bluuebunny/crossref_metadata_2025_split huggingface dataset:
Allocating the weights in the linear opinion pool
Authors: Genest Christian , McConway Kevin J. 
Published: July 2007

AbstractA standard approach to the combination of probabilistic opinions involves taking a weighted linear average of the individuals distributions. This paper reviews some of the possible interpretations that have been proposed for these weights in the literature on expert use. Several paradigms for selecting weights are also considered. Special attention is devoted to the Bayesian mechanism used for updating expert weights in the face of new information. An asymptotic result is proved which highlights the importance of choosing the initial weights carefully.
A test of space‐time arma modelling and forecasting of hotel data
Authors: Pfeifer Phillip E. , Bodily Samuel E. 
Published: July 2007

AbstractThis paper describes the application of space‐time ARMA modelling to demand‐related data from eight hotels from a single hotel chain in a large US city. Important spatial characteristics of the space‐time process are incorporated into the model using a simple weighting matrix based on driving distances between the hotels. Using a hold‐out sample, the forecasting performance of this space‐time approach was found to be superior to eight separate univariate ARMA models.
Robust exponential smoothing
Authors: Cipra Tomáš 
Published: July 2007

AbstractThe paper is devoted to robust modifications of exponential smoothing for time series with outliers or long‐tailed distributions. Classical exponential smoothing applied to such time series is sensitive to the presence of outliers or long‐tailed distributions and may give inadequate smoothing and forecasting results. First, simple and double exponential smoothing in the L1 norm (i.e. based on the least absolute deviations) are discussed in detail. Then, general exponential smoothing is made robust, replacing the least squares approach by M‐estimation in such a way that the recursive character of the final formulas is preserved. The paper gives simple algorithmic procedures which preserve advantageous features of classical exponential smoothing and, in addition, which are less sensitive to outliers. Robust versions are compared numerically with classical ones.
Forecasting the federal budget with time‐series models
Authors: Baghestani Hamid , McNown Robert 
Published: July 2007

AbstractThe stochastic properties of conventionally denned federal expenditures and revenues are examined, and cointegration is found. Alternative time‐series models‐univariate ARIMA models, vector autoregressions in levels and differences, and an error correction model‐are specified and estimated using quarterly data from 1955:1 through 1979:4. Updated forecasts for up to three years beyond the sample period are evaluated against actual expenditures, revenues and the deficit. The vector autoregression in levels shows evidence of nonstationarity, which leads to strong biases in the forecasts. The remaining models produce forecasts that are satisfactory by the mean squared error criterion, and the magnitudes of biases at the longer horizons are significantly smaller than those of the official forecasts.
Prediction in the one‐way error component model with serial correlation
Authors: Baltagi Badi H. , Li Ql 
Published: July 2007

AbstractThis paper derives the best linear unbiased predictor for a one‐way error component model with serial correlation. A transformation derived by Baltagi and Li (1991) is used to show how the forecast can be easily computed from the GLS estimates and residuals. This result is useful for panel data applications which utilize the error component specification and exhibit serial correlation in the remainder disturbance term. Analytical expressions for this predictor are given when the remainder disturbances follow (1) an AR(1) process, (2) an AR(2) process, (3) a special AR(4) process for quarterly data, and (4) an MA(1) process.
A flexible model of technological diffusion incorporating economic factors with an application to the spread of colour television ownership in the UK
Authors: Karshenas Massou D. , Stoneman Paul 
Published: July 2007

AbstractIn this paper a new encompassing flexible diffusion model of the epidemic type is proposed and its properties explored. Economic factors are incorporated directly into the model. The new model allows freer interaction between exogenous and endogenous factors in the diffusion process than existing models of this type. The model is applied to the diffusion of colour television ownership in the UK and outperforms the existing models in the literature. The exogenous factors are shown to play a more dominant role in the diffusion of colour television in the UK than existing models allow.
On the use of dispersion measures from NAPM surveys in business cycle forecasting
Authors: Dasgupta Susmita , Lahiri Kajal 
Published: July 2007

AbstractQualitative survey data on changes in production, inventory, new order, and employment collected every month by the National Association of Purchasing Managers are analysed over 1948–90. The Probability method we use generates time‐series estimates of cross‐section variabilities across firms. It is shown that these diffusion measures have additional explanatory power in the prediction of business cycle turning points.
Forecasting stock returns an examination of stock market trading in the presence of transaction costs
Authors: Pesaran M. Hashem , Timmermann Allan 
Published: July 2007

