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Most of the information security management research involving fear appeals is guided by either protection motivation theory or the extended parallel processing model. Over time, extant research has extended these theories, as well as their derivative theories, in a variety of ways, leading to several theoretical and empirical inconsistencies. The large body of fragmented, and sometimes conflicting, research has muddied the broader understanding of what drives protection- and defensive motivation. We provide guidance to the security discourse by offering the first study in the literature to employ two-stage meta-analytic structural equation modeling (TSSEM), which combines covariance-based structural equation modeling and meta-analysis. Information systems (IS) researchers have traditionally used meta-analysis for structural equation modeling for such purposes—an approach that has several serious statistical flaws. Using 341 systematically selected empirical security articles (representing 383 unique studies) and TSSEM, we pool a large series of five datasets to test six models, from which we examine the effects of constructs and paths in the security fear-appeals literature. We compare and test six versions of models inspired by issues in the broader fear-appeals literature. We confirm the importance of both the threat- and coping-appraisal processes; establish the central role of fear and that it has greater importance than threat; show that efficacy is a stronger predictor of protection motivation than is threat; demonstrate that response costs as currently measured are ineffective but that maladaptive rewards have a strong negative effect on protection motivation and a positive effect on defensive motivation; and provide evidence that dual models of danger control and fear control should be used. [ABSTRACT FROM AUTHOR]

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing. [ABSTRACT FROM AUTHOR]

This study investigates the cortical correlates of motor response control and monitoring, using the Theory of Event Coding (TEC) as a framework to investigate signals related to low‐level sensory processing of motor reafference and high‐level response monitoring, including verification of response outcomes with the internal model. We used a visuomotor paradigm with two targets at different distances from the participant. For the recorded movement‐related cortical potentials (MRCPs), we analyzed their different components and assessed the movement phases during which they are active. Residual iteration decomposition (RIDE) and multivariate pattern analysis (MVPA) were used for this analysis. Using RIDE, we separated MRCPs into signals related to different parallel processes of visuomotor transformation: stimulus processing (S‐cluster), motor response preparation and execution (R‐cluster), and intermediate processes (C‐cluster). We revealed sequential activation in the R‐cluster, with execution‐related negative components and positive contralateral peaks reflecting reafference processing. We also identified the motor post‐imperative negative variation within the R‐cluster, highlighting the response outcome evaluation process included in the action file. Our findings extend the understanding of C‐cluster signals, typically associated with stimulus–response mapping, by demonstrating C‐activation from the preparatory stages through to response termination, highlighting its participation in action monitoring. In addition, we highlighted the ability of MVPA to identify movement‐related attribute encoding: where statistical analysis showed independence of stimulus processing activity from movement distance, MVPA revealed distance‐related differences in the S‐cluster within a time window aligned with the lateralized readiness potential (LRP). This highlights the importance of integrating RIDE and MVPA to uncover the intricate neural dynamics of motor control, sensory integration, and response monitoring. [ABSTRACT FROM AUTHOR]

Background: Although significant advances have been made in the conceptualization of sustainability, having pragmatic, psychometrically valid tools remains a need within the field. Our previous work has developed frameworks and tools to assess both program sustainability and clinical sustainability capacity. This work presents new, psychometrically tested short versions of the Program Sustainability Assessment Tool (PSAT) and the Clinical Sustainability Assessment Tool (CSAT). Methods: These methods were conducted in identical, parallel processes for the CSAT and PSAT. Previously collected data for these instruments was obtained across a variety of settings, contexts, and participants. We first conducted testing to determine cronbach's alpha of shortened domains (3 items each) and then conducted Confirmatory Factor Analysis to ensure that the domains were still appropriate for the tool. After, the team met to review the results and determine the final versions of the short PSAT and short CSAT. Results: The short PSAT retained cronbach's alpha's of 0.82 – 0.91 for each domain of the tool, with which maintains excellent reliability for the tool. Confirmatory factor analysis highlights that the short PSAT retains conceptual distinction across the 8 domains, with CFI scores greater than 0.90, RMSEA scores below 0.6, and SRMR scores less than 0.08. The short CSAT had cronbach's alpha of 0.84 – 0.92 for each of the domains of the tool, also suggesting excellent reliability of the domains within the measure after dropping two items/domain. Confirmatory factor analysis of the short CSAT meets the same specifications as above, again highlighting conceptual distinction across the domains. Conclusion: Each tool was able to be shortened to three items per domain while maintaining strong psychometric properties. This results in a tool that takes less time to complete, meeting one of the key calls for pragmatic measures within implementation science. This advances our abilities to measure and test sustainability within implementation science. [ABSTRACT FROM AUTHOR]

Recently, the convolutional neural network (CNN) has gained widespread adoption in the hyperspectral image (HSI) classification owing to its remarkable feature extraction capability. However, the fixed acceptance domain of CNN restricts it to Euclidean image data only, making it difficult to capture complex information in hyperspectral data. To overcome this problem, much attention has been paid to the graph attention network (GAT), which can effectively model graph structure and capture complex dependencies between nodes. However, GAT usually acts on superpixel nodes, which may lead to the loss of pixel-level information. To better integrate the advantages of both, we propose a CNN-assisted multi-hop graph attention network (CMGAT) for HSI classification. Specifically, a parallel dual-branch architecture is first constructed to simultaneously capture spectral-spatial features from hyperspectral data at the superpixel and pixel levels using GAT and CNN, respectively. On this basis, the multi-hop and multi-scale mechanisms are further employed to construct a multi-hop GAT module and a multi-scale CNN module to capture diverse feature information. Secondly, an attention module is cascaded before the multi-scale CNN module to improve classification performance. Eventually, the output information from the two branches is weighted and fused to produce the classification result. We performed experiments on four benchmark HSI datasets, including Indian Pines (IP), University of Pavia (UP), Salinas Valley (SV) and WHU-Hi-LongKou (LK). The results demonstrate that the proposed method outperforms several deep learning methods, achieving overall accuracies of 95.67%, 99.04%, 99.55% and 99.51%, respectively, even with fewer training samples. [ABSTRACT FROM AUTHOR]

Subitizing is the ability to appraise a number of small quantities (up to four) rapidly and precisely. This system, however, can be impaired by distractors presented along with targets to be enumerated. To better understand whether this limitation arises in perceptual circuits or in the response selection stage, we investigated whether subitizing can endure in simultaneous comparison tasks. Participants were asked to compare the number of dots in two sets on the left and right sides of the screen, presented either simultaneously or sequentially. For comparing within the numerosity range (6–32 dots), both the error rate and reaction time increased steadily as the ratio between the two numbers compared approached "1." Namely, a phenomenon labeled the ratio effect was revealed. For comparison with small numbers (<5), the sequential comparison task was errorless despite the ratio, suggesting the feature of subitizing. Individual efficiency (measured by the inverse efficiency score [IES]) did not correlate between number ranges in sequential comparison, suggesting that distinct mechanisms were involved. However, we found that in simultaneous tasks, error rate and efficiency showed an increase as the ratios of the two numbers compared approached "1." This is similar to the ratio effect revealed in the comparison for moderate numbers. Individual efficiency within these two ranges correlated, indicating that the enumeration within these two ranges was based on a single mechanism. These results suggest that subitizing cannot process sets in parallel, and numerosity takes the job whenever subitizing fails. [ABSTRACT FROM AUTHOR]

Graph pattern matching is widely used in real-world applications, such as social network analysis. Since the traditional subgraph isomorphism is NP-complete and often too restrictive to catch sensible matches, relaxed graph pattern matching models are used. However, existing algorithms suffer from limited linear scalability and restricted degrees of parallelism. In this paper, we propose fast parallel algorithms, GPGS and GPDS, for graph simulation and dual simulation, respectively. They make most use of the GPU performance by adopting the edge-centric processing model. We perform parallel computations on the data graph edges to evaluate the matching constraints for each vertex allowing for fast and scalable algorithms. To the best of our knowledge, we present the first GPU-based algorithms for graph simulation and dual simulation. Extensive experiments on synthetic and real-world data graphs demonstrate that our algorithms significantly outperform existing methods, achieving up to 74.8 × acceleration for GPGS and up to 114.2 × acceleration for GPDS. [ABSTRACT FROM AUTHOR]

Flux flattening and power uprating of large heavy water power reactors (HWRs) are demonstrated as an application of an accelerator-driven photoneutron source (ADS) in the ADS-CANDU concept where an array of electron linear accelerators is configured around the periphery of a subcritical CANDU-6 core. The localized ADS generated through (e−,γ,n) reactions in the HWR lattice perturbs the reactor power distribution by increasing the power of low-power bundles and depressing the power at the core center relative to the fundamental mode power distribution. Gross power uprating is feasible when the system is near critical, but the ADS array consumes tens of megawatts electric exceeding the power gained by a factor of more than 2 for the conservative ADS performance specifications assumed in the analysis. Several important challenges of fixed-source Monte Carlo simulations of near-critical multiplying media are investigated including severe load imbalance issues with distributed-memory parallel computing architecture and correlated local tallies in nonanalog (implicit absorption) Monte Carlo radiation transport. All subcritical fixed-source simulations in the study readily exceed the default random number stride used in most production Monte Carlo codes, and the stride exceedance causes both bias in local tally results (bundle powers) and spatial autocorrelation of these errors/biases in the large core. A legacy stride exceedance is critically reviewed, and the conclusions and subsequent interpretations of those conclusions are rejected. Several classes of radiation transport Monte Carlo problems are likely to be susceptible to stride exceedance, and this issue needs to be promptly addressed by the Monte Carlo analyst and code developer communities. [ABSTRACT FROM AUTHOR]

Advancements in technology have driven the miniaturization of embedded systems, making them more cost-effective and energy-efficient for wireless applications. As a result, the number of connectable devices in Internet of Things (IoT) networks has increased significantly, creating the challenge of linking them effectively and economically. The space industry has long recognized this challenge and invested in satellite infrastructure for IoT networks, exploiting the potential of edge computing technologies. In this context, it is of critical importance to enhance the onboard computing capabilities of satellites and develop enabling technologies for their advancement. This is necessary to ensure that satellites are able to connect devices while reducing latency, bandwidth utilization, and development costs, and improving privacy and security measures. This paper presents the GPU@SAT DevKit: an ecosystem for testing a high-performance, general-purpose accelerator designed for FPGAs and suitable for edge computing tasks on satellites. This ecosystem provides a streamlined way to exploit GPGPU processing in space, enabling faster development times and more efficient resource use. Designed for FPGAs and tailored to edge computing tasks, the GPU@SAT accelerator mimics the parallel architecture of a GPU, allowing developers to leverage its capabilities while maintaining flexibility. Its compatibility with OpenCL simplifies the development process, enabling faster deployment of satellite-based applications. The DevKit was implemented and tested on a Zynq UltraScale+ MPSoC evaluation board from Xilinx, integrating the GPU@SAT IP core with the system's embedded processor. A client/server approach is used to run applications, allowing users to easily configure and execute kernels through a simple XML document. This intuitive interface provides end-users with the ability to run and evaluate kernel performance and functionality without dealing with the underlying complexities of the accelerator itself. By making the GPU@SAT IP core more accessible, the DevKit significantly reduces development time and lowers the barrier to entry for satellite-based edge computing solutions. The DevKit was also compared with other onboard processing solutions, demonstrating similar performance. [ABSTRACT FROM AUTHOR]

In this study, we investigate the underlying causes of drift in the time history curves of measured parameters obtained through strain electrical measurements and assess their impacts on load measurements. To address the challenge of efficiently processing large volumes of aircraft load data, we propose and analyze a multi-level parallel algorithm specifically designed for the data processing of aircraft load measurements. To achieve this objective, we discuss parallel processing at both medium- and fine-grained levels and develop two distinct parallel processing algorithms: one for coarse- and medium-grained aircraft-type data streams, and another for medium- and fine-grained takeoff and landing data streams. The efficacy of these algorithms is validated through the processing of load data measured on a specific aircraft wing. The results demonstrate that the proposed approach offers a novel technical pathway for large-scale scientific computations and enhances data processing efficiency in the domain of aircraft load spectrum analysis. [ABSTRACT FROM AUTHOR]

The scale and complexity of big data are growing continuously, posing severe challenges to traditional data processing methods, especially in the field of clustering analysis. To address this issue, this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update (BDTMCDIncreUpdate), which combines distributed computing, storage technology, and incremental update techniques to provide an efficient and effective means for clustering analysis. Firstly, the original dataset is divided into multiple sub-blocks, and distributed computing resources are utilized to process the sub-blocks in parallel, enhancing efficiency. Then, initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results. When new data arrives, incremental update technology is employed to update the core tensor and factor matrix, ensuring that the clustering model can adapt to changes in data. Finally, by combining the updated core tensor and factor matrix with historical computational results, refined clustering results are obtained, achieving real-time adaptation to dynamic data. Through experimental simulation on the Aminer dataset, the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy (ACC) and normalized mutual information (NMI) metrics, achieving an accuracy rate of 90% and an NMI score of 0.85, which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios. Therefore, the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis, integrating distributed computing, incremental updates, and tensor-based multi-clustering techniques. It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments. This method shows great potential in real-world applications where dynamic data growth is common, and it is of significant importance for advancing the development of data analysis technology. [ABSTRACT FROM AUTHOR]

This article introduces a paradigm shift in radar technology with field programmable gate array (FPGA)-driven Phase estimation using floating point arithmetic (FPA). Leveraging FPGA's parallel processing and the precision of FPA, this work promises enhanced accuracy and efficiency. The proposed system's key performance metrics include the following: number of slices: 20,941, number of look-up tables (LUTs): 22,371, number of digital signal processing (DSP) blocks: 2, delay: 112.9 ns, and power consumption: 7.2 mw. A comparative analysis showcases advantages in area utilization, LUT, and DSP blocks despite a trade-off with delay. The presented methodology and results demonstrate the feasibility of real-time phase estimation at GHz rates, positioning this approach as transformative for next-gen radar systems. [ABSTRACT FROM AUTHOR]

Through analyses of Russian voicing assimilation and German dorsal fricative assimilation, this article argues for a restricted version of structure preservation and a stratal model of Optimality Theory. Structure preservation (Kiparsky 1985) prohibits the creation of allophones early in the phonological computation. The parallel architecture of OT undermines the assumptions of structure preservation, which has been widely rejected within OT. This article demonstrates that processes that are both neutralizing and non-structure-preserving, and which involve overlapping sets of targets and triggers, such as Russian voicing assimilation, result in a ranking paradox in parallel OT. Purely allophonic processes, such as German fricative assimilation, do not pose the same difficulties. Analyses of both processes are proposed within the framework of Stratal OT. The use of multiple strata eliminates the ranking paradox illustrated in Russian voicing assimilation and accounts for the interaction of German fricative assimilation and umlaut. This account predicts that non-structure-preserving neutralization cannot take place at the earliest level of evaluation but must apply after the rich base is filtered to the language-specific inventory and stem-level processes are applied. In the case of Russian, this is substantiated by application of assimilation across clitic boundaries, requiring phrase-level application. [ABSTRACT FROM AUTHOR]

A new optimization strategy based on system performance optimization life cycle (SPOLC) is introduced for high-performance lattice Boltzmann simulations of three-dimensional decaying isotropic turbulence. This strategy improves the performance of turbulent flow simulations in periodic boxes at different resolutions using the lattice Boltzmann method (LBM). The strategy improves the performance by modifying the lattice Boltzmann model, mathematical representation, computational algorithm, software implementation, and computing hardware utilization. The modifications include: (1) Establishing the slice concept as a logical grouping layer added to the LBM, applying an aggregation–disaggregation mechanism, enabling two-dimensional (2D) operation on the three-dimensional (3D) model, (2) improving lattice data access pattern by using an alternative one-dimensional (1D) array for numerical representation instead of the 3D cubic representation, (3) major reduction in memory access iterations by switching from function-wise iteration method to lattice-wise iteration method by applying code fusion to the streaming, velocity and collision model functions and iterations, (4) applying process parallelization and data vectorization, (5) achieving a much more efficient utilization of modern compute units by increasing the adaption of stream processing model. Furthermore, a correctness validation process has been applied by conducting lattice-wise value comparisons between the proposed solution output and the original implementation output. Simulations of decaying isotropic turbulence at resolutions ranging from 323 to 5123 using the LBM are carried out for these purposes. Calculations are performed on two systems with distinct specifications, to validate the effectiveness and portability of the SPOLC strategy. The calculation times are significantly reduced after applying the SPOLC strategy on S1 with the lattice Boltzmann relaxation time τ=0.503 by over 69% compared to S2’s original time, increasing to 95.47% at higher resolutions. Different features of the flow fields are depicted and their characteristics are discussed. Thin tubes are visualized, and the energy spectra are studied. All fields are initialized by a forced turbulent field simulated in a previous study using the LBM. [ABSTRACT FROM AUTHOR]

