting a user's action or inaction, such as page visitation in the case of news item recommendation or webpage bookmarking in the bookmarking scenario. Usually this kind of data are extremely sparse (a small fraction are positive examples), therefore ambiguity arises in the interpretation of the non-positive examples. Negative examples and unlabeled positive examples are mixed together and we are typically unable to distinguish them. For example, we cannot really attribute a user not bookmarking a page to a lack of interest or lack of awareness of the page. Previous research addressing this one-class problem only considered it as a classification task. In this paper, we consider the one-class problem under the CF setting. We propose two frameworks to tackle OCCF. One is based on weighted low rank approximation; the other is based on negative example sampling. The experimental results show that our approaches significantly outperform the baselines.

Membrane-Type Acoustic Metamaterial with Negative Dynamic Mass

Abstract: We present the experimental realization and theoretical understanding of a membrane-type acoustic metamaterial with very simple construct, capable of breaking the mass density law of sound attenuation in the 100--1000 Hz regime by a significant margin ($\ensuremath{\sim}200$ times). Owing to the membrane's weak elastic moduli, there can be low-frequency oscillation patterns even in a small elastic film with fixed boundaries defined by a rigid grid. The vibrational eigenfrequencies can be tuned by placing a small mass at the center of the membrane sample. Near-total reflection is achieved at a frequency between two eigenmodes where the in-plane average of normal displacement is zero. By using finite element simulations, negative dynamic mass is explicitly demonstrated at frequencies around the total reflection frequency. Excellent agreement between theory and experiment is obtained.

Mars: a MapReduce framework on graphics processors

Abstract: We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth, but are harder to program since their architectures are designed as a special-purpose co-processor and their programming interfaces are typically for graphics applications. As the first attempt to harness GPU's power for MapReduce, we developed Mars on an NVIDIA G80 GPU, which contains over one hundred processors, and evaluated it in comparison with Phoenix, the state-of-the-art MapReduce framework on multi-core CPUs. Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interface. It is up to 16 times faster than its CPU-based counterpart for six common web applications on a quad-core machine.

Ruthenium-catalyzed azide-alkyne cycloaddition: scope and mechanism.

Abstract: The catalytic activity of a series of ruthenium(II) complexes in azide−alkyne cycloadditions has been evaluated. The [Cp*RuCl] complexes, such as Cp*RuCl(PPh3)2, Cp*RuCl(COD), and Cp*RuCl(NBD), were among the most effective catalysts. In the presence of catalytic Cp*RuCl(PPh3)2 or Cp*RuCl(COD), primary and secondary azides react with a broad range of terminal alkynes containing a range of functionalities selectively producing 1,5-disubstituted 1,2,3-triazoles; tertiary azides were significantly less reactive. Both complexes also promote the cycloaddition reactions of organic azides with internal alkynes, providing access to fully-substituted 1,2,3-triazoles. The ruthenium-catalyzed azide−alkyne cycloaddition (RuAAC) appears to proceed via oxidative coupling of the azide and alkyne reactants to give a six-membered ruthenacycle intermediate, in which the first new carbon−nitrogen bond is formed between the more electronegative carbon of the alkyne and the terminal, electrophilic nitrogen of the azide. This ...

HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management

Abstract: Radio spectrum resource is of fundamental importance for wireless communication. Recent reports show that most available spectrum has been allocated. While some of the spectrum bands (e.g., unlicensed band, GSM band) have seen increasingly crowded usage, most of the other spectrum resources are underutilized. This drives the emergence of open spectrum and dynamic spectrum access concepts, which allow unlicensed users equipped with cognitive radios to opportunistically access the spectrum not used by primary users. Cognitive radio has many advanced features, such as agilely sensing the existence of primary users and utilizing multiple spectrum bands simultaneously. However, in practice such capabilities are constrained by hardware cost. In this paper, we discuss how to conduct efficient spectrum management in ad hoc cognitive radio networks while taking the hardware constraints (e.g., single radio, partial spectrum sensing and spectrum aggregation limit) into consideration. A hardware-constrained cognitive MAC, HC-MAC, is proposed to conduct efficient spectrum sensing and spectrum access decision. We identify the issue of optimal spectrum sensing decision for a single secondary transmission pair, and formulate it as an optimal stopping problem. A decentralized MAC protocol is then proposed for the ad hoc cognitive radio networks. Simulation results are presented to demonstrate the effectiveness of our proposed protocol.

