Quantum Computing For Computer Scientists

Quantum computing is coming, so a lot of people are trying to articulate why we want it and how it works. Most of the explanations are either hardcore physics talking about spin and entanglement, or very breezy and handwaving which can be useful to get a little understanding but isn’t useful for applying the technology. Microsoft Research has a video that attempts to hit that spot in the middle — practical information for people who currently work with traditional computers. You can see the video below.

The video starts with basics you’d get from most videos talking about vector representation and operations. You have to get through about 17 minutes of that sort of thing until you get into qubits. If you glaze over on math, listen to the “index array” explanations [Andrew] gives after the math and you’ll be happier.

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It’s All In The Libs – Building A Plugin System Using Dynamic Loading

Shared libraries are our best friends to extend the functionality of C programs without reinventing the wheel. They offer a collection of exported functions, variables, and other symbols that we can use inside our own program as if the content of the shared library was a direct part of our code. The usual way to use such libraries is to simply link against them at compile time, and let the linker resolve all external symbols and make sure everything is in place when creating our executable file. Whenever we then run our executable, the loader, a part of the operating system, will try to resolve again all the symbols, and load every required library into memory, along with our executable itself.

But what if we didn’t want to add libraries at compile time, but instead load them ourselves as needed during runtime? Instead of a predefined dependency on a library, we could make its presence optional and adjust our program’s functionality accordingly. Well, we can do just that with the concept of dynamic loading. In this article, we will look into dynamic loading, how to use it, and what to do with it — including building our own plugin system. But first, we will have a closer look at shared libraries and create one ourselves.

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Keep it Close: A Private Git Server Crash Course

At this point, everyone has already heard that Microsoft is buying GitHub. Acquisitions of this scale take time, but most expect everything to be official by 2019. The general opinion online seems to be one of unease, and rightfully so. Even if we ignore Microsoft’s history of shady practices, there’s always an element of unease when somebody new takes over something you love. Sometimes it ends up being beneficial, the beginning of a new and better era. But sometimes…

Let’s not dwell on what might become of GitHub. While GitHub is the most popular web-based interface for Git, it’s not the only one. For example GitLab, a fully open source competitor to GitHub, is reporting record numbers of new repositories being created after word of the Microsoft buyout was confirmed. But even GitLab, while certainly worth checking out in these uncertain times, might be more than you strictly need.

Let’s be realistic. Most of the software projects hackers work on don’t need even half the features that GitHub/GitLab offer. Whether you’ve simply got a private project you want to maintain revisions of, or you’re working with a small group collaboratively in a hackerspace setting, you don’t need anything that isn’t already provided by the core Git software.

Let’s take a look at how quickly and easily you can setup a private Git server for you and your colleagues without having to worry about Microsoft (or anyone else) having their fingers around your code.

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Arduino and Pidgin C++

What do you program the Arduino in? C? Actually, the Arduino’s byzantine build processes uses C++. All the features you get from the normal libraries are actually C++ classes. The problem is many people write C and ignore the C++ features other than using object already made for them. Just like traders often used pidgin English as a simplified language to talk to non-English speakers, many Arduino coders use pidgin C++ to effectively code C in a C++ environment. [Bert Hubert] has a two-part post that isn’t about the Arduino in particular, but is about moving from C to a more modern C++.

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Calm Down: It’s Only Assembly Language

Based on [Ben Jojo’s] title — x86 Assembly Doesn’t have to be Scary — we assume that normal programmers fear assembly. Most hackers don’t mind it, but we also don’t often have an excuse to program assembly for desktop computers.

In fact, the post is really well suited for the typical hacker because it focuses the on real mode of an x86 processor after it boots. What makes this tutorial a little more interesting than the usual lecture is that it has interactive areas, where a VM runs your code in the browser after assembling with NASM.

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Using TensorFlow To Recognize Your Own Objects

When the time comes to add an object recognizer to your hack, all you need do is choose from many of the available ones and retrain it for your particular objects of interest. To help with that, [Edje Electronics] has put together a step-by-step guide to using TensorFlow to retrain Google’s Inception object recognizer. He does it for Windows 10 since there’s already plenty of documentation out there for Linux OSes.

You’re not limited to just Inception though. Inception is one of a few which are very accurate but it can take a few seconds to process each image and so is more suited to a fast laptop or desktop machine. MobileNet is an example of one which is less accurate but recognizes faster and so is better for a Raspberry Pi or mobile phone.

Collage of images for card datasetYou’ll need a few hundred images of your objects. These can either be scraped from an online source like Google’s images or you get take your own photos. If you use the latter approach, make sure to shoot from various angles, rotations, and with different lighting conditions. Fill your background with various other things and even have some things partially obscuring your objects. This may sound like a long, tedious task, but it can be done efficiently. [Edje Electronics] is working on recognizing playing cards so he first sprinkled them around his living room, added some clutter, and walked around, taking pictures using his phone. Once uploaded, some easy-to-use software helped him to label them all in around an hour. Note that he trained on 24 different objects, which are the number of different cards you get in a pinochle deck.

You’ll need to install a lot of software and do some configuration, but he walks you through that too. Ideally, you’d use a computer with a GPU but that’s optional, the difference being between three or twenty-four hours of training. Be sure to both watch his video below and follow the steps on his Github page. The Github page is kept most up-to-date but his video does a more thorough job of walking you through using the software, such as how to use the image labeling program.

Why is he training an object recognizer on playing cards? This is just one more step in making a blackjack playing robot. Previously he’d done an impressive job using OpenCV, even though the algorithm handled non-overlapping cards only. Google’s Inception, however, recognizes partially obscured cards. This is a very interesting project, one which we’ll be keeping an eye on. If you have any ideas for him, leave them in the comments below.

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Machine Learning Crash Course From Google

We’ve been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer vision, and even classifying radio signals. If you’ve yet to climb the learning curve, you might be interested in a new free class from Google using TensorFlow.

Of course, we’ve covered tutorials for TensorFlow before, but this is structured as a 15 hour class with 25 lessons and 40 exercises. Of course, it is also from the horse’s mouth, so to speak. Google says the class will answer questions like:

  • How does machine learning differ from traditional programming?
  • What is loss, and how do I measure it?
  • How does gradient descent work?
  • How do I determine whether my model is effective?
  • How do I represent my data so that a program can learn from it?
  • How do I build a deep neural network?

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