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Cloud computing is one of the promising technologies that offers costeffective choices for processing and storing the huge volumes of data. In today’s world, data is the most important asset that one can have but it needs to be handled and protected properly. Portability of data can be increased by reducing the size of the data to be stored because of the limited storage space. As a result, data compression has arisen significantly. Data compression is a useful technique for reducing data size and increasing the effectiveness of data transit and storage. Data compression reduces the size of a data file while using lossy or lossless compression. One of the newest techniques for data compression is data duplication, which can reduce the amount of data saved while removing unnecessary data and maintaining an exact copy of the data. This analysis presents an Efficient data compression and storage technique with key management authentication in cloud space. This approach uses Regressive probabilistic key encryption (RPKE) to encrypt the cloud data and Lempel-Ziv-77-Huffman coding (LZ77-HM) is used to compress the huge amounts of cloud data. The Performance of presented approach is evaluated in terms of compression ratio and compression rate. [ABSTRACT FROM AUTHOR]

Technologies like data-driven methods for battery state estimation, fleet monitoring and cloud-based BMSs are emerging. However, challenges in data compression and storage hinder their widespread adoption. This paper addresses these issues by proposing a novel, efficient lossy voltage data compression method for measurements in electric vehicles. The method is grounded in polynomial regression and enables the use of the adaptive method without the need for parameters or training of the model which, representing an improvement over existing techniques. At a compression rate of 99.75% in an ambient temperature of 25 °C on average across all drive cycles compared, the root mean square error (RMSE) was 5.62 mV. Impressively, at a compression rate of 99%, the RMSE decreased to 3.12 mV. Furthermore, an implementation on a low-power STM32 microcontroller can compress 600 data points in just 35 milliseconds, demonstrating its suitability for real-time applications. These results highlight the potential of our approach to significantly improve the efficiency and accuracy of voltage measurement compression in electric vehicles, paving the way for advancements in electric vehicle technology. [ABSTRACT FROM AUTHOR]

Visualization of numerical results in computer communications is very important such that some very small differences are sometimes crucial, distinguishable, and descriptive for comparison among some state-of-the-art techniques. For the issue of data quality evaluation and compression rates in internet of multimedia things, there are many metrics traditionally, for instance, peak signal-to-noise ratio (PSNR) is strongly able to describe non-sensitive (and relatively ambiguous) results of mean square error and since PSNR is normally between 10 and 100 for most of the lossy techniques, it can plotted with using any graphical/visualization tool. However, the results of compression rates for aggregation techniques may be a little complicated on which using a non-flexible mathematical operator like logarithm may have an unsuitable effect with ignoring the small differences while plotting the results. The aim behind this paper is to introduce a new metric entitled average capacity index (ACI), as a non-linear visualization approach/scaling mechanism, to be usable in evaluating capacity results of data hiding and aggregation algorithms based on bar charts. Some examples with synthetic and real data will show that the proposed metric outperforms the existing conventional tools in terms of statistical measures and visual presentation. [ABSTRACT FROM AUTHOR]

Graph summarization has become integral for managing and analyzing large-scale graphs in diverse real-world applications, including social networks, biological networks, and communication networks. Existing methods for graph summarization often face challenges, being either computationally expensive, limiting their applicability to large graphs, or lacking the incorporation of node attributes. In response, we introduce SsAG, an efficient and scalable lossy graph summarization method designed to preserve the essential structure of the original graph. SsAG computes a sparse representation (summary) of the input graph, accommodating graphs with node attributes. The summary is structured as a graph on supernodes (subsets of vertices of G), where weighted superedges connect pairs of supernodes. The methodology focuses on constructing a summary graph with k supernodes, aiming to minimize the reconstruction error (the difference between the original graph and the graph reconstructed from the summary) while maximizing homogeneity with respect to the node attributes. The construction process involves iteratively merging pairs of nodes. To enhance computational efficiency, we derive a closed-form expression for efficiently computing the reconstruction error (RE) after merging a pair, enabling constant-time approximation of this score. We assign a weight to each supernode, quantifying their contribution to the score of pairs, and utilize a weighted sampling strategy to select the best pair for merging. Notably, a logarithmic-sized sample achieves a summary comparable in quality based on various measures. Additionally, we propose a sparsification step for the constructed summary, aiming to reduce storage costs to a specified target size with a marginal increase in RE. Empirical evaluations across diverse real-world graphs demonstrate that SsAG exhibits superior speed, being up to 17 × faster, while generating summaries of comparable quality. This work represents a significant advancement in the field, addressing computational challenges and showcasing the effectiveness of SsAG in graph summarization. [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]

Light-field images (LFIs) are gaining increased attention within the field of 3D imaging, virtual reality, and digital refocusing, owing to their wealth of spatial and angular information. The escalating volume of LFI data poses challenges in terms of storage and transmission. To address this problem, this paper introduces an MSHPE (most-similar hierarchical prediction encoding) structure based on light-field multi-view images. By systematically exploring the similarities among sub-views, our structure obtains residual views through the subtraction of the encoded view from its corresponding reference view. Regarding the encoding process, this paper implements a new encoding scheme to process all residual views, achieving lossless compression. High-efficiency video coding (HEVC) is applied to encode select residual views, thereby achieving lossy compression. Furthermore, the introduced structure is conceptualized as a layered coding scheme, enabling progressive transmission and showing good random access performance. Experimental results demonstrate the superior compression performance attained by encoding residual views according to the proposed structure, outperforming alternative structures. Notably, when HEVC is employed for encoding residual views, significant bit savings are observed compared to the direct encoding of original views. The final restored view presents better detail quality, reinforcing the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

The emerging field of molecular cavity polaritons has stimulated a surge of experimental and theoretical activities and presents a unique opportunity to develop the many-body simulation methodology. This paper presents a numerical scheme for the extraction of key kinetic information of lossy cavity polaritons based on the transfer tensor method (TTM). Steady state, relaxation timescales, and oscillatory phenomena can all be deduced directly from a set of transfer tensors without the need for long-time simulation. Moreover, we generalize TTM to disordered systems by sampling dynamical maps and achieve fast convergence to disordered-averaged dynamics using a small set of realizations. Together, these techniques provide a toolbox for characterizing the interplay of cavity loss, disorder, and cooperativity in polariton relaxation and allow us to predict unusual dependences on the initial excitation state, photon decay rate, strength of disorder, and the type of cavity models. Thus, using the example of cavity polaritons, we have demonstrated significant potential in the use of the TTM toward both the efficient computation of long-time polariton dynamics and the extraction of crucial kinetic information about polariton relaxation from a small set of short-time trajectories. [ABSTRACT FROM AUTHOR]

To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. Instead, we propose that EBM parameterizations, as a lossy data compression problem, can be better formulated using established physical rules widely used in computational physics and chemistry, and different biogeochemical processes can be more robustly integrated within a reactive‐transport framework. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process‐level hypotheses to test in empirical studies. Finally, while empirical models with more parameters are often less robust, physical rules‐based models can be more robust and show lower predictive equifinality, stemming from their enhanced consistency in representations of processes, interactions and spatial scaling. Plain Language Summary: Robust ecosystem biogeochemical models are needed to provide humanity with predictions to understand and manage interactions between terrestrial ecosystems and the climate. However, existing models do not fully achieve this target because of their wide use of statistical relationships derived from empirical observations. We argue that wider adoption of physical rules can help develop better ecosystem biogeochemical models to meet with society's needs. This can be achieved by deeper interdisciplinary collaboration between scientists from fields in soils, biology, chemistry, physics, and mathematics. Development of improved biogeochemical models will better position society to adapt to climate change. Key Points: The popular empirically based modeling approaches limit improvement of existing ecosystem biogeochemical model predictionsPhysical rules‐based approaches will help develop scaling consistent ecosystem biogeochemical modelsInter‐disciplinary collaboration can accelerate development and adoption of physical rules‐based ecosystem biogeochemical models [ABSTRACT FROM AUTHOR]

Securing data during transmission is critical to prevent unauthorized access, interception, or modification of the data. Data can be communicated securely while maintaining its confidentiality, integrity, and availability by using cryptographic algorithms and measures. In the proposed work, a hybrid data compression algorithm is proposed to increase the amount of input data that is encrypted using the Advanced Encryption Standard (AES) cryptography method to boost security level, and it can be utilized to carry out the lossy compacting Steganography method. By reducing the quantity of data transmitted, this technique can enable speedy transmission over a sluggish internet connection or use less space on different storage devices. The cover image is compressed using Discrete Wavelet Transform (DWT), which reduces the cover image's dimensions by lossyly compressing the image. The ordinary text is converted to hexadecimal format from text. The encrypted data will then be inserted into the compressed cover picture using the least significant bit (LSB) with Image vector array (IVA). Bits per pixel (BPP), Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were some of the metrics we used to evaluate the proposed technique. [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]

In recent years, deep learning has been widely employed in the field of image compression, the most significant of which is the lossy image compression method on the basis of convolutional neural networks. And we study the application of convolutional neural networks to remote sensing image compression. Consequently, we implement an enhanced residual attention module (ERAM) on the primary and hyper coders. ERAM can apply a spatial attention mechanism to generate importance masks for potential features of the original image. And the importance mask can adaptively adjust the bit distribution and allocate more bits to substantial target regions and fewer bits to unimportant background regions. Additionally, ERAM has the capacity to lessen the channel redundancy of potential features. Furthermore, an accurate probability estimate of a potential feature can affect to a great degree the effectiveness of entropy coding and hence image compression. We propose to utilise the discrete mixed Laplace entropy model to parameterize the probability distribution of latent features, which can improve the compression efficiency of latent features in the entropy encoding process. In the field of remote sensing image compression, the experiments demonstrate that the algorithm described in this paper has good subjective effects as well as good objective effects. [ABSTRACT FROM AUTHOR]

Objective. The aim of the study is to develop methods for data compression in relation to monitoring systems.Method. The studies were carried out mainly experimentally, by processing data generated by the information-measuring system of the Altai State Technical University in the process of meteorological observations, temperature control and control of the consumption of such energy resources as hot and cold water, heat supply. However, when it was proposed to change their structure and presentation form to compact the stored data, the degree of compression was found theoretically.Result. The results of studies conducted with the participation of the author of the publication in the field of data processing, transmission and storage are presented. Methods for streaming data compression “on the fly” as it arrives, both lossy and lossless, as well as methods based on optimizing the structure of databases designed to store information collected during monitoring, are considered. The main attention is paid to the consideration of data compression created in the process of temperature monitoring, since it is the control and monitoring of temperature processes that is most common in various technical process control systems, heat metering systems both on the consumer side (housing and communal services) and on the side of the heat supply organization.Conclusion. In the vast majority of cases, lossless compression allows you to compress the information received from the monitoring system by more than 10 times, and lossy compression is much stronger, and without losing the pragmatic value of the stored information.

In the process of data services, compressing and indexing data can reduce storage costs, improve query efficiency, and thus enhance the quality of data services. However, different service requirements have diverse demands for data precision. Traditional lossy compression techniques fail to meet the precision requirements of different data due to their fixed compression parameters and schemes. Additionally, error-bounded lossy compression techniques, due to their tightly coupled design, cannot achieve high compression ratios under high precision requirements. To address these issues, this paper proposes a lossy compression technique based on error control. Instead of imposing precision constraints during compression, this method first uses the JPEG compression algorithm for multi-level compression and then manages data through a tree-based index structure to achieve error control. This approach satisfies error control requirements while effectively avoiding tight coupling. Additionally, this paper enhances data restoration effects using a deep learning network and provides a range query processing algorithm for the tree-based index to improve query efficiency. We evaluated our solution using ocean data. Experimental results show that, while maintaining data precision requirements (PSNR of at least 39 dB), our compression ratio can reach 64, which is twice that of the SZ compression algorithm.

Featured Application: The methods of digital steganography in image files presented in this research may find application in numerous areas and industries, because they offer great capacity and may be combined with cryptography. ① Hidden communication (in public or monitored channels, when encryption is not possible or difficult to use, contacts between whistleblowers and journalists or informants and police, to send dangerous contents, privacy protection, data security); ② military and intelligence (conceal strategic information during war and occupation operations); ③ business (protection against industrial/corporate espionage, hidden communication between business partners or company branches); ④ property protection (watermarking of images to embed information about the author, additional comments or some metadata, fraud detection); ⑤ other areas (bypassing censorship in countries violating human rights, sending viruses, tracking users). As can be seen, potential applications lie in both white hat and black hat fields of operation. In this paper, several ideas of data hiding in WebP images are presented. WebP is a long-known, but not very poplar file format that provides lossy or lossless compression of data, in the form of a still image or an animation. A great number of WebP features are optional, so the structure of the image offers great opportunities for data hiding. The article describes distinct approaches to steganography divided into two categories: format-based and data-based. Among format-based methods, we name simple injection, multi-secret steganography that uses thumbnails, hiding a message in metadata or in a specific data chunk. Data-based methods achieve secret concealment with the use of a transparent, WebP-specific algorithm that embeds bits by choosing proper prediction modes and alteration of the color indexing transform. The capacity of presented techniques varies. It may be unlimited for injection, up to a few hundred megabytes for other format-based algorithms, or be content-dependent in data-based techniques. These methods fit into the container modification branch of steganography. We also present a container selection technique which benefits from available WebP compression parameters. Images generated with the described methods were tested with three applications, including the Firefox web browser, GNU Image Manipulation Program, and ImageMagick. Some of the presented techniques can be combined in order to conceal more than one message in a single carrier. [ABSTRACT FROM AUTHOR]

Smart objects are increasingly widespread and their ecosystem, also known as the Internet of Things (IoT), is relevant in many application scenarios. The huge amount of temporally annotated data produced by these smart devices demands efficient techniques for the transfer and storage of time series data. Compression techniques play an important role toward this goal and, even though standard compression methods could be used with some benefit, there exist several ones that specifically address the case of time series by exploiting their peculiarities to achieve more effective compression and more accurate decompression in the case of lossy compression techniques. This article provides a state-of-the-art survey of the principal time series compression techniques, proposing a taxonomy to classify them considering their overall approach and their characteristics. Furthermore, we analyze the performances of the selected algorithms by discussing and comparing the experimental results that were provided in the original articles. The goal of this article is to provide a comprehensive and homogeneous reconstruction of the state-of-theart, which is currently fragmented across many articles that use different notations and where the proposed methods are not organized according to a classification. [ABSTRACT FROM AUTHOR]

Numerical modeling of the direct current (DC) potential field for the mise-a-la-masse (MALM) method traditionally depends on the specific source under loss-free dielectric consideration. In this paper, we propose a numerical technique for modeling DC potential fields in charged lossy dielectric media. A numerical solver of charged current transportation is first presented using finite different method, then the DC potential is integrated from all unit current elements with the Legendre function polynomial. A new preconditioner is also proposed for MALM surveying to reduce the condition number to accurately solve the equation. This new technique is verified through comparisons with numerical cases and field surveys. The basic problem formulation is general, but it is directly applicable in MALM surveying as a geophysical technique where the DC potential produced by charged lossy dielectric media is of interest. [ABSTRACT FROM AUTHOR]

This paper is concerned with the lossy compression of general random variables, specifically with rate-distortion theory and quantization of random variables taking values in general measurable spaces such as, e.g. manifolds and fractal sets. Manifold structures are prevalent in data science, e.g. in compressed sensing, machine learning, image processing and handwritten digit recognition. Fractal sets find application in image compression and in the modeling of Ethernet traffic. Our main contributions are bounds on the rate-distortion function and the quantization error. These bounds are very general and essentially only require the existence of reference measures satisfying certain regularity conditions in terms of small ball probabilities. To illustrate the wide applicability of our results, we particularize them to random variables taking values in (i) manifolds, namely, hyperspheres and Grassmannians and (ii) self-similar sets characterized by iterated function systems satisfying the weak separation property. [ABSTRACT FROM AUTHOR]

Scattering from anisotropic geometries of arbitrary shape is relatively difficult to interpret physically, involving the intricate interplay between material and geometric effects. Insights into complex scattering mechanisms are often enabled by modal methods that decompose the response into the well-understood multipolar resonances. Here, we extend the generalized normal mode expansion to lossy and anisotropic scatterers. Unique to the method is that it decomposes the total response of any anisotropic resonator into the modes of the corresponding isotropic resonator. This disentangles the material and geometric contributions to the scattering of any anisotropic resonator. Furthermore, the method can identify absorption and scattering resonances with separate sets of modes. We illustrate our method by considering an infinitely long cylinder with concentric metallic/dielectric layers, targeting the complex case of an effective hyperbolic response. We show that by scanning the material composition of the hyperbolic medium, we can achieve any desired scattering effect, including backscattering cancellation. [ABSTRACT FROM AUTHOR]

We propose a universal ensemble for the random selection of rate–distortion codes which is asymptotically optimal in a sample-wise sense. According to this ensemble, each reproduction vector, x ^ , is selected independently at random under the probability distribution that is proportional to 2 − L Z (x ^) , where L Z (x ^) is the code length of x ^ pertaining to the 1978 version of the Lempel–Ziv (LZ) algorithm. We show that, with high probability, the resulting codebook gives rise to an asymptotically optimal variable-rate lossy compression scheme under an arbitrary distortion measure, in the sense that a matching converse theorem also holds. According to the converse theorem, even if the decoder knew the ℓ-th order type of source vector in advance (ℓ being a large but fixed positive integer), the performance of the above-mentioned code could not have been improved essentially for the vast majority of codewords pertaining to source vectors in the same type. Finally, we present a discussion of our results, which includes among other things, a clear indication that our coding scheme outperforms the one that selects the reproduction vector with the shortest LZ code length among all vectors that are within the allowed distortion from the source vector. [ABSTRACT FROM AUTHOR]

