D2^2: Decentralized Training over Decentralized Data

While training a machine learning model using multiple workers, each of which collects data from their own data sources, it would be most useful when the data collected from different workers can be {\em unique} and {\em different}. Ironically, recent analysis of decentralized parallel stochastic gradient descent (D-PSGD) relies on the assumption that the data hosted on different workers are {\em not too different}. In this paper, we ask the question: {\em Can we design a decentralized parallel stochastic gradient descent algorithm that is less sensitive to the data variance across workers?} In this paper, we present D$^2$, a novel decentralized parallel stochastic gradient descent algorithm designed for large data variance \xr{among workers} (imprecisely, "decentralized" data). The core of D$^2$ is a variance blackuction extension of the standard D-PSGD algorithm, which improves the convergence rate from $O\left({\sigma \over \sqrt{nT}} + {(n\zeta^2)^{\frac{1}{3}} \over T^{2/3}}\right)$ to $O\left({\sigma \over \sqrt{nT}}\right)$ where $\zeta^{2}$ denotes the variance among data on different workers. As a result, D$^2$ is robust to data variance among workers. We empirically evaluated D$^2$ on image classification tasks where each worker has access to only the data of a limited set of labels, and find that D$^2$ significantly outperforms D-PSGD

Non-perturbative Dynamical Decoupling Control: A Spin Chain Model

This paper considers a spin chain model by numerically solving the exact model to explore the non-perturbative dynamical decoupling regime, where an important issue arises recently (J. Jing, L.-A. Wu, J. Q. You and T. Yu, arXiv:1202.5056.). Our study has revealed a few universal features of non-perturbative dynamical control irrespective of the types of environments and system-environment couplings. We have shown that, for the spin chain model, there is a threshold and a large pulse parameter region where the effective dynamical control can be implemented, in contrast to the perturbative decoupling schemes where the permissible parameters are represented by a point or converge to a very small subset in the large parameter region admitted by our non-perturbative approach. An important implication of the non-perturbative approach is its flexibility in implementing the dynamical control scheme in a experimental setup. Our findings have exhibited several interesting features of the non-perturbative regimes such as the chain-size independence, pulse strength upper-bound, noncontinuous valid parameter regions, etc. Furthermore, we find that our non-perturbative scheme is robust against randomness in model fabrication and time-dependent random noise

Understanding the Barrier of Elder’s Adoption of B2C Online Commerce: Comparison Between Different Product Types and Ages

With the coming of aging society, the market for elderly people will become more and more important. The elder will become an important potential market for business. Therefore, understand elder’s adoption of online commerce is becoming a critical issue in MIS field. The purpose of this study is attempted to understand the barrier of elder’s adoption of B2C online commerce. Additionally, we compared the results across different product types and ages. Survey study was employed in this study. The main result discovered that the barriers affecting user adoption of B2C online commerce would differ with the variation of age range and product type. For the elder, risk barrier and image barrier dominate the decisions for tangible products, while value barrier and risk barrier dominate for the intangible products. This research result can become a practical reference. For both academic and business, this study has a certain contribution in the development of electronic commerce