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Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we consider training sets constructed from coupled-cluster (CC) and density functional theory (DFT) data. We report that multitask surrogates can predict at CC-level accuracy with a reduction in data generation cost by over an order of magnitude. Of note, our approach allows the training set to include DFT data generated by a heterogeneous mix of exchange–correlation functionals without imposing any artificial hierarchy on functional accuracy. More generally, the multitask framework can accommodate a wider range of training set structures—including the full disparity between the different levels of fidelity—than existing kernel approaches based on Δ-learning although we show that the accuracy of the two approaches can be similar. Consequently, multitask regression can be a tool for reducing data generation costs even further by opportunistically exploiting existing data sources. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s. [ABSTRACT FROM AUTHOR]

When silica‐filled rubber compounds are deformed, structural modifications in the material's bulk lead to irreversible damage, the most significant of which is cavitation appearing within the interfaces of interconnected polymer and filler networks. This work introduces a new method to analyze cavitation in industrial‐grade rubbers based on ultra small‐angle x‐ray scattering. This method employs a specially designed multi‐sample stretching device for high‐throughput measurements with statistical relevance. The proposed data reduction approach allows for early detection and quantification of cavitation while providing at the same time information on the hierarchical filler structures at length scales ranging from the primary particle size to large silica agglomerates over four orders of magnitude. To validate the method, the scattering of SSBR rubber compounds filled with highly dispersible silica at different ratios was measured under quasi‐static strain. The strain was applied in incremental steps up to a maximum achievable elongation or breakage of the sample. From the measurements performed in multiple repetitions, it was found that the minimum strain necessary for cavity formation and the size evolution of the cavities with increasing strain are comparable between these samples. The sample with the highest polymer content showed the lowest rate of cavity formation and higher durability of silica structures. The structural stability of the compounds was determined by the evolution of the filler hierarchical structures, obtained by fitting data across the available strain range. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an essential platform for industrial applications since it enables massive systems connecting many IoT devices for analytical data collection. This attribute is responsible for the exponential development in the amount of data created by IoT devices. IoT devices can generate voluminous amounts of data, which may place extraordinary demands on their limited resources, data transfer bandwidths, and cloud storage. Using lightweight IoT data compression techniques is a practical way to deal with these problems. This paper presents adaptable lightweight SZ lossy compression algorithm for IoT devices (SZ4IoT), a lightweight and adjusted version of the SZ lossy compression method. The SZ4IoT is a local (non-distributed) and interpolation-based compressor that can accommodate any sensor data type and can be implemented on microcontrollers with low resources. It operates on univariate and multivariate time series. It was implemented and tested on various devices, including the ESP32, Teensy 4.0, and RP2040, and evaluated on multiple datasets. The experiments of this paper focus on the compression ratio, compression and decompression time, normalized root mean square error (NRMSE), and energy consumption and prove the effectiveness of the proposed approach. The compression ratio outperforms LTC, WQT RLE, and K RLE by two, three, and two times, respectively. The proposed SZ4IoT decreased the consumed energy for the data size 40 KB by 31.4, 29.4, and 27.3% compared with K RLE, LTC, and WQT RLE, respectively. In addition, this paper investigates the impact of stationary versus non-stationary time series datasets on the compression ratio. [ABSTRACT FROM AUTHOR]

Inference of evolutionary and demographic parameters from a sample of genome sequences often proceeds by first inferring identical-by-descent (IBD) genome segments. By exploiting efficient data encoding based on the ancestral recombination graph (ARG), we obtain three major advantages over current approaches: (i) no need to impose a length threshold on IBD segments, (ii) IBD can be defined without the hard-to-verify requirement of no recombination, and (iii) computation time can be reduced with little loss of statistical efficiency using only the IBD segments from a set of sequence pairs that scales linearly with sample size. We first demonstrate powerful inferences when true IBD information is available from simulated data. For IBD inferred from real data, we propose an approximate Bayesian computation inference algorithm and use it to show that even poorly-inferred short IBD segments can improve estimation. Our mutation-rate estimator achieves precision similar to a previously-published method despite a 4 000-fold reduction in data used for inference, and we identify significant differences between human populations. Computational cost limits model complexity in our approach, but we are able to incorporate unknown nuisance parameters and model misspecification, still finding improved parameter inference. Author summary: Samples of genome sequences can be informative about the history of the population from which they were drawn, and about mutation and other processes that led to the observed sequences. However, obtaining reliable inferences is challenging, because of the complexity of the underlying processes and the large amounts of sequence data that are often now available. A common approach to simplifying the data is to use only genome segments that are very similar between two sequences, called identical-by-descent (IBD). The longer the IBD segment the more informative it is about recent shared ancestry, and current approaches restrict attention to IBD segments above a length threshold. We instead are able to use IBD segments of any length, allowing us to extract much more information from the sequence data. To reduce the computational burden we identify subsets of the available sequence pairs that lead to little information loss. Our approach exploits recent advances in inferring the genealogical history underlying the sample of sequences. Computational cost still limits the size and complexity of problems our method can handle, but where feasible we obtain dramatic improvements in the power of inferences. [ABSTRACT FROM AUTHOR]

Low-discrepancy points (also called Quasi-Monte Carlo points) are deterministically and cleverly chosen point sets in the unit cube, which provide an approximation of the uniform distribution. We explore two methods based on such low-discrepancy points to reduce large data sets in order to train neural networks. The first one is the method of Dick and Feischl (J Complex 67:101587, 2021), which relies on digital nets and an averaging procedure. Motivated by our experimental findings, we construct a second method, which again uses digital nets, but Voronoi clustering instead of averaging. Both methods are compared to the supercompress approach of (Stat Anal Data Min ASA Data Sci J 14:217–229, 2021), which is a variant of the K-means clustering algorithm. The comparison is done in terms of the compression error for different objective functions and the accuracy of the training of a neural network. [ABSTRACT FROM AUTHOR]

