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AbstractSynchronous molecular fluorescence with multivariate calibration was investigated to assay seven polycyclic aromatic hydrocarbons (PAHs) and three oxygenated derivatives (O-PAHs) in printer toner. Great sensitivity and a good data compression capacity were achieved; therefore, a relatively small number of factors were used. Correlation coefficients above 0.95 for the calibration functions were obtained, and recoveries lie between 70 and 130% in analyzing synthetic samples (13 PCs). For the real samples assayed, recoveries between 81 and 125% were obtained when compared to the results obtained by gas chromatography – tandem mass spectrometry (GC-MS/MS). Despite a certain loss in accuracy when compared to chromatography, the proposed methodology is faster, simpler, and lower in cost. The multivariate tool proved to be an expeditious alternative for screening toner in relation to the presence and abundance of PAHs and derivatives. [ABSTRACT FROM AUTHOR]

We explore an error‐bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data, methodologies tailored for unstructured mesh data are lacking; for example, one can compress nodal data as 1D arrays, neglecting the spatial coherency of the mesh nodes. Inspired by the SZ compressor, which predicts and quantizes values in a multidimensional array, we dynamically reorganize nodal data into sequences. Each sequence starts with a seed cell; based on a predefined traversal order, the next cell is added to the sequence if the current cell can predict and quantize the nodal data in the next cell with the given error bound. As a result, one can efficiently compress the quantized nodal data in each sequence until all mesh nodes are traversed. This paper also introduces a suite of novel error metrics, namely continuous mean squared error (CMSE) and continuous peak signal‐to‐noise ratio (CPSNR), to assess compression results for unstructured mesh data. The continuous error metrics are defined by integrating the error function on all cells, providing objective statistics across nonuniformly distributed nodes/cells in the mesh. We evaluate our methods with several scientific simulations ranging from ocean‐climate models and computational fluid dynamics simulations with both traditional and continuous error metrics. We demonstrated superior compression ratios and quality than existing lossy compressors. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency. [ABSTRACT FROM AUTHOR]

Three-dimensional mesh compression is vital to support advances in many scenarios, such as 3D web-based applications. Existing 3D mesh methods usually require complex data structures and time-consuming processing. Given a mesh represented by its vertices and triangular faces, we present a novel, fast, and straightforward encoding algorithm. Our method encodes the mesh connectivity data based on an upper triangular matrix which is easily recovered by its underlying decoding process. Our technique encodes the mesh edges in linear time without losing any face in the process. Results show that our method provides a connectivity compression rate of 55.29 and an average total compression rate of 27.09. Furthermore, our approach achieves, on average, a similar compressing rate of state-of-the-art algorithms, such as OpenCTM, which considers geometry and connectivity, while our approach considers only their connectivity. [ABSTRACT FROM AUTHOR]

Porous media such as aerogels can exhibit unique properties including low thermal conductivity, low bulk density, and low sound velocity. However, the limited mechanical properties of aerogels restrict their widespread application. This study focuses on understanding the mechanical behavior of aggregated silica aerogels by investigating their microstructural connectivity and densification mechanisms under uniaxial compression. The interparticle connectivity is generated using the diffusion‐limited cluster–cluster aggregation (DLCA) algorithm, and the particle connections are modeled by beam elements that account for contact interaction. The mechanical response of representative volume elements (RVEs) is analyzed in both linear and nonlinear regimes while applying periodic boundary conditions. The model is correlated with experimental compression test data to validate the simulation results. With increasing compressive strain, load transitions between multiple backbone paths appear in the network structure. Thus, the simulation model provides insight into the compression process. Moreover, the simulation model enables the examination of the influence of various model parameters and facilitates the evaluation of the power–law relationship between the elasticity modulus and porosity of aerogels. [ABSTRACT FROM AUTHOR]

In this study, the Discrete Element Method (DEM) was employed as a computational approach to analyse the bulk behaviour of wheat (Triticum aestivum L., cv. 'Pionier') under compressive loading. A laboratory compression apparatus was utilised as a basis for the acquisition of data from compression tests. To ensure accurate DEM predictions, the physical, mechanical, and interaction properties of wheat kernels were experimentally determined for two different moisture contents (12.4% and 25.4% w.b.). Five particle models were constructed to represent the geometric shape of the kernel, with the model composed of five sub-spheres identified as the most suitable, ensuring an optimal balance between computational complexity and simulation accuracy. The bulk compression results from DEM simulations were compared to experimental data, revealing a good agreement for volumetric strain, bulk density increase, and bulk compressibility within the specified range of applicability. Simulations provided valuable insights into temporal and spatial variations of forces and deformations acting on individual particles, thereby enhancing the understanding of bulk behaviour at different compression levels. The moisture content was found to significantly affect the particle compressive forces, deformation capabilities, and bulk compression characteristics. In conclusion, this study demonstrated the great potential of DEM in predicting the bulk behaviour of wheat under compression, providing valuable information for practical storage and handling processes. • Bulk behaviour of wheat during compressive loading was studied using DEM. • DEM-relevant modelling parameters were determined experimentally. • Bulk volumetric strain, bulk density and compressibility were investigated. • Mechanical response of particles under bulk compression was analysed. • Moisture content effect on product properties and compression characteristics was examined. [ABSTRACT FROM AUTHOR]