Diabetes, characterized by persistently high blood glucose levels, has been identified as a hazardous health condition, potentially leading to severe complications such as heart attacks, strokes, and heart failure. This study introduces a fog-based remote health monitoring system designed to mitigate the devastating impacts of diabetes and hypoglycemia. This system persistently monitors health parameters including glucose levels, carbohydrate intake, physical activities, heart rate, and blood pressure. It additionally supports advanced services such as feature extraction, distributed local storage, and enhanced security. The traditional cloud-based architecture, while effective, often results in significant latency due to the processing of vast amounts of data. By bringing computing servers closer to users, Fog computing addresses this issue, reducing latency, and increasing security, resource accessibility, and on-demand scaling. In this context, the proposed system aims to minimize latency and network usage while addressing critical issues such as security, access control, and privacy. It employs lossy data compression at the gateway level to decrease network bandwidth and enhance efficiency. Furthermore, the system introduces a novel Load Balancing mechanism to distribute the load among fog nodes evenly. It utilizes lightweight cryptographic algorithms, efficient key exchange protocols, and digital signatures to ensure confidentiality, authentication, and user privacy. The performance of the proposed framework was evaluated in terms of average processing time, energy consumption management, computational resource distribution, latency, and network usage. When compared with other systems, the proposed framework demonstrated superior results, thus validating its effectiveness. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) obtained satisfactory results on different vision tasks; however, they usually suffer from large models and massive parameters during model deployment. While DNN compression can reduce the memory footprint of deep model effectively, so that the deep model can be deployed on portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the application that needs fast computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new model compression method, termed dictionary-pair-based fast data-free DNN compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our method performs tensor decomposition of DNN model with a fast dictionary-pair learning-based reconstruction approach, which can be deployed on different weight layers (e.g., convolution and fully connected layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-driven fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Moreover, automatic hyper-parameter tuning and shared-dictionary mechanism is proposed to improve the model performance and availability. Extensive experiments on popular DNN models (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss. [ABSTRACT FROM AUTHOR]

Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely on trial and error to assess lossy compression performance. As a strong data-driven effort toward quantifying lossy compressibility of scientific datasets, we provide a statistical framework to predict compression ratios of lossy compressors. Our method is a two-step framework where (i) compressor-agnostic predictors are computed and (ii) statistical prediction models relying on these predictors are trained on observed compression ratios. Proposed predictors exploit spatial correlations and notions of entropy and lossyness via the quantized entropy. We study 8+ compressors on 6 scientific datasets and achieve a median percentage prediction error less than 12%, which is substantially smaller than that of other methods while achieving at least a 8.8× speedup for searching for a specific compression ratio and 7.8× speedup for determining the best compressor out of a collection. [ABSTRACT FROM AUTHOR]

Despite the above cautions, there are likely some applications where the representation of variability provided in the authors' model would be sufficient for purpose, for example, in application areas where there is not a large sensitivity to temperature variability. Introduction The authors are to be congratulated for a thought-provoking paper on the use of statistical models for a form of data compression for climate model output. The authors propose an interesting potential solution to this problem: do not trying to unconditionally compress precipitation, but then develop a statistical model for temperature conditional on precipitation as a compression of temperature. [Extracted from the article]

We present ATC, a C++ library for advanced Tucker-based lossy compression of dense multidimensional numerical data in a shared-memory parallel setting, based on the sequentially truncated higher-order singular value decomposition (ST-HOSVD) and bit plane truncation. Several techniques are proposed to improve speed, memory usage, error control and compression rate. First, a hybrid truncation scheme is described which combines Tucker rank truncation and TTHRESH quantization. We derive a novel expression to approximate the error of truncated Tucker decompositions in the case of core and factor perturbations. We parallelize the quantization and encoding scheme and adjust this phase to improve error control. Implementation aspects are described, such as an ST-HOSVD procedure using only a single transposition. We also discuss several usability features of ATC, including the presence of multiple interfaces, extensive data type support, and integrated downsampling of the decompressed data. Numerical results show that ATC maintains state-of-the-art Tucker compression rates while providing average speed-up factors of 2.2 to 3.5 and halving memory usage. Our compressor provides precise error control, deviating only 1.4% from the requested error on average. Finally, ATC often achieves higher compression than non-Tucker-based compressors in the high-error domain. [ABSTRACT FROM AUTHOR]

Objective. The wearable electrocardiogram (ECG) monitoring device is an effective tool for diagnosing intermittent heart diseases. However, the massive amount of ECG data increases power consumption during wireless transmission, thereby reducing the monitoring duration of the wearable device. Here, we presented a novel method to solve this problem. Method. To reduce the data size, we used a down-sampling operation to compress signals. Then, we designed a signal-referenced network to reconstruct the original signals from the compressed ones. We validated the proposed method on the China Physiological Signal Challenge 2018 database, used the root mean square error (RMSE) to evaluate the performance of the proposed network, and evaluated the effectiveness of the reconstructed signals via the F1-score of an ECG signal classifier. Main Result. The classifier used in this paper achieved an F1-score of 84% on 500 Hz signals reconstructed from 25 Hz, 89% from 50 Hz, 90% from 125 Hz, and 95% from 250 Hz. The RMSE of these four sampling rates was 0.10 mV, 0.08 mV, 0.05 mV, and 0.04 mV, respectively. Significance. The experimental result shows that the proposed network has a good performance when reconstructing signals. Furthermore, our method can remove the computational load of compressing signals from wearable devices. [ABSTRACT FROM AUTHOR]

For hyperspectral images (HSI) compressed sensing reconstruction, the 3D total variation (3DTV) is a powerful regulation term encoding the spatial-spectral local smooth prior structure. The term is calculated by supposing the sparsity structure on a gradient map along the spatial and spectral direction. In some real scenes, however, the gradient maps along different directions of HSI are not always sparse. Actually, TV constraints established on the gradient map of the original HSI are more effective in most cases. In this paper, instead of imposing sparsity on gradient maps themselves directly, compressed sensing (CS) reconstruction for HSI is formulated as an optimization problem utilizing a novel regulation term named multi-TV (MTV), which combines the sparsity prior for the gradient map and the TV projection along with other directions of the gradient map. We also develop a workable utility algorithm based on the alternating direction method of multipliers (ADMM) to effectively deal with the optimization problem. The proposed MTV term can easily replace the conventional 3DTV term and be embedded into hyperspectral CS (HCS) reconstruction to improve its performance. Experimental results show that compared with similar state-of-the-art methods, the proposed MTV term can significantly improve reconstruction precision for HCS. [ABSTRACT FROM AUTHOR]

Nowadays, there is a clear trend toward increasing the number of remote-sensing images acquired and their average size. This leads to the need to compress the images for storage, dissemination, and transfer over communication lines where lossy compression techniques are more popular. The images to be compressed or some of their components are often noisy. They must therefore be compressed taking into account the properties of the noise. Due to the noise filtering effect obtained during lossy compression of noisy images, an optimal operating point (OOP) may exist. The OOP is a parameter that controls the compression for which the quality of the compressed image is closer (closest) to the corresponding noise-free image than the quality of the noisy (original, uncompressed) image according to some quantitative criterion (metric). In practice, it is important to know whether the OOP exists for a given image, because if the OOP exists, it is appropriate to perform the compression in the OOP or at least in its neighborhood. Since the real image is absent in practice, it is impossible to determine a priori whether the OOP exists or not. Here, we focus on three-channel-remote-sensing images and show that it is possible to easily predict the existence of the OOP. Furthermore, it is possible to predict the metric values or their improvements with appropriate accuracy for practical use. The BPG (better portable graphics) encoder is considered a special case of an efficient compression technique. As an initial design step, the case of additive white Gaussian noise with equal variance in the three components is considered. While previous research was mainly focused on predicting the improvement (reduction) of the PSNR and PSNR-HVS-M metrics, here we focus on the modern visual quality metrics, namely PSNR-HA and MDSI. We also discuss what to do if, according to the prediction, an OOP is absent. Examples of lossy compression of noisy three-channel remote sensing images are given. It is also shown that the use of three-dimensional compression provides a compression ratio increase by several times compared with component-wise compression in the OOP. [ABSTRACT FROM AUTHOR]

Rate distortion theory was developed for optimizing lossy compression of data, but it also has applications in statistics. In this paper, we illustrate how rate distortion theory can be used to analyze various datasets. The analysis involves testing, identification of outliers, choice of compression rate, calculation of optimal reconstruction points, and assigning "descriptive confidence regions" to the reconstruction points. We study four models or datasets of increasing complexity: clustering, Gaussian models, linear regression, and a dataset describing orientations of early Islamic mosques. These examples illustrate how rate distortion analysis may serve as a common framework for handling different statistical problems. [ABSTRACT FROM AUTHOR]

This paper discusses and analyzes the quasistatic optimal plasmonic dipole resonance of a small dielectric particle embedded in a lossy surrounding medium. The optimal resonance at any given frequency is defined by the complex valued dielectric constant that maximizes the absorption of the particle under the quasistatic approximation and a passivity constraint. In particular, for an ellipsoid aligned along the exciting field, the optimal material property is given by the complex conjugate of the pole position associated with the polarizability of the particle. In this paper, we employ the classical Mie theory to analyze this approximation for spherical particles in a lossy surrounding medium. It turns out that the quasistatic optimal plasmonic resonance is valid, provided that the electrical size of the particle is sufficiently small at the same time as the external losses are sufficiently large. Hence, it is important to note that this approximation cannot be used for a lossless medium, and which is also obvious, since the quasistatic optimal dipole absorption becomes unbounded for this case. Moreover, it turns out that the optimal normalized absorption cross sectional area of the small dielectric sphere has a very subtle limiting behavior and is, in fact, unbounded even in full dynamics when both the electrical size and the exterior losses tend to zero at the same time. A detailed analysis is carried out to assess the validity of the quasistatic estimation of the optimal resonance, and numerical examples are included to illustrate the asymptotic results. [ABSTRACT FROM AUTHOR]

This study aims at the problem of reconstruction the unknown links in debt networks among enterprises. We use the topological matrix of the enterprise debt network as the object of reconstruction and use the time series data of accounts receivable and payable as input and output information in the debt network to establish an underdetermined linear system about the topological matrix of the debt network. We establish an iteratively reweighted least-squares algorithm, which is an algorithm in compressed sensing. This algorithm uses reweighted ℓ 2 -minimization to approximate ℓ 1 -norm of the target vectors. We solve the ℓ 1 -minimization problem of the underdetermined linear system using the iteratively reweighted least-squares algorithm and obtain the reconstructed topological matrix of the debt network. Simulation experiments show that the topology matrix reconstruction method of enterprise debt networks based on compressed sensing can reconstruct over 70% of the unknown network links, and the error is controlled within 2%. [ABSTRACT FROM AUTHOR]

The state-of-the-art performance for several real-world problems is currently reached by deep and, in particular, convolutional neural networks (CNN). Such learning models exploit recent results in the field of deep learning, leading to highly performing, yet very large neural networks with typically millions to billions of parameters. As a result, such models are often redundant and excessively oversized, with a detrimental effect on the environment in terms of unnecessary energy consumption and a limitation to their deployment on low-resource devices. The necessity for compression techniques able to reduce the number of model parameters and their resource demand is thereby increasingly felt by the research community. In this paper we propose the first extensive comparison, to the best of our knowledge, of the main lossy and structure-preserving approaches to compress pre-trained CNNs, applicable in principle to any existing model. Our study is intended to provide a first and preliminary guidance to choose the most suitable compression technique when there is the need to reduce the occupancy of pre-trained models. Both convolutional and fully-connected layers are included in the analysis. Our experiments involved two pre-trained state-of-the-art CNNs (proposed to solve classification or regression problems) and five benchmarks, and gave rise to important insights about the applicability and performance of such techniques w.r.t. the type of layer to be compressed and the category of problem tackled. [ABSTRACT FROM AUTHOR]