Compressive sensing introduces novel perspectives on non‐uniform sampling, leading to substantial reductions in acquisition cost and cycle time compared to current seismic exploration practices. Non‐uniform spatial sampling, achieved through source and/or receiver areal distributions, and non‐uniform temporal sampling, facilitated by simultaneous‐source acquisition schemes, enable compression and/or reduction of seismic data acquisition time and cost. However, acquiring seismic data using compressive sensing may encounter challenges such as an extremely low signal‐to‐noise ratio and the generation of interference noise from adjacent sources. A significant challenge to this innovative approach is to demonstrate the translation of theoretical gains in sampling efficiency into operational efficiency in the field. In this study, we propose a spatial compression scheme based on compressive sensing theory, aiming to obtain an undersampled survey geometry by minimizing the mutual coherence of a spatial sampling operator. Building upon an optimised spatial compression geometry, we subsequently consider temporal compression through a simultaneous‐source acquisition scheme. To address challenges arising from the recorded compressed seismic data in the non‐uniform temporal and spatial domains, such as missing traces and crosstalk noise, we present a joint deblending and reconstruction algorithm. Our proposed algorithm employs the iterative shrinkage‐thresholding method to solve an ℓ2–ℓ1 optimization problem in the frequency–wavenumber–wavenumber (ω–kx–ky) domain. Numerical experiments demonstrate that the proposed algorithm produces excellent deblending and reconstruction results, preserving data quality and reliability. These results are compared with non‐blended and uniformly acquired data from the same location illustrating the robustness of the application. This study exemplifies how the theoretical improvements based on compressive sensing principles can significantly impact seismic data acquisition in terms of spatial and temporal sampling efficiency. [ABSTRACT FROM AUTHOR]

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent. [ABSTRACT FROM AUTHOR]

HPC trends favor algorithms and implementations that reduce data motion relative to FLOPS. We investigate the use of lossy compressed data arrays in place of traditional IEEE floating point arrays to store the primary data of calculations. Simulation is fundamentally an exercise in controlled approximation, and error introduced by finite-precision arithmetic (or lossy compression) is just one of several sources of error that need to be managed to ensure sufficient accuracy in a computed result. We describe ZFP, a compressed numerical format designed for in-memory storage of multidimensional arrays, and summarize theoretical results that demonstrate that the error of repeated lossy compression can be bounded and controlled. Furthermore, we establish a relationship between grid resolution and compression-induced errors and show that, contrary to conventional floating point, ZFP reduces finite-difference errors with finer grids. We present example calculations that demonstrate data reduction by 4x or more with negligible impact on solution accuracy. Our results further demonstrate several orders-of-magnitude increase in accuracy using ZFP over IEEE floating point and Posits for the same storage budget. [ABSTRACT FROM AUTHOR]

Tactile intent recognition systems, which are highly desired to satisfy human's needs and humanized services, shall be accurately understanding and identifying human's intent. They generally utilize time‐driven sensor arrays to achieve high spatiotemporal resolution, however, which encounter inevitable challenges of low scalability, huge data volumes, and complex processing. Here, an event‐driven intent recognition touch sensor (IR touch sensor) with in‐sensor computing capability is presented. The merit of event‐driven and in‐sensor computing enables the IR touch sensor to achieve ultrahigh resolution and obtain complete intent information with intrinsic concise data. It achieves critical signal extraction of action trajectories with a rapid response time of 0.4 ms and excellent durability of >10 000 cycles, bringing an important breakthrough of tactile intent recognition. Versatile applications prove the integrated functions of the IR touch sensor for great interactive potential in all‐weather environments regardless of shading, dynamics, darkness, and noise. Unconscious and even hidden action features can be perfectly extracted with the ultrahigh recognition accuracy of 98.4% for intent recognition. The further auxiliary diagnostic test demonstrates the practicability of the IR touch sensor in telemedicine palpation and therapy. This groundbreaking integration of sensing, data reduction, and ultrahigh‐accuracy recognition will propel the leapfrog development for conscious machine intelligence. [ABSTRACT FROM AUTHOR]

The editorial in "Frontiers in Physics" discusses the challenges and advancements in developing detectors for photon science, focusing on X-ray imaging detectors and sensors. The text highlights the need for detectors to meet the performance increase of new photon sources like Free Electron Lasers and Diffraction-Limited Storage Rings. It also addresses the challenges of data reduction and processing, as well as operational complexities in running imaging systems at photon science facilities. The editorial emphasizes the importance of simplifying system integration and calibration to enhance user interest and data quality in imaging detectors for photon science. [Extracted from the article]

Background/Objectives: This paper will compare the outcomes—safety and efficacy—of three minimally invasive glaucoma surgeries (MIGSs),the Hydrus Microstent, iStent, and Gonioscopy-Assisted Transluminal Trabeculotomy (GATT), for intraocular pressure (IOP) reduction in patients with primary open-angle glaucoma (POAG). Methods: A literature search of Ovid Medline and Embase identified studies evaluating the Hydrus, iStent, and GATT. Data on IOP reduction, medication use, and complications were analyzed. Results: Studies show the Hydrus, iStent, and GATT reduce IOP and medication burden in POAG patients, with some complications. For the Hydrus, studies showed 37.09% (27.5 ± 4.4 to 17.3 ± 3.7 mmHg) and 25% (16.8 to 12.6 mmHg) IOP reduction. Meanwhile, medication burden decreased from 2.5 ± 0.7 to 1.0 and from 2.1 to 1.15. For the iStent, studies showed a 36.39% (21.1 to 13.4 mmHg) and 8.19% (17.1 to 15.7 mmHg) IOP drop. Medication burden decreased from 2.87 to 1.24 and from 1.7 to 0.26. For GATT, studies showed a 49.33% (27.70 ± 10.30 to 14.04 ± 3.75) and 39.09% (26.40 ± 6.37 to 16.08 ± 2.38) IOP drop. Medication burden reduced from 3.73 ± 0.98 to 1.82 ± 1.47 and from 3.12 ± 0.80 to 0.45 ± 0.96. Conclusions: The Hydrus, iStent, and GATT are effective alternatives to trabeculectomy for mild to moderate POAG. They reduce and control IOP and dependence on medications with manageable safety profiles. In all three options, there were some clinically significant complications based on the p-value. For the Hydrus, it was PAS. For the iStent, they were PAS, FB sensation, IOP spikes, and microhyphema. For GATT, it was IOP spikes. However, further long-term studies, especially randomized controlled trials, are needed to support these results. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is an ever‐expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog‐cloud‐enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC‐IoT, a novel two‐phase clustering‐based approach designed to optimize the management of IoT‐generated data. In the first phase, which is performed in the fog layer, we used the K‐means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K‐means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC‐IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC‐IoT very suitable for resource‐constrained IoT environments. [ABSTRACT FROM AUTHOR]

Introduction: Knee osteoarthritis (KOA) is a major health issue affecting millions worldwide. This study employs machine learning algorithms to analyze human gait using kinematic data, aiming to enhance the diagnosis and detection of KOA. By adopting this approach, we contribute to the development of an effective diagnostic methods for KOA, a prevalent joint condition. Methods: The methodology is structured around several critical steps to optimize the model's performance. These steps include extracting kinematic features from video data to capture essential gait dynamics, applying data filtering and reduction techniques to remove noise and enhance data quality, and calculating key gait parameters to boost the model's predictive power. The machine learning model trains on these refined features, validates through cross-validation for robust performance assessment, and tests on unseen data to ensure generalizability. Results: Our approach demonstrates significant improvements in classification accuracy, highlighting its potential for early and precise KOA detection. The model achieves a high classification accuracy, indicating its effectiveness in distinguishing KOA-related gait patterns. Discussion: Furthermore, a comparative analysis with another model trained on the same dataset demonstrates the superiority of our method, suggesting that the proposed approach serves as a reliable tool for early KOA detection and potentially improves clinical diagnostic workflows. [ABSTRACT FROM AUTHOR]

Block-coder style data compression schemes, such as JPEG, ZFP and SPERR, rely on de-correlating transformations in order to efficiently store data with entropy encoding schemes. These de-correlating transformations are typically reliant on a uniform spatiotemporal sampling of the transformations and/or the data. Thus, for nonuniform data which may include spatial (and temporal) nonuniformity, such as those arising from partial differential equations solveing over complex geometries and unstructured grids, there is not an efficient de-correlating transformation nor a data compression pipeline that sufficiently adapts to this nonuniformity. We presented the manifold harmonic basis (MHB) as a de-correlating transformation, and prod d a collocation method over the nonuniform domain for solving the eigen problem that defines the MHB. Additionally, we demonstrated a modified version of ZFP's compression pipeline that adapted to the problems presented by nonuniformity in a block-coder style compression scheme. This resulted in a state-of-the-art spatial data compression technique for nonuniform data which we call Mesh-Float-Zip (MFZ). We illustrated the performance of the proposed compression strategy and compared its accuracy against ZFP and SZ compressors (applied to both original and reshuffled data) on three scientific computing datasets. [ABSTRACT FROM AUTHOR]

MGARD (MultiGrid Adaptive Reduction of Data) is an algorithm for compressing and refactoring scientific data, based on the theory of multigrid methods. The core algorithm is built around stable multilevel decompositions of conforming piecewise linear $ C^0 $ finite element spaces, enabling accurate error control in various norms and derived quantities of interest. In this work, we extend this construction to arbitrary order Lagrange finite elements $ \mathbb{Q}_p $, $ p \geq 0 $, and propose a reformulation of the algorithm as a lifting scheme with polynomial predictors of arbitrary order. Additionally, a new formulation using a compactly supported wavelet basis is discussed, and an explicit construction of the proposed wavelet transform for uniform dyadic grids is described. [ABSTRACT FROM AUTHOR]

Cancer is a complex disease driven by genomic alterations, and tumor sequencing is becoming a mainstay of clinical care for cancer patients. The emergence of multi-institution sequencing data presents a powerful resource for learning real-world evidence to enhance precision oncology. GENIE BPC, led by American Association for Cancer Research, establishes a unique database linking genomic data with clinical information for patients treated at multiple cancer centers. However, leveraging sequencing data from multiple institutions presents significant challenges. Variability in gene panels can lead to loss of information when analyses focus on genes common across panels. Additionally, differences in sequencing techniques and patient heterogeneity across institutions add complexity. High data dimensionality, sparse gene mutation patterns, and weak signals at the individual gene level further complicate matters. Motivated by these real-world challenges, we introduce the Bridge model. It uses a quantile-matched latent variable approach to derive integrated features to preserve information beyond common genes and maximize the utilization of all available data, while leveraging information sharing to enhance both learning efficiency and the model's capacity to generalize. By extracting harmonized and noise-reduced lower-dimensional latent variables, the true mutation pattern unique to each individual is captured. We assess model's performance and parameter estimation through extensive simulation studies. The extracted latent features from the Bridge model consistently excel in predicting patient survival across six cancer types in GENIE BPC data. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of estimating the principal eigenspace over an online data stream. First-order optimization methods are appealing choices for this problem thanks to their high efficiency and easy implementation. The existing first-order solvers, however, require either per-step orthonormalization or matrix inversion, which empirically puts pressure to the parameter tuning, and also incurs extra costs of rank augmentation. To get around these limitations, we introduce a penalty-like term controlling the distance from the Stiefel manifold into matrix Krasulina's method, and propose the first orthonormalization- and inversion-free incremental PCA scheme (Domino). The Domino is shown to achieve the computational speed-up, and own the ability of automatic correction on the numerical rank. It also maintains the advantage of Krasulina's method, e.g., variance reduction on low-rank data. Moreover, both of the asymptotic and non-asymptotic convergence guarantees are established for the proposed algorithm. [ABSTRACT FROM AUTHOR]

Makassar City as the largest city in Eastern Indonesia is known to have a high crime rate. On the other hand, the most common and often faced community dynamics in Makassar city is community mobility that triggers crowds. So as a preventive effort, the police have the authority to issue a crowd permit to regulate community activities. This research aims to analyze the regulations and mechanisms for granting crowd permits and to identify the methods of police supervision of crowd activities in the Makassar Port Police area. The type of research used is empirical juridical research. Data collection is done through interviews and observations. The technique of analyzing research data starts from data reduction, data presentation, to verification and conclusion drawing. The results showed that: (1) Regulations in granting crowd permits are regulated in Law of the Republic of Indonesia Number 2 of 2002 concerning the Indonesian National Police and several Field Guidelines of the National Police Chief. The mechanism for granting permits includes: First, the criteria for activities are given for activities with large masses. Second, the flow of services has been listed in the task guidelines with clear steps. (2) Supervision of crowd activities is carried out through open maintenance of public security and order by the Samapta Bhayangkara (Sabhara) unit and closed by the Intelligence and Security Unit and the Criminal Investigation Unit. Meanwhile, supervision of police members is carried out through control by police administrative staff, discipline enforcement by the Profession and Security Unit, and post-activity and periodic consolidation and evaluation once a year. Thus, the authority of the Police in granting crowd permits in the Makassar Port Police area has been running well, although there are still shortcomings in terms of supervision, especially time security and police availability to accompany crowd activities until completion. [ABSTRACT FROM AUTHOR]

The aim of this research is to find out how the Village Fund Allocation Policy (ADD) is implemented in Kontumere Village, Kabawo District, Muna Regency and to find out the factors that influence the Implementation of the Village Fund Allocation Policy (ADD). In this research, data collection was carried out in the following way, collecting data or information through: observation, semi-structured interviews (open interviews), documentation. Data analysis methods include data reduction, data presentation, and drawing conclusions. When viewed from the perspective of achieving objectives, the results of this research can be concluded that the implementation of the Village Fund Allocation Policy in Kontumere Village, Kabawo District, Muna District has not been achieved comprehensively or optimally. This is influenced by several inhibiting factors, namely (1) communication, the inhibiting factor in this communication is socialization to the public regarding the non-existent ADD policy (weakness). This results in the difficulty of inviting community participation in implementing ADD and in monitoring activities (treatment/threats). (2) resources, lack of ability of implementers to operate computers (Weakness), and lack of adequate village income support (Treatment/Threat), (3) Attitude of Implementers, The inhibiting factor in the attitude of implementers is the lack of responsiveness of ADD implementers (Weakness/Weakness) which considers the ADD policy to be a mere routine policy (Treatment/Threat). (4) Bureaucratic structure, namely the lack of coordination between policy implementers. [ABSTRACT FROM AUTHOR]

In this article, the idea of essential information‐based compression is extended to trilinear datasets. This basically boils down to identifying and labelling the essential rows (ERs), columns (ECs) and tubes (ETs) of such three‐dimensional datasets that allow by themselves to reconstruct in a linear way the entire space of the original measurements. ERs, ECs and ETs can be determined by exploiting convex geometry computational approaches such as convex hull or convex polytope estimations and can be used to generate a reduced version of the data at hand. These compressed data and their uncompressed counterpart share the same multilinear properties and their factorisation (carried out by means of, for example, parallel factor analysis–alternating least squares [PARAFAC‐ALS]) yield, in principle, indistinguishable results. More in detail, an algorithm for the assessment and extraction of the essential information encoded in trilinear data structures is here proposed. Its performance was evaluated in both real‐world and simulated scenarios which permitted to highlight the benefits that this novel data reduction strategy can bring in domains like multiway fluorescence spectroscopy and imaging. [ABSTRACT FROM AUTHOR]

This article describes the main features of the impacts of global observations on the reduction of errors in the data assimilation (DA) cycle carried out in the Brazilian Global Atmospheric Model (BAM) at Center for Weather Forecast and Climate Studies [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)]. These results show the importance of studying and evaluating the contribution of each observation to the DA system, therefore, two experiments (exp1/exp2) were performed with different configurations of the BAM model, with exp2 presenting the best fit between the Gridpoint Statistical Interpolation (GSI) and BAM systems. The BAM model was validated by the statistical metrics of root mean-square error and correlation anomaly, but this validation is not explored in this paper. A metric was applied that does not depend on the adjoint-based method, but only on the residuals that are made available in the GSI system for the observation space, given by the total impact, the fractional impact and the fractional beneficial impact. In general, the average daily showed that the observations of the global system that contribute most to the reduction of errors in the DA cycle are from the pilot balloon data (−3.54/−3.45 J kg−1)and the profilers (−2.13/−1.97 J kg−1), and the smallest contributions came from the land (−0.28/−0.29 J kg−1) and sea (−0.44/−0.44 J kg−1) surfaces. The same pattern was observed for the synoptic times presented. However, when verifying the fraction of the impact by each type of observation, it was found that the radiance data (64.88/30.30%), followed by radiosondes (14.85/27.42%) and satellite winds (11.03/22.70%), are the most important fractions for both experiments. These results show that the DA system is working to generate the best analyses at the research center and that the deficiencies found in some observations can be adjusted to improve the development of the GSI and the BAM model, since together, the entire database used is evaluated, as well as the forecast model itself, indicating the relationship between the assertiveness of the atmospheric model and the DA system used at the research center. [ABSTRACT FROM AUTHOR]

This study discusses communication patterns in conflict interaction in premarital couples experiencing toxic relationships. A toxic relationship is unhealthy, characterized by unsupportive attitudes, conflicts in which one tries to destroy the other, competition, disrespect, and lack of cohesiveness. This research employed a descriptive method. Data were collected through interviews. The data validity testing was performed using the source triangulation technique. The data analysis technique utilized the Miles and Huberman analysis model, consisting of data collection, data reduction, data presentation, and verification. This study discovered that conflicts experienced by premarital couples experiencing toxic relationships stemmed from different goals, poor communication, and lack of fulfillment of needs. These results revealed that both partners decided to stay in toxic relationships with different conflict resolution strategies. The first partner used the conflict avoidance style, whereas the second one applied the compromise conflict style. Factors influencing the use of conflict resolution styles for the first couple were perceptions of conflict, relationship dominance, and family closeness, whereas the second partner was influenced by emotional attachment and personality factors. The communication patterns adopted are also different, namely, the pattern of unbalanced separation in the first couple and the balance pattern in the second couple. [ABSTRACT FROM AUTHOR]

The pursuit of effective models with high detection accuracy has sparked great interest in anomaly detection of internet traffic. The issue still lies in creating a trustworthy and effective anomaly detection system that can handle massive data volumes and patterns that change in real-time. The detection techniques used, especially the feature selection methods and machine learning algorithms, are crucial to the design of such a system. The fundamental difficulty in feature selection is selecting a smaller subset of features that are more related to the class but are less numerous. To reduce the dimensionality of the dataset, this research offered a multi-feature selection technique (MFST) using four filter techniques: fast correlationbased filter, significance feature evaluator, chi-square, and gain ratio. Each technique's output vector is put via ranker and Borda voting filters. The feature with the highest number of votes and rank values will be selected from the dataset. The performance of the given MFST framework was the best when compared to the four strategies listed above functioning alone; three different classifiers were employed to test the accuracy. C4.5, nave Bayes, and support vector machine. The experiment outcomes employed ten datasets of different sizes with 10,000-300,000 instances. Only 8 out of 248 characteristics were chosen, with classifiers percentages averaging 65%, 93.8%, and 95.5%. [ABSTRACT FROM AUTHOR]

Mucosal‐associated invariant T cells (MAIT) are innate‐like lymphocytes enriched in mucosal organs where they contribute to antimicrobial defense. APECED is an inborn error of immunity characterized by immune dysregulation and chronic mucocutaneous candidiasis. Reduction in the frequency of circulating MAITs has been reported in many inborn errors of immunity, but only in a few of them, the functional competence of MAITs has been assessed. Here, we show in a cohort of 24 patients with APECED, that the proportion of circulating MAITs was reduced compared with healthy age and sex‐matched controls (1.1% vs. 2.6% of CD3+ T cells; p < 0.001) and the MAIT cell immunophenotype was more activated. Functionally the IFN‐γ secretion of patient MAITs after stimulation was comparable to healthy controls. We observed in the patients elevated serum IFN‐γ (46.0 vs. 21.1 pg/mL; p = 0.01) and IL‐18 (42.6 vs. 13.7 pg/mL; p < 0.001) concentrations. Lower MAIT proportion did not associate with the levels of neutralizing anti‐IL‐22 or anti‐IL‐12/23 antibodies but had a clear negative correlation with serum concentrations of IFN‐γ, IL‐18, and protein C‐reactive protein. Our data suggest that reduction of circulating MAITs in patients with APECED correlates with chronic type 1 inflammation but the remaining MAITs are functionally competent. [ABSTRACT FROM AUTHOR]

A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification. [ABSTRACT FROM AUTHOR]

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Background: Nutritional issues among children remain a significant health concern, with stunting being a national priority. Data of the Indonesian Nutrition Status Survey shows an increase in the prevalence of stunting in West Nusa Tenggara (NTB) from 31.4% (2021) to 32.7% (2022). Dompet Dhuafa Foundation has a Kawasan Sehat program to support the government in promoting healthy lifestyles, particularly stunting prevention. Objectives: Evaluating the Kawasan Sehat program in efforts to prevent and manage stunting in Layanan Kesehatan Cuma-Cuma area of NTB. Methods: This evaluative research used a mixed-methods approach. Data were collected through interviews, observations, and document reviews. Informants were selected using purposive sampling. Qualitative data analysis was conducted in stages of data reduction, presentation, and interpretation. Quantitative data were analyzed descriptively to assess stunting prevalence and the achievement of program indicators. Results: Kawasan Sehat program has contributed to preventing and managing stunting. The input components effectively support its implementation, with adequate human resources and budget acquired from partnerships. The program was carried out systematically, including planning involving stakeholders, execution using a combination of community empowerment and charity, and supervision. Various interventions are implemented, including support for pregnant women, breastfeeding mothers, infants, and toddlers. The success of the output was evidenced by the decrease in stunting prevalence in Kawasan Sehat NTB and the achievement of the program indicators. Conclusions: Kawasan Sehat program for stunting intervention has been successful according to input, process, and output. Community empowerment should continuously be strengthened to encourage communities to adopt clean and healthy living behaviors. [ABSTRACT FROM AUTHOR]

This study introduces a novel deep learning-based approach for skin cancer diagnosis and treatment planning to overcome existing limitations. The proposed system employs a series of innovative algorithms, including IQQO for preprocessing, TSSO for cancer region isolation, and FA-MFC for data dimensionality reduction. The USSL-Net DCNN extracts hidden features, and the BGR-QNN enables multi-class classification. Evaluated on Kaggle and ISIC-2019 datasets, the model achieves impressive accuracy, up to 96.458% for Kaggle and 94.238% for ISIC-2019. This hybrid deep learning technique shows great potential for improving skin cancer classification, thus enhancing diagnosis and treatment outcomes and ultimately reducing mortality rates. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected. [ABSTRACT FROM AUTHOR]

The main aim of this research is to analyze and describe the performance of the police in SKCK services at Tomohon Police. The approach used is descriptive qualitative. The number of informants in this research was 10 people. Data collection techniques use observation, interviews and documentation. Data analysis through data collection, data reduction, data presentation and confirmation of conclusions. The research results based on the four research focus indicators show that: 1) the SKCK service performance of police employees is not optimal, resulting in slow work of employees; 2) Obstacles that occur, such as if there are many applicants who apply for SKCK, there can be a lot of queues and it takes quite a long time for each person, so it gives the impression that the service is slow and unsatisfactory and there are still many applicants who do not understand the online SKCK registration even though the steps have been explained; 3) There are still police employees who are not disciplined, such as those who are late coming to the office; 4) The public's response regarding SKCK services is good and positive but they still receive constructive complaints, suggestions and complaints. It was concluded that the performance of the SKCK service for police employees was not optimal because there were still problems in the SKCK service. In performance management, it is important for superiors to measure, manage and improve the performance of police employees continuously. [ABSTRACT FROM AUTHOR]

This research aims to analyze and describe the administration of the new village government. The research method used is descriptive qualitative. The number of informants in this study was 5 people. Data collection uses observation, interview and documentation techniques. Data analysis through data reduction, data presentation and drawing conclusions. The research results show that based on research conducted by researchers, it can be concluded that not everything can be implemented. Among them are 27 village administrations according to PERMENDAGRI No. 47 of 2016 and it was found that there are 3 village administrations that have not been implemented, namely the development activity book, development results inventory book and activity support cash book. The cause of the obstacles to implementing this administration is the lack of technical guidance from the government districts about how they should manage village administration, then the lack of tools needed to manage village administration. [ABSTRACT FROM AUTHOR]

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (SGP.DES) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of SGP.DES supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the SGP.DES framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that SGP.DES outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear relationship between the original data and the dimensionality-reduced representation, leading to better interpretability. In this research, we present a tied-weight autoencoder as a dimensionality reduction model with the merit of both linear and nonlinear methods. Although the tied-weight autoencoder is a nonlinear dimensionality reduction model, we approximate it to function as a linear model. This is achieved by removing the hidden layer units that are largely inactivated by the input data, while preserving the model's effectiveness. We evaluate the proposed model by comparing its performance with other linear and nonlinear models using benchmark datasets. Our results show that the proposed model performs comparably to the nonlinear model of a similar autoencoder structure to the proposed model. More importantly, we show that the proposed model outperforms the linear models in various metrics, including the mean square error, data reconstruction, and the classification of low-dimensional projections of the input data. Thus, our study provides general recommendations for best practices in dimensionality reduction. [ABSTRACT FROM AUTHOR]

The effect of regulation on virtually every aspect of the lives of US citizens has led to an understandable impulse to measure this total impact. It has led to various attempts to count the total number of regulations and regulatory requirements, and to total the costs and benefits of regulation. And these counting mechanisms have played prominent roles in discussions over statutory changes designed to reform the process by which we write regulations. However, counting regulations in a meaningful way and measuring their cumulative economic impact is an astonishingly difficult task. For this reason, there have been a wide variety of methods that scholars and advocates have employed in the effort to do so. This article is an attempt to catalog the most prominent methods of counting regulations and measuring regulatory impact in the United States, describe their strengths and weaknesses, and suggest alternative approaches to attack this important question. We suggest both using large language models and detailed analysis of Paperwork Reduction Act data and, at the opposite extreme, doing more qualitative work on the consequences of regulation on individuals, firms, and industries. [ABSTRACT FROM AUTHOR]

Temporal knowledge graphs (TKGs) are a form of knowledge representation constructed based on the evolution of events at different time points. It provides an additional perspective by extending the temporal dimension for a range of downstream tasks. Given the evolving nature of events, it is essential for TKGs to reason about non-existent or future events. Most of the existing models divide the graph into multiple time snapshots and predict future events by modeling information within and between snapshots. However, since the knowledge graph inherently suffers from missing data and uneven data distribution, this time-based division leads to a drastic reduction in available data within each snapshot, which makes it difficult to learn high-quality representations of entities and relationships. In addition, the contribution of historical information changes over time, distinguishing its importance to the final results when capturing information that evolves over time. In this paper, we introduce CH-TKG (Contrastive Learning and Historical Information Learning for TKG Reasoning) to addresses issues related to data sparseness and the ambiguity of historical information weights. Firstly, we obtain embedding representations of entities and relationships with evolutionary dependencies by R-GCN and GRU. On this foundation, we introduce a novel contrastive learning method to optimize the representation of entities and relationships within individual snapshots of sparse data. Then we utilize self-attention and copy mechanisms to learn the effects of different historical data on the final inference results. We conduct extensive experiments on four datasets, and the experimental results demonstrate the effectiveness of our proposed model with sparse data. [ABSTRACT FROM AUTHOR]

In the field of recommendation algorithms, Knowledge Graphs are often utilized as supplementary information to enhance recommendation accuracy. However, while applying Knowledge Graphs enriches recommendation information, it also introduces potentially misleading effects due to Knowledge Graph noise. To address these challenges, we propose a method to achieve Knowledge Graph Noise Reduction and Knowledge Perception Enhancement through positive contrast learning. The method employs Fourier Transform, Inverse Transform, and Convolution Optimization Computation techniques to transform and analyze Knowledge Graph triplet information in the frequency domain. The process filters and reduces noise through Convolution Optimization Computation by integrating frequency domain feature information, eliminating misleading relational information that cannot effectively infer user preferences. Subsequently, Positive Contrastive Learning enhances the acquired ternary information and improves the applicability of the information to recognize user preference information accurately. The proposed method reduces noise and enhances knowledge perception, strengthens the application of strong relationships, reduces the impact of weak relationships, and improves recommendation accuracy by utilizing frequency-domain features. The KG-CFCL_RippleNet and KG-CFCL_MKR models validate the effectiveness of this optimization method, and significant improvements are achieved in the area of book, music, and movie recommendations compared to existing models. Experiments demonstrate the advantages of KG-CFCL in terms of noise reduction, Knowledge Perception Enhancement, and data optimization, improving the interpretability of the models. [ABSTRACT FROM AUTHOR]

In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models. [ABSTRACT FROM AUTHOR]

Dimensionality reduction and clustering are crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, treated independently in the current process, hindering their mutual benefits. The latest methods jointly optimize these tasks through deep clustering. However, contrastive learning, with powerful representation capability, can bridge the gap that common deep clustering methods face, which requires pre-defined cluster centers. Therefore, a dual-level contrastive clustering method with nonuniform sampling (nsDCC) is proposed for scRNA-seq data analysis. Dual-level contrastive clustering, which combines instance-level contrast and cluster-level contrast, jointly optimizes dimensionality reduction and clustering. Multi-positive contrastive learning and unit matrix constraint are introduced in instance- and cluster-level contrast, respectively. Furthermore, the attention mechanism is introduced to capture inter-cellular information, which is beneficial for clustering. The nsDCC focuses on important samples at category boundaries and in minority categories by the proposed nearest boundary sparsest density weight assignment algorithm, making it capable of capturing comprehensive characteristics against imbalanced datasets. Experimental results show that nsDCC outperforms the six other state-of-the-art methods on both real and simulated scRNA-seq data, validating its performance on dimensionality reduction and clustering of scRNA-seq data, especially for imbalanced data. Simulation experiments demonstrate that nsDCC is insensitive to "dropout events" in scRNA-seq. Finally, cluster differential expressed gene analysis confirms the meaningfulness of results from nsDCC. In summary, nsDCC is a new way of analyzing and understanding scRNA-seq data. [ABSTRACT FROM AUTHOR]

