Electrically-driven phase transition in magnetite nanostructures

Magnetite (Fe$_{3}$O$_{4}$), an archetypal transition metal oxide, has been used for thousands of years, from lodestones in primitive compasses[1] to a candidate material for magnetoelectronic devices.[2] In 1939 Verwey[3] found that bulk magnetite undergoes a transition at T$_{V}$ $\approx$ 120 K from a high temperature "bad metal" conducting phase to a low-temperature insulating phase. He suggested[4] that high temperature conduction is via the fluctuating and correlated valences of the octahedral iron atoms, and that the transition is the onset of charge ordering upon cooling. The Verwey transition mechanism and the question of charge ordering remain highly controversial.[5-11] Here we show that magnetite nanocrystals and single-crystal thin films exhibit an electrically driven phase transition below the Verwey temperature. The signature of this transition is the onset of sharp conductance switching in high electric fields, hysteretic in voltage. We demonstrate that this transition is not due to local heating, but instead is due to the breakdown of the correlated insulating state when driven out of equilibrium by electrical bias. We anticipate that further studies of this newly observed transition and its low-temperature conducting phase will shed light on how charge ordering and vibrational degrees of freedom determine the ground state of this important compound.Comment: 17 pages, 4 figure

The Output Gap, the Labor Wedge, and the Dynamic Behavior of Hours

We use a standard quantitative business cycle model with nominal price and wage rigidities to estimate two measures of economic ineffciency in recent U.S. data: the output gap - the gap between the actual and effcient levels of output - and the labor wedge - the wedge between households' marginal rate of substitution and firms' marginal product of labor. We establish three results. (i ) The output gap and the labor wedge are closely related, suggesting that most ineffciencies in output are due to the ineffcient allocation of labor. (ii ) The estimates are sensitive to the structural interpretation of shocks to the labor market, which is ambiguous in the model. (iii ) Movements in hours worked are essentially exogenous, directly driven by labor market shocks, whereas wage rigidities generate a markup of the real wage over the marginal rate of substitution that is acyclical. We conclude that the model fails in two important respects: it does not give clear guidance concerning the effciency of business cycle uctuations, and it provides an unsatisfactory explanation of labor market and business cycle dynamics

Bayesian Analysis of DSGE Models

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the nonlinear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models, and a DSGE model that was solved with a second-order perturbation method. (JEL C11, C32, C51, C52

Test Data Compression and Test Time Reduction Using an Embedded Microprocessor

Abstract—Systems-on-a-chip (SOCs) with many complex intellectual property cores require a large volume of data for manufacturing test. The computing power of the embedded processor in a SOC can be used to test the cores within the chip boundary, reducing the test time and memory requirements. This paper discusses techniques that use the computing power of the embedded processor in a more sophisticated way. The processor can generate and reuse random numbers to construct test patterns and selectively apply only those patterns that contribute to the fault coverage, significantly reducing the pattern generation time, the total number of test applications and, hence, the test time. It can also apply deterministic test patterns that have been compressed using the characteristics of the random patterns as well as those of the deterministic patterns themselves, which leads to high compression of test data. We compare three fast run-length coding schemes which are easily implemented and effective for test-data compression. We also demonstrate the effectiveness of the proposed approach by applying it to some benchmark circuits and by comparing it with other available techniques. Index Terms—Built-in self test (BIST), data compression/decompression, embedded core testing, processor-based testing, random pattern generation, system-on-a-chip (SOC), test time. I

A hybrid method for classifying cognitive states from fMRI data

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees