Pricing the Financial Heston Model Using Parallel Finite Difference Method on GPU CUDA

An option is a financial instrument in which two parties agree to exchange assets at a price or strike and the date or maturity is predetermined. Options can provide investors with information to set strategies so they can increase profits and reduce risk. Option prices need to be accurately evaluated according to reality and quickly so that the resulting value can be utilized at the best momentum. Valuation of option prices can use the Heston equation model which has advantages compared to other equation models because the assumption of volatility is not constant with time or stochastic volatility. The volatility that is not constant with time corresponds to reality because the underlying asset as a basis can experience fluctuations. The Heston equation has a disadvantage because it is a derivative equation that is difficult to solve. One way to solve derivative equations easily is to use a numerical solution to the finite difference method of non-uniform grids because the Heston equation can be assumed to be a parabolic equation. The numerical solution of the finite difference method can solve derivative equations flexibly and do not require matrix processing. But it requires a heavy and slow computing process because there are many elements of calculation and iteration. This study proposes a numerical solution to the finite difference method by using the Compute Unified Device Architecture (CUDA) parallel programming to solve the Heston equation model that applies the concept of stochastic volatility to get accurate and fast results. The results of this research proved 15.52 times faster in conducting parallel computing processes with error of 0.0016.

Developing Students\u27 Reading Culture for Academic Reading Level Through Metacognitive Strategies

The development of reading culture for this academic level used the metacognitive strategies. This research was a case study in wihich the data sources were randomly drawn from various print sources by cutting off the core of the text to be analyzed. The technique of data analysis was done with two steps. The first step was to identify each data based on the categories that contained in the cognitive domain. The second step was the results of the analysis of the cognitive domains viewed based on the metacognitive strategies that included the process of planning, monitoring, and evaluate. The results of the research are that reading culture at the academic level can be developed through metacognition strategy. The steps that can be done are the reader performs cognitive process starting from remembering, understanding, applying, analyzing, evaluating, and creating. Then, every cognitive process is reviewed that the Academic reading culture can be developed with metacognitive strategies that are the type of intensive reading includes, reading comprehension, reading critically, reading creatively, reading interpretative, and reading reflective

Mendidik Calon Pendidik

Mendidik adalah sebuah proses. Ia akan memerlukan waktu. Sebab, apapun namanya, sebuah proses adalah terjalinnya hubungan antara berbagai besaran dalam kurun waktu tertentu. Dalam hal ini pendidikan merupakan alat sekaligus, tempat terjadinya proses itu. Dengan kata lain, pendidikan adalah proses transformasi nilai yang diberikan oleh pendidik kepada terdidik (Burrow, 1984, hal. 51). Transformasi ini akan berjalan terus sesuai dengan mekanisme pendidikann yang telah ditetapkan. Dari sini, akan terlihat keterlibatan masing-masing pihak dalam proses pendidikan ini. Proses pendidikan ini, yang dipandang jauh lebih luas dari proses pengajaran, (Lock, 1976, hal. 17) akan menjembatani kesenjangan antara subjek didik dan objek didik. Namun, itu akan memerlukan proses. Dan, proses ini memerlukan waktu yang relatif lama. Inilah sebabnya, proses pendidikan itu terjadi dalam jangka waku yang sangat panjang. Bahkan, bisa dikatakan seumur hidup. Konsep ini tentu tidak bedebihan, mengingat pendidikan itu bisa diterjemahkan ke dalam berbagai segi, menurut pandangan masing-masing pihak. Artinya, jika pendidikan ditinjau melalui pendekatan human behavior, maka ia berorientasi pada USAha untuk merubah tingkah laku seseorang. Terlepas dari akibat buruk yang mungkin terjadi selama proses pendidikan itu berlangsung (Lock, ibid, hal. 29)

Seninya Mengajar, dan Mengajar Seni

Disadari bahwa ketika pengajar berdiri di depan kelas. ia telah memerankandirinya sendiri sebagai aktor. Siswa bisa dianggap sebagai publik atau penonton.Dan, kelas dianggap sebagai panggung pertunjukan. Dengan demikian, pengajarmoo tidal< mau harus berakting ketika ia rnenyampaikan materi ajarannya. Untukitulah, pengajar nampaknya rnemerlukan bekal pengetaQuan prinsip dasar dramatuTgi.Pengetahuan akan prinsip dasar dramaturgi ini akan menyebabkan pengajarmampu berakting dan menempatkan dirinya dengan tepat dan benar di depan kelas

A Novel Space-time Discontinuous Galerkin Method for Solving of One-dimensional Electromagnetic Wave Propagations

In this paper we propose a high-order space-time discontinuous Galerkin (STDG) method for solving of one-dimensional electromagnetic wave propagations in homogeneous medium. The STDG method uses finite element Discontinuous Galerkin discretizations in spatial and temporal domain simultaneously with high order piecewise Jacobi polynomial as the basis functions. The algebraic equations are solved using Block Gauss-Seidel iteratively in each time step. The STDG method is unconditionally stable, so the CFL number can be chosen arbitrarily. Numerical examples show that the proposed STDG method is of exponentially accuracy in time

Deep Convolutional Level Set Method for Image Segmentation

Level Set Method is a popular method for image segmentation. One of the problems in Level Set Method is finding the right initial surface parameter, which implicitly affects the curve evolution and ultimately the segmentation result. By setting the initial curve too far away from the target object, Level Set Method could potentially miss the target altogether, whereas by setting the initial curve as general as possible "“ i.e. capturing the whole image "“ makes Level Set Method susceptible to noise. Recently, deep-learning methods, especially Convolutional Neural Network (CNN), have been proven to achieve state-of-the-art performance in many computer vision tasks such as image classification and detection. In this paper, a new method is proposed, called Deep Convolutional Level Set Method (DCLSM). The idea is to use the CNN object detector as a prior for Level Set Method segmentation. Using DCLSM it is possible to significantly improve the segmentation accuracy and precision of the classic Level Set Method. It was also found that the prior used in the proposed method is the lower and upper bound for DCLSM's precision and recall, respectively