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Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security and privacy guarantees. It also seeks to virtualize the Internet as many meta computers. In meta computing, tasks can be assigned to containers at edge nodes for processing, based on container images with multiple layers. The dynamic and resource-constrained nature of meta computing environments requires an optimal container migration strategy for mobile users to minimize latency. However, the problem of container migration in meta computing has not been thoroughly explored. To address this gap, we present low-latency, layer-aware container migration strategies that consider both proactive and passive migration. Specifically: 1) We formulate the container migration problem in meta computing, taking into account layer dependencies to reduce migration costs and overall task duration by considering four delays. 2) We introduce a reinforcement learning algorithm based on policy gradients to minimize total latency by identifying layer dependencies for action selection, making decisions for both proactive and passive migration. Expert demonstrations are introduced to enhance exploitation. 3) Experiments using real data trajectories show that the algorithm outperforms baseline algorithms, achieving lower total latency., Comment: to be published in IEEE ICMC 2024

Zn metal battery has been considered a promising alternative energy storage technology in renewable energy storage and grid storage. It is well-known that the surface orientation of a Zn metal anode is vital to the reversibility of a Zn metal battery. Herein, the (101)-oriented thin Zn metal anode (down to 2 {\mu}m) is electrodeposited on a Cu surface by adding dimethyl sulfoxide (DMSO) electrolyte additive in ZnSO4 aqueous solution. Scanning electron microscope (SEM) observation indicates the formation of flat terrace-like compact (101)-oriented surfaces. Insitu optical observation confirms that the (101)-oriented surfaces can be reversibly plated and stripped. DFT calculations reveal two mechanisms for the nucleation and growth of the Zn-(101) surface: (1) formation of Zn(101)//Cu(001) could lower the interface energy as compared to Zn(002)//Cu(001); (2) large reconstruction of the Zn (101) surface with DMSO and H2O absorption. Raman, XPS, and ToF-SIMS characterizations indicate that adding DMSO in ZnCl2 could facilitate the formation of ZnO-based SEI on Zn metal surface, while OH- and S-based SEI can be obtained with DMSO in ZnSO4. The electrochemical testings are performed, which demonstrates a higher cyclability for the (101)-oriented Zn in the half cell as well as a lower charge transfer barrier with respect to the (002)-dominated surface of the same electrode thickness. Zn||V2O5 full cells are further assembled, showing better capacity retention for the (101)-Zn as compared to the (002)-Zn with the same thickness (5 {\mu}m, 3 {\mu}m, and 2 {\mu}m). We hope this study to spur further interest in the control of Zn metal surface crystallographic orientation towards ultrathin Zn metal anodes., Comment: 21 Pages 6 Figures

We consider the setup of nonparametric {\em blind regression} for estimating the entries of a large $m \times n$ matrix, when provided with a small, random fraction of noisy measurements. We assume that all rows $u \in [m]$ and columns $i \in [n]$ of the matrix are associated to latent features $x_{\text{row}}(u)$ and $x_{\text{col}}(i)$ respectively, and the $(u,i)$-th entry of the matrix, $A(u, i)$ is equal to $f(x_{\text{row}}(u), x_{\text{col}}(i))$ for a latent function $f$. Given noisy observations of a small, random subset of the matrix entries, our goal is to estimate the unobserved entries of the matrix as well as to "de-noise" the observed entries. As the main result of this work, we introduce a nearest-neighbor-based estimation algorithm, and establish its consistency when the underlying latent function $f$ is Lipschitz, the underlying latent space is a bounded diameter Polish space, and the random fraction of observed entries in the matrix is at least $\max \left( m^{-1 + \delta}, n^{-1/2 + \delta} \right)$, for any $\delta > 0$. As an important byproduct, our analysis sheds light into the performance of the classical collaborative filtering algorithm for matrix completion, which has been widely utilized in practice. Experiments with the MovieLens and Netflix datasets suggest that our algorithm provides a principled improvement over basic collaborative filtering and is competitive with matrix factorization methods. Our algorithm has a natural extension to the setting of tensor completion via flattening the tensor to matrix. When applied to the setting of image in-painting, which is a $3$-order tensor, we find that our approach is competitive with respect to state-of-art tensor completion algorithms across benchmark images., Comment: 27 pages, 3 figures. To appear in IEEE Transactions on Information Theory