Some things you find on GitHub are more interesting and frightening than others.
This one is both and some more. What is it you ask?
R2 Bitcoin Arbitrager is an automatic arbitrage trading application targeting Bitcoin exchanges.
So it’s buying and selling Bitcoins. And it’s doing this on different markets. On the topic of arbitrage Wikipedia has something to say:
In economics and finance, arbitrage is the practice of taking advantage of a price difference between two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices at which the unit is traded.
For example, an arbitrage opportunity is present when there is the opportunity to instantaneously buy something for a low price and sell it for a higher price.
Now this already is the second version of the tool and already 2 years old. See it as some sort of interesting archeological specimem. Please refrain to actually so something harmful with it.
I am writing this down here because apart from it’s obvious horrors this is a good starting point to understand how these computer-trading-systems do work in principle.
Given that an architectural drawing is also included it gives all sorts of starting points to thoughts.
Also. What could possibly go wrong if a tool to buy/sell on actual markets with actual bitcoins is confident enough to include the “maxTargetProfit” configuration option. Effectively setting the top-line of profit you’re going to make!!!111
It’s 3.8 kg and delivers 25.5 kg of force. Impressive! And it’s in stores (in Japan).
The “Every Muscle Suit” has a lot going for it. Weighing just 3.8 kilograms, the pneumatic artificial muscle suit is powerful enough to generate up to 25.5 kilogram-force and effectively relieves pressure on users’ backs when performing activities like heavy lifting.
Best of all, its streamlined design conceals an advanced air pressure system that doesn’t require electricity or batteries.
Please read this first paragraph and let it settle:
At the core of the BrainScaleS wafer-scale hardware system (see Figure 90) is an uncut wafer built from mixed-signal ASICs , named High Input Count Analog Neural Network chips (HICANNs), which provide a highly configurable substrate that physically emulates adaptively spiking neurons and dynamic synapses (Schemmel et al. (2010), Schemmel et al. (2008)).
I’ve highlighted in bold the portion that I want you to think about once more. We are not talking about chips, dies or cut-up wafers.
We are talking about real-size, huge, fully developed wafers filled with logic. For the sole purpose of brain scale neural network research and development…
The Neuromorphic Computing Platform allows neuroscientists and engineers to perform experiments with configurable neuromorphic computing systems. The platform provides two complementary, large-scale neuromorphic systems built in custom hardware at locations in Heidelberg, Germany (the “BrainScaleS” system, also known as the “physical model” or PM system) and Manchester, United Kingdom (the “SpiNNaker” system, also known as the “many core” or MC system). Both systems enable energy-efficient, large-scale neuronal network simulations with simplified spiking neuron models. The BrainScaleS system is based on physical (analogue) emulations of neuron models and offers highly accelerated operation (104 x real time). The SpiNNaker system is based on a digital many-core architecture and provides real-time operation.
The machine-learning tooling is getting better. Take a look at Perceptilabs:
Fast modeling With our drag and drop GUI we enable fast model development.
Increased transparency The statistical dashboard increases the model’s transparency during training. Get a better understanding of your model with instant feedback on the operations outputs. We enable fast error debugging with our custom code editor.
Flexibility Full flexible options for plugins and importing. Execute any custom Python code in our code editor.
The world is awash in bullshit. Politicians are unconstrained by facts. Science is conducted by press release. Higher education rewards bullshit over analytic thought. Startup culture elevates bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit — and take advantage of our lowered guard to bombard us with bullshit of the second order. The majority of administrative activity, whether in private business or the public sphere, seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit.
We’re sick of it. It’s time to do something, and as educators, one constructive thing we know how to do is to teach people. So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bullshit, seeing through it, and combating it with effective analysis and argument.
This application generates a random medieval city layout of a requested size. The generation method is rather arbitrary, the goal is to produce a nice looking map, not an accurate model of a city. Maybe in the future I’ll use its code as a basis for some game or maybe not.
“Shepard’s Pi” is one continous song that lasts for 999,999,999 hours, or about 114 years.
Canton Becker’s music generating algorithm composed this music using the first one billion digits of pi (p). Each digit (3.1415…) determines four seconds of music, supplying the “turn signals” used to determine every musical expression.
Because the numbers in pi never repeat, each of the million hours of “Shepard’s Pi” music are in fact unique. By fast forwarding to some distant moment in the song, you are virtually guaranteed to find yourself listening to something that nobody else including Canton himself has ever heard before.
Apparently my choice of purchasing the HD-DVD drive for the Xbox 360 will ultimately pay off!! As we all know Bluray won that format war back in the days.
But now it seems that this below would be useable for something:
Over the life of nuclear fuel, inhomogeneous structures develop, negatively impacting thermal properties. New fuels are under development, but require more accurate knowledge of how the properties change to model performance and determine safe operational conditions.
Measurement systems capable of small–scale, pointwise thermal property measurements and low cost are necessary to measure these properties and integrate into hot cells where electronics are likely to fail during fuel investigation. This project develops a cheaper, smaller, and easily replaceable Fluorescent Scanning Thermal Microscope (FSTM) using the blue laser and focusing circuitry from an Xbox HD-DVD player.
As RISC-V progressively challenges the existing ARM processor ecosystem it’s interesting to see more and more software projects popping up that aim that RISC-V architecture.
