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Search Results: 1 - 10 of 177350 matches for " Laurence T. Yang "
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The Cross-Sectional Risk Premium of Decomposed Market Volatility in UK Stock Market  [PDF]
Yan Yang, Laurence Copeland
Open Journal of Social Sciences (JSS) , 2014, DOI: 10.4236/jss.2014.27006

We decompose UK market volatility into short- and long-run components using EGARCH component model and examine the cross-sectional prices of the two components. Our empirical results suggest that these two components are significantly priced in the cross-section and the negative risk premia are consistent with the existing literature. The Fama-French three-factor model is improved by the inclusion of the two volatility components. However, our ICAPM model using market excess return and the decomposed volatility components as state variables compares inferiorly to the traditional three-factor model.

COSR: A Reputation-Based Secure Route Protocol in MANET
Fei Wang,Furong Wang,Benxiong Huang,Laurence T. Yang
EURASIP Journal on Wireless Communications and Networking , 2010, DOI: 10.1155/2010/258935
Abstract: Now, the route protocols defined in the Mobile Ad Hoc Network (MANET) are constructed in a common assumption which all nodes contained in such networks are trustworthy and cooperative. Once malicious or selfish nodes exist, all route paths built by these protocols must be broken immediately. According to the secure problems within MANET, this paper proposes Cooperative On-demand Secure Route (COSR), a novel secure source route protocol, against malicious and selfish behaviors. COSR measures node reputation (NR) and route reputation (RR) by contribution, Capability of Forwarding (CoF) and recommendation upon Dynamic Source Route (DSR) and uses RR to balance load to avoid hotpoint. Furthermore, COSR defines path collection algorithm by NR to enhance efficiency of protocol. At last, we verify COSR through GloMoSim. Results show that COSR is secure and stable.
Multimedia Communications over Next Generation Wireless Networks
Liang Zhou,Athanasios V. Vasilakos,Laurence T. Yang,Naixue Xiong
EURASIP Journal on Wireless Communications and Networking , 2010, DOI: 10.1155/2010/896041
Guest Editorial
Athanasios V. Vasilakos,Neal N. Xiong,Laurence T. Yang,Chuan Lin
Journal of Communications , 2010, DOI: 10.4304/jcm.5.1.1-4
Abstract: Journal of Communications (JCM) is a top venue for high quality research that advances state-of-the-art contributions in the area of the new technologies. The focus is on Dependable Computing for Ubiquitous Services (DCUS). The latest developments in theories, systems, methods, algorithms and applications in communications have enabled new dimensions in dependable computing for ubiquitous services, such as dependable hardware and dependable software. As applications of dependable computing have permeated in every aspects of daily life, the dependability of computing has become increasingly critical. The aim of the special issue is to provide fast publication outlet for refereed, high quality original research papers in the various aspects of advances in Reliability and its applications. The special issue would focus on the research challenges and issues in the design and implementation (theories, technologies, architecture and applications) on DCUS. DCUS shall always welcome all research results on the traditional and on-going developing reliability technologies and next generation reliability technologies. Novel techniques, algorithms, architectures, and experiences regarding DCUS are invited. In this special issue, we present several papers for Dependable Computing for Ubiquitous Services. The first paper “A Sudoku-based Secret Image Sharing Scheme with Reversibility” by C.-C. Chang, P. Lin, Z. Wang, and M. Li, derives the secret shadows and generates the meaningful shadow images by adopting the sudoku. In this scheme, the sudoku grid is setting to 16×16 and divided into sixteen 4×4 blocks. Thus, authors can embed 4×(t-1) secret bits into each pixel pair of the host image. Besides, the embeddable secret capacity can be improved according to the threshold t in the (t, n)-threshold sharing system. The experiments show that the shadows can be successfully camouflaged in the host image with satisfactory quality. The distortion of the embedded host pixels is limited within range [0, 3]. Moreover, the proposed scheme provides a large capacity for embedded secret data. The second paper “On Evaluating BGP Routing Stress Attack” by Wenping Deng, Peidong Zhu, Xicheng Lu, and Bernhard Plattner, investigates a new attack on BGP routing system inspired from synchronization and resonance in complex system. The attack applies routing stress by periodically injecting and propagating excessive BGP routing advertisements, which are beyond the processing ability and the storage capacity of the BGP routers in the routing system. Authors first describe a BGP routing stress
Safety Challenges and Solutions in Mobile Social Networks
Yashar Najaflou,Behrouz Jedari,Feng Xia,Laurence T. Yang,Mohammad S. Obaidat
Computer Science , 2013,
Abstract: Mobile social networks (MSNs) are specific types of social media which consolidate the ability of omnipresent connection for mobile users/devices to share user-centric data objects among interested users. Taking advantage of the characteristics of both social networks and opportunistic networks, MSNs are capable of providing an efficient and effective mobile environment for users to access, share, and distribute data. However, lack of a protective infrastructure in these networks has turned them in to convenient targets for various perils. This is the main impulse why MSNs carry disparate and intricate safety concerns and embrace divergent safety challenging problems. In this paper, we aim to provide a clear categorization on safety challenges and a deep exploration over some recent solutions in MSNs. This work narrows the safety challenges and solution techniques down from opportunistic networks (OppNets) and delay tolerant networks (DTNs) to MSNs with the hope of covering all the work proposed around security, privacy and trust in MSNs. To conclude, several major open research issues are discussed and future research directions are outlined.
