Local martingale difference approach for service selection with dynamic QoS

AbstractUsers in Service-oriented architecture (SOA) seek the best Quality of service (QoS) by service selection from the candidates responding in succession. In case the QoS changes dynamically, choosing one service and stop the searching is problematic for a service user who makes the choice online. Lack of accurate knowledge of service distribution, the user is unable to make a good decision. The Local Martingale Difference (LMD) approach is developed in this paper to help users to achieve optimal results, in the sense of probability. The stopping time is proved to be bounded to ensure the existence of an optimal solution first. Then, a global estimation over the time horizon is transformed to a local determination based on current martingale difference to make the algorithm feasible. Independent of any predetermined threshold or manual intervention, LMD enables users to stop around the optimal time, based on the information collected during the stochastic process. Verified to be efficient by comparison with three traditional methods, LMD is adaptable in vast applications with dynamic QoS

Adherence to medication interventions: Following attending physicians or online support?

Medication interventions are clinical interventions that delay or prevent the recurrence. In this research, we built upon the social network theory (SNT) to examine how trust in attending physicians and different sources of online social support would affect patients\u27 adherence to medication interventions. We conducted a mixed-method approach for different types of target populations. An online survey involving 311 patients with recent hospitalization experience was conducted, and the results confirmed that accessing support from online professionals made patients deviate from the medication interventions. Besides, patients’ trust in ability of their attending physicians would promote the adherence behaviors. Considering more senior patients, we conducted ten in-depth interviews to obtain further insight into patients\u27 dilemmas and challenges in integrating eHealth platforms into their treatment. This research contributes to the existing literature by revealing the potential problems in eHealth platform development and operation, in integrating the eHealth platform with physical healthcare systems

Numerical approximations for a three-component Cahn–Hilliard phase-field model based on the invariant energy quadratization method

How to develop efficient numerical schemes while preserving the energy stability at the discrete level is a challenging issue for the three component Cahn-Hilliard phase-field model. In this paper, we develop first and second order temporal approximation schemes based on the "Invariant Energy Quadratization" approach, where all nonlinear terms are treated semi-explicitly. Consequently, the resulting numerical schemes lead to a well-posed linear system with the symmetric positive definite operator to be solved at each time step. We rigorously prove that the proposed schemes are unconditionally energy stable. Various 2D and 3D numerical simulations are presented to demonstrate the stability and the accuracy of the schemes

Generation of Intense High-Order Vortex Harmonics

This paper presents the method for the first time to generate intense high-order optical vortices that carry orbital angular momentum in the extreme ultraviolet region. In three-dimensional particle-in-cell simulation, both the reflected and transmitted light beams include high-order harmonics of the Laguerre-Gaussian (LG) mode when a linearly polarized LG laser pulse impinges on a solid foil. The mode of the generated LG harmonic scales with its order, in good agreement with our theoretical analysis. The intensity of the generated high-order vortex harmonics is close to the relativistic region, and the pulse duration can be in attosecond scale. The obtained intense vortex beam possesses the combined properties of fine transversal structure due to the high-order mode and the fine longitudinal structure due to the short wavelength of the high-order harmonics. Thus, the obtained intense vortex beam may have extraordinarily promising applications for high-capacity quantum information and for high-resolution detection in both spatial and temporal scales because of the addition of a new degree of freedom

Crustal and mantle velocity models of southern Tibet from finite frequency tomography

Using traveltimes of teleseismic body waves recorded by several temporary local seismic arrays, we carried out finite‐frequency tomographic inversions to image the three‐dimensional velocity structure beneath southern Tibet to examine the roles of the upper mantle in the formation of the Tibetan Plateau. The results reveal a region of relatively high P and S wave velocity anomalies extending from the uppermost mantle to at least 200 km depth beneath the Higher Himalaya. We interpret this high‐velocity anomaly as the underthrusting Indian mantle lithosphere. There is a strong low P and S wave velocity anomaly that extends from the lower crust to at least 200 km depth beneath the Yadong‐Gulu rift, suggesting that rifting in southern Tibet is probably a process that involves the entire lithosphere. Intermediate‐depth earthquakes in southern Tibet are located at the top of an anomalous feature in the mantle with a low Vp, a high Vs, and a low Vp/Vs ratio. One possible explanation for this unusual velocity anomaly is the ongoing granulite‐eclogite transformation. Together with the compressional stress from the collision, eclogitization and the associated negative buoyancy force offer a plausible mechanism that causes the subduction of the Indian mantle lithosphere beneath the Higher Himalaya. Our tomographic model and the observation of north‐dipping lineations in the upper mantle suggest that the Indian mantle lithosphere has been broken laterally in the direction perpendicular to the convergence beneath the north‐south trending rifts and subducted in a progressive, piecewise and subparallel fashion with the current one beneath the Higher Himalaya

Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer’s Disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP), which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multi-class classification in AD diagnosis. Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of the proposed method over other state-of-the-art methods

Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis

Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer’s disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods

Linoleic acid suppresses colorectal cancer cell growth by inducing oxidant stress and mitochondrial dysfunction

Some polyunsaturated fatty acids (PUFAs), if not all, have been shown to have tumoricidal action, but their exact mechanism(s) of action is not clear. In the present study, we observed that n-6 PUFA linoleic acid (LA) inhibited tumor cell growth at high concentrations (above 300 μM); while low concentrations (100-200 μM) promoted proliferation. Analysis of cell mitochondrial membrane potential, reactive oxygen species (ROS) formation, malondialdehyde (MDA) accumulation and superoxide dismutase (SOD) activity suggested that anti-cancer action of LA is due to enhanced ROS generation and decreased cell anti-oxidant capacity that resulted in mitochondrial damage. Of the three cell lines tested, semi-differentiated colorectal cancer cells RKO were most sensitive to the cytotoxic action of LA, followed by undifferentiated colorectal cancer cell line (LOVO) while the normal human umbilical vein endothelial cells (HUVEC) were the most resistant (the degree of sensitivity to LA is as follows: RKO > LOVO > HUVEC). LA induced cell death was primed by mitochondrial apoptotic pathway. Pre-incubation of cancer cells with 100 μM LA for 24 hr enhanced sensitivity of differentiated and semi-differentiated cells to the subsequent exposure to LA. The relative resistance of LOVO cells to the cytotoxic action of LA is due to a reduction in the activation of caspase-3. Thus, LA induced cancer cell apoptosis by enhancing cellular oxidant status and inducing mitochondrial dysfunction