A SVD accelerated kernel-independent fast multipole method and its application to BEM

The kernel-independent fast multipole method (KIFMM) proposed in [1] is of almost linear complexity. In the original KIFMM the time-consuming M2L translations are accelerated by FFT. However, when more equivalent points are used to achieve higher accuracy, the efficiency of the FFT approach tends to be lower because more auxiliary volume grid points have to be added. In this paper, all the translations of the KIFMM are accelerated by using the singular value decomposition (SVD) based on the low-rank property of the translating matrices. The acceleration of M2L is realized by first transforming the associated translating matrices into more compact form, and then using low-rank approximations. By using the transform matrices for M2L, the orders of the translating matrices in upward and downward passes are also reduced. The improved KIFMM is then applied to accelerate BEM. The performance of the proposed algorithms are demonstrated by three examples. Numerical results show that, compared with the original KIFMM, the present method can reduce about 40% of the iterating time and 25% of the memory requirement.Comment: 19 pages, 4 figure

Efficiency improvement of the frequency-domain BEM for rapid transient elastodynamic analysis

The frequency-domain fast boundary element method (BEM) combined with the exponential window technique leads to an efficient yet simple method for elastodynamic analysis. In this paper, the efficiency of this method is further enhanced by three strategies. Firstly, we propose to use exponential window with large damping parameter to improve the conditioning of the BEM matrices. Secondly, the frequency domain windowing technique is introduced to alleviate the severe Gibbs oscillations in time-domain responses caused by large damping parameters. Thirdly, a solution extrapolation scheme is applied to obtain better initial guesses for solving the sequential linear systems in the frequency domain. Numerical results of three typical examples with the problem size up to 0.7 million unknowns clearly show that the first and third strategies can significantly reduce the computational time. The second strategy can effectively eliminate the Gibbs oscillations and result in accurate time-domain responses

The Applications of Utility Theory in Insurance Industry

Abstract: In this paper, The Applications of Utility Theory in insurance industry are discussed from two ways.First of all we consider the insurance pricing from both insurers and insured , and makes the strict explanation from the value example to the St. Petersburg paradox. .Then we discuss insurance pricing between the risk swap agreement insurers and give the value example. Key words: Utility Theory, Utility function, Insurance premium,expected Utility, Risk Theory（LIU, WANG & GUO. 2007

Big-Data Based Analysis for Communication Effect of Science-Technology Public Accounts On Social Media

Public accounts on social media have become important channels for information dissemination. Well-designed public social media accounts are vital to better communicate science and technology (S-T) achievements. This article defines the S-T communication concept and proposes the analyzing dimensions. In order to measure the communication effect, this research collected 7,246 articles from S-T public accounts on WeChat. We analysis these massive data incorporating neural network (NN) and multivariate linear regression (MLR) model. The evaluation indicator system of communication effect includes three levels indicators. The research found the following factors affecting the S-T communication effect in different degrees: the number of active fans on Science Technology Public Accounts on Social Media (STPA-SM), locations where the articles are published, the authentication status of STPA-SM, and so on. Finally, the article proposes some strategic suggestions for improving the communication effects of S-T achievements through STPA-SM

Robust occupancy inference with commodity WiFi

Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. in this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity wifi infrastructure. depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. we evaluate the system on a hardware testbed deployed in a 600m 2 workspace with 25 occupants for 6 weeks. experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness

SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images

Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images. In this work, we, for the first time, propose a framework \textit{SyreaNet} for UIE that integrates both synthetic and real data under the guidance of the revised underwater image formation model and novel domain adaptation (DA) strategies. First, an underwater image synthesis module based on the revised model is proposed. Then, a physically guided disentangled network is designed to predict the clear images by combining both synthetic and real underwater images. The intra- and inter-domain gaps are abridged by fully exchanging the domain knowledge. Extensive experiments demonstrate the superiority of our framework over other state-of-the-art (SOTA) learning-based UIE methods qualitatively and quantitatively. The code and dataset are publicly available at https://github.com/RockWenJJ/SyreaNet.git.Comment: 7 pages; 10 figure

Sum Rate Analysis of MU-MIMO with a 3D MIMO Base Station Exploiting Elevation Features

Although the three-dimensional (3D) channel model considering the elevation factor has been used to analyze the performance of multiuser multiple-input multiple-output (MU-MIMO) systems, less attention is paid to the effect of the elevation variation. In this paper, we elaborate the sum rate of MU-MIMO systems with a 3D base station (BS) exploiting different elevations. To illustrate clearly, we consider a high-rise building scenario. Due to the floor height, each floor corresponds to an elevation. Therefore, we can analyze the sum rate performance for each floor and discuss its effect on the performance of the whole building. This work can be seen as the first attempt to analyze the sum rate performance for high-rise buildings in modern city and used as a reference for infrastructure

Diethyl 4,6-diacetamido­isophthalate

In the title compound, C16H20N2O6, two intra­molecular N—H⋯O hydrogen bonds occur, in which the carbonyl O atoms of the ethyl acetate groups serve as the acceptor atoms; both motifs generate S(6) rings. In the crystal, mol­ecules are linked by weak C—H⋯O links (with the acceptor O atoms part of the amide groups), generating [001] chains