1,329 research outputs found
Experimental quantum key distribution with active phase randomization
Phase randomization is an important assumption made in many security proofs
of practical quantum key distribution (QKD) systems. Here, we present the first
experimental demonstration of QKD with reliable active phase randomization. One
key contribution is a polarization-insensitive phase modulator, which we added
to a commercial phase-coding QKD system to randomize the global phase of each
bit. We also proposed a simple but useful method to verify experimentally that
the phase is indeed randomized. Our result shows very low QBER (<1%). We expect
this active phase randomization process to be a standard part in future QKD
set-ups due to its significance and feasibility.Comment: 3 pages, 3 figures, RevTE
Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks
Spiking Neural Networks (SNNs) are more biologically plausible and
computationally efficient. Therefore, SNNs have the natural advantage of
drawing the sparse structural plasticity of brain development to alleviate the
energy problems of deep neural networks caused by their complex and fixed
structures. However, previous SNNs compression works are lack of in-depth
inspiration from the brain development plasticity mechanism. This paper
proposed a novel method for the adaptive structural development of SNN
(SD-SNN), introducing dendritic spine plasticity-based synaptic constraint,
neuronal pruning and synaptic regeneration. We found that synaptic constraint
and neuronal pruning can detect and remove a large amount of redundancy in
SNNs, coupled with synaptic regeneration can effectively prevent and repair
over-pruning. Moreover, inspired by the neurotrophic hypothesis, neuronal
pruning rate and synaptic regeneration rate were adaptively adjusted during the
learning-while-pruning process, which eventually led to the structural
stability of SNNs. Experimental results on spatial (MNIST, CIFAR-10) and
temporal neuromorphic (N-MNIST, DVS-Gesture) datasets demonstrate that our
method can flexibly learn appropriate compression rate for various tasks and
effectively achieve superior performance while massively reducing the network
energy consumption. Specifically, for the spatial MNIST dataset, our SD-SNN
achieves 99.51\% accuracy at the pruning rate 49.83\%, which has a 0.05\%
accuracy improvement compared to the baseline without compression. For the
neuromorphic DVS-Gesture dataset, 98.20\% accuracy with 1.09\% improvement is
achieved by our method when the compression rate reaches 55.50\%
The Application of Carbon Footprint Analysis in Hunan Province
Based on interpreting carbon footprint’s definition and its effecting factors, making positive analyses by using the data of cities in Hunan Province from 2005 to 2009, this paper constructs the calculating model of carbon footprint and analyses the relationship between carbon footprint and population, economy development level, industrial structure and energy structure. Meanwhile, on the basis of above analyses, this paper puts forward effective ways to advance the low-carbon development of Hunan Province from four aspects
Applicability of the Friedberg-Lee-Zhao method
Friedberg, Lee and Zhao proposed a method for effectively evaluating the
eigenenergies and eigen wavefunctions of quantum systems. In this work, we
study several special cases to investigate applicability of the method.
Concretely, we calculate the ground-state eigenenergy of the Hellmann potential
as well as the Cornell potential, and also evaluate the energies of the systems
where linear term is added to the Coulomb and harmonic oscillator potentials as
a perturbation. The results obtained in this method have a surprising agreement
with the traditional method or the numerical results. Since the results in this
method have obvious analyticity compared to that in other methods, and because
of the simplicity for calculations this method can be applied to solving the
Schr\"{o}dinger equation and provides us better understanding of the physical
essence of the concerned systems. But meanwhile applications of the FLZ method
are restricted at present, especially for certain potential forms, but due to
its obvious advantages, it should be further developed.Comment: 14 pages,no figure
Degree correlation effect of bipartite network on personalized recommendation
In this paper, by introducing a new user similarity index base on the
diffusion process, we propose a modified collaborative filtering (MCF)
algorithm, which has remarkably higher accuracy than the standard collaborative
filtering. In the proposed algorithm, the degree correlation between users and
objects is taken into account and embedded into the similarity index by a
tunable parameter. The numerical simulation on a benchmark data set shows that
the algorithmic accuracy of the MCF, measured by the average ranking score, is
further improved by 18.19% in the optimal case. In addition, two significant
criteria of algorithmic performance, diversity and popularity, are also taken
into account. Numerical results show that the presented algorithm can provide
more diverse and less popular recommendations, for example, when the
recommendation list contains 10 objects, the diversity, measured by the hamming
distance, is improved by 21.90%.Comment: 9 pages, 3 figure
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) have received considerable attention not only
for their superiority in energy efficient with discrete signal processing, but
also for their natural suitability to integrate multi-scale biological
plasticity. However, most SNNs directly adopt the structure of the
well-established DNN, rarely automatically design Neural Architecture Search
(NAS) for SNNs. The neural motifs topology, modular regional structure and
global cross-brain region connection of the human brain are the product of
natural evolution and can serve as a perfect reference for designing
brain-inspired SNN architecture. In this paper, we propose a Multi-Scale
Evolutionary Neural Architecture Search (MSE-NAS) for SNN, simultaneously
considering micro-, meso- and macro-scale brain topologies as the evolutionary
search space. MSE-NAS evolves individual neuron operation, self-organized
integration of multiple circuit motifs, and global connectivity across motifs
through a brain-inspired indirect evaluation function, Representational
Dissimilarity Matrices (RDMs). This training-free fitness function could
greatly reduce computational consumption and NAS's time, and its
task-independent property enables the searched SNNs to exhibit excellent
transferbility and scalability. Extensive experiments demonstrate that the
proposed algorithm achieves state-of-the-art (SOTA) performance with shorter
simulation steps on static datasets (CIFAR10, CIFAR100) and neuromorphic
datasets (CIFAR10-DVS and DVS128-Gesture). The thorough analysis also
illustrates the significant performance improvement and consistent
bio-interpretability deriving from the topological evolution at different
scales and the RDMs fitness function
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