Future surveys could obtain tighter constraints on the cosmological parameters with the galaxy power spectrum than with the cosmic microwave background. However, the inclusion of multiple overlapping tracers, redshift bins, and more non-linear scales means that generating the necessary ensemble of simulations for model-fitting presents a computational burden. In this work, we combine full-shape fitting of galaxy power spectra, analytical covariance matrix estimates, the massively optimized parameter estimation and data compression (MOPED) method, and the Taylor expansion interpolation of the power spectrum for the first time to constrain the cosmological parameters directly from a state-of-the-art set of galaxy clustering measurements. We find it takes less than a day to compute the analytical covariance while it takes several months to calculate the simulated ones. Combining MOPED with the Taylor expansion interpolation of the power spectrum, we can constrain the cosmological parameters in just a few hours instead of a few days. We also find that even without a priori knowledge of the best-fitting cosmological or galaxy bias parameters, the analytical covariance matrix with the MOPED compression still gives consistent cosmological constraints to within 0.1σ after two iterations. Therefore, the pipeline we have developed here can significantly speed up the analysis for future surveys. [ABSTRACT FROM AUTHOR]

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics. [ABSTRACT FROM AUTHOR]

Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications. [ABSTRACT FROM AUTHOR]

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process, and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and, therefore, hindering its broad adoption. In this paper, we present Genie, to the best of our knowledge the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder. Genie, an open-source encoder compliant with ISO/IEC's MPEG-G standard, delivers state-of-the-art genomic data compression ratios and guarantees long-term data sustainability and decodability through interoperability. [ABSTRACT FROM AUTHOR]

As an important method of image processing, image differentiation enables edge detection of targets to achieve recognition of object features and information compression and the computation speed can be boosted by optical information techniques. Traditional optical image differentiation methods mainly rely on spatial spectrum filtering by using classical 4f system, while some work focus on 1D or single‐direction differentiation. It is only in recent years that the rapid development of metasurfaces has facilitated image differentiation methods. In this work, a Laplace operation device is demonstrated based on a silicon hollow brick dielectric resonant metasurface for incident light field. The optical transfer function (OTF) required by the optical Laplace operation can be obtained by exciting an angularly selective toroidal dipole (TD) resonance supported by the metasurface. This hollow silicon brick differentiator not only realizes 2D second‐order edge detection but also has a numerical aperture close to 0.4 and a broad working wavelength range >100 nm and can be directly integrated with imaging systems. Such metadevice may be potentially applied in the fields of optical sensing, microscopy, machine vision, biomedical imaging, etc. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) human mesh sequences obtained by 3D scanning equipment are often used in film and television, games, the internet, and other industries. However, due to the dense point cloud data obtained by 3D scanning equipment, the data of a single frame of a 3D human model is always large. Considering the different topologies of models between different frames, and even the interaction between the human body and other objects, the content of 3D models between different frames is also complex. Therefore, the traditional 3D model compression method always cannot handle the compression of the 3D human mesh sequence. To address this problem, we propose a sequence compression method of 3D human mesh sequence based on the Laplace operator, and test it on the complex interactive behavior of a soccer player bouncing the ball. This method first detects the mesh separation degree of the interactive object and human body, and then divides the sequence into a series of fragments based on the consistency of separation degrees. In each fragment, we employ a deformation algorithm to map keyframe topology to other frames, to improve the compression ratio of the sequence. Our work can be used for the storage of mesh sequences and mobile applications by providing an approach for data compression. [ABSTRACT FROM AUTHOR]

Motivation The quality scores data (QSD) account for 70% in compressed FastQ files obtained from the short and long reads sequencing technologies. Designing effective compressors for QSD that counterbalance compression ratio, time cost, and memory consumption is essential in scenarios such as large-scale genomics data sharing and long-term data backup. This study presents a novel parallel lossless QSD-dedicated compression algorithm named PQSDC, which fulfills the above requirements well. PQSDC is based on two core components: a parallel sequences-partition model designed to reduce peak memory consumption and time cost during compression and decompression processes, as well as a parallel four-level run-length prediction mapping model to enhance compression ratio. Besides, the PQSDC algorithm is also designed to be highly concurrent using multicore CPU clusters. Results We evaluate PQSDC and four state-of-the-art compression algorithms on 27 real-world datasets, including 61.857 billion QSD characters and 632.908 million QSD sequences. (1) For short reads, compared to baselines, the maximum improvement of PQSDC reaches 7.06% in average compression ratio, and 8.01% in weighted average compression ratio. During compression and decompression, the maximum total time savings of PQSDC are 79.96% and 84.56%, respectively; the maximum average memory savings are 68.34% and 77.63%, respectively. (2) For long reads, the maximum improvement of PQSDC reaches 12.51% and 13.42% in average and weighted average compression ratio, respectively. The maximum total time savings during compression and decompression are 53.51% and 72.53%, respectively; the maximum average memory savings are 19.44% and 17.42%, respectively. (3) Furthermore, PQSDC ranks second in compression robustness among the tested algorithms, indicating that it is less affected by the probability distribution of the QSD collections. Overall, our work provides a promising solution for QSD parallel compression, which balances storage cost, time consumption, and memory occupation primely. Availability and implementation The proposed PQSDC compressor can be downloaded from https://github.com/fahaihi/PQSDC. [ABSTRACT FROM AUTHOR]