LiFeAs: An intrinsic FeAs-based superconductor with Tc=18 K

Abstract: The synthesis and properties of LiFeAs, a high-${T}_{c}$ Fe-based superconducting stoichiometric compound, are reported. Single crystal x-ray studies reveal that it crystallizes in the tetragonal PbFCl type (P4/nmm) with $a=3.7914(7)\text{ }\text{\AA{}}$ and $c=6.364(2)\text{ }\text{\AA{}}$. Unlike the known isoelectronic undoped intrinsic FeAs compounds, LiFeAs does not show any spin-density wave behavior but exhibits superconductivity at ambient pressures without chemical doping. It exhibits a respectable transition temperature of ${T}_{c}=18\text{ }\text{K}$ with electronlike carriers and a very high critical field, ${\text{H}}_{c2}(0)g80\text{ }\text{T}$. LiFeAs appears to be the chemical equivalent of the infinite layered compound of the high-${T}_{c}$ cuprates.

Superconducting Fe-based compounds (A1-xSrx)Fe2As2 with A=K and Cs with transition temperatures up to 37 K.

Abstract: New high-${T}_{c}$ Fe-based superconducting compounds, $A{\mathrm{Fe}}_{2}{\mathrm{As}}_{2}$ with $A=\mathrm{K}$, Cs, $\mathrm{K}/\mathrm{Sr}$, and $\mathrm{Cs}/\mathrm{Sr}$, were synthesized. The ${T}_{c}$ of ${\mathrm{KFe}}_{2}{\mathrm{As}}_{2}$ and ${\mathrm{CsFe}}_{2}{\mathrm{As}}_{2}$ is 3.8 and 2.6 K, respectively, which rises with partial substitution of Sr for K and Cs and peaks at 37 K for 50%--60% Sr substitution, and the compounds enter a spin-density-wave state with increasing electron number (Sr content). The compounds represent $p$-type analogs of the $n$-doped rare-earth oxypnictide superconductors. Their electronic and structural behavior demonstrate the crucial role of the (${\mathrm{Fe}}_{2}{\mathrm{As}}_{2}$) layers in the superconductivity of the Fe-based layered systems, and the special feature of having elemental $A$ layers provides new avenues to superconductivity at higher ${T}_{c}$.

Transfer learning via dimensionality reduction

Abstract: Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this paper, we consider transfer learning via dimensionality reduction. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source domain data and the target domain data are the same or close to each other. Onto this latent feature space, we project the data in related domains where we can apply standard learning algorithms to train classification or regression models. Thus, the latent feature space can be treated as a bridge of transferring knowledge from the source domain to the target domain. The main contribution of our work is that we propose a new dimensionality reduction method to find a latent space, which minimizes the distance between distributions of the data in different domains in a latent space. The effectiveness of our approach to transfer learning is verified by experiments in two real world applications: indoor WiFi localization and binary text classification.

Optimized scale-and-stretch for image resizing

Abstract: We present a "scale-and-stretch" warping method that allows resizing images into arbitrary aspect ratios while preserving visually prominent features. The method operates by iteratively computing optimal local scaling factors for each local region and updating a warped image that matches these scaling factors as closely as possible. The amount of deformation of the image content is guided by a significance map that characterizes the visual attractiveness of each pixel; this significance map is computed automatically using a novel combination of gradient and salience-based measures. Our technique allows diverting the distortion due to resizing to image regions with homogeneous content, such that the impact on perceptually important features is minimized. Unlike previous approaches, our method distributes the distortion in all spatial directions, even when the resizing operation is only applied horizontally or vertically, thus fully utilizing the available homogeneous regions to absorb the distortion. We develop an efficient formulation for the nonlinear optimization involved in the warping function computation, allowing interactive image resizing.

