Darwin's theory of evolution is a simple but powerful framework that explains how complexity can come from simplicity: how everything biological around us - from the microbial biofilms on your teeth to the majestic redwood trees - emerged from the very simplest of beginnings. How exactly this happened is, of course, a matter of intense research. Each species is finely adapted to thrive in its environment, which in turn has shaped that species' evolutionary history. But those environmental forces exerted on a species occurred over a very long period of time, in the often very distant past. How can we understand which environmental features were responsible for which adaptations we see today?

There have been very few video game characters as well conceived as Nathan Drake. Exuding charm and determination, strength and vulnerability, he is the sort of male lead great Hollywood screenwriters aspire to create, but that video games have tended to bypass in favour of gritty, cynical sociopaths on mindless quests for retribution. Nathan is a character you care for and want to protect, even when he makes awful decisions that will hurt the people he loves. In Uncharted 4 he gets the conclusion he and his fans deserve – a rollicking, globe-trotting adventure that manages to be funny and exciting, yet also touched with sadness. We soon begin to realise that Nathan's quest to discover Libertalia, the fabled anarchist utopia set up by pirate Henry Avery, is symbolic of his whole career as a treasure hunter.

As Artificial Intelligence (AI) becomes more of a talking point in esoteric circles, two organisations working on a project based in Pittsburgh are using it to expand personalised healthcare. Teams from Carnegie Mellon University and the University of Pittsburgh are part of project financed by a University of Pittsburgh Medical Center for six years at between 10 million and 20 million per year. Computerworld has a report that the project's taking data from EHRs, diagnostic imaging, prescriptions, genomic profiles, insurance records and wearable devices to create healthcare plans by disease and specific types of people. These personal traits are seen as foundation to develop apps, machine learning tools and services from the project. A smartphone app for people to use to live healthier lives and ward off some diseases and condition's scheduled for release in about a year.

Samsung's Artik 10 developer board will compete with the Raspberry Pi 3, but not on price. The Artik 10 is priced at US 150, and can be ordered from Digi-Key's website. That's a much higher price than the 35 Raspberry Pi 3, but the Artik 10 offers a better GPU and more storage. The Artik 10 has all its components mashed onto a circuit board, much like Raspberry Pi. The board is targeted at enthusiasts looking to make smart gadgets, appliances, robots, drones, sensor devices, and industrial automation equipment.

Nick Lane is an evolutionary biochemist at University College London who thinks about the big questions of life: how it began, how it is maintained, why we age and die, and why we have sex. Shunning the habit of our times to regard these as questions for evolutionary genetics, Lane insists that our fundamental biochemical mechanisms--particularly those through which living cells generate energy--may determine or limit these facts of life. Lane has been steadily constructing an alternative, complementary view of evolution to the one in which genes compete for reproductive success and survival. He has argued that some of the big shifts during evolutionary history, such as the appearance of complex cells called eukaryotes (like our own) and the emergence of multicellular life forms, are best understood by considering the energetic constraints. Lane's book Life Ascending: The Ten Great Inventions of Evolution was awarded the 2010 Royal Society Science Books Prize, the top prize in the United Kingdom for books on science. His 2015 book The Vital Question: Why Is Life the Way It Is? has been described as "game-changing" and "brimming with bold and important ideas." It offers a new, detailed model for how life might have begun by harnessing the incipient chemical energy at deep-sea vents. Bill Gates called The Vital Question "an amazing inquiry into the origins of life."

The purpose of this article is to demonstrate how the practical Data Scientist can implement a Locality Sensitive Hashing system from start to finish in order to drastically reduce the search time typically required in high dimensional spaces when finding similar items. Locality Sensitive Hashing accomplishes this efficiency by exponentially reducing the amount of data required for storage when collecting features for comparison between similar item sets. In other words, Locality Sensitive Hashing successfully reduces a high dimensional feature space while still retaining a random permutation of relevant features which research has shown can be used between data sets to determine an accurate approximation of Jaccard similarity [2,3]. The concept of Locality Sensitive Hashing has been around for some time now with publications dating back as far as 1999 [1] exploring its use for breaking the curse of dimensionality in nearest neighbor query problems. Since this time various applications of Locality Sensitive Hashing have been making appearances in academic publications all over the world.

In Mediterranean waters, off the coast of France, a diver recently visited the shipwreck La Lune -- a vesssel in King Louis XIV's fleet -- which lay untouched and unexplored on the ocean bottom since it sank in 1664. But the wreck's first nonaquatic visitor in centuries wasn't human -- it was a robot. Dubbed "OceanOne," the bright orange diving robot resembles a mecha-mermaid. It measures about 5 feet (1.5 meters) in length and has a partly human form: a torso, a head -- with stereoscopic vision -- and articulated arms. Its lower section holds its computer "brain," a power supply, and an array of eight multidirectional thrusters.

Nobody wants to be fat and men don't often want to be seen as effeminate, but for those in the gay community who live at the intersection of such identities, life can be like the worst case of double jeopardy. For Jamal Lewis however, who is also black and gender deviant, being fat and effeminate is a source of power, and a subject worthy of exploration in a documentary titled "No Fats, No Femmes." "For me, I'm just interested in the spaces that people are afraid to occupy," said Lewis, who uses "he-she" as a gender pronoun. "I think there is something to be learned from what we are most afraid of and so, if that's what I was taught to be afraid of, well [forget] that. I am the Fat Femme." Jamal Lewis, director of "No Fats, No Femmes," poses for a portrait on Third Street and Broadway in Los Angeles, Calif.

Being overweight carries with it a social stigma, as does being a man who embraces the feminine, but for those in the gay community who live at the intersection of such identities, life can be like the worst case of double jeopardy. To Jamal Lewis, however, who is also black and who identifies as "gender deviant," being fat and effeminate is a source of power and a subject worthy of exploration in a documentary titled "No Fats, No Femmes." "For me, I'm just interested in the spaces that people are afraid to occupy," said Lewis, who uses "he-she" as a gender pronoun. "I think there is something to be learned from what we are most afraid of, and so, if that's what I was taught to be afraid of, well [forget] that. I am the Fat Femme."

Distributional (or distribution-valued) data are a new type of data arising from several sources and are considered as realizations of distributional variables. A new set of fuzzy c-means algorithms for data described by distributional variables is proposed. The algorithms use the $L2$ Wasserstein distance between distributions as dissimilarity measures. Beside the extension of the fuzzy c-means algorithm for distributional data, and considering a decomposition of the squared $L2$ Wasserstein distance, we propose a set of algorithms using different automatic way to compute the weights associated with the variables as well as with their components, globally or cluster-wise. The relevance weights are computed in the clustering process introducing product-to-one constraints. The relevance weights induce adaptive distances expressing the importance of each variable or of each component in the clustering process, acting also as a variable selection method in clustering. We have tested the proposed algorithms on artificial and real-world data. Results confirm that the proposed methods are able to better take into account the cluster structure of the data with respect to the standard fuzzy c-means, with non-adaptive distances.