Exponential Weighting Research Articles

Modeling Volatility and Valuing Derivatives Under Anchoring

We develop a complete-markets model with volatility smiles, tractability, and intuitive appeal as an anchoring or habit-formation model. Like traditional stochastic volatility models, it is invariant to a multiplicative scaling of the stock price levels. The anchoring effect is that the volatility depends on the relative value of the current stock price compared to its past history, with an exponential weighting.

A novel adaptive discrete cosine transform-domain filter for gap-inpainting of high resolution PET scanners.

Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.

Identification of rare variants for hypertension with incorporation of linkage information.

We conducted linkage analysis using the genome-wide association study data on chromosome 3, and then assessed association between hypertension and rare variants of genes located in the regions showing evidence of linkage. The rare variants were collapsed if their minor allele frequencies were less than or equal to the thresholds: 0.01, 0.03, or 0.05. In the collapsing process, they were either unweighted or weighted by the nonparametric linkage log of odds scores in 2 different schemes: exponential weighting and cumulative weighting. Logistic regression models using the generalized estimating equations approach were used to assess association between the collapsed rare variants and hypertension adjusting for age and gender. Evidence of association from the weighted and unweighted collapsing schemes with minor allele frequencies ≤0.01, after accounting for multiple testing, was found for genes DOCK3 (p = 0.0090), ARMC8 (p = 1.29E-5), KCNAB1 (p = 5.8E-4), and MYRIP (p = 5.79E-6). DOCK3 and MYRIP are newly discovered. Incorporating linkage scores as weights was found to help identify rare causal variants with a large effect size.

Bi-Directional Training for Adaptive Beamforming and Power Control in Interference Networks

We study distributed algorithms for adapting transmit beamformers and linear receiver filters in a Time-Division Duplex Multiple-Input Multiple-Output (MIMO) interference network. Each transmitter transmits a single beam, and neither the transmitters nor receivers have a priori Channel State Information (CSI). Given a fixed set of powers, we present an adaptive version of the Max-SINR algorithm: pilot symbols are alternately transmitted in the forward direction (transmitters to receivers) and in the reverse direction (receivers to transmitters). Unlike previous channel estimation schemes, transmissions in each direction are synchronized across the source or destination nodes, and the pilots are used to update the filters/beams directly using a least squares criterion. To improve the performance with limited training, we include exponential weighting of the least squares objective across data frames. In addition, bi-directional training can be used to implement analog interference pricing for power control: training in the forward direction is used to measure received signal-to-interference plus noise ratios (SINRs) and interference prices, and those estimates combined with synchronous backward training are used to update the powers. Given sufficient training this method achieves the same performance as interference pricing updates with perfect CSI. Numerical results are presented that illustrate the performance of these methods in different settings.

The Multivariate EWMA Model and Health Care Monitoring

We introduce the construction of MEWMA (Multivariate exponentially weighted moving average) process control in the field of bio surveillance. Such introduction will both improve the reliability of data collected in bio surveillance, better interpretation of the results, improvement in the quality of results and standardization of results when more than two variables are involved. We propose sensitivity ratios as a measure of the effects of the mean shift and dispersion shift in processes under study. Using these sensitivity measures, we designed the optimal exponential weighting factor, which is consistent to results reported in control chart applications. Although ARL (average run length) is the usual measure for control chart performance in multivariate process control, it is by no means the only criterion, however, at the moment it is most widely used criterion for decision making. We suggest addition study of other criteria. For example Medial Run Length, Days to Completion, Direction of Eorrors and others.

Multiple Model Identification for a High Purity Distillation Column Process Based on EM Algorithm

Due to the strong nonlinearity and transition dynamics between different operating points of the high purity distillation column process, it is difficult to use a single model for modeling such a process. Therefore, the multiple model based approach is introduced for modeling the high purity distillation column plant under the framework of the expectation maximization (EM) algorithm. In this paper, autoregressive exogenous (ARX) models are adopted to construct the local models of this chemical process at different operating points, and the EM algorithm is used for identification of local models as well as the probability that each local model takes effect. The global model is obtained by aggregating the local models using an exponential weighting function. Finally, the simulation performed on the high purity distillation column demonstrates the effectiveness of the proposed method.

