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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]

With the IoT trend, wireless sensors are gaining growing interest. This is due to the possibility of installing them in locations inaccessible to wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize sampling to a sufficient rate to catch important information without excessive energy consumption. One way to achieve sufficient sampling is by using an algorithm for adaptive sampling named dynamic sampling rate algorithm (DSRA); however, this algorithm requires an expert to set and tune its parameters, which might not always be readily available. This study aims to further develop this algorithm to be machine learning based to tune these parameters. To achieve this goal, the algorithm was modified and an optimization strategy that considers a predetermined error threshold was developed. Then the algorithm was implemented using simulated and real data with a set of predetermined errors thresholds to observe its performance. The results showed that the developed algorithm exhibited adaptive sampling behavior, and it could collect data efficiently depending on the predetermined error threshold. Based on the results, it is possible to conclude that the developed algorithm endows sensors with adaptive sampling capabilities based on the signal rate of change. [ABSTRACT FROM AUTHOR]

Environmental vibrations and other factors cause significant random errors in raw data collected during TBM tunnelling. Such errors can reach up to 25% and will severely reduce the performance of data mining and machine learning model. To improve the data quality and find a suitable denoising method for TBM data, this study first proposed two assurance indicators to evaluate the effectiveness of data denoising, such as distortion degree (θ) and denoising magnitude (λ). Then, the effects of the data denoising schemes were compared. The results demonstrate that: (1) The Convlove19 scheme has better performance and is recommended for the denoising processing for TBM data. It can preserve the statistical characteristics of the raw data to the maximum extent possible and can improve the performance of the machine learning model by 15∼25%. (2) "Boring cycle segmentation after denoising strategy" (SAD) has better performance than "Boring cycle segmentation before denoising strategy" (SBD) and is recommended. The findings in this study can help with TBM data cleaning and quality improvement, as well as provide a reference for noise reduction of other data with similar features. [ABSTRACT FROM AUTHOR]

Smart grazing is a relatively difficult field of digital agriculture. Restricted by the geographical conditions of pastures, poor network infrastructure and low economic output, conventional IoT systems are difficult to apply in the field of grazing. In this paper, we propose the Space-Air-Ground integrated Grazing IoT(SAG-GIoT) system based on the background of yak grazing production in the Qinghai-Tibet Plateau, and define three smart grazing management application scenarios: (1) daily grazing supervision, (2) UAV grazing, (3) searching for yaks. To this end, we have designed the three-tier technical architecture of SAG-GIoT, and developed collar, base station and grazing management system. We designed the all-terrain network service scheme with the BeiDou Satellite-Base Station Sender(BDS-BSS) and Small Base Stations(SBSs), and verified the daily grazing supervision test in long-term. UAV grazing test was carried out in pasture, and a flexible communication networking was realized through the UAV Based Sation(UAV-BS). With the guidance of UAV searching and APP positioning, taking Handled Base Stations(HBSs) in hand, we quickly and accurately find the lost yaks. SAG-GIoT system is characterized as low cost, flexible deployment and global service, and has broad application prospects. Article highlights: SAG-GIoT system boosts yak herding efficiency on the Tibetan Plateau, which reduces the risk of livestock loss. Drones and satellite networks solve the problem of signal coverage in vast pastoral areas, which is expected be a universal network for wild. This system is efficient and feasible, with further cost reduction, it is expected to be widely applied and promoted in pastoral areas. [ABSTRACT FROM AUTHOR]

AbstractMany data problems contain some reference or normal conditions, upon which to compare newly collected data. This scenario occurs in data collected as part of clinical trials to detect adverse events, or for measuring climate change against historical norms. The data is typically multivariate, and often the normal ranges are specified by a multivariate normal distribution. The work presented in this paper develops methods to compare the new sample against the reference distribution with high-dimensional visualisation. It uses a projection pursuit guided tour to produce a sequence of low-dimensional projections steered towards those where the new sample is most different from the reference. A new projection pursuit index is defined for this purpose. The tour visualisation also includes drawing of the projected ellipse, which is computed analytically, corresponding to the reference distribution. The methods are implemented in the R package, tourr. [ABSTRACT FROM AUTHOR]

This article focuses on the practical issue of a recent theoretical method proposed for trend estimation in high dimensional time series. This method falls within the scope of the low-rank matrix factorization methods in which the temporal structure is taken into account. It consists of minimizing a penalized criterion, theoretically efficient but which depends on two constants to be chosen in practice. We propose a two-step strategy to solve this question based on two different known heuristics. The performance and a comparison of the strategies are studied through an important simulation study in various scenarios. In order to make the estimation method with the best strategy available to the community, we implemented the method in an R package TrendTM which is presented and used here. Finally, we give a geometric interpretation of the results by linking it to PCA and use the results to solve a high-dimensional curve clustering problem. The package is available on CRAN. [ABSTRACT FROM AUTHOR]

So far, most of the about 5700 exoplanets have been discovered mainly with radial velocity and transit methods. These techniques are sensitive to planets in close orbits, not being able to probearge star–planet separations. μ-lensing is the indirect method that allows us to probe the planetary systems at the snow-line and beyond, but it is not a repeatable observation. On the contrary, direct imaging (DI) allows for the detection and characterization ofow mass companions at wide separation (≤5–6 au). The main challenge of DI is that a typical planet–star contrast ranges from 10−6, for a young Jupiter in emittedight, to 10−9 for Earth in reflectedight. In theast two decades, aot of efforts have been dedicated to combiningarge (D ≥ 5 m) telescopes (to reduce the impact of diffraction) with coronagraphs and high-order adaptive optics (to correct phase errors induced by atmospheric turbulence), with sophisticated image post-processing, to reach such a contrast between the star and the planet in order to detect and characterize cooler and closer companions to nearby stars. Building on the first pioneering instrumentation, the second generation of high-contrast imagers, SPHERE, GPI, and SCExAO, allowed us to probe hundreds of stars (e.g., 500–600 stars using SHINE and GPIES), contributing to a better understanding of the demography and the occurrence of planetary systems. The DI offers a possible clear vision for studying the formation and physical properties of gas giant planets and brown dwarfs, and the future DI (space and ground-based) instruments with deeper detectionimits will enhance this vision. In this paper, we briefly review the methods, the instruments, the main sample of targeted stars, the remarkable results, and the perspective of this rising technique. [ABSTRACT FROM AUTHOR]

The surge in data generation has prompted a shift to big data, challenging the notion that "more data equals better performance" due to processing and time constraints. In this evolving artificial intelligence and machine learning landscape, instance selection (IS) has become crucial for data reduction without compromising model quality. Traditional IS methods, though efficient, often struggle with large, complex datasets in data mining. This study evaluates graph reduction techniques, grounded in graph theory, as a novel approach for instance selection. The objective is to leverage the inherent structures of data represented as graphs to enhance the effectiveness of instance selection. We evaluated 35 graph reduction techniques across 29 classification datasets. These techniques were assessed based on various metrics, including accuracy, F1 score, reduction rate, and computational times. Graph reduction methods showed significant potential in maintaining data integrity while achieving substantial reductions. Top techniques achieved up to 99% reduction while maintaining or improving accuracy. For instance, the Multilevel sampling achieved an accuracy effectiveness score of 0.8555 with 99.16% reduction on large datasets, while Leiden sampling showed high effectiveness on smaller datasets (0.8034 accuracy, 97.87% reduction). Computational efficiency varied widely, with reduction times ranging from milliseconds to minutes. This research advances the theory of graph-based instance selection and offers practical application guidelines. Our findings indicate graph reduction methods effectively preserve data quality and boost processing efficiency in large, complex datasets, with some techniques achieving up to 160-fold speedups in model training at high reduction rates. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents a new technique for creating spatial sounds based on a convolution processor. The main objective of this research was to propose a new method for generating a set of impulse responses that guarantee a realistic spatial experience based on the fusion of amplitude data acquired from an omnidirectional microphone and directional data acquired from an intensity probe. The advantages of the proposed approach are its versatility and easy adaptation to playback in a variety of multi-speaker systems, as well as a reduction in the amount of data, thereby simplifying the measurement procedure required to create any set of channel responses at the post-production stage. This paper describes the concept behind the method, the data acquisition method, and the signal processing algorithm required to generate any number of high-quality channel impulse responses. Experimental results are presented to confirm the suitability of the proposed solution by comparing the results obtained for a traditional surround 5.1 recording system and the proposed approach. This study aims to highlight the potential of intensity impulse responses in the audio recording and virtual reality industries. [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]

