Error Correction Approach Research Articles

Evaluating the resilience of variational quantum algorithms to leakage noise

As we are entering the era of constructing practical quantum computers, suppressing the inevitable noise to accomplish reliable computational tasks will be the primary goal. Leakage noise, as the amplitude population leaking outside the qubit subspace, is a particularly damaging source of error that error correction approaches cannot handle. However, the impact of this noise on the performance of variational quantum algorithms (VQAs), a type of near-term quantum algorithms that is naturally resistant to a variety of noises, is yet unknown. Here, {we consider a typical scenario with the widely used hardware-efficient ansatz and the emergence of leakage in two-qubit gates}, observing that leakage noise generally reduces the expressive power of VQAs. Furthermore, we benchmark the influence of leakage noise on VQAs in real-world learning tasks. Results show that, both for data fitting and data classification, leakage noise generally has a negative impact on the training process and final outcomes. Our findings give strong evidence that VQAs are vulnerable to leakage noise in most cases, implying that leakage noise must be effectively suppressed in order to achieve practical quantum computing applications, whether for near-term quantum algorithms and long-term error-correcting quantum computing.

Stock Market Capitalisation, Banks’ Lending and Deposit Interest Rate in Relation to Financial Liberalisation Theory in Nigeria (1985-2021)

The paper examines the impact of deposit rates on savings, lending rates on credits and market capitalization of Nigerian Exchange group limited from 1985 to 2021, using co-integration and vector error correction approach. The specific objectives were to estimate the short-and long-run association as well as the error correction mechanism of lending interest rate and deposit interest rate and stock market capitalization in Nigeria. The outcome of the exercise validated the hypothesis of financial liberalisation theory that deposit rates on savings, lending rates on credits on market capitalization of Nigerian Exchange group limited. Deposit interest rate and lending interest rate have significant explanatory power for stock market capitalization, which is consistent with our expectations in line with financial liberalization theory in Nigeria. The regulatory body should mandate banks to channel their mobilised savings to investors in form of loans at reasonable interest rate. Hence, the pointer should be to identify those constraints and bottlenecks that are making it difficult for banks to make loans available to investors. The issue of high interest rate with hidden transaction costs must be vigorously addressed by the monetary authorities; Monetary authorities should pursue policies that will encourage the saving culture of the people. This could be done by increasing the deposit rate which would lure people to deposit their money in banks thereby increasing the supply of loanable funds. This would lead to a fall in interest rate and eventually rise in investment in stock market development.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Climate change, food security and economic growth nexus in the Gambia: Evidence from an econometrics analysis

This paper aims to test econometrically to analysis climate change on food security and other which includes economic growth, population growth and agriculture sector. This paper employed data for the period 1971–2020 from open data bank from (WDI) World development indicator and analyzed this data using Vector Autoregressive Approach (VAR), Granger Causality Approach, Autoregressive Distributed lag mode approach (ARDL) and Error Correction Approach (ECM). Empirical evidence displays, food security growth has strong optimistic correlation with the agriculture sector but negative correlation with rainfall variation. Finally, Population growth has significant negative impact on food security in a short term and insignificant negative impact on food security in the long term. Policy recommendation is that since agriculture is important for food security in the rural Gambia. The government should work on planting more trees for better agriculture and environment, Food security intervention programs, and Poverty reduction programs, provision of more adaptive capacity indicators, Quality and affordable education for all, System changes to bring quality of governance and institution and economic that is working for all and sundry, and consider export economy, the author noted.

K-Mer Spectrum-Based Error Correction Algorithm for Next-Generation Sequencing Data.

In the mid-1970s, the first-generation sequencing technique (Sanger) was created. It used Advanced BioSystems sequencing devices and Beckman's GeXP genetic testing technology. The second-generation sequencing (2GS) technique arrived just several years after the first human genome was published in 2003. 2GS devices are very quicker than Sanger sequencing equipment, with considerably cheaper manufacturing costs and far higher throughput in the form of short reads. The third-generation sequencing (3GS) method, initially introduced in 2005, offers further reduced manufacturing costs and higher throughput. Even though sequencing technique has result generations, it is error-prone due to a large number of reads. The study of this massive amount of data will aid in the decoding of life secrets, the detection of infections, the development of improved crops, and the improvement of life quality, among other things. This is a challenging task, which is complicated not just by a large number of reads and by the occurrence of sequencing mistakes. As a result, error correction is a crucial duty in data processing; it entails identifying and correcting read errors. Various k-spectrum-based error correction algorithms' performance can be influenced by a variety of characteristics like coverage depth, read length, and genome size, as demonstrated in this work. As a result, time and effort must be put into selecting acceptable approaches for error correction of certain NGS data.

