Computational Burden Research Articles

Improved Representations of Longwave Surface Emissivity to Reduce Surface and Atmospheric Heating Biases in Earth System Models

AbstractMany Earth system models (ESMs) approximate surface emissivity as a broadband constant. This approximation reduces the computational burden, yet omits the spectral structure of emissivity and atmospheric absorption. Neglecting spectral variation in surface emission introduces biases in longwave (LW) atmospheric fluxes and heating. Biases are strongest over surfaces with strongly varying emissivity and minimal atmospheric opacity. We examine these biases over water, ice, and snow surfaces. We partition spectral emissivity into the 16 spectral bands utilized by a single‐column atmospheric radiative transfer model (RRTMG_LW) commonly used in ESMs. We quantify flux and heating biases introduced by broadband assumptions relative to the spectrally resolved case for standard atmospheric profiles over each surface type. Current assumptions tend to overestimate upwelling surface fluxes; for example, the greybody assumption overestimates flux by 1.6 W/m2 (0.52%) at the bottom of a mid‐latitude winter atmosphere over ice, and by 2.33 (1.0%) at the top of atmosphere. The blackbody assumption tends to artificially cool Earth's surface, stabilizing the lower troposphere. Interestingly, the optimal broadband emissivity can deviate from the Planck‐weighted mean by up to 3% depending on surface type and atmospheric profile. We investigate bias sensitivity to surface temperature, cloud water path, and atmospheric water vapor. Bias is most sensitive to water vapor content, and least sensitive to cloud water path. Lastly, we show that a modified greybody method with updated broadband values can reduce total surface flux bias up to 1.69 , comparable to a five‐band approach and at a fraction of the computational cost.

Quantum delegated and federated learning via quantum homomorphic encryption

Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients’ private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing. In addition, in the proposed quantum federated learning scenario, there is less computational burden on local quantum devices from the client side, since the server can operate on encrypted quantum data without extracting any information. We further prove that certain quantum speedups in supervised learning carry over to private delegated learning scenarios employing quantum kernel methods. Our results provide a valuable guide toward privacy-guaranteed quantum learning on the cloud, which may benefit future studies and security-related applications.

Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits

BackgroundMilk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causal variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 180,217 cattle were imputed to the sequence level and large-scale genome-wide association study (GWAS) followed by fine-mapping and evolutionary and functional annotation were carried out to identify and prioritize new association signals.ResultsUsing the imputed sequence data of a large cattle dataset, we identified 50,876 significant variants, confirming many known and identifying previously unreported candidate variants for milk (MY), fat (FY), and protein yield (PY). Genome-wide significant signals were fine-mapped with the Bayesian approach that determines the credible variant sets and generates the probability of causality for each signal. The variants with the highest probabilities of being causal were further classified using external information about the function and evolution, making the prioritization for subsequent validation experiments easier. The top potential causal variants determined with fine-mapping explained a large percentage of genetic variance compared to random ones; 178 variants explained 11.5%, 104 explained 7.7%, and 68 variants explained 3.9% of the variance for MY, FY, and PY, respectively, demonstrating the potential for causality.ConclusionsOur findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. These can also come with both computational and time burdens, as presented in our study. Although milk production traits in cattle are comprehensively investigated, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown. With constantly growing sample sizes, we expect more insights into the genetic architecture of milk production traits in the future.

DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data.

To ensure privacy protection and alleviate computational burden, we propose a fast subsmaling procedure for the Cox model with massive survival datasets from multi-centered, decentralized sources. The proposed estimator is computed based on optimal subsampling probabilities that we derived and enables transmission of subsample-based summary level statistics between different storage sites with only one round of communication. For inference, the asymptotic properties of the proposed estimator were rigorously established. An extensive simulation study demonstrated that the proposed approach is effective. The methodology was applied to analyze a large dataset from the U.S. airlines.

High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity

Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. Despite these advantages, the large number of Gaussian points and associated internal parameters result in high storage demands. To address this challenge, we propose a pruning strategy applied during the Gaussian densification and pruning phases. Our approach integrates learnable Gaussian masks with a contribution-based pruning mechanism, further enhanced by an opacity update strategy to facilitate the pruning process. This method effectively eliminates redundant Gaussian points and those with minimal contributions to scene construction. Additionally, during the Gaussian parameter compression phase, we employ a combination of teacher–student models and vector quantization to compress the spherical harmonic coefficients. Extensive experimental results demonstrate that our approach reduces the storage requirements of original 3D Gaussian models by over 30 times, with only a minor degradation in rendering quality.

