Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Many systems are partially stochastic in nature. We have derived data driven approaches for extracting stochastic state machines (Markov models) directly from observed data. This chapter provides an overview of our approach with numerous practical applications. We have used this approach for inferring shipping patterns, exploiting computer system side-channel information, and detecting botnet activities. For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems and Securit

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Viewing the Future? Virtual Reality In Journalism

Journalism underwent a flurry of virtual reality content creation, production and distribution starting in the final months of 2015. The New York Times distributed more than 1 million cardboard virtual reality viewers and released an app showing a spherical video short about displaced refugees. The Los Angeles Times landed people next to a crater on Mars. USA TODAY took visitors on a ride-along in the "Back to the Future" car on the Universal Studios lot and on a spin through Old Havana in a bright pink '57 Ford. ABC News went to North Korea for a spherical view of a military parade and to Syria to see artifacts threatened by war. The Emblematic Group, a company that creates virtual reality content, followed a woman navigating a gauntlet of anti- abortion demonstrators at a family planning clinic and allowed people to witness a murder-suicide stemming from domestic violence.In short, the period from October 2015 through February 2016 was one of significant experimentation with virtual reality (VR) storytelling. These efforts are part of an initial foray into determining whether VR is a feasible way to present news. The year 2016 is shaping up as a period of further testing and careful monitoring of potential growth in the use of virtual reality among consumers

The interplay between helicity and rotation in turbulence: implications for scaling laws and small-scale dynamics

Invariance properties of physical systems govern their behavior: energy conservation in turbulence drives a wide distribution of energy among modes, observed in geophysical or astrophysical flows. In ideal hydrodynamics, the role of helicity conservation (correlation between velocity and its curl, measuring departures from mirror symmetry) remains unclear since it does not alter the energy spectrum. However, with solid body rotation, significant differences emerge between helical and non-helical flows. We first outline several results, like the energy and helicity spectral distribution and the breaking of strict universality for the individual spectra. Using massive numerical simulations, we then show that small-scale structures and their intermittency properties differ according to whether helicity is present or not, in particular with respect to the emergence of Beltrami-core vortices (BCV) that are laminar helical vertical updrafts. These results point to the discovery of a small parameter besides the Rossby number; this could relate the problem of rotating helical turbulence to that of critical phenomena, through renormalization group and weak turbulence theory. This parameter can be associated with the adimensionalized ratio of the energy to helicity flux to small scales, the three-dimensional energy cascade being weak and self-similar

A Stability Analysis for the Acceleration-based Robust Position Control of Robot Manipulators via Disturbance Observer

This paper proposes a new nonlinear stability analysis for the acceleration-based robust position control of robot manipulators by using Disturbance Observer (DOb). It is shown that if the nominal inertia matrix is properly tuned in the design of DOb, then the position error asymptotically goes to zero in regulation control and is uniformly ultimately bounded in trajectory tracking control. As the bandwidth of DOb and the nominal inertia matrix are increased, the bound of error shrinks, i.e., the robust stability and performance of the position control system are improved. However, neither the bandwidth of DOb nor the nominal inertia matrix can be freely increased due to practical design constraints, e.g., the robust position controller becomes more noise sensitive when they are increased. The proposed stability analysis provides insights regarding the dynamic behavior of DOb-based robust motion control systems. It is theoretically and experimentally proved that non-diagonal elements of the nominal inertia matrix are useful to improve the stability and adjust the trade-off between the robustness and noise sensitivity. The validity of the proposal is verified by simulation and experimental results.Comment: 9 pages, 9 figures, Journa

Improving Search through A3C Reinforcement Learning based Conversational Agent

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.Comment: 17 pages, 7 figure

Towards machines that understand people

The ability to estimate the state of a human partner is an insufficient basis on which to build cooperative agents. Also needed is an ability to predict how people adapt their behavior in response to an agent's actions. We propose a new approach based on computational rationality, which models humans based on the idea that predictions can be derived by calculating policies that are approximately optimal given human-like bounds. Computational rationality brings together reinforcement learning and cognitive modeling in pursuit of this goal, facilitating machine understanding of humans.peerReviewe