Feature selection is an effective data reduction technique. SHapley Additive exPlanations (SHAP) can be used to provide a feature importance ranking for models built with labeled or unlabeled data. Thus, one may use the SHAP feature importance ranking in a feature selection technique by selecting the k highest ranking features. Furthermore, this SHAP-based feature selection technique is applicable regardless of the availability of labels for data. We use the Kaggle Credit Card Fraud detection dataset to simulate three label availability scenarios. When no labeled data is available, unsupervised learners should be used. We explore feature selection for data reduction with Isolation Forest and SHAP for this case. When data of one class is available, a one-class classifier, such as Gaussian Mixture Model (GMM) can be used in combination with SHAP for determining feature importance, and for feature selection. Finally, if labeled data from both classes is available a binary-class classifier can be used in conjunction with SHAP for data reduction. Our contribution is to provide a comparative analysis of features selected in the three label availability scenarios. Our primary conclusion is that feature sets may be reduced with SHAP without compromising performance. To the best of our knowledge, this is the first study to explore a feature analysis technique, applicable in the three label availability scenarios. [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]

Highly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e - 04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential. [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]

Power consumption (PC) data are fundamental for optimizing energy use and managing industrial operations. However, with the widespread adoption of data-driven technologies in the energy sector, maintaining the integrity and quality of these data has become a significant challenge. Missing or incomplete data, often caused by equipment failures or communication disruptions, can severely affect the accuracy and reliability of data analyses, ultimately leading to poor decision-making and increased operational costs. To address this, we propose a Robust Momentum-Enhanced Non-Negative Tensor Factorization (RMNTF) model, which integrates three key innovations. First, the model utilizes adversarial loss and L 2 regularization to enhance its robustness and improve its performance when dealing with incomplete data. Second, a sigmoid function is employed to ensure that the results remain non-negative, aligning with the inherent characteristics of PC data and improving the quality of the analysis. Finally, momentum optimization is applied to accelerate the convergence process, significantly reducing computational time. Experiments conducted on two publicly available PC datasets, with data densities of 6.65% and 4.80%, show that RMNTF outperforms state-of-the-art methods, achieving an average reduction of 16.20% in imputation errors and an average improvement of 68.36% in computational efficiency. These results highlight the model's effectiveness in handling sparse and incomplete data, ensuring that the reconstructed data can support critical tasks like energy optimization, smart grid maintenance, and predictive analytics. [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]

In this study, a new technique for active control of the flow around a corner is proposed and a key parameter dominating the flow deflection angle is proposed. In the technique, a microjet array is used for controlling the deflection of the flow at 33 m/s ~ 54 m/s around the 25-degree corner with a small downstream-facing step, the surface of which is lined with the micro-orifices from which jets are injected into the flow. The flow velocities around the corner are measured using a PIV (particle image velocimetry) technique under each condition for injecting the microjets into the flow. The results reveal that a vortex is produced by the microjet array and the flow past the corner is pulled into the low-pressure region near the vortex core, i.e., the flow that has passed the corner deflects downward to the vortex. The results also reveal that the flow deflection angle increases with the supply pressure, i.e., the deflection angle increases with the jet Mach number. In addition, a parameter in the form of a momentum coefficient is introduced for data reduction by considering that the flow deflection is induced by a Rankine's combined vortex. As a result, the relationship between the momentum coefficient and the streamline slope is expressed by a single linear relation regardless of the flow speed, which suggests that the flow deflection angle is controllable precisely by using the microjet array. [ABSTRACT FROM AUTHOR]

In a finite buffer queuing system, the congestion mostly occurs due to the higher blocking probability. In this article, the author has presented a Modified Bulk Service (MBS) rule for the finite buffer queueing system under assumptions that the server can accept a customer during ongoing service if serving batch size is lower; however, the time spent in serving the lower batch size is elapsed. Various performance metrics are discussed. [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]

This research aims to describe and analyze the Implementation of Gender Mainstreaming (PUG) in Supporting Sustainable Development in Kotamobagu City. This research uses a descriptive qualitative approach, with 10 predetermined informants with data collection techniques namely observation, interviews and documentation with data analysis techniques through data reduction, data presentation and verification conclusion drawing. The results showed that the implementation of gender mainstreaming in Kotamobagu City faces significant challenges in various aspects that have an impact on its not optimal implementation. From the commitment sub-focus, limited understanding of the apparatus and inadequate budget allocations cause gender responsive policies and programs to be unevenly distributed at all levels of government, so that vulnerable groups such as people with disabilities and people with economic limitations have not been optimally accommodated. In the policy sub-focus, the lack of capacity of government officials and the unequal distribution of resources, especially in remote areas, further exacerbates the gap between policies and their implementation in the field. After that, in the resource sub-focus, limited time and budget for continuous training hinder the strengthening of apparatus competencies in integrating gender perspectives into the planning and implementation of development programs. In addition, the disaggregated data management subfocus is still constrained by inadequate information systems and weak coordination between institutions, so that existing data has not been fully utilized to design gender-based policies. Therefore, it is necessary to strengthen commitment, increase human resource capacity, effective data management systems, and cross-sector coordination to support inclusive sustainable development in Kotamobagu City. [ABSTRACT FROM AUTHOR]

The national population administration policy that has been determined (formulation) by the government, especially the service policy for issuing Electronic Identity Cards (e-KTP) for Indonesian citizens and foreigners residing in Indonesia must be implemented properly and by the implementor. The implementor who is given the task and responsibility is the population and civil registry office. This research was conducted with the aim of analyzing and explaining the implementation of population administration order policies, especially the issuance of electronic identity cards which are focused on aspects or variables of communication, resources, disposition, and organizational structure at the Population and Civil Registration Office of Bolaang Mongondow Regency. This research uses a naturalistic (qualitative) approach. Informants who were used as data sources were determined purposively (purposive sampling) and snowball sampling. Data collection techniques were interviews, observations, and documents. Data analysis is an interactive qualitative analysis with data analysis activities, namely data reduction, data display, and conclution drawing / verification The results showed that the implementation of the Electronic Identity Card (e-KTP) issuance service policy for Indonesian citizens and foreigners residing in Indonesia as the focus and sub-focus of the research has not been implemented as it should. For this reason, it is recommended that the various variables that are the focus and sub-focus of this research as variables that determine the implementation of public policy need to be optimized both in terms of quantity and quality. [ABSTRACT FROM AUTHOR]

This work presents the first step in the development of the VISION toolkit, a set of Python tools that allows easy, efficient, and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources from being used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, are sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing, and handling a large number of highly temporally resolved model files, resulting in a process which is data-, labour-, and time-intensive. In this paper we focus on comparison between model data and in situ observations (from aircraft, ships, buoys, sondes, etc.). A standalone code, In-Situ Observations Simulator, or ISO_simulator for short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. These model data are then written to NetCDF files that can be efficiently archived due to their small sizes and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in situ data. By interpolating global gridded hourly files onto observation locations, we reduce data output for a typical climate resolution run, from ∼3 Gb per model variable per month to ∼15 Mb per model variable per month (a 200-times reduction in data volume). The VISION toolkit is relatively fast to run and can be automated to process large volumes of data at once, allowing efficient data analysis over a large number of years. Although this code was initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models. [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]

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]

This article presents an investigation of the use of machine learning methodologies for the prediction of surface roughness in milling operations, using sensor data as the primary source of information. The sensors, which included current transformers, a microphone, and displacement sensors, captured comprehensive machining signals at a frequency of 10 kHz. The signals were subjected to preprocessing using the Savitzky–Golay filter, with the objective of isolating relevant moments of active material machining and reducing noise. Two machine learning models, namely Elastic Net and neural networks, were employed for the prediction purposes. The Elastic Net model demonstrated effective handling of multicollinearity and reduction in the data dimensionality, while the neural networks, utilizing the ReLU activation function, exhibited the capacity to capture complex, nonlinear patterns. The models were evaluated using the coefficient of determination (R²), which yielded values of 0.94 for Elastic Net and 0.95 for neural networks, indicating a high degree of predictive accuracy. These findings illustrate the potential of machine learning to optimize manufacturing processes by facilitating precise predictions of surface roughness. Elastic Net demonstrated its utility in reducing dimensionality and selecting features, while neural networks proved effective in modeling complex data. Consequently, these methods exemplify the efficacy of integrating data-driven approaches with robust predictive models to improve the quality and efficiency of surface. [ABSTRACT FROM AUTHOR]

Finite mixture of Gaussians are often used to classify two- (units and variables) or three- (units, variables and occasions) way data. However, two issues arise: model complexity and capturing the true cluster structure. Indeed, a large number of variables and/or occasions implies a large number of model parameters; while the existence of noise variables (and/or occasions) could mask the true cluster structure. The approach adopted in the present paper is to reduce the number of model parameters by identifying a sub-space containing the information needed to classify the observations. This should also help in identifying noise variables and/or occasions. The maximum likelihood model estimation is carried out through an EM-like algorithm. The effectiveness of the proposal is assessed through a simulation study and an application to real data. [ABSTRACT FROM AUTHOR]

Spectroscopic data allows for the obtaining of relevant information about the composition of samples and has been used for research in scientific disciplines such as chemistry, geology, archaeology, Mars research, pharmacy, and medicine, as well as important industrial use. In archaeology, it allows the characterization and classification of artifacts and ecofacts, the analysis of patterns, the characterization and study of the exchange of materials, etc. Spectrometers provide a large amount of data, the so-called "big data" type, which requires the use of multivariate statistical techniques, mainly principal component analysis, cluster analysis, and discriminant analysis. This work is focused on reducing the dimensionality of the data by selecting a small subset of variables to characterize the samples and presents a mathematical methodology for the selection of the most efficient variables. The objective is to identify a subset of variables based on spectral features that allow characterization of the samples under study with the least possible errors when performing quantitative analyses or discriminations between different samples. The subset is not predetermined and, in each case, is obtained for each set of samples based on the most important features of the samples under study, which allows for a good fit to the data. The reduction of the number of variables to an important performance based on the previously chosen difference between features, with a great fit to the raw data. Thus, instead of 2151 variables, a minimum optimal subset of 32 valleys and 31 peaks is obtained for a minimum difference between peaks or between valleys of 20 nm. This methodology has been applied to a sample of minerals and rocks extracted from the ECOSTRESS 1.0 spectral library. [ABSTRACT FROM AUTHOR]

In this work, an optical asymmetric scheme for image encryption and authentication is proposed. Our proposal uses an information authentication process for phase encrypted data in the Collins diffraction domain. A partial phase component of the optical image encrypted is used in the decryption stage to validate the grayscale encrypted data. Meanwhile, the use of phase component will be facilitating the design and implementation of optical encryption schemes. The limited phase data makes the scheme more secure owing to difficult reorganization of the confidential information. In addition to security increases, a reduction of encrypted data is achieved by selecting some parts of the phase component of the encrypted data for the decryption process. Therefore, this development strategy efficiently facilitates optical information transfer and storage. Numerical simulations verify the resistance of the system against noise, cropping attacks, and potential attacks. [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]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Motivation: As data sets increase in size and complexity with advancing technology, flexible and interpretable data reduction methods that quantify information preservation become increasingly important. Results: Super Partition is a large-scale approximation of the original Partition data reduction algorithm that allows the user to flexibly specify the minimum amount of information captured for each input feature. In an initial step, Genie, a fast, hierarchical clustering algorithm, forms a super-partition, thereby increasing the computational tractability by allowing Partition to be applied to the subsets. Applications to high dimensional data sets show scalability to hundreds of thousands of features with reasonable computation times. Availability and implementation: Super Partition is a new function within the partition R package, available on the CRAN repository (https://cran.r-project.org/web/packages/partition/index.html). [ABSTRACT FROM AUTHOR]