Data‐Driven Dynamic Event‐Triggered Load Frequency Control for Multi‐Area Interconnected Power Systems With Random Delays

ABSTRACTThis paper investigates a load frequency control issue for multi‐area interconnected unknown power systems with random communication delays. First, a model‐free adaptive control scheme is established by building an equivalent data‐relationship model between the area control error and corresponding control input. Then, a dynamic event‐triggered scheme is designed to improve resource utilization and reduce computational burden. Furthermore, random communication delays in the feedback and forward channels are considered. The results show that the proposed method is independent of any model information about the power system, only using the controlled system's control input and output data. Several simulation results validate the effectiveness of the proposed control scheme.

A Fog Computing and Blockchain-based Anonymous Authentication Scheme to Enhance Security in VANET Environments

Authentication of vehicles and users, integrity of exchanged messages, and privacy preservation are essential features in VANETs. VANETs are used to collect information on road conditions, vehicle location and speed, and traffic congestion data. The open exchange of information within VANETs poses serious security threats. Furthermore, existing schemes have higher communication and computational costs, making them incompatible with resource-constrained VANET applications. This study proposes a multifactor authentication and privacy-preserving security scheme for VANETs based on blockchain and fog computing to meet all these requirements. The proposed scheme uses fingerprints and Quick Response (QR) codes as a multifactor to authenticate vehicle users and fog-cloud computing techniques to reduce the computational burden on RSUs and improve service quality and resilience. Additionally, the scheme synchronizes a consistent ledger across all RSUs using blockchain technology to store and distribute vehicle authentication statuses. Through a thorough comparison with relevant current protocols, the scheme shows a much-reduced computing expense and communication burden in situations with high vehicle density within a timeframe of 6.3846 ms and 544 bytes for communication costs. In addition, the proposed scheme demonstrates a successful balance between efficacy and complexity, protecting confidentiality, anonymous authentication, and ensuring integrity and conditional tracking. Formal and informal security analysis showed that the proposed scheme is more reliable, practical, and secure against many hostile attacks, such as modification attacks, 51% attacks, Sybil attacks, and MITM attacks.

Enumeration Approach to Atom-to-Atom Mapping Accelerated by Ising Computing.

Chemical reactions are regarded as transformations of chemical structures, and the question of which atoms in the reactants correspond to which atoms in the products has attracted chemists for a long time. Atom-to-atom mapping (AAM) is a procedure that establishes such correspondence(s) between the atoms of reactants and products in a chemical reaction. Currently, automatic AAM tools play a pivotal role in various chemoinformatics tasks. However, achieving accurate automatic AAM for complex or unknown reactions within a reasonable computation time remains a significant challenge due to the combinatorial nature of the problem and the difficulty in applying appropriate reaction rules. In this study, we propose a rule-free AAM algorithm, which enumerates all atom-to-atom correspondences that minimize the number of bond cleavages and formations during the reaction. To reduce the computational burden associated with the combinatorial optimization (i.e., minimizing bond changes), we introduce Ising computing, a computing paradigm that has gained significant attention for its efficiency in solving hard combinatorial optimization problems. We found that our Ising computing framework outperforms conventional combinatorial optimization algorithms in terms of computation times, making it feasible to solve the AAM problem without reaction rules in an acceptable time. Furthermore, our AAM algorithm successfully found the correct AAM solution for all problems in a benchmark data set. In contrast, conventional AAM algorithms based on chemical heuristics failed for several problems. Specifically, these algorithms either failed to find the optimal solution in terms of bond changes, or they identified only one optimal solution, which was incorrect when multiple optimal solutions exist. These results emphasize the importance of enumerating all optimal correspondences that minimize bond changes, which is effectively achieved by our Ising-computing framework.

