253 research outputs found
Two Piggybacking Codes with Flexible Sub-Packetization to Achieve Lower Repair Bandwidth
As a special class of array codes, piggybacking codes are MDS codes
(i.e., any out of nodes can retrieve all data symbols) that can achieve
low repair bandwidth for single-node failure with low sub-packetization . 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 with , where . We show that our first piggybacking codes have
lower repair bandwidth for any single-node failure than the existing
piggybacking codes when , and .
Moreover, we propose second piggybacking codes such that the sub-packetization
is a multiple of the number of parity nodes (i.e., ), 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
and
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
pairing state, a three dimensional analogy of the 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 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
IL-1β promotes ADAMTS enzyme-mediated aggrecan degradation through NF-κB in human intervertebral disc
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
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
In recent years, the explosion of web videos makes text-video retrieval
increasingly essential and popular for video filtering, recommendation, and
search. Text-video retrieval aims to rank relevant text/video higher than
irrelevant ones. The core of this task is to precisely measure the cross-modal
similarity between texts and videos. Recently, contrastive learning methods
have shown promising results for text-video retrieval, most of which focus on
the construction of positive and negative pairs to learn text and video
representations. Nevertheless, they do not pay enough attention to hard
negative pairs and lack the ability to model different levels of semantic
similarity. To address these two issues, this paper improves contrastive
learning using two novel techniques. First, to exploit hard examples for robust
discriminative power, we propose a novel Dual-Modal Attention-Enhanced Module
(DMAE) to mine hard negative pairs from textual and visual clues. By further
introducing a Negative-aware InfoNCE (NegNCE) loss, we are able to adaptively
identify all these hard negatives and explicitly highlight their impacts in the
training loss. Second, our work argues that triplet samples can better model
fine-grained semantic similarity compared to pairwise samples. We thereby
present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to
construct partial order triplet samples by automatically generating
fine-grained hard negatives for matched text-video pairs. The proposed TPM-CL
designs an adaptive token masking strategy with cross-modal interaction to
model subtle semantic differences. Extensive experiments demonstrate that the
proposed approach outperforms existing methods on four widely-used text-video
retrieval datasets, including MSR-VTT, MSVD, DiDeMo and ActivityNet.Comment: Accepted by ACM MM 202
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