Two Piggybacking Codes with Flexible Sub-Packetization to Achieve Lower Repair Bandwidth

As a special class of array codes, $(n,k,m)$ piggybacking codes are MDS codes (i.e., any $k$ out of $n$ nodes can retrieve all data symbols) that can achieve low repair bandwidth for single-node failure with low sub-packetization $m$. In this paper, we propose two new piggybacking codes that have lower repair bandwidth than the existing piggybacking codes given the same parameters. Our first piggybacking codes can support flexible sub-packetization $m$ with $2\leq m\leq n-k$, where $n - k > 3$. We show that our first piggybacking codes have lower repair bandwidth for any single-node failure than the existing piggybacking codes when $n - k = 8,9$, $m = 6$ and $30\leq k \leq 100$. Moreover, we propose second piggybacking codes such that the sub-packetization is a multiple of the number of parity nodes (i.e., $(n-k)|m$), by jointly designing the piggyback function for data node repair and transformation function for parity node repair. We show that the proposed second piggybacking codes have lowest repair bandwidth for any single-node failure among all the existing piggybacking codes for the evaluated parameters $k/n = 0.75, 0.8, 0.9$ and $n-k\geq 4$

Pairing Symmetries of Unconventional High Temperature Superconductivity in a Zinc-Blende Structure

We classify the pairing symmetries of three-dimensional superconductivity in the zinc-blende structure which can support an electronic environment to host unconventional high temperature superconductivity, and calculate the pairing symmetry in the presence of strong electron-electron correlation by the slave boson mean-field approach. We find that the $d_{2z^2-x^2-y^2} \pm id_{x^2-y^2}$ pairing state, a three dimensional analogy of the $d\pm id$ pairing in a two dimensional square lattice, is ubiquitously favored near half filling upon hole doping in both single-orbital and three-orbital models. However, unlike the two dimensional counterpart, the Bogoliubov quasiparticle spectrum of the three dimensional state upholds the full $T_d$ point group symmetry and encompasses point nodes along certain high symmetric lines.Comment: 9pages,6 figures and 7 tabulation

Generalized Simple Regenerating Codes: Trading Sub-packetization and Fault Tolerance

Maximum distance separable (MDS) codes have the optimal trade-off between storage efficiency and fault tolerance, which are widely used in distributed storage systems. As typical non-MDS codes, simple regenerating codes (SRCs) can achieve both smaller repair bandwidth and smaller repair locality than traditional MDS codes in repairing single-node erasure. In this paper, we propose {\em generalized simple regenerating codes} (GSRCs) that can support much more parameters than that of SRCs. We show that there is a trade-off between sub-packetization and fault tolerance in our GSRCs, and SRCs achieve a special point of the trade-off of GSRCs. We show that the fault tolerance of our GSRCs increases when the sub-packetization increases linearly. We also show that our GSRCs can locally repair any singe-symbol erasure and any single-node erasure, and the repair bandwidth of our GSRCs is smaller than that of the existing related codes

Characterization the coding and non-coding RNA components in the transcriptome of invasion weed Alternanthera philoxeroides

Alternanthera philoxeroides is a notorious invasive weed worldwide, but it still lacks a genome information currently. In this study, we collected 4 groups of A. philoxeroides Illumina RNA-seq data (62.5 Gb) and performed a comprehensive de novo assembling. Totally, 421,372 unigenes were obtained with a total length of 230,842,460 bp, with 43,430 (10.31%) unigenes longer than 1000 bp. Then 119,222 (28.3%) unigenes were functional annotated and 235,885 (56.0%) were grouped into reliable lncRNAs reservoir. Besides, 534 tRNA and 234 rRNAs were identified in assembly sequences. Additionally, 131,624 microsatellites were characterized in 106,761 sequences. Then SSR primers were developed for the amplification of 40,752 microsatellites in 36,329 sequences. The miRNAs are key post-transcriptional regulators, about 86 candidate miRNA sequences were detected from A. philoxeroides assembly, and miRNA target genes prediction revealed possible functions of them in growth and development as well as stress responding processes. These results provide a vital basis for sequence-based studies of A. philoxeroides in the future, especially gene function analysis

Constructing quantum dots@flake g-C3N4 isotype heterojunctions for enhanced visible-light-driven NADH regeneration and enzymatic hydrogenation

The authors thank the financial support from National Natural Science Funds of China (21406163, 91534126, 21621004), Tianjin Research Program of Application Foundation and Advanced Technology (15JCQNJC10000), Open Funding Project of the National Key Laboratory of Biochemical Engineering (2015KF-03), and the Program of Introducing Talents of Discipline to Universities (B06006). X.W. also acknowledges financial support from The Carnegie Trust for the Universities of Scotland (70265) and The Royal Society (RG150001 and IE150611).Peer reviewedPostprin

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization