Correction Term Research Articles

Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction

Asynchronous federated learning (AFL) is a distributed machine learning technique that allows multiple devices to collaboratively train deep learning models without sharing local data. However, AFL suffers from low efficiency due to poor client model training quality and slow server model convergence speed, which are a result of the heterogeneous nature of both data and devices. To address these issues, we propose Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction (FedAC). Our framework consists of three key components. The first component is client weight evaluation based on temporal gradient, which evaluates the client weight based on the similarity between the client and server update directions. The second component is adaptive server update with prospective weighted momentum, which uses an asynchronous buffered update strategy and a prospective weighted momentum with adaptive learning rate to update the global model in server. The last component is client update with fine-grained gradient correction, which introduces a fine-grained gradient correction term to mitigate the client drift and correct the client stochastic gradient. We conduct experiments on real and synthetic datasets, and compare with existing federated learning methods. Experimental results demonstrate effective improvements in model training efficiency and AFL performance by our framework.

Exact Insulated-Tip Fin Length Correction for Tip Convection Compensation

Abstract This study details the derivation of a length correction term for computing the heat transfer performance of one-dimensional, straight, convecting-tip fins using the insulated-tip fin solution. Use of this corrected length in the insulated-tip fin solution produces the identical heat transfer rate and temperature profile as those computed using the more complex convecting-tip fin equations. The analysis derives the length correction equation from fundamental principles and produces a simple, closed-form expression valid for all fin cross-sectional shapes. Furthermore, the valid parameter range where this length correction is applicable, and outside of which no exact length correction is possible, is quantified.

Quasi-P-Wave Reverse Time Migration in TTI Media with a Generalized Fractional Convolution Stencil

In seismic modeling and reverse time migration (RTM), incorporating anisotropy is crucial for accurate wavefield modeling and high-quality images. Due to the trade-off between computational cost and simulation accuracy, the pure quasi-P-wave equation has good accuracy to describe wave propagation in tilted transverse isotropic (TTI) media. However, it involves a fractional pseudo-differential operator that depends on the anisotropy parameters, making it unsuitable for resolution using conventional solvers for fractional operators. To address this issue, we propose a novel pure quasi-P-wave equation with a generalized fractional convolution operator in TTI media. First, we decompose the conventional pure quasi-P-wave equation into an elliptical anisotropy equation and a fractional pseudo-differential correction term. Then, we use a generalized fractional convolution stencil to approximate the spatial-domain pseudo-differential term through the solution of an inverse problem. The proposed approximation method is accurate, and the wavefield modeling method based on it also accurately describes quasi-P-wave propagation in TTI media. Moreover, it only increases the computational cost for calculating mixed partial derivatives compared to those in vertical transverse isotropic (VTI) media. Finally, the proposed wavefield modeling method is utilized in RTM to correct the anisotropic effects in seismic imaging. Numerical RTM experiments demonstrate the flexibility and viability of the proposed method.

Uncovered interest rate parity phenomenon and determinants of domestic interest rates: an analysis of Pakistan and China economies

The core objective of this article is to explore the viability of uncovered interest parity and to reconnoiter major determinants of domestic interest rates of the Pakistan and China economies. It is imperative to study the interest rate parity that assistances to comprehend that exactly how exchange rate and domestic interest rate influence foreign interest rate of two economies in emerging trends in international trade. The World Development Indicators is main source of data gathering, and it ranges from 1980 to 2023 for the analysis of both models. This study considers two models, Engle–Granger causality has been applied in Model 1, to analyze the uncovered interest parity, and findings indicate that the exchange rate of each country has no effect on the interest rate of the other country. However, Model 2 investigates the main determinants of domestic interest rate and their long-run associations for Pakistan and China. To capture the long-run association, ARDL estimation technique has been deployed, and findings show the long-run relations and co-integration among the policy variables of both countries. In Pakistan, consumer price index and exchange rate hold positive linking with the domestic interest rate; however, output level is adversely correlated. The error correction term is 91%, confirming the rapidity of equilibrium adjustment when a shock occurs. Regarding the Chinese economy, consumer price index and exchange rate have a negative impact on China domestic interest rate. The results of the error correction model indicate an 80% speed of convergence in the long run. This study suggests that improvement in productivity and exchange rate may assist both economies to experience steady exchange rate and interest rate.

Assessing the long-term impact of macroeconomic and environment dynamics: Does sustainable energy production shape the environmental landscape of south ASIAN nations?