Metaheuristic is one of the techniques to improve the security of image encryption. However, existing metaheuristic image cryptosystems based on metaheuristic may have convergence difficulties during the optimization process, which cause insecurity and slow convergence. Besides, the time cost of the parallel execution model applied to metaheuristic image cryptosystems is not low enough. Therefore, a parallel many‐objective optimized key generation framework is proposed. Firstly, if four or more security indicators of cryptosystem, which are the results of security test, need to be optimized, the many‐objective optimization algorithm is employed to the proposed framework. With method adjusts the chaotic system parameters as the optimization key, which effectively avoid the convergence difficulty of the encryption key. Secondly, a master‐slave parallel model is improved to metaheuristic encryption. The model allocates the most time‐consuming fitness calculation work to slave nodes, which makes the modified model more reasonable and thus reduces the encryption time. To evaluate the performance of the proposed framework, a specific encryption scheme is constructed, that utilizes a 2D quadric map and many‐objective optimization algorithm based on dominance and decomposition (MOEA/DD) to optimize five security indicators. Experimental results reveal that this scheme has good security performances and less parallel encryption time. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Vehicles (IoV), the demand for secure and efficient signature verification is becoming increasingly urgent. To meet this need, we propose an efficient SM9 aggregate signature scheme implemented on Field-Programmable Gate Array (FPGA). The scheme includes both fault-tolerant and non-fault-tolerant aggregate signature modes, which are designed to address challenges in various network environments. We provide security proofs for these two signature verification modes based on a K-ary Computational Additive Diffie–Hellman (K-CAA) difficult problem. To handle the numerous parallelizable elliptic curve point multiplication operations required during verification, we utilize FPGA's parallel processing capabilities to design an efficient parallel point multiplication architecture. By the Montgomery point multiplication algorithm and the Barrett modular reduction algorithm, we optimize the single-point multiplication computation unit, achieving a point multiplication speed of 70776 times per second. Finally, the overall scheme was simulated and analyzed on an FPGA platform. The experimental results and analysis indicate that under error-free conditions, the proposed non-fault-tolerant aggregate mode reduces the verification time by up to 97.1% compared to other schemes. In fault-tolerant conditions, the proposed fault-tolerant aggregate mode reduces the verification time by up to 77.2% compared to other schemes. When compared to other fault-tolerant aggregate schemes, its verification time is only 28.9% of their consumption, and even in the non-fault-tolerant aggregate mode, the verification time is reduced by at least 39.1%. Therefore, the proposed scheme demonstrates significant advantages in both error-free and fault-tolerant scenarios. [ABSTRACT FROM AUTHOR]

With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. Current graph data protection algorithms lack sufficient research on the characteristics of social users, while simultaneously incurring significant time and space overhead. Additionally, the strategies lack adaptability in incorporating noise, often resulting in a subsequent decrease in data availability. To address these issues, a novel approach called the Information Entropy-driven Adaptive Differential Privacy Protection Scheme (IEA-DP) is presented for the release of social data in this study. The proposed solution initially designs the InfomapMerge algorithm to divide the data into communities based on the characteristics of social networks, thereby enabling parallel processing, and mitigating the time and space overhead. Subsequently, the Adaptive Edge Modification Algorithm (AEMA) is proposed to optimize the noise addition process by adaptively adding noise based on the score of node importance. This effectively reduces the amount of added noise, increasing data availability. Finally, the experimental results conducted on six public datasets demonstrate that the IEA-DP scheme strikes a desirable balance between data availability and privacy. [ABSTRACT FROM AUTHOR]

In process mining, an event log is a structured collection of recorded events that describes the execution of processes within an organization. The completeness of event logs is crucial for ensuring accurate and reliable process models. Incomplete event logs, which can result from system errors, manual data entry mistakes, or irregular operational patterns, undermine the integrity of these models. Addressing this issue is essential for constructing accurate models. This research aims to enhance process model performance and robustness by transforming incomplete event logs into complete ones using a process discovery algorithm. Genetic process mining, a type of process discovery algorithm, is chosen for its ability to evaluate multiple candidate solutions concurrently, effectively recovering missing events and improving log completeness. However, the original form of the genetic process mining algorithm is not optimized for handling incomplete logs, which can result in incorrect models being discovered. To address this limitation, this research proposes a modified approach that incorporates timing information to better manage incomplete logs. By leveraging timing data, the algorithm can infer missing events, leading to process tracking and reconstruction which is more accurate. Experimental results validate the effectiveness of the modified algorithm, showing higher fitness and precision scores, improved process model comparisons, and a good level of coverage without errors. Additionally, several advanced metrics for conformance checking are presented to further validate the process models and event logs discovered by both algorithms. [ABSTRACT FROM AUTHOR]

Color discrimination is fundamental to human behavior. We find bananas by coarsely searching for yellow but then differentiate nuances of yellow to pick the best exemplars. How does the brain adjust the resolution of color selectivity to our changing needs? Here, we analyze the brain magnetic response in the human visual cortex to show that color selectivity is adaptively set by coarse-and fine-resolving processes running in parallel at different hierarchical levels. Those include a gain enhancement in the higher-lever cortex of color units tuned away from the target to resolve very similar colors and a coarsely resolving gain enhancement in the mid-level cortex of units tuned to the target. Our findings suggest that attention operates on a form of multiresolution representation of color at different levels in the visual hierarchy, which keeps selectivity adaptive to a changing resolution context. [ABSTRACT FROM AUTHOR]

All‐optical logic gates (AOLGs) have attracted considerable attention for their high computing speed, low power consumption, and parallel processing; however, chemical design of new luminescent materials is a challenge to explore a facile platform for undertaking AOLGs. Here, an AOLG system is developed based on the organic–inorganic hybrid halides [NH3(CH2)2NH3]2ZnCl6:Mn2+ with tricolor luminescence under dual‐wavelength excitation, and the intrinsic emission mechanisms are further addressed. When employing 275 and 365 nm UV excitations as input signals, the AOLGs conduct Boolean operations “OR, YES, AND” via identifying the green (G) and red (R) emissions and their intensity ratio (R/G) as output signals. This work further demonstrates the AOLG operation imaging and image processing via hybrid halide emitters, indicating promise in optical computing applications. [ABSTRACT FROM AUTHOR]

Trends over the past two decades indicate that much of the performance gains of commercial optimization solvers is due to improvements in x86 hardware. To continue making progress, it is critical to consider alternative/specialized massively parallel computing architectures. In this work, we detail the development of an open-source source code transformation approach built using Symbolics.jl to construct McCormick-based relaxations of functions that enables their effective parallelized evaluation. We then apply this approach in a novel parallelized branch-and-bound routine that offloads lower- and upper-bounding problems to a GPU. The effectiveness of this new approach is demonstrated on three nonconvex problems of interest, where it yields convergence time improvements of 22–118x compared to an equivalent serial CPU implementation and in two cases outperforms vanilla branch-and-bound versions of existing state-of-the-art solvers that use tighter bounding techniques. This work exemplifies how deterministic global optimizers using alternative hardware architectures can compete with—or eventually outclass—even the most powerful serial CPU implementations, and to the best of the authors' knowledge, represents the first successful demonstration of deterministic global optimization using a GPU. [ABSTRACT FROM AUTHOR]

Summary: Power system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far. [ABSTRACT FROM AUTHOR]

Benefiting from low power consumption and high processing speed, there is a growing interest in diffraction neural networks (DNNs), which are typically showcased with 3D printing devices, leading to large volumes, high costs, and low levels of integration. Metasurfaces can desirably manipulate wavefronts of electromagnetic waves, providing a compact platform for mimicking DNNs with novel functions. Although multi‐wavelength and multi‐target recognition provides a richer and more detailed understanding of complex environments, existing architectures are primarily trained to classify a single target at a specific wavelength. A metasurface approach is proposed to design multiplexed DNNs that can classify multiple targets and spatial sequences across various wavelengths in multiple channels. To realize multi‐task processing, the dielectric metasurface is designed based on phase and wavelength multiplexing, which can integrate multi‐target DNNs with different tasks such as operating at distinct wavelengths and classifying diverse targets. The efficacy of this method is exemplified through the numerical simulation and experimental demonstration of recognizing a single target with two wavelengths, two targets at a single wavelength, and two targets at dual wavelengths. This compact metasurface approach enables the design of multi‐target and multi‐wavelength DNNs, opening a new window to develop massively parallel processing and versatile artificial intelligence systems. [ABSTRACT FROM AUTHOR]

With the advantages of parallel processing, high‐speed operation, and ultra‐low power consumption, optical computing presents immense potential in this age of information explosion. As a fundamental optical computing component, optical differentiator has extensive applications, particularly excelling in the real‐time processing and recognition of high‐frame‐rate image information. An ideal optical differentiator needs to adapt to different application scenarios, such as arbitrary operating wavelengths, complex or even transparent targets. Furthermore, performing differential operations of varying orders on target optical field can extract multi‐level information. Unfortunately, most current differential computing devices can only meet part of the requirements, which limits the attained information dimensions and hinders the development of their practical application. Herein, a universal platform based on liquid crystals (LCs) is proposed to achieve 0th‐, 1st‐, and 2nd‐order differential operations of amplitude‐typed or even phase‐typed objects within a broadband range. The switching of different differentiation orders can be accomplished by straightforward adjusting the applied voltages of the differentiator. This approach is capable of extracting more feature information including bright‐field, outline, and edges images of versatile targets. With advanced characteristics of multi‐dimension, broadband and multifunction, the work can flourish applications in fields of image processing, biomedical imaging and optical analog computation, etc. [ABSTRACT FROM AUTHOR]

Background: Intensity‐modulated proton therapy (IMPT) optimizes spot intensities and position, providing better conformability. However, the successful application of IMPT is dependent upon addressing the challenges posed by range and setup uncertainties. In order to address the uncertainties in IMPT, robust optimization is essential. Purpose: This study aims to develop a novel fast algorithm for robust optimization of IMPT with minimum monitor unit (MU) constraint. Methods and Materials: The study formulates a robust optimization problem and proposes a novel, fast algorithm based on the alternating direction method of multipliers (ADMM) framework. This algorithm enables distributed computation and parallel processing. Ten clinical cases were used as test scenarios to evaluate the performance of the proposed approach. The robust optimization method (RBO‐NEW) was compared with plans that only consider nominal optimization using CTV (NMO‐CTV) without handling uncertainties and PTV (NMO‐PTV) to handle the uncertainties, as well as with conventional robust‐optimized plans (RBO‐CONV). Dosimetric metrics, including D95, homogeneity index, and Dmean, were used to evaluate the dose distribution quality. The area under the root‐mean‐square dose (RMSD)–volume histogram curves (AUC) and dose–volume histogram (DVH) bands were used to evaluate the robustness of the treatment plan. Optimization time cost was also assessed to measure computational efficiency. Results: The results demonstrated that the RBO plans exhibited better plan quality and robustness than the NMO plans, with RBO‐NEW showing superior computational efficiency and plan quality compared to RBO‐CONV. Specifically, statistical analysis results indicated that RBO‐NEW was able to reduce the computational time from 389.70±207.40$389.70\pm 207.40$ to 228.60±123.67$228.60\pm 123.67$ s (p<0.01$p<0.01$) and reduce the mean organ‐at‐risk (OAR) dose from 9.38±12.80$9.38\pm 12.80$% of the prescription dose to 9.07±12.39$9.07\pm 12.39$% of the prescription dose (p<0.05$p<0.05$) compared to RBO‐CONV. Conclusion: This study introduces a novel fast robust optimization algorithm for IMPT treatment planning with minimum MU constraint. Such an algorithm is not only able to enhance the plan's robustness and computational efficiency without compromising OAR sparing but also able to improve treatment plan quality and reliability. [ABSTRACT FROM AUTHOR]

This article addresses the long-standing problem of program correctness for programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level models expressed as transition systems. The implementation language underlying the method is based on the concurrency model of actors and active objects. The method defines program correctness in terms of a simulation relation between the transition system that specifies the program semantics of the parallel program and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the concurrency model of the implementation language considered in this article. As a proof of concept, we apply our method to the correctness of a parallel simulator of multicore memory systems. [ABSTRACT FROM AUTHOR]

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO. [ABSTRACT FROM AUTHOR]

Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique properties of the TPs and their remarkable performance. Nowadays, the demand for high-quality images (2D signals) is increasing and is expected to continue growing. The processing of these signals requires the generation of accurate and fast polynomials. The existing algorithms generate the TPs sequentially, and this is considered as computationally costly for high-order and larger-sized polynomials. To this end, we present a new efficient solution to overcome the limitation of sequential algorithms. The presented algorithm uses the parallel processing paradigm to leverage the computation cost. This is performed by utilizing the multicore and multithreading features of a CPU. The implementation of multithreaded algorithms for computing TP coefficients segments the computations into sub-tasks. These sub-tasks are executed concurrently on several threads across the available cores. The performance of the multithreaded algorithm is evaluated on various TP sizes, which demonstrates a significant improvement in computation time. Furthermore, a selection for the appropriate number of threads for the proposed algorithm is introduced. The results reveal that the proposed algorithm enhances the computation performance to provide a quick, steady, and accurate computation of the TP coefficients, making it a practical solution for different applications. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the malignancies with high mortality rates worldwide, and its timely detection and accurate diagnosis are crucial for improving patient prognosis. To address the limitations of traditional image segmentation techniques and the U-Net network in capturing fine image features, this study proposes an improved model based on the U-Net architecture, named RHEU-Net. By replacing traditional convolution modules in the encoder and decoder with improved residual modules, the network's feature extraction capabilities and gradient stability are enhanced. A Hybrid Gated Attention (HGA) module is integrated before the skip connections, enabling the parallel processing of channel and spatial attentions, optimizing the feature fusion strategy, and effectively replenishing image details. A Multi-Scale Feature Enhancement (MSFE) layer is introduced at the bottleneck, utilizing multi-scale feature extraction technology to further enhance the expression of receptive fields and contextual information, improving the overall feature representation effect. Testing on the LiTS2017 dataset demonstrated that RHEU-Net achieved Dice scores of 95.72% for liver segmentation and 70.19% for tumor segmentation. These results validate the effectiveness of RHEU-Net and underscore its potential for clinical application. [ABSTRACT FROM AUTHOR]

Task‐related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network‐level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal‐hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small‐world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6‐months or below and above 6‐months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6‐months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network. [ABSTRACT FROM AUTHOR]

Long-strip imaging is an important way of improving the coverage and acquisition efficiency of remote sensing satellite data. During the agile maneuver imaging process of the satellite, the LuoJia3-01 satellite can obtain a sequence of array long-strip images with a certain degree of overlap. Limited by the relative accuracy of satellite attitude, there will be relative misalignment between the sequence frame images, requiring high-precision geometric processing to meet the requirements of large-area remote sensing applications. Therefore, this study proposes a new method for the geometric correction of long-strip images without ground control points (GCPs) through GPU acceleration. Firstly, through the relative orientation of sequence images, the relative geometric errors between the images are corrected frame-by-frame. Then, block perspective transformation and image point densified filling (IPDF) direct mapping processing are carried out, mapping the sequence images frame-by-frame onto the stitched image. In this way, the geometric correction and image stitching of the sequence frame images are completed simultaneously. Finally, computationally intensive steps, such as point matching, coordinate transformation, and grayscale interpolation, are processed in parallel using GPU to further enhance the program's execution efficiency. The experimental results show that the method proposed in this study achieves a stitching accuracy of less than 0.3 pixels for the geometrically corrected long-strip images, an internal geometric accuracy of less than 1.5 pixels, and an average processing time of less than 1.5 s per frame, meeting the requirements for high-precision near-real-time processing applications. [ABSTRACT FROM AUTHOR]

Applying blockchain technology to the cloud manufacturing platform (CMfg) endowed it with features such as decentralized resource management, distributed data storage, and platform robustness. However, during the implementation of service optimization composition, challenges are encountered, including the need for centralized resource scheduling, data entry dependence on the moment of block generation, and a lack of dynamic adaptability in the execution scheme. To address this, this paper proposed a solution in the form of cloud dual-chain oriented to service optimization composition. The cloud dualchain comprised a manufacturing service chain (MSC) and a process data chain (PDC). It segregated the management of service resources and composition scheme into distinct chains, allowing the CMfg to reconcile blockchain characteristics with real-world service collaboration needs. The manufacturing service within the cloud dual-chain was self-managed by the enterprise, and the MSC chain was formed by smart contracts based on task orders, balancing distributed and centralized management. The PDC, generated based on service composition, recorded process data in parallel. In case of service failure, a new service was invoked in the corresponding MSC node, updating the information in the PDC node to ensure dynamic adaptability of the execution plan. Experimental results demonstrate that cloud dual-chain facilitates flexible service management, distributed parallel entry of process data, and provides dynamic adaptability to execution scheme. Cloud dual-chain can effectively support the development of CMfg. [ABSTRACT FROM AUTHOR]

In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively. [ABSTRACT FROM AUTHOR]

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What's more, these models often overlook the exploitation of PAN images' inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network's feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Purpose: Previous studies examining the inter-relations between serial and discrete naming with reading have found that the ability to efficiently process multiple items presented in a sequence (indexed by serial naming) is a unique predictor of word- and text-reading fluency. However, conclusions have been tempered by the concurrent nature of the available data and the uniformly low demands of the materials (words and texts). Here we go beyond previous studies by using more varied materials to examine the relations of serial and discrete naming with the discrete reading of words and the serial reading of word lists and connected text over time. Method: Two hundred and eight English-speaking Canadian children (51% female, Mage = 7.2 years) were followed from Grade 2 to Grade 5 and were assessed on serial and discrete digit naming and serial and discrete word reading at both measurement points. Results: Strong associations between discrete naming and discrete reading already from Grade 2 indicated that short and high-frequency words were processed in parallel early in development. By Grade 5, when word recognition was presumably automatized, serial naming accounted for unique variance in serial reading of word lists and connected texts after controlling for discrete word reading. More importantly, Latent Change Score modeling indicated that serial naming was the main predictor of growth in serial reading from Grade 2 to Grade 5. Conclusion: These findings suggest that, beyond individual word recognition, reading fluency development also requires efficient processing of multiple items presented in serial format (termed "cascaded processing"). [ABSTRACT FROM AUTHOR]