Cloud computing faces challenges in increasing efficiency through task scheduling and load balancing. Various load-balancing methods, including the Throttled policy, help evenly distribute user requests among virtual machines. The article compares the cost of the clonal selection algorithm with the throttled load-balancing policy and the clonal selection algorithm with the distributed service broker policy. It also introduces two new algorithms based on the artificial immune system, aiming to efficiently respond to user requests at the lowest cost. Additionally, the article presents two new algorithms resulting from combining an evolutionary algorithm with artificial immune system mechanisms. These algorithms have been evaluated and compared using the Cloud Analyst simulator, demonstrating their superior performance in terms of response time, data center processing time, data transfer cost, and overall cost. The results indicate that ACODTH algorithm has an average response time of 0.35 milliseconds shorter than ACO. Additionally, the ACONSA algorithm shows a 0.44-millisecond reduction in average data center processing time compared to ACONSA. When considering the overall cost, the AIS_DSBP algorithm is projected to be 3.13$ cheaper than AIS_TLP. Similarly, the CSA_DSBP algorithm is expected to have a total data transfer cost of 3.14$ lower than CSA. [ABSTRACT FROM AUTHOR]

Ordinary 3D Baryon Acoustic Oscillations (BAO) data are model-dependent, requiring the assumption of a cosmological model to calculate comoving distances during data reduction. Throughout the present-day literature, the assumed model is Λ CDM. However, it has been pointed out in several recent works that this assumption can be inadequate when analyzing alternative cosmologies, potentially biasing the Hubble constant ( H 0 ) low, thus contributing to the Hubble tension. To address this issue, 3D BAO data can be replaced with 2D BAO data, which are only weakly model-dependent. The impact of using 2D BAO data, in combination with alternative cosmological models beyond Λ CDM, has been explored for several phenomenological models, showing a promising reduction in the Hubble tension. In this work, we accommodate these models in the theoretically robust framework of bimetric gravity. This is a modified theory of gravity that exhibits a transition from a (possibly) negative cosmological constant in the early universe to a positive one in the late universe. By combining 2D BAO data with cosmic microwave background and type Ia supernovae data, we find that the inverse distance ladder in this theory yields a Hubble constant of H 0 = (71.0 ± 0.9) km / s / Mpc , consistent with the SH0ES local distance ladder measurement of H 0 = (73.0 ± 1.0) km / s / Mpc . Replacing 2D BAO with 3D BAO results in H 0 = (68.6 ± 0.5) km / s / Mpc from the inverse distance ladder. We conclude that the choice of BAO data significantly impacts the Hubble tension, with ordinary 3D BAO data exacerbating the tension, while 2D BAO data provide results consistent with the local distance ladder. [ABSTRACT FROM AUTHOR]

Deduplication and compression are powerful techniques to reduce the ratio between the quantity of logical data stored and the physical amount of consumed storage. Deduplication can impose significant performance overheads, as duplicate detection for large systems induces random accesses to the backend storage. These random accesses have led to the concern that deduplication for primary storage and HDDs are not compatible. Most inline data reduction solutions are therefore optimized for SSDs and discourage their use for HDDs, even for sequential workloads. In this work, we show that these concerns are valid if and only if the lessons learned from deduplication research are not applied. We have therefore investigated data reduction solutions for primary storage based on the RedHat Virtual Disk Optimizer (VDO) and show that directly applying them can decrease sequential write performance for HDDs by 36×. We then show that slight modifications to VDO plus the integration of a very small SSD area significantly improve performance even beyond the performance without data reduction enabled, making HDDs more cost-efficient for a wide range of mostly sequential cloud workloads than SSDs. Additionally, these VDO optimizations do not require to maintain different code bases for HDDs and SSDs, and we therefore provide the first data reduction solution applicable to both storage media. [ABSTRACT FROM AUTHOR]

Storage savings and data confidentiality are two primary goals for outsourced storage. However, encryption by design destroys the content redundancy within plaintext data, so there exist design tensions when combining encryption with data reduction techniques (i.e., deduplication, delta compression, and local compression). We present EDRStore, an outsourced storage system that realizes encrypted data reduction to achieve both storage savings and data confidentiality. EDRStore's core idea is a careful design of the encryption and data reduction workflows. It proposes new key generation and encryption schemes to preserve the content similarity of encrypted data for deduplication and delta compression. It further proposes selective local compression based on content similarity, so as to achieve storage savings of encrypted data from both delta compression and local compression. Evaluation on real-world datasets shows that EDRStore achieves higher storage savings than existing encrypted storage approaches and incurs moderate performance overhead compared with plaintext storage. [ABSTRACT FROM AUTHOR]

As the data volume continues to grow exponentially, there is an increasing demand for large storage system capacity. Data compression techniques effectively reduce the volume of written data, enhancing space efficiency. As a result, many modern SSDs have already incorporated data compression capabilities. However, data compression introduces additional processing overhead in critical I/O paths, potentially affecting system performance. Currently, most compression solutions in flash-based storage systems employ fixed compression algorithms for all incoming data without leveraging differences among various data access patterns. This leads to sub-optimal compression efficiency. This article proposes a data-type-aware Flash Translation Layer (DAFTL) scheme to maximize space efficiency without compromising system performance. First, we propose an I/O behavior prediction method to forecast future access on specific data. Then, DAFTL matches data types with distinct I/O behaviors to compression algorithms of varying intensities, achieving an optimal balance between performance and space efficiency. Specifically, it employs higher-intensity compression algorithms for less frequently accessed data to maximize space efficiency. For frequently accessed data, it utilizes lower-intensity but faster compression algorithms to maintain system performance. Finally, an improved compact compression method is proposed to effectively eliminate page fragmentation and further enhance space efficiency. Extensive evaluations using a variety of real-world workloads, as well as the workloads with real data we collected on our platforms, demonstrate that DAFTL achieves more data reductions than other approaches. When compared to the state-of-the-art compression schemes, DAFTL reduces the total number of pages written to the SSD by an average of 8%, 21.3%, and 25.6% for data with high, medium, and low compressibility, respectively. In the case of workloads with real data, DAFTL achieves an average reduction of 10.4% in the total number of pages written to SSD. Furthermore, DAFTL exhibits comparable or even improved read and write performance compared to other solutions. [ABSTRACT FROM AUTHOR]

