Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset

Many recent papers have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these papers generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of vintage data synchronized with the Fed's Greenbook forecast. This dataset consists of a large number of variables, as observed at the time of each Greenbook forecast since 1979. Thus, we can compare real-time large dataset predictions to both simple univariate methods and to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook's advantage in evaluating the current state of the economy, neither large dataset methods nor the Greenbook process offers much advantage over a univariate autoregressive forecast.

Selecting ELL Textbooks: A Content Analysis of Ethnicity Depicted in Illustrations and Writing

In an effort to respond to the need for culturally appropriate English Language Learning(ELL) resources for adolescent immigrants, the researchers gathered 64 textbooks actually in use in eight Milwaukee middle schools to analyze their content for the range of diversity of ethnicity depicted in illustrations and written text. The eight school settings selected provided a broad range of materials to analyze. In addition, these materials reflect both public and Catholic teachers’ resource selection in predominantly Latino and Southeast Asian American classroom contexts. The settings were chosen with the advice of administrators and teachers as schools they perceived to be of greatest need for ELL curriculum and instruction development. Based upon their findings, the researchers draw some initial conclusions and recommendations for the selection of culturally appropriate textbooks that fit the cultural contexts of the learners. Finally, the study provides as appendices the bibliography of textbooks under analysis and sample coding instruments used to analyze the content of these textbooks

Control of Multi-level Voltage States in a Hysteretic SQUID Ring-Resonator System

In this paper we study numerical solutions to the quasi-classical equations of motion for a SQUID ring-radio frequency (rf) resonator system in the regime where the ring is highly hysteretic. In line with experiment, we show that for a suitable choice of of ring circuit parameters the solutions to these equations of motion comprise sets of levels in the rf voltage-current dynamics of the coupled system. We further demonstrate that transitions, both up and down, between these levels can be controlled by voltage pulses applied to the system, thus opening up the possibility of high order (e.g. 10 state), multi-level logic and memory.Comment: 8 pages, 9 figure

Biomechanical Determinants of the Reactive Strength Index During Drop Jumps

The Reactive Strength Index (RSI) is often used to quantify drop-jump (DJ) performance; however, not much is known about its biomechanical determinants. The purpose of this study was to investigate the correlations between the RSI and several biomechanical variables calculated from DJ performed with different initial drop heights. Twelve male NCAA Division I basketball players performed DJs from drop heights of 30, 45, and 60 cm. Force plates were used to calculate DJ performance parameters (ie, DJ height, contact time, and RSI) and DJ biomechanical variables (ie, vertical stiffness and eccentric/concentric energetics). Regression analyses were used to assess the correlations between variables at each drop height, and ANOVAs were used to assess the differences of all variables across drop heights. Follow-up analyses used 2 neural networks to determine if DJ performance and biomechanical data could accurately classify DJ trials by drop-height condition. Vertical-stiffness values were significantly correlated with RSI at each height but did not change across drop heights. Surprisingly, the RSI and other DJ parameters also did not vary across drop height, which resulted in the inability of these variables to accurately classify DJ trials. Given that vertical stiffness did not change across drop height and was highly correlated with RSI at each height, the RSI appears to reflect biomechanical behavior related to vertical stiffness during DJ. However, the inability of the RSI to accurately classify drop-height condition questions the use of RSI profiles established from DJs from different heights

Learning to Skim Text

Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q\&A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy

Structure and control of self-sustained target waves in excitable small-world networks

Small-world networks describe many important practical systems among which neural networks consisting of excitable nodes are the most typical ones. In this paper we study self-sustained oscillations of target waves in excitable small-world networks. A novel dominant phase-advanced driving (DPAD) method, which is generally applicable for analyzing all oscillatory complex networks consisting of nonoscillatory nodes, is proposed to reveal the self-organized structures supporting this type of oscillations. The DPAD method explicitly explores the oscillation sources and wave propagation paths of the systems, which are otherwise deeply hidden in the complicated patterns of randomly distributed target groups. Based on the understanding of the self-organized structure, the oscillatory patterns can be controlled with extremely high efficiency.Comment: 16 pages 5 figure

Equine digital tendons show breed‐specific differences in their mechanical properties that may relate to athletic ability and predisposition to injury

Background Throughout the ages, human subjects have selected horse breeds for their locomotor capacities. Concurrently, tissue properties may have diversified because of specific requirements of different disciplines. Objectives The aim of this study was to compare the biomechanical properties of tendons with different functions between equine breeds traditionally selected for racing or sport. Study design This study used ex vivo tendons and compared the mechanical properties of the common digital extensor tendon (CDET) and superficial digital flexor tendon (SDFT) between racehorses (Thoroughbred [TB]) and sports horses (Friesian Horse [FH], Warmblood [WB]). Methods The SDFT and CDET of FH (n = 12), WBs (n = 12) and TBs (n = 8) aged 3-12 years were harvested. The cross sectional area (cm(2)), maximal load (N), ultimate strain (%), ultimate stress (MPa) and elastic modulus (MPa) were determined and tested for significant differences between the breeds (P<0.05). Results The SDFT from WB horses had a significantly lower elastic modulus than TB horses and failed at a higher strain and load than both FHs and TBs. The mechanical properties of the CDET did not differ between breeds. In agreement with previous studies, the CDET failed at a higher stress and had a higher elastic modulus than the SDFT and, for the WB group of horses only, failed at a significantly lower strain. Interestingly, the mode of failure differed between breeds, particularly with respect to the FHs. Main limitations The exercise history of horses used in this study was unknown and the age-range was relatively large; both these factors may have influenced the absolute properties reported in this study. Conclusions This study shows for the first time that mechanical properties of the SDFT differ between breeds. These properties are likely to be related to selection for high-speed vs. an extravagant elastic gait and may be an important indicator of performance ability. The is available in Spanish - see Supporting Informatio