We explore the possibility of assuming largely flat syntactic structures in Simpler Syntax, suggesting that these are plausible alternatives to conventional hierarchical structures. We consider the implications of flat structure for analyses of various linguistic phenomena in English, including heavy NP shift, extraposition, topicalization and constituent order variation in the VP. We also sketch a general strategy to circumvent some of the problems flat structure is said to cause for semantic interpretation. Our proposals eliminate the need for movement, unpronounced copies and feature-bearing nodes postulated to trigger syntactic operations. We assume the Parallel Architecture and use declarative schemas to establish direct correspondences between phonology on the one hand and syntactic and semantic structures on the other. The resulting picture is one in which narrow syntax can be relatively stable across languages and constructions, largely reflecting the structure of human thought, and the main source of linguistic variation is the linearization of conceptual and syntactic structures. Unlike other minimalist theories that reach a similar conclusion, the theory we propose takes mappings to phonology to be central to the architecture of grammar. [ABSTRACT FROM AUTHOR]

Deep cascaded networks have been extensively studied and applied to accelerate Magnetic Resonance Imaging (MRI) and have shown promising results. Most existing works employ a large cascading number for the sake of superior performances. However, due to the lack of proper guidance, the reconstruction performance can easily reach a plateau and even face degradation if simply increasing the cascading number. In this paper, we aim to boost the reconstruction performance from a novel perspective by proposing a parallel architecture called DIRECTION that fully exploits the guiding value of the reconstruction residual of each subnetwork. Specifically, we introduce a novel Reconstruction Residual-Based Feature Modulation Mechanism (RRFMM) which utilizes the reconstruction residual of the previous subnetwork to guide the next subnetwork at the feature level. To achieve this, a Residual Attention Modulation Block (RAMB) is proposed to generate attention maps using multi-scale residual features to modulate the image features of the corresponding scales. Equipped with this strategy, each subnetwork within the cascaded network possesses its unique optimization objective and emphasis rather than blindly updating its parameters. To further boost the performance, we introduce the Cross-Stage Feature Reuse Connection (CSFRC) and the Reconstruction Dense Connection (RDC), which can reduce information loss and enhance representative ability. We conduct sufficient experiments and evaluate our method on the fastMRI knee dataset using multiple subsampling masks. Comprehensive experimental results show that our method can markedly boost the performance of cascaded networks and significantly outperforms other compared state-of-the-art methods quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

The relation between the improvement of mechanical quality factor (Qm) and internal electric field (Ei) generated during the aging process in lead‐free piezoelectric BiFeO3‐BaTiO3 ceramics is investigated for hard piezoelectric application of power transducers. The change of built‐in Ei depends on the ferroelectric polarization (P) direction during the aging process. For the aging process with P parallel to Ei, Qm and Ei increases but Qm and Ei decreases with P antiparallel to Ei. These results are attributed to the migration of defects by depolarization fields and domain‐wall pinning. This work provides the systematic guidance to develop high‐power piezoelectric transducers with lead‐free ceramics. [ABSTRACT FROM AUTHOR]

Spatial statistical modeling involves processing an n×n$$ n\times n $$ symmetric positive definite covariance matrix, where n$$ n $$ denotes the number of locations. However, when n$$ n $$ is large, processing this covariance matrix using traditional methods becomes prohibitive. Thus, coupling parallel processing with approximation can be an elegant solution by relying on parallel solvers that deal with the matrix as a set of small tiles instead of the full structure. The approximation can also be performed at the tile level for better compression and faster execution. The tile low‐rank (TLR) approximation has recently been used to compress the covariance matrix, which mainly relies on ordering the matrix elements, which can impact the compression quality and the efficiency of the underlying solvers. This work investigates the accuracy and performance of location‐based ordering algorithms. We highlight the pros and cons of each ordering algorithm and give practitioners hints on carefully choosing the ordering algorithm for TLR approximation. We assess the quality of the compression and the accuracy of the statistical parameter estimates of the Matérn covariance function using TLR approximation under various ordering algorithms and settings of correlations through simulations on irregular grids. Our conclusions are supported by an application to daily soil moisture data in the Mississippi Basin area. [ABSTRACT FROM AUTHOR]

The performance of sorting algorithms has a great impact on many computationally intensive applications. Researchers worked on parallelizing many sorting algorithms on various interconnection networks to improve their sequential counterpart performance. One of these interconnection networks is the optical chained-cubic tree (OCCT). In this paper, a parallel bucket sort (PBS) algorithm is presented and applied to the OCCT interconnection network. This PBS algorithm is evaluated analytically and by simulation in terms of various performance metrics including parallel runtime, computation time, communication time, concatenation time, speedup and efficiency, for a different number of processors, dataset sizes and data distributions including random and descending distributions. Simulation results show that the highest obtained speedup is approximately 912x on OCCT using 1020 processors, which means that the parallel runtime of the PBS on 1020 processors is 912 times faster than the sequential runtime of BS on a single processor. [ABSTRACT FROM AUTHOR]

Suggesting a new reading of A74 in an exclusively physiological context and in combination with a new interpretation of B100, this article argues for a new reconstruction of Empedocles' respiration theory, involving two complementary and parallel respiratory processes, where the pressure of the innate heat acts together with the movement of the blood to drive the air in and out and reach all body parts and organs. [ABSTRACT FROM AUTHOR]

Machine learning models play a critical role in applications such as image recognition, natural language processing, and medical diagnosis, where accuracy and efficiency are paramount. As datasets grow in complexity, so too do the computational demands of classification techniques. Previous research has achieved high accuracy but required significant computational time. This paper proposes a parallel architecture for Ensemble Machine Learning Models, harnessing multicore CPUs to expedite performance. The primary objective is to enhance machine learning efficiency without compromising accuracy through parallel computing. This study focuses on benchmark ensemble models including Random Forest, XGBoost, ADABoost, and K Nearest Neighbors. These models are applied to tasks such as wine quality classification and fraud detection in credit card transactions. The results demonstrate that, compared to single-core processing, machine learning tasks run 1.7 times and 3.8 times faster for small and large datasets on quad-core CPUs, respectively. [ABSTRACT FROM AUTHOR]

