A Tutorial on Bayesian Nonparametric Models

A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number ofclusters in mixture models or the number of factors in factor analysis. In this tutorial we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.Comment: 28 pages, 8 figure

Stochastic Acceleration of Low Energy Electrons in Plasmas with Finite Temperature

This paper extends our earlier work on the acceleration of low-energy electrons by plasma turbulence to include the effects of finite temperature of the plasma. We consider the resonant interaction of thermal electrons with the whole transverse branch of plasma waves propagating along the magnetic field. We show that our earlier published results for acceleration of low-energy electrons can be applied to the case of finite temperature if a sufficient level of turbulence is present. From comparison of the acceleration rate of the thermal particles with the decay rate of the waves with which they interact, we determine the required energy density of the waves as a fraction of the magnetic energy density, so that a substantial fraction of the background plasma electrons can be accelerated. The dependence of this value on the plasma parameter alpha = omega_pe / Omega_e (the ratio of electron plasma frequency to electron gyrofrequency), plasma temperature, and turbulence spectral parameters is quantified. We show that the result is most sensitive to the plasma parameter alpha. We estimate the required level of total turbulence by calculating the level of turbulence required for the initial acceleration of thermal electrons and that required for further acceleration to higher energies

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Cooperative Transmission for Wireless Relay Networks Using Limited Feedback

To achieve the available performance gains in half-duplex wireless relay networks, several cooperative schemes have been earlier proposed using either distributed space-time coding or distributed beamforming for the transmitter without and with channel state information (CSI), respectively. However, these schemes typically have rather high implementation and/or decoding complexities, especially when the number of relays is high. In this paper, we propose a simple low-rate feedback-based approach to achieve maximum diversity with a low decoding and implementation complexity. To further improve the performance of the proposed scheme, the knowledge of the second-order channel statistics is exploited to design long-term power loading through maximizing the receiver signal-to-noise ratio (SNR) with appropriate constraints. This maximization problem is approximated by a convex feasibility problem whose solution is shown to be close to the optimal one in terms of the error probability. Subsequently, to provide robustness against feedback errors and further decrease the feedback rate, an extended version of the distributed Alamouti code is proposed. It is also shown that our scheme can be generalized to the differential transmission case, where it can be applied to wireless relay networks with no CSI available at the receiver.Comment: V1: 27 pages, 1 column, 6 figures. Submitted to IEEE Transactions on Signal Processing, February 2, 2009. V2: 30 pages, 1 column, 8 figures. Revised version submitted to IEEE Transactions on Signal Processing, July 23, 200

Distance Dependent Infinite Latent Feature Models

Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. We present a generalization of the IBP, the distance dependent Indian buffet process (dd-IBP), for modeling non-exchangeable data. It relies on distances defined between data points, biasing nearby data to share more features. The choice of distance measure allows for many kinds of dependencies, including temporal and spatial. Further, the original IBP is a special case of the dd-IBP. In this paper, we develop the dd-IBP and theoretically characterize its feature-sharing properties. We derive a Markov chain Monte Carlo sampler for a linear Gaussian model with a dd-IBP prior and study its performance on several non-exchangeable data sets.Comment: 28 pages, 9 figure

Reinstated episodic context guides sampling-based decisions for reward.

How does experience inform decisions? In episodic sampling, decisions are guided by a few episodic memories of past choices. This process can yield choice patterns similar to model-free reinforcement learning; however, samples can vary from trial to trial, causing decisions to vary. Here we show that context retrieved during episodic sampling can cause choice behavior to deviate sharply from the predictions of reinforcement learning. Specifically, we show that, when a given memory is sampled, choices (in the present) are influenced by the properties of other decisions made in the same context as the sampled event. This effect is mediated by fMRI measures of context retrieval on each trial, suggesting a mechanism whereby cues trigger retrieval of context, which then triggers retrieval of other decisions from that context. This result establishes a new avenue by which experience can guide choice and, as such, has broad implications for the study of decisions

Multitasking versus multiplexing: Toward a normative account of limitations in the simultaneous execution of control-demanding behaviors