The Gaussian law reigns supreme in the information theory of analog random variables. This paper showcases a number of information theoretic results which find elegant counterparts for Cauchy distributions. New concepts such as that of equivalent pairs of probability measures and the strength of real-valued random variables are introduced here and shown to be of particular relevance to Cauchy distributions. [ABSTRACT FROM AUTHOR]

The unsteady adjoint method used in gradient‐based optimization in 2D and, particularly, 3D industrial problems modeled by unsteady PDEs may have significant storage requirements and/or computational cost. The reason for this is that the backward in time integration of the adjoint equations requires the previously computed instantaneous flow fields to be available at each time‐step. This article proposes remedies to this problem, by extending/upgrading relevant techniques proposed by the group of authors as well as other researchers. Their applicability is wide, even if these remedies are herein demonstrated in shape optimization problems in unsteady fluid mechanics. Check‐pointing is in widespread use as it reduces the memory footprint and CPU cost of the optimization with a controllable computational overhead. Alternatively, flow field time‐series can be stored in a lossless or lossly compressed form. The novelty of this article is the development of a Compressed Coarse‐grained Check‐Pointing strategy for second‐order accurate schemes in time, by optimally combining check‐pointing and lossy compression. The latter includes (a) the incremental Proper Generalized Decomposition (iPGD) algorithm and (b) a hybridization of the iPGD with the ZFP and Zlib algorithms. This is implemented within OpenFOAM, which is used to solve the flow and adjoint equations and conduct the optimization, and assessed in 2D/3D aerodynamic shape optimization problems on unstructured grids. Effectiveness in data reduction, computational cost, and reconstruction accuracy are compared, vis‐à‐vis also to the "standard" binomial check‐pointing technique after adjusting it to second‐order accurate schemes in time. [ABSTRACT FROM AUTHOR]

A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method's effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%. [ABSTRACT FROM AUTHOR]

The accuracy of the first-order multiplicative (Ott–Clemmow) and additive (van der Waerden) modified saddle point integration methods for the Sommerfeld problem of a vertical Hertzian dipole over a lossy half-space is investigated using two theoretically equivalent formulations with either a positive or a negative image term extracted. It is demonstrated that whereas the additive method leads to the same asymptotic field representation irrespective of the sign of the image, the multiplicative variant yields distinct results in these two cases, both of which differ from the unique result of the additive method. It is further found that the positive-image Ott–Clemmow method yields the well-known Norton formula, but the negative-image variant is inaccurate and predicts a zero field in the on-surface transmitter–receiver configuration. It is also demonstrated that the first-order multiplicative method yields an incomplete representation of the first order in the inverse distance. [ABSTRACT FROM AUTHOR]

To improve the viewer’s Quality of Experience (QoE) and optimize computer graphics applications, 3D model quality assessment (3D-QA) has become an important task in the multimedia area. Point cloud and mesh are the two most widely used digital representation formats of 3D models, the visual quality of which is quite sensitive to lossy operations like simplification and compression. Therefore, many related studies such as point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality of distorted 3D models. However, most previous studies utilize full-reference (FR) metrics, which indicates they can not predict the quality level in the absence of the reference 3D model. Furthermore, few 3D-QA metrics consider color information, which significantly restricts their effectiveness and scope of application. In this paper, we propose a no-reference (NR) quality assessment metric for colored 3D models represented by both point cloud and mesh. First, we project the 3D models from 3D space into quality-related geometry and color feature domains. Then, the 3D natural scene statistics (3D-NSS) and entropy are utilized to extract quality-aware features. Finally, a support vector regression (SVR) model is employed to regress the quality-aware features into visual quality scores. Our method is validated on the colored point cloud quality assessment database (SJTU-PCQA), the Waterloo point cloud assessment database (WPC), and the colored mesh quality assessment database (CMDM). The experimental results show that the proposed method outperforms most compared NR 3D-QA metrics with competitive computational resources and greatly reduces the performance gap with the state-of-the-art FR 3D-QA metrics. The code of the proposed model is publicly available now at https://github.com/zzc-1998/NR-3DQA. [ABSTRACT FROM AUTHOR]

Invertible color-to-grayscale conversion is a research issue in grayscale image colorization, which is a complex problem resulting from information loss. This paper presents an innovative invertible color-to-grayscale conversion idea by independently compressing the chromaticity plane and hiding it into the corresponding luminance plane as a watermark. Since the chromaticity and luminance planes are orthogonal in the proposed method, they can be processed efficiently without mutual influence. By using an efficient lossy compression operation, we can save more chromatic information. By using two high-capacity data hiding techniques (reversible watermarking (RW) and least significant bit (LSB) substitution), we can embed the compressed chromatic information into the luminance plane perfectly. For the purpose of integrality authentication, the luminance plane is hashed as part of the embedded information before the embedding. Experimental results have shown that higher quality of reconstructed color images can be achieved by using RW, but the quality of the synthesized grayscale drops sharply. By using LSB substitution, we can obtain high-quality synthesized grayscale and reconstructed color images simultaneously. Furthermore, we have compared the proposed LSB-based scheme with several recently reported state-of-the-art methods to validate the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Recently, magnetic resonance imaging (MRI) reconstruction based on deep learning has become popular. Nevertheless, reconstruction of highly undersampled MR images is still challenging due to severe aliasing effects. In this study we built a deep fusion connection network (DFCN) to efficiently utilize the correlation information between adjacent slices. The proposed method was evaluated with online public IXI dataset and Calgary-Campinas-359 dataset. The results show that DFCN can generate the best reconstruction images in de-aliasing and restoring tissue structure compared with several state-of-the-art methods. The mean value of the peak signal-to-noise ratio could reach 34.16 dB, the mean value of the structural similarity is 0.9626, and the mean value of the normalized mean square error is 0.1144 on T 2 -weighted brain data of IXI dataset under 10× acceleration. Additionally, the mean value of the peak signal-to-noise ratio could reach 30.17 dB, the mean value of the structural similarity is 0.9259, and the mean value of the normalized mean square error is 0.1294 on T 1 -weighted brain data of Calgary-Campinas-359 dataset under 10× acceleration. With the correlation information between adjacent slices as prior knowledge, our method can dramatically eliminate aliasing effects and enhance the reconstruction quality of undersampled MR images. [ABSTRACT FROM AUTHOR]

