A Class of MSR Codes for Clustered Distributed Storage

Clustered distributed storage models real data centers where intra- and cross-cluster repair bandwidths are different. In this paper, exact-repair minimum-storage-regenerating (MSR) codes achieving capacity of clustered distributed storage are designed. Focus is given on two cases: $\epsilon=0$ and $\epsilon=1/(n-k)$, where $\epsilon$ is the ratio of the available cross- and intra-cluster repair bandwidths, $n$ is the total number of distributed nodes and $k$ is the number of contact nodes in data retrieval. The former represents the scenario where cross-cluster communication is not allowed, while the latter corresponds to the case of minimum cross-cluster bandwidth that is possible under the minimum storage overhead constraint. For the $\epsilon=0$ case, two types of locally repairable codes are proven to achieve the MSR point. As for $\epsilon=1/(n-k)$, an explicit MSR coding scheme is suggested for the two-cluster situation under the specific condition of $n = 2k$.Comment: 9 pages, a part of this paper is submitted to IEEE ISIT201

Hierarchical Coding for Distributed Computing

Coding for distributed computing supports low-latency computation by relieving the burden of straggling workers. While most existing works assume a simple master-worker model, we consider a hierarchical computational structure consisting of groups of workers, motivated by the need to reflect the architectures of real-world distributed computing systems. In this work, we propose a hierarchical coding scheme for this model, as well as analyze its decoding cost and expected computation time. Specifically, we first provide upper and lower bounds on the expected computing time of the proposed scheme. We also show that our scheme enables efficient parallel decoding, thus reducing decoding costs by orders of magnitude over non-hierarchical schemes. When considering both decoding cost and computing time, the proposed hierarchical coding is shown to outperform existing schemes in many practical scenarios.Comment: 7 pages, part of the paper is submitted to ISIT201

Interferon-inducible protein SCOTIN interferes with HCV replication through the autolysosomal degradation of NS5A

Hepatitis C virus (HCV) utilizes autophagy to promote its propagation. Here we show the autophagy-mediated suppression of HCV replication via the endoplasmic reticulum (ER) protein SCOTIN. SCOTIN overexpression inhibits HCV replication and infectious virion production in cells infected with cell culture-derived HCV. HCV nonstructural 5A (NS5A) protein, which is a critical factor for HCV RNA replication, interacts with the IFN-beta-inducible protein SCOTIN, which transports NS5A to autophagosomes for degradation. Furthermore, the suppressive effect of SCOTIN on HCV replication is impaired in both ATG7-silenced cells and cells treated with autophagy or lysosomal inhibitors. SCOTIN does not affect the overall flow of autophagy; however, it is a substrate for autophagic degradation. The physical association between the transmembrane/proline-rich domain (TMPRD) of SCOTIN and Domain-II of NS5A is essential for autophagosomal trafficking and NS5A degradation. Altogether, our findings suggest that IFN-beta-induced SCOTIN recruits the HCV NS5A protein to autophagosomes for degradation, thereby restricting HCV replication.1110Ysciescopu

Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment

Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. Recent findings have shown that the accuracy and robustness of unsupervised word translation (UWT) can be improved by making use of visual observations, which are universal representations across languages. In this work, we investigate the potential of using not only visual observations but also pretrained language-image models for enabling a more efficient and robust UWT. Specifically, we develop a novel UWT method dubbed Word Alignment using Language-Image Pretraining (WALIP), which leverages visual observations via the shared embedding space of images and texts provided by CLIP models (Radford et al., 2021). WALIP has a two-step procedure. First, we retrieve word pairs with high confidences of similarity, computed using our proposed image-based fingerprints, which define the initial pivot for the word alignment. Second, we apply our robust Procrustes algorithm to estimate the linear mapping between two embedding spaces, which iteratively corrects and refines the estimated alignment. Our extensive experiments show that WALIP improves upon the state-of-the-art performance of bilingual word alignment for a few language pairs across different word embeddings and displays great robustness to the dissimilarity of language pairs or training corpora for two word embeddings.Comment: In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings