“Signals are types of messages sent by an operating system to a process such as a Perl program. Signals provide a method for communicating with a process, for example when running a command line program pressing control-c will send the interrupt signal (‘SIGINT’) to the program by default terminating it. Signals are often unexpected and if not handled can leave your Perl program or data in an unfinished state. This article describes some useful Perl programming tools for gracefully handling signals…”
use sigtrap qw/die normal-signals/;
“Human logic, unlike that of the machines which we program and use every day, isn’t perfect. We make mistakes, we establish bad mental habits, and we have many cognitive biases that negatively impact our ability to be successful engineers. I want to go over five of the most common biases that I see on a regular basis as a software engineer…”
“Let me say from the beginning that this is not meant as a flame post. I’m genuinely interested in some issues related to Erlang’s adoption and how people outside of its community see its fitness for the domain where the insiders know it shines – building distributed systems…”
“what do Erlang programmers think about Go stealing some of the mindshare (and job-share) in the area of building distributed systems? Why would if be a good option? Or not an option at all? Just professional opinions based on your experience with Erlang please…”
“Choosing the right EC2 instance can be tricky. If you’ve tried spinning up the same application in different regions, which is business as usual when it comes to cross-region disaster recovery, you know not every instance is always available in different regions. As a result, the challenge is to find an available EC2 instance that most closely meets your needs. While the AWS documentation does point you in the right direction, the information you need to make an educated decision is scattered across several pages…”
“A team of scientists in Finland has used a topographical self-reported method to reveal the effects that different emotional states have on bodily sensations. After five experiments and over 700 participants from Finland, Sweden and Taiwan, who reported where on their bodies they felt different emotions, the scientists discovered surprising consistencies. Their research findings were published in the Proceedings of the National Academy of Sciences…”
“Torch7 is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to an easy and fast scripting language, LuaJIT, and an underlying C implementation.
Among other things, it provides:
a powerful N-dimensional array
lots of routines for indexing, slicing, transposing, …
amazing interface to C, via LuaJIT
linear algebra routines
neural network, and energy-based models
numeric optimization routines
To get an overview of the Torch7 ecosystem, check this page…”
“Every decision is risky business. Selecting the best time to stop and act is crucial. When Microsoft prepares to introduce Word 2020, it must decide when to quit debugging and launch the product. When a hurricane veers toward Florida, the governor must call when it’s time to stop watching and start evacuating. Bad timing can be ruinous. Napoleon learned that the hard way after invading Russia. We face smaller-consequence stopping decisions all the time, when hunting for a better parking space, responding to a job offer or scheduling retirement.
The basic framework of all these problems is the same: A decision maker observes a process evolving in time that involves some randomness. Based only on what is known, he or she must make a decision on how to maximize reward or minimize cost. In some cases, little is known about what’s coming. In other cases, information is abundant. In either scenario, no one predicts the future with full certainty. Fortunately, the powers of probability sometimes improve the odds of making a good choice.
While much of mathematics has roots that reach back millennia to Euclid and even earlier thinkers, the history of probability is far shorter. And its lineage is, well, a lot less refined. Girolamo Cardano’s famed 1564 manuscript De Ludo Aleae, one of the earliest writings on probability and not published until a century after he wrote it, primarily analyzed dice games. Although Galileo and other 17th-century scientists contributed to this enterprise, many credit the mathematical foundations of probability to an exchange of letters in 1654 between two famous French mathematicians, Blaise Pascal and Pierre de Fermat. They too were concerned with odds and dice throws—for example, whether it is wise to bet even money that a pair of sixes will occur in 24 rolls of two fair dice. Some insisted it was, but the true probability of a double six in 24 rolls is about 49.1 percent…”
“If there is one highly underrated concept in philosophy today, it is computation. Why is it so important? Because computationalism is the new mechanism. For millennia, philosophers have struggled when they wanted to express or doubt that the universe can be explained in a mechanical way, because it is so difficult to explain what a machine is, and what it is not. The term computation does just this: it defines exactly what machines can do, and what not. If the universe/the mind/the brain/bunnies/God is explicable in a mechanical way, then it is a computer, and vice versa…”