Francis C. M. Lau

Bio: Francis C. M. Lau is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Low-density parity-check code & Decoding methods. The author has an hindex of 57, co-authored 680 publications receiving 12306 citations. Previous affiliations of Francis C. M. Lau include University of Alberta & Shantou University.

A C-LSTM Neural Network for Text Classification

Abstract: Neural network models have been demonstrated to be capable of achieving remarkable performance in sentence and document modeling. Convolutional neural network (CNN) and recurrent neural network (RNN) are two mainstream architectures for such modeling tasks, which adopt totally different ways of understanding natural languages. In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification. C-LSTM utilizes CNN to extract a sequence of higher-level phrase representations, and are fed into a long short-term memory recurrent neural network (LSTM) to obtain the sentence representation. C-LSTM is able to capture both local features of phrases as well as global and temporal sentence semantics. We evaluate the proposed architecture on sentiment classification and question classification tasks. The experimental results show that the C-LSTM outperforms both CNN and LSTM and can achieve excellent performance on these tasks.

Chaos-based digital communication systems

Abstract: Publisher Summary This chapter reviews the common chaos-based digital modulation schemes. It also explains the corresponding coherent and noncoherent demodulation schemes. The two multiple-access schemes, based on the differential chaos-shift-keying (DCSK) or the frequency-modulated DCSK modulation (FM-DCSK), and the one multiple-access scheme based on the chaotic frequency modulation are also discussed. In the chaos-based communication systems, the detection schemes are broadly classified into the coherent and noncoherent types. The coherent systems require an exact replica of the chaotic carrier used to carry the information to be reproduced at the receiver, while the noncoherent systems have no such requirement. The chaos-based digital modulation schemes occupy a much wider bandwidth than is required to transmit a stream of binary symbols. Allowing multiple users to share the same bandwidth ensures an efficient use of the spectrum. When the chaos-shift-keying (CSK) signals are decoded based on the estimation of the bit energy, the threshold of the detector is expected to shift with the noise level. This leads to a large number of errors. The DCSK modulation scheme can be useful in overcoming such a problem.

Load Balancing in Parallel Computers: Theory and Practice

Abstract: Foreword. Preface. 1. Introduction. 2. A Survey of Nearest-Neighbor Load Balancing Algorithms. 3. The GDE Method. 4. GDE on Tori and Meshes. 5. The Diffusion Method. 6. GDE Versus Diffusion. 7. Termination Detection of Load Balancing. 8. Remapping with the GDE Method. 9. Load Distribution in Combinatorial Optimizations. 10. Conclusions. References. Index.

Online job dispatching and scheduling in edge-clouds

Abstract: In edge-cloud computing, a set of edge servers are deployed near the mobile devices such that these devices can offload jobs to the servers with low latency. One fundamental and critical problem in edge-cloud systems is how to dispatch and schedule the jobs so that the job response time (defined as the interval between the release of a job and the arrival of the computation result at its device) is minimized. In this paper, we propose a general model for this problem, where the jobs are generated in arbitrary order and times at the mobile devices and offloaded to servers with both upload and download delays. Our goal is to minimize the total weighted response time over all the jobs. The weight is set based on how latency sensitive the job is. We derive the first online job dispatching and scheduling algorithm in edge-clouds, called OnDisc, which is scalable in the speed augmentation model; that is, OnDisc is (1 + e)-speed O(1/e)-competitive for any constant e ∊ (0,1). Moreover, OnDisc can be easily implemented in distributed systems. Extensive simulations on a real-world data-trace from Google show that OnDisc can reduce the total weighted response time dramatically compared with heuristic algorithms.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Hierarchical Attention Networks for Document Classification

Abstract: We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences.

Table Of Integrals Series And Products

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A Survey on Mobile Edge Computing: The Communication Perspective

Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

On the capacity of a cellular CDMA system

Abstract: It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >