Large Dimensionality Research Articles

A study on identifying representative trips for mobility service design

AbstractRecently, with growing interest in urban mobility patterns, the demand for collecting and analysing origin‐destination (OD) data is increasing. Due to the large scale and dimensionality of OD data, there are two issues in analysing the data: big‐data storage and major pattern extraction. To deal with two issues at the same time, this study suggests a principal control analysis‐based major demand identification method to improve the usability of microscopic OD data. Especially, this study focuses on finding principal components that preserve major patterns from OD data with small random noise so that the data can be effectively used for mobility service design. The proposed method is applied to smart card data of Seoul and Sejong and extracted major demand patterns from peak‐ and non‐peak hour data of these cities. The degree of daily regularity, reconstruction accuracy, and compression rate of the reconstructed data is analysed varying sets of principal components. The obtained results show that the major demands contain a low volume and a large volume of demand and with lower‐order principal components, major demands can be efficiently extracted by removing randomly appearing small‐volume demand. In addition, the trade‐off behaviour is observed between the degree of daily regularity and reconstruction accuracy depending on the compression rate. Based on the observations, it can be found that the loss of major demand patterns could be prevented when targeting a reconstruction accuracy of 90–95% and the proposed method can reduce the data size while preserving major mobility patterns.

The magnetic maze: a system with tunable scale invariance

Random magnetic field configurations are ubiquitous in nature. Such fields lead to a variety of dynamical phenomena, including localization and glassy physics in some condensed matter systems and novel transport processes in astrophysical systems. Here we consider the physics of a charged quantum particle moving in a “magnetic maze”: a high-dimensional space filled with a randomly chosen vector potential and a corresponding magnetic field. We derive a path integral description of the model by introducing appropriate collective variables and integrating out the random vector potential, and we solve for the dynamics in the limit of large dimensionality. We derive and analyze the equations of motion for Euclidean and real-time dynamics, and we calculate out-of-time-order correlators. We show that a special choice of vector potential correlations gives rise, in the low temperature limit, to a novel scale-invariant quantum theory with a tunable dynamical exponent. Moreover, we show that the theory is chaotic with a tunable chaos exponent which approaches the chaos bound at low temperature and strong coupling.

Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis.

Allergy disorders caused by biological particles, such as the proteins in some airborne pollen grains, are currently considered one of the most common chronic diseases, and European Academy of Allergy and Clinical Immunology forecasts indicate that within 15 years 50% of Europeans will have some kind of allergy as a consequence of urbanization, industrialization, pollution, and climate change. The aim of this study was to monitor and analyze the dissemination of information about pollen symptoms from December 2006 to January 2022. By conducting a comprehensive evaluation of public comments and trends on Twitter, the research sought to provide valuable insights into the impact of pollen on sensitive individuals, ultimately enhancing our understanding of how pollen-related information spreads and its implications for public health awareness. Using a blend of large language models, dimensionality reduction, unsupervised clustering, and term frequency-inverse document frequency, alongside visual representations such as word clouds and semantic interaction graphs, our study analyzed Twitter data to uncover insights on respiratory allergies. This concise methodology enabled the extraction of significant themes and patterns, offering a deep dive into public knowledge and discussions surrounding respiratory allergies on Twitter. The months between March and August had the highest volume of messages. The percentage of patient tweets appeared to increase notably during the later years, and there was also a potential increase in the prevalence of symptoms, mainly in the morning hours, indicating a potential rise in pollen allergies and related discussions on social media. While pollen allergy is a global issue, specific sociocultural, political, and economic contexts mean that patients experience symptomatology at a localized level, needing appropriate localized responses. The interpretation of tweet information represents a valuable tool to take preventive measures to mitigate the impact of pollen allergy on sensitive patients to achieve equity in living conditions and enhance access to health information and services.

Audio feature enhancement based on quaternion filtering and deep hashing

This paper aims to solve the problems of difficult convergence of audio model training, large data demand, and large dimensionality of storage space for audio-generated feature vectors. To this end, this paper proposes the use of quaternion Gabor filtering to suppress the background information of the spectrogram and reduce the interference of the data for the case of insufficient data alignment between audio data and image data after shifting the domain. In addition, different scales of window lengths and frame shifts are used to capture the connections between different vocal objects. To address the problem that the generated feature vectors are large dimensional, we use a deep hash module to map high-dimensional features to low-dimensional features and use a probability function to make the learned samples more consistent with the overall distribution. In the experimental evaluation, the proposed method was evaluated on the environmental sound classification dataset and the music genre classification dataset. The proposed method uses only a common backbone network and improves the accuracy of audio recognition.

