Phylogenetic approaches are finding more and more applications outside the field of biology. Astrophysics is no exception since an overwhelming amount of multivariate data has appeared in the last twenty years or so. In particular, the diversification of galaxies throughout the evolution of the Universe quite naturally invokes phylogenetic approaches. We have demonstrated that Maximum Parsimony brings useful astrophysical results, and we now proceed toward the analyses of large datasets for galaxies. In this talk I present how we solve the major difficulties for this goal: the choice of the parameters, their discretization, and the analysis of a high number of objects with an unsupervised NP-hard classification technique like cladistics. 1. Introduction How do the galaxy form, and when? How did the galaxy evolve and transform themselves to create the diversity we observe? What are the progenitors to present-day galaxies? To answer these big questions, observations throughout the Universe and the physical modelisation are obvious tools. But between these, there is a key process, without which it would be impossible to extract some digestible information from the complexity of these systems. This is classification. One century ago, galaxies were discovered by Hubble. From images obtained in the visible range of wavelengths, he synthetised his observations through the usual process: classification. With only one parameter (the shape) that is qualitative and determined with the eye, he found four categories: ellipticals, spirals, barred spirals and irregulars. This is the famous Hubble classification. He later hypothetized relationships between these classes, building the Hubble Tuning Fork. The Hubble classification has been refined, notably by de Vaucouleurs, and is still used as the only global classification of galaxies. Even though the physical relationships proposed by Hubble are not retained any more, the Hubble Tuning Fork is nearly always used to represent the classification of the galaxy diversity under its new name the Hubble sequence (e.g. Delgado-Serrano, 2012). Its success is impressive and can be understood by its simplicity, even its beauty, and by the many correlations found between the morphology of galaxies and their other properties. And one must admit that there is no alternative up to now, even though both the Hubble classification and diagram have been recognised to be unsatisfactory. Among the most obvious flaws of this classification, one must mention its monovariate, qualitative, subjective and old-fashioned nature, as well as the difficulty to characterise the morphology of distant galaxies. The first two most significant multivariate studies were by Watanabe et al. (1985) and Whitmore (1984). Since the year 2005, the number of studies attempting to go beyond the Hubble classification has increased largely. Why, despite of this, the Hubble classification and its sequence are still alive and no alternative have yet emerged (Sandage, 2005)? My feeling is that the results of the multivariate analyses are not easily integrated into a one-century old practice of modeling the observations. In addition, extragalactic objects like galaxies, stellar clusters or stars do evolve. Astronomy now provides data on very distant objects, raising the question of the relationships between those and our present day nearby galaxies. Clearly, this is a phylogenetic problem. Astrocladistics 1 aims at exploring the use of phylogenetic tools in astrophysics (Fraix-Burnet et al., 2006a,b). We have proved that Maximum Parsimony (or cladistics) can be applied in astrophysics and provides a new exploration tool of the data (Fraix-Burnet et al., 2009, 2012, Cardone \& Fraix-Burnet, 2013). As far as the classification of galaxies is concerned, a larger number of objects must now be analysed. In this paper, I

We give a complete characterization of the complexity of best-arm identification in one-parameter bandit problems. We prove a new, tight lower bound on the sample complexity. We propose the `Track-and-Stop' strategy, which we prove to be asymptotically optimal. It consists in a new sampling rule (which tracks the optimal proportions of arm draws highlighted by the lower bound) and in a stopping rule named after Chernoff, for which we give a new analysis.

Black-box alpha (BB-$\alpha$) is a new approximate inference method based on the minimization of $\alpha$-divergences. BB-$\alpha$ scales to large datasets because it can be implemented using stochastic gradient descent. BB-$\alpha$ can be applied to complex probabilistic models with little effort since it only requires as input the likelihood function and its gradients. These gradients can be easily obtained using automatic differentiation. By changing the divergence parameter $\alpha$, the method is able to interpolate between variational Bayes (VB) ($\alpha \rightarrow 0$) and an algorithm similar to expectation propagation (EP) ($\alpha = 1$). Experiments on probit regression and neural network regression and classification problems show that BB-$\alpha$ with non-standard settings of $\alpha$, such as $\alpha = 0.5$, usually produces better predictions than with $\alpha \rightarrow 0$ (VB) or $\alpha = 1$ (EP).

The satisfiability problem for SPARQL patterns is undecidable in general, since the expressive power of SPARQL 1.0 is comparable with that of the relational algebra. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound-constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is efficiently decidable by simple checks on bound variables and on the use of literals. The paper also points out that satisfiability for the so-called `well-designed' patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.

This year's Computex Taipei 2016 will mark a turning point for Asia's biggest tradeshow, as Taiwan begins asserting itself as being not only the center of the global ICT supply chain but also as a key partner for building the global technology ecosystem and driving innovation. This new positioning for the show will be highlighted in new exhibits including InnoVEX - a startup village - and iSTyle, which will feature a collection of Apple MFi certified products and accessories. A packed schedule of keynotes, panels, forums, and demos provide a rare opportunity for startups to connect with international VCs, angel investors, potential partners, and future customers in a single venue. InnoVEX events will focus on startup technology and entrepreneurial issues, from securing funding and partnerships to building and managing company growth. Participating companies will showcase the latest developments in peripherals, accessories and software for MFi certified products including cables, chargers, and connectors.

As SAP continues to makes its own transition into the cloud, the company is also focused on delivering applications that will help customers make that same change simpler. At this year's recently held SAP Sapphire Now conference in Orlando, Florida, a key theme for the company was introducing machine learning to its HANA cloud platform. CEO Bill McDermott predicted that over the next five to 10 years the hype will be around machine learning, artificial intelligence, and augmented reality. "I think very strongly that intelligent applications will fundamentally change the way you do work in the enterprise and the way you collaborate with your trading partners outside of the enterprise," McDermott said. He went on to say that machine learning has the capabilities to help businesses make more informed decisions around how they can better serve their end-customer.

The difference between Google's generated images of 2015, and the images generated in 2016. Last June, Google wrote that it was teaching its artificial intelligence algorithms to generate images of objects, or "dream." The A.I. tried to generate pictures of things it had seen before, like dumbbells. But it ran into a few problems. It was able to successfully make objects shaped like dumbbells, but each had disembodied arms sticking out from the handles, because arms and dumbbells were closely associated.

This post teaches you how to implement your own spam filter in under 100 lines of Python code. While doing this hands-on exercise, you'll work with natural language data, learn how to detect the words spammers use automatically, and learn how to use a Naive Bayes classifier for binary classification. The task is to distinguish between two types of emails, "spam" and "non-spam" often called "ham". The machine learning classifier will detect that an email is spam if it is characterised by certain features. The textual content of the email – words like "Viagra" or "lottery" or phrases like "You've won a 100,000,000 dollars!

Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive. Users asked each AI questions using normal language, not the robotic commands you're used to using with digital assistants.

The advancement of artificial intelligence may lead to sentient machines being granted'human' rights, Oxford University professor for the public understanding of science, Marcus du Sautoy, has said. Speaking at The Hay Literary Festival (via The Telegraph), Sautoy said: "It's getting to a point where we might be able to say this thing has a sense of itself and maybe there is a threshold moment where suddenly this consciousness emerges. One of the things I address in my new book is how can you tell whether my smartphone will ever be conscious. "The fascinating thing is that consciousness for a decade has been something that nobody has gone anywhere near because we didn't know how to measure it. We now have a telescope into the brain and it's given us an opportunity to see things that we've never been able to see before.