Enzymatic hydrogelation of small molecules.

Abstract: Enzymes, a class of highly efficient and specific catalysts in Nature, dictate a myriad of reactions that constitute various cascades in biological systems. Self-assembly, a process prevalent in Nature, also plays important roles in biology, from maintaining the integrity of cells to performing cellular functions and inducing abnormalities that cause disease. To explore enzyme-regulated molecular self-assembly in an aqueous medium will help to understand and control those important biological processes. On the other hand, certain small organic molecules self-assemble in water to form molecular nanofibers and result in a hydrogel, which is referred to as a "supramolecular hydrogel" (and the small molecules are referred to as "supramolecular hydrogelators"). Supramolecular hydrogelators share common features, such as amphiphilicity and supramolecular interactions (pi-pi interactions, hydrogen bonding, and charge interactions among the molecules, among others) that result in nanostructures and form the three-dimensional networks as the matrices of hydrogels. In this Account, we discuss the use of enzymes to trigger and control the self-assembly of small molecules for hydrogelation, which takes place in vitro or in vivo, extra- or intracellularly. Using phosphatase, thermolysin, beta-lactamase, and phosphatase/kinase as examples, we illustrate the design and application of enzyme-catalyzed or -regulated formation of supramolecular hydrogels that offer a new strategy for detecting the activity of enzymes, screening for enzyme inhibitors, typing bacteria, drug delivery systems, and controlling the fate of cells. Since the expression and distribution of enzymes differ by the types and states of cells, tissues, and organs, using an enzymatic reaction to convert precursors into hydrogelators that self-assemble into nanofibers as the matrices of the hydrogel, one can control the delivery, function, and response of a hydrogel according to a specific biological condition or environment, thus providing an accessible route to create sophisticated materials for biomedicine. Particularly, intracellular enzymatic hydrogelation of small molecules offers a unique means for scientists to integrate molecular self-assembly with inherent enzymatic reactions inside cells for developing new biomaterials and therapeutics at the supramolecular level and improving the basic understanding of dynamic molecular self-assembly in water.

Growth of nanowires

Abstract: The tremendous interest in nanoscale structures such as quantum dots (zero-dimension) and wires (quasi-one-dimension) stems from their size-dependent properties. One-dimensional (1D) semiconductor nanostructures are of particular interest because of their potential applications in nanoscale electronic and optoelectronic devices. For 1D semiconductor nanomaterials to have wide practical application, however, several areas require further development. In particular, the fabrication of desired 1D nanomaterials with tailored atomic structures and their assembly into functional devices are still major challenges for nanotechnologists. In this review, we focus on the status of research on the formation of nanowire structures via highly anisotropic growth of nanocrystals of semiconductor and metal oxide materials with an emphasis on the structural characterization of the nucleation, initial growth, defects and interface structures, as well as on theoretical analyses of nanocrystal formation, reactivity and stability. We review various methods used and mechanisms involved to generate 1D nanostructures from different material systems through self-organized growth techniques including vapor–liquid–solid growth, oxide-assisted chemical vapor deposition (without a metal catalyst), laser ablation, thermal evaporation, metal-catalyzed molecular beam epitaxy, chemical beam epitaxy and hydrothermal reaction. 1D nanostructures grown by these technologies have been observed to exhibit unusual growth phenomena and unexpected properties, e.g., diameter-dependent and temperature-dependent growth directions, structural transformation by enhanced photothermal effects and phase transformation induced by the point contact reaction in ultra-thin semiconductor nanowires. Recent progress in controlling growth directions, defects, interface structures, structural transformation, contacts and hetero-junctions in 1D nanostructures is addressed. Also reviewed are the quantitative explorations and predictions of some challenging 1D nanostructures and descriptions of the growth mechanisms of 1D nanostructures, based on the energetic, dynamic and kinetic behaviors of the building block nanostructures and their surfaces and/or interfaces.

