Browsing by Subject "Data reduction"

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Open Access
Are stock prices too volatile to be justified by the dividend discount model?
(Elsevier, 2007) Akdeniz, L.; Salih, A. A.; Ok, S. T.
This study investigates excess stock price volatility using the variance bound framework of LeRoy and Porter [The present-value relation: tests based on implied variance bounds, Econometrica 49 (1981) 555-574] and of Shiller [Do stock prices move too much to be justified by subsequent changes in dividends? Am. Econ. Rev. 71 (1981) 421-436.]. The conditional variance bound relationship is examined using cross-sectional data simulated from the general equilibrium asset pricing model of Brock [Asset prices in a production economy, in: J.J. McCall (Ed.), The Economics of Information and Uncertainty, University of Chicago Press, Chicago (for N.B.E.R.), 1982]. Results show that the conditional variance bounds hold, hence, our hypothesis of the validity of the dividend discount model cannot be rejected. Moreover, in our setting, markets are efficient and stock prices are neither affected by herd psychology nor by the outcome of noise trading by naive investors; thus, we are able to control for market efficiency. Consequently, we show that one cannot infer any conclusions about market efficiency from the unconditional variance bounds tests.
Open Access
Big-data streaming applications scheduling based on staged multi-armed bandits
(Institute of Electrical and Electronics Engineers, 2016) Kanoun, K.; Tekin, C.; Atienza, D.; Van Der Schaar, M.
Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single stream input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new systematic and efficient methodology and associated algorithms for online learning and energy-efficient scheduling of Big-Data streaming applications with multiple streams on many core systems with resource constraints. We formalize the problem of multi-stream scheduling as a staged decision problem in which the performance obtained for various resource allocations is unknown. The proposed scheduling methodology uses a novel class of online adaptive learning techniques which we refer to as staged multi-armed bandits (S-MAB). Our scheduler is able to learn online which processing method to assign to each stream and how to allocate its resources over time in order to maximize the performance on the fly, at run-time, without having access to any offline information. The proposed scheduler, applied on a face detection streaming application and without using any offline information, is able to achieve similar performance compared to an optimal semi-online solution that has full knowledge of the input stream where the differences in throughput, observed quality, resource usage and energy efficiency are less than 1, 0.3, 0.2 and 4 percent respectively.
Open Access
Combined filtering and key-frame reduction of motion capture data with application to 3DTV
(WSCG, 2006-01-02) Önder, Onur; Erdem, Ç.; Erdem, T.; Güdükbay, Uğur; Özgüç, Bülent
A new method for combined filtering and key-frame reduction of motion capture data is proposed. Filtering of motion capture data is necessary to eliminate any jitter introduced by a motion capture system. Key-frame reduction, on the other hand, allows animators to easily edit motion data by representing animation curves with a significantly smaller number of key frames. The proposed technique achieves key frame reduction and jitter removal simultaneously by fitting a Hermite curve to motion capture data using dynamic programming. Copyright © UNION Agency - Science Press.
Open Access
E-museum: web-based tour and information system for museums
(IEEE, 2006) Baştanlar, Y.; Altıngövde, İsmail Şenol; Aksay, A.; Alav, O.; Çavuş, Özge; Yardımcı, Y.; Ulusoy, Ozgur; Güdükbay, Uğur; Çetin, A. Enis; Akar, G. B.; Aksoy, Selim
A web-based system - consisting of data entrance, access and retrieval modules - is constructed for museums. Internet users that visit the e-museum, are able to view the written and visual information belonging to the artworks in the museum, are able to follow the virtual tour prepared for the different sections of the museum, are able to browse the artworks according to certain properties, are able to search the artworks having the similar visual content with the viewed artwork. © 2006 IEEE.
Open Access
Effective early termination techniques for text similarity join operator
(Springer, Berlin, Heidelberg, 2005) Özalp, S. A.; Ulusoy, Özgür
Text similarity join operator joins two relations if their join attributes are textually similar to each other, and it has a variety of application domains including integration and querying of data from heterogeneous resources; cleansing of data; and mining of data. Although, the text similarity join operator is widely used, its processing is expensive due to the huge number of similarity computations performed. In this paper, we incorporate some short cut evaluation techniques from the Information Retrieval domain, namely Harman, quit, continue, and maximal similarity filter heuristics, into the previously proposed text similarity join algorithms to reduce the amount of similarity computations needed during the join operation. We experimentally evaluate the original and the heuristic based similarity join algorithms using real data obtained from the DBLP Bibliography database, and observe performance improvements with continue and maximal similarity filter heuristics. © Springer-Verlag Berlin Heidelberg 2005.