Accurate segmentation of COVID-19 lesion regions in lung CT images aids physicians in analyzing and diagnosing patients' conditions. However, the varying morphology and blurred contours of these regions make this task complex and challenging. Existing methods utilizing Transformer architecture lack attention to local features, leading to the loss of detailed information in tiny lesion regions. To address these issues, we propose a multi-scale feature fusion network, ECF-Net, based on channel enhancement. Specifically, we leverage the learning capabilities of both CNN and Transformer architectures to design parallel channel extraction blocks in three different ways, effectively capturing diverse lesion features. Additionally, to minimize irrelevant information in the high-dimensional feature space and focus the network on useful and critical information, we develop adaptive feature generation blocks. Lastly, a bidirectional pyramid-structured feature fusion approach is introduced to integrate features at different levels, enhancing the diversity of feature representations and improving segmentation accuracy for lesions of various scales. The proposed method is tested on four COVID-19 datasets, demonstrating mIoU values of 84.36%, 87.15%, 83.73%, and 75.58%, respectively, outperforming several current state-of-the-art methods and exhibiting excellent segmentation performance. These findings provide robust technical support for medical image segmentation in clinical practice. [ABSTRACT FROM AUTHOR]

In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP. [ABSTRACT FROM AUTHOR]

Longitudinal studies on mainstream children and training studies in the dyslexic population suggest that visual attention span (VAS) abilities contribute to reading acquisition. We evaluated to what extent VAS training in beginning readers might enhance later literacy skills. A large cohort of 453 children was followed from the beginning to the end of Grade 1. A first group of students was trained on a custom‐designed digital application—called EVASION—that targeted VAS abilities. Another group used the GraphoGame application, while the third was a “business‐as‐usual” group. A total training time of 10 h was recommended; training was performed during the regular school day, under the sole supervision of teachers. Pre–post intervention assessment revealed higher VAS, higher reading fluency improvement, and higher postintervention spelling skills in the EVASION group. Children who spent more time playing with EVASION improved more in both VAS and literacy skills. In the whole population, VAS enhancement predicted reading fluency improvement and posttraining spelling skills, independently of other reading related skills and of the class effect. The overall findings suggest that training VAS in the classroom might prevent difficulties in learning to read and spell. Evidence for longitudinal effects of VAS training on literacy skills support a causal relationship. In improving multiletter parallel processing, training would translate into better orthographic learning, yielding higher reading fluency and spelling skills. [ABSTRACT FROM AUTHOR]

Recent advancements in neuromorphic computing have led to the development of hardware architectures inspired by Spiking Neural Networks (SNNs) to emulate the efficiency and parallel processing capabilities of the human brain. This work focuses on testing the HEENS architecture, specifically designed for high parallel processing and biological realism in SNN emulation, implemented on a ZYNQ family FPGA. The study applies this architecture to the classification of digits using the well-known MNIST database. The image resolutions were adjusted to match HEENS' processing capacity. Results were compared with existing work, demonstrating HEENS' performance comparable to other solutions. This study highlights the importance of balancing accuracy and efficiency in the execution of applications. HEENS offers a flexible solution for SNN emulation, allowing for the implementation of programmable neural and synapticmodels. It encourages the exploration of novel algorithms and network architectures, providing an alternative for real-time processing with efficient energy consumption. [ABSTRACT FROM AUTHOR]

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time–frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios. [ABSTRACT FROM AUTHOR]

Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excellent results. However, CNNs cannot model global features well due to the influence of the local receptive field; these global features are crucial for classifying music signals with temporal properties. Transformers can capture long-range dependencies within an image thanks to adopting the self-attention mechanism. Nevertheless, there are still performance and computational cost gaps between Transformers and existing CNNs. In this paper, we propose a hybrid architecture (CNN-TE) based on CNN and Transformer encoder for MGC. Specifically, we convert the audio signals into mel spectrograms and feed them into a hybrid model for training. Our model employs a CNN to initially capture low-level and localized features from the spectrogram. Subsequently, these features are processed by a Transformer encoder, which models them globally to extract high-level and abstract semantic information. This refined information is then classified using a multi-layer perceptron. Our experiments demonstrate that this approach surpasses many existing CNN architectures when tested on the GTZAN and FMA datasets. Notably, it achieves these results with fewer parameters and a faster inference speed. [ABSTRACT FROM AUTHOR]

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without decryption, providing strong security for sensitive information. However, computational and memory demands for FHE are significant challenges, particularly in the Number Theoretic Transform (NTT) phase. This paper presents three efficient Twiddle Factor Generators (TFGs) to address these challenges: the Half-Memory TFG, the On-the-fly Serial TFG, and the On-the-fly Parallel TFG. The Half-Memory TFG reduces memory usage by storing only half of the twiddle factors and calculating the rest as needed. The On-the-fly Serial TFG eliminates memory requirements by computing twiddle factors, while the On-the-fly Parallel TFG enhances computational speed through parallel processing. Implemented on the FPGA KCU105 board, these TFGs demonstrated significant improvements in hardware resource utilization and computational efficiency. The Half-Memory TFG effectively reduces memory footprint, the On-the-fly Serial TFG eliminates memory usage with acceptable computational overhead, and the On-the-fly Parallel TFG offers superior performance for high-throughput applications. These innovations make FHE more practical for real-world applications, contributing to the broader goal of enabling secure, privacy-preserving computations on encrypted data. [ABSTRACT FROM AUTHOR]

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional  ω K imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the  G r o u p - N s t r e a m  processing model, and the  F t h r e a d - G r o u p - N s t r e a m  processing model. The  F t h r e a d - G r o u p - N s t r e a m  processing model utilizes  F t h r e a d ,  G r o u p , and  N s t r e a m  for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm's performance in dynamic parallelism and alleviating the issue of serial execution within the  G r o u p - N s t r e a m  processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring. [ABSTRACT FROM AUTHOR]