The nineteenth-century conception that linguistic structure was to be explained by recourse to the histories of languages was largely abandoned with the rise of synchronic theories in the twentieth century, but has re...
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The nineteenth-century conception that linguistic structure was to be explained by recourse to the histories of languages was largely abandoned with the rise of synchronic theories in the twentieth century, but has recently returned to prominence. Whereas traditional generative theories of language have tended to attribute crosslinguistic regularities to constraints imposed on the class of possible grammars by the human Language Faculty, some scholars have argued that this is often a mistake: that there are no (or at least very few) real substantive universals of language, and that the regularities in question arise from common paths of diachronic change having their basis in factors outside of the defining properties of the set of cognitively accessible grammars. This review surveys evidence for that position, primarily in phonology but also in morphology and syntax. I argue that in phonology, there are at present no convincingly demonstrated substantive universals governing the set of possible regularities, and that the generalizations we find should be attributed to a combination of contingent historical developments and biases in the learning algorithm that relates available data to the grammars learners acquire. In morphology and syntax, I argue that some apparent generalizations are indeed the product of diachronic change rather than synchronic constraint.
One of the fundamental problems in wireless sensor networks is providing area coverage to fulfill a certain task. This problem in directional sensor networks is more challenging because of limited sensing angle of dir...
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ISBN:
(纸本)9781509034352
One of the fundamental problems in wireless sensor networks is providing area coverage to fulfill a certain task. This problem in directional sensor networks is more challenging because of limited sensing angle of directional sensors. This paper addresses the problem of deployment and orientation of a specific number of directional sensor nodes in order to maximize the area coverage. First, we present an optimization model for this problem. Then, we propose a distributed payoff based learning algorithm in which each sensor tries to maximize its own coverage relative to the coverage of its neighbors by relocating toward uncovered positions and selecting an appropriate working direction. Simulation results demonstrate the performance of proposed algorithm.
In the communication aspect of a computer network, data are sent by packets. If the communication channel is not completely safe, then the arrival of the packets must be acknowledged. In the data-acknowledgment proble...
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In the communication aspect of a computer network, data are sent by packets. If the communication channel is not completely safe, then the arrival of the packets must be acknowledged. In the data-acknowledgment problem, the goal is to determine the time of sending acknowledgments. Here we present a new online algorithm for it, where the algorithm itself is a parameter-learning extension of the alarming algorithms. The efficiency of the algorithm is then investigated by testing it experimentally, and it is demonstrated that the new parameter-learning algorithm performs significantly better than the original one.
The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus *** the inherently high re...
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The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus *** the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption *** a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the ***,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value *** results show that this approach is feasible in searching for the control strategy of *** this method has its own advantage comparing with the CDCS ***,a test based on a real PHEB was carried out to verify the applicable of the proposed method.
Artificial neural networks are one of the most efficient methods for pattern recognition and have a vast range of applications for aiding medical decision making. The proposed study applies feed-forward back-propagati...
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Artificial neural networks are one of the most efficient methods for pattern recognition and have a vast range of applications for aiding medical decision making. The proposed study applies feed-forward back-propagation neural networks as a classifier and compares the combination of nine learning algorithms and three activation functions to build a knowledge-based system with the best network architecture for predicting the severity of autism. The performances of the derived models were evaluated based on statistical criteria such as mean squared error (MSE), mean absolute percentage error (MAPE), root mean squared error (RMSE), regression (R value), training time and number of epochs. The study findings showed that the optimal performance was achieved by model MLP_LM_104 trained on Levenberg-Marquardt (LM) back-propagation algorithm having network topology of 40-10-4 with purelin and tansig activation functions in hidden and output layers. The regression coefficients for training, validation and test datasets were 0.996, 0.996 and 0.994, respectively. The MSE, RMSE and MAPE were 2.26 x 10(-4), 1.50 x 10(-2) and 1.13, respectively. Furthermore, BFGS quasi-Newton (BFG), conjugate gradient, gradient descent and resilient back-propagation (RP) algorithms did not perform well. Models trained with BFG algorithms required longer training time, whereas the performance of models trained on RP algorithm got worse as the numbers of hidden neurons were increased.
With the prevalence of GPS-enabled smart phones, Location Based Social Network (LBSN) has emerged and become a hot research topic during the past few years. As one of the most important components in LBSN, Points-of-I...
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With the prevalence of GPS-enabled smart phones, Location Based Social Network (LBSN) has emerged and become a hot research topic during the past few years. As one of the most important components in LBSN, Points-of-Interests (POIs) has been extensively studied by both academia and industry, yielding POI recommendations to enhance user experience in exploring the city. In conventional methods, rating vectors for both users and POIs are utilized for similarity calculation, which might yield inaccuracy due to the differences of user biases. In our opinion, the rating values themselves do not give exact preferences of users, however the numeric order of ratings given by a user within a certain period provides a hint of preference order of POIs by such user. Firstly, we propose an approach to model users preference by employing utility theory. Secondly, We devise a collection-wise learning method over partial orders through an effective stochastic gradient descent algorithm. We test our model on two real world datasets, i.e., Yelp and TripAdvisor, by comparing with some state-of-the-art approaches including PMF and several user preference modeling methods. In terms of MAP and Recall, we averagely achieve 15% improvement with regard to the baseline methods. The results show the significance of comparative choice in a certain time window and show its superiority to the existing methods. (C) 2015 Elsevier Ltd. All rights reserved.
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