AbstractThe paper presents new evidence on the predictability of excess returns on common stocks for the Standard and Poor's 500 and the Dow Jones Industrial portfolios at the monthly, quarterly, and annual frequencies. It shows that recursive predictions obtained on the basis of the excess returns regressions are capable of correctly predicting a statistically significant proportion of the signs of the actual returns. The paper also shows that the switching portfolios constructed on the basis of the signs of the recursive predictions mean‐variance dominate the respective market portfolios when trading takes place on a quarterly or annual basis. This result holds even under a high transaction cost scenario. However, due to the larger number of transactions at the monthly frequency the monthly switching portfolios only mean‐variance dominate the respective market portfolios when transaction costs are zero or low.
Combining var estimation and state space model reduction for simple good predictions
Authors: Gilbert Paul D. 
Published: July 2007

AbstractA combination of VAR estimation and state space model reduction techniques are examined by Monte Carlo methods in order to find good, simple to use, procedures for determining models which have reasonable prediction properties. The presentation is largely graphical. This helps focus attention on the aspects of the model determination problem which are relatively important for forecasting. One surprising result is that, for prediction purposes, knowledge of the true structure of the model generating the data is not particularly useful unless parameter values are also known. This is because the difficulty in estimating parameters of the true model causes more prediction error than results from a more parsimonious approximate model.
A uniform network for VLSI massively parallel computers
Authors: Iwasaki Kazuhiko , Iseli Christian , Sato Yuji 
Published: July 2007

AbstractSymmetrical Network Topologies (SNT's) for VLSI massively parallel computers are proposed. VLSI massively parallel computers can be expected to integrate more than dozens of processing elements. It is desired that the network in such a VLSI system is regular.SNT consists of a two‐dimensional end‐around mesh with supplementary symmetrical branch connections, and therefore, it satisfies the condition of regularity. In this paper it is shown that the SNT's have desirable characteristics with regard to diameter and average distance. That is, the SNTs have smaller diameters and average distance compared to the hypercubes with the same number of nodes. For example, for 4096 nodes, the degree, diameter and average distance of a hypercube are 12, 12 and 6.001, respectively. The degree, diameter and average distance of the SNT can be 8, 8 and 5.324, respectively. It is shown also that SNTs are maximally connected graphs resulting in a fault‐tolerant network. Results show that SNT's are well suited for the VSLI implementation of massively parallel computers.
High speed display algorithm for 3D medical images using multilayer range image
Authors: Ban Hideyuki , Suzuki Ryuuichi 
Published: July 2007

AbstractWe propose a high‐speed, three‐dimensional display method for objects represented by voxels intended for three‐dimensional image diagnostic systems that can be operated interactively. The method extends the concept of the conventional range image and introduces the multilayer range image that stores the distance from the projection plane to the object regardless of whether the object is visible or not. A range image is generated on the surface of any projection plane from the multilayer range images of 6 projection planes perpendicular to the coordinate axes and displayed in three dimensions after shading. Fast speeds are achieved by selecting the multilayer range images that correspond to the projection direction for processing and by eliminating operations on voxels which are not needed in the processing. We were able to display a 2563—voxel object on a 22 MIPS workstation in about 0.5 seconds. Moreover, we anticipate a response of less than one second on the next generation of 100 MIPS workstations.
Extracting global structures using perceptual grouping
Authors: Hiratsuka Satoshi , Ohnishi Noboru , Sugie Noboru 
Published: July 2007

AbstractOne of the problems in the study of image recognition is to extract the global structure, e.g., the contour of the object, with a high reliability. There already exists a technique in which the model figure is pre‐determined. This gives a fairly satisfactory result. It is essentially desirable, however, that the system can handle the unknown figure.This study considers a approach to such a problem, where the perceptual grouping as pointed out by Gestalt psychologists is applied. More precisely, the method is as follows. The connected component elements are extracted from the input image. Then, based on the factors for perceptual grouping (such as proximity, similarity, good continuity and closure), which are quantitatively given expressions by the psychological experiment conducted by the authors, elements which have high possibilities of forming a global structure are determined and connected, based on such information as relative position of elements.Using the evaluation function, the optimal candidate for the figure is extracted from the result. When observing overlapped multiple figures, a human can separate the figures (figure segmentation). This aspect also is considered in this study. The foregoing idea is implemented on a computer. Simulation experiments are made for several patterns, and the operation of the proposed system is verified.
Bidirectional feature map for robotic arm control
Authors: Hirano Takashi , Sase Mikiya , Kosugi Yukio 
Published: July 2007

AbstractWhen a neural network is used as an arm position controller, it is hoped that the network realizes at least two functions: (1) implementation of a desired input‐output relationship; (2) ensuring the ability of generalization. However, most well‐known networks, such as Back Propagation, and Feature Map clones do not satisfy these requirements simultaneously.Based on a new architecture and the learning rules, this paper proposes a Bidirectional Feature Map model which has these functions. Then the network for an arm controller with newly proposed constraints is applied to avoid ill‐posed situations inherent to articulation mechanisms with excessive freedom.
A method of search space pruning based on search state dominance
Authors: Fujino Takayuki , Fujiwara Hideo 
Published: July 2007

