Static and Dynamic Error Correction of a Computer-Aided Mechanical Navigation Linkage for Arthroscopic Hip Surgery

While beneficially decreasing the necessary incision size, arthroscopic hip surgery increases the surgical complexity due to loss of joint visibility. To ease such difficulty, a computer-aided mechanical navigation system was developed to present the location of the surgical tool relative to the patient¿s hip joint. A preliminary study reduced the position error of the tracking linkage with limited static testing trials. In this study, a correction method, including a rotational correction factor and a length correction function, was developed through more in-depth static testing. The developed correction method was then applied to additional static and dynamic testing trials to evaluate its effectiveness. For static testing, the position error decreased from an average of 0.384 inches to 0.153 inches, with an error reduction of 60.5%. Three parameters utilized to quantify error reduction of dynamic testing did not show consistent results. The vertex coordinates achieved 29.4% of error reduction, yet with large variation in the upper vertex. The triangular area error was reduced by 5.37%, however inconsistent among all five dynamic trials. Error of vertex angles increased, indicating a shape torsion using the developed correction method. While the established correction method effectively and consistently reduced position error in static testing, it did not present consistent results in dynamic trials. More dynamic paramters should be explored to quantify error reduction of dynamic testing, and more in-depth dynamic testing methodology should be conducted to further improve the accuracy of the computer-aided nagivation system.

The Time-Series Properties on Housing Prices: A Case Study of the Southern California Market

We examine the time-series relationship between housing prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the housing price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the housing prices in these eight MSAs, a purchasing power parity finding for the housing prices in Southern California. Second, we perform temporal Granger causality tests revealing intertwined temporal relationships. The Santa Anna MSA leads the pack in temporally causing housing prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experienced the largest number of temporal effects from other MSAs, six of the seven, excluding only Los Angeles. The Santa Barbara MSA proved the most isolated in that it temporally caused housing prices in only two other MSAs (Los Angels and Oxnard) and housing prices in the Santa Anna MSA temporally caused prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different MSAs. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

"Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix

We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

Nonstationary brand variables in category management: A cointegration perspective

Category-management models serve to assist in the development of plans for pricing and promotions of individual brands. Techniques to solve the models can have problems of accuracy and interpretability because they are susceptible to spurious regression problems due to nonstationary time-series data. Improperly stated nonstationary systems can reduce the accuracy of the forecasts and undermine the interpretation of the results. This is problematic because recent studies indicate that sales are often a nonstationary time-series. Newly developed correction techniques can account for nonstationarity by incorporating error-correction terms into the model when using a Bayesian Vector Error-Correction Model. The benefit of using such a technique is that shocks to control variates can be separated into permanent and temporary effects and allow cointegration of series for analysis purposes. Analysis of a brand data set indicates that this is important even at the brand level. Thus, additional information is generated that allows a decision maker to examine controllable variables in terms of whether they influence sales over a short or long duration. Only products that are nonstationary in sales volume can be manipulated for long-term profit gain, and promotions must be cointegrated with brand sales volume. The brand data set is used to explore the capabilities and interpretation of cointegration.

Quantum-error correction on linear-nearest-neighbor qubit arrays

A quantum circuit implementing 5-qubit quantum-error correction on a linear-nearest-neighbor architecture is described. The canonical decomposition is used to construct fast and simple gates that incorporate the necessary swap operations allowing the circuit to achieve the same depth as the current least depth circuit. Simulations of the circuit's performance when subjected to discrete and continuous errors are presented. The relationship between the error rate of a physical qubit and that of a logical qubit is investigated with emphasis on determining the concatenated error correction threshold.

Continuous quantum error correction by cooling

We describe an implementation of quantum error correction that operates continuously in time and requires no active interventions such as measurements or gates. The mechanism for carrying away the entropy introduced by errors is a cooling procedure. We evaluate the effectiveness of the scheme by simulation, and remark on its connections to some recently proposed error prevention procedures.

