We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from dynamical systems are not independent and moreover, usual mixing concepts may not be appropriate for measuring the dependence among these observations. By employing the $\mathcal{C}$-mixing concept to measure the dependence, we conduct statistical analysis on the consistency and convergence of the kernel density estimator. Our main results are as follows: First, we show that with properly chosen bandwidth, the kernel density estimator is universally consistent under $L_1$-norm; Second, we establish convergence rates for the estimator with respect to several classes of dynamical systems under $L_1$-norm. In the analysis, the density function $f$ is only assumed to be H\"{o}lder continuous which is a weak assumption in the literature of nonparametric density estimation and also more realistic in the dynamical system context. Last but not least, we prove that the same convergence rates of the estimator under $L_\infty$-norm and $L_1$-norm can be achieved when the density function is H\"{o}lder continuous, compactly supported and bounded. The bandwidth selection problem of the kernel density estimator for dynamical system is also discussed in our study via numerical simulations.

Effective placement of charging stations plays a key role in Electric Vehicle (EV) adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be selected with respect to the concerns of both (a) the charging station service provider, such as the demand at the candidate sites and the budget for deployment, and (b) the EV user, such as charging station reachability and short waiting times at the station. This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a mixed-packing-and- covering optimization framework that models competing concerns of the service provider and EV users; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and set cover algorithms. The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data.

This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for the system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering the system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. More generally, these advances allow for a robust and non-parametric estimation of system timescale causal structures from subsampled time series data.

It used to be that one great technology defined an era. The steam engine, for example, served as the catalyst for the rise of the industrial age. Nowadays, however, a number of amazing technical advances and inventions are contending for bragging rights as the leading technology of our times. I would argue that one is particularly worthy of such boasting: machine learning. Although it has been in slow and steady development for years and has been used in a few enterprise applications, it has recently burst onto the scene in response to the explosion of data in today's increasingly connected digital world.

A computer program has been developed that can tell if a person is lying by analysing linguistic cues in emails, texts and even online dating profiles. Researchers from City University in London detailed their findings in a paper set to be published in Journal of Management Information Systems. The paper, titled "Untangling a Web of Lies: Exploring Automated Detection of Deception in Computer-Mediated Communication," describes tell-tale signs that someone is lying. Deceitful emailers have a tendency to avoid personal pronouns--such as "I" and "me"--while also including language that flatters the recipient. The paper's authors say their findings could be used within a business context, to identify corporate deception and spot when a customer might not be telling the truth.

In the pixelated cube world of "Minecraft," players can create almost anything their hearts desire. Now, Microsoft is using the popular world-building game to build and test artificial intelligence in the fictional environment. Microsoft has made a platform for artificial intelligence (AI) research using a modified version of "Minecraft" that will become available to the public following a limited release to select researchers. Project Malmo (formerly known as Project AIX) allows anyone from ambitious amateur coders to advanced computer scientists to build and test artificial intelligence in the "Minecraft" environment. "We?re trying to put out the tools that will allow people to make progress on those really, really hard research questions," Katja Hofmann, the project's lead researcher, said in a Microsoft blog post announcing the release.

Big tech firms acquiring artificial intelligence startups are paying top dollar for them and are increasingly looking for those yet to bring in revenue, bucking the trend of wider tech M&A. "A good AI engineer is worth more than many company chief executives right now," said Victor Basta of advisory firm Magister Advisors which identified the pursuit of talent in AI as one of the most significant drivers of acquisitions in the sector, leading to significantly large sums being spent on relatively small and early stage startups. The analysis found that the average acquisition was valued at 2.4m ( 1.8m) per employee for AI startups based on 26 deals in Europe, the US and Israel since 2014. Twitter's surprise 150m deal for Magic Pony last month had a significantly higher value of 10m per employee. It also noted that there were more companies chasing each startup as the sector attracts a greater range of businesses which would traditionally not have competed with each other in the race to build their AI capabilities.

Phone (US): 1 (425) 440-5100 ... (long distance cost may apply) (1C4A) Unfamiliar with how to do this on Skype? Add the contact "join.conference" to your skype contact list first. To participate in the teleconference, make a skype call to "join.conference", then open the dial pad (see platform-specific instructions below) and enter the Conference ID: 843758# when prompted. You can indicate that you want to ask a question verbally by clicking on the "hand" button, and wait for the moderator to call on you; or, type and send your question into the chat window at the bottom of the screen. Just add the room as a buddy - (in our case here) summit_20160317@soaphub.org ... Handy for mobile devices!

The race to acquire artificial intelligence talent has inverted the "laws" of M&A, with pre-revenue AI firms such as UK-based Magic Pony being sold to Twitter for about 10m per employee. Magister Advisors, the global M&A advisory firm to the technology industry, notes that AI firms without revenues are more valuable than those with, as buyers look for pristine competitive advantage, and that Britain is amongst top tier for AI innovation. Twitter just paid 150m for 14-person Magic Pony, a UK-based AI visual search company barely anyone had heard of before the deal. At 10m per employee it marks a high water mark in AI for what is essentially a team acquisition. Magister has tracked 26 AI driven deals since 2014 in the US, Europe and Israel, 11 of which involved companies with less than 50 employees which were acquired largely, or entirely, for the team and capability. Across all 11 deals, the median price paid per employee has reached 2.4m, meaning a high quality AI company with 40 employees would be valued at near 100m - even if it had little or no revenue.

So everyone in China has been following the European Championship in France, and this time it's the game between France and Iceland that made a lot of noise, generating 2.8 billion media impressions! It was fascinating for Chinese people as they really admired this team from Iceland. They discovered that Iceland is a country of only 330 thousand people, which is just a city for them. Shanghai for example has 25 million inhabitants! For a country so small to reach that stage of a Soccer Championship was just amazing for them.