The executive

The sudden resignation of Tung Chee-hwa as chief executive (CE) of the Hong Kong Special Administrative Region (HKSAR) in March 2005 surprised Hong Kong. Donald Tsang, the former chief secretary for administration, won the CE by-election on 16 June 2005. He was then appointed by the Central People’s Government (CPG) and assumed office on 21 June 2005. Handpicked by former President Jiang Zemin, Tung had earned the trust of the CPG. Unfortunately, the performance of Tung’s government was regarded by many of his fellow citizens as far from satisfactory. The reasons for the under-performance were various. The 1997 Asian financial crisis provided too much of a challenge and burden for the newly-formed HKSAR government. Tung himself, underprepared and ill-equipped to lead the executive, found it hard to mount a prompt and effective response to the crisis. The institutional design for selecting the CE and forming the executive was also flawed. This chapter therefore examines the establishment and functions within the executive branch of the HKSAR. How the CE and the executive govern, and their interaction with the Legislative Council in terms of general policymaking and budgetary decision-making processes, are also discussed. Lastly, possible developments and the emerging reforms are considered

The executive

With the election of Leung Chun-ying as the fourth chief executive (CE) of the Hong Kong Special Admininstration Region (HKSAR), the people of Hong Kong are keen to have a new government with policy visions and leadership. This public expectation is running high because Hong Kong has been suffering from a seris of critical and deep-seated socio-economic problems: the polarization of wealth, low social mobility, the subtraction of public (government) services, and so on. The inability of the HKSAR governments to address these problems has its roots in the governing system in general and the institutional design for selecting the CE and forming the executive in particular. This chapter therefore first examines the establishment and functions of the executive of the HKSAR, and then the way the CE and the executive govern, and their interaction with the Legislative Council (Legco) in terms of general policy-making and budgetary decision-making processes. Lastly, various challenges faced by the executive are considered

Holographic de Sitter Universe

We propose to embed de Sitter space into five dimensional anti-de Sitter space to compute some physical quantities of interest, using the AdS/CFT correspondence. The static de Sitter can be considered as the conformal structure of the boundary of the hyperbolic AdS5 with a horizon, thus energy as well as entropy can be computed. The global dS can be embedded into a half-global AdS5, and the dS entropy can be regarded as entanglement entropy in this case. Finally, the inflationary dS can also be regarded as the boundary of AdS5, and this can be extended to include general cosmology with a positive cosmological constant, however at the price of introducing a naked null singularity in the bulk unless the cosmology is pure de Sitter space.Comment: 12 pages, minor modifications and more references adde

Moving Object Detection in Video Using Saliency Map and Subspace Learning

Moving object detection is a key to intelligent video analysis. On the one hand, what moves is not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone not to be detected. So there are undesirable false alarms and missed alarms in many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper, we propose to incorporate a saliency map into an incremental subspace analysis framework where the saliency map makes estimated background has less chance than foreground (i.e., moving objects) to contain salient objects. The proposed objective function systematically takes account into the properties of sparsity, low-rank, connectivity, and saliency. An alternative minimization algorithm is proposed to seek the optimal solutions. Experimental results on the Perception Test Images Sequences demonstrate that the proposed method is effective in reducing false alarms and missed alarms

Cascade Learning by Optimally Partitioning

Cascaded AdaBoost classifier is a well-known efficient object detection algorithm. The cascade structure has many parameters to be determined. Most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically. Their objective functions are not directly related to minimum computation cost. These algorithms are not guaranteed to have optimal solution in the sense of minimizing computation cost. On the assumption that a strong classifier is given, in this paper we propose an optimal cascade learning algorithm (we call it iCascade) which iteratively partitions the strong classifiers into two parts until predefined number of stages are generated. iCascade searches the optimal number ri of weak classifiers of each stage i by directly minimizing the computation cost of the cascade. Theorems are provided to guarantee the existence of the unique optimal solution. Theorems are also given for the proposed efficient algorithm of searching optimal parameters ri. Once a new stage is added, the parameter ri for each stage decreases gradually as iteration proceeds, which we call decreasing phenomenon. Moreover, with the goal of minimizing computation cost, we develop an effective algorithm for setting the optimal threshold of each stage classifier. In addition, we prove in theory why more new weak classifiers are required compared to the last stage. Experimental results on face detection demonstrate the effectiveness and efficiency of the proposed algorithm.Comment: 17 pages, 20 figure

