Search

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]

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]

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]

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]

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]

Synaptic devices have gained prominence as viable alternatives to conventional complementary metal‐oxide‐semiconductor‐based (CMOS) electronics in the information processing field owing to their inherent advantages in analog and parallel operations. The potential of synaptic devices has not yet been fully utilized for logic operations because only the conventional binary logic structure has been applied to analog synaptic devices, leading to the loss of advantages unique to analog signals. To resolve this issue, an innovative concept is proposed: an analog logic gate that can perform parallel operations at the device level and is coupled with logic reconfigurations, enabling comprehensive analog computation. This logic gate comprises two synaptic devices with different retention characteristics, adjusted by the side‐chain engineering of an organic polymer. The long‐term and short‐term synaptic devices serve as reconfigurable synapse for logic mode selection and parallel processable logic synapse, respectively. In this study, the reconfigurable and parallel processable synaptic logic circuit is effectively implemented in a personalized disease risk diagnostic system. This innovative approach not only allows for the simultaneous computation of analog‐formed diagnostic data, but also enhances both computing efficiency and system complexity in general synaptic systems. [ABSTRACT FROM AUTHOR]

Diversion from the syntactic norm, as manifested in the absence of otherwise expected lexical and syntactic material, has been extensively studied in theoretical syntax. Such modifications are observed in headlines, telegrams, labels, and other specialized contexts, collectively referred to as “reduced” registers. Focusing on search queries, a type of reduced register, I propose that they are generated by a simpler grammar that lacks a full‐fledged syntactic component. The analysis is couched in the Parallel Architecture framework, whose assumption of relative independence of linguistic components—their parallelism—and the rejection of syntactocentrism are essential to explain properties of queries. [ABSTRACT FROM AUTHOR]

Bearings are foundational supporting components in diverse mechanical systems, essential for the reliable operation of these systems through real‐time monitoring and precise health state assessment. However, vibration signals from bearings in practical equipment often contain excessive noise and redundant information, complicating health state assessment. To address this challenge, this paper proposes a neural network‐based method named parallel encoder‐decoder (PED). This method features a parallel architecture that combines the long short‐term memory network and the temporal convolutional network for the encoder, along with a self‐attention module for the decoder. PED is adept at learning the temporal representations hidden in original signals and filtering vibration signals to remove noise and redundant information. Additionally, a multi‐objective loss function is developed to enhance the prediction results. A normalized Mahalanobis distance‐based metric is then employed to compare residual signals during bearing operation with those under normal conditions. The case study evaluates the PED observer's proficiency in accurately predicting vibration signals and assessing the performance of health indicator curves, demonstrating the proposed PED observer's superiority over conventional networks. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real‐time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real‐time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in‐depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller‐led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non‐processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real‐time data in the process of parallel processing, as well as the evaluation and detection of real‐time data replacement. [ABSTRACT FROM AUTHOR]

3D nanolithography based on two‐photon polymerization (TPP) allows for the high‐precision fabrication of nearly arbitrary 3D micro/nanostructures, finding extensive applications in areas such as micro‐optics, micro‐mechanics, and biomedicine. However, the large size, complexity of optical systems, and high costs have significantly constrained the widespread adoption of 3D nanolithography technology in both scientific research and industry. In this study, a metasurface is introduced, for the first time, into 3D nanolithography resulting in the construction of a miniaturized and simplified TPP system that achieved efficient multi‐focus parallel processing with high uniformity. A microlens array is fabricated, showcasing the system's application capacity to generate an array of devices with high consistency and quality. It is believed that the utilization of metasurface devices will provide a novel TPP operating platform, enabling richer and more flexible printing functionalities while maintaining system miniaturization and low cost. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown great performance in computer vision tasks, from image classification to pattern recognition. However, CNNs′${\rm CNNs}^{\prime }$ superior performance arises at the expense of high computational costs, which restricts their employment in real‐time decision‐making applications. Computationally intensive convolutions can be offloaded to optical metasurfaces, enabling sub‐picosecond latency and nearly zero energy consumption, but the currently reported approaches require additional bulk optics and can only process polarized light, which limits their practical usages in integrated lightweight systems. To solve these challenges, a novel design of the metasurface‐based optical convolutional accelerator is experimentally demonstrated, offering an ultra‐compact volume of 0.016 mm3${\rm mm}^{3}$, a low cross‐talk of ‐20 dB, polarization insensitivity, and is capable of implementing multiple convolution operations and extracting simultaneously various features from light‐encoded images. The ultra‐compact metasurface‐based optical accelerator can be compactly integrated with a digital imaging system to constitute an optical‐electronic hybrid CNN, which experimentally achieves a consistent accuracy of 96 % in arbitrarily polarized MNIST digits classification. The proposed ultra‐compact metasurface‐based optical convolutional accelerator paves the way for power‐efficient edge‐computing platforms for a range of machine vision applications. [ABSTRACT FROM AUTHOR]