In the previous works on heat stress detection, various state-of-the-art machine learning techniques had been used to detect stress patterns from electroencephalographic (EEG) signals. Since the handcrafted feature engineering-based approaches pose certain limitations and are sensitive to transform the nonlinearities of EEG, deep learning techniques have drawn attention in the domain of EEG-based applications. Moreover, existing approaches for stress detection consider the whole frequency band (delta to gamma) which conceals the redundant and lossy information that increased the false detection rate. Also, these approaches were implemented only to identify the stress in binary classes and confined their application to identifying the level of stress. Therefore, a multi-scale channel-wise convolution-based multi-level heat stress assessment has been proposed in this paper. The novel contributions of this work are (1) designing a multi-scale convolutional neural network (MS-CNN) for extracting precise information from individual frequency bands of EEG, (2) use of sparse connectivity to reduce the redundant information and increase the performance with less trainable parameters, and (3) multi-level heat stress assessment for precise identification of the severity of stress. The proposed approaches were evaluated on pre-recorded data of 10 rats in a simulated laboratory environment. The high accuracy of approximately 96–97% and 90–92% has been achieved for binary and multi-level stress detection. There is approximately a 6 to 50% reduction in the trainable parameters with a 2% improvement of accuracy achieved on adopting channel-wise convolution over MS-CNN and deep convolutional neural network (DCNN). This demonstrates the effectiveness of the adopted multiscale feature extraction and sparse connectivity to improve the performance with a less complex model. [ABSTRACT FROM AUTHOR]

4D-STEM datasets obtained from EMPAD detectors broke the TEM resolution limits of 0.4Å in 2018 [[37]] and 0.25Å in 2021 [[38]] by using electron ptychography. Optical microscopes inspired the first TEM, and since then several new imaging modalities have been implemented, such as electron holography [[6]], Lorentz electron microscopy [[8]], and scanning TEM (STEM) [[9]]. Multidimensional Electron Microscopy Enabled by Detector Advances Electronic detectors have been used for TEM image acquisition since the early nineties. Keywords: electron microscope; edge computer; high-performance computing; 4D STEM; electron detectors EN electron microscope edge computer high-performance computing 4D STEM electron detectors 10 19 10 11/29/22 20221101 NES 221101 Introduction Ernst Ruska built the first transmission electron microscope (TEM) during his doctoral studies, and it celebrates its eightieth anniversary this year [[1]-[3]]. [Extracted from the article]

Federated learning (FL) is an emerging collaborative machine learning (ML) framework that enables training of predictive models in a distributed fashion where the communication among the participating nodes are facilitated by a central server. To deal with the communication bottleneck at the server, decentralized FL (DFL) methods advocate rely on local communication of nodes with their neighbors according to a specific communication network. In DFL, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e., averaging over the network) steps. As the size of the ML models grows, the limited communication bandwidth among the nodes does not permit communication of full-precision messages; hence, it is becoming increasingly common to require that messages be lossy, compressed versions of the local parameters. The requirement of communicating compressed messages gives rise to the important question: given a fixed communication budget, what should be our communication strategy to minimize the (training) loss as much as possible? In this article, we explore this direction, and show that in such compressed DFL settings, there are benefits to having multiple gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for, e.g., by means of reducing the precision of compressed information. In particular, we show that having ${\mathcal O}(\log \frac{1}{\epsilon })$ O (log 1 ε) gradient iterations with constant step size - and ${\mathcal O}(\log \frac{1}{\epsilon })$ O (log 1 ε) gossip steps between every pair of these iterations - enables convergence to within $\epsilon$ ε of the optimal value for a class of non-convex problems that arise in the training of deep learning models, namely, smooth non-convex objectives satisfying Polyak-Łojasiewicz condition. Empirically, we show that our proposed scheme bridges the gap between centralized gradient descent and DFL on various machine learning tasks across different network topologies and compression operators. [ABSTRACT FROM AUTHOR]

Fiber optic sensors (FOSs) based on the lossy mode resonance (LMR) technique have gained substantial attention from the scientific community. The LMR technique displays several important features over the conventional surface plasmon resonance (SPR) phenomenon, for planning extremely sensitive FOSs. Unlike SPR, which mainly utilizes the thin film of metals, a wide range of materials such as conducting metal oxides and polymers support LMR. The past several years have witnessed a remarkable development in the field of LMR-based fiber optic sensors; through this review, we have tried to summarize the overall development of LMR-based fiber optic sensors. This review article not only provides the fundamental understanding and detailed explanation of LMR generation but also sheds light on the setup/configuration required to excite the lossy modes. Several geometries explored in the literature so far have also been addressed. In addition, this review includes a survey of the different materials capable of supporting lossy modes and explores new possible LMR supporting materials and their potential applications in sensing. [ABSTRACT FROM AUTHOR]

The remarkable spreading of medical devices relying on electromagnetic (EM) fields stimulates a deeper analysis of their interactions with the human body. The electric field generated by a radio-frequency (RF) source, such as an antenna, can be transmitted inside biological tissues and used in disease treatment, rehabilitation, and noninvasive imaging. In this context, it is important to facilitate the penetration of the electric field inside tissues. Matching layers (MLs) consisting of dielectric media or impedance surfaces provide an approach to maximize the field penetration. However, the presence of a source in close proximity to the body complicates the EM analysis and the design of such a structure. Here, we analyze an analytical method based on the wave-transmission-chain matrix and the wave-impedance concept, as defined by Schelkunoff for near-field sources. Examples of the usefulness of this approach in designing an ML that could improve the penetration of the electric field inside tissues are presented. The results obtained analytically are finally compared with those of a full-wave solver, showing good agreement. [ABSTRACT FROM AUTHOR]

