6,857 research outputs found
Revisit of directed flow in relativistic heavy-ion collisions from a multiphase transport model
We have revisited several interesting questions on how the rapidity-odd
directed flow is developed in relativistic Au+Au collisions at
= 200 and 39 GeV based on a multiphase transport model. As the
partonic phase evolves with time, the slope of the parton directed flow at
midrapidity region changes from negative to positive as a result of the later
dynamics at 200 GeV, while it remains negative at 39 GeV due to the shorter
life time of the partonic phase. The directed flow splitting for various quark
species due to their different initial eccentricities is observed at 39 GeV,
while the splitting is very small at 200 GeV. From a dynamical coalescence
algorithm with Wigner functions, we found that the directed flow of hadrons is
a result of competition between the coalescence in momentum and coordinate
space as well as further modifications by the hadronic rescatterings.Comment: 8 pages, 8 figures, version after major revisio
Boundary feedback stabilization of the undamped Timoshenko beam with both ends free
AbstractIn this paper, we study a boundary feedback system of a class of nonuniform undamped Timoshenko beam with both ends free. We give some sufficient conditions and some necessary conditions for the system to have exponential stability. Our method is based on the operator semigroup technique, the multiplier technique, and the contradiction argument of the frequency domain method
Building quantum neural networks based on swap test
Artificial neural network, consisting of many neurons in different layers, is
an important method to simulate humain brain. Usually, one neuron has two
operations: one is linear, the other is nonlinear. The linear operation is
inner product and the nonlinear operation is represented by an activation
function. In this work, we introduce a kind of quantum neuron whose inputs and
outputs are quantum states. The inner product and activation operator of the
quantum neurons can be realized by quantum circuits. Based on the quantum
neuron, we propose a model of quantum neural network in which the weights
between neurons are all quantum states. We also construct a quantum circuit to
realize this quantum neural network model. A learning algorithm is proposed
meanwhile. We show the validity of learning algorithm theoretically and
demonstrate the potential of the quantum neural network numerically.Comment: 10 pages, 13 figure
Concatenation of the Gottesman-Kitaev-Preskill code with the XZZX surface code
Bosonic codes provide an alternative option for quantum error correction. An
important category of bosonic codes called the Gottesman-Kitaev-Preskill (GKP)
code has aroused much interest recently. Theoretically, the error correction
ability of GKP code is limited since it can only correct small shift errors in
position and momentum quadratures. A natural approach to promote the GKP error
correction for large-scale, fault-tolerant quantum computation is concatenating
encoded GKP states with a stabilizer code. The performance of the XZZX
surface-GKP code, i.e., the single-mode GKP code concatenated with the XZZX
surface code is investigated in this paper under two different noise models.
Firstly, in the code-capacity noise model, the asymmetric rectangular GKP code
with parameter is introduced. Using the minimum weight perfect
matching decoder combined with the continuous-variable GKP information, the
optimal threshold of the XZZX-surface GKP code reaches when
, compared with the threshold of the standard
surface-GKP code. Secondly, we analyze the shift errors of two-qubit gates in
the actual implementation and build the full circuit-level noise model. By
setting the appropriate bias parameters, the logical error rate is reduced by
several times in some cases. These results indicate the XZZX surface-GKP codes
are more suitable for asymmetric concatenation under the general noise models.
We also estimate the overhead of the XZZX-surface GKP code which uses about 291
GKP states with the noise parameter 18.5 dB () to
encode a logical qubit with the error rate , compared with
the qubit-based surface code using 3041 qubits to achieve almost the same
logical error rate.Comment: 17 pages, 10 figure
ZCURVE_V: a new self-training system for recognizing protein-coding genes in viral and phage genomes
BACKGROUND: It necessary to use highly accurate and statistics-based systems for viral and phage genome annotations. The GeneMark systems for gene-finding in virus and phage genomes suffer from some basic drawbacks. This paper puts forward an alternative approach for viral and phage gene-finding to improve the quality of annotations, particularly for newly sequenced genomes. RESULTS: The new system ZCURVE_V has been run for 979 viral and 212 phage genomes, respectively, and satisfactory results are obtained. To have a fair comparison with the currently available software of similar function, GeneMark, a total of 30 viral genomes that have not been annotated by GeneMark are selected to be tested. Consequently, the average specificity of both systems is well matched, however the average sensitivity of ZCURVE_V for smaller viral genomes (< 100 kb), which constitute the main parts of viral genomes sequenced so far, is higher than that of GeneMark. Additionally, for the genome of Amsacta moorei entomopoxvirus, probably with the lowest genomic GC content among the sequenced organisms, the accuracy of ZCURVE_V is much better than that of GeneMark, because the later predicts hundreds of false-positive genes. ZCURVE_V is also used to analyze well-studied genomes, such as HIV-1, HBV and SARS-CoV. Accordingly, the performance of ZCURVE_V is generally better than that of GeneMark. Finally, ZCURVE_V may be downloaded and run locally, particularly facilitating its utilization, whereas GeneMark is not downloadable. Based on the above comparison, it is suggested that ZCURVE_V may serve as a preferred gene-finding tool for viral and phage genomes newly sequenced. However, it is also shown that the joint application of both systems, ZCURVE_V and GeneMark, leads to better gene-finding results. The system ZCURVE_V is freely available at: . CONCLUSION: ZCURVE_V may serve as a preferred gene-finding tool used for viral and phage genomes, especially for anonymous viral and phage genomes newly sequenced
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