AbstractEST (Equivalent STate hashing) algorithm, which reduces the search space form a different point of view from the conventional methods, was proposed for test generation by Giraldi and Bushnell. In this method the processes and results of the test pattern searches are stored and used to avoid fruitless searches to reduce the search space. In this paper the concept of search state equivalence, which is introduced for EST, is extended to that of search state dominance. Based on that concept an algorithm DST (Dominant STate hashing) is proposed. The search state dominance can prune the search space more effectively than the search state equivalence.
Method for predicting utterances using a layered plan recognition model—disambiguation of speech recognition candidates by focusing on speaker intentions
Authors: Yamaoka Takayuki , Iida Hitoshi 
Published: July 2007

AbstractA context‐sensitive method that predicts a subsequent utterance to reduce ambiguity in speech recognition candidates for use in a spoken‐language processing system is described. Current speech recognition technology cannot accomplish perfect speech recognition, and in many cases ambiguities remain in the results. On the other hand, linguistic processing techniques assume error‐free input. Therefore the effectiveness of this processing is reduced when there are multiple candidates in the input. In particular, in spite of the importance to dialogue translation of the degree of ambiguity in the sentence‐final expressions that indicate the speaker's intention and colloquial fragmentary expressions that are characteristic of spoken language, conventional techniques do not address this problem.This paper describes first a method for dialogue understanding by means of a layered plan recognition method which uses knowledge on the conduct of a dialogue as well as knowledge concerning the topic, and the use of stacks to manage the states of understand‐ing that this method produces. Then a method is presented for obtaining abstract contextual information concerning the next utterance by referring to the stack contents.Next, a method of applying that contextual information in the selection of speech recognition candidates is described. Finally, pragmatic knowledge concerning utterance intention is specified, and the effectiveness of these methods in reducing the ambiguity of expressions concerning utterance intention is demonstrated by experiment.
A fault‐tolerant parallel processor modeled by a two‐dimensional linear cellular automaton
Authors: Kawanaka Masataka , Tsunoyama Masahiro , Naito Sachio 
Published: July 2007

AbstractA number of methods for constructing fault‐tolerant parallel processors using one dimensional linear cellular automaton model have already been proposed. When the methods are used, however, a large amount of hardware is required to realize the connections among processing elements for executing a given algorithm, and for reconfiguring the processor quickly when faults occur in the processor. Moreover, these processors require long communication distances between processing elements so that the method is difficult to be used for large scale processors. In this paper, a method for constructing parallel processors based on a two dimensional cellular automaton model is proposed to alleviate the problems. In the processor based on this model, processing elements are connected in two dimension and the average communication distance can be reduced and a higher fault‐tolerance due to the increase in the number of communication paths from 2 to 4.
Performance prediction of word recognition using the transition information between phonemes or between characters
Authors: Otsuki Takashi , Makino Shozo , Ito Akinori , Sone Toshio 
Published: July 2007

AbstractThis paper considers word recognition based on the existence of the transition between phonemes and characters with complete segmentation between phonemes and characters. A method is proposed which estimates theoretically the relation between the phoneme/character recognition score and the word recognition score. The word recognition score can be estimated by the evaluation formula based on the number of short‐distance words in the dictionary for the case where the word dictionary is used. The traditional evaluation formula for the word recognition score, however, has a problem in that the accuracy of estimation is deteriorated rapidly with the decrease of the phoneme/character recognition score and the increase of the short‐distance words.From such a viewpoint, this paper proposes a new evaluation formula with a higher estimation accuracy. In this paper, the sequence that can be generated based on the transition information is used as the item in the word dictionary. Then the number of items increases exponentially with the length, making it difficult to estimate the number of short‐distance words. For this problem, this paper proposes an algorithm that calculates the number of short‐distance words in a polynomial time. For approximately 5000 important words in Japanese, the estimated word recognition score for the phoneme recognition score of 90 percent is given approximately 72 percent by the proposed method and approximately 66 percent by the traditional method. It is approximately 78 percent by simulation. Thus, it is shown that the proposed method gives a better approximation than the traditional method.
Enhancing the image quality of high‐resolution MR angiography
Authors: Sano Koichi , Koizumi Hideaki , Takane Atushi 
Published: July 2007