Unifying methods for optimal control in non-Markovian quantum systems via process tensors.

The large dimensionality of environments is the limiting factor in applying optimal control to open quantum systems beyond the Markovian approximation. Various methods exist to simulate non-Markovian systems, which effectively reduce the environment to a number of active degrees of freedom. Here, we show that several of these methods can be expressed in terms of a process tensor in the form of a matrix-product-operator, which serves as a unifying framework to show how they can be used in optimal control and to compare their performance. The matrix-product-operator form provides a general scheme for computing gradients using back propagation and allows the efficiency of the different methods to be compared via the bond dimensions of their respective process tensors.

Spatial-Aware and Load Balancing Distributed Data Partitioning Strategies for Content-Based Multimedia Retrieval.

Content-Based Multimedia Retrieval (CBMR) has become very popular in several applications, driven by the growing routine use of multimedia data. Since the datasets used in real-world applications are very large and descriptor's dimensionality is high, querying is an expensive, albeit important functionality. Further, exact search is prohibitive in most cases, motivating the use of Approximate Nearest Neighbour Search (ANNS) algorithms, trading accuracy for performance. These have been mainly developed targeting a sequential execution in a single node. However, the large and increasing datasets used and the high query loads submitted to those systems typically surpass the memory and computing resources available in a single node. This motivated the development of parallel distributed memory ANNS solutions to meet the computing capabilities required by those applications. A common problem that must be handled when using distributed memory systems is data partitioning and its impact on load imbalance. Several data partitioning approaches have already been proposed, including elaborated spatial-aware strategies. However, little effort has been put into carefully analyzing the performance of those strategies at scale. Here, we evaluated the commonly used data partitioning strategies in ANNS and identified their limitations to propose a novel class of partitioning algorithms that can minimize load imbalance while improving data locality to attain high performance on the distributed memory search. Experimentally, we found that our proposed algorithms (SABBS and SABBSR) improved search performance by up to 1.64× compared to the best previous solution. In a distributed memory weak scaling evaluation, with up to 12 billion 128-dimensional descriptors and 60 compute nodes, the gains were maintained as the system scaled with our novel approaches. These results demonstrate the efficiency of our new algorithms for billion-scale ANNS and the importance of considering not only data locality but also data and load imbalance in the data partitioning.

Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method.

Accurate estimation of pandemic likelihood in every US state of interest and at any time. Coronavirus disease 2019 (COVID-19) is an infectious illness with a high potential for global dissemination and low rates of fatality and morbidity, placing some strains on national public health systems. This research intends to benchmark a novel technique, that enables hazard assessment, based on available clinical data, and dynamically observed patient numbers while taking into account pertinent territorial and temporal mapping. Multicentre, population-based, and biostatistical strategies have been utilized to process raw/unfiltered medical survey data. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. First, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case,-not to mention challenges with system dimensionality higher than 2D. Assessing outbreak risks of future outbreaks in any nation/region of interest. Existing bio-statistical approaches do not always have the benefits of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. Apply contemporary, novel statistical/reliability techniques directly to raw/unfiltered clinical data. The current study outlines a novel bio-system hazard assessment technique that is particularly suited for multi-regional environmental, bio, and public health systems, observed over a representative period. With the use of the Gaidai multivariate hazard assessment approach, epidemic outbreak spatiotemporal risks may be properly assessed. Based on raw/unfiltered clinical survey data, the Gaidai multivariate hazard assessment approach may be applied to a variety of public health applications. The study's primary finding was an assessment of the risks of epidemic outbreaks, along with a matching confidence range. Future global COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-COV2) epidemic risks have been examined in the current study; however, COVID-19/SARS-COV2 infection transmission mechanisms have not been discussed.

Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests

Normalizing flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions but also deliver density estimation by construction. We propose here an in-depth comparison of coupling and autoregressive flows, both based on symmetric (affine) and non-symmetric (rational quadratic spline) bijectors, considering four different architectures: real-valued non-Volume preserving (RealNVP), masked autoregressive flow (MAF), coupling rational quadratic spline (C-RQS), and autoregressive rational quadratic spline (A-RQS). We focus on a set of multimodal target distributions of increasing dimensionality ranging from 4 to 400. The performances were compared by means of different test statistics for two-sample tests, built from known distance measures: the sliced Wasserstein distance, the dimension-averaged one-dimensional Kolmogorov–Smirnov test, and the Frobenius norm of the difference between correlation matrices. Furthermore, we included estimations of the variance of both the metrics and the trained models. Our results indicate that the A-RQS algorithm stands out both in terms of accuracy and training speed. Nonetheless, all the algorithms are generally able, without too much fine-tuning, to learn complicated distributions with limited training data and in a reasonable time of the order of hours on a Tesla A40 GPU. The only exception is the C-RQS, which takes significantly longer to train, does not always provide good accuracy, and becomes unstable for large dimensionalities. All algorithms were implemented using TensorFlow2 and TensorFlow Probability and have been made available on GitHub.