In order to improve the emotional expression effect of jewellery, this article combines artificial intelligence technology to analyse the emotional expression in jewellery design. Aiming at the characteristics of high‐gloss materials such as strong reflectivity and translucency, starting from the classic ray tracing algorithm, this article strengthens the reflected light and projected light on the surface of the material, and proposes a realistic rendering algorithm for high‐gloss materials. Moreover, this article introduces the CUDA parallel computing architecture to give full play to the computing power of multi‐core computers, accelerate the calculation speed of the ray tracing algorithm, save the consumption of storage space, and improve the speed of real‐time rendering under the premise of ensuring realism. The experimental research results show that the emotional expression system for smart jewellery design proposed in this article has a certain effect, which can effectively improve the effect of jewellery design. [ABSTRACT FROM AUTHOR]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

Durations in the several seconds' range are cognitively accessible during active timing. Functional neuroimaging studies suggest the engagement of the basal ganglia (BG) and supplementary motor area (SMA). However, their functional relevance and arrangement remain unclear because non‐timing cognitive processes temporally coincide with the active timing. To examine the potential contamination by parallel processes, we introduced a sensory control and a motor control to the duration‐reproduction task. By comparing their hemodynamic functions, we decomposed the neural activities in multiple brain loci linked to different cognitive processes. Our results show a dissociation of two cortical neural circuits: the SMA for both active timing and motor preparation, followed by a prefrontal–parietal circuit related to duration working memory. We argue that these cortical processes represent duration as the content but at different levels of abstraction, while the subcortical structures, including the BG and thalamus, provide the logistic basis of timing by coordinating the temporal framework across brain structures. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a population pharmacokinetic-pharmacodynamic model of subcutaneously administered bupivacaine in a novel extended-release microparticle formulation for postoperative pain management. Bupivacaine was administered subcutaneously in the lower leg to 28 healthy male subjects in doses from 150 to 600 mg in a phase 1 randomized, placebo-controlled, double-blind, dose-ascending study with two different microparticle formulations, LIQ865A and LIQ865B. Warmth detection threshold was used as a surrogate pharmacodynamic endpoint. Population pharmacokinetic-pharmacodynamic models were fitted to plasma concentration-effect-time data using non-linear mixed-effects modelling. The pharmacokinetics were best described by a two-compartment model with biphasic absorption as two parallel absorption processes: a fast, zero-order process and a slower, first-order process with two transit compartments. The slow absorption process was found to be dose-dependent and rate-limiting for elimination at higher doses. Apparent bupivacaine clearance and the transit rate constant describing the slow absorption process both appeared to decrease with increasing doses following a power function with a shared covariate effect. The pharmacokinetic-pharmacodynamic relationship between plasma concentrations and effect was best described by a linear function. This model gives new insight into the pharmacokinetics and pharmacodynamics of microparticle formulations of bupivacaine and the biphasic absorption seen for several local anaesthetics. [ABSTRACT FROM AUTHOR]

Nanoparticles (NPs) generated by pulsed‐laser ablation in liquids (PLAL) have benefited many key applications due to their versatility, enlarged surface area, and high purity. However, scaling up NPs production represents one of the main requisites to commercialize this technology. The established upscaling strategy demands high power and repetition rate laser source with fast scanning systems, which are not widely available and costly. Herein, a cost‐effective alternative is proposed, the addition of static diffractive optical elements to achieve parallel processing through the multi‐beam PLAL (MB‐PLAL). In MB‐PLAL, the optimum repetition rate is reduced to compensate laser energy splitting, hence achieving a higher interpulse distance, reducing pulse shielding, and increasing NPs productivity. MB‐PLAL with 11 beams reached a factor 4 productivity increase for iron–nickel alloy (Fe50Ni50) NPs compared to the single‐beam setup (0.4–1.6 g h−1), and a factor 3 increase for gold (Au) NPs (0.32–0.94 g h−1). The scalability of the proposed MB‐PLAL technique setup is confirmed by Au and Fe50Ni50 NPs productivity experiments using 1, 6, and 11 beams, showing a linear increase in productivity. [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]

