arXiv subjects

Chao Fang

Publications and source records attributed to Chao Fang.

66 records · Page 4Linked to original sources

arXiv2019

Thermodynamics of the Bardeen Black Hole in Anti-de Sitter Space

We first study the thermodynamics of Bardeen-AdS black hole by the $T$-$r_{h}$ diagram, where T is the Hawking temperature and $r_{h}$ is the radius of event horizon. The cut-off radius which is the minimal radius of the thermodynamical stable Bardeen black hole can be got, and the cut-off radius is the same with the result of the heat capacity analysis. Moreover, by studying the parameter $g$, which is interpreted as a gravitationally collapsed magnetic monopole arising in a specific form of non-linear electrodynamics, in the Bardeen black hole, we can get a critical value $g_{m}$ and different phenomenons with different values of parameter $g$. For $g>g_{m}$, there is no second order phase transition. We also research the thermodynamical stability of the Bardeen black hole by the Gibbs free energy and the heat capacity. In addition, the phase transition is discussed.

hep-th↗

arXiv2019

Training Deep Neural Networks Using Posit Number System

With the increasing size of Deep Neural Network (DNN) models, the high memory space requirements and computational complexity have become an obstacle for efficient DNN implementations. To ease this problem, using reduced-precision representations for DNN training and inference has attracted many interests from researchers. This paper first proposes a methodology for training DNNs with the posit arithmetic, a type- 3 universal number (Unum) format that is similar to the floating point(FP) but has reduced precision. A warm-up training strategy and layer-wise scaling factors are adopted to stabilize training and fit the dynamic range of DNN parameters. With the proposed training methodology, we demonstrate the first successful training of DNN models on ImageNet image classification task in 16 bits posit with no accuracy loss. Then, an efficient hardware architecture for the posit multiply-and-accumulate operation is also proposed, which can achieve significant improvement in energy efficiency than traditional floating-point implementations. The proposed design is helpful for future low-power DNN training accelerators.

cs.LG↗

arXiv2018

MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction

Beta-turn prediction is useful in protein function studies and experimental design. Although recent approaches using machine-learning techniques such as SVM, neural networks, and K-NN have achieved good results for beta-turn pre-diction, there is still significant room for improvement. As previous predictors utilized features in a sliding window of 4-20 residues to capture interactions among sequentially neighboring residues, such feature engineering may result in incomplete or biased features, and neglect interactions among long-range residues. Deep neural networks provide a new opportunity to address these issues. Here, we proposed a deep dense inception network (DeepDIN) for beta-turn prediction, which takes advantages of the state-of-the-art deep neural network design of the DenseNet and the inception network. A test on a recent BT6376 benchmark shows that the DeepDIN outperformed the previous best BetaTPred3 significantly in both the overall prediction accuracy and the nine-type beta-turn classification. A tool, called MUFold-BetaTurn, was developed, which is the first beta-turn prediction tool utilizing deep neural networks. The tool can be downloaded at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html.

q-bio.BM↗

arXiv2018

Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks

Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy is the limited capacity of the methods; in particular, the traditional machine-learning methods like SVM may not extract high-level features well to distinguish between turn or non-turn. Hence, it is worthwhile exploring new machine-learning methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are very effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provides a useful example for the community.

q-bio.QM↗

arXiv2018

Discussion of a possible corrected black hole entropy

Einstein's equation could be interpreted as the first law of thermodynamic near the spherically symmetric horizon. Through recalling the Einstein gravity with a more general static spherical symmetric metric, we find that the entropy would have a correction in Einstein gravity. By using this method, we investigate the Eddington-inspired Born-Infeld (EiBI) gravity. Without matter field, we can also derive the first law in EiBI gravity. With an electromagnetic field, as the field equations have a more general spherically symmetric solution in EiBI gravity, we find that correction of the entropy could be generalized to EiBI gravity. Furthermore, we point out that the Einstein gravity and EiBI gravity might be equivalent on the event horizon. At last, under EiBI gravity with the electromagnetic field, a specific corrected entropy of black hole is given.

gr-qc↗

arXiv2018

NAE-SAT-based probabilistic membership filters

Probabilistic membership filters are a type of data structure designed to quickly verify whether an element of a large data set belongs to a subset of the data. While false negatives are not possible, false positives are. Therefore, the main goal of any good probabilistic membership filter is to have a small false-positive rate while being memory efficient and fast to query. Although Bloom filters are fast to construct, their memory efficiency is bounded by a strict theoretical upper bound. Weaver et al. introduced random satisfiability-based filters that significantly improved the efficiency of the probabilistic filters, however, at the cost of solving a complex random satisfiability (SAT) formula when constructing the filter. Here we present an improved SAT filter approach with a focus on reducing the filter building times, as well as query times. Our approach is based on using not-all-equal (NAE) SAT formulas to build the filters, solving these via a mapping to random SAT using traditionally-fast random SAT solvers, as well as bit packing and the reduction of the number of hash functions. Paired with fast hardware, NAE-SAT filters could result in enterprise-size applications.