AbstractIn MR angiography, which is effective for screening of aneurysms, investigation for the quality of high‐resolution angiography method using the combination of asymmetric measurement with half scan reconstruction are presented. In addition to avoiding the flow void by the asymmetric measurement, it was found that there were improvements of: (1) blood contrast by isolation of small vessels and static regions; (2) blood continuation by interpolation in 3‐dimensional space; and (3) relative S/N of small vessels with the increasing resolution. Experimental results show that good contrast image is obtained by this method with no penalty of scanning time.
An efficient algorithm for solving optimization problems on Hopfield‐type neural networks
Authors: Tanaka Toshio , Higuchi Tetsuya , Furuya Tatsumi 
Published: July 2007

AbstractWhen solving optimization problems on Hopfield neural networks, good solutions are not obtained due to convergence to local minima of the energy function. The Boltzmann machine can escape from local minima because of its stochastic behavior, but the computation time is very long to reach the (semi‐) global minimum.To avoid these problems, this paper proposes an efficient algorithm for solving optimization problems by the use of binary Hopfield neural networks. This algorithm selects a neuron which reduces the energy function value the most to reduce the energy of the network efficiently. Those algorithms are applied to the traveling salesman problem (TSP) with 10 cities. Near‐optimal solutions were obtained more efficiently than an ordinary TSP algorithm because the energy level of the network can reach lower energy states. The computation time was reduced by one order of magnitude because the energy level of the network reached lower energy states much faster than an ordinary TSP algorithm.
Robust and fast text‐line extraction using local linearity of the text‐line
Authors: Goto Hideaki , Aso Hirotomo 
Published: July 2007

AbstractText region extraction is a necessary process before character recognition is done for document images. This paper describes a new algorithm, Linear Segment Linking (LSL), for text‐line extraction from document images. The algorithm groups together the piecewise linear elements in the document images, which may be assumed to be text lines, and then extracts them from the images. The algorithm requires less knowledge about document structure and is robust for distortion of the image. The primitive rectangles are introduced for the intermediate representation of image. It is easier and faster to create them than the usual circumscribing rectangles. A method of splitting the bridges between neighboring text lines is proposed. Combining the bridge splitting process with the text line extraction, the locally touching text lines will be extracted as individual ones.
The learning of linear neural nets with anti‐hebbian rules
Authors: Matsuoka Kiyotoshi , Kawamoto Mitsuru 
Published: July 2007

AbstractHebbian rule might be the most popular one as an unsupervised learning model of neural nets. Recently, the opposite of the Hebbian rule, i.e., the so‐called anti‐Hebbian rule, has drawn attention as a new learning paradigm. This paper first clarifies some fundamental properties of the anti‐Hebbian rule, and then shows that a variety of networks can be acquired by some anti‐Hebbian rules.
Control of associative dynamics by matching features in chaotic neural network
Authors: Deguchi Toshinori , Ishii Naohiro 
Published: July 2007

AbstractWhen patterns are stored by using associative learning in a chaotic neural network consisting of chaotic neurons that easily produces chaos, whether dynamic (chaotic) or static (non‐chaotic) remembering occurs can be controlled by varying the parameters of the chaotic neurons. A pattern can be searched by using this dynamic remembering as a sampling procedure. If features of the output pattern of the network become similar to desired features during the search, the chaotic state is changed to a static remembering state so that the output pattern has desired features. In this paper, the state is controlled by using a model of presynaptic inhibition similar to that seen in real nerve cells (not using the parameters of the chaotic neurons). Extraction and comparison of the features are performed by back‐propagation networks. The extraction of features under this method is amenable to visual representation of its trajectory in feature space.
Improving the performance of global communication on a three‐dimensional torus network
Authors: Kawakura Yasushi , Tanabe Noboru , Oyanagi Shigeru 
Published: July 2007

AbstractA high‐speed one‐to‐all broadcasting algorithm is proposed whose performance does not deteriorate much when the number of processors is increased in a massively parallel computer. For the network topology, 3D torus networks are considered. Two methods are discussed for a system which broadcasts by repeating one‐to‐one communications. One uses paths having a smaller maximum transfer number to reduce the number of transfers, and the other presets the hardware to reduce the overhead of individual one‐to‐one communications. These methods are evaluated using a double loop model which consists of an inner loop for local processing and an outer loop for global communications. When these methods are used, the scalability increases and for a 32K processor system a 4.2 times speedup in program execution can be achieved.
LSI module placement using the kohonen network
Authors: Inoue Takeshi , Abe Shigeo , Kayama Masahiro 
Published: July 2007

AbstractThis paper discusses a method for solving an LSI module placement problem using the Kohonen network. First, it is clarified how the neighborhood relations affect the solution called feature map. Specifically, each synapse weight vector is located at the gravitational center of the neighboring synapse weight vectors. Then, it is shown how to set the neighborhood relations to locate the synapse weight vectors at the lattice points, which is necessary in solving the placement problem. Finally, it is shown by computer simulations that performance of the modified Kohonen network is between those of the simulated annealing and the Min‐cut methods.