Boolean Matrix Factorization (BMF) helps unveil hidden patterns in boolean datasets and is a powerful tool in machine learning. However, when dealing with large datasets, reducing data size becomes crucial for BMF algorithms. In this paper, we revisit and propose novel data reduction approaches for BMF algorithms based on Formal Concept Analysis (FCA), aiming to minimize the impact of data reduction on factor quality. Specifically, we introduce the concept of intent vectors , and present incremental algorithms along with their associated theorems for capturing and quantifying these vectors, thereby facilitating a reduction in data size. More importantly, we propose two innovative approaches based on FCA principles that effectively identify and eliminate redundant rows in datasets through distinct deletion strategies. The first approach incrementally deletes rows while preserving the intent vectors of attribute concepts, thus maintaining the quality of factors. The second approach progressively removes rows from the reduced dataset by the first approach, by gradually adjusting the amount of concept loss to minimize any degradation in factor quality. Experiments demonstrate that our first reduction algorithm significantly decreases data size without degrading factor quality, consistently outperforming current leading algorithms with a 100 % success rate. Our second algorithm outperformed the existing algorithm in 72 out of 96 comparisons, greatly reducing data size with minimal loss in factor quality. [ABSTRACT FROM AUTHOR]

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Abstract Smart grazing is a relatively difficult field of digital agriculture. Restricted by the geographical conditions of pastures, poor network infrastructure and low economic output, conventional IoT systems are difficult to apply in the field of grazing. In this paper, we propose the Space-Air-Ground integrated Grazing IoT(SAG-GIoT) system based on the background of yak grazing production in the Qinghai-Tibet Plateau, and define three smart grazing management application scenarios: (1) daily grazing supervision, (2) UAV grazing, (3) searching for yaks. To this end, we have designed the three-tier technical architecture of SAG-GIoT, and developed collar, base station and grazing management system. We designed the all-terrain network service scheme with the BeiDou Satellite-Base Station Sender(BDS-BSS) and Small Base Stations(SBSs), and verified the daily grazing supervision test in long-term. UAV grazing test was carried out in pasture, and a flexible communication networking was realized through the UAV Based Sation(UAV-BS). With the guidance of UAV searching and APP positioning, taking Handled Base Stations(HBSs) in hand, we quickly and accurately find the lost yaks. SAG-GIoT system is characterized as low cost, flexible deployment and global service, and has broad application prospects.

Data classification and feature/attribute selection approaches play important role in enabling organizations to extract meaningful insights from vast and complex datasets. Besides, the accuracy and processing time are two parameters of interest to determine which approach is favourable or suitable for enormous data. Moreover, the presence of redundant, incomplete, noisy and inconsistent data made more concern to accuracy and computational resources. The issue of incomplete data is addressed in limited studies due to its complexities, particularly on data classification and accuracy as well as attribute selection. The limited tolerance relation between objects is the favourable approach used in this scenario. However, the accuracy and the data classification rate need to be improved. In this paper, a new approach called extended limited tolerance relation with the similarity precision among objects to improve the data classification with high accuracy will be presented and the feature/attribute selection is performed using conditional entropy. Comparative analysis and experiment result between the proposed approach with limited tolerance relation approach in terms of data classification and accuracy are presented. The proposed approach comparatively improved the accuracy with better data classification rate and feature selection while preserving the consistency of the information in incomplete information systems that is worthy of attention.

Clustering a graph when the clusters can overlap can be seen from three different angles: We may look for cliques that cover the edges of the graph with bounded overlap, we may look to add or delete few edges to uncover the cluster structure, or we may split vertices to separate the clusters from each other. Splitting a vertex v means to remove it and to add two new copies of v and to make each previous neighbor of v adjacent with at least one of the copies. In this work, we study underlying computational problems regarding the three angles to overlapping clusterings, in particular when the overlap is small. We show that the above-mentioned covering problem is NP -complete. We then make structural observations that show that the covering viewpoint and the vertex-splitting viewpoint are equivalent, yielding NP-hardness for the vertex-splitting problem. On the positive side, we show that splitting at most k vertices to obtain a cluster graph has a problem kernel with O (k) vertices. Finally, we observe that combining our hardness results with structural observations and a so-called critical-clique lemma yields a simple alternative NP-hardness proof for the Cluster Editing With Vertex Splitting problem, where we add or delete edges and split vertices to obtain a cluster graph. [ABSTRACT FROM AUTHOR]