Open Access
Evaluating predictive performance of judgemental extrapolations from simulated currency series
(Elsevier, 1999) Pollock, A. C.; Macaulay, A.; Önkal-Atay, D.; Wilkie-Thomson, M. E.
Judgemental forecasting of exchange rates is critical for financial decision-making. Detailed investigations of the potential effects of time-series characteristics on judgemental currency forecasts demand the use of simulated series where the form of the signal and probability distribution of noise are known. The accuracy measures Mean Absolute Error (MAE) and Mean Squared Error (MSE) are frequently applied quantities in assessing judgemental predictive performance on actual exchange rate data. This paper illustrates that, in applying these measures to simulated series with Normally distributed noise, it may be desirable to use their expected values after standardising the noise variance. A method of calculating the expected values for the MAE and MSE is set out, and an application to financial experts' judgemental currency forecasts is presented.
Open Access
Feedback-labelling synergies in judgmental stock price forecasting
(Elsevier, 2004) Goodwin, P.; Önkal-Atay, D.; Thomson, M. E.; Pollock, A. C.; Macaulay, A.
Research has suggested that outcome feedback is less effective than other forms of feedback in promoting learning by users of decision support systems. However, if circumstances can be identified where the effectiveness of outcome feedback can be improved, this offers considerable advantages, given its lower computational demands, ease of understanding and immediacy. An experiment in stock price forecasting was used to compare the effectiveness of outcome and performance feedback: (i) when different forms of probability forecast were required, and (ii) with and without the presence of contextual information provided as labels. For interval forecasts, the effectiveness of outcome feedback came close to that of performance feedback, as long as labels were provided. For directional probability forecasts, outcome feedback was not effective, even if labels were supplied. Implications are discussed and future research directions are suggested.
Open Access
Financial earthquakes, aftershocks and scaling in emerging stock markets
(Elsevier BV, 2004) Selçuk, F.
This paper provides evidence for scaling laws in emerging stock markets. Estimated parameters using different definitions of volatility show that the empirical scaling law in every stock market is a power law. This power law holds from 2 to 240 business days (almost 1 year). The scaling parameter in these economies changes after a change in the definition of volatility. This finding indicates that the stock returns may have a multifractal nature. Another scaling property of stock returns is examined by relating the time after a main shock to the number of aftershocks per unit time. The empirical findings show that after a major fall in the stock returns, the stock market volatility above a certain threshold shows a power law decay, described by Omori's law. © 2003 Elsevier B.V. All rights reserved.
Open Access
Human action recognition using distribution of oriented rectangular patches
(Springer, 2007-10) İkizler, Nazlı; Duygulu, Pınar
We describe a "bag-of-rectangles" method for representing and recognizing human actions in videos. In this method, each human pose in an action sequence is represented by oriented rectangular patches extracted over the whole body. Then, spatial oriented histograms are formed to represent the distribution of these rectangular patches. In order to carry the information from the spatial domain described by the bag-of-rectangles descriptor to temporal domain for recognition of the actions, four different methods are proposed. These are namely, (i) frame by frame voting, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis by rectangular patches, (iii) a classifier based approach using SVMs, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the descriptor. The detailed experiments are carried out on the action dataset of Blank et. al. High success rates (100%) prove that with a very simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations. © Springer-Verlag Berlin Heidelberg 2007.
Open Access
Improvement of face detection algorithms for news videos
(IEEE, 2005) Ikizler, Nazlı; Duygulu, Pınar
People are the most important subjects in news videos and for proper retrieval of person images, face detection is a very crucial step. However, face detection and recognition in news videos is a very challenging task due to the huge irregularities and high noise level in the data. This study presents a method that combines skin detection and Schneiderman-Kanade face detection, for improving the face detection performance in news videos for a better retrieval. This method has been tested on TRECVID 2003 dataset and the results are very promising. © 2005 IEEE.
