Asymmetric Data Research Articles

A new multimodal and asymmetric bivariate circular distribution

Multimodal and asymmetric bivariate circular data arise in several different disciplines and fitting appropriate distribution plays an important role in the analysis of such data. In this paper, we propose a new bivariate circular distribution which can be used to model both asymmetric and multimodal bivariate circular data simultaneously. In fact the proposed density covers unimodality as well as multimodality, symmetry as well as asymmetry of circular bivariate data. A number of properties of the proposed density are presented. A Bayesian approach with MCMC scheme is employed for statistical inference. Three real datasets and a simulation study are provided to illustrate the performance of the proposed model in comparison with alternative models such as finite mixture Cosine model.

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Weighted Average Information Criterion for Selection of an Asymmetric Price Relationship

TThis study evaluates the performance of the recently developed model selection criteria (WIC) against commonly used alternatives (AIC and BIC) in terms of their ability to recover the true asymmetric data generating process. Monte Carlo simulation results indicate that the performance of the model selection methods depends on the sample size, the difference in asymmetric adjustment parameters and the amount of noise in the model used in the application. WIC outperforms AIC and BIC under stable conditions such as a large sample and small noise levels. Additionally, WIC outperforms AIC and BIC as the difference between asymmetric adjustment speeds increases. These results suggest that WIC is a very reliable and useful criterion in asymmetric price transmission model selection.

A taxonomy on misbehaving nodes in delay tolerant networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented.

A Bayesian Analysis of Two-Piece Distributions Based on the Scale Mixtures of Normal Family

The current paper seeks to present a Bayesian approach for the estimation of the parameters of the two-piece scale mixtures of normal distributions. This is a rich family of light/heavy-tailed symmetric/asymmetric distributions that includes, as a special case, the heavy-tailed scale mixtures of normal distributions, and is flexible in computations for modeling symmetric and asymmetric data. A Bayesian approach is possible from the specification of hierarchical representations of the proposed family. We illustrate the usefulness of our approach with both real and simulated data.

Regression models with asymmetric data for estimating thyroglobulin levels one year after the ablation of thyroid cancer.

A key biomarker in the study of differentiated thyroid cancer is thyroglobulin. Measurements of the levels of this protein in the blood are determined using laboratory instruments that cannot detect very small concentrations below a threshold, generating left-censored measurements. In the presence of censoring, ordinary least-squares regression models generate biased parameter estimates; therefore, it is necessary to resort to more complex models that consider the censored observations and the behavior of the distribution of the response variable, such as censored and mixed regression models. These techniques were used to model the relationship between thyroglobulin levels in individuals with differentiated thyroid cancer before and after treatment with radioactive iodine (I-131). Log-normal, log-skew-normal, log-power-normal, and log-generalized-gamma probability distributions were used to model the behavior of errors in the adjusted models. Log-generalized-gamma distribution yielded the best results according to the established model selection criteria.

Multimedia Multicast Services in 5G Networks: Subgrouping and Non-Orthogonal Multiple Access Techniques

The expected growth in mobile video demand over broadband cellular networks is one of the key factors driving the wireless industry to develop the fifth generation of network technology. This scenario is fueling the need for group-oriented services (i.e., multicast and broadcast) in order to efficiently manage the radio resources, and consequently grant different groups of users simultaneous access to the same multimedia content with differentiated quality of service. The evolved multimedia broadcast multicast service (eMBMS), standardized by the Third Generation Partnership Project, is one of the technologies likely to be extended to 5G systems with the aim of addressing point-to-multipoint services. In addition, non-orthogonal multiple access (NOMA) techniques are also being considered as a driver to increase the efficient use of the spectrum in multi-user environments with asymmetric data delivery. The present article proposes the joint use of subgrouping multicast techniques and NOMA in an eMBMS-like scenario. Performance is evaluated in envisaged 5G environments, where different quality video services are delivered to a group of users interested in the same contents.

