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

ANALYZING HIGHWAY DAMAGE COSTS ATTRIBUTED TO TRUCK TRAFFIC OF PROCESSED MEAT AND RELATED INDUSTRIES IN SOUTHWEST KANSAS

Kansas is one of the leaders in meat production in the United States. In the southwest Kansas region, there are more than three hundred feed yards and several of the biggest meat processing plants in the nation. Heavy trucks (e.g., tractor-trailers) have been used primarily for transporting processed meat, meat byproducts, grain, and other related products. With the continuous growth of the industries, there will be more trucks on highways transporting meat and meat-related products in southwest Kansas. These trucks cause noteworthy damages to Kansas highway pavements, which in turn leads to more frequent maintenance actions and ultimately more traffic delays and congestions. The primary objective of this research was to estimate the highway damage costs attributed to the truck traffic associated with the processed meat (beef) and related industries in southwest Kansas. The researcher developed a systematic pavement damage estimation procedure that synthesized several existing methodologies including Highway Economic Requirements System (HERS) and American Association of State Highway and Transportation Officials (AASHTO) methods. In this research project, the highway section of US 50/400 between Dodge City to Garden City in Kansas was selected and its pavement data was collected for analysis. Outcomes of this research will be beneficial for the selection of cost-effective transportation modes for the meat processing and related industries in southwest Kansas. It will also help highway agents to assess highway maintenance needs and to set up maintenance priorities. Meanwhile, the analysis results will be valuable for the determination of reasonable user costs. Based on findings of this research, recommendations on the selection of transportation modes are provided and promising future research tasks are suggested at the end of the thesis as well

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