Improving reliability using phase distribution aware LDPC code for holographic data storage.

The iterative Fourier transform (IFT) algorithm is an effective solution for phase retrieval in phase-type holographic data storage systems, but introduces a higher phase error rate. As a result, data reliability becomes a significant issue. In this paper, to improve reliability and decrease decoding latency, we propose a phase distribution aware low-density parity-check (LDPC) code [called point data abstraction library (PDAL)] with outstanding error correcting capability. After experiencing IFT, we first investigate the phase distribution characteristics and find that the adjacent phase distribution is more likely to cross, resulting in higher phase shift percentages. Then, using phase distribution, PDAL optimizes LDPC codes with higher precision decoding information by dynamically applying the phase threshold based on the phase error rate. When the phase error rate is 0.04, the bit error rate, decoding iteration times, and decoding failure rate are all reduced by 51.5%, 26.9%, and 51.8% on average, respectively, compared with traditional LDPC code without exploiting phase distribution. PDAL, which is an efficient and practical error correction approach for phase-modulated holographic data storage, can improve data reliability by boosting error correction performance.

Modeling the 2D Inundation Simulation Based on the ANN-Derived Model with Real-Time Measurements at Roadside IoT Sensors

This study aims to develop a smart model for the two-dimensional (2D) inundation simulation based on the derived artificial neural network (ANN) model with real-time measurements at the roadside IoT (Internet of Things) sensors; in detail, the flooding zones and associated area can be quantified by combining the inundation-depth estimates at the ungauged locations (defined by the virtual IoT sensor, VIOT) via the corresponding inundation-estimation equations, established using the ANN-derived model with the measurements at the IoT sensors (named SM_EID_VIOT model). Moreover, the resulting inundation-depth estimates at the ungauged locations from the proposed SM_EID_VIOT model can be improved by means of the real-time error-correction approach for the 2D inundation simulation. To demonstrate the reliability of the results from the proposed SM_EID_VIOT model, 1000 simulations of the rainfall-induced flood events within the study area of the Miaoli City of Northern Taiwan are generated as the model-training and validation datasets. Consequently, the proposed SM_EID_VIOT could estimate the inundation depths with an acceptable accuracy at the ungauged locations in time and space based on a low root mean square error (RMSE) of under 0.01 m and a high coefficient of determination (R2) of over 0.8; and it also can delineate the flooding zone to quantify the corresponding area in high reliability in terms of the precision ratio of about 0.7.

How Does Corruption Affect Currency Substitution? Evidence from Nigeria

The article examines the impact of corruption on currency substitution in Nigeria over the 1986–2017 period, using the auto-regressive distributed lag and vector error-correction approaches. The results indicate that corruption and currency substitution are co-integrated. Moreover, corruption promotes currency substitution in the short run, which confirms the safe-haven hypothesis. An increase in income leads to an increase in currency substitution, which further supports the safe-haven hypothesis. Exchange rate depreciation tends to promote currency substitution, especially in the short-run. Finally, inflation and interest rate promote currency substitution in Nigeria. Therefore, it would be necessary for policymakers to explore ways of reducing corruption, ensure macroeconomic stability, foster the equitable distribution of growth and promote productivity to enhance savers’ confidence in the local currency.