OPTIMISATION OF BUILDING ENERGY SYSTEM TECHNOLOGY CONFIGURATION USING MULTI-CRITERIA DECISION MAKING METHODS / DAUGIAKRITERIŲ METODŲ TAIKYMAS RACIONALIAM PASTATO ENERGETINĖS SISTEMOS TECHNOLOGIJŲ DERINIUI NUSTATYTI

This article presents the evaluation and optimization algorithm of the building energy system. Two main objectives have been achieved: the optimal configuration of the building energy system has been defined, which minimizes the use of non-renewable sources and reduces the environmental impact of the building. Energy demand for the house has been simulated employing DesignBuilder software. Five configurations of technologies for the building energy system have been chosen and simulated applying Polysun software in order to define the seasonal energy efficiency of the generators of each configuration. Multi-criteria decision making methods SAW (Simple Additive Weight), COPRAS (COmplex PRoportion ASsessment) and MEW (Multiplicative Exponential Weighting) have been used for finding the optimal decision on this case study. Article in Lithuanian. Santrauka Pateikiamas mažai energijos vartojančio gyvenamojo namo energetinės sistemos vertinimo ir optimizavimo algoritmas. Šio tyrimo metu, siekiant nustatyti racionalų realiai pritaikomą pastato energetinės sistemos technologijų derinį, įgyvendinti du pagrindiniai tikslai: parinktas derinys, kurį taikant maksimaliai išnaudojami atsinaujinantieji energijos ištekliai ir sumažinamos sistemos išmetamų CO2 dujų emisijos. DesignBuilder kompiuterine programa atliktas energinis modeliavimas pastato energijos reikmėms nustatyti. Esamam gyvenamajam namui parinkti penki energetinės sistemos technologijų deriniai. Atliktas derinių modeliavimas Polysun programa, nustatytas kiekvieno derinio generatoriaus sezoninis energinis efektyvumas. Įvairiapusiškai racionaliam sprendimui priimti buvo taikyti daugiakriterio vertinimo metodai: SAW (Simple Additive Weight), COPRAS (COmplex PRoportion ASsessment) ir MEW (Multiplicative Exponential Weighting).

An Image Filtering Algorithm for Removing Mixed Noise

In order to remove salt-and-pepper noise and Gaussian noise in image, a novel filtering algorithm is proposed in this paper. The novel algorithm can preserve image edge details as much as possible. Firstly, five-median-binary code (FMBC) is proposed and used to describe local edge type of image. Secondly, median filter algorithm is improved to remove salt-and-pepper noise by using FMBC. Then, local enhanced bilateral filter with FMBC and a new type of exponential weighting function is used to remove Gaussian noise. Simulation results show that the algorithm proposed in this paper is very effective not only in filtering mixed noise but also in preserving edge details.

Intensity based image registration by minimizing exponential function weighted residual complexity

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen–Tsallis.

Sin2ψ-based residual stress gradient analysis by energy-dispersive synchrotron diffraction constrained by small gauge volumes. I. Theoretical concept

The influence of the gauge volume size and shape on the analysis of steep near-surface residual stress gradients by means of energy-dispersive synchrotron diffraction is studied theoretically. Cases are considered where the irradiated sample volume is confined by narrow-slit systems, in both the primary and the diffracted beam, to dimensions comparable to the `natural' 1/einformation depth τ1/eof the X-rays. It is shown that the ratio between τ1/e, defined by the material's absorption, and the immersion depthhGVof the gauge volume into the sample is the crucial parameter that shapes thedψhklor ∊ψhklversussin2ψ distributions obtained in the Ψ mode of X-ray stress analysis. Since the actual information depth 〈z〉GVto which the measured X-ray signal has to be assigned is a superposition of geometrical and exponential weighting functions, ambiguities in the conventional plot of the Laplace stressesversus〈z〉GVmay occur for measurements performed using narrow-slit configurations. To avoid conflicts in data analysis in these cases, a modified formalism is proposed for the evaluation of the real space residual stress profiles σ||(z), which is based on a two-dimensional least-squares fit procedure.

Comparison of sequential monitoring methods for environmental time series with stochastic cycle

The paper compares recursive methods for detecting change points in environmental time series. Timely identification of peaks and troughs is important for planning defense actions and preventing risks. We consider linear nonparametric methods, such as time-varying coefficients, double exponential smoothers and prediction error statistics. These methods are often used in surveillance, forecasting and control, and their common features are sequential computation and exponential weighting of data. The new approach proposed here is to select their coefficients by maximizing the difference between subsequent peaks and troughs detected on past data. We compare the methods with applications to meteorological, astronomical and ecological data, and Monte-Carlo simulations.