<p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This study delves into the intricate interconnections among the financial sector, macroeconomic dynamics, and the advancement of sustainable energy production in South Asia. Utilizing data from 1987 to 2022 in Bangladesh, India, Pakistan, and Sri Lanka, the research employs the Structural Vector Autoregressive (SVAR) method to analyze financial market responses. The findings reveal nuanced insights into the complex relationship between economic and environmental factors, fostering a comprehensive understanding of the region's intricate nexus between financial markets and sustainable energy initiatives. In the Economic Growth model, the negative coefficient for CO2 emissions underscores environmental concerns, indicating an inverse relationship with economic growth. Conversely, positive coefficients for non-fossil energy production, Gross Fixed Capital Formation, stock exchange value, and domestic credit emphasize their positive impact on economic growth. A positive GDP coefficient in the CO2 emission model signifies heightened carbon emissions with economic growth, while a negative coefficient for non-fossil energy production suggests potential environmental benefits. The nearly unitary positive coefficient for Gross Fixed Capital Formation highlights its significant role in driving carbon emissions. The Non-Fossil Energy Production model reveals an inverse relationship between GDP and clean energy, suggesting a potential reliance on fossil fuels during economic growth. Positive coefficients for CO2 emissions, Gross Fixed Capital Formation, stock exchange value, and domestic credit highlight factors influencing non-fossil energy production. Long-run stable coefficients affirm variable significance, while a negative error correction term suggests convergence in developing countries. These models offer nuanced insights into the intricate relationships among economic, environmental, and energy variables, guiding sustainable development policies in South Asia.</span></p>

Manual versus machine: How accurately does the Medical Text Indexer (MTI) classify different document types into disease areas?

The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary developed by the U.S. National Library of Medicine (NLM) for classifying journal articles. It is increasingly used by researchers studying medical innovation to classify text into disease areas and other categories. Although this process was once manual, human indexers are now assisted by algorithms that automate some of the indexing process. NLM has made one of their algorithms, the Medical Text Indexer (MTI), available to researchers. MTI can be used to easily assign MeSH descriptors to arbitrary text, including from document types other than publications. However, the reliability of extending MTI to other document types has not been studied directly. To assess this, we collected text from grants, patents, and drug indications, and compared MTI's classification to expert manual classification of the same documents. We examined MTI's recall (how often correct terms were identified) and found that MTI identified 78% of expert-classified MeSH descriptors for grants, 78% for patents, and 86% for drug indications. This high recall could be driven merely by excess suggestions (at an extreme, all diseases being assigned to a piece of text); therefore, we also examined precision (how often identified terms were correct) and found that most MTI outputs were also identified by expert manual classification: precision was 53% for grant text, 73% for patent text, and 64% for drug indications. Additionally, we found that recall and precision could be improved by (i) utilizing ranking scores provided by MTI, (ii) excluding long documents, and (iii) aggregating to higher MeSH categories. For simply detecting the presence of any disease, MTI showed > 94% recall and > 87% precision. Our overall assessment is that MTI is a potentially useful tool for researchers wishing to classify texts from a variety of sources into disease areas.

First-order quantum correction of thermodynamics in a charged accelerating AdS black hole with gauge potential

In this paper, we study the tunneling radiation from a charged-accelerating AdS black hole with gauge potential under the impact of quantum gravity. Using the semi-classical phenomenon known as the Hamilton–Jacobi ansatz, it is studied that tunneling radiation occurs via the horizon of a black hole and also employs the Lagrangian equation using the generalized uncertainty principle. Furthermore, we investigate the impact of charge, gauge potential, and first order correction parameters on the temperature as well as the stable and unstable states of the black hole. We also compute thermodynamic properties such as entropy, internal energy, Helmholtz free energy, enthalpy, specific heat, and Gibbs free energy under the impact of the correction parameter for the black hole. We calculate the logarithmic modification terms for entropy around the equilibrium state to analyze the impacts of logarithmic correction. In the presence of the correction terms, we also check the validity of the thermodynamics. It examines the graphical representation of the influence of logarithmic correction on the thermodynamic properties of black hole stability as well as charged, accelerating, and gauge potential parameters.