IF-EDAAN: An information fusion-enhanced domain adaptation attention network for unsupervised transfer fault diagnosis

Unlike domain adaptation methods that rely on single-source information for transfer diagnosis, multi-source information-based domain adaptation methods can leverage the extensive diagnostic features derived from multiple sources of data. However, the issues of potential feature conflicts, the critical fault information loss, and high computational burdens still hinder effective applications of multi-source information domain adaptation for transfer diagnosis. For resolving these issues, this study proposes an unsupervised multi-source information domain adaptation approach for transfer fault diagnosis, which utilizes an information fusion-enhanced domain adaptation attention network (IF-EDAAN). Firstly, an information fusion method that converts multi-source information into a fused image using principal component analysis and signal-to-image conversion is employed to enhance and compress data from both the source and target domains. Then, a parameter-free attention mechanism (PFAM) module is proposed to adaptively focus on the domain-invariant temporal and spatial features of information fusion samples. Subsequently, the weight assignment module and joint maximum mean discrepancy metric strategy are proposed to mitigate negative transfer, thus enabling the effective extraction and alignment of domain-invariant temporal and spatial features. Finally, experiment validations on two rotating machinery datasets have been comprehensively elaborated to verify the efficacy and advantages of our proposed IF-EDAAN approach for transfer fault diagnosis across different working conditions. Experiment results have proved that our proposed IF-EDAAN approach can rapidly adapt to new transfer diagnostic scenarios with impressive performance and outperform several mainstream unsupervised domain adaptation approaches.

Application of Karhunen-Loève Transform Algorithms in Hydrogeological Parameter Compression: A Comparative Study

ABSTRACT This paper proposes a parameter compression method using the Karhunen-Loève Transform (K-L) to reduce the computational burden of parameter inversion in groundwater models. Two spatial distributions of hydraulic conductivity (K) were generated using Kriging interpolation and Sequential Gaussian Simulation (SGS), and the K-L transform was applied to reduce their dimensionality. The method’s generality was assessed by varying model boundaries, extreme values of K, and simulation periods. The results showed that sequentially adding different boundary conditions resulted in high accuracy in model output. The model was most accurate when K values were within the range of 50-100 m/d, and its performance decreased as the range of K expanded. The model also exhibited high accuracy across various time periods within a single stress period. Dimensionality reduction through Kriging-KL and SGS- KL resulted in high accuracy of model-simulated heads, with R2 values exceeding 0.80 and 0.98 respectively. Overall, the K-L transform efficiently reduces parameter dimensions without compromising model performance. SGS-KL showed greater robustness under varying parameters, indicating its suitability for complex groundwater systems. This study contributes to efficient groundwater model inversion and provides valuable insights into the characteristics of groundwater systems.

Uncertainty analysis method for diagnosing multi-point defects in urban drainage systems

Urban drainage system (UDS) plays a key role in city urbanization, where defective pipes can lead to seepage. Previous studies have identified the locations of defects in UDS using inverse optimization models. However, the unique optimal solution neglects uncertainty analysis, which may lead to misdiagnosis. In addition, the multi-point defect diagnosis has heavy computational burden due to high dimensional parameters space. To address these issues, this paper proposes a hybrid method that leverages the genetic algorithm (GA) to identify probable space, and then utilizes the adaptive Metropolis (AM) to provide an estimation of the posterior probability distribution (PPD). Firstly, a multi-population GA is employed for the maximum exploration within the model space. Then, AM algorithm is used to explore the final PPD of each pipe defect parameter. The metrics accuracy (ACC), Matthews correlation coefficient (MCC) and mean absolute error (MAE) are used to evaluate the diagnosis performance. A synthetic UDS case with randomized multi-point seepage scenarios is used to validate the method. Results indicate that the proposed hybrid method is effective in diagnosing multi-point defect, with 0.91, 0.78 of the hybrid method and 0.87, 0.69 of the DiffeRential Evolution Adaptive Metropolis method for the ACC and MCC, respectively. Meanwhile, the diagnosis speed has increased by 32%. The result PPD passes the 90% confidence interval validation. The proposed method can provide effective uncertainty analysis to reduce misdiagnosis of the traditional method.

A novel scheme for enhanced content integrity, authentication, and privacy in information centric network using lightweight blockchain-based homomorphic integrity and authentication

Information-centric networks (ICNs) face challenges in ensuring content integrity and authentication while preserving user privacy. Homomorphic encryption has been widely used to protect content privacy within ICNs. Blockchain technology is widely applied in ICNs to ensure content integrity. Conventional blockchain-based authentication schemes rely on computationally expensive homomorphic encryption to perform secure data transmission. Also, the existing cryptography methods comprise confidentiality, and integrity while sharing content in ICNs. This paper proposes a novel lightweight blockchain-based homomorphic integrity and authentication (Light-BHIA) scheme to increase the integrity and privacy of content within ICNs. This scheme leverages the transparency and immutability of blockchain, the privacy-preserving properties of homomorphic encryption, and decentralized trust mechanisms to achieve secure and verifiable content delivery. The proposed work utilizes a lightweight homomorphic encryption scheme specifically designed for ICNs, reducing computational burdens by using resource-constrained devices. Light-BHIA uses the permissioned blockchain with an efficient consensus mechanism for increasing the trustworthiness of content delivery within ICNs. Authorized recipients can perform integrity and authentication checks on the encrypted content without decryption, maintaining confidentiality. The comparative study reveals proposed scheme achieved a 25% reduction in registration delay and confirms higher integrity, privacy, and confidentiality.