Here’s one project that aims to develop (and explain along the way) how to create an operating system from scratch. On top of the RISC-V specifics this tutorial also aims to teach how this all can be done in a programming language called Rust.
Keep in mind that all of this is done on a baremetal system. No other software is running.
RISC-V (“risk five”) and the Rust programming language both start with an R, so naturally they fit together. In this blog, we will write an operating system targeting the RISC-V architecture in Rust (mostly). If you have a sane development environment for RISC-V, you can skip the setup parts right to bootloading. Otherwise, it’ll be fairly difficult to get started.
This tutorial will progressively build an operating system from start to something that you can show your friends or parents — if they’re significantly young enough. Since I’m rather new at this I decided to make it a “feature” that each blog post will mature as time goes on. More details will be added and some will be clarified. I look forward to hearing from you!
Can you display VGA and play audio on a Cortex-M4 in pure Rust? The short answer is yes, yes you can! Minus the hand-unrolled assembler loop for fixing the phase error in the RGB output. But we don’t talk about that in polite company.
The Atari Joystick interface works, but two Joysticks would be more fun
The PS/2 Keyboard via the Atmega works, but the pinout was mirrored so you have to put the connector under the PCB :/
The RTC works
VGA Output works
The MIDI Out seems to work when looped to MIDI In, as does the MIDI Though.
The MIDI In seems to receive data when connected to my electronic drum kit
The Audio output seems to work quite nicely
The SD card works, but the power supply can’t handle hot-insertion of the SD card and it makes the TM4C reboot. More capacitors / some current limiting probably required.
I can load games and programs from the SD card into the 24 KiB of free Application RAM. You can interact with these games via the PS/2 Keyboard and Joystick. I can play simple games (like Snake) and play three channels of 8-bit wavetable audio simultaneously. I’ve even got a 6502 Emulator running a copy of 6502 Enhanced BASIC, if you want to go old school!
The synergistically incorporated CNT–metal hierarchical architectures offer record-high broadband optical absorption with excellent electrical and structural properties as well as industrial-scale producibility.
Usually when we visited lectures the notes and explanations where given on a chalk board or a projector. With the lecturer looking away from the audience most of the time.
This is where Light Boards come in handy. They allow the lecturer to face his audience and give explanations on a board…
I was made aways by Ryan Heffernans tweet on the project he did together with his son. He built one of these light boards! The short clip above shows his son on their board.
My son and I built a lightboard. You write on it like a whiteboard, but you can face your audience and the writing is illuminated. Commercial versions cost around $10k, but we made ours for $400 in parts from Home Depot. Here’s how.
And what reminded me of this astonishing achievement.
Think of this: You are flying at >34k miles per hour. You are >18.5 billion miles away from earth. (It’ll take >16 hours at light speed one-way trip from earth to you). And on top, you are still able to send data back to earth at 159 bytes per second.
In quantum mechanics, wave function collapse occurs when a wave function—initially in a superposition of several eigenstates—reduces to a single eigenstate due to interaction with the external world. This interaction is called an “observation”. It is the essence of a measurement in quantum mechanics which connects the wave function with classical observables like position and momentum. Collapse is one of two processes by which quantum systems evolve in time; the other is the continuous evolution via the Schrödinger equation. Collapse is a black box for a thermodynamically irreversible interaction with a classical environment. Calculations of quantum decoherence predict apparent wave function collapse when a superposition forms between the quantum system’s states and the environment’s states. Significantly, the combined wave function of the system and environment continue to obey the Schrödinger equation.
After my first stationary trainer broke I bought a new one with the capability to measure wattage and also to apply resistance measured by the watt.
After looking at my average speeds, heart-rates and times on the device I was able to build a quite detailed understanding of the broader picture. What effects my power output and what does not. The effects of nutrition and health to what the body will deliver while being asked the exact same power output curve than the last time.
In a nutshell the numbers tell me that I am usually at a mediocre wattage of 150W constant load doing about 40 km/h average. My reserves usually allow me to go for 1-2 hours without a break doing this.
So far so good. Now I’ve found out from more serious cyclers that there’s something like “Functional Threshold Power“. I do regular have tests at the doctors to check for any heart-rate issues.
Reading about this Functional Threshold Power my curiousity is sparked.
How much could I do? Should I even go for measuring it?
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity.
AI and deep-learning is not always necessary or helpful. In this case impressive results have been achieved without the use of any of the hyped technologies.
In this case you give the algorithms two inputs. A video base that you want to stylize and a base picture that resembles the style you want to achieve.
We introduce a new example-based approach to video stylization, with a focus on preserving the visual quality of the style, user controllability and applicability to arbitrary video. Our method gets as input one or more keyframes that the artist chooses to stylize with standard painting tools. It then automatically propagates the stylization to the rest of the sequence. To facilitate this while preserving visual quality, we developed a new type of guidance for state-of-art patch-based synthesis, that can be applied to any type of video content and does not require any additional information besides the video itself and a user-specified mask of the region to be stylized. We further show a temporal blending approach for interpolating style between keyframes that preserves texture coherence, contrast and high frequency details. We evaluate our method on various scenes from real production setting and provide a thorough comparison with prior art.
Apparently there also is a Windows demo available in which you are supposedly be able to create your own stylized short clips. But as I wanted to try it out it threw a lot of funky messages regarding the application to be specifically untrustworthy / possibly malicious. So be aware and cautious.