Structure Constant of the Yang-Lee Edge Singularity

This paper studies the Yang-Lee singularity of the 2-dimensional Ising model on the cylinder via transfer matrix and finite-size scaling techniques. These techniques enable a measurement of the 2-point and 3-point correlations and a comparison of a measurement of a corresponding universal amplitude with a prediction for the amplitude from the (A4,A1) minimal conformal field theory.Comment: 1 figur

Approximate Bayesian Computation in State Space Models

A new approach to inference in state space models is proposed, based on approximate Bayesian computation (ABC). ABC avoids evaluation of the likelihood function by matching observed summary statistics with statistics computed from data simulated from the true process; exact inference being feasible only if the statistics are sufficient. With finite sample sufficiency unattainable in the state space setting, we seek asymptotic sufficiency via the maximum likelihood estimator (MLE) of the parameters of an auxiliary model. We prove that this auxiliary model-based approach achieves Bayesian consistency, and that - in a precise limiting sense - the proximity to (asymptotic) sufficiency yielded by the MLE is replicated by the score. In multiple parameter settings a separate treatment of scalar parameters, based on integrated likelihood techniques, is advocated as a way of avoiding the curse of dimensionality. Some attention is given to a structure in which the state variable is driven by a continuous time process, with exact inference typically infeasible in this case as a result of intractable transitions. The ABC method is demonstrated using the unscented Kalman filter as a fast and simple way of producing an approximation in this setting, with a stochastic volatility model for financial returns used for illustration

Neural signature of fictive learning signals in a sequential investment task

Reinforcement learning models now provide principled guides for a wide range of reward learning experiments in animals and humans. One key learning (error) signal in these models is experiential and reports ongoing temporal differences between expected and experienced reward. However, these same abstract learning models also accommodate the existence of another class of learning signal that takes the form of a fictive error encoding ongoing differences between experienced returns and returns that "could-have-been-experienced" if decisions had been different. These observations suggest the hypothesis that, for all real-world learning tasks, one should expect the presence of both experiential and fictive learning signals. Motivated by this possibility, we used a sequential investment game and fMRI to probe ongoing brain responses to both experiential and fictive learning signals generated throughout the game. Using a large cohort of subjects (n = 54), we report that fictive learning signals strongly predict changes in subjects' investment behavior and correlate with fMRI signals measured in dopaminoceptive structures known to be involved in valuation and choice

Proportional-integral-plus (PIP) control of the ALSTOM gasifier problem

Although it is able to exploit the full power of optimal state variable feedback within a non-minimum state-space (NMSS) setting, the proportional-integral-plus (PIP) controller is simple to implement and provides a logical extension of conventional proportional-integral and proportional-integral-derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically by the NMSS formulation of the problem when the process is of greater than first order or has appreciable pure time delays. The present paper applies the PIP methodology to the ALSTOM benchmark challenge, which takes the form of a highly coupled multi-variable linear model, representing the gasifier system of an integrated gasification combined cycle (IGCC) power plant. In particular, a straightforwardly tuned discrete-time PIP control system based on a reduced-order backward-shift model of the gasifier is found to yield good control of the benchmark, meeting most of the specified performance requirements at three different operating points

High Resolution XRF Sediment Analysis of Late Season Precipitation Events in a High Arctic Glaciated Watershed: Svalbard, Norway

Sediment transport in High Arctic watersheds have historically been dominated by melt-induced processes (Woo and McCann, 1994). However, in Svalbard, the last decade has experienced increased discharge variability and late season precipitation events (Nowak and Hodson, 2013). This study provides a detailed description of sedimentation corresponding to these late season precipitation events in Linnevatnet, western Spitsbergen. Annual sediment traps of three consecutive years (2011-2012, 2013-2014, and 2014-2015) were examined through the coupling of high-resolution X-ray fluorescence (XRF) analysis with lower resolution grain size and magnetic susceptibility measurements. Geochemical signatures were compared temporally and geographically across the basin. Zirconium counts and Fe/Ti ratios (Cuven et al., 2010) were used to delineate events and seasonal boundaries. All three years experienced heavy late season precipitation events, resulting in peaks of coarse sediment deposition coupled with variable Ca content. Principle Component Analysis was run on 10 elemental constituents (Al, Si, K, Ca, Ti, Mn, Fe, Rb, Sr, and Zr) in order to examine the relationship between them. The 2013-2014 year showed a strong relationship (PC1\u3e0.5) between all 10 elements and the first principle component (PC1), suggesting the elements varied together. The 2013-2014 sediment budget was dominated (\u3e40%) by a single late August precipitation event. Multiple late season precipitation events in 2011-2012 and 2014-2015, on the other hand, were characterized by increased variance in sediment geochemistry

A Case Grammar Analysis of the Representation of African-Americans in Current Fifth Grade Social Studies Textbooks.

The portrayal of ethnic minorities in American history textbooks has been examined over recent years by numerous researchers (Banks, 1969; Agostino and Barone, 1985; Garcia and Tanner, 1985; Garcia, 1986; Lamott, 1988; Thomas and Alawiye, 1993). Most of these studies have focused upon factual veracity and the selection of life roles through which certain groups have been portrayed. Life roles include employment, daily routines, and positions of prominence achieved

The Multidimensional Reading Instruction Observation Scale

The Multidimensional Reading Instruction Observation Scale is a formative evaluative instrument which can be used to judge the quality of reading instruction by recording the nature of the interaction between the student and the teacher along three dimensions critical to quality instruction: cognitive processes, affective processes, and management skills. Cognitive processes are those behaviors which are directed toward acquiring strategies or skills (to improve reading). Affective processes are those behaviors which influence the self-concept of the learner. Management skills are those behaviors which demonstrate ability to utilize components of the learning environment effectively

The Reading Teacher as a Workplace Literacy Consultant

A number of leaders in industry and government have asserted that many workers in this country lack the basic skills to perform adequately on the job; they claim that a wave of workplace illiteracy is sweeping this nation as never before. Whether the cause of this problem is perceived to be a deficit in the educational delivery system or a lag in skills due to the sudden technological explosion, it seems that there are many adults who need training in basic workplace skills in order to obtain and keep employment

Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models

A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics for the observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a 'match' between observed and simulated summaries are retained, and used to estimate the inaccessible posterior. With no reduction to a low-dimensional set of sufficient statistics being possible in the state space setting, we define the summaries as the maximum of an auxiliary likelihood function, and thereby exploit the asymptotic sufficiency of this estimator for the auxiliary parameter vector. We derive conditions under which this approach - including a computationally efficient version based on the auxiliary score - achieves Bayesian consistency. To reduce the well-documented inaccuracy of ABC in multi-parameter settings, we propose the separate treatment of each parameter dimension using an integrated likelihood technique. Three stochastic volatility models for which exact Bayesian inference is either computationally challenging, or infeasible, are used for illustration. We demonstrate that our approach compares favorably against an extensive set of approximate and exact comparators. An empirical illustration completes the paper.Comment: This paper is forthcoming at the Journal of Computational and Graphical Statistics. It also supersedes the earlier arXiv paper "Approximate Bayesian Computation in State Space Models" (arXiv:1409.8363

Excitation Spectrum at the Yang-Lee Edge Singularity of 2D Ising Model on the Strip

At the Yang-Lee edge singularity, finite-size scaling behavior is used to measure the low-lying excitation spectrum of the Ising quantum spin chain for free boundary conditions. The measured spectrum is used to identify the CFT that describes the Yang-Lee edge singularity of the 2D Ising model for free boundary conditions.Comment: 7 pages, 1 figur