In this work, we have used the standard symmetric key block cipher data encryption algorithm RC5(32,16,8) for the encryption of digital greyscale images. For this, we have only considered the nonzero elements of the sparse matrix of the images which have been generated using the sparse representation technique. The security strength of the proposed technique is verified through different quality parameters, e.g. information entropy, correlation coefficients, NPCR, UACI, and NIST test. Our proposed algorithm achieved the maximum information entropy value of 7.9977 for the cameraman image and nearly zero correlation coefficient values establish the robustness of the cryptosystem. The highest NPCR and UACI values obtained in our work are 99.6916 and 33.9605, which are closer to the maximum theoretical values. The proposed technique is also compared with some of the contemporary works to validate the credibility of our proposed algorithm. [ABSTRACT FROM AUTHOR]

To improve the efficiency of multi-coil data compression and recover the compressed image reversibly, increasing the possibility of applying the proposed method to medical scenarios. A deep learning algorithm is employed for MR coil compression in the presented work. The approach introduces a variable augmentation network for invertible coil compression (VAN-ICC). This network utilizes the inherent reversibility of normalizing flow-based models. The aim is to enhance the readability of the sentence and clearly convey the key components of the algorithm. By applying the variable augmentation technology to image/k-space variables from multi-coils, VAN-ICC trains the invertible network by finding an invertible and bijective function, which can map the original data to the compressed counterpart and vice versa. Experiments conducted on both fully-sampled and under-sampled data verified the effectiveness and flexibility of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep learning-based approaches demonstrated that VAN-ICC carries much higher compression effects. The proposed method trains the invertible network by finding an invertible and bijective function, which improves the defects of traditional coil compression method by utilizing inherent reversibility of normalizing flow-based models. In addition, the application of variable augmentation technology ensures the implementation of reversible networks. In short, VAN-ICC offered a competitive advantage over other traditional coil compression algorithms. [ABSTRACT FROM AUTHOR]

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to develop a wearable system for providing CNC machine operators with visual and tactile perception of triaxial cutting forces, thereby assisting operators in industrial environments to enhance work efficiency and prevent mechanical failures. To achieve this goal, we successfully integrated a virtual machining tool simulator with the remote-control wearable system (RCWS). Using the 'King Path' milling parameters, we employed the simulation software developed by the AIM-HI team to calculate static and dynamic cutting forces, converting this data into vibrational commands for the RCWS to generate corresponding tactile feedback. Furthermore, we conducted extensive experiments, testing various data conversion methods, including three sampling techniques and two data compression strategies, aiming to provide accurate tactile feedback related to cutting forces under different operating conditions. [ABSTRACT FROM AUTHOR]

The Polar Qualification System (PQS) was applied on hue spectra fingerprinting to describe color changes in tomato during storage. The cultivar 'Pitenza' was harvested at six different maturity stages, and half of the samples were subjected to gaseous 1-methylcyclopropene (1-MCP) treatment. Reference color parameters were recorded with a vision system colorimeter instrument, and the fruit pigment concentration was assessed with the DA-index®. Additionally, acoustic firmness (Stiffness) was measured. All acquired reference parameters were used to grade fruit in the supply chain. The applied 1-MCP treatments were used to control the ripening of climacteric horticultural produce. Both the DA-index® and stiffness values, presented as chlorophyll concentration and acoustic firmness, showed significant differences among maturity stages and treated and control samples and in their kinetics during storage. The machine vision parameter PQS-X was significantly affected by 1-MCP treatment (F = 10.18, p < 0.01), while PQS-Y was primarily affected by storage time (F = 18.18, p < 0.01) and maturity stage (F = 11.15, p < 0.01). A significant correlation was achieved for acoustic firmness with normalized color (r > 0.78) and PQS-Y (r > 0.80), as well as for the DA-index® (r > 0.9). The observed color changes agreed with the reference measurements. The significant statistical effect on the PQS coordinates suggests that hue spectra fingerprinting with this data compression technique is suitable for quality assessment based on color. [ABSTRACT FROM AUTHOR]

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation. [ABSTRACT FROM AUTHOR]

DLSIA (Deep Learning for Scientific Image Analysis) is a Python‐based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy‐to‐use architectures, such as autoencoders, tunable U‐Nets and parameter‐lean mixed‐scale dense networks (MSDNets). Additionally, this article introduces sparse mixed‐scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA‐instantiated networks and training scripts are employed in multiple applications, including inpainting for X‐ray scattering data using U‐Nets and MSDNets, segmenting 3D fibers in X‐ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis. [ABSTRACT FROM AUTHOR]

High-quality cardiopulmonary resuscitation (CPR) and training are important for successful revival during out-of-hospital cardiac arrest (OHCA). However, existing training faces challenges in quantifying each aspect. This study aimed to explore the possibility of using a three-dimensional motion capture system to accurately and effectively assess CPR operations, particularly about the non-quantified arm postures, and analyze the relationship among them to guide students to improve their performance. We used a motion capture system (Mars series, Nokov, China) to collect compression data about five cycles, recording dynamic data of each marker point in three-dimensional space following time and calculating depth and arm angles. Most unstably deviated to some extent from the standard, especially for the untrained students. Five data sets for each parameter per individual all revealed statistically significant differences (p < 0.05). The correlation between Angle 1′ and Angle 2′ for trained (rs = 0.203, p < 0.05) and untrained students (rs = −0.581, p < 0.01) showed a difference. Their performance still needed improvement. When conducting assessments, we should focus on not only the overall performance but also each compression. This study provides a new perspective for quantifying compression parameters, and future efforts should continue to incorporate new parameters and analyze the relationship among them. [ABSTRACT FROM AUTHOR]

Network traffic prediction is an important network monitoring method, which is widely used in network resource optimization and anomaly detection. However, with the increasing scale of networks and the rapid development of 5-th generation mobile networks (5G), traditional traffic forecasting methods are no longer applicable. To solve this problem, this paper applies Long Short-Term Memory (LSTM) network, data augmentation, clustering algorithm, model compression, and other technologies, and proposes a Cluster-based Lightweight PREdiction Model (CLPREM), a method for real-time traffic prediction of 5G mobile networks. We have designed unique data processing and classification methods to make CLPREM more robust than traditional neural network models. To demonstrate the effectiveness of the method, we designed and conducted experiments in a variety of settings. Experimental results confirm that CLPREM can obtain higher accuracy than traditional prediction schemes with less time cost. To address the occasional anomaly prediction issue in CLPREM, we propose a preprocessing method that minimally impacts time overhead. This approach not only enhances the accuracy of CLPREM but also effectively resolves the real-time traffic prediction challenge in 5G mobile networks. [ABSTRACT FROM AUTHOR]

The classical JPEG compression is a rich source of cues for forensic image analysis. However, this compression standard will in the near future be complemented by a new, highly efficient learning-based compression standard called JPEG AI. JPEG AI is fundamentally different from classical JPEG. Hence, its forensic traces can also be expected to be fundamentally different. We argue that there is a pressing need for image forensics research to investigate these traces. In this work, we characterize forensic compression traces of different AI compression algorithms. Our analysis investigates AI compression artifacts in frequency domain and in spatial domain. Both domains exhibit similar artifacts that likely stem from upsampling operations of the decoders. Additionally, we report for one AI codec another artifact in homogeneous regions. We also investigate the artifact detectability in several scenarios including unseen AI compression traces and postprocessing. Here, frequency and autocorrelation features are better on additive noise and classical JPEG post-compression, while RGB features perform better on blurred and downsampled images. [Display omitted] • Characterization of forensic cues of different AI-based image codecs. • Frequency domain and autocorrelation exhibit periodic artifacts. • Artifacts stem likely from upsampling operations and homogeneous regions. • Experiments show that these artifacts are well detectable with deep neural networks. • Further experiments show that detection generalization of artifacts is challenging. [ABSTRACT FROM AUTHOR]

Hydrocarbon polymers are used in a wide variety of practical applications. In the field of dynamic compression at extreme pressures, these polymers are used at several high energy density (HED) experimental facilities. One of the most common polymers is poly(methyl methacrylate) or PMMA, also called Plexiglass® or Lucite®. Here, we present high-fidelity, hundreds of GPa range experimental shock compression data measured on Sandia's Z machine. We extend the principal shock Hugoniot for PMMA to more than threefold compression up to 650 GPa and re-shock Hugoniot states up to 1020 GPa in an off-Hugoniot regime, where experimental data are even sparser. These data can be used to put additional constraints on tabular equation of state (EOS) models. The present results provide clear evidence for the need to re-examine the existing tabular EOS models for PMMA above ∼120 GPa as well as perhaps revisit EOSs of similar hydrocarbon polymers commonly used in HED experiments investigating dynamic compression, hydrodynamics, or inertial confinement fusion. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases. [ABSTRACT FROM AUTHOR]

A lossless data compression code based on a binary-coded ternary number representation is investigated. The code is complete, universal, synchronizable, and supports a direct search in a compressed file. A simple monotonous encoding and very fast decoding algorithms are constructed owing to code properties. Experiments show that in natural language text compression, the new code outperforms byte-aligned codes SCDC and RPBC either in compression ratio or in decoding speed. [ABSTRACT FROM AUTHOR]

In this work we propose a low rank approximation of areal, particularly three dimensional, data utilizing additional weights. This way we enable effective compression if additional information indicates that parts of the data are of higher interest than others. The guiding example are high fidelity finite element simulations of an abdominal aortic aneurysm, i.e. a deformed blood vessel. The additional weights encapsulate the areas of high stress, which we assume indicates the rupture risk of the aorta. The stress values on the grid are modeled as a Gaussian Markov random field and we define our approximation as a basis of vectors that solve a series of optimization problems. Each of these problems describes the minimization of an expected weighted quadratic loss. We provide an effective numerical heuristic to compute the basis under general conditions, which relies on the sparsity of the precision matrix to ensure acceptable computing time even for large grids. We explicitly explore two such bases on the surface of a high fidelity finite element grid and show their efficiency for compression. Finally, we utilize the approach as part of a larger model to predict the van Mises stress in areas of interest using low and high fidelity simulations. [ABSTRACT FROM AUTHOR]

The symmetric information bottleneck (SIB), an extension of the more familiar information bottleneck, is a dimensionality-reduction technique that simultaneously compresses two random variables to preserve information between their compressed versions. We introduce the generalized symmetric information bottleneck (GSIB), which explores different functional forms of the cost of such simultaneous reduction. We then explore the data set size requirements of such simultaneous compression. We do this by deriving bounds and root-mean-squared estimates of statistical fluctuations of the involved loss functions. We show that in typical situations, the simultaneous GSIB compression requires qualitatively less data to achieve the same errors compared to compressing variables one at a time. We suggest that this is an example of a more general principle that simultaneous compression is more data efficient than independent compression of each of the input variables. [ABSTRACT FROM AUTHOR]

Aeolian and kaolin deposits contribute greatly to infrastructural damage during the rainy seasons because of massive collapse settlements. The prediction of wetting-induced collapse potential and compressibility behavior under partly saturated states is essential for the development of infrastructure on these deposits. In this study, a general constitutive model was developed from the suction-controlled compression and wetting-induced collapse tests. The effect of particle orientation resulting from various initial compaction conditions, drying paths, and wetting paths on the yielding behavior of soil was investigated. A new collapsible soil model (CoSM) was presented by considering the wetting-induced changes to the clay fabric associations in the collapsible soils. The proposed CoSM requires eight parameters for evaluating the mechanical behavior. These model parameters can be readily estimated from simple compression tests, which is the major advantage of the model. The derived equations were capable of predicting three crucial mechanical characteristics, namely, loading-collapse yield, compression, and collapse behavior from the basic compression data. The model shows an excellent agreement with the measured data for two kaolin soils from the present work and several collapsible soils from the literature. The generalized model is capable of predicting mechanical behavior of collapsible soils with various initial compaction states and loading stress histories. [ABSTRACT FROM AUTHOR]

Jeffbenite (Mg3Al2Si3O12) is a tetragonal phase found in so far only in superdeep diamonds, and its thermoelastic parameters are a prerequisite for determining entrapment pressures as it is regarded as a potential indicator for superdeep diamonds. In this study, the thermoelastic properties of synthetic Fe3+‐jeffbenite were measured up to 33.7 GPa and 750 K. High‐temperature static compression data were fitted, giving (∂KT0/∂T)P = −0.0107 (4) GPa/K and αT = 3.50 (3) × 10−5 K−1. The thermoelastic properties and phase stability are applied to modeling isomekes, or P‐T paths intersecting possible conditions of entrapment in diamond. We calculate that under ideal exhumation, jeffbenite entrapped at mantle transition zone conditions will exhibit a high remnant pressure at 300 K (Pinc) of ∼5.0 GPa. Elastic geobarometry on future finds of jeffbenite inclusions can use the new equation of state to estimate entrapment pressures for this phase with still highly uncertain stability field in the mantle. Plain Language Summary: Ongoing superdeep diamonds research is providing new insights into the Earth's deep mantle. Natural superdeep diamonds and its inclusions can show compelling evidence for retrograde conversion from the lower mantle or transition zone precursors; along with carbonate melt‐peridotite reactions. Jeffbenite with a composition of Mg3Al2Si3O12, found in so far only in superdeep diamonds can be regarded as a potential indicator mineral for superdeep diamonds. Recent synthesis of Fe3+‐rich jeffbenite provides an opportunity for in‐situ measurements to study the thermodynamic properties of jeffbenite at deep‐mantle conditions. Thus, in this study, we explored the high pressure and temperature stability and thermoelastic properties of Fe‐bearing jeffbenite up to 33.7 GPa and 750 K. The thermoelastic data and phase stability were measured and the results are applied to modeling the isomekes, or P‐T paths intersecting possible conditions of entrapment and along which the pressure on the inclusion is equal to the external pressure on the diamond host. Our finding can be applied to determining entrapment pressures in such diamond inclusions in future finds and its primary or retrograde history is essential in understanding mantle dynamics and the hidden consequences of plate tectonics. Key Points: We measure the phase stability and thermoelastic properties on synthetic Fe‐bearing jeffbeniteThe thermoelastic data are applied to modeling isomekes and the remnant pressure (Pinc) is determinedThe smaller thermal expansivity of jeffbenite gives rise to a broader pressure and temperature stability field in the upper lower mantle [ABSTRACT FROM AUTHOR]

We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that incorporates several functionalities enhancing its performance and applicability for both industrial and academic studies. The code has been re-designed with a focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. This new version includes several major improvements: magnetisation vector inversion, inversion of multi-component magnetic data, wavelet compression, improved handling of topography with support for non-uniform grids, a new and efficient parallelisation scheme, a flexible parameter file, and optimised input–output operations. Extensive testing has been conducted on a large synthetic dataset and field data from a prospective area of the Eastern Goldfields (Western Australia) to explore new functionalities with a focus on inversion for magnetisation vectors and magnetic susceptibility, respectively. Results demonstrate the effectiveness of Tomofast-x 2.0 in real-world studies in terms of both the recovery of subsurface features and performances on shared and distributed memory machines. Overall, with its updated features, improved capabilities, and performances, the new version of Tomofast-x provides a free open-source, validated advanced and versatile tool for constrained gravity and magnetic inversion. [ABSTRACT FROM AUTHOR]

Effective compression of point clouds is essential for implementing virtual and mixed reality applications, which require encoding millions or even tens of millions of points. This paper offers a new geometric compression for point clouds based on sparse cascaded residuals and sparse attention. A sparse cascaded residual module is posited to connect multiple residual modules through shortcuts, thereby augmenting the network's learning capacity and compression efficacy. The authors developed a sparse attention module to acquire global features by computing interdependencies among points, enhancing compression performance to a greater extent. Trade‐off parameters are employed to optimize the rate and distortion. The authors' method outperforms the state‐of‐the‐art open‐source method regarding rate‐distortion on the ShapeNet, ModelNet, and Microsoft Voxelized Upper Bodies datasets, with average bjøntegaard‐delta (BD)‐rate gains of −14.44% and −15.38%. [ABSTRACT FROM AUTHOR]

There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting. [ABSTRACT FROM AUTHOR]

Test data volume (TDV) is the main issue for lessening the test data volume for system-on-a-chip (SoC), lessening test time, average power, and peak power. Several optimization algorithms have been presented previously to decrease the TDV, but none of the algorithm provides sufficient results. To overcome these issues, a novel test data compression technique for CSA using hosted cuckoo optimization algorithm is proposed in this manuscript for lessening the test data volume during fault analysis. The conflicting bits volume is decreased to achieve the ratio of better compression with reduced test application time (TAT) including test data volume. The proposed algorithm achieves fast global searching capability. Hosted cuckoo optimization (HCOA) algorithm is used to find the optimal solution for the constrained problems. The proposed method is executed in MATLAB. The proposed method attains 23.84%, 27.94%, 32.84% higher compression ratio compared with the existing methods. [ABSTRACT FROM AUTHOR]

The object of research is the method and algorithm of arithmetic addition of binomial numbers generated by binary binomial counting systems. The lack of binomial arithmetic, in particular the operation of adding binary binomial numbers, in a certain way prevents their introduction into information systems and the construction of information and communication technologies based on them for combinatorial optimization, generation of combinatorial objects, data compression and encryption. In the framework of the proposed approach, instead of operating with binomial coefficients, only operations with their upper and lower parameters are carried out. At the same time, the weighting coefficients of binary binomial numbers, which are added to each other, are represented in the form of two-component tuples. Taking this into account, this paper presents an algorithm for binomial arithmetic addition using dynamic arrays. The main idea, which is included in the structure of the algorithm of binomial arithmetic addition based on dynamic arrays, is that the transition from a two-dimensional model of summation to a one-dimensional one is carried out. At the same time, only available, existing binomial coefficients are placed in the dynamic array. Accordingly, the search for binomial coefficients equal to or greater than the quantitative equivalent takes place in much smaller areas. In comparison with the algorithm based on matrix models, this quite significantly reduces the amount of time spent when performing the summation operation, and also reduces the requirements for the amount of memory required for placing two-component tuples of the assembly array. In the course of the research, a several-fold decrease in the number of machine cycles required to search for the necessary elements in the dynamic array was practically confirmed. This leads to an increase in the performance of the presented algorithm of binomial arithmetic addition based on dynamic arrays. In turn, this leads to the acceleration of solving information tasks of combinatorial optimization, generation of combinatorial objects, data compression and encryption, for the solution of which the operation of adding binary binomial numbers is used. [ABSTRACT FROM AUTHOR]

We evaluate pressure consistency of equations of state (EOS) for NaCl and Au at 300 K. The simultaneous measurements of unit-cell volumes (V) with ruby R1 line shifts (Δλ) in a helium (He) loaded diamond cell effectively remove potential systematic errors. Compression and decompression data were automatically collected at 1 sec interval, yielding a dense dataset with >8,000 (V, Δλ) pairs each for NaCl and Au. Solidification of He has noticeable effects on both V and Δλ (hence P). Only data up to ~14 GPa, or 6000 (V, P) pairs, can be considered hydrostatic within the resolution. The P–V data are fitted to the Rydberg-Vinet and the 3rd order Birch-Murnaghan EOS. Predicted pressures of these EOSs agree with those given by the Ruby2020 ruby scale to within ±0.05 GPa. Overall, pressures predicted by the Rydberg-Vinet EOS are in better agreement with the average of commonly used NaCl and Au pressure scales. [ABSTRACT FROM AUTHOR]

Ultra-long-distance and high-precision lightning location technology is an important means to realize low-cost and wide-area lightning detection. This paper carried out research on the high-precision location technology of very-low-frequency (VLF) lightning electromagnetic pulse based on the Asia-Pacific Lightning Location Network (APLLN) deployed in 2018. Two key technologies are proposed in this paper: one is the calculation method of signal arrival time using very-low-frequency lightning electromagnetic pulse waveform, and the other is the compression transmission technology of lightning electromagnetic pulse waveform based on a signal principal component analysis. The results of a comparison and evaluation of the improved APLLN with the ADTD system show that the APLLN has a relative location efficiency of 69.1% and an average location error within the network of 4.5 km. [ABSTRACT FROM AUTHOR]

Three-dimensional discrete element method (DEM) is employed to model the published calibration chamber test of a closed-ended displacement pile driven in sand. To achieve large chamber-to-pile and pile-to-particle size ratios for unbiased predictions, a high-performance graphics processing unit-powered DEM code is utilised in conjunction with the axisymmetric assumption and particle refinement method. The DEM parameters are first calibrated against the drained triaxial compression test data on Fontainebleau sand. Then, the DEM modelling of pile driving demonstrates quantitative agreement with the experimental counterpart in terms of cone resistance, stress contours, and distributions. [ABSTRACT FROM AUTHOR]

Self-supervised monocular depth prediction is a widely researched field that aims to provide a better scene understanding. However, most existing methods prioritize prediction accuracy over computation cost, which can hinder the deployment of these methods in real-world applications. Our objective is to propose a solution that efficiently compresses the depth map while maintaining a high level of accuracy for navigation purpose. The proposed method is an expansion of the work presented in N-QGN, which utilizes a quadtree representation for compression. This approach has already shown promising results, but we aim to improve it further by making it more accurate, faster, and easier to train. Therefore, we introduce a new method that directly predicts the quadtree structure, resulting in a more consistent prediction, and we revise the network architecture to be lighter and produce state-of-the-art accuracy results, depending on the data compression rate. The new implementation is also faster, making it more suitable for real-time applications. Experiments have been conducted on various scene configuration highlighting the capability of the method to efficiently predicting a reliable quadtree depth representation of the scene at low computation cost and high accuracy. • Proposed method compresses depth maps into quadtree while maintaining accuracy. • Network predicts quadtree structure and depth values simultaneously for consistency. • Network uses sparse convolutions to predict required info, making it lightweight and accurate. • Method is designed to be fast and lightweight for real-time applications. • Experiments demonstrate method's ability to predict efficient quadtree depth representation. [ABSTRACT FROM AUTHOR]

The need to know a few singular triplets associated with the largest singular values of a third‐order tensor arises in data compression and extraction. This paper describes a new method for their computation using the t‐product. Methods for determining a couple of singular triplets associated with the smallest singular values also are presented. The proposed methods generalize available restarted Lanczos bidiagonalization methods for computing a few of the largest or smallest singular triplets of a matrix. The methods of this paper use Ritz and harmonic Ritz lateral slices to determine accurate approximations of the largest and smallest singular triplets, respectively. Computed examples show applications to data compression and face recognition. [ABSTRACT FROM AUTHOR]

