Minimize nesting is often a neat idea though.
Cauchy-Lorentz: “Something alarmingly mathematical is happening, and you should probably pause to Google my name and check what field I originally worked in.”
Sun’s three-dimensional magnetic field during one full solar rotation (top), and
composite image generated from
photographs taken on the day of the total eclipse (bottom left) vs. the model’s predictions (bottom right).
Credits: Predictive Science Inc./Miloslav Druckmüller, Peter Aniol, Shadia Habbal/NASA Goddard, Joy Ng
Using machine learning for cross-lingual and cross-platform rumor verification
Preprint.- Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content – PDF – Code (GitHub)
Interesting, I guess over time it will be quite trusted depending precisely on the trust that platforms can offer, in this case Twitter, Google and its Chinese counterpart Baidu. However, common sense and education (natural intelligence as opposed to artificial intelligence) will remain essential as long as there are lots of folks who only believe (and share regardless of reliability) what fits in their pre-established and static schemes, and little lambs that treat as truth revealed all the stupid things they read on MSM or see on YouTube.
Multi-purpose silicon chip created for quantum information processing
The Bristol team has been using silicon photonic chips as a way to try to build quantum computing components on a large scale and today’s result, published in the journal Nature Photonics, demonstrates it is possible to fully control two qubits of information within a single integrated chip. This means any task that can be achieved with two qubits, can be programmed and realised with the device.
Image credit: Xiaogang Qiang/University of Bristol
Hi! I am new to Medium and made this tutorial on the Pandas Framework for Python. Please tell me what you think!
A cool short introduction to Python + Pandas. Thanks!
Actually I’m a R enthusiast, but I’d say the tool (Python, R, Julia, C++, Java, etc…) is a secondary factor, the real core of data science is Mathematics, singularly Statistic, then Computer Science, and only after that, the tool. In my opinion both Python and R (with or without Jupyter) are powerful enough to enter happily in this wonderful field.
Image from Wikipedia article “Exploratory data analysis”:
As Sean Carroll says in his FB page:
The shortest math paper ever reminds us why mathematicians think that P doesn’t equal NP, even if they can’t yet prove it. It’s much easier to check solutions to problems (P) than it is to actually solve them (NP).
Below you can see how a CDC 600 computer looks like around 1964-1969.
[Image credit: Jitze Couperus, Supercomputer – The Beginnings – Flickr]
Blockchain rudimentals, cryptography and computer science aren’t needed in order to grasp the basic principles. A cool infographic. Below “the record”, “the block” and “the chain”:
My first code in Mathematica goes back to V2 (1991) for the first reliable graphical environment from Microsoft (Windows 3.0, 3.1), lab reports improved instantly. Many things have changed in three decades in the world of computer algebra systems, even to the point to be of (irremediably) minority use, mostly because there are plenty alternatives both for high (Matlab, Octave, Sage) and low level (C, C++, Fortran…), or both (Python, Java, R…). But for people of a couple of generations (those born in the 60s and 70s or so) coming from an almost purely analogical world, seeing a pioneer (back then and now) of that generation as Stephen Wolfram (1959), posting this about the software that he himself had developed from the scratch before his 30 birthday, well, it makes us… happily nostalgic.