Open Access
Intraday dynamics of stock market returns and volatility
(Elsevier BV, 2006) Selçuk, F.; Gençay, R.
This paper provides new empirical evidence for intraday scaling behavior of stock market returns utilizing a 5 min stock market index (the Dow Jones Industrial Average) from the New York Stock Exchange. It is shown that the return series has a multifractal nature during the day. In addition, we show that after a financial "earthquake", aftershocks in the market follow a power law, analogous to Omori's law. Our findings indicate that the moments of the return distribution scale nonlinearly across time scales and accordingly, volatility scaling is nonlinear under such a data generating mechanism. © 2006 Elsevier B.V. All rights reserved.
Open Access
Neural networks for improved target differentiation and localization with sonar
(Pergamon Press, 2001) Ayrulu, B.; Barshan, B.
This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-flight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61-90%) employing multiple sensor nodes. A sensor node is a pair of transducers with fixed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. Copyright © 2001 Elsevier Science Ltd.
Open Access
A new directional acoustic lens: V-groove lens
(IEEE, 1993) Bozkurt, Ayhan; Yaralıoğlu, G. Göksenin; Atalar, Abdullah; Köymen, Hayrettin
A new directional acoustic lens is introduced. The geometry is very similar to the line-focus lens except the lens cavity, which is shaped as a groove with flat-bottom V cross section. The slanted planar edges of the groove are inclined in order to generate waves incident on the object surface at a critical angle. Hence, the edges of the groove act like two wedge transducers facing each other. The cross section of the lens is the same as that of the Lamb Wave Lens. Therefore, it enjoys the same sensitivity to surface wave excitations. On the other hand, since the cross section remains the same along one of the lateral directions, it has directional properties very similar to that of the Line Focus Beam Lens. The waves normally incident on the object surface generated from the flat-bottom, interfere with those at the critical angle, giving rise to a V(Z) effect. Calculated responses of the lens are presented for silicon (001) surface as a function of crystal orientation. The calculated curves are compared with measurement results. The leaky wave velocities are extracted from the measurement results using the conventional FFT algorithm. A new model based algorithm is proposed for extracting the velocity information from V(Z) data.
Open Access
Nonrectangular wavelets for multiresolution mesh analysis and compression
(SPIE, 2006) Köse, Kıvanç; Çetin, A. Enis; Güdükbay, Uğur; Onural, Levent
We propose a new Set Partitioning In Hierarchical Trees (SPIHT) based mesh compression framework. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed and SPIHT is applied on the wavelet domain data. The method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the ID data stream created by SPIHT algorithm. Nearly perfect reconstruction is possible if full length of 1D data is received.
Open Access
On reduced order modeling of flexible structures from frequency response data
(IEEE, 2014) Demir, Okan; Özbay, Hitay
In order to identify the dominant flexible modes of a flexible structure with an input/output delay, a numerical method is proposed. The method uses a frequency domain approach (frequency response data) to estimate the resonating frequencies and damping coefficients of the flexible modes, as well as the amount of the time delay. A sequential NLLS (Non-Linear Least Squares) curve fitting procedure is adopted. It is illustrated that such a Newtonian optimization method has the capability of finding the parameters of a reduced order transfer function by minimizing a cost function involving nonlinearities such as exponential and fractional terms.
Open Access
Parsing Turkish using the lexical functional grammar formalism
(Springer/Kluwer Academic Publishers, 1995) Güngördü, Z.; Oflazer, K.
This paper describes our work on parsing Turkish using the lexical-functional grammar formalism [11]. This work represents the first effort for wide-coverage syntactic parsing of Turkish. Our implementation is based on Tomita's parser developed at Carnegie Mellon University Center for Machine Translation. The grammar covers a substantial subset of Turkish including structurally simple and complex sentences, and deals with a reasonable amount of word order freeness. The complex agglutinative morphology of Turkish lexical structures is handled using a separate two-level morphological analyzer, which has been incorporated into the syntactic parser. After a discussion of the key relevant issues regarding Turkish grammar, we discuss aspects of our system and present results from our implementation. Our initial results suggest that our system can parse about 82% of the sentences directly and almost all the remaining with very minor pre-editing. © 1995 Kluwer Academic Publishers.