Human Visual Search Does Not Maximize the Post-Saccadic Probability of Identifying Targets
Camille Morvan ,Laurence T. Maloney
PLOS Computational Biology , 2012, DOI: 10.1371/journal.pcbi.1002342
Abstract: Researchers have conjectured that eye movements during visual search are selected to minimize the number of saccades. The optimal Bayesian eye movement strategy minimizing saccades does not simply direct the eye to whichever location is judged most likely to contain the target but makes use of the entire retina as an information gathering device during each fixation. Here we show that human observers do not minimize the expected number of saccades in planning saccades in a simple visual search task composed of three tokens. In this task, the optimal eye movement strategy varied, depending on the spacing between tokens (in the first experiment) or the size of tokens (in the second experiment), and changed abruptly once the separation or size surpassed a critical value. None of our observers changed strategy as a function of separation or size. Human performance fell far short of ideal, both qualitatively and quantitatively.
MLDS: Maximum Likelihood Difference Scaling in R
Kenneth Knoblauch,Laurence T. Maloney
Journal of Statistical Software , 2008,
Abstract: The MLDS package in the R programming language can be used to estimate perceptual scales based on the results of psychophysical experiments using the method of difference scaling. In a difference scaling experiment, observers compare two supra-threshold differences (a,b) and (c,d) on each trial. The approach is based on a stochastic model of how the observer decides which perceptual difference (or interval) (a,b) or (c,d) is greater, and the parameters of the model are estimated using a maximum likelihood criterion. We also propose a method to test the model by evaluating the self-consistency of the estimated scale. The package includes an example in which an observer judges the differences in correlation between scatterplots. The example may be readily adapted to estimate perceptual scales for arbitrary physical continua.
Ubiquitous Log Odds: A Common Representation of Probability and Frequency Distortion in Perception, Action, and Cognition
Hang Zhang,Laurence T. Maloney
Frontiers in Neuroscience , 2012, DOI: 10.3389/fnins.2012.00001
Abstract: In decision from experience, the source of probability information affects how probability is distorted in the decision task. Understanding how and why probability is distorted is a key issue in understanding the peculiar character of experience-based decision. We consider how probability information is used not just in decision-making but also in a wide variety of cognitive, perceptual, and motor tasks. Very similar patterns of distortion of probability/frequency information have been found in visual frequency estimation, frequency estimation based on memory, signal detection theory, and in the use of probability information in decision-making under risk and uncertainty. We show that distortion of probability in all cases is well captured as linear transformations of the log odds of frequency and/or probability, a model with a slope parameter, and an intercept parameter. We then consider how task and experience influence these two parameters and the resulting distortion of probability. We review how the probability distortions change in systematic ways with task and report three experiments on frequency distortion where the distortions change systematically in the same task. We found that the slope of frequency distortions decreases with the sample size, which is echoed by findings in decision from experience. We review previous models of the representation of uncertainty and find that none can account for the empirical findings.
Content Based Image Retrieval System using Feature Classification with Modified KNN Algorithm
T. Dharani,I. Laurence Aroquiaraj
Computer Science , 2013,
Abstract: Feature means countenance, remote sensing scene objects with similar characteristics, associated to interesting scene elements in the image formation process. They are classified into three types in image processing, that is low, middle and high. Low level features are color, texture and middle level feature is shape and high level feature is semantic gap of objects. An image retrieval system is a computer system for browsing, searching and retrieving images from a large image database. Content Based Image Retrieval is a technique which uses visual features of image such as color, shape, texture to search user required image from large image database according to user requests in the form of a query. MKNN is an enhancing method of KNN. The proposed KNN classification is called MKNN. MKNN contains two parts for processing, they are validity of the train samples and applying weighted KNN. The validity of each point is computed according to its neighbors. In our proposal, Modified K-Nearest Neighbor can be considered a kind of weighted KNN so that the query label is approximated by weighting the neighbors of the query.
The Hamiltonian Brain
Laurence Aitchison,té Lengyel
Quantitative Biology , 2014,
Abstract: A venerable history of models have shown that simple and complex cell responses in the primary visual cortex (V1) are adapted to the statistics of natural images. These models are based, either explicitly or implicitly, on the assumption that neural responses represent maximum likelihood or a posteriori inferences in a generative model trained on natural images and thus provide a normative account of steady-state neural responses in V1. However, such models have very different structural and dynamical properties to the brain. In particular, these models violate Dale's law, have gradient ascent, rather than oscillatory dynamics, and approach a single fixed point in response to fixed input. We give a solution to all of these problems, by suggesting that the brain samples possible explanations of incoming data using a mechanism inspired by Hamiltonian Monte Carlo. Our sampler has recurrent connections that obey Dale's law, gives rise to oscillatory dynamics, and continues sampling in response to fixed input, rather than reaching a single fixed point. These properties of our model allow it to match three key aspects of neural responses in V1. First, the oscillation frequency increases with stimulus contrast. Second, there are large transient increases in firing rate upon stimulus onset. Third, excitation and inhibition are balanced, and inhibition lags excitation.
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