cs.DS↗

arXiv2017

MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks

Motivation: Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning, which has been successfully applied to various research fields such as image classification and voice recognition, provides a new opportunity to significantly improve the secondary structure prediction accuracy. Although several deep-learning methods have been developed for secondary structure prediction, there is room for improvement. MUFold-SS was developed to address these issues. Results: Here, a very deep neural network, the deep inception-inside-inception networks (Deep3I), is proposed for protein secondary structure prediction and a software tool was implemented using this network. This network takes two inputs: a protein sequence and a profile generated by PSI-BLAST. The output is the predicted eight states (Q8) or three states (Q3) of secondary structures. The proposed Deep3I not only achieves the state-of-the-art performance but also runs faster than other tools. Deep3I achieves Q3 82.8% and Q8 71.1% accuracies on the CB513 benchmark.

q-bio.QM↗

arXiv2017

The generalization of charged AdS black hole specific volume and number density

In this paper, by proposing a generalized $specific~volume$, we restudy the $P-V$ criticality of charged AdS black holes in the extended phase space. The results show that most of the previous conclusions can be generalized without change, but the ratio $\tildeρ_c$ should be $3 \tildeα/16$ in general case. Further research on the thermodynamical phase transition of black hole leads us to a natural interpretation of our assumption, and more black hole properties can be generalized. Finally, we study the number density for charged AdS black hole in higher dimensions, the results show the necessity of our assumption.

gr-qc↗

arXiv2016

Content-Centric and Software-Defined Networking with Big Data

Many communities have researched the application of novel network architectures such as Content-Centric Networking (CCN) and Software-Defined Networking (SDN) to build the future Internet. Another emerging technology which is big data analysis has also won lots of attentions from academia to industry. Many splendid researches have been done on CCN, SDN, and big data, which all have addressed separately in the traditional literature. In this paper, we propose a novel network paradigm to jointly consider CCN, SDN, and big data, and provide the architecture internal data flow, big data processing and use cases which indicate the benefits and applicability. Simulation results are exhibited to show the potential benefits relating to the proposed network paradigm. We refer to this novel paradigm as Data-Driven Networking (DDN).

cs.NI↗

arXiv2016

borealis - A generalized global update algorithm for Boolean optimization problems

Optimization problems with Boolean variables that fall into the nondeterministic polynomial (NP) class are of fundamental importance in computer science, mathematics, physics and industrial applications. Most notably, solving constraint-satisfaction problems, which are related to spin-glass-like Hamiltonians in physics, remains a difficult numerical task. As such, there has been great interest in designing efficient heuristics to solve these computationally difficult problems. Inspired by parallel tempering Monte Carlo in conjunction with the rejection-free isoenergetic cluster algorithm developed for Ising spin glasses, we present a generalized global update optimization heuristic that can be applied to different NP-complete problems with Boolean variables. The global cluster updates allow for a wide-spread sampling of phase space, thus considerably speeding up optimization. By carefully tuning the pseudo-temperature (needed to randomize the configurations) of the problem, we show that the method can efficiently tackle optimization problems with over-constraints or on topologies with a large site-percolation threshold. We illustrate the efficiency of the heuristic on paradigmatic optimization problems, such as the maximum satisfiability problem and the vertex cover problem.

physics.comp-ph↗

arXiv2014

Global quantum discord in infinite quantum spin chains

In this paper, we study global quantum discord (GQD) in infinite-size spin chains. For this purpose, in the framework of matrix product states (MPSs), we propose an effective procedure to calculate GQD (denoted as Gn) for consecutive n-site subchains in infinite chains. For a spin-1/2 three-body interaction model, whose ground state can be exactly expressed as MPSs, We use the procedure to study Gn with n up to $24$. Then for a spin-1/2 XXZ chain, we firstly use infinite time-evolving block decimation (iTEBD) algorithm to obtain the approximate wavefunction in the from of MPSs, and then figure out Gn with n up to $18$. In both models, Gn shows an interesting linear growth as the increase of n, that is, Gn = k*n+b. Moreover, in non-critical regions the slope $k$ of Gn converges very fast, while in critical regions it converges relatively slow, and the behaviors are explained in a clear physical picture with the short-range and long-range correlations. Based on these results, we propose to use Gn/n to describe the global correlations in infinite chains. Gn/n has twofold physical meanings. Firstly, it can be regarded as "global discord per site", very similar to "energy per site" or "magnetization per site" in quantum magnetic systems. Secondly, Gn/n (when n is large enough) describes the quantum correlation between a single site and an (n-1)-site block. Then we successfully apply our theory to an exactly soluble infinite-size spin XY chain which is beyond the matrix product formula, and the Hamiltonian can reduce to the transverse-field Ising model and the XX model. The relation between GQD and quantum phase transitions in these models is discussed.

quant-ph↗

arXiv2012

The trouble with asymptotically safe inflation

In this paper we investigate the perturbation theory of the asymptotically safe inflation and we find that all modes of gravitational waves perturbation become ghosts in order to achieve a large enough number of e-folds. Formally we can calculate the power spectrum of gravitational waves perturbation, but we find that it is negative. It indicates that there is serious trouble with the asymptotically safe inflation.

hep-th↗