An ensemble learning interpretation of geometric semantic genetic programming

AbstractGeometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to produce candidate solutions. GSGP has shown considerable success in improving the performance of GP in terms of program correctness, however this comes at the expense of exponential program growth. Subsequent attempts to address this growth have not fully-exploited the fact that GSGP searches by producing linear combinations of existing solutions. This paper examines this property of GSGP and frames the method as an ensemble learning approach by redefining mutation and crossover as examples of boosting and stacking, respectively. The ensemble interpretation allows for simple integration of regularisation techniques that significantly reduce the size of the resultant programs. Additionally, this paper examines the quality of parse tree base learners within this ensemble learning interpretation of GSGP and suggests that future research could substantially improve the quality of GSGP by examining more effective initialisation techniques. The resulting ensemble learning interpretation leads to variants of GSGP that substantially improve upon the performance of traditional GSGP in regression contexts, and produce a method that frequently outperforms gradient boosting.

Environmental outlook of ASEAN-5 through the lens of green bonds, environmental technologies and financialization

Sustainable development goals call for urgent action with an aim to spur socio-economic growth along with the protection of the environment and resources. However, these goals are difficult to be achieved when finances are not mobilized to mitigate harmful resources. In return, numerous green bond manifestations appeared in the market, which are viewed as a bridge to have successful implementation of sustainable development goals. Therefore, the study is aimed to explore the relevance of green bonds, financialization, and sustainable environmental change mitigation technologies for environmental change in ASEAN economies. The initial outcomes confirm the long-run cointegration association between study variables and also explore the slope heterogeneity and cross-sectional dependencies. To explore the causal connections between the study variables, the current study employs CS-ARDL technique. The study finds significant contributions of sustainable environmental change mitigation technologies and green bonds in the reduction of pollution, hence improving climate quality. The short-run infers similar results with relatively lower magnitudes of coefficients as compared to the long-run. Negative error correction terms are used to confirm the stability of the model, which implies sustained convergence towards “steady-state equilibrium” in case of expected deviations. The results offer practical implications and recommendations.

Study of energy loss, range, and stopping time for proton in germanium and copper materials

Abstract In this study, the energy loss, range, and stopping time were calculated using the Bethe theory for protons in germanium and copper materials over an energy range of 0.01–1,000 MeV. The mathematical equations were programmed and implemented using MATLAB2013 to obtain the results for the relative mass stopping power and range, illustrated in tables and graphs. The computed results from the Bethe and Ziegler equations were compared with the experimental results from Pstar. The results showed a difference between the experimental and theoretical results at the beginning of the range used in the Bethe equation because it is a quantized equation that is not suitable for low energies. Hence, a correction term was proposed for the Bethe equation to correct the results at low energies, which produced negative values. On the other hand, the Ziegler equation showed good agreement along the range used as it is a semi-empirical equation. The range and stopping time of the incident particle increased with increasing incident particle energy in the mentioned materials.

CorrDiff: Corrective Diffusion Model for Accurate MRI Brain Tumor Segmentation.

Accurate segmentation of brain tumors in MRI images is imperative for precise clinical diagnosis and treatment. However, existing medical image segmentation methods exhibit errors, which can be categorized into two types: random errors and systematic errors. Random errors, arising from various unpredictable effects, pose challenges in terms of detection and correction. Conversely, systematic errors, attributable to systematic effects, can be effectively addressed through machine learning techniques. In this paper, we propose a corrective diffusion model for accurate MRI brain tumor segmentation by correcting systematic errors. This marks the first application of the diffusion model for correcting systematic segmentation errors. Additionally, we introduce the Vector Quantized Variational Autoencoder (VQ-VAE) to compress the original data into a discrete coding codebook. This not only reduces the dimensionality of the training data but also enhances the stability of the correction diffusion model. Furthermore, we propose the Multi-Fusion Attention Mechanism, which can effectively enhances the segmentation performance of brain tumor images, and enhance the flexibility and reliability of the corrective diffusion model. Our model is evaluated on the BRATS2019, BRATS2020, and Jun Cheng datasets. Experimental results demonstrate the effectiveness of our model over state-of-the-art methods in brain tumor segmentation.

AI based 1-D P- and S-wave velocity models for the greater alpine region from local earthquake data