We present a new algorithm for incrementally updating the tensor train decomposition of a stream of tensor data. This new algorithm, called the tensor train incremental core expansion (TT-ICE), improves upon the current state-of-the-art algorithms for compressing in tensor train format by developing a new adaptive approach that incurs significantly slower rank growth and guarantees compression accuracy. This capability is achieved by limiting the number of new vectors appended to the TT-cores of an existing accumulation tensor after each data increment. These vectors represent directions orthogonal to the span of existing cores and are limited to those needed to represent a newly arrived tensor to a target accuracy. We provide two versions of the algorithm: TT-ICE and TT-ICE accelerated with heuristics (TT-ICE* ). We provide a proof of correctness for TT-ICE and empirically demonstrate the performance of the algorithms in compressing large-scale video and scientific simulation datasets. Compared to existing approaches that also use rank adaptation, TT-ICE* achieves 57× higher compression and up to reduction in computational time. [ABSTRACT FROM AUTHOR]

To authenticate a controller area network (CAN) data frame, a message authentication code (MAC) must be sent along with the CAN frame, but there is no space reserved for the MAC in the CAN frame. Recently, difference-based compression (DBC) algorithms have been used to create a space inside the frame. DBC has the advantage of being very efficient, but its drawback is that, if an error occurs in one frame, the effects of that error propagate to subsequent frames. In this paper, a CAN data compression algorithm is proposed that compresses the current frame without relying on previous frames. Therefore, an error generated in one frame cannot be propagated to subsequent frames. In addition, a CAN signal grouping technique is proposed based on entropy analysis. To efficiently authenticate CAN frames, the length of the compressed data must be 4 bytes or less (4BL). Simulation shows that the 4BL-compression ratio of a Kia Sorento vehicle is 99.36% in the DBC method, but 100% in the proposed method. In an LS Mtron tractor, the 4BL-compression ratio is 98.58% in the DBC method, but 100% in the proposed method. In addition, the execution time of the proposed compression algorithm is only 27.39% of that of the DBC algorithm. The results show that the proposed algorithm has better compression characteristics for CAN security than the DBC algorithms. [ABSTRACT FROM AUTHOR]

The Lyman-α three-dimensional correlation functions have been widely used to perform cosmological inference using the baryon acoustic oscillation scale. While the traditional inference approach employs a data vector with several thousand data points, we apply near-maximal score compression down to tens of compressed data elements. We show that carefully constructed additional data beyond those linked to each inferred model parameter are required to preserve meaningful goodness of fit tests that guard against unknown systematics, and to avoid information loss due to non-linear parameter dependences. We demonstrate, on suites of realistic mocks and Data Release 16 data from the Extended Baryon Oscillation Spectroscopic Survey, that our compression approach is lossless and unbiased, yielding a posterior that is indistinguishable from that of the traditional analysis. As an early application, we investigate the impact of a covariance matrix estimated from a limited number of mocks, which is only well conditioned in compressed space. [ABSTRACT FROM AUTHOR]

In the hyperspectral image (HSI) classification tasks, various deep learning models have achieved remarkable success. However, most deep learning models are compute-intensive, requiring significant computing power, time, and other resources. It becomes a challenge to pursue better results while saving computational resources. Therefore, a novel dual-input ultralight multi-head self-attention learning network (DUMS-LN) is proposed for HSI classification. The proposed DUMS-LN consists of three main core modules, namely the high-dimensional reduced module (HDRM), lightweight multi-head self-attention (LMHSA) module, and linearized hierarchical conversion module (LHCM). HDRM is used as a pre-processing module with efficient data compression and combines spatial and spectral information extraction from the raw data to provide cleaner and more comprehensive feature data for subsequent processing. In addition, the core computational module of DUMS-LN is the LMHSA module, which is lightweight but possesses better data processing capability than the traditional multi-head self-attention module. Finally, the LHCM divides the model into two phases, reducing the dimensionality of the feature data phase by phase so that the LMHSA module can perform feature extraction at different levels. Experiments on four benchmark HSI datasets show that the proposed DUMS-LN outperforms the comparison HSI classification algorithms regarding speed and classification accuracy. [ABSTRACT FROM AUTHOR]

Background: As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved. Results: Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support. Conclusions: In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/. All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis. [ABSTRACT FROM AUTHOR]

The network structure of digital watermarking algorithm based on deep learning is usually encoder‐noise layer‐decoder. Most of the existing encoders suffer from the problem of insufficient feature extraction, and the introduction of simulated differentiable joint photographic experts group (JPEG) compression in the noise layer cannot ensure the robustness under real JPEG. In this paper, a watermarking algorithm based on multi‐scale auto‐encoder is proposed, which can effectively extract the image feature information by combining with the channel attention mechanism. At the same time, some parameters of decoder and encoder are shared to reduce redundant feature embedding and improve extraction accuracy. This paper also proposes a robust training scheme against JPEG compression, which can guide the model to store the watermark in the low‐frequency region needed for decoding. Experimental results show that the peak signal‐to‐noise ratio (PSNR) of the proposed algorithm is above 48 and the decoding rate is above 99% under JPEG compression with quality factor Q = 50. Moreover, this scheme can effectively promote the combination of noise layer in training. In addition, the proposed algorithm is also robust to other common network noises. [ABSTRACT FROM AUTHOR]