Euclid preparation

We investigate the accuracy of the perturbative galaxy bias expansion in view of the forthcoming analysis of the Euclid spectroscopic galaxy samples. We compare the performance of a Eulerian galaxy bias expansion using state-of-the-art prescriptions from the effective field theory of large-scale structure (EFTofLSS) with a hybrid approach based on Lagrangian perturbation theory and high-resolution simulations. These models are benchmarked against comoving snapshots of the flagship I N-body simulation at z = (0.9, 1.2, 1.5, 1.8), which have been populated with Hα galaxies leading to catalogues of millions of objects within a volume of about 58 h−3 Gpc3. Our analysis suggests that both models can be used to provide a robust inference of the parameters (h, ωc) in the redshift range under consideration, with comparable constraining power. We additionally determine the range of validity of the EFTofLSS model in terms of scale cuts and model degrees of freedom. From these tests, it emerges that the standard third-order Eulerian bias expansion – which includes local and non-local bias parameters, a matter counter term, and a correction to the shot-noise contribution – can accurately describe the full shape of the real-space galaxy power spectrum up to the maximum wavenumber of kmax = 0.45 h Mpc−1, and with a measurement precision of well below the percentage level. Fixing either of the tidal bias parameters to physically motivated relations still leads to unbiased cosmological constraints, and helps in reducing the severity of projection effects due to the large dimensionality of the model. We finally show how we repeated our analysis assuming a volume that matches the expected footprint of Euclid, but without considering observational effects, such as purity and completeness, showing that we can get constraints on the combination (h, ωc) that are consistent with the fiducial values to better than the 68% confidence interval over this range of scales and redshifts.

A novel LSTM-integrated non-intrusive ROM for reliability analysis of hysteretic systems with large stochastic dimension

Failure probability estimation of hysteretic systems subjected to random process excitation appears in many practical engineering applications, such as structures subjected to earthquake and vibration control. This task is computationally challenging due to the need for repeated solutions of a high-resolution model. This cost grows significantly with the stochastic dimension — characterized by the number of random variables, in this case, arising from the discretization of the random excitation. To address this issue, a novel non-intrusive reduced order model (ROM) is proposed in this paper. Accordingly, the complexity due to spatial discretization is first reduced using proper orthogonal decomposition. However, interpolation among response time histories in the resulting reduced solution space is computationally infeasible due to large stochastic dimensionality. An existing ROM developed by the authors is now improved by adopting a long short-term memory (LSTM) network for this interpolation. Furthermore, this adoption required two additional modifications in the mathematical structure of the ROM related to data compression and nonlinear coupling. The proposed methodology can be seamlessly integrated with black-box nonlinear solvers. Through detailed numerical studies on a beam on Winkler foundation and a multi-storied building frame subjected to stationary and non-stationary excitations, the proposed ROM is found to be accurate, efficient, and robust with respect to the frequency band of interest. The ROM has shown reduction of two orders of magnitude in the computational time. Furthermore, the proposed ROM is inherently parallelizable and holds promise to be scaled to other types of nonlinear systems with large stochastic dimensionality.

Hyperspectral Imaging and Machine Learning as a Nondestructive Method for Proso Millet Seed Detection and Classification.

Millet is a small-seeded cereal crop with big potential. There are many different cultivars of proso millet (Panicum miliaceum L.) with different characteristics, bringing forth the issue of sorting which are important for growers, processors, and consumers. Current methods of grain cultivar detection and classification are subjective, destructive, and time-consuming. Therefore, there is a need to develop nondestructive methods for sorting the cultivars of proso millet. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900-1700 nm) to discriminate between different cultivars of proso millet seeds was evaluated. A total of 5000 proso millet seeds were randomly obtained and investigated from the ten most popular cultivars in the United States, namely Cerise, Cope, Earlybird, Huntsman, Minco, Plateau, Rise, Snowbird, Sunrise, and Sunup. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied, and the first two principal components were used as spectral features for building the classification models because they had the largest variance. The classification performance showed prediction accuracy rates as high as 99% for classifying the different cultivars of proso millet using a Gradient tree boosting ensemble machine learning algorithm. Moreover, the classification was successfully performed using only 15 and 5 selected spectral features (wavelengths), with an accuracy of 98.14% and 97.6%, respectively. The overall results indicate that NIR hyperspectral imaging could be used as a rapid and nondestructive method for the classification of proso millet seeds.

