Jurnal Singkat

https://github.com/kivy/python-for-android

http://www.economist.com/blogs/graphicdetail/2015/05/daily-chart-3 MORE religious countries tend to be less innovative, according to a paper published last month by America’s National Bureau of Economic Research. In “Forbidden Fruits: The Political Economy of Science, Religion, and Growth”, Roland Benabou of Princeton and Davide Ticche and Andrea Vindigni of the IMT Institute for Advanced Studies Lucca find a strong negative correlation between innovation, as measured by patents, and religiosity, measured by the share of a population that self-identifies as religious. “I am interested in how people form beliefs that are relevant to economics,” says Mr Benabou. “That thought takes you to belief with a capital B, and that’s religion.” MORE religious countries tend to be less innovative, according to a paper published last month by America’s National Bureau of Economic Research. In “Forbidden Fruits: The Political Economy of Science, Religion, and Growth”, Ro

http://www.businessinsider.co.id/why-this-google-engineer-gave-up-on-silicon-valley-2017-3/ Why this Google engineer gave up on Silicon Valley and moved back to India Read more at http://www.businessinsider.co.id/why-this-google-engineer-gave-up-on-silicon-valley-2017-3/ By all accounts, including her own, Nupur Dave had the dream life. A native of India, she had spent the past decade living in the US. She was working at Google at the perk-filled “Googleplex” headquarters in Mountain View, California, at a job she loved. And she had obtained a permanent residence, her green card. She was a manager for a part of Google called Network Content Distribution, the network tech that makes Google run faster (in geek speak: it’s Google’s homegrown alternative to a content distribution network like Akamai). And the opportunities for promotion were plentiful. “I got to travel all over the world, attend conferences,” she told Business Insider.”It was great. The team was

https://www.lrb.co.uk/v39/n07/daniel-soar/the-most-expensive-weapon-ever-built On the night of 12 January, there was a series of explosions at Mezzeh military airport on the outskirts of Damascus. A few warehouses were destroyed but no one died. The Syrian government blamed rocket attacks launched from inside Israel. The targets were missile systems, sources close to the Israeli government said, that could have been delivered to Hizbullah in Lebanon. According to some reports, however, the attacks were carried out not by rockets but by fighter jets – specifically, the Lockheed Martin F-35 Lightning II, the Joint Strike Fighter, a US-built ‘fifth generation’ stealth jet with super-advanced avionics that has been under development for the last twenty years at a cost of many billions of dollars. In early March, a reporter for the Figaro , Georges Malbrunot, said that according to French intelligence sources the strikes had unquestionably been carried out by a pair of F-35

https://sysprogs.com/w/how-we-turned-8-popular-stm32-boards-into-powerful-logic-analyzers/ How We Turned 8 Popular STM32 Boards into Powerful Logic Analyzers March 23, 2017 Ivan Shcherbakov The idea of making a “soft logic analyzer” that will run on top of popular prototyping boards has been crossing my mind since we first got acquainted with the STM32 Discovery and Nucleo boards. The STM32 GPIO is blazingly fast and the built-in DMA controller looks powerful enough to handle high bandwidths. So having that in mind, we spent several months perfecting both software and firmware side and here is what we got in the end. Capturing the signals The main challenge when using a microcontroller like STM32 as a core of a logic analyzer is dealing with sampling irregularities. Unlike FPGA-based analyzers, the microcontroller has to share the same resources to load instructions from memory, read/write the program state and capture the external inputs from the G

http://asia.nikkei.com/Business/Companies/What-GE-saw-but-Toshiba-didn-t TOKYO -- The links and parallels between General Electric and  Toshiba  go way back. Today, however, it is their glaring differences that perhaps stand out most. Thomas Edison, the American inventor who founded GE, used to say "make money" a lot. Ichisuke Fujioka, a Toshiba founder, learned how to make an incandescent lightbulb from Edison and began mass-producing it in Japan. Neither GE nor Toshiba produces incandescent lightbulbs anymore, and both have sold off home appliance businesses. Today, GE is the world's 11th-largest company by market capitalization. Toshiba, in contrast, is on the verge of collapse due to fraudulent accounting practices and massive losses stemming from its nuclear operations. Why did Toshiba stumble? The difference between the two old lightbulb makers appears to lie in their current business models -- how they make money. Meltdown Hideki Wakabayashi,

6.849: Geometric Folding Algorithms: Linkages, Origami, Polyhedra (Fall 2010) http://courses.csail.mit.edu/6.849/fall10/lectures/

https://teachyourselfcs.com/ Teach Yourself Computer Science If you’re a self-taught engineer or bootcamp grad, you owe it to yourself to learn computer science. Thankfully, you can give yourself a world-class CS education without investing years and a small fortune in a degree program 💸. There are plenty of resources out there, but some are better than others. You don’t need yet another “200+ Free Online Courses” listicle. You need answers to these questions: Which subjects should you learn, and why? What is the best book or video lecture series for each subject? This guide is our attempt to definitively answer these questions. TL;DR: Study all nine subjects below, in roughly the presented order, using either the suggested textbook or video lecture series, but ideally both. Aim for 100-200 hours of study of each topic, then revist favorites throughout your career 🚀.

Problem Solving with Algorithms and Data Structures using Python (2015) http://interactivepython.org

https://www.wired.com/2017/03/physics-teachers-really-teach-numerical-calculations/ I love including numerical calculations in my introductory and advanced physics courses. I find that including coding in the introductory physics course forces students to think in different ways to better understand physics. Numerical calculations are such an integral part of real world physics that it would be wrong to not include them in the intro courses. And at this point, I think my colleagues have run out of excuses for not including numerical calculations in these classes. What is a numerical calculation? You might know it by another name—computational physics, numerical models, or even coding in physics. The basic idea is break a problem into many smaller problems. If you want an example, have a look at this older post on the three-body-problem . Although it’s usually the easiest to create a numerical calculation with a computer, you could technically do it on paper. This is th

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