Subsystem Rényi Entropy of Thermal Ensembles for SYK-like models

The Sachdev-Ye-Kitaev model is an N-modes fermionic model with infinite range random interactions. In this work, we study the thermal Rényi entropy for a subsystem of the SYK model using the path-integral formalism in the large-N limit. The results are consistent with exact diagonalization [1] and can be well approximated by thermal entropy with an effective temperature [2] when subsystem size M ≤ N/2. We also consider generalizations of the SYK model with quadratic random hopping term or U(1) charge conservation

Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View

Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With the proposed formulation, the nodes in the network are regarded as players and the local combiner of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, we further propose two error-aware adaptive filtering algorithms. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of our analysis and proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Red recombination enables a wide variety of markerless manipulation of porcine epidemic diarrhea virus genome to generate recombinant virus

Porcine epidemic diarrhea virus (PEDV) is a member of the genera Alphacoronavirus that has been associated with acute watery diarrhea and vomiting in swine. Unfortunately, no effective vaccines and antiviral drugs for PEDV are currently available. Reverse genetics systems are crucial tools for these researches. Here, a PEDV full-length cDNA clone was constructed. Furtherly, three PEDV reporter virus plasmids containing red fluorescent protein (RFP), Nano luciferase (Nluc), or green fluorescence protein (GFP) were generated using Red recombination with the GS1783 E. coli strain. These reporter-expressing recombinant (r) PEDVs showed similar growth properties to the rPEDV, and the foreign genes were stable to culture up to P9 in Vero cells. Using the Nluc-expressing rPEDV, the replication of PEDV was easily quantified, and a platform for rapid anti-PEDV drug screening was constructed. Among the three drugs, Bergenin, Umifenovir hydrochloride (Arbidol), and Ganoderma lucidum triterpenoids (GLTs), we found that GLTs inhibited PEDV replication mainly after the stage of virus “Entry”. Overall, this study will broaden insight into the method for manipulating the PEDV genome and provide a powerful tool for screening anti-PEDV agents