Determination of f0−σf_0-\sigma mixing angle through Bs0→J/Ψ f0(980)(σ)B_s^0 \to J/\Psi~f_0(980)(\sigma) decays

We study $B_s^0 \to J/\psi f_0(980)$ decays, the quark content of $f_0(980)$ and the mixing angle of $f_0(980)$ and $\sigma(600)$. We calculate not only the factorizable contribution in QCD facorization scheme but also the nonfactorizable hard spectator corrections in QCDF and pQCD approach. We get consistent result with the experimental data of $B_s^0 \to J/\psi f_0(980)$ and predict the branching ratio of $B_s^0 \to J/\psi \sigma$. We suggest two ways to determine $f_0-\sigma$ mixing angle $\theta$. Using the experimental measured branching ratio of $B_s^0 \to J/\psi f_0(980)$, we can get the $f_0-\sigma$ mixing angle $\theta$ with some theoretical uncertainties. We suggest another way to determine $f_0-\sigma$ mixing angle $\theta$ using both of experimental measured decay branching ratios $B_s^0 \to J/\psi f_0(980) (\sigma)$ to avoid theoretical uncertainties.Comment: arXiv admin note: substantial text overlap with arXiv:0707.263

Designing the Right IT Services for the Bottom of the Pyramid

This article introduces ways to create proper IT services which can help organizations serve the emerging rural markets. The research is based on a two-year action research project which derives a framework for building IT services for the low-income consumers in the rural area of China. The framework is proposed to understand the characteristics that the consumers in the rural area use the IT services, especially in China. The framework helps IT companies and governmental agencies design and improve the IT services they provided to the emerging market

Online Tensor Learning: Computational and Statistical Trade-offs, Adaptivity and Optimal Regret

We investigate a generalized framework for estimating latent low-rank tensors in an online setting, encompassing both linear and generalized linear models. This framework offers a flexible approach for handling continuous or categorical variables. Additionally, we investigate two specific applications: online tensor completion and online binary tensor learning. To address these challenges, we propose the online Riemannian gradient descent algorithm, which demonstrates linear convergence and the ability to recover the low-rank component under appropriate conditions in all applications. Furthermore, we establish a precise entry-wise error bound for online tensor completion. Notably, our work represents the first attempt to incorporate noise in the online low-rank tensor recovery task. Intriguingly, we observe a surprising trade-off between computational and statistical aspects in the presence of noise. Increasing the step size accelerates convergence but leads to higher statistical error, whereas a smaller step size yields a statistically optimal estimator at the expense of slower convergence. Moreover, we conduct regret analysis for online tensor regression. Under the fixed step size regime, a fascinating trilemma concerning the convergence rate, statistical error rate, and regret is observed. With an optimal choice of step size we achieve an optimal regret of $O(\sqrt{T})$. Furthermore, we extend our analysis to the adaptive setting where the horizon T is unknown. In this case, we demonstrate that by employing different step sizes, we can attain a statistically optimal error rate along with a regret of $O(\log T)$. To validate our theoretical claims, we provide numerical results that corroborate our findings and support our assertions

MiR-1/133 attenuates cardiomyocyte apoptosis and electrical remodeling in mice with viral myocarditis

Background: The role of miR-1 and miR-133 in regulating the expression of potassium and calcium ion channels, and mediating cardiomyocyte apoptosis in mice with viral myocarditis (VMC) is investigated herein. Methods: Male Balb/c mice were randomly divided into groups: control group, VMC group, VMC + miR-1/133 mimics group, or VMC + miR-1/133 negative control (NC) group. VMC was induced with coxsackievirus B3 (CVB3). MiR-1/133 mimics ameliorated cardiac dysfunction in VMC mice and was compared to the VMC+NC group. Results: Hematoxylin and eosin staining showed a well-arranged myocardium without inflammatory cell infiltration in the myocardial matrix of the control group. However, in the VMC and VMC+NC groups, the myocardium was disorganized and swollen with necrosis, and the myocardial matrix was infiltrated with inflammatory cells. These changes were alleviated by miR-1/133 mimics. TUNEL staining revealed decreased cardiomyocyte apoptosis in the VMC + miR-1/133 mimics group compared with the VMC group. In addition, miR-1/133 mimics up-regulated the expression of miR-1 and miR-133, the potassium channel genes Kcnd2 and Kcnj2, as well as Bcl-2, and down-regulated the expression of the potassium channel suppressor gene Irx5, L-type calcium channel subunit gene a1c (Cacna1c), Bax, and caspase-9 in the myocardium of VMC mice. MiR-1/133 also up-regulated the protein levels of Kv4.2 and Kir2.1, and down-regulated the expression of CaV1.2 in the myocardium of VMC mice. Conclusions: MiR-1 and miR-133 decreased cardiomyocyte apoptosis by mediating the expression of apoptosis-related genes in the hearts of VMC mice

Scale-based surface understanding using diffusion smoothing

The research discussed in this thesis is concerned with surface understanding from the viewpoint of recognition-oriented, scale-related processing based on surface curvatures and diffusion smoothing. Four problems below high level visual processing are investigated: 1) 3-dimensional data smoothing using a diffusion process; 2) Behaviour of shape features across multiple scales, 3) Surface segmentation over multiple scales; and 4) Symbolic description of surface features at multiple scales. In this thesis, the noisy data smoothing problem is treated mathematically as a boundary value problem of the diffusion equation instead of the well-known Gaussian convolution, In such a way, it provides a theoretical basis to uniformly interpret the interrelationships amongst diffusion smoothing, Gaussian smoothing, repeated averaging and spline smoothing. It also leads to solving the problem with a numerical scheme of unconditional stability, which efficiently reduces the computational complexity and preserves the signs of curvatures along the surface boundaries. Surface shapes are classified into eight types using the combinations of the signs of the Gaussian curvature K and mean curvature H, both of which change at different scale levels. Behaviour of surface shape features over multiple scale levels is discussed in terms of the stability of large shape features, the creation, remaining and fading of small shape features, the interaction between large and small features and the structure of behaviour of the nested shape features in the KH sign image. It provides a guidance for tracking the movement of shape features from fine to large scales and for setting up a surface shape description accordingly. A smoothed surface is partitioned into a set of regions based on curvature sign homogeneity. Surface segmentation is posed as a problem of approximating a surface up to the degree of Gaussian and mean curvature signs using the depth data alone How to obtain feasible solutions of this under-determined problem is discussed, which includes the surface curvature sign preservation, the reason that a sculptured surface can be segmented with the KH sign image alone and the selection of basis functions of surface fitting for obtaining the KH sign image or for region growing. A symbolic description of the segmented surface is set up at each scale level. It is composed of a dual graph and a geometrical property list for the segmented surface. The graph describes the adjacency and connectivity among different patches as the topological-invariant properties that allow some object's flexibility, whilst the geometrical property list is added to the graph as constraints that reduce uncertainty. With this organisation, a tower-like surface representation is obtained by tracking the movement of significant features of the segmented surface through different scale levels, from which a stable description can be extracted for inexact matching during object recognition