On the Nevanlinna characteristic of f(z+η) and difference equations in the complex plane

Abstract: We investigate the growth of the Nevanlinna characteristic of f(z+η) for a fixed η∈C in this paper. In particular, we obtain a precise asymptotic relation between T(r,f(z+η)) and T(r,f), which is only true for finite order meromorphic functions. We have also obtained the proximity function and pointwise estimates of f(z+η)/f(z) which is a discrete version of the classical logarithmic derivative estimates of f(z). We apply these results to give new growth estimates of meromorphic solutions to higher order linear difference equations. This also allows us to solve an old problem of Whittaker (Interpolatory Function Theory, Cambridge University Press, Cambridge, 1935) concerning a first order difference equation. We show by giving a number of examples that all of our results are best possible in certain senses. Finally, we give a direct proof of a result in Ablowitz, Halburd and Herbst (Nonlinearity 13:889–905, 2000) concerning integrable difference equations.

Characterization of mechanical behavior of woven fabrics: Experimental methods and benchmark results

Abstract: Textile composites made of woven fabrics have demonstrated excellent mechanical properties for the production of high specific-strength products. Research efforts in the woven fabric sheet forming are currently at a point where benchmarking will lead to major advances in understanding both the strengths and the limitations of existing experimental and modeling approaches. Test results can provide valuable information for the material characterization and forming process design of woven composites if researchers know how to interpret the results obtained from varying test methods appropriately. An international group of academic and industry researchers has gathered to design and conduct benchmarking tests of interest to the composite sheet forming community. Shear deformation is the dominative deformation mode for woven fabrics in forming; therefore, trellis-frame (picture-frame) and bias-extension tests for both balanced and unbalanced fabrics have been conducted and compared through this collaborative effort. Tests were conducted by seven international research institutions on three identical woven fabrics. Both the variations in the setup of each research laboratory and the normalization methods used to compare the test results are presented and discussed. With an understanding of the effects of testing variations on the results and the normalization methods, numerical modeling efforts can commence and new testing methods can be developed to advance the field.

Skeleton extraction by mesh contraction

Abstract: Extraction of curve-skeletons is a fundamental problem with many applications in computer graphics and visualization. In this paper, we present a simple and robust skeleton extraction method based on mesh contraction. The method works directly on the mesh domain, without pre-sampling the mesh model into a volumetric representation. The method first contracts the mesh geometry into zero-volume skeletal shape by applying implicit Laplacian smoothing with global positional constraints. The contraction does not alter the mesh connectivity and retains the key features of the original mesh. The contracted mesh is then converted into a 1D curve-skeleton through a connectivity surgery process to remove all the collapsed faces while preserving the shape of the contracted mesh and the original topology. The centeredness of the skeleton is refined by exploiting the induced skeleton-mesh mapping. In addition to producing a curve skeleton, the method generates other valuable information about the object's geometry, in particular, the skeleton-vertex correspondence and the local thickness, which are useful for various applications. We demonstrate its effectiveness in mesh segmentation and skinning animation.