In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for binary variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differences between the choice of Ising solvers (simulated annealing, quantum annealing and simulated quenching) and the strategies of the BBO algorithms (BOCS, FMQA and their variations) are discussed for further development of the BBO techniques. [ABSTRACT FROM AUTHOR]

JPEG steganalysis aims to detect stego JPEG images. For some robust steganography methods, in order to enhance stego images’ robustness of resisting JPEG recompression from lossy channel such SNS or photo sharing websites, steganographer may intentionally recompress cover image several times with quantization matrix of targeted channel, which thereby make it possible to transmit stego data in such channel for better disguise. In addition, there are huge number of cover JPEG images may be recompressed for various reasons, such as processing by some tools. Thus a better steganalysis method for such images is needed. In this paper, we investigate the steganalysis method for images recompressed with the same quantization matrix, namely, discriminate recompressed JPEG cover images and its stego images. We present some observed phenomenon on recompressed JPEG images, and design methods to enhance the sensitivity of feature based and deep model based steganalysis methods for this task. To verify their effectiveness with different acquisition of recompression prior-knowledge, we conduct experiments in various settings including conventional setting and mixing samples of different recompressing times in training. Their results demonstrate that the proposed method can notably improve detection accuracy on recompressed JPEG images. [ABSTRACT FROM AUTHOR]

In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization. Previous CNN quantization techniques tend to ignore the joint statistics of weights and activations, producing sub-optimal CNN performance at a given quantization bit-rate, or consider their joint statistics during training only and do not facilitate efficient compression of already trained CNN models. We optimally transform (decorrelate) and quantize the weights post-training using a rate–distortion framework to improve compression at any given quantization bit-rate. Transform quantization unifies quantization and dimensionality reduction (decorrelation) techniques in a single framework to facilitate low bit-rate compression of CNNs and efficient inference in the transform domain. We first introduce a theory of rate and distortion for CNN quantization and pose optimum quantization as a rate–distortion optimization problem. We then show that this problem can be solved using optimal bit-depth allocation following decorrelation by the optimal End-to-end Learned Transform (ELT) we derive in this paper. Experiments demonstrate that transform quantization advances the state of the art in CNN compression in both retrained and non-retrained quantization scenarios. In particular, we find that transform quantization with retraining is able to compress CNN models such as AlexNet, ResNet and DenseNet to very low bit-rates (1–2 bits). [ABSTRACT FROM AUTHOR]

In the context of electromagnetic compatibility ventilation panels with small quasi-periodic openings exist. The standard finite element (FE) method leads to a large number of unknowns for ventilation panels. An efficient multiscale FE approach is introduced to cope with the multiple scale problem. To this end, special functions are computed by solving cell problems for a single aperture with FEs of higher order to enhance the FE space. The proposed technique is able to cope with varying thickness es of the metallic sheet of the ventilation panel and arbitrary shapes of apertures, as well as lossy materials and frequencies in a wide range. The introduced method improves essentially the accuracy of coarse FE simulations for ventilation panels in 3-D. [ABSTRACT FROM AUTHOR]

This paper proposes a new method that combines checkpointing methods with error-controlled lossy compression for large-scale high-performance full-waveform inversion (FWI), an inverse problem commonly used in geophysical exploration. This combination can significantly reduce data movement, allowing a reduction in run time as well as peak memory. In the exascale computing era, frequent data transfer (e.g., memory bandwidth, PCIe bandwidth for GPUs, or network) is the performance bottleneck rather than the peak FLOPS of the processing unit. Like many other adjoint-based optimization problems, FWI is costly in terms of the number of floating-point operations, large memory footprint during backpropagation, and data transfer overheads. Past work for adjoint methods has developed checkpointing methods that reduce the peak memory requirements during backpropagation at the cost of additional floating-point computations. Combining this traditional checkpointing with error-controlled lossy compression, we explore the three-way tradeoff between memory, precision, and time to solution. We investigate how approximation errors introduced by lossy compression of the forward solution impact the objective function gradient and final inverted solution. Empirical results from these numerical experiments indicate that high lossy-compression rates (compression factors ranging up to 100) have a relatively minor impact on convergence rates and the quality of the final solution. [ABSTRACT FROM AUTHOR]

This paper presents research of lossy coding impact on speech recognition with convolutional neural networks. For this purpose, google speech commands dataset containing utterances of 30 words was encoded using four most common all-purpose codecs: mp3, aac, wma and ogg. A convolutional neural network was taught using part of the original files and later tested with the rest of the files, as well as their counterparts encoded with different codecs and bitrates. The same network model was also taught using mp3 encoded data showing the biggest loss in effectiveness of the previous network. Results show that lossy coding does have an effect on speech recognition, especially for low bitrates. [ABSTRACT FROM AUTHOR]

Quantization is an important technique to transform the input sample values from a large set (or a continuous range) into the output sample values in a small set (or a finite set). It has been applied broadly for lossy-data compression, pattern recognition, probability density estimation, and clustering. Vector quantization (VQ) is a prevalent image-compression technique, which treats image matrices as stretched vectors and then finds the representative stretched vectors accordingly for a given image data set. One can use tensor data representation to directly characterize the original two-dimensional image data rather than stretch the image matrix into a long vector so as to destroy the original two-dimensional data structure. In this work, we propose a new tensor quantization (TQ) framework which does not need to reduce the dimensionality of the original image data and destroy the original two-dimensional spatial relationship among data; these two serious drawbacks of vector quantization are well known. We first present tensor calculus and then propose a new parallel tensor-inversion algorithm for TQ thereupon. We also establish the pertinent theoretical proof to justify that our proposed new TQ approach is superior to the existing VQ approach especially as the image dimension becomes large. Finally, numerical experiments to evaluate the image-compression performances of VQ and TQ are demonstrated and their corresponding computational-complexities are also compared. [ABSTRACT FROM AUTHOR]