Risk Analysis of Shanghai Inter-Bank Offered Rate - A GARCH-VaR Approach

The inter-bank offered rate widely used by Chinese commercial banks is Shanghai Inter-Bank Offered Rate (Shibor). Shibor has experienced significant development since it was created. It offers different products by duration. Despite its importance in China’s financial market, Shibor’s risk has largely remained unexplored. Making contribution to existing literature on risk management of Shibor, this paper investigates risk of Shanghai Inter- Bank Offered Rate (Shibor) utilizing GARCH-VaR method. The VaR of each product is calculated and compared while GARCH model is designed for a simpler calculation. In order to have a clearer view of Chinese commercial banks, the data selected is Shibor data sample from 2006 to 2016, which is measured by GARCH-VaR model and verified effectiveness by chi-square test. Empirical results show strong evidence for the need of Chinese commercial banks to change the status quo so that the great fluctuation and abnormal situation can be avoided. Policy implication, involving the interest rate management and internal problem in commercial banks, is proposed for financial regulators

Fast exact algorithms for optimization problems in resource allocation and switched linear systems

University of Minnesota Ph.D. dissertation.June 2019. Major: Industrial Engineering. Advisor: Qie He. 1 computer file (PDF); x, 138 pages.Discrete optimization is a branch of mathematical optimization where some of the decision variables are restricted to real values in a discrete set. The use of discrete decision variables greatly expands the scope and capacity of mathematical optimization models. In the era of big data, efficiency and scalability are increasingly important in evaluating the performance of an algorithm. However, discrete optimization problems usually are challenging to solve. In this thesis, we develop new fast exact algorithms for discrete optimization problems arising in the field of resource allocation and switched linear systems. The first problem is the discrete resource allocation problem with nested bound constraints. It is a fundamental problem with a wide variety of applications in search theory, economics, inventory systems, etc. Given $B$ units of resource and $n$ activities, each of which associated with a convex allocation cost $f_i(\cdot)$, we aim to find an allocation of resources to the $n$ activities, denoted by \bm{x} \in \Ze^n, to minimize the total allocation cost $\sum\limits_{i = 1}^{n} f_i(x_i)$ subject to the total amount of resource constraint as well as lower and upper bound constraints on total resource allocated to subsets of activities. We develop a $\Theta(n^2\log\frac{B}{n})$-time algorithm for it. It is an infeasibility-guided divide-and-conquer algorithm and the worst-case complexity is usually not achieved. Numerical experiments demonstrate that our algorithm significantly outperforms a state-of-the-art optimization solver and the performance of our algorithm is competitive compared to the algorithm with the best worst-case complexity for this problem in the literature. The second problem is the minimum convex cost network flow problem on the dynamic lot size network. In the dynamic lot size network, there are one source node and $n$ sink nodes with demand $d_i, i = 1, \dots, n$. Let $B = \sum_{i=0}^{n}d_i$ be the total demand. We aim to find a flow $\bm{x}$ to minimize the total arc cost and satisfy all the flow balance and capacity constraints. Many optimization models in the literature can be seen as special cases of this problem, including dynamic lot-sizing problem and speed optimization. It is also a generalization of the first problem. We develop the Scaled Flow-improving Algorithm. For the continuous problem, our algorithm finds a solution that is at most $\epsilon$ away from an optimal solution in terms of the infinity norm in $O(n^2\log{\frac{B}{n\epsilon}})$ time. For the integer problem, our algorithm terminates in $O(n^2\log\frac{B}{n})$ time. Our algorithm has the best worst-case complexity in the literature. In particular, it solves the discrete resource allocation problem with nested bound constraints in $O(n\log{n}\log\frac{B}{n})$ time and it also achieves the best worst-case complexity for that problem. We conduct extensive numerical experiments on instances with a variety of convex objectives. The numerical result demonstrates the efficiency of our algorithm in solving large-sized instances. The last problem is the optimal control problem in switched linear systems. We consider the following dynamical system that consists of several linear subsystems: $K$ matrices, each chosen from the given set of matrices, to maximize a convex function over the product of the $K$ matrices and the given vector.This simple problem has many applications in operations research and control, yet a moderate-sized instance is challenging to solve to optimality for state-of-the-art optimization software. We prove the problem is NP-hard. We propose a simple exact algorithm for this problem. Our algorithm runs in polynomial time when the given set of matrices has the oligo-vertex property, a concept we introduce for a set of matrices. We derive several easy-to-verify sufficient conditions for a set of matrices to have the oligo-vertex property. In particular, we show that a pair of binary matrices has the oligo-vertex property. Numerical results demonstrate the clear advantage of our algorithm in solving large-sized instances of the problem over one state-of-the-art global solver. We also pose several open questions on the oligo-vertex property and discuss its potential connection with the finiteness property of a set of matrices, which may be of independent interest