Ordered smoothers with exponential weighting

The main goal in this paper is to propose a new approach to deriving oracle inequalities related to the exponential weighting method. The paper focuses on recovering an unknown vector from noisy data with the help of the family of ordered smoothers [12]. The estimators withing this family are aggregated using the exponential weighting method and the aim is to control the risk of the aggregated estimate. Based on the natural probabilistic properties of the unbiased risk estimate, we derive new oracle inequalities for the mean square risk and show that the exponential weighting permits to improve Kneip’s oracle inequality.

Sparse Estimation by Exponential Weighting

Consider a regression model with fixed design and Gaussian noise where the regression function can potentially be well approximated by a function that admits a sparse representation in a given dictionary. This paper resorts to exponential weights to exploit this underlying sparsity by implementing the principle of sparsity pattern aggregation. This model selection take on sparse estimation allows us to derive sparsity oracle inequalities in several popular frameworks, including ordinary sparsity, fused sparsity and group sparsity. One striking aspect of these theoretical results is that they hold under no condition in the dictionary. Moreover, we describe an efficient implementation of the sparsity pattern aggregation principle that compares favorably to state-of-the-art procedures on some basic numerical examples.

Evaluation of Recursive Detection Methods for Turning Points in Financial Time Series

Summary Timely identification of turning points in economic time series is important for planning control actions and achieving profitability. This paper compares sequential methods for detecting peaks and troughs in stock values and deciding the time to trade. Three semi-parametric methods are considered: double exponential smoothing, time-varying parameters and prediction error statistics. These methods are widely used in monitoring, forecasting and control, and their common features are recursive computation and exponential weighting of observations. The novelty of this paper is the selection of smoothing and alarm coefficients for maximisation of the gain (the difference in level between subsequent peaks and troughs) of sample data. The methods are compared on applications to leading financial series and with simulation experiments.

Exponential weighting and oracle inequalities for projection estimates

We consider the problem of recovering an unknown vector from noisy data. The vector is estimated using a family of projection estimates, and the goal is finding a sufficiently good convex combination of these estimates based on the observations. We study an aggregation method for constructing estimates related to the so-called exponential weighting and present an upper bound on the mean-square risk of this method.

SU-E-J-172: Development of a Video Guided Real-Time Patient Motion Monitoring System for Helical Tomotherpay.

We developed a video image-guided real-time patient motion monitoring system for helical Tomotherapy (VGRPM-Tomo), and its clinical utility was evaluated using a motion phantom. The VGRPM-Tomo consisted of three components: an image acquisition device consisting of two PC-cams, a main control computer with a radiation signal controller and warning system, and patient motion analysis software, which was developed in house. The system was designed for synchronization with a beam on/off trigger signal to limit operation during treatment time only and to enable system automation. In order to detect the patient motion while the couch is moving into the gantry, a reference image, which continuously updated its background by exponential weighting filter (EWF), is compared with subsequent live images using the real-time frame difference-based analysis software. When the error range exceeds the set criteria (δ_movement) due to patient movement, a warning message is generated in the form of light and sound. The described procedure repeats automatically for each patient. A motion phantom, which operates by moving a distance of 0.1, 0.2, 0.5, and 1.0 cm for 1 and 2 sec, respectively, was used to evaluate the system performance at maximum couch speed (0.196 cm/sec) in a Helical Tomotherapy (HD, Hi-art, Tomotherapy, USA). We measured the optimal EWF factor (a) and δ_movement, which is the minimum distance that can be detected with this system, and the response time of the whole system. The optimal a for clinical use ranged from 0.85 to 0.9. The system was able to detect phantom motion as small as 0.2 cm with tight δ_movement, 0.1% total number of pixels in the reference image. The measured response time of the whole system was 0.1 sec. The VGRPM-tomo can contribute to reduction of treatment error caused by the motion of patients and increase the accuracy of treatment dose delivery in HD. This work was supported by the Technology Innovation Program, 10040362, Development of an integrated management solution for radiation therapy funded by the Ministry of Knowledge Economy (MKE, Korea). This idea is protected by a Korean patent (patent no. 10-1007367).

