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.

Jamie Metzl is a Senior Fellow for Technology and National Security at the Atlantic Council. After 4 billion years of evolution by one set of rules, our species is about to begin evolving by another. Overlapping and mutually reinforcing revolutions in genetics, information technology, artificial intelligence, big data analytics, and other fields are providing the tools that will make it possible to genetically alter our future offspring should we choose to do so. Nearly everybody wants to have cancers cured and terrible diseases eliminated. Most of us want to live longer, healthier and more robust lives. Genetic technologies will make that possible. But the very tools we will use to achieve these goals will also open the door to the selection for and ultimately manipulation of non-disease-related genetic traits -- and with them a new set of evolutionary possibilities.

After being stranded in the Mediterranean for three days, fear had overcome Alou Sango. "I thought that we would all die, because there was nothing left, the petrol had finished," he says of his journey from Libya. Like thousands before him, Sango boarded an overcrowded boat to escape the country's turmoil after being unable to return to his native Mali. But after days at sea the captain lost his way and, without a GPS position to give to the Italian authorities, the 100 or so passengers were losing hope. Their rubber dinghy was finally spotted by a Chinese vessel, which picked up the migrants and took them to Italy, where Sango, now 24, is studying through a Rome-based charity, Sant'Egidio Community.

Dreams are successions of images, ideas, emotions, and sensations that occur usually involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not definitively understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

We present algorithms to effectively represent a set of Markov decision processes (MDPs), whose optimal policies have already been learned, by a smaller source subset for lifelong, policy-reuse-based transfer learning in reinforcement learning. This is necessary when the number of previous tasks is large and the cost of measuring similarity counteracts the benefit of transfer. The source subset forms an `$\epsilon$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<\epsilon$ regret in $M_p$. Our contributions are as follows. We present EXP-3-Transfer, a principled policy-reuse algorithm that optimally reuses a given source policy set when learning for a new MDP. We present a framework to cluster the previous MDPs to extract a source subset. The framework consists of (i) a distance $d_V$ over MDPs to measure policy-based similarity between MDPs; (ii) a cost function $g(\cdot)$ that uses $d_V$ to measure how good a particular clustering is for generating useful source tasks for EXP-3-Transfer and (iii) a provably convergent algorithm, MHAV, for finding the optimal clustering. We validate our algorithms through experiments in a surveillance domain.