Modeling the determinants of renewable energy consumption in Nigeria: Evidence from Autoregressive Distributed Lagged in error correction approach

Different solutions have been designed to solve the climate change impacts. One recommended solution is to use renewable energy sources to power up economic activities. Renewable energy lowers the carbon footprint, generates no emissions and reduces harmful air pollutants. This research investigates the determinants of renewable energy consumption in Nigeria from 1960 to 2019 using the Parameterization Autoregressive Distributed Lagged in Error Correction approach. Nigeria is used as a case study because of its position and role in African and global economies. In the long-run, gross domestic product and financial development promote renewable energy consumption, and fossil fuel energy consumption harms renewable energy consumption. As gross domestic product increases by 1%, renewable energy consumption increases by 0.11%. A 1% increase in fossil fuel energy consumption decreases renewable energy consumption by 0.0008%. As financial development increases by 1%, renewable energy consumption increases by 0.026%. In the short-run, trade openness has a negative impact on renewable energy consumption. A 1% increase in trade openness decreases renewable energy consumption by 0.005%. Therefore, it is suggested that the Nigerian government continue to create policies that increase the Nigerian economy's productive capacity and regulate the importation of fossil fuel-related goods in favor of renewable energy products.

Financial deepening and sectoral performance in emerging markets: evidence from the Nigerian agricultural and manufacturing sectors

Purpose. The formal sector in Nigeria has experienced some transformation in the last 20 years. This has led to advancement and deepening of sector. Despite the progression achieved in financial sector, it has not yielded the desired impacts on the economy as experienced in other emerging economies. Hence, this research assessed the advantageous effects of financial deepening on sectoral advancement in Nigeria.
 Methodology / approach. The research used the proxies of broad money (M2) to GDP ratio, and the ratio of credits to private sector to GDP for financial deepening. The data regarding time series were retrieved from Thomson Reuters DataStream and Augmented Dickey-Fuller unit root test and Johansen co integration approaches used for the long-oriented effects and Error Correction approach for short orientation estimation. 
 Results. The impact of financial deepening was positive and statistically significant in explaining agricultural growth, but the performance of the manufacturing sector was not significantly impacted in the reviewed period, hence government should make efforts to reduce the interest rate on loan bestowed to the private sector.
 Originality / scientific novelty. The variables assortments have never been describing in the literature. Particularly, this study considers financial deepening as a significant determinant of sectoral development in the Nigerian context. 
 Practical value / implications. According to the results of the study, it is recommended to increase credits to the manufacturing sector through specialized banks and ensures the growth of the financial industry in terms of provision of quality financial instruments, sound financial and money markets, and transparent legislation for healthy competition in the industry for better sectoral performance.

Using Machine Learning to Mitigate Single-Event Upsets in RF Circuits and Systems

The present paper applies the k-nearest neighbor (k-NN) machine learning algorithm to detect and correct single-event upsets (SEUs). In particular, this work focuses on SEUs in RF systems carrying modulated data resulting from single-event transients in RF signals. Pulsed-laser measurements were performed, and the resulting SEUs were used to train separate k-NN algorithms to detect and correct these upsets. The results show that the algorithm correctly classified data into “upset” and “no upset” 99.2% of the time. In addition, the number of symbol upsets was reduced by 30%. Some of the challenges to implementing and deploying this error correction approach in flight systems are discussed. The results of the present work demonstrate the potential benefits of applying machine learning techniques to the field of radiation effects.

AN ECONOMIC ANALYSIS OF THE DETERMINANTS OF DOMESTIC DEMAND FOR TEA IMPORTS IN IRAQ USING THE AUTOREGRESSIVE DISTRIBUTED LAG TECHNIQUE (ARDL) FOR THE PERIOD (1990-2020)

This study was aimed to estimate the domestic demand function on tea imports in Iraq using the Autoregressive Distributed Lag model (ARDL) for co-integration during the period 1990-2020.The data are verified for stationary by using unit root tests basis on Augmented Dickey - Fuller Test. The existence of a long-run equilibrium relationship among tea import quantity, current prices of tea and coffee buying, gross national income, and number of buyers is proved using bounds test method to co-integration. Results of estimating short and long-run models of the demand of tea imports by using the unrestricted error correction approach showed that the error correction parameter is adjusted annually by 140%. Results of estimating price elasticity of demand in the short run showed that the absolute coefficient of price elasticity of demand is less than one and more than zero, which means that tea is a necessary commodity for the Iraqi shopper. Additionally the cross elasticity coefficient had a positive sign in the short and long run, which means that coffee, is an alternative good for tea from the viewpoint of the Iraqi consumer. With regarding to the coefficient value of the income elasticity of demand, it was positive and less than one in the short and long run, which means that tea, is a necessary normal commodity for Iraqi buyers. The study recommended that it is necessary to diversify the sources of national income in the local economy by utilizing all available resources in order to reduce dependence on oil revenues, which were a major source of financing imports in the country.

