Holographic Van der Waals phase transition for a hairy black hole

The Van der Waals(VdW) phase transition in a hairy black hole is investigated by analogizing its charge, temperature, and entropy as the temperature, pressure, and volume in the fluid respectively. The two point correlation function(TCF), which is dual to the geodesic length, is employed to probe this phase transition. We find the phase structure in the temperature$-$geodesic length plane resembles as that in the temperature$-$thermal entropy plane besides the scale of the horizontal coordinate. In addition, we find the equal area law(EAL) for the first order phase transition and critical exponent of the heat capacity for the second order phase transition in the temperature$-$geodesic length plane are consistent with that in temperature$-$thermal entropy plane, which implies that the TCF is a good probe to probe the phase structure of the back hole.Comment: Accepted by Advances in High Energy Physics(The special issue: Applications of the Holographic Duality to Strongly Coupled Quantum Systems

γγ→tcˉ+ctˉ\gamma\gamma \to t\bar{c}+c\bar{t} in a supersymmetric theory with an explicit R-parity violation

We studied the process $\gamma\gamma \to t\bar{c}+c\bar{t}$ in a $R_{p}$ violating supersymmetric Model with the effects from both B- and L-violating interactions. The calculation shows that it is possible to detect a $R_{p}$ violating signal at the Next Linear Collider. Information about the B-violating interaction in this model could be obtained under very clean background, if we take the present upper bounds for the parameters in the supersymmetric $\rlap/ R_{p}$ interactions. Even if we can not detect a signal of $\rlap/R_{p}$ in the experiment, we may get more stringent constraints on the heavy-flavor $\rlap/R_{p}$ couplings.Comment: 16 pages, 6 figure

Thermodynamics and weak cosmic censorship conjecture of the torus-like black hole

After studying the energy-momentum relation of charged particles' Hamilton-Jacobi equations, we discuss the laws of thermodynamics and the weak cosmic censorship conjecture in torus-like black holes. We find that both the first law of thermodynamic as well as the weak cosmic censorship conjecture are valid in both the normal phase space and extended phase space. However, the second law of thermodynamics is only valid in the normal phase space. Our results show that the first law and weak cosmic censorship conjecture do not depend on the phase spaces while the second law depends. What's more, we find that the shift of the metric function that determines the event horizon take the same form in different phase spaces, indicating that the weak cosmic censorship conjecture is independent of the phase space.Comment: 15 page

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\%