Simulation techniques for generalized Gaussian densities

This contribution deals with Monte Carlo simulation of generalized Gaussian random variables. Such a parametric family of distributions has been proposed in many applications in science to describe physical phenomena and in engineering, and it seems also useful in modeling economic and financial data. For values of the shape parameter a within a certain range, the distribution presents heavy tails. In particular, the cases a=1/3 and a=1/2 are considered. For such values of the shape parameter, different simulation methods are assessed.Generalized Gaussian density, heavy tails, transformations of rendom variables, Monte Carlo simulation, Lambert W function

Time-frequency analysis of locally stationary Hawkes processes

Locally stationary Hawkes processes have been introduced in order to generalise classical Hawkes processes away from stationarity by allowing for a time-varying second-order structure. This class of self-exciting point processes has recently attracted a lot of interest in applications in the life sciences (seismology, genomics, neuro-science,...), but also in the modelling of high-frequency financial data. In this contribution we provide a fully developed nonparametric estimation theory of both local mean density and local Bartlett spectra of a locally stationary Hawkes process. In particular we apply our kernel estimation of the spectrum localised both in time and frequency to two data sets of transaction times revealing pertinent features in the data that had not been made visible by classical non-localised approaches based on models with constant fertility functions over time.Comment: Bernoulli journal, A Para{\^i}tr

Implementasi Laporan Keuangan Menggunakan Aplikasi Accurate pada Sekolah Luar Biasa Perguruan Al-Azhar

In every life and process of human development there is always something called calculating numbers. Calculations will often involve accounting knowledge. Accounting science is a pure science that humans use to solve their financial problems to become more organized and structured. Likewise with trading companies or service companies that use helper applications to complete their financial processes to make them more efficient and stable. Such as the Al-Azhar School for Special Education which is used as a forum for completing the preparation of financial administration calculations using accounting data which is applied to the method of completion using Accurate software. The accounting data used are in the form of general journals, ledgers, balance sheets and income statements. This calculation is carried out in a thorough and good way using accurate software based on the correct guide within a certain time to complete the financial administration calculation process until it is finished

An Advanced GPR Modelling Framework: The Next Generation of gprMax

ACKNOWLEDGMENT The authors would like to acknowledge financial support for this work from The Defence Science and Technology Laboratory (Dstl), UK. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/). This work benefited from networking activities carried out within the EU funded COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar

Database Applications in Science Data Systems for Low-Cost Satellite Missions

As in financial and business applications. satellite systems accumulate data sets over some time period. Certain portions of this data lend themselves to storage within a commercial database system. The advantages and disadvantages of using commercial database systems for storage of data related to satellite systems are discussed. The types of data stored in satellite science data systems are outlined. Case studies from MSX. NEAR and TIMED. three satellite missions developed at The Johns Hopkins University Applied Physics Laboratory. are reviewed. An example of database logical architecture to support science data queries is presented. Implementation lessons in INGRES and ORACLE are noted. Issues related to the user community in each case are discussed. In conclusion. a summary of the lessons learned from all case studies is presented

Quantum-inspired computational imaging

Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip

Saturn Platform: Foundation Model Operations and Generative AI for Financial Services

Saturn is an innovative platform that assists Foundation Model (FM) building and its integration with IT operations (Ops). It is custom-made to meet the requirements of data scientists, enabling them to effectively create and implement FMs while enhancing collaboration within their technical domain. By offering a wide range of tools and features, Saturn streamlines and automates different stages of FM development, making it an invaluable asset for data science teams. This white paper introduces prospective applications of generative AI models derived from FMs in the financial sector

Political stability and innovation in Africa

This paper examines the role of institutional factors, primarily, political stability, in shaping the dynamics of innovation activity in today’s modern society. Using panel data consisting of 26 African countries from the period between 1996 and 2016 as well as employing the negative binomial estimate, we model the impact of those factors on the number of applications for patents per resident. GDP per capita, education and also other institutional factors including the rule of law and financial freedom. The results reveal a strong positive effect of the rule of law on patenting activity. © Foundation of International Studies, 2020 © CSR, 2020.Deutscher Akademischer Austauschdienst, DAADMinistry of Education and Science of the Russian Federation, MinobrnaukaMinisterstwo Nauki i Szkolnictwa Wyższego, MNiSWThe authors are thankful to the Russian Ministry of Education and Science, DAAD, The Ministry of Science and Higher Education in Poland for financial support to carry out this research. Thanks (IOS, Regensburg) for hosting James Okrah for the period of his research internship with them, with special thanks to Dr Alexander Nepp (Supervisor), Dr. Olga Popova (IOS, Regensburg), Prof. Richard Frensch (IOS, Regensburg) and Dr Stefan Huber for their support