Survival Analysis Based Qos Recommendation for Bus Transportation using Deep Learning

Public transportation play an important role in metropolitan cities.Bus services are an important part of public transportation systems, which are frequently chosen because they are convenient, economical, environmentally beneficial, and less taxing on the infrastructure. The temporal patterns of bus transportation incidents are examined in this study using survival analysis, with a focus on the amount of time until certain events like delays, malfunctions, or safe arrivals at destinations. High-dimensional data is being generated at an increasing rate due to technological advancements. As a result, it is quite difficult to analyze such a dataset adequately. Algorithms for machine learning (ML) have become popular tools for modeling complex and nonlinear interactions in a variety of real-world applications, including as the analysis of high-dimensional survival data. Multilayer Deep Neural Network (DNN) models have achieved impressive results recently. Thus, using Keras and TensorFlow, a Cox-based DNN prediction survival model (DNNSurv model) was created. Its findings, however, were limited to survival datasets with large sample sizes or high dimensionality. Using high dimensional survival data, the proposed work will evaluate the DNNSurv model's prediction ability and compare it to a well-known machine learning survival prediction model (random survival forest). The Proposed Work also offers the best settings for a number of hyperparameters, including tuning parameter selection, for this reason. The suggested approach showed through data analysis that, when compared to the ML model, the DNNSurv model performed better overall in terms of the two primary survival prediction evaluation measures (i.e., the concordance index and the time-dependent AUC).

Ensemble classification to predict botnet and its impact on IoT networks

The IoT (Internet of Things) has been rapidly growing to make a stronger influence on huge industrial systems. Since cybercriminals have made IoT a target for harmful operations such as botnets, an attack on the end nodes is now a possibility. Protecting IoT infrastructure with a typical intrusion detection system is difficult because of its vastness, variety, and minimal resource availability. Harmful "bot sources” (servers) control a botnet, which is made up of hacked devices that are employed for several illicit purposes like sending spam, initiating DoS attacks, and stealing personal data. Because bot sources generate network traffic while conversing with their bots, Intrusion Detection Systems could benefit from analyzing traffic on the network to detect Botnet traffic. DDoS attacks and spam distribution are common uses for botnets.As machine learning (ML) approaches are employed in various niches of security, it appears practical and workable to use ML to detect botnets. ML has been used in various researches to detect botnets. However, the results are either unreliable or limited to certain types of botnets or devices. Current approaches attempt to solve these issues by presenting models that are trained on botnet features, but the large dimensionality of feature values and the reliance on botnet features alone are serious drawbacks. In order to address these constraints, a novel botnet and its impact detection by a typical ensemble classification approach has been proposed by the current work. The framework uses the correlated traffic-flow and botnet features to train the classifier, which is titled as "Ensemble Classification to Predict Botnet and its Impact (EC-PBI) on IoT Networks". The experimental study is a cross validation approach that is performed using a multi-label, fourfold strategy. Performance analysis of the proposed approach was done by comparing it with contemporary models. Results prove its efficiency in detecting botnet and its impact.

Decentralized Mixed Effects Modeling in COINSTAC.

Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.

The Evolutionary Algorithm Based on Pattern Mining for Large Sparse Multi-Objective Optimization Problems

Most of the parameters in these sparse solutions are zeroed out, giving them their defining characteristic. In the actual world, several multiobjective optimization problems display Pareto-optimal solutions. A large dimensionality is often involved in Sparse Multiobjective Optimization Problems (SMOPs), which presents difficulties for evolutionary algorithms in terms of effectively discovering optimum solutions. The PMMOEA Framework, an acronym for Pattern Mining-based Multi-objective Evolutionary Algorithm, is introduced in this article. The purpose of developing this framework was to address optimization problems on both a big and small scale. The framework's goal is to reduce the search space and lessen the impact of the curse of dimensionality by finding the sparse sequence of Pareto-optimal solutions. Using the evolutionary pattern mining technique, the suggested PMMOEA Framework finds the maximum and minimum values of the sets of non-zero variables in Pareto-optimal solutions. Additional exploitation of these recognized sets to restrict dimensions occurs during the generation of child solutions. To further enhance performance, the framework has a digital mutation operator and a binary crossover operator. This guarantees that there are few solutions. The suggested solution outperforms existing evolutionary algorithms on eight benchmark issues and four real-world scenarios when it comes to handling large-scale SMOPs.

