From Alexa and self-driving cars to job applicant screening processes, artificial intelligence is fast becoming the norm in business. But it also could start playing far bigger roles in security, helping law enforcement and other protective agents figure out who's up to no good. As Fredrick Kunkle of The Washington Post reports, there's now an AI-based kiosk designed to detect whether travelers are fibbing. Designed by Aaron Elkins, assistant professor of the Fowler College of Business Administration at San Diego State University, the new AI lie detector goes by the name Automated Virtual Agent for Truth Assessments in Real Time, or AVATAR for short. Once you've scanned your ID or passport, the kiosk asks you a bunch of questions.

Masters of Illusion Magic prevails for another summer as this showcase for prestidigitators starts its fourth season, with Dean Cain ("Lois & Clark: The New Adventures of Superman") returning as the series' host. Killjoys Still reeling from the devastating losses they suffered last season, Dutch, Johnny and D'avin (Hannah John-Kamen, Aaron Ashmore, Luke Macfarlane) go on the offensive in the season premiere of this space-set action drama. The Great British Baking Show The cooks are tasked with whipping up a better batter for baked goods in this new episode; with judges Mary Berry and Paul Hollywood. Dark Matter A rival of Tabor captures one of Adrian's (Mishka Thebaud) friends and demands a specific data file in exchange for her safe return in a new episode of this science fiction drama. Correspondents Eva Pilgrim and Nick Watt report on tense situations that can quickly become dangerous and deadly.

Bangalore-based Niki.ai, which runs an artificial intelligence-powered personal assistant, has raised $2 million (about Rs 13 crore) in a Series A round of funding from San Francisco-based fund SAP.iO and existing investor Unilazer Ventures. VCCircle had exclusively reported on this development last month. Haresh Chawla of private equity firm True North, and Arihant Patni of Hive Technologies also invested, besides some US- and Germany-based investors, the company said on Wednesday. Software giant SAP launched SAP.iO with an initial investment of $35 million in March this year. The fund seeks to make early-stage investments in software startups with an aim to expand the SAP ecosystem.

Scientists have developed an AI artist whose masterpieces could pass off as human-made. The system builds upon earlier techniques to generate art and learn about style through observation, but unlike earlier approaches, the new network also has the ability to become creative. When put to the test, the researchers found that humans could not tell the difference between those created by the system and artwork made by contemporary human artists – and sometimes, the AI-generated images even scored higher. The system builds upon earlier techniques to generate art and learn about style through observation, but unlike earlier approaches, the new network also has the ability to become creative. Like the GAN system, the Creative Adversarial Network (CAN) also uses two sub-networks.

Implicit schemes are popular methods for the integration of time dependent PDEs such as hyperbolic and parabolic PDEs. However the necessity to solve corresponding linear systems at each time step constitutes a complexity bottleneck in their application to PDEs with rough coefficients. We present a generalization of gamblets introduced in \cite{OwhadiMultigrid:2015} enabling the resolution of these implicit systems in near-linear complexity and provide rigorous a-priori error bounds on the resulting numerical approximations of hyperbolic and parabolic PDEs. These generalized gamblets induce a multiresolution decomposition of the solution space that is adapted to both the underlying (hyperbolic and parabolic) PDE (and the system of ODEs resulting from space discretization) and to the time-steps of the numerical scheme.

We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic active learning scheme that identifies the intervention which is optimally informative about all of the unknown functions jointly, given previously observed data. We test the derived algorithms on simple examples, to demonstrate that they produce a structured exploration policy that significantly improves on unstructured base-lines.

We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression. This convex relaxation of reduced-rank multinomial regression has the advantage of leveraging underlying structure among the response categories to make better predictions. We apply our method, nuclear penalized multinomial regression (NPMR), to Major League Baseball play-by-play data to predict outcome probabilities based on batter-pitcher matchups. The interpretation of the results meshes well with subject-area expertise and also suggests a novel understanding of what differentiates players.

Institute for Theoretical Physics, University of Cologne, Z ulpicher Straße 77, 50937 Cologne, Germany The inverse Ising problem seeks to reconstruct the parameters of an Ising Hamiltonian on the basis of spin configurations sampled from the Boltzmann measure. Over the last decade, many applications of the inverse Ising problem have arisen, driven by the advent of large-scale data across different scientific disciplines. Recently, strategies to solve the inverse Ising problem based on convex optimisation have proven to be very successful. Examples are the pseudolikelihood method and interaction screening. In this paper, we establish a link between approaches to the inverse Ising problem based on convex optimisation and the statistical physics of disordered systems. We characterise the performance of an arbitrary objective function and calculate the objective function which optimally reconstructs the model parameters. We evaluate the optimal objective function within a replica-symmetric ansatz and compare the results of the optimal objective function with other reconstruction methods. Apart from giving a theoretical underpinning to solving the inverse Ising problem by convex optimisation, the optimal objective function outperforms state-of-the-art methods, albeit by a small margin. The advent of large-scale data across different scientific disciplines, especially biology, has inspired many applications of the inverse Ising problem. Over the last decade, the inverse Ising problem has been used to analyze neural firing patterns [1] and gene expression data [2], to infer biological fitness landscapes [3, 4], and to analyze financial data [5].

In this paper we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types. Based on these observations we provide methods to detect the subscription type of customers by using information about their personal call statistics, and also their egocentric networks simultaneously. The key of our first approach is to cast this classification problem as a problem of graph labelling, which can be solved by max-flow min-cut algorithms. Our experiments show that, by using both user attributes and relationships, the proposed graph labelling approach is able to achieve a classification accuracy of $\sim 87\%$, which outperforms by $\sim 7\%$ supervised learning methods using only user attributes. In our second problem we aim to infer the subscription type of customers of external operators. We propose via approximate methods to solve this problem by using node attributes, and a two-ways indirect inference method based on observed homophilic structural correlations. Our results have straightforward applications in behavioural prediction and personal marketing.

Peter Bloem Knowledge Representation and Reasoning Group VU University Amsterdam De Boelelaan 1105, 1081 HV Amsterdam, NL Steven de Rooij † Mathematical Institute University of Leiden Niels Bohrweg 1, 2333 CA Leiden, NL (Dated: February 9, 2018) We present an Expectation-Maximization algorithm for the fractal inverse problem: the problem of fitting a fractal model to data. In our setting the fractals are Iterated Function Systems (IFS), with similitudes as the family of transformations. The data is a point cloud in R H with arbitrary dimensionH . Each IFS defines a probability distribution on R H, so that the fractal inverse problem can be cast as a problem of parameter estimation. We show that the algorithm reconstructs well-known fractals from data, with the model converging to high precision parameters. We also show the utility of the model as an approximation for datasources outside the IFS model class.