Too Little or Too Much? Untangling the Relationship Between Corporate Philanthropy and Firm Financial Performance

Abstract: What is the relationship between corporate philanthropy and corporate financial performance? Some scholars argue that corporate philanthropy facilitates stakeholder cooperation and helps secure access to critical resources controlled by those stakeholders, suggesting that corporate philanthropy should be positively associated with corporate financial performance. In contrast, other scholars take a negative stance, suggesting that corporate philanthropy diverts valuable corporate resources and tends to inhibit corporate financial performance. Existing empirical studies have not found conclusive evidence on the corporate philanthropy--financial performance relationship. Integrating and extending existing perspectives, this study develops the argument that the relationship between corporate philanthropy and financial performance is best captured by an inverse U-shape. In addition, it posits that the inverse U-shaped relationship varies with the level of dynamism in firms' operational environment. Using a panel data set of 817 firms listed in the Taft Corporate Giving Directory from 1987 to 1999, we find strong support for these arguments.

Strategic Capacity Rationing to Induce Early Purchases

Abstract: Dynamic pricing offers the potential to increase revenues. At the same time, it creates an incentive for customers to strategize over the timing of their purchases. A firm should ideally account for this behavior when making its pricing and stocking decisions. In particular, we investigate whether it is optimal for a firm to create rationing risk by deliberately understocking products. Then, the resulting threat of shortages creates an incentive for customers to purchase early at higher prices. But when does such a strategy make sense? If it is profitable to create shortages, what is the optimal amount of rationing risk to create? We develop a stylized model to study this problem. In our model, customers have heterogeneous valuations for the firm's product and face declining prices over two periods. Customers are assumed to have identical risk preferences and know the price path and fill rate in each period. Via its capacity choice, the firm is able to control the fill rate and, hence, the rationing risk faced by customers. Customers behave strategically and weigh the payoff of immediate purchases against the expected payoff of delaying their purchases. We analyze the capacity choice that maximizes the firm's profits. First, we consider a monopoly market and characterize conditions under which rationing is optimal. We examine how the optimal amount of rationing is affected by the magnitude of price changes over time and the degree of risk aversion among customers. We then analyze an oligopoly version of the model and show that competition reduces the firms' ability to profit from rationing. Indeed, there exists a critical number of firms beyond which a rationing equilibrium cannot be supported.

Manipulating Charge-Transfer Character with Electron-Withdrawing Main-Group Moieties for the Color Tuning of Iridium Electrophosphors†

Abstract: A new and synthetically versatile strategy has been developed for the phosphorescence color tuning of cyclometalated iridium phosphors by simple tailoring of the phenyl ring of ppy (Hppy=2-phenylpyridine) with various main-group moieties in [Ir(ppy-X)(2)(acac)] (X=B(Mes)(2), SiPh3, GePh3, NPh2, POPh2, OPh, SPh, SO2Ph). This can be achieved by shifting the charge-transfer character from the pyridyl groups in some traditional iridium ppy-type complexes to the electron-withdrawing main-group moieties and these assignments were supported by theoretical calculations.

Contracting and Information Sharing Under Supply Chain Competition

Abstract: We investigate contracting and information sharing in two competing supply chains, each consisting of one manufacturer and one retailer. The two supply chains are identical, except they may have di...

Trade-Off and Pecking Order Theories of Debt

Abstract: Taxes, bankruptcy costs, transactions costs, adverse selection, and agency conflicts have all been advocated as major explanations for the corporate use of debt financing. These ideas have often been synthesized into the trade-off theory and the pecking order theory of leverage. This chapter reviews these theories and the related evidence and identifies a number of important empirical stylized facts. To understand the evidence, it is important to recognize the differences among private firms, small public firms, and large public firms. Private firms use retained earnings and bank debt heavily; small public firms make active use of equity financing; and large public firms primarily use retained earnings and corporate bonds. The available evidence can be interpreted in several ways. Direct transaction costs and indirect bankruptcy costs appear to play important roles in a firm’s choice of debt. The relative importance of the other factors remains open to debate. No currently available model appears capable of simultaneously accounting for all of the stylized facts.