A novel bio-inspired design method for porous structures: Variable-periodic Voronoi tessellation

This paper introduces a novel approach, namely Variable-Periodic Voronoi Tessellation (VPVT), for the bio-inspired design of porous structures. The method utilizes distributed points defined by a variable-periodic function to generate Voronoi tessellation patterns, aligning with a wide diversity of artificial or natural cellular structures. In this VPVT design method, the truss-based architecture can be fully characterized by design variables, such as frequency factors, thickness factors. This approach enables the optimal design of porous structures for both mechanical performance and functionality. The varied, anisotropic cell shapes and sizes of VPVT porous structures provide significantly greater design flexibility compared to typical isotropic porous structures. In addition, the VPVT method not only can design micro-macro multiscale materials, but is also applicable for the design of meso-macro scale truss-based porous structures, such as architecture constructions, biomedical implants, and aircraft frameworks. This work employs a Surrogate-assisted Differential Evolution (SaDE) method to perform the optimization process. Numerical examples and experiments validate that the proposed design achieves about 51.1% and 47.8% improvement in compliance performance and damage strength, respectively, than existing studies

Throughput of Hybrid UAV Networks with Scale-Free Topology

Unmanned Aerial Vehicles (UAVs) hold great potential to support a wide range of applications due to the high maneuverability and flexibility. Compared with single UAV, UAV swarm carries out tasks efficiently in harsh environment, where the network resilience is of vital importance to UAV swarm. The network topology has a fundamental impact on the resilience of UAV network. It is discovered that scale-free network topology, as a topology that exists widely in nature, has the ability to enhance the network resilience. Besides, increasing network throughput can enhance the efficiency of information interaction, improving the network resilience. Facing these facts, this paper studies the throughput of UAV Network with scale-free topology. Introducing the hybrid network structure combining both ad hoc transmission mode and cellular transmission mode into UAV Network, the throughput of UAV Network is improved compared with that of pure ad hoc UAV network. Furthermore, this work also investigates the optimal setting of the hop threshold for the selection of ad hoc or cellular transmission mode. It is discovered that the optimal hop threshold is related with the number of UAVs and the parameters of scale-free topology. This paper may motivate the application of hybrid network structure into UAV Network.Comment: 15 pages, 7 figure

Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation

Neighbor discovery (ND) is a key step in wireless ad hoc network, which directly affects the efficiency of wireless networking. Improving the speed of ND has always been the goal of ND algorithms. The classical ND algorithms lose packets due to the collision of multiple packets, which greatly affects the speed of the ND algorithms. Traditional methods detect packet collision and implement retransmission when encountering packet loss. However, they does not solve the packet collision problem and the performance improvement of ND algorithms is limited. In this paper, the successive interference cancellation (SIC) technology is introduced into the ND algorithms to unpack multiple collision packets by distinguishing multiple packets in the power domain. Besides, the multi-packet reception (MPR) is further applied to reduce the probability of packet collision by distinguishing multiple received packets, thus further improving the speed of ND algorithms. Six ND algorithms, namely completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC), and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and verified by simulation. The simulation results show that SIC and MPR reduce the ND time of SBA by 69.02% and CRA by 66.03% averagely.Comment: 16 pages, 16 figure

LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?

