"The Predication Semantics Model: The Role of Predicate: Class in Text Comprehension and Recall"

This paper presents and tests the predication semantics model, a computational model of text comprehension. It goes beyond previous case grammar approaches to text comprehension in employing a propositional rather than a rigid hierarchical tree notion, attempting to maintain a coherent set of propositions in working memory. The authors' assertion is that predicate class contains semantic information that readers use to make generally accurate predictions of a given proposition. Thus, the main purpose of the model-which works as a series of input and reduction cycles-is to explore the extent to which predicate categories play a role in reading comprehension and recall. In the reduction phase of the model, the propositions entered into the memory during the input phase are decreased while coherence is maintained among them. In an examination of the working memory at the end of each cycle, the computational model maintained coherence for 70% of cycles. The model appeared prone to serial dependence in errors: the coherence problem appears to occur because (unlike real readers) the simulation docs not reread when necessary. Overall, the experiment suggested that the predication semantics model is robust. The results suggested that the model emulates a primary process in text comprehension: predicate categories provide semantic information that helps to initiate and control automatic processes in reading, and allows people to grasp the gist of a text even when they have only minimal background knowledge. While needing refinement in several areas presenting minor problems-for example, the lack of a sufficiently complex memory to ensure that when the simulation of the model goes wrong it does not, as at present, stay wrong for successive intervals-the success of the model even at the current restrictive level of detail demonstrates the importance of the semantic information in predicate categories.

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting; Markov chain Monte Carlo

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We pro- pose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis aects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more eficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting, Markov chain Monte Carlo.

Results at 24 months from the prospective, randomized, multicenter Investigational Device Exemption trial of ProDisc-C versus anterior cervical discectomy and fusion with 4-year follow-up and continued access patients.

BackgroundCervical total disk replacement (TDR) is intended to address pain and preserve motion between vertebral bodies in patients with symptomatic cervical disk disease. Two-year follow-up for the ProDisc-C (Synthes USA Products, LLC, West Chester, Pennsylvania) TDR clinical trial showed non-inferiority versus anterior cervical discectomy and fusion (ACDF), showing superiority in many clinical outcomes. We present the 4-year interim follow-up results.MethodsPatients were randomized (1:1) to ProDisc-C (PDC-R) or ACDF. Patients were assessed preoperatively, and postoperatively at 6 weeks and 3, 6, 12, 18, 24, 36, and 48 months. After the randomized portion, continued access (CA) patients also underwent ProDisc-C implantation, with follow-up visits up to 24 months. Evaluations included Neck Disability Index (NDI), Visual Analog Scale (VAS) for pain/satisfaction, and radiographic and physical/neurologic examinations.ResultsRandomized patients (103 PDC-R and 106 ACDF) and 136 CA patients were treated at 13 sites. VAS pain and NDI score improvements from baseline were significant for all patients (P < .0001) but did not differ among groups. VAS satisfaction was higher at all time points for PDC-R versus ACDF patients (P = .0499 at 48 months). The percentage of patients who responded yes to surgery again was 85.6% at 24 months and 88.9% at 48 months in the PDC-R group, 80.9% at 24 months and 81.0% at 48 months in the ACDF group, and 86.3% at 24 months in the CA group. Five PDC-R patients (48 months) and no CA patients (24 months) had index-level bridging bone. By 48 months, approximately 4-fold more ACDF patients required secondary surgery (3 of 103 PDC-R patients [2.9%] vs 12 of 106 ACDF patients [11.3%], P = .0292). Of these, 6 ACDF patients (5.6%) required procedures at adjacent levels. Three CA patients required secondary procedures (24 months).ConclusionsOur 4-year data support that ProDisc-C TDR and ACDF are viable surgical options for symptomatic cervical disk disease. Although ACDF patients may be at higher risk for additional surgical intervention, patients in both groups show good clinical results at longer-term follow-up

