Parameter identification of Li-ion battery models is important for efficiently charge and discharge the most widely used energy storage devices. In this work, we propose a simplified battery model with a parameter ide...
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Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is a...
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Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is an active field of research in the space weather ***,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive *** the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant *** this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.
We introduce partial optical frequency division to generate 10 GHz microwaves with -147 dBc/Hz phase noise at 20 kHz offset. Electronic tuning covers the 8-12 GHz frequency range, while preserving low phase noise.
ISBN:
(纸本)9781957171258
We introduce partial optical frequency division to generate 10 GHz microwaves with -147 dBc/Hz phase noise at 20 kHz offset. Electronic tuning covers the 8-12 GHz frequency range, while preserving low phase noise.
Quantum data-syndrome (QDS) codes are a class of quantum error-correcting codes that protect against errors both on the data qubits and on the syndrome itself via redundant measurement of stabilizer group elements. On...
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ISBN:
(数字)9798350382846
ISBN:
(纸本)9798350382853
Quantum data-syndrome (QDS) codes are a class of quantum error-correcting codes that protect against errors both on the data qubits and on the syndrome itself via redundant measurement of stabilizer group elements. One way to define a QDS code is to choose a syndrome measurement code, a classical block code that encodes the syndrome of the underlying quantum code by defining additional stabilizer measurements. We propose the use of primitive narrow-sense BCH codes as syndrome measurement codes. We show that these codes asymptotically require
$O(t\log \ell)$
extra measurements, where
$\ell$
is the number of stabilizer generators of the quantum code and
$t$
is the number of syndrome measurement errors corrected by the BCH code. Previously, the best known general method of constructing QDS codes out of quantum codes required
$O(t^{3}\log \ell)$
extra measurements. As the number of additional syndrome measurements is a reasonable metric for the amount of additional time a general QDS code requires, we conclude that our construction protects against the same number of syndrome errors with significantly less time overhead.
We study the chirality of staggered quarks on the Dirac eigenvalue spectrum using deep learning (DL) techniques. The Kluberg-Stern method to construct staggered bilinear operators conserves continuum property such as ...
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We demonstrate quantum memory of single-photon-level coherent pulses of 880 GHz bandwidth with 95.6(3)% storage efficiency in collisionally broadened barium vapor. We measure 26(1)% total efficiency, limited by contro...
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Fizeau drag means that the speed of light can be regulated by the flow of water, owing to the momentum interaction between photons and moving media. However, the dragging of heat is intrinsically elusive, due to the a...
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Fizeau drag means that the speed of light can be regulated by the flow of water, owing to the momentum interaction between photons and moving media. However, the dragging of heat is intrinsically elusive, due to the absence of momentum in thermal diffusion. Here, we design a spatiotemporal thermal metamaterial based on heat transfer in porous media to demonstrate the diffusive analog to Fizeau drag. The space-related inhomogeneity and time-related advection enable the diffusive Fizeau drag effect. Thanks to the spatiotemporal coupling, different propagating speeds of temperature fields can be observed in two opposite directions, thus facilitating nonreciprocal thermal profiles. The phenomenon of diffusive Fizeau drag stands robustly even when the direction of advection is perpendicular to the propagation of temperature fields. These results could pave an unexpected way toward realizing the nonreciprocal and directional transport of mass and energy.
Continuous-variable (CV) teleportation is a fundamental protocol in quantum information science. A number of experiments have been designed to simulate ideal teleportation under realistic conditions. In this paper, we...
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Continuous-variable (CV) teleportation is a fundamental protocol in quantum information science. A number of experiments have been designed to simulate ideal teleportation under realistic conditions. In this paper, we detail an analytical approach for determining optimal input states for quantifying the performance of CV unidirectional and bidirectional teleportation. The metric that we consider for quantifying performance is the energy-constrained channel fidelity between ideal teleportation and its experimental implementation, and along with this, our focus is on determining optimal input states for distinguishing the ideal process from the experimental one. We prove that, under certain energy constraints, the optimal input state in unidirectional as well as bidirectional teleportation is a finite entangled superposition of twin-Fock states saturating the energy constraint. Moreover, we also prove that, under the same constraints, the optimal states are unique; that is, there is no other optimal finite entangled superposition of twin-Fock states.
For the first time, we show high-fidelity generation of Liquid Argon Time Projection Chamber (LArTPC-like) data using a generative neural network. This demonstrates that methods developed for natural images do transfe...
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For the first time, we show high-fidelity generation of Liquid Argon Time Projection Chamber (LArTPC-like) data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments.
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