A Novel Automatic Probe-to-Pad Alignment Error Correction Approach

In wafer test, the probe mark center shall be close to the pad center as much as possible. But the traditional probe-to-pad alignment (PTPA) process cannot always meet the requirements. So a novel automatic error correction approach was developed in this paper to improve the PTPA process. The approach is based on a classic two dimension rigid motion model and enhanced by integrating an iteratively reweighted least squares algorithm. The singular value decomposition method is also used to seek the solutions. Using the probe mark and pad geometric information, it first best fits the probe mark centers to the targets, then it expands the minimum probe mark to pad edge clearance to the largest. The experiments proved its good feasibility, accuracy and efficiency.

An Orbital Error Correction Model Based on Triplet Network and Shrunken Estimation

Orbital error, one of major error sources of InSAR observations, is characterized by long wavelength artifacts which can downgrade the monitoring accuracy, especially for wideswath SAR missions. In this paper, we present a novel approach for time series orbital error correction, with an emphasis on the computational and estimation efficiency of orbital error parameters over wide-area scale scenes. The proposed method combines the temporal triplet network and shrunken estimator, which integrates <i>L</i>²-Norm with <i>L</i>¹-Norm regularization, also known as Lasso regularization. The rationale behind it is to first determine the initial orbital parameters by utilizing the traditional polynomial-based method in the spatial domain. Next, in order to weaken the interference of phase unwrapping errors and other undesired phase contributions, an additional correction procedure is implemented through building up redundant triplet network in the time domain and further a shrunken estimation method. Experiments on synthetic data and real Sentinel-1 datasets covering Eastern California confirm that the presented method can better balance the accuracy and computational efficiency. The proposed approach may therefore be useful for the processing of emerging big data InSAR.

A Novel Approach for Asymmetric Quantum Error Correction With Syndrome Measurement

Most of the quantum error correction methods are symmetric. Symmetric methods are implemented by considering the amplitude of bit flip(X) and phase flip(Z) errors as same. With the quantum experiments, it is observed that the amplitude of Z errors are more compared to X errors. Due to which the need of asymmetric error correction has increased. This paved a path for the development of asymmetric error correction methods. In this paper, we discussed the concept of asymmetric quantum error correction (AQEC) and proposed an efficient approach for AQEC with encoding, syndrome measurement and decoding operations with increased fidelity to 85.89% and reduced circuit depth to 48%.

Supply Response of Field Crops to Price and Environmental Factors in Traditional Rainfed Agriculture

This study aimed to estimate supply response crops, i.e., sesame, groundnut, sorghum, and millet, in traditional rainfed agriculture, in North Kordofan State, Sudan, from 1990 to 2015. The response is estimated as the yield and area responses to prices, temperature, and rainfall. The study depended mainly on secondary data obtained from the records of the Ministry of agriculture and animal resources, Elobied Crops Market, and Elobied Airport Metrological Station. The co-integration and vector error correction approaches were applied to estimate the response. The results found that the estimated responses of crop yield in the long run to price were negative and inelastic for sesame, groundnut, and elastic for sorghum. It was positive and inelastic for yield millet yield. The estimated responses of crop area to price in the long run were negative and inelastic for sesame, sorghum, millet, and elastic for groundnut. The estimated responses of crop yield in the long run to temperature were negative and elastic for all crops and ranged from-24.01 to -197.83. The estimated responses of crop area in the long run to temperature were positive and elastic and ranged from 37.121 to 411.747.The estimated responses of crop yield to rainfall index, in the long run, were positive and elastic for groundnut (2.357), sorghum (4.667), and millet (1.142), but it is inelastic for sesame (0.509). The responses of crop area to rainfall index were negative and elastic and ranged from -13.745 to -1.086. The study concluded that rainfall index and temperature factors are the most dominating factors influencing yield and area behavior in the long-run, hence farmers’ decisions.

