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.

Machines and medicine have gone hand in hand for the past couple of decades, although for the most part they are used as tools where they can scan a person's body, but at the end of the day it's up to the doctor to interpret the findings. Like a machine could detect spots on a person's lungs, but it won't know what it means. However it seems that in the future, it is possible that with an algorithm, AIs could detect if we have depression. This is thanks to an ongoing research project called SimSensei developed by researchers at the University of Southern California. The idea is that with the use of a Kinect, it will be able to "read" a person's body language to look out for signs that could hint at depression, like nervousness, anxiety, and so on.

Prostate cancer is the second most common cancer in men in the United States. This buildup of abnormal cells in a man's prostate -- a gland below the bladder and in front of the rectum -- often has no early symptoms and usually grows very slowly. Treatment choices depend on many factors. More than half of prostate cancers stay within the gland and don't become life-threatening. But doctors don't have a way to reliably predict which tumors will progress and which are unlikely to cause problems.

Would you trust a robot to help deliver your baby? Robots could eventually play integral roles in labor wards, according to findings from MIT's Computer Science and Artificial Intelligence Laboratory. Robots are currently employed in hospitals to carry out simple actions, like dispensing medication. But can they understand patient needs and make scheduling decisions? The researchers have been working for the past two years to determine whether robots can be more than just helpful companions. They've been conducting experiments to see if a robot can serve as an effective "resource nurse."

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.

Another accident involving Tesla's autopilot system has been reported, this time in Montana when a Model X veered off the road and hit a post. Early on Sunday morning on a highway near Whitehall, a Tesla veered off to the right into a wooden guardrail, according to the Detroit Free Press, stopping the car before it left the road. The driver told a highway patrol officer that the car's driver assist feature had been engaged. Tesla on Tuesday said data suggested that the driver's hands were not on the wheel when the accident occurred. The company confirmed that the driver had enabled autosteer on an undivided mountain road, a Tesla spokesperson said in a short statement, adding that it is looking into the crash. "No force was detected on the steering wheel for over two minutes after autosteer was engaged," Tesla said, adding that it was contrary to the system's terms of use.

In 2012, Dennis Mortensen had 1,019 meetings, each of which required an average of roughly eight back-and-forth emails to schedule. Every time Mortensen comes across a contact interested in meeting with him, the CEO and founder of New York City-based artificial intelligence firm x.ai simply sends them a return email copying Amy, who takes care of the rest. "In raw numbers, I've saved about an hour every day -- an hour which I would otherwise have to use in really rudimentary work where I add not much value," Mortensen said of Amy's help scheduling meetings. Virtual assistants like Amy are becoming more common. Just as household technology platforms like Apple's "Siri" and Microsoft's "Cortana" has helped consumers navigate their lives more easily, other forms of rudimentary artificial intelligence platforms are starting to proliferate the market, many of them upending traditional business roles.