Confidence Level and Sensitivity Limits in High Contrast Imaging

In long adaptive optics corrected exposures, exoplanet detections are currently limited by speckle noise originating from the telescope and instrument optics, and it is expected that such noise will also limit future high-contrast imaging instruments for both ground and space-based telescopes. Previous theoretical analysis have shown that the time intensity variations of a single speckle follows a modified Rician. It is first demonstrated here that for a circular pupil this temporal intensity distribution also represents the speckle spatial intensity distribution at a fix separation from the point spread function center; this fact is demonstrated using numerical simulations for coronagraphic and non-coronagraphic data. The real statistical distribution of the noise needs to be taken into account explicitly when selecting a detection threshold appropriate for some desired confidence level. In this paper, a technique is described to obtain the pixel intensity distribution of an image and its corresponding confidence level as a function of the detection threshold. Using numerical simulations, it is shown that in the presence of speckles noise, a detection threshold up to three times higher is required to obtain a confidence level equivalent to that at 5sigma for Gaussian noise. The technique is then tested using TRIDENT CFHT and angular differential imaging NIRI Gemini adaptive optics data. It is found that the angular differential imaging technique produces quasi-Gaussian residuals, a remarkable result compared to classical adaptive optic imaging. A power-law is finally derived to predict the 1-3*10^-7 confidence level detection threshold when averaging a partially correlated non-Gaussian noise.Comment: 29 pages, 13 figures, accepted to Ap

Inner shelf sediments off Chesapeake Bay. III, Heavy minerals

The heavy minerals in the sand sized fraction of 112 grab samples collected off the Virginia coast were analyzed for their variations in mineralogy. The main purpose was to characterize the heavy mineral suite and to delineate potentially important economic areas

A Millimeter-scale Single Charged Particle Dosimeter for Cancer Radiotherapy

This paper presents a millimeter-scale CMOS 64$\times$64 single charged particle radiation detector system for external beam cancer radiotherapy. A 1$\times$1 $\mu m^2$ diode measures energy deposition by a single charged particle in the depletion region, and the array design provides a large detection area of 512$\times$512 $\mu m^2$. Instead of sensing the voltage drop caused by radiation, the proposed system measures the pulse width, i.e., the time it takes for the voltage to return to its baseline. This obviates the need for using power-hungry and large analog-to-digital converters. A prototype ASIC is fabricated in TSMC 65 nm LP CMOS process and consumes the average static power of 0.535 mW under 1.2 V analog and digital power supply. The functionality of the whole system is successfully verified in a clinical 67.5 MeV proton beam setting. To our' knowledge, this is the first work to demonstrate single charged particle detection for implantable in-vivo dosimetry

Visual Representation Determines Search Difficulty: Explaining Visual Search Asymmetries

In visual search experiments there exist a variety of experimental paradigms in which a symmetric set of experimental conditions yields asymmetric corresponding task performance. There are a variety of examples of this that currently lack a satisfactory explanation. In this paper, we demonstrate that distinct classes of asymmetries may be explained by virtue of a few simple conditions that are consistent with current thinking surrounding computational modeling of visual search and coding in the primate brain. This includes a detailed look at the role that stimulus familiarity plays in the determination of search performance. Overall, we demonstrate that all of these asymmetries have a common origin, namely, they are a consequence of the encoding that appears in the visual cortex. The analysis associated with these cases yields insight into the problem of visual search in general and predictions of novel search asymmetries

Are critical finite-size scaling functions calculable from knowledge of an appropriate critical exponent?

Critical finite-size scaling functions for the order parameter distribution of the two and three dimensional Ising model are investigated. Within a recently introduced classification theory of phase transitions, the universal part of the critical finite-size scaling functions has been derived by employing a scaling limit that differs from the traditional finite-size scaling limit. In this paper the analytical predictions are compared with Monte Carlo simulations. We find good agreement between the analytical expression and the simulation results. The agreement is consistent with the possibility that the functional form of the critical finite-size scaling function for the order parameter distribution is determined uniquely by only a few universal parameters, most notably the equation of state exponent.Comment: 11 pages postscript, plus 2 separate postscript figures, all as uuencoded gzipped tar file. To appear in J. Phys. A