DNF-SC-PNC: a new physical-layer network coding scheme for two-way relay channels with asymmetric data length

To improve the bit error rate (BER) performance of physical-layer network coding (PNC) in data length asymmetric two-way relay channels (TWRC), a new PNC scheme named combined denoise-and-forward and superposition coded physical-layer network coding (DNF-SC-PNC) is proposed, and the decoding algorithm of the scheme is improved. In the new scheme, the mixed information is denoised and superposed at the relay node, which will be broadcasted to the destination nodes. The destination nodes can use successive interference cancellation (SIC) or likelihood rate (LLR) algorithm for decoding. Theoretical analysis and simulation results show that DNF-SC-PNC can provide better BER performance and better throughput rate performance when the data length is asymmetric. Furthermore, we also proved that LLR algorithm can provide better performance than SIC algorithm in data length asymmetric TWRC.

$$\ell _1$$-Norm Based Central Point Analysis for Asymmetric Radial Data

Multivariate asymmetric radial data clouds with irregularly positioned “spokes” and “clutters” are commonly seen in real life applications. In identifying the spoke directions of such data, a key initial step is to locate a central point from which each spoke extends and diverges. In this technical note, we propose a novel method that features a preselection procedure to screen out candidate points that have sufficiently many data points in the vicinity and identifies the central point by solving an $$\ell _1$$ -norm constrained discrete optimization program. Extensive numerical experiments show that the proposed method is capable of providing central points with superior accuracy and robustness compared with other known methods and is computationally efficient for implementation.

Asymmetric MDS with Categorical External Information Based on Radius Model

Asymmetric dissimilarity data can be described for the asymmetric relations between subjects and observed in various domains such as marketing research. In the situation, visualization of such the asymmetric relations is very useful for the researchers. Asymmetric MDS is one of the powerful tool for such the visualization. It is sure that Asymmetric MDS is useful, however, we cannot interpret the estimated low-dimensions unlike PCA. In the practical situation, there are many situations such that both asymmetric dissimilarity data and multivariate data for the same subjects. Therefore, in this paper, we proposed new asymmetric MDS with the multivariate data as an external information. The advantage of the proposed method is that we can interpret the estimated dimensions like PCA by using the information of the multivariate data.

关于非对称数据的ESMD改进算法

关于非对称数据的ESMD改进算法

Deep Learning for Detection of Object-Based Forgery in Advanced Video

Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

On a Backward Heat Conduction Problem Associated with Asymmetric Final Data

In this work, we investigate an abstract parabolic problem associated with the final data, with one specific case of the abstract parabolic problem being considered in polar coordinates. Specially, the present paper gives the first treatment which is not only more general but also more applicable in the case the backward heat problem (BHP) is not symmetric and not axisymmetric in polar coordinates. In general, the above problem is severely ill-posed in the sense of Hadamard. Here, we propose the modified quasi-boundary value (MQBV) method in order to obtain the stability of the regularized solution for the problem. To our knowledge, our results are new and they improve the results of two previous papers (see Cheng and Fu (Inverse Probl. Sci. Eng. 17(8), 1085–1093 2009), (Acta Math. Sinica, English Series 26(11), 2157–2164 2010)) while the authors only considered axisymmetric or radially symmetric data. Finally, a numerical example is given to demonstrate the feasibility and efficiency of our method.

Penrose inequality in anti–de Sitter space

For asymptotically flat spacetimes the Penrose inequality gives an initial data test for the weak cosmic censorship hypothesis. We give a formulation of this inequality for asymptotically anti--de Sitter (AAdS) spacetimes, and show that the inequality holds for time asymmetric data in spherical symmetry. Our analysis is motivated by the constant-negative-spatial-curvature form of the AdS black hole metric.

Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes

This article investigates whether uncompetitive pricing tactics are being employed in the retail petrol market in Australia through examining the effect of a change in daily oil prices on monthly petrol prices. To do so, we incorporate asymmetry into the coefficients of a normalised beta weighting function within an Asymmetric Mixed Data Sampling (AMIDAS) framework. This enables us to examine both the timing, and the lagged marginal effects, of a change in retail petrol prices in response to a change in the oil price. We find evidence of asymmetries in both the timing, and magnitude, of retail petrol prices to a change in the oil price. Specifically, we find that while price falls are slowly and symmetrically passed onto consumers, price increases are more delayed, but higher in intensity over time. Depending on the capital city, when retailers eventually do increase petrol prices, in response to an oil price rise with delay, the price rise is between 2.1 and 3.4 times more than when retailers reduce petrol prices in response to a fall in the price of oil. This finding is consistent with retailers delaying substantial petrol price rises in order to mask the existence of uncompetitive practices.

New copulas based on general partitions-of-unity and their applications to risk management (part II)

Abstract We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature. In particular, we consider negative binomial and Poisson copulas and present a solution to the problem of fitting such copulas to highly asymmetric data in arbitrary dimensions.

A flexible semiparametric regression model for bimodal, asymmetric and censored data

ABSTRACTIn this paper, we propose a new semiparametric heteroscedastic regression model allowing for positive and negative skewness and bimodal shapes using the B-spline basis for nonlinear effects. The proposed distribution is based on the generalized additive models for location, scale and shape framework in order to model any or all parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. We motivate the new model by means of Monte Carlo simulations, thus ignoring the skewness and bimodality of the random errors in semiparametric regression models, which may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. An iterative estimation process and some diagnostic methods are investigated. Applications to two real data sets are presented and the method is compared to the usual regression methods.

How do daily changes in oil prices affect US monthly industrial output?

Detecting asymmetry has become increasingly difficult using single frequency data. This paper goes beyond the prevailing use of aggregate/averaged data in order to provide a more in-depth treatment of the dynamic effects of the price of crude oil on industrial output growth. To do so, we propose an Asymmetric Mixed Data Sampling (AMIDAS) model to examine if there is any concealed evidence of asymmetry arising from daily effects of the price of crude oil on monthly changes in industrial output in the United States (US). We find that this model is able to detect dynamic asymmetric impacts of a high frequency independent variable on a low frequency dependent variable more effectively than when the high frequency variable is aggregated up at the time interval of the low frequency variable. We find that, in comparison with the marginal lagged effects of a rise in the daily price of crude oil, the effects of a fall in the daily price of crude oil are more sluggish as it takes longer for the effects of the oil price drop to die off over time. This finding implies that a fall in the price of crude oil shifts the supply curve rightward less and at a much slower pace than an equivalent price rise shifts it to the left.

Two‐Photon Absorption‐Free Ultrafast Optical Switching in Carbon‐Rich SixC1−x Microring

Ultrafast Kerr effect switching in near‐infrared two‐photon absorption (TPA)‐free and free‐carrier‐absorption (FCA)‐free nonstoichiometric silicon carbide (SixC1−x) microring waveguides is performed and investigated. With pulsed return‐to‐zero on–off‐keying (PRZ‐OOK) stream for the Si‐rich SixC1−x‐based all‐optical switches, the inversely modulated probe reveals an asymmetric bit shape data extinction ratio (ER) of only 8.7 dB owing to the TPA + FCA effect. By eliminating the FCA effects in nearly stoichiometric SixC1−x microring waveguide, the high‐speed wavelength conversion of a data stream with an ER of 14 dB can be observed when coinciding the probe wavelength with the transmission dip. Slightly deviating the probe wavelength from the transmission dip, most probe power still remains in bus waveguide to cause a strong FCA effect to degrade the ER down to 7.4 dB. A symmetrically converted and inverted data stream with high on/off extinction can be observed in the C‐rich SixC1−x‐based microring waveguide with completely suppressed trailing‐edge response. To further approach the upper limitation on bandwidth, the wavelength‐converted and sign‐reversible PRZ‐OOK data switching at a bit rate up to 12 Gbit s−1 can be obtained with similar ER of better than 20 dB, as contributed mainly by the enhanced Kerr nonlinearity in the C‐rich SixC1−x.

Efeito da massagem miofascial sobre a dor e a propriocepção pós-mastectomia radical

Introdução: o câncer de mama, dentre as neoplasias malignas, tem sido o responsável pelos maiores índices de mortalidade no mundo. Diversos problemas podem estar relacionados à cirurgia, como a dor e as alterações na sensibilidade, os quais são responsáveis por uma maior tensão miofascial, que leva à diminuição na amplitude dos movimentos, estando diretamente relacionados à propriocepção. Objetivos: analisar o efeito agudo da abordagem indireta no tecido conjuntivo sobre a dor e a propriocepção de mulheres submetidas à mastectomia radical modificada. Método: a pesquisa é do tipo quase experimental com pré e pós-teste, composta por mulheres com idades entre 40 e 72 anos. Foram realizadas as seguintes avaliações: questionário sociodemográfico; questionário Disabilities of the Arm, Shoulder and Hand; Escala Analógica da dor e cinesiômetro para a avaliação da propriocepção. Após, as mulheres foram submetidas à aplicação da massagem miofascial. Para a análise dos dados, foi realizado o teste de normalidade de Shapiro-Wilk, sendo que nos dados simétricos, foi utilizado o Teste T Pareado, e nos dados assimétricos, foi utilizado o teste Wilcoxon. Resultados: participaram da pesquisa 12 mulheres. Os resultados demonstraram que houve redução nas médias, o qual, estatisticamente, não foram significativos em relação à dor e à propriocepção, pré e pós-abordagem indireta do tecido conjuntivo. Conclusão: concluiu-se que não houve melhora da dor e propriocepção após massagem miofascial em mulheres mastectomizadas.