Encouraging sustainable mobility patterns is at the forefront of policymaking at all scales of governance as the collective consciousness surrounding climate change continues to expand. Not every community, however, possesses the necessary economic or socio-cultural capital to encourage modal shifts away from private motorized vehicles towards active modes. The current literature on `soft' policy emphasizes the importance of tailoring behavior change campaigns to individual or geographic context. Yet, there is a lack of insight and appropriate tools to promote active mobility and overcome transport disadvantage from the local community perspective. The current study investigates the promotion of walking and cycling adoption using a series of focus groups with local residents in two geographic communities, namely Chicago's (1) Humboldt Park neighborhood and (2) suburb of Evanston. The research approach combines traditional qualitative discourse analysis with quantitative text-mining tools, namely topic modeling and sentiment analysis. The analysis uncovers the local mobility culture, embedded norms and values associated with acceptance of active travel modes in different communities. We observe that underserved populations within diverse communities view active mobility simultaneously as a necessity and as a symbol of privilege that is sometimes at odds with the local culture. The mixed methods approach to analyzing community member discourses is translated into policy findings that are either tailored to local context or broadly applicable to curbing automobile dominance. Overall, residents of both Humboldt Park and Evanston envision a society in which multimodalism replaces car-centrism, but differences in the local physical and social environments would and should influence the manner in which overarching policy objectives are met.

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume {\it all the data are already in one place}, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with "minimal" communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing; the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while about 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing.

DINARD, FRANCE - Foreign ministers of Group of Seven nations on Saturday pushed North Korea to continue denuclearization negotiations with the United States while vowing to maintain pressure on Pyongyang to encourage it to give up its nuclear weapons and ballistic missile programs. In a communique issued after a two-day meeting in Dinard, western France, the ministers also expressed serious concern about the situation in the East and South China seas -- a veiled criticism of China's militarization of outposts in disputed areas of the South China Sea and its attempts to undermine Japan's control of the Senkaku Islands in the East China Sea. The Senkakus are administered by Japan, but claimed by China and Taiwa, which call them the Diaoyu and Tiaoyutai, respectively. During the meeting, some G7 members touched on China's expanding global ambitions through its signature Belt and Road Initiative infrastructure project, a Japanese official said. But the communique makes no reference to the initiative in an apparent effort to demonstrate unity among the group.

Tesla has reported its biggest ever drop in vehicle sales in the company's history. The electric car maker revealed the first quarter of 2019 saw 31 per cent less sale deliveries than the previous quarter, representing a fall of nearly 30,000 vehicles. It is the first time in nearly two years that Tesla has experienced a quarter-to-quarter sales decline but still represents a significant increase from the first quarter of 2018. We'll tell you what's true. You can form your own view.

One day in 1992, Lawrence Mattis opened up his mail to find an unsolicited screenplay from two unknown writers. It was a dark, nasty, almost defiantly uncommercial tale of cannibalism and class warfare--the type of story that few execs in Hollywood would want to tell. Yet it was exactly the kind of movie Mattis was looking for. Only a few years earlier, Mattis, in his late twenties, had abandoned a promising legal career to start a talent company, Circle of Confusion, with the aim of discovering new writers to represent. He'd set up shop in New York City, despite being told repeatedly that his best hope for finding talent was to be in Los Angeles. Before that strange script showed up, Mattis was starting to wonder if those naysayers had been right. "I'd only sold a few options that paid about five hundred dollars each," Mattis says. "I was starting to think about going back to law. Then I get this letter from these two kids, saying'Could you please read our script?'" The screenplay, titled Carnivore, was a horror tale set in a soup kitchen, where the bodies of the rich are used to feed the poor. "It was funny, it was visceral, and it made it clear that whoever wrote it really knew movies," Mattis says. Its writers were Lilly and Lana Wachowski, two self-described "schmoes from Chicago" who, in later years, would be referred to by many colleagues and admirers simply as "the Wachowskis." By the time they contacted Mattis, the Wachowskis had been collaborating for years, having spent their childhood creating radio plays, comic books, and their own role-playing game. They'd been raised in a middle-class neighborhood on Chicago's South Side by their mother, a nurse and artist, and their father, a businessman. Growing up, their parents had encouraged them to discover art, especially film.