Open Access
Prediction of protein subcellular localization based on primary sequence data
(IEEE, 2004) Özarar, M.; Atalay, V.; Çetin-Atalay, Rengül
Subcellular localization is crucial for determining the functions of proteins. A system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order is designed. The approach for prediction is to find the most frequent motifs for each protein in a given class based on clustering via self organizing maps and then to use these most frequent motifs as features for classification by the help of multi layer perceptrons. This approach allows a classification independent of the length of the sequence. In addition to these, the use of a new encoding scheme is described for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. The statistical test results of the system is presented on a four class problem. P2SL achieves slightly higher prediction accuracy than the similar studies.
Open Access
Protein folding rates correlate with heterogeneity of folding mechanism
(American Physical Society, 2004) Öztop, B.; Ejtehadi, M. R.; Plotkin, S. S.
The folding rates of protein were shown to correlate with the degree of heterogeneity in the formation of native contacts. It was shown that both experimental rates and simulated free energy barriers for 2-state proteins depend on the degree of heterogeneity present in the folding process. Heterogeneity due to variance in the distribution of native loop lengths, and variance in the distribution of φ values, were observed to increase folding rates and reduce folding barriers. The observed effect due to φ variance was found to be the most statistically significant, because φ variance captures both heterogeneity arising from native topology and that arising from energetics.
Open Access
Query expansion with terms selected using lexical cohesion analysis of documents
(Elsevier Ltd, 2007-07) Vechtomova, O.; Karamuftuoglu, M.
We present new methods of query expansion using terms that form lexical cohesive links between the contexts of distinct query terms in documents (i.e., words surrounding the query terms in text). The link-forming terms (link-terms) and short snippets of text surrounding them are evaluated in both interactive and automatic query expansion (QE). We explore the effectiveness of snippets in providing context in interactive query expansion, compare query expansion from snippets vs. whole documents, and query expansion following snippet selection vs. full document relevance judgements. The evaluation, conducted on the HARD track data of TREC 2005, suggests that there are considerable advantages in using link-terms and their surrounding short text snippets in QE compared to terms selected from full-texts of documents. © 2006 Elsevier Ltd. All rights reserved.
Open Access
Using data compression for increasing memory system utilization
(Institute of Electrical and Electronics Engineers, 2009-06) Ozturk, O.; Kandemir, M.; Irwin, M. J.
The memory system presents one of the critical challenges in embedded system design and optimization. This is mainly due to the ever-increasing code complexity of embedded applications and the exponential increase seen in the amount of data they manipulate. The memory bottleneck is even more important for multiprocessor-system-on-a-chip (MPSoC) architectures due to the high cost of off-chip memory accesses in terms of both energy and performance. As a result, reducing the memory-space occupancy of embedded applications is very important and will be even more important in the next decade. While it is true that the on-chip memory capacity of embedded systems is continuously increasing, the increases in the complexity of embedded applications and the sizes of the data sets they process are far greater. Motivated by this observation, this paper presents and evaluates a compiler-driven approach to data compression for reducing memory-space occupancy. Our goal is to study how automated compiler support can help in deciding the set of data elements to compress/ decompress and the points during execution at which these compressions/decompressions should be performed. We first study this problem in the context of single-core systems and then extend it to MPSoCs where we schedule compressions and decompressions intelligently such that they do not conflict with application execution as much as possible. Particularly, in MPSoCs, one needs to decide which processors should participate in the compression and decompression activities at any given point during the course of execution. We propose both static and dynamic algorithms for this purpose. In the static scheme, the processors are divided into two groups: those performing compression/ decompression and those executing the application, and this grouping is maintained throughout the execution of the application. In the dynamic scheme, on the other hand, the execution starts with some grouping but this grouping can change during the course of execution, depending on the dynamic variations in the data access pattern. Our experimental results show that, in a single-core system, the proposed approach reduces maximum memory occupancy by 47.9% and average memory occupancy by 48.3% when averaged over all the benchmarks. Our results also indicate that, in an MPSoC, the average energy saving is 12.7% when all eight benchmarks are considered. While compressions and decompressions and related bookkeeping activities take extra cycles and memory space and consume additional energy, we found that the improvements they bring from the memory space, execution cycles, and energy perspectives are much higher than these overheads. © 2009 IEEE.