Stochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in the theory of stochastic control. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations, and martingale duality methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples from the world of finance: portfolio allocation, option hedging, real options, optimal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematicians interested in financial applications and practitioners wishing to know more about the use of stochastic optimization methods in finance.

Continuous-time stochastic control and optimization with financial applications / Huyen Pham

The Semantic Web 10

Network Analysis: Methodological Foundations

NOAH（New York Online Access to Health，纽约健康在线）网站于1994年由纽约城市图书馆发起创建，旨在合并多种类型医学图书馆的资源，为用户提供高质量卫生保健信息。网站由纽约图书馆员和卫生保健专家查找、筛选和组织信息，全文卫生保健信息均是最近发表的、准确无偏倚的医学资源。网站编辑委员会对筛选和使用的所有主题和链接负责。语言以英语和西班牙语为主。

Noah

Research advances of magnesium and magnesium alloys worldwide in 2021

Industry 4.0 is a shift from the previous industry shape. Automation is as a result of technology involving electrical energy. Changes occur and lead to paperless and humanless, but these changes require preparation both in the industrial world and in other supporting worlds. Data is a different side of technology. Data involves many different concepts from the industrial world, even though either data of the industrial world involve the same technology. Specifically, data management is different than industry management. This paper reviews integrated management based on data science, a science that studies the behavior of data. As a result, integration requires data structuring steps from Industry 4.0 and structuring data for Industry 4.0.

https://robots.iopscience.iop.org/article/10.1088/1757-899X/1122/1/012037/pdf

Industry 4.0: Data science perspective

Human eye can easily distinguish objects in images, such as animals, plants or non-living objects that they already recognized. However, the same matter doesn't hold for computers that do not have the capability to recognize objects in images just like humans do. However, in computer vision world, an image can be represented by bag of visual words method using its local features to identify the image characteristics in order to distinguish objects in the image. In the process, bag of visual words has some stages in determining the characteristics of the interest points. In this study, the determination of the interest point is modified using the speed-up robust feature with histogram of oriented gradients in order to enhance the recognition of blurred images. Performance of this modification shows a consistent accuracy of 70% by using 5-scale detection of interest points, which can recognize blur patterns in images that is not only focused on prediction of image degradation model to classify blur area on images like those found in previous research works.

Modification of speed-up robust feature method with histogram of oriented gradient in image blur classification

Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q.

/pdf/enhancing-performance-of-parallel-self-organizing-map-on-59h4gxued6.pdf

Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q

Here, different optical soliton wave solutions for a couple of nonlinear Schrodinger's equations (NLSEs), namely, the nonlinear spin dynamics of Heisenberg ferromagnetic spin chains (HFSC) and the ...

Mahyuddin K. M. Nasution

Papers

Industry 4.0: Data science perspective

Modification of speed-up robust feature method with histogram of oriented gradient in image blur classification

Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q

Nonlinear spin dynamics of a couple of nonlinear Schrödinger's equations by the improved form of an analytical method

Microwave absorption and electromagnetic evaluations of the single and bilayer absorbers of sphere like BFO and pompon flower-like CoGd ferrites