SUMMARY The recent rapid improvement of machine learning techniques had a large impact on the way seismological data can be processed. During the last years several machine learning algorithms determining seismic onset times have been published facilitating the automatic picking of large data sets. Here we apply the deep neural network PhaseNet to a network of over 900 permanent and temporal broad-band stations that were deployed as part of the AlpArray research initiative in the Greater Alpine Region (GAR) during 2016–2020. We selected 384 well distributed earthquakes with ML ≥ 2.5 for our study and developed a purely data-driven pre-inversion pick selection method to consistently remove outliers from the automatic pick catalogue. This allows us to include observations throughout the crustal triplication zone resulting in 39 599 P and 13 188 S observations. Using the established VELEST and the recently developed McMC codes we invert for the 1-D P- and S-wave velocity structure including station correction terms while simultaneously relocating the events. As a result we present two separate models differing in the maximum included observation distance and therefore their suggested usage. The model AlpsLocPS is based on arrivals from ≤130 km and therefore should be used to consistently (re)locate seismicity based on P and S observations. The model GAR1D_PS includes the entire observable distance range of up to 1000 km and for the first time provides consistent P- and S-phase synthetic traveltimes for the entire Alpine orogen. Comparing our relocated seismicity with hypocentral parameters from other studies in the area we quantify the absolute horizontal and vertical accuracy of event locations as ≈2.0 and ≈6.0 km, respectively.

Spin-Symmetry Breaking and Hyperfine Couplings in Transition-Metal Complexes Revisited Using Density Functionals Based on the Exact-Exchange Energy Density.

A small set of mononuclear manganese complexes evaluated previously for their Mn hyperfine couplings (HFCs) has been analyzed using density functionals based on the exact-exchange energy density─in particular, the spin symmetry breaking (SSB) found previously when using hybrid functionals. Employing various strong-correlation corrected local hybrids (scLHs) and strong-correlation corrected range-separated local hybrids (scRSLHs) with or without additional corrections to their local mixing functions (LMFs) to mitigate delocalization errors (DE), the SSB and the associated dipolar HFCs of [Mn(CN)4]2-, MnO3, [Mn(CN)4N]-, and [Mn(CN)5NO]2- (the latter with cluster embedding) have been examined. Both strong-correlation (sc)-correction and DE-correction terms help to diminish SSB and correct the dipolar HFCs. The DE corrections are more effective, and the effects of the sc corrections depend on their damping factors. Interestingly, the DE-corrections reduce valence-shell spin polarization (VSSP) and thus SSB by locally enhancing exact-exchange (EXX) admixture near the metal center and thereby diminishing spin-density delocalization onto the ligand atoms. In contrast, sc corrections diminish EXX admixture locally, mostly on specific ligand atoms. This then reduces VSSP and SSB as well. The performance of scLHs and scRSLHs for the isotropic Mn HFCs has also been analyzed, with particular attention to core-shell spin-polarization contributions. Further sc-corrected functionals, such as the KP16/B13 construction and the DM21 deep-neural-network functional, have been examined.

The Role of the National Gas Policy on the Nigerian Electricity Supply Industry and Economic Growth

This study examined the relationship between real GDP, electricity supply, and the National Gas Policy (NGP) in Nigeria, using annual time series data from 1981 to 2019. To achieve its aim, the study explores how the NGP might influence future economic growth, natural gas reserves and electricity supply. Econometric techniques are employed for analysis, where real GDP is dependent variable, and the NGP is the dummy explanatory variable alongside other explanatory variables. The Augmented Dickey-Fuller and Phillip Perron Methods were used for unit root tests. The ARDL bounds tests indicated evidence of co-integration and long-run relationships among the variables. From the results, electricity supply and the NGP have a positive and significant relationship with real GDP. Consumer Price Index (CPI), a control variable, had a negative and significant relationship with real GDP in the short run. Also, the Error Correction Term is -0.2228. Consequently, the speed of adjustment for the model to return to long-run equilibrium after a shock is 22.28%. The study also found that the current two (2) percent annual growth rate of proven NG reserves is inadequate to cater for the rapid growth in the NG sector considering the positive effect of the NGP. The study, therefore, recommends that policymakers should continue to promote the NGP programme because it contributes positively to real GDP. In addition, consideration should be given to a diverse energy mix policy to reduce the over-reliance of electricity generation on NG-fired power plants, consequently, policymakers should promote policies that will lead to the diversification of the energy source for power generation.

W4Λ: Leveraging Λ Coupled-Cluster for Accurate Computational Thermochemistry Approaches.

High-accuracy composite wave function methods like Weizmann-4 (W4) theory, high-accuracy extrapolated ab initio thermochemistry (HEAT), and the Feller-Peterson-Dixon (FPD) approach enable sub-kJ/mol accuracy in gas-phase thermochemical properties. Their biggest computational bottleneck is the evaluation of the valence post-CCSD(T) correction term. We demonstrate here, for the W4-17 thermochemistry benchmark and subsets thereof, that the Λ coupled-cluster expansion converges more rapidly and smoothly than the regular coupled-cluster series. By means of CCSDT(Q)Λ and CCSDTQ(5)Λ, we can considerably (up to an order of magnitude) accelerate W4- and W4.3-type calculations without loss in accuracy, leading to the W4Λ and W4.3Λ computational thermochemistry protocols.