Effects of surfactant treatment on mechanical and electrical properties of CNT/epoxy nanocomposites

Abstract: Surfactant has been successfully applied to enhance the dispersion of carbon nanotubes (CNTs) in polymer and the properties of nanocomposite. CNTs were treated with a nonionic surfactant Triton X-100, and its effects on dispersion state, surface chemistry, structure and morphology of CNTs, as well as on the thermomechanical, mechanical and electrical properties of CNT/epoxy nanocomposites were evaluated. The mechanical properties such as impact fracture toughness, flexural strength and modulus, the thermomechanical properties, as well as the electrical conductivity of the nanocomposite all showed significant improvements after the treatment. The above observations are attributed to the “bridging” effects between the CNT and epoxy, which are introduced by the hydrophobic and hydrophilic segments of the nonionic surfactant. The enhanced interfacial interactions gave rise to improved dispersion and wetting of CNTs in polymer matrix, enhancing the mechanical and fracture properties of the nanocomposite. Unlike chemical functionalization techniques, however, the surfactant treatment exhibited little adverse effect on electrical conducting behavior of the nanocomposite.

Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks

Abstract: Cognitive radio is being recognized as an intelligent technology due to its ability to rapidly and autonomously adapt operating parameters to changing environments and conditions. In order to reliably and swiftly detect spectrum holes in cognitive radios, spectrum sensing must be used. In this paper, we consider cooperative spectrum sensing in order to optimize the sensing performance. We focus on energy detection for spectrum sensing and find that the optimal fusion rule is the half-voting rule. Next, the optimal detection threshold of energy detection is determined numerically. Finally, we propose a fast spectrum sensing algorithm for a large network which requires fewer than the total number of cognitive radios to perform cooperative spectrum sensing while satisfying a given error bound.

Isolation Enhancement Between Two Closely Packed Antennas

Abstract: This paper introduces a coupling element to enhance the isolation between two closely packed antennas operating at the same frequency band. The proposed structure consists of two antenna elements and a coupling element which is located in between the two antenna elements. The idea is to use field cancellation to enhance isolation by putting a coupling element which artificially creates an additional coupling path between the antenna elements. To validate the idea, a design for a USB dongle MIMO antenna for the 2.4 GHz WLAN band is presented. In this design, the antenna elements are etched on a compact low-cost FR4 PCB board with dimensions of 20times40times1.6 mm3. According to our measurement results, we can achieve more than 30 dB isolation between the antenna elements even though the two parallel individual planar inverted F antenna (PIFA) in the design share a solid ground plane with inter-antenna spacing (Center to Center) of less than 0.095 lambdao or edge to edge separations of just 3.6 mm (0.0294 lambdao). Both simulation and measurement results are used to confirm the antenna isolation and performance. The method can also be applied to different types of antennas such as non-planar antennas. Parametric studies and current distribution for the design are also included to show how to tune the structure and control the isolation.

Opportunities and Challenges of Peer-to-Peer Internet Video Broadcast

Abstract: There have been tremendous efforts and many technical innovations in supporting real-time video streaming in the past two decades, but cost-effective large-scale video broadcast has remained an elusive goal. Internet protocol (IP) multicast represented an earlier attempt to tackle this problem but failed largely due to concerns regarding scalability, deployment, and support for higher level functionality. Recently, peer-to-peer based broadcast has emerged as a promising technique, which has been shown to be cost effective and easy to deploy. This new paradigm brings a number of unique advantages such as scalability, resilience, and effectiveness in coping with dynamics and heterogeneity. While peer-to-peer applications such as file download and voice-over-IP have gained tremendous popularity, video broadcast is still in its early stages, and its full potential remains to be seen. This paper reviews the state-of-the-art of peer-to-peer Internet video broadcast technologies. We describe the basic taxonomy of peer-to-peer broadcast and summarize the major issues associated with the design of broadcast overlays. We closely examine two approaches - tree-based and data-driven - and discuss their fundamental tradeoff and potential for large-scale deployment. Lastly, we outline the key challenges and open problems and highlight possible avenues for future directions.