Sliding-Window Turbo Equalization and Its Reduced-Complexity Decoding Techniques

Turbo equalization is a cooperative error-correcting approach to achieve a target bit-error rate over inter-symbol interference channels. In this paper, we proposed a new turbo equalization, called sliding-window turbo equalization (SW-TE), consisting of the Bahl-Cocke-Jelinek-Raviv detector and a spatially coupled, low-density, parity-check decoder. Moreover, the Protograph-based Extrinsic Information Transfer (P-EXIT) chart was modified to investigate the asymptotic behavior of SW-TE. Our analyses showed that SW-TE outperformed conventional turbo equalization in terms of decoding threshold. Based on the P-EXIT chart analysis, we further proposed a guideline of the reduced-complexity decoding techniques for SW-TE, eliminating unnecessary branch metric updates during turbo iterative decoding. Our simulations corroborated the analytical results, showing that SW-TE outperformed conventional turbo equalization while maintaining an acceptable level of complexity.

Testing the Validity of the Long Run Neutrality of Money in Nigeria

Abstract Research background: There is no consensus among scholars on the interaction effect between money supply, price, and wages despite various studies conducted to that effect. Purpose: This study investigates whether the neutrality of money assumption holds in the long run in Nigeria, using annual data from 1970 to 2018. Research methodology: The study utilized the Johansen cointegration test and the Vector Error Correction (VECM) approach for estimation. Results: The results from the Phillips curve model contradict the classical school of economics assumption that money is neutral in the long run. This implies that in the Nigerian economy, money is not neutral in the long run. The long run Fishers’ effect model shows that the coefficient of LOG (CPI) exhibits a negative sign and is statistically significant at a 5% significant level, thus contradicting the hypothesis which states that a one percent increase in consumer prices will lead to an increase in the rate of interest by one percent. The coefficient of nominal money supply indicates a negative sign and insignificant statistically on the interest rate. The Short-run estimated results showed that the coefficient of the error correction term ECM (–1) indicates a negative sign and is significant statistically in the Fishers’ effect model. The result shows the actual and equilibrium values are corrected with adjustment speeds equal to 31% yearly. Novelty: The study recommends that the Central Bank of Nigeria should ensure an effective implementation of monetary targeting measures in fine-tuning the economy and curbing inflationary pressures.

Data-driven dynamical mean-field theory: An error-correction approach to solve the quantum many-body problem using machine learning

Machine learning opens new avenues for modelling correlated materials. Quantum embedding approaches, such as the dynamical mean-field theory (DMFT), provide corrections to first-principles calculations for strongly correlated materials, which are poorly described at lower levels of theory. Such embedding approaches are computationally demanding on classical computing architectures, and hence remain restricted to small systems, which limits the scope of applicability without exceptional computational resources. Here we outline a data-driven machine learning process for solving the Anderson Impurity Model (AIM) - the central component of DMFT calculations. The key advance is the use of an ensemble error-correction approach to generate fast and accurate solutions of AIM. An example calculation of the Mott transition using DMFT in the single band Hubbard model is given as an example of the technique, and is validated against the most accurate available method. This new approach is called data-driven dynamical mean-field theory (\textbf{d$^{3}$MFT}).

Dysfunctional Association Between Public Expenditure Growth and National Consumption Cost: A Vector Error Correction Approach

The management of public expenditure is crucial due to its effect on people’s standard of living. In line with this, this study investigated public expenditure growth and national consumption costs using a vector error correction approach. The objective is to determine the effect of capital and recurrent expenditure on the consumer price index. The dependent variable in this study is national consumption measured by the consumer price index, while the independent variable is public expenditure measured by capital and recurrent expenditures. An ex post facto research design was adopted, and data for the study spanning from 1981 to 2019 was sourced from the World Bank. Descriptive statistics, unit root tests, cointegration tests and vector error correction estimates were all conducted with the aid of EViews 9. Based on the results, we concluded that there is a positive but insignificant relationship between the consumer price index and capital and recurrent expenditures of the government in Nigeria. We recommend that Nigeria’s government should increase expenditure on projects that will improve the economy and the living standards of the people. Additionally, there is an urgent need for proper monitoring of allocations contained in the budget to ensure efficient and effective utilization of funds, as this will help to improve the standard of living of the people and improve the economy.