A fast approach for analyzing spatio-temporal patterns in ischemic heart disease mortality across US counties (1999–2021)

Ischaemic heart disease (IHD) remains the primary cause of mortality in the US. This study focuses on using spatio-temporal disease mapping models to explore the temporal trends of IHD at the county level from 1999 to 2021. To manage the computational burden arising from the high-dimensional data, we employ scalable Bayesian models using a ”divide and conquer” strategy. This approach allows for fast model fitting and serves as an efficient procedure for screening spatio-temporal patterns. Additionally, we analyze trends in four regional subdivisions, West, Midwest, South and Northeast, and in urban and rural areas. The dataset on IHD contains missing data, and we propose a procedure to impute the omitted information. The results show a slowdown in the decrease of IHD mortality in the US after 2014 with a slight increase noted after 2019. However, differences exists among the counties, the four big geographical regions, and rural and urban areas.

Co-LLaVA: Efficient Remote Sensing Visual Question Answering via Model Collaboration

Large vision language models (LVLMs) are built upon large language models (LLMs) and incorporate non-textual modalities; they can perform various multimodal tasks. Applying LVLMs in remote sensing (RS) visual question answering (VQA) tasks can take advantage of the powerful capabilities to promote the development of VQA in RS. However, due to the greater complexity of remote sensing images compared to natural images, general-domain LVLMs tend to perform poorly in RS scenarios and are prone to hallucination phenomena. Multi-agent debate for collaborative reasoning is commonly utilized to mitigate hallucination phenomena. Although this method is effective, it comes with a significant computational burden (e.g., high CPU/GPU demands and slow inference speed). To address these limitations, we propose Co-LLaVA, a model specifically designed for RS VQA tasks. Specifically, Co-LLaVA employs model collaboration between Large Language and Vision Assistant (LLaVA-v1.5) and Contrastive Captioners (CoCas). It combines LVLM with a lightweight generative model, reducing computational burden compared to multi-agent debate. Additionally, through high-dimensional multi-scale features and higher-resolution images, Co-LLaVA can enhance the perception of details in RS images. Experimental results demonstrate the significant performance improvements of our Co-LLaVA over existing LVLMs (e.g., Geochat, RSGPT) on multiple metrics of four RS VQA datasets (e.g., +3% over SkySenseGPT on “Rural/Urban” accuracy in the test set of RSVQA-LR dataset).

Vertical Memristive Crossbar Array for Multilayer Graph Embedding and Analysis.

Graph data structures effectively represent objects and their relationships, enabling the modeling of complex connections in various fields. Recent work demonstrate that metal at diagonal crossbar arrays (m-CBA) can effectively represent planar graphs. However, they are unsuitable for representing multilayer graphs having multiple relationships across different layers. Using conventional software, embedding multilayer graphs in high-dimensional Euclidean spaces introduces significant mathematical complexity and computational burden, often resulting in information loss. This study proposes a unique graph embedding (mapping) method utilizing a fabricated vertical m-CBA (vm-CBA), where a custom-built measurement system thoroughly validated its functionality. This structure directly maps multilayer graphs into a 3D vm-CBA, accurately representing inter-layer and intra-layer connections. The practical link prediction and information scores across various real-world datasets demonstrated that vm-CBA achieved enhanced accuracy compared to conventional embeddings, even with a significantly decreased number of operations.

Parallel Primal-Dual Method with Linearization for Structured Convex Optimization

This paper presents the Parallel Primal-Dual (PPD3) algorithm, an innovative approach to solving optimization problems characterized by the minimization of the sum of three convex functions, including a Lipschitz continuous term. The proposed algorithm operates in a parallel framework, simultaneously updating primal and dual variables, and offers potential computational advantages. This parallelization can greatly accelerate computation, particularly when run on parallel computing platforms. By departing from traditional primal-dual methods that necessitate strict parameter constraints, the PPD3 algorithm removes reliance on the spectral norm of the linear operator, significantly reducing the computational burden associated with its evaluation. As the problem size grows, calculating the spectral norm, which is essential for many primal-dual methods, becomes progressively more expensive. In addition, adaptive step sizes are computed to accelerate the convergence process. In contrast to most primal-dual approaches that employ a fixed step size constrained by a global upper limit throughout all iterations, the adaptive step size is typically greater and may result in faster convergence. An O(1/k) ergodic convergence rate is proved theoretically. Applications in Fused LASSO and image inpainting demonstrate the method’s efficiency in computation time and convergence rate compared to state-of-the-art algorithms.