The effect of 3D surface roughness on acoustic wave propagation in a cylindrical waveguide

This paper studies the acoustic wave scattering and attenuation in a cylindrical waveguide (pipe) with wall roughness varying along all three dimensions and roughness height smaller than the acoustic wavelength. Using the decomposition of the acoustic wave field into deterministic and random components, small perturbation method and Fourier transform the analytical solution of a 3-D averaged acoustic wave field is obtained. The correction term describing the mechanism of wave attenuation caused by roughness and determined by the modal cross-talk is also derived. The solution for the plane wave is validated in the frequency range extended well beyond the second cut-off frequency, where the crosstalk between the fundamental and non-axisymmetric modes are observed. The analytical solution is compared with the numerical results obtained with the Monte-Carlo method and Finite Element solver. The numerical study results have demonstrated a close agreement with the analytical solution for the averaged sound field, dispersion curves, and the wave attenuation effect expressed as the wavenumber correction term. A key novelty of this work is a comprehensive analysis of wave dispersion and cut-off frequency changes due to the presence of 3-D wall roughness in a pipe.

A Variant of the Second-Moment Method for k-Eigenvalue Calculations

The second-moment (SM) method is a linear variant of the quasi-diffusion (QD) method for accelerating the iterative convergence of Sn source calculations. It has several significant advantages relative to the QD method, diffusion synthetic acceleration, and nonlinear diffusion acceleration. Here, we define a variant of this method for k-eigenvalue calculations that retains the advantages of the original method, and we computationally demonstrate the efficacy of the method for simple example calculations. In particular, this method has two important properties. First, it is a linear acceleration scheme requiring only the solution of a pure k-eigenvalue diffusion equation with a corrective source term as opposed to a k-eigenvalue drift-diffusion equation. Second, unconditional stability is achieved even when the diffusion equation is not discretized in a manner consistent with the Sn spatial discretization. We are unaware of any other scheme that has these properties. We also show a connection between our method and the k-eigenvalue acceleration technique of Barbu and Adams, which motivated us to develop our SM method.

Fintech inclusion in natural resource utilization, trade openness, resource productivity, recycling and minimizing waste generation: Does technology really drive economies toward green growth?

Ever since the industrial revolution, financial sector is considered a significant claimant of driving growth in society. However, adverse eco-friendly projects by financial means raise various concerns that either financial market products benefit environment. In this lieu, the present study aims to analyze the role of fintech in natural resource utilization, trade openness, resource productivity and recycling and minimizing waste generation in China. For this purpose, data from the 1995 to 2021 period is analyzed using the “Quantile Autoregressive Distributed lag (QARDL) model. The Granger causality test is also used to analyze the causal association between variables and to know about their relative significance. According to the findings, the error correction term (ECM) has an expected negative sign with statistical significance across different quantiles showing that the relationship between these variables reverts to the long-run equilibrium. QARDL short run and long run estimation indicates that fintech promotes natural resource utilization, resource productivity, trade openness, and recycling of waste materials and management with the significance of these effects varying at different quantiles. The findings of Granger Causality indicate that a bidirectional association is present between fintech and recycling and waste management, and unidirectional causality is found to be present from fintech to resource productivity. The study recommends the government formulate and implement policies aimed at promoting fintech and integrating them with streamlined natural resources and industrial and trade sectors.

Consortium blockchain private key protection scheme based on rational secret sharing and blockchain

In a consortium blockchain, the loss of a private key can result in significant losses for users, making the protection of private keys particularly crucial. Traditional distributed private key protection methods are susceptible to threats such as node silence and collusion, leading to the inability to reconstruct the private key smoothly. To address this issue, this paper proposes a private key protection scheme based on rational secret sharing and blockchain. Users act as secret distributors, distributing secrets to nodes on the blockchain through secret sharing. When users need to use the private key, nodes share sub-keys. To incentivize nodes to actively respond to user requests, an incentive-compatible evolutionary game model is constructed. Dynamic incentive strategies are then adjusted through smart contracts to ensure that nodes honestly share sub-keys to reconstruct the user’s private key. This proposed scheme is the first to introduce evolutionary game theory into secret sharing to address issues of node silence or non-responsiveness. Through scheme analysis, it is found that the proposed scheme exhibits good performance in terms of correctness, security, and fairness.

Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography

Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modelling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and in vivo measurements in 3D.