Shape restricted additive hazards models: Monotone, unimodal, and U-shape hazard functions.

We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time-dependent covariate with monotone or U-shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study.

CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes

Given the pronounced impact COVID-19 continues to have on society—infecting 700 million reported individuals and causing 6.96 million deaths—many deep learning works have recently focused on the virus’s diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices.

Nonparametric Bayesian functional clustering with applications to racial disparities in breast cancer

AbstractAs we have easier access to massive data sets, functional analyses have gained more interest. However, such data sets often contain large heterogeneities, noises, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This paper considers noisy information reduction in functional analyses from two perspectives: functional clustering to group similar observations and thus reduce the sample size and functional variable selection to reduce the dimensionality. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this paper proposes a nonparametric Bayesian functional clustering and peak point selection method via weighted Dirichlet process mixture (WDPM) modeling that automatically clusters and provides accurate estimations, together with conditional Laplace prior, which is a conjugate variable selection prior. The proposed method is named WDPM‐VS for short, and is able to simultaneously perform the following tasks: (1) Automatic cluster without specifying the number of clusters or cluster centers beforehand; (2) Cluster for heterogeneously behaved functions; (3) Select vibrational peak points; and (4) Reduce noisy information from the two perspectives: sample size and dimensionality. The method will greatly outperform its comparison methods in root mean squared errors. Based on this proposed method, we are able to identify biological factors that can explain the breast cancer racial disparities.

High-capacity medical information hiding scheme with P-tensor product secret image sharing for wireless body area networks

Given the escalating demand for information transmission in the resource-restricted wireless body area networks (WBANs), securing medical data and minimizing energy consumption are paramount. We present an innovative information hiding scheme that employs P-tensor product secret image sharing (PTP-SIS) and multiple information-hiders for WBANs. Considering the remarkable compression performance demonstrated by compressed sensing (CS), as well as its link to Shamir's (k, n) secret sharing method, using CS rather than traditional secret sharing techniques for image processing purposes could potentially help alleviate transmission burden of sensor nodes. However, utilizing CS to process high-dimensional signals poses a significant challenge due to the large dimensionality of the measurement matrix. The P-tensor product (PTP) is a tool that offers outstanding universality and can overcome the dimension limitations of traditional matrix multiplication, it is applied in the secret sharing stage. Moreover, applying the ideas of secret sharing leads to less stress on an individual device as well as lower communication spending, and multiple hiders have the ability to independently embed additional secrets into shadow images for efficient information backup. Theoretical analysis and experiments validate that the proposed scheme ensures information security and transmission efficiency while achieving superior image recovery performance and higher information embedding rates compared to other approaches.

Pacific Ocean Windspeeds Prediction by Gaidai Multivariate Risks Evaluation Method, Utilizing Self-Deconvolution

Abstract The current study advances research on the consequences of global climate change by utilizing the novel Gaidai multivariate risks evaluation methodology to conduct spatiotemporal analysis of areal windspeeds. Multidimensional structural and environmental dynamic systems that have been either physically observed or numerically simulated over a representative time-lapse are particularly suitable for the Gaidai risks evaluation methodology. Current research also presents a novel non-parametric deconvolution extrapolation method. As this study has shown, given in situ environmental input, it is possible to accurately predict environmental system hazard risks, based even on a limited underlying dataset. Furthermore, because of their complex nonlinear cross-correlations between various environmental system-critical dimensions or components and large dimensionality, environmental dynamic systems are difficult to handle using traditional methods for evaluating risks. In the North Pacific, close to the Hawaiian Islands, NOAA buoys gathered raw in situ wind speed data, which has been utilized in the current study. Areal ocean wind speeds constitute quite a complex environmental dynamic system that is challenging to analyze because of its nonlinear, multidimensional, cross-correlated nature. Global warming had impacts on ocean windspeeds in the recent decade. Developing novel state-of-the-art environmental system risk evaluation methods is a principal component of modern offshore structural analysis in light of adverse weather. The advocated novel risk/hazard assessment approach may be used for resilient island cities design, especially those that are near ocean shore and hence exposed to extreme weather.