Fast correction method for the translation position error of ptychography

Abstract Translation position errors in ptychography can significantly reduce the reconstruction quality. It is crucial to obtain the accurate translation positions between the probe and the object. Current methods for correcting the translation position error are applied into the iterative process of ptychography that will increase the computation time of a single iteration, and take much time to correct the translation position error and obtain a high reconstruction quality. A sub-pixel block matching method is proposed to ultrafast correct the translation position errors and greatly reduce the correction time. This method is applied in the preprocessing stage before iterations that does not increase the computational burden of a single iteration in ptychography and can quickly correct the translation position error. Simulations and experiments demonstrate that our method can significantly improve computational efficiency with ensuring correction accuracy.

Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG

IntroductionApproximately 15 million premature infants are born each year, many of whom face risks of neurological impairments. Accurate assessment of brain maturity is crucial for timely intervention and treatment planning. Electroencephalography (EEG) is a noninvasive method commonly used for this purpose. However, using all channels and features for brain maturity assessment can lead to high computational burden and overfitting, which can decrease the performance of the prediction system.MethodsIn this study, we propose an automatic prediction framework based on EEG to predict functional brain age (FBA) for assessing brain maturity in preterm infants. To optimize channel selection, we combine Binary Particle Swarm Optimization (BPSO) with Forward Addition (FA) and Backward Elimination (BE) methods. For feature selection, we combine the Pearson Correlation Coefficient (PCC), Recursive Feature Elimination (RFE), and Support Vector Regression (SVR) model.ResultsThe proposed framework achieved a prediction accuracy of 76.71% within ±1 week and 94.52% within ±2 weeks. Effective channel and feature selection significantly improved model performance while reducing computational costs.DiscussionThese results demonstrate that optimizing channel and feature selection can enhance the performance of FBA prediction in preterm infants, offering a more efficient and accurate tool for brain maturity assessment.

ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool

Abstract. The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance but remains a challenge due to their inherent complexity, which is reflected in input–output relationships often characterised by multiple interactions between the parameters, thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict the impact of input parameter variations on model output, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the effects of varying the values of Noah-MP model parameters with a relatively small number of model runs by simulating a sea breeze circulation over an idealised flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis for complex models, guiding the user through the different steps and allowing for a simplification and automatisation of the process.

A method framework of cruciate ligaments segmentation and reconstruction from MRI images

Segmenting anterior and posterior cruciate ligaments (ACL/PCL) presents challenges in medical imaging due to diverse characteristics, including size, shape, and intensity. Our study uses superpixel-based spectral clustering for knee cruciate ligament segmentation in 2D DICOM slices, renowned for generating high-quality clusters. The proposed method addresses the challenges by (i) identifying the ligamentous region (ROI) through superpixel-based computation, (ii) extracting features (intensity-based, shape-based, geometric complexity, and Scale-Invariant Feature Transform) from the ROI, and (iii) segmenting knee ligament tissues using spectral clustering on the extracted features. Superpixel-based spectral clustering addresses the challenge of constructing a dense similarity matrix and significantly reduces the computational burden. Furthermore, 3D visualization of ligament structures is performed using the Visualization Toolkit (VTK). We evaluated our proposed approach on a dataset of knee MRI slices, assessing the results via the dice score, average surface distance (ASD), and root mean squared error (RMSE) metrics. Our method achieved an average dice score of 0.912 for ACL segmentation and 0.896 for PCL segmentation, outperforming other clustering methods. These scores showed an enhancement of 10.7% and 14.9% in segmentation accuracy for the ACL and PCL, respectively. Furthermore, reduced error margins were demonstrated with the mean ASD values of 1.60 and 1.78 and the mean RMSE values of 1.76 and 1.86 for ACL and PCL, respectively. These results show the effectiveness of the proposed method for cruciate ligament segmentation and its potential for increasing the segmentation accuracy and speed, offering significant advantages over manual segmentation by reducing time and expertise.