In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, particularly graph data. Dynamic graphs, which capture temporal network evolution patterns, are ubiquitous in real-world web data. Evaluating LLMs' competence in understanding spatial-temporal information on dynamic graphs is essential for their adoption in web applications, which remains unexplored in the literature. In this paper, we bridge the gap via proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic graphs, to the best of our knowledge, for the first time. Specifically, we propose the LLM4DyG benchmark, which includes nine specially designed tasks considering the capability evaluation of LLMs from both temporal and spatial dimensions. Then, we conduct extensive experiments to analyze the impacts of different data generators, data statistics, prompting techniques, and LLMs on the model performance. Finally, we propose Disentangled Spatial-Temporal Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal understanding abilities. Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks. The data and codes will be open-sourced at publication time

Analysis of the Association between Intestinal Microflora and Long-lived Elderly People

The intestinal microbiota is the cornerstone of the human intestinal microecosystem and plays an unnegligible role in the growth and health maintenance of the human body. In recent years, many studies have been committed to exploring the potential connection of gut flora and the elderly population. The changes of gut flora are affected by various factors such as age increase, disease, medication, living habits, nutritional structure, and the intestinal flora is expected to be applied to the comprehensive evaluation of elderly health and longevity in the future. Based on this, the research progress of the general elderly and its related influencing factors

Mechanical Properties of Steel-FRP Composite Bars under Tensile and Compressive Loading

The factory-produced steel-fiber reinforced polymer composite bar (SFCB) is a new kind of reinforcement for concrete structures. The manufacturing technology of SFCB is presented based on a large number of handmade specimens. The calculated stress-strain curves of ordinary steel bar and SFCB under repeated tensile loading agree well with the corresponding experimental results. The energy-dissipation capacity and residual strain of both steel bar and SFCB were analyzed. Based on the good simulation results of ordinary steel bar and FRP bar under compressive loading, the compressive behavior of SFCB under monotonic loading was studied using the principle of equivalent flexural rigidity. There are three failure modes of SFCB under compressive loading: elastic buckling, postyield buckling, and no buckling (ultimate compressive strength is reached). The increase in the postyield stiffness of SFCB rsf can delay the postyield buckling of SFCB with a large length-to-diameter ratio, and an empirical equation for the relationship between the postbuckling stress and rsf is suggested, which can be used for the design of concrete structures reinforced by SFCB to consider the effect of reinforcement buckling

A novel data-driven multi-energy load forecasting model

With the increasing concern on energy crisis, the coordination of multiple energy sources and low-carbon economic operation of integrated energy system (IES) have drawn more and more attention in recent years. In IES, accurate and effective multi-energy load forecasting becomes a research hotspot, especially using the high-performance data mining and machine learning algorithms. However, due to the huge difference in energy utilization between IES and traditional energy systems, the load forecasting of IES is more difficult and complex. In fact, in IES, load forecasting is not only related to external factors such as meteorological parameters and different seasons, but the correlation between energy consumption of different types of loads also plays an important role. In order to deal with the strong coupling and high uncertainty issues in IES, a novel data-driven multi-energy load forecasting model is proposed in this paper. Firstly, a feature extraction method based on Uniform Manifold Approximation and Projection (UMAP) for multi-energy load of the IES is developed, which reduces the dimension of the complex nonlinear input data. Then, considering multi-energy coupling correlation, a combined TCN-NBeats model is proposed for the joint prediction of multi-energy loads, aiming to improve the prediction accuracy through ensemble learning. Finally, the numerical case analysis using the multi-energy consumption data of an actual campus verifies the effectiveness and accuracy of the proposed data-driven multi-energy load forecasting model