Stabilizer formalism for operator quantum error correction

Operator quantum error correction is a recently developed theory that provides a generalized and unified framework for active error correction and passive error avoiding schemes. In this Letter, we describe these codes using the stabilizer formalism. This is achieved by adding a gauge group to stabilizer codes that defines an equivalence class between encoded states. Gauge transformations leave the encoded information unchanged; their effect is absorbed by virtual gauge qubits that do not carry useful information. We illustrate the construction by identifying a gauge symmetry in Shor's 9-qubit code that allows us to remove 3 of its 8 stabilizer generators, leading to a simpler decoding procedure and a wider class of logical operations without affecting its essential properties. This opens the path to possible improvements of the error threshold of fault-tolerant quantum computing.

Operator quantum error correction

This paper is an expanded and more detailed version of the work [1] in which the Operator Quantum Error Correction formalism was introduced. This is a new scheme for the error correction of quantum operations that incorporates the known techniques - i.e. the standard error correction model, the method of decoherence-free subspaces, and the noiseless subsystem method - as special cases, and relies on a generalized mathematical framework for noiseless subsystems that applies to arbitrary quantum operations. We also discuss a number of examples and introduce the notion of unitarily noiseless subsystems.

Pricing-to-market using EGARCH-error correction model

In this paper, the authors use an exponential generalized autoregressive conditional heteroscedastic (EGARCH) error-correction model (ECM), that is, EGARCH-ECM, to estimate the pass-through effects of foreign exchange (FX) rates and producers’ prices for 20 U.K. export sectors. The long-run adjustment of export prices to FX rates and producers’ prices is within the range of -1.02% (for the Textiles sector) and -17.22% (for the Meat sector). The contemporaneous pricing-to-market (PTM) coefficient is within the range of -72.84% (for the Fuels sector) and -8.05% (for the Textiles sector). Short-run FX rate pass-through is not complete even after several months. Rolling EGARCH-ECMs show that the short and long-run effects of FX rate and producers’ prices fluctuate substantially as are asymmetry and volatility estimates before equilibrium is achieved.

Pairing Repeated Reading and Systematic Error Correction: Impact on Fluency of 18 Year Old Students on a Small Caribbean Island

This study investigates the impact of a combined treatment of Systematic Error Correction and Repeated Reading on reading rate and errors for 18 year olds with undiagnosed reading difficulties on a Caribbean Island. In addition to direct daily measures of reading accuracy, the Reading Self Perception Scale was administered to determine whether the intervention was associated with changes in the way the student perceives himself as a reader.

Analysis of the Bovespa Futures and Spot Indexes With High Frequency Data. Chinese Business Review

Data from the World Federation of Exchanges show that Brazil’s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariate forecasting models based on intraday data from the futures and spot markets of the BOVESPA index. The interest is to verify if there exist arbitrage opportunities in Brazilian financial market. To this end, three econometric forecasting models were built: ARFIMA, vector autoregressive (VAR), and vector error correction (VEC). Furthermore, it presents the results of a Granger causality test for the aforementioned series. This type of study shows that it is important to identify arbitrage opportunities in financial markets and, in particular, in the application of these models on data of this nature. In terms of the forecasts made with these models, VEC showed better results. The causality test shows that futures BOVESPA index Granger causes spot BOVESPA index. This result may indicate arbitrage opportunities in Brazil.

Price transmission dynamics between ADRs and their underlying foreign security: The case of Banco de Colombia S.A.- Bancolombia.

This paper analyzes the dynamics ofthe American Depositary Receipt (ADR) of a Colombian bank (Bancolombia) in relation to its pricing factors (underlying (preferred) shares price, exchange rate and the US market index). The aim is to test if there is a long-term relation among these variables that would imply predictability. One cointegrating relation is found allowing the use of a vector error correction model to examine the transmission of shocks to the underlying prices, the exchange rate, and the US market index. The main finding of this paper is that in the short run, the underlying share price seems to adjust after changes in the ADR price, pointing to the fact that the NYSE (trading market for the ADR) leads the Colombian market. However, in the long run, both, the underlying share price and the ADR price, adjust to changes in one another.