One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: "So today I'm going to be talking about different approaches to building…" He stalled, as though just realising that he was stating his momentous ambition out loud. And then he said it: "AGI". AGI stands for artificial general intelligence, a hypothetical computer program that can perform intellectual tasks as well as, or better than, a human. AGI will be able to complete discrete tasks, such as recognising photos or translating languages, which are the single-minded focus of the multitude of artificial intelligences (AIs) that inhabit our phones and computers. But it will also add, subtract, play chess and speak French. It will also understand physics papers, compose novels, devise investment strategies and make delightful conversation with strangers. It will monitor nuclear reactions, manage electricity grids and traffic flow, and effortlessly succeed at everything else. AGI will make today's most advanced AIs look like pocket calculators. The only intelligence that can currently attempt all these tasks is the kind that humans are endowed with. But human intelligence is limited by the size of the skull that houses the brain. Its power is restricted by the puny amount of energy that the body is able to provide. Because AGI will run on computers, it will suffer none of these constraints. Its intelligence will be limited only by the number of processors available.

Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the significant modalities of the data. In such applications, each base kernel represents one dimension of the data or is derived from one specific descriptor. Therefore, MKL finds an optimal weighting scheme for the given kernels to increase the classification accuracy. Nevertheless, the majority of the works in this area focus on only binary classification problems or aim for linear separation of the classes in the kernel space, which are not realistic assumptions for many real-world problems. In this paper, we propose a novel multi-class MKL framework which improves the state-of-the-art by enhancing the local separation of the classes in the feature space. Besides, by using a sparsity term, our large-margin multiple kernel algorithm (LMMK) performs discriminative feature selection by aiming to employ a small subset of the base kernels. Based on our empirical evaluations on different real-world datasets, LMMK provides a competitive classification accuracy compared with the state-of-the-art algorithms in MKL. Additionally, it learns a sparse set of non-zero kernel weights which leads to a more interpretable feature selection and representation learning.

Hiring developer talent is a business priority, but not all roles are created equal. As startups introduce new ways to apply technologies and large enterprises continue their quest to digitally transform, hiring needs to evolve for all companies looking to hire top tech talent. Data from Hired's marketplace reveals that global demand for blockchain engineers is through the roof, at a 517% increase year over year. For developers interested in blockchain roles, don't let the titles fool you. For engineers with an expertise in blockchain, they typically hold titles such as backend engineer, systems engineer or solutions architect, with blockchain being listed as a desired skill for the role.

We propose a new approach for constructing synthetic pseudo-panel data from cross-sectional data. The pseudo panel and the preferences it intends to describe is constructed at the individual level and is not affected by aggregation bias across cohorts. This is accomplished by creating a high-dimensional probabilistic model representation of the entire data set, which allows sampling from the probabilistic model in such a way that all of the intrinsic correlation properties of the original data are preserved. The key to this is the use of deep learning algorithms based on the Conditional Variational Autoencoder (CVAE) framework. From a modelling perspective, the concept of a model-based resampling creates a number of opportunities in that data can be organized and constructed to serve very specific needs of which the forming of heterogeneous pseudo panels represents one. The advantage, in that respect, is the ability to trade a serious aggregation bias (when aggregating into cohorts) for an unsystematic noise disturbance. Moreover, the approach makes it possible to explore high-dimensional sparse preference distributions and their linkage to individual specific characteristics, which is not possible if applying traditional pseudo-panel methods. We use the presented approach to reveal the dynamics of transport preferences for a fixed pseudo panel of individuals based on a large Danish cross-sectional data set covering the period from 2006 to 2016. The model is also utilized to classify individuals into 'slow' and 'fast' movers with respect to the speed at which their preferences change over time. It is found that the prototypical fast mover is a young woman who lives as a single in a large city whereas the typical slow mover is a middle-aged man with high income from a nuclear family who lives in a detached house outside a city.

The first product of Apple's self-driving electric car project codenamed "Titan", could actually be an electric van instead of a car, the media reported. "According to multiple unnamed sources of German business publication -- Manager Magazin, prototypes of Apple's work have been seen painted in black and silver and the main highlight is that vans are being tested, rather than cars," Apple Insider reported on Thursday. In August 2018, Tesla's former engineering Vice President, Doug Field was appointed by Apple to lead team "Titan". "The'Apple Car' which could be arriving between 2023 and 2025, has undergone development at Apple in a variety of different. Originally working on an entire vehicle, the project changed its focus towards self-driving vehicle systems, though there are some signs it is shifting back towards overall vehicular design," the report said.