Sensitivity enhancement for maximally resolved two‐dimensional NMR by nonuniform sampling

Resolving NMR signals which are separated in frequency on the order of their line widths requires obtaining the time domain free induction decay for a maximum time tmax = πT2 , where T2 is the transverse relaxation time of the given signals. Unfortunately, samples acquired beyond ∼1.26T2 contribute more noise than signal to the data; and samples in the range of about (0.75-1.26)× T2 have a negligible effect on the signal-to-noise ratio (SNR). Therefore, one must sacrifice SNR to reach evolution times of πT2 . One can preserve resolution in a shorter total experimental time by selecting a reduced set of samples from the Nyquist grid according to an exponential probability density which is on the order of the T2 of the signals. This practice is widely termed nonuniform sampling (NUS). We derive analytic theory for the enhancement of the intrinsic SNR of NUS time domain data compared with uniformly sampled data when the total experimental times are equivalent. This theory is general for any tmax and exponential weighting and is further carefully validated with simulations. Enhancements of SNR in the time domain on the order of twofold are routinely available when tmax ∼ πT2 and are reflected in the subsequent maximum entropy reconstructed spectra. SNR enhancement by NUS is demonstrated to be helpful in enabling the acquisition of HMQC spectra of dilute bile salts in which high resolution in the indirect carbon dimension is required.

Efficient MMSE Estimation and Uncertainty Processing for Multienvironment Robust Speech Recognition

This paper presents a feature compensation framework based on minimum mean square error (MMSE) estimation and stereo training data for robust speech recognition. In our proposal, we model the clean and noisy feature spaces in order to obtain clean feature estimates. However, unlike other well-known MMSE compensation methods such as SPLICE or MEMLIN, which model those spaces with Gaussian mixture models (GMMs), in our case every feature space is characterized by a set of prototype vectors which can be alternatively considered as a vector quantization (VQ) codebook. The discrete nature of this feature space characterization introduces two significative advantages. First, it allows the implementation of a very efficient MMSE estimator in terms of accuracy and computational cost. On the other hand, time correlations can be exploited by means of hidden Markov modeling (HMM). In addition, a novel subregion-based modeling is applied in order to accurately represent the transformation between the clean and noisy domains. In order to deal with unknown environments, a multiple-model approach is also explored. Since this approach has been shown quite sensitive to incorrect environment classification, we adapt two uncertainty processing techniques, soft-data decoding and exponential weighting, to our estimation framework. As a result, environment miss-classifications are concealed, allowing a better performance under unknown environments. The experimental results on noisy digit recognition show a relative improvement of 87.93% in word accuracy regarding the baseline when clean acoustic models are used, while a 4.54% is achieved with multi-style trained models.

BUILDING EXTERNAL PARTITIONS ASSESMENT USING MULTI-CRITERIA DECISION MAKING METHODS / IŠORINIŲ PASTATO ATITVARŲ VERTINIMAS TAIKANT DAUGIAKRITERINIUS METODUS

The paper presents a model for selection of building’s external elements. Were analyzed 6 variations of walls and windows area ratio. The authors determined the criteria of those alternatives, which are as follows: heat losses through the building external walls, heat losses through the building windows, heat losses through the bearer thermal bridges, heat losses above the rated air infiltration, external heat inflows in the building from the outside, the building’s energy consumption without consideration of heating system performance, and walls and windows price ratio. According to available data, the initial matrix of a problem was determined. By the use of multiple assessment methods, such as SAW (Simple Additive Weight) COPRAS (Complex Proportion Assessment) and MEW (Multiplicative Exponential Weighting) were calculated the optimal variant of a window and wall aria variation.

Mixing It Up: Measuring Diffusion in Supercooled Liquid Solutions of Methanol and Ethanol at Temperatures near the Glass Transition

How do liquid mixtures cooled to temperatures below their freezing point behave? We address this question using nanoscale films of methanol and ethanol supercooled liquid solutions of varying composition (0−100% methanol) at temperatures near their glass transition, Tg. The permeation of Kr through these films is used to determine the diffusivities of the supercooled liquid mixtures. We find that the temperature-dependent diffusivities of the mixtures are well-fit by a Vogel−Fulcher−Tamman equation, indicating that the mixtures exhibit fragile behavior at temperatures just above their Tg. Further, for a given temperature, the composition-dependent diffusivity is well-fit by a Vignes-type equation, that is, the diffusivity of any mixture can be predicted using an exponential weighting of the pure methanol and ethanol diffusivities. These results show that deeply supercooled liquid mixtures can be used to provide valuable insight into the properties of normal liquid mixtures.