PENERAPAN MODEL PEMBELAJARAN PROBLEM SOLVING UNTUK MENINGKATKAN HASIL BELAJAR KETERAMPILAN LOB BERTAHAN PERMAINAN BULUTANGKIS DI SMP N 2 LEMBANG (Penelitian Tindakan Kelas)

Penelitian ini bertujuan untuk mengungkapkan apakah penerapan metode pembelajaran Problem Solving pada pembelajaran aktivitas permainan Bulutangkis dalam pembelajaran pendidikan jasmani dapat meningkatkan hasil belajar lob bertahan di Sekolah Menengah Pertama (SMP). Penelitian dilaksanakan dengan metode penelitian tindakan kelas yang terdiri atas tahapan perencanaan tindakan, pelaksanaan tindakan, observasi dan refleksi. Penelitian dilaksanakan terhadap siswa kelas VIIi SMPN 2 Lembang yang berjumlah 37 siswa terdiri dari 21 siswa laki-laki dan 16 siswa perempuan. Proses penelitian terlebih dahulu dilakukan observasi pada Pra-siklus lalu dilakukan penelitian selama dua siklus masing-masing siklus terdiri dari dua tindakan penelitian. Setiap tindakan menggunakan tugas gerak pukuluan lob bertahan yang dikemas dalam bentuk penggunaan gerak dasar, permainan sederhana dan distimulus dengan berbagai masalah melalui variasi pemberian shutlecock. Data dikumpulkan dengan menggunakan instrumen observasi dan catatan lapangan. Semua data yang terkumpul dianalisis menggunakan teknik prosentase psikomotor. Kemudian setelah dilakukan penelitian pada siklus 2 tindakan 4 nilai aspek psikomotor siswa mengalami kenaikan 12,5%. Berdasarkan hasil analisis data tersebut, dapat disimpulkan bahwa penerapan model pembelajaran problem solving dapat meningkatkan hasil belajar keterampilan lob bertahan permainan bulutangkis siswa di SMPN 2 Lembang. This study aims to discover whether the implementation of problem-solving learning method in learning badminton game during physical education class can improve the learning outcomes of defensive lob in Junior High School level. The research was conducted using a Classroom Action Research method that consists of four stages, namely; action planning, application of action, observation and reflection. The study was conducted on a class of 37 seventh graders, consisting of 21 male students and 16 female students, in SMPN 2 Lembang. The research was started by doing an observation in the pre-cycle stage, then it was continued by the following two cycles, each cycle consists of two research actions. Every action related to defensive lob motion packaged were done under the disguise of basic motion, simple game and stimulated with a numerous issue through variations of shuttlecock pass. Data were collected using observation instruments and field notes. Every collected data was analyzed using psychomotor percentage techniques. Then, after research was done on the second cycle of action, four of the students’ psychomotor aspects were increased up to 12.5%. Based on the results of the data analysis, it can be concluded that the application of problem-solving learning model can improve the learning outcomes of defensive lob skill in badminton games of students at SMPN 2 Lembang. Keywords: Game, Badminton, Defensive Lob, Problem Solving

Bounds on Query Convergence

The problem of finding an optimum using noisy evaluations of a smooth cost function arises in many contexts, including economics, business, medicine, experiment design, and foraging theory. We derive an asymptotic bound E[ (x_t - x*)^2 ] >= O(1/sqrt(t)) on the rate of convergence of a sequence (x_0, x_1, >...) generated by an unbiased feedback process observing noisy evaluations of an unknown quadratic function maximised at x*. The bound is tight, as the proof leads to a simple algorithm which meets it. We further establish a bound on the total regret, E[ sum_{i=1..t} (x_i - x*)^2 ] >= O(sqrt(t)) These bounds may impose practical limitations on an agent's performance, as O(eps^-4) queries are made before the queries converge to x* with eps accuracy.Comment: 6 pages, 2 figure

Defining Illegal Insider Trading—Lessons From the European Community Directive on Insider Trading

The EC made a bold move towards defining precisely what conduct constitutes improper trading on nonpublic information with its Insider Trading Directive. The differences between the EC and US laws on insider trading are examined

Access Control Synthesis for Physical Spaces

Access-control requirements for physical spaces, like office buildings and airports, are best formulated from a global viewpoint in terms of system-wide requirements. For example, "there is an authorized path to exit the building from every room." In contrast, individual access-control components, such as doors and turnstiles, can only enforce local policies, specifying when the component may open. In practice, the gap between the system-wide, global requirements and the many local policies is bridged manually, which is tedious, error-prone, and scales poorly. We propose a framework to automatically synthesize local access control policies from a set of global requirements for physical spaces. Our framework consists of an expressive language to specify both global requirements and physical spaces, and an algorithm for synthesizing local, attribute-based policies from the global specification. We empirically demonstrate the framework's effectiveness on three substantial case studies. The studies demonstrate that access control synthesis is practical even for complex physical spaces, such as airports, with many interrelated security requirements

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.Comment: 12 pages, 9 figure

User's guide for atmospheric carbon monoxide transport model

In the winter months of Fairbanks, Alaska, a highly stable air temperature inversion creates high levels of carbon monoxide (CO) concentrations. As an aid to understanding this problem, a CO transport computer model has been created which provides a useful tool when used in conjunction with other measurement and analytic studies of traffic, meteorology, emissions control, zoning, and parking management. The model is completely documented and illustrated with several examples. Named ACOSP (Atmospheric CO Simulation Program), it predicts expected CO concentrations within a specific geographic area for a defined set of CO sources. At the present time, the model is programmed to consider automobile emissions as the major CO source and may include estimates of stationary sources. The model is coded for computer solution in the FORTRAN programming language and uses the finite-element method of numerical solution of the basic convective-diffusion equations. Although it has a potential for real-time analysis and control, at the present time the model will be most valuable for investigating and understanding the physical processes which are responsible for high CO levels and for testing remedial control measures at high speed and low cost