2,178 research outputs found
Quantum Phase Liquids-Fermionic Superfluid without Phase Coherence
We investigate the two dimensional generalized attractive Hubbard model in a
bipartite lattice, and and a "quantum phase liquid" phase, in which the
fermions are paired but don't have phase coherence at zero temperature, in
analogy to quantum spin liquid phase. Then, two types of topological quantum
phase liquids with a small external magnetic field-Z2 quantum phase liquids and
chiral quantum phase liquids-are discussed.Comment: 7 pages, 2 figure
Word Embedding based Correlation Model for Question/Answer Matching
With the development of community based question answering (Q&A) services, a
large scale of Q&A archives have been accumulated and are an important
information and knowledge resource on the web. Question and answer matching has
been attached much importance to for its ability to reuse knowledge stored in
these systems: it can be useful in enhancing user experience with recurrent
questions. In this paper, we try to improve the matching accuracy by overcoming
the lexical gap between question and answer pairs. A Word Embedding based
Correlation (WEC) model is proposed by integrating advantages of both the
translation model and word embedding, given a random pair of words, WEC can
score their co-occurrence probability in Q&A pairs and it can also leverage the
continuity and smoothness of continuous space word representation to deal with
new pairs of words that are rare in the training parallel text. An experimental
study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new
method's promising potential.Comment: 8 pages, 2 figure
Encoder-Decoder-Based Intra-Frame Block Partitioning Decision
The recursive intra-frame block partitioning decision process, a crucial
component of the next-generation video coding standards, exerts significant
influence over the encoding time. In this paper, we propose an encoder-decoder
neural network (NN) to accelerate this process. Specifically, a CNN is utilized
to compress the pixel data of the largest coding unit (LCU) into a fixed-length
vector. Subsequently, a Transformer decoder is employed to transcribe the
fixed-length vector into a variable-length vector, which represents the block
partitioning outcomes of the encoding LCU. The vector transcription process
adheres to the constraints imposed by the block partitioning algorithm. By
fully parallelizing the NN prediction in the intra-mode decision, substantial
time savings can be attained during the decision phase. The experimental
results obtained from high-definition (HD) sequences coding demonstrate that
this framework achieves a remarkable 87.84\% reduction in encoding time, with a
relatively small loss (8.09\%) of coding performance compared to AVS3 HPM4.0
Using LC-MS/MS-based targeted proteomics to monitor the pattern of ABC transporters expression in the development of drug resistance
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