Delta encoding is a data reduction technique capable of calculating the differences (i.e., delta) among very similar files and chunks. It is widely used for various applications, such as synchronization replication, backup/archival storage, cache compression, and so on. However, delta encoding is computationally costly due to its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches either run at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this article, we propose Gdelta, a fast delta encoding approach with a high compression ratio. The key idea behind Gdelta is the combined use of five techniques: (1) employing an improved Gear-based rolling hash to replace Adler32 hash for fast scanning overlapping words of similar chunks, (2) adopting a quick array-based indexing for word-matching, (3) applying a sampling indexing scheme to reduce the cost of traditional building full indexes for base chunks' words, (4) skipping unmatched words to accelerate delta encoding through non-redundant areas, and (5) last but not least, after word-matching, further batch compressing the remainder to improve the compression ratio. Our evaluation results driven by seven real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 3.5X∼25X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10%∼240%. [ABSTRACT FROM AUTHOR]

High-fidelity computational fluid dynamics simulations play an essential role in predicting complex aerodynamic flow fields, but their employment are hindered due to the high computational burdens involving fine spatial discretizations. While recent data-driven methods offer a promising avenue for performance improvements, they often face challenges related to excessive reliance on labeled data and insufficient accuracy. Consequently, we propose a novel hybrid model, which integrates a deep learning model into the fluid simulation workflow, harnessing the predictive capabilities to accelerate the fluid simulations. The acceleration is performed by a coarse-to-fine flow field mapping. To mitigate over-reliance on labeled data, the model is first pre-trained using pseudo-labeled data and then fine-tuned with a new designed attention mechanism. Acceleration efficiency of the hybrid model is demonstrated through two cases: aerodynamic simulations of an airfoil and a spherical blunt cone under varied operating conditions. Numerical experiments reveal that the proposed model achieves a substantial reduction in labeled data as well as prediction accuracy improvement, in comparison with traditional data-driven methods. [ABSTRACT FROM AUTHOR]

The geometric accuracy reduction of 3D models derived from oblique photogrammetry is crucial for the security of high-precision data. However, the existing research predominantly focuses on 2D data, which is difficult to meet the demand of 3D model accuracy reduction. This paper proposes a bounded product function-based algorithm for reducing the geometric accuracy of terrain heights in oblique photogrammetry 3D models. The algorithm leverages the continuity and differentiability at any order of bounded functions to ensure that data deformation remains smooth, continuous, and gradual, which effectively avoids large-scale abrupt changes and maintains consistency of the spatial form of data. Additionally, by incorporating the periodicity of trigonometric functions, the robustness of the proposed algorithm is further enhanced. Experimental results demonstrate that the proposed algorithm can effectively control the reduction of data precision. At the same time, the algorithm can maintain the spatial form of data and meet the requirements of data availability, and the algorithm has high security and is conducive to the security disclosure of data. [ABSTRACT FROM AUTHOR]

Researchers of 5G-sliced Wireless Networks are faced with the task of achieving, at the same time, (i) mandatory isolation among network slices and (ii) the effective utilization of resource at Fifth Generation Base Station (gNB). This article proposes the second version of the Preemption-based service Prioritization (PP2) scheme that merges the capabilities of the classical Resource Reservation (RR) and Service Prioritization (SP) schemes and realizes the coexistence of non-TCP streaming and TCP elastic traffic at gNB. Analytical methods are given for Dynamic Resource Sharing (DRS) and Dynamic Resource Reallocation (DRR) with data rate reduction and service preemption. As main results, under some baseline scenario, the PP2 scheme, in comparison with RR, does increase the admission probability up to 99.999% at the system while ensuring 100% in system capacity utilization. [ABSTRACT FROM AUTHOR]

In order to improve image recognition technologies in an IoT environment, we propose a data reduction scheme for a feature-extractable CMOS image sensor and present simulation results for object recognition using feature data. We evaluated the accuracy of the simulated feature data in object recognition based on YOLOX trained with a feature dataset. According to our simulation results, the obtained object recognition accuracy was 56.6 % with the large-scale COCO dataset, even though the amount of data was reduced by 97.7 % compared to conventional RGB color images. When the dataset was replaced with the RAISE RAW image dataset for more accurate simulation, the object recognition accuracy improved to 76.3 % . Furthermore, the feature-extractable CMOS image sensor can switch its operation mode between RGB color image mode and feature data mode. When the trigger for switching from feature data mode to RGB color image mode was set to the detection of a large-sized person, the feature data achieved an accuracy of 93.5 % with the COCO dataset. [ABSTRACT FROM AUTHOR]

Purpose: To assess the potential for accelerating continuous‐wave (CW) T1ρ dispersion measurement with compressed sensing approach via studying the effect that the data reduction has on the ability to detect differences between intact and degenerated articular cartilage with different spin‐lock amplitudes and to assess quantitative bias due to acceleration. Methods: Osteochondral plugs (n = 27, 4 mm diameter) from femur (n = 14) and tibia (n = 13) regions from human cadaver knee joints were obtained from commercial biobank (Science Care, USA) under Ethical permission 134/2015. MRI of specimens was performed at 9.4T with magnetization prepared radial balanced SSFP (bSSFP) readout sequence, and the CWT1ρ relaxation time maps were computed from the measured data. The relaxation time maps were evaluated in the cartilage zones for different acceleration factors. For reference, Osteoarthritis Research Society International (OARSI) grading and biomechanical measurements were performed and correlated with the MRI findings. Results: Four‐fold acceleration of CWT1ρ dispersion measurement by compressed sensing approach was feasible without meaningful loss in the sensitivity to osteoarthritic (OA) changes within the articular cartilage. Differences were significant between intact and OA groups in the superficial and transitional zones, and CWT1ρ correlated moderately with the reference measurements (0.3 < r < 0.7). Conclusion: CWT1ρ was able to differentiate between intact and OA cartilage even with four‐fold acceleration. This indicates that acceleration of CWT1ρ dispersion measurement by compressed sensing approach is feasible with negligible loss in the sensitivity to osteoarthritic changes in articular cartilage. [ABSTRACT FROM AUTHOR]