Why is it that behaviors that rely on control, so striking in their diversity and flexibility, are also subject to such striking limitations? Typically, people cannot engage in more than a few—and usually only a single—control-demanding task at a time. This limitation was a defining element in the earliest conceptualizations of controlled processing; it remains one of the most widely accepted axioms of cognitive psychology, and is even the basis for some laws (e.g., against the use of mobile devices while driving). Remarkably, however, the source of this limitation is still not understood. Here, we examine one potential source of this limitation, in terms of a trade-off between the flexibility and efficiency of representation (“multiplexing”) and the simultaneous engagement of different processing pathways (“multitasking”). We show that even a modest amount of multiplexing rapidly introduces cross-talk among processing pathways, thereby constraining the number that can be productively engaged at once. We propose that, given the large number of advantages of efficient coding, the human brain has favored this over the capacity for multitasking of control-demanding processes.National Science Foundation (U.S.). Graduate Research Fellowship Progra

Generation of optimal trajectories for Earth hybrid pole sitters

A pole-sitter orbit is a closed path that is constantly above one of the Earth's poles, by means of continuous low thrust. This work proposes to hybridize solar sail propulsion and solar electric propulsion (SEP) on the same spacecraft, to enable such a pole-sitter orbit. Locally-optimal control laws are found with a semi-analytical inverse method, starting from a trajectory that satisfies the pole-sitter condition in the Sun-Earth circular restricted three-body problem. These solutions are subsequently used as first guess to find optimal orbits, using a direct method based on pseudospectral transcription. The orbital dynamics of both the pure SEP case and the hybrid case are investigated and compared. It is found that the hybrid spacecraft allows savings on propellant mass fraction. Finally, it is shown that for sufficiently long missions, a hybrid pole-sitter, based on mid-term technology, enables a consistent reduction in the launch mass for a given payload, with respect to a pure SEP spacecraft

Active current sheets and hot flow anomalies in Mercury's bow shock

Hot flow anomalies (HFAs) represent a subset of solar wind discontinuities interacting with collisionless bow shocks. They are typically formed when the normal component of motional (convective) electric field points toward the embedded current sheet on at least one of its sides. The core region of an HFA contains hot and highly deflected ion flows and rather low and turbulent magnetic field. In this paper, we report first observations of HFA-like events at Mercury identified over a course of two planetary years. Using data from the orbital phase of the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission, we identify a representative ensemble of active current sheets magnetically connected to Mercury's bow shock. We show that some of these events exhibit unambiguous magnetic and particle signatures of HFAs similar to those observed earlier at other planets, and present their key physical characteristics. Our analysis suggests that Mercury's bow shock does not only mediate the flow of supersonic solar wind plasma but also provides conditions for local particle acceleration and heating as predicted by previous numerical simulations. Together with earlier observations of HFA activity at Earth, Venus and Saturn, our results confirm that hot flow anomalies are a common property of planetary bow shocks, and show that the characteristic size of these events is of the order of one planetary radius.Comment: 39 pages, 15 figures, 2 table

Comparing the Performance of Hyperbolic and Circular Rod Quadrupole Mass Spectrometers with Applied Higher Order Auxiliary Excitation

This work applies higher order auxiliary excitation techniques to two types of quadrupole mass spectrometers (QMSs): commercial systems and spaceborne instruments. The operational settings of a circular rod geometry commercial system and an engineering test-bed for a hyperbolic rod geometry spaceborne instrument were matched, with the relative performance of each sensor characterized with and without applied excitation using isotopic measurements of Kr+. Each instrument was operated at the limit of the test electronics to determine the effect of auxiliary excitation on extending instrument capabilities. For the circular rod sensor, with applied excitation, a doubling of the mass resolution at 1% of peak transmission resulted from the elimination of the low-mass side peak tail typical of such rod geometries. The mass peak stability and ion rejection efficiency were also increased by factors of 2 and 10, respectively, with voltage scan lines passing through the center of stability islands formed from auxiliary excitation. Auxiliary excitation also resulted in factors of 6 and 2 in peak stability and ion rejection efficiency, respectively, for the hyperbolic rod sensor. These results not only have significant implications for the use of circular rod quadrupoles with applied excitation as a suitable replacement for traditional hyperbolic rod sensors, but also for extending the capabilities of existing hyperbolic rod QMSs for the next generation of spaceborne instruments and low-mass commercial systems