MOSUL, IRAQ – Iraqi forces said Monday that they have taken more territory from jihadists and were searching for militants and bombs on the edge of the Old City as they press an offensive in west Mosul. They are also striking IS with armed drones as part of a renewed push launched on March 5 that has forced the jihadis out of several neighborhoods and key sites, including the famed Mosul museum. West Mosul is the most-populated urban area still held by the jihadis, followed by Syria's Raqa, which is also a key target in the U.S.-led anti-IS campaign. Iraq's Joint Operations Command announced additional gains on Monday, saying that forces from the elite Counter-Terrorism Service had recaptured the Al-Nafat and Mosul al-Jadida neighborhoods. Lt. Gen. Raed Shakir Jawdat said that forces from the Rapid Response Division, another special forces unit, and the federal police were working to search and clear territory on the edge of Mosul's Old City.

This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the ``model-and-solve'' functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the model-and-solve paradigm. It would be misleading to conclude that constraint programming is ``better'' than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.

The multi-label classification framework, where each observation can be associated with a set of labels, has generated a tremendous amount of attention over recent years. The modern multi-label problems are typically large-scale in terms of number of observations, features and labels, and the amount of labels can even be comparable with the amount of observations. In this context, different remedies have been proposed to overcome the curse of dimensionality. In this work, we aim at exploiting the output sparsity by introducing a new loss, called the sparse weighted Hamming loss. This proposed loss can be seen as a weighted version of classical ones, where active and inactive labels are weighted separately. Leveraging the influence of sparsity in the loss function, we provide improved generalization bounds for the empirical risk minimizer, a suitable property for large-scale problems. For this new loss, we derive rates of convergence linear in the underlying output-sparsity rather than linear in the number of labels. In practice, minimizing the associated risk can be performed efficiently by using convex surrogates and modern convex optimization algorithms. We provide experiments on various real-world datasets demonstrating the pertinence of our approach when compared to non-weighted techniques.

The goal of data clustering is to partition data points into groups to minimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is not a vector but a point pattern or a set of points. Moreover, many existing clustering methods require the user to specify the number of clusters, which is not available in advance. This paper proposes a new class of models for data clustering that addresses set-valued data as well as unknown number of clusters, using a Dirichlet Process mixture of Poisson random finite sets. We also develop an efficient Markov Chain Monte Carlo posterior inference technique that can learn the number of clusters and mixture parameters automatically from the data. Numerical studies are presented to demonstrate the salient features of this new model, in particular its capacity to discover extremely unbalanced clusters in data.

When we say "I know why he was late", we know not only the fact that he was late, but also an explanation of this fact. We propose a logical framework of "knowing why" inspired by the existing formal studies on why-questions, scientific explanation, and justification logic. We introduce the Ky_i operator into the language of epistemic logic to express "agent i knows why phi" and propose a Kripke-style semantics of such expressions in terms of knowing an explanation of phi. We obtain two sound and complete axiomatizations w.r.t. two different model classes depending on different assumptions about introspection.

Intel CEO Brian Krzanich, BMW CEO Harald Krueger, and Mobileye CTO and cofounder Amnon Shashua pose after a press conference in Munich on July 1, 2016. Intel started making lots of noise about the autonomous car market last year. But it's a long slog getting into a market like automotive, where it can take years to get designed into a vehicle. On Monday, the chip giant announced it would just buy its way into the market with a $15.3 billion acquisition of Mobileye, a leading provider of advanced driver assistant systems based in Israel. A massive consolidation spree is sweeping the semiconductor industry.

Thousands have flooded into Austin, Texas to experience the 31st Annual South by Southwest Convention and Festivals. Check out some of the sights and sounds from the first day. Osaka University roboticist Hiroshi Ishiguro returned SXSW, this time bringing two robots, shown at left. Don't be too concerned about the rise of humanoid robots, because they're still not ready for prime time. But you might want to keep a wary eye on the machine-learning systems that power them.

You're awake, you're sentient, you might even be upright. You're not comatose or dead, and it's reasonable to assume that if you were on some kind of powerful mind-altering drug then you wouldn't be reading this. The point is, you're here, and you're alive, so therefore you're conscious. OK then, since you're conscious and I'm conscious and everyone else is conscious, go ahead. Does it belong to the mind or the body, or does it exist outside both? Is consciousness part of our souls, or does it live in the things we create – our art, our music, our cities and wars? Could it be mechanical or electronic, and, if so, what makes it operate? Most pressingly of all, is it possible we have now made for ourselves a new kind of consciousness, one which exists independently? If so, then what the hell have we got ourselves into? The search for a definition of consciousness must lay claim to be the world's longest-running detective story. We've had our best minds on it ever since we developed brains big enough to ask questions and, still, we seem to be stumped. Plato and Aristotle couldn't fix it; Kant, Hume and Locke tried different angles; Schroedinger, Heisenberg and Einstein remained in awe before it. None of them came up with the final formula, the definitive, nailed-it for ever, silences-all-critics answer. Lately though, the hunt seems to have changed gear. Despite big differences about how best to conduct the search and where to look, several of the most persistent sleuths have found themselves disconcertingly close to agreement. No-one is yet at the stage when they are ready to call a press conference and announce to the world they have finally apprehended the suspect, but they have at least begun to converge on these two leads: the Omega Point and the Singularity. Pierre Teilhard de Chardin is an improbable prophet, partly because he's dead, and partly because he's still associated with a famous palaeontological fraud.

Five actors gathered in a room on Lafayette Street, in downtown Manhattan, to start rehearsing a new work for the Public Theatre, "Joan of Arc: Into the Fire." Written by David Byrne, formerly of the Talking Heads, the show recast the enduring, improbable story of Joan--a teen-age girl in medieval France who experienced divine visions, led an army to defeat an occupying power, and was burned at the stake for heresy--as a rock musical that spoke to the current political moment. It was early January, and, that morning, U.S. intelligence officials had arrived at Trump Tower to brief the President-elect, Donald Trump, on the findings of an investigation into the recent election, in which they had concluded that President Vladimir Putin, of Russia, had acted to insure the defeat of Hillary Clinton. Inauguration Day was looming, and the rehearsal room had a troubled mood that reflected more than the ordinary anxieties of creating a show. The actors arranged four tables into a rectangle and sat down with Alex Timbers, the director of "Joan of Arc." Timbers, who is thirty-eight, is tall and fine-featured. He wore a denim shirt and black jeans that hung off his lanky, slightly hunched frame. His hair is dark and thick, and he frequently runs a hand through it, like a Romantic poet on deadline. Despite the air of disquiet, Timbers, who talks like a cool high-school teacher--lots of vocal fry, the repeated use of "awesome"--addressed the cast with rousing enthusiasm. He explained that, though the show had been in development for two years, it remained a work in progress. "I don't think anything is sacred--we are going to be building this together," Timbers said to the actors, all of whom were men except for Jo Lampert, a thirty-one-year-old newcomer, who was to play Joan. Timbers presented a scale model of the stage design, which had been conceived by Chris Barreca. When built, the set would be black and austere, and filled with enormous L.E.D. screens. A staircase extended from wing to wing, and at center stage there was a vertiginous platform. The set was on a turntable, and as it revolved it represented everything from a cathedral to a prison tower.

Of all the prejudices of pundits, presentism is the strongest. It is the assumption that what is happening now is going to keep on happening, without anything happening to stop it. If the West has broken down the Berlin Wall and McDonald's opens in St. Petersburg, then history is over and Thomas Friedman is content. If, by a margin so small that in a voice vote you would have no idea who won, Brexit happens; or if, by a trick of an antique electoral system designed to give country people more power than city people, a Donald Trump is elected, then pluralist constitutional democracy is finished. The liberal millennium was upon us as the year 2000 dawned; fifteen years later, the autocratic apocalypse is at hand. You would think that people who think for a living would pause and reflect that whatever is happening usually does stop happening, and something else happens in its place; a baby who is crying now will stop crying sooner or later. Exhaustion, or a change of mood, or a passing sound, or a bright light, something, always happens next. But for the parents the wait can feel the same as forever, and for many pundits, too, now is the only time worth knowing, for now is when the baby is crying and now is when they're selling your books.