Lanthanum-Cerium Based Bulk Metallic Glasses with Superior Glass-Forming Ability

A quinary (La0.5Ce0.5)65Al10(Co0.6Cu0.4)25 alloy with superior glass-forming ability (GFA), identified by the formation of fully glassy rod of 32 mm in diameter by tilt-pour casting, was reported. By comparing with the GFA of quarternary (La0.5Ce0.5)65Al10TM25 and ternary Ln65Al10TM25 alloys (Ln = La or Ce; TM = Co or Cu), we suggest that the strong frustration of crystallization by utilizing the coexistence of La-Ce and Co-Cu to complicate competing crystalline phases is helpful to construct BMG component with superior GFA.Comment: 15 pages, 4 figures, 1 tabl

Learning Sampling Distributions for Efficient Object Detection

Object detection is an important task in computer vision and learning systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection. By sampling particle windows from a proposal distribution (PD), MPW avoids exhaustively scanning the image. Despite its success, it is unknown how to determine the number of stages and the number of particle windows in each stage. Moreover, it has to generate too many particle windows in the initialization step and it redraws unnecessary too many particle windows around object-like regions. In this paper, we attempt to solve the problems of MPW. An important fact we used is that there is large probability for a randomly generated particle window not to contain the object because the object is a sparse event relevant to the huge number of candidate windows. Therefore, we design the proposal distribution so as to efficiently reject the huge number of non-object windows. Specifically, we propose the concepts of rejection, acceptance, and ambiguity windows and regions. This contrasts to MPW which utilizes only on region of support. The PD of MPW is acceptance-oriented whereas the PD of our method (called iPW) is rejection-oriented. Experimental results on human and face detection demonstrate the efficiency and effectiveness of the iPW algorithm. The source code is publicly accessible.Comment: 14 pages, 13 figure

Charge redistribution at the antiferromagnetic phase transition in SrFeAsF compound

The relationship between spin, electron, and crystal structure has been one of the foremost issues in understanding the superconducting mechanism since the discovery of iron-based high temperature superconductors. Here, we report M\"ossbauer and first-principles calculations studies of the parent compound SrFeAsF with the largest temperature gap ($\sim$50\,K) between the structural and antiferromagnetic (AFM) transitions. Our results reveal that the structural transition has little effect on the electronic structure of the compound SrFeAsF while the development of the AFM order induces a redistribution of the charges near the Fermi level.Comment: 6 Pages, 7 Figure

Magnetohydrodynamics on Heterogeneous architectures: a performance comparison

We present magneto-hydrodynamic simulation results for heterogeneous systems. Heterogeneous architectures combine high floating point performance many-core units hosted in conventional server nodes. Examples include Graphics Processing Units (GPU's) and Cell. They have potentially large gains in performance, at modest power and monetary cost. We implemented a magneto-hydrodynamic (MHD) simulation code on a variety of heterogeneous and multi-core architectures --- multi-core x86, Cell, Nvidia and ATI GPU --- in different languages, FORTRAN, C, Cell, CUDA and OpenCL. We present initial performance results for these systems. To our knowledge, this is the widest comparison of heterogeneous systems for MHD simulations. We review the different challenges faced in each architecture, and potential bottlenecks. We conclude that substantial gains in performance over traditional systems are possible, and in particular that is possible to extract a greater percentage of peak theoretical performance from some systems when compared to x86 architectures.Comment: 8 pages, 2 figure

Video Summarization with Attention-Based Encoder-Decoder Networks

This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named Attentive encoder-decoder networks for Video Summarization (AVS), in which the encoder uses a Bidirectional Long Short-Term Memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on three video summarization benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches,with remarkable improvements from 0.8% to 3% on two datasets,respectively..Comment: 9 pages, 7 figure