This work demonstrates that the electrodeposition of highly stressed films on compliant ribbons is a robust process to obtain helical structures with excellent mechanical stability and potentially high thermal and electrical conductance. Electrodeposition on end‐tethered ribbons alters their axial and bending stiffness while imparting mechanical stress to drive the formation of a helix with a microscale diameter and pitch in a controlled and scalable manner. The process generates helices with diameters and pitches between 80 and 200 µm and lengths as large as several millimeters. The approach is amenable to parallel processing a large number of 3D structures on any substrate, including large‐area semiconductor wafers. This phenomenon is explained in terms of the change of stress gradients as material is added. Applications of the fabricated helices include antennas, metamaterials, and slow‐wave structures in frequency ranges not previously attainable. [ABSTRACT FROM AUTHOR]

Glioma brain tumour is one of the life‐threatening diseases in the world. Tumour substructure segmentation by physicians is a time‐consuming task with the magnetic resonance imaging (MRI) technique due to the size of clinical data. An automatic and well‐trained method is essential to detect and segment the tumour which increase the survival of the patients. The proposed work aims to produce high accuracy on glioma substructures segmentation with less computation time using deep learning. From the literature survey, the following challenges are found: (i) computing complex spatial boundaries between normal and tumour tissues, (ii) feature reduction and (iii) overfitting problems. Hence, we proposed a fully automatic glioma tumour segmentation using a residual‐inception block (RIB) with a modified 3D U‐Net (RIBM3DU‐Net). It includes three phases: pre‐processing, modified 3D U‐Net segmentation and post‐processing. From the results, RIB with U‐Net enhances the segmentation accuracy. GPU parallel architecture reduces the computation time while training and testing. For quantitative analysis, comprehensive experiment results were computed and compared with state‐of‐the‐art methods. It achieves better Dice scores on enhancing tumour, tumour core and complete tumour of 87%, 87% and 94%, respectively. GPU speedup folds yield up to 48× when compared with CPU. Quantitatively, 3D glioma volume is rendered from the obtained segmented results and estimated using the Cavalieri estimator. [ABSTRACT FROM AUTHOR]

Ovarian abnormality like polycystic ovarian disease (PCOD) is one of the most common diseases among women worldwide. PCOD not only has an impact on infertility but also hurts the psychological well‐being of women affecting their quality of life. In this study, a two‐class pattern learning problem is designed for the classification of PCOD. In total, 37 clinical parameters and abdominal ultrasound images of women are collected under the proper ethical protocol. Using only clinical data, an accuracy of 93.7% is obtained using Random Forest as the classifier which is further improved to 95.54% by using a Randomized Search CV during Random Forest classification. The ultrasound images are classified using the proposed Attention‐UNet architecture and a mean Dice score of 0.945 is obtained indicating more accurate segmentation. The segmented images are passed through the state‐of‐the‐art EfficientNet B6 for the classification of PCOS and non‐PCOS and recorded an accuracy of 95.47%. Using big data architecture Pyspark, the performance is further enhanced to 96.8% and 96.3% for clinical and ultrasound images respectively along with the reduced computational speed. The results of these classifiers are then used to create metadata and a customized Artificial Neural Network is applied for the final prediction of PCOD and non‐PCOD. From the results, it can be seen that the stacking model outperformed with an accuracy of 98.12% when compared to the single classifier. Our proposed method has very good performance with less computation, contributing a new architecture to evaluate PCOD and hence helping to improve the wellness of women. [ABSTRACT FROM AUTHOR]

Degrammaticalisation is an oft‐dismissed category of language change. In this paper evidence is provided for its existence, its triggers, and its conditions. This case study details the development of an understudied Old Italo‐Romance indefinite, covelle, a polarity‐sensitive item roughly translating as 'anything' which originated from a Latin free relative with Free‐Choice interpretation. It is shown that in Old Italo‐Romance the original Free Choice use was expanded to further nonveridical contexts. Subsequently, a curious development of covelle in Old Romagnol, a 16th‐century Gallo‐Italic variety, is examined whereby polar neutralisation (loss of sensitivity to polarity) of the indefinite was taking place. Contextually, a process of degrammaticalisation had begun through which the indefinite pronoun developed into the classifier 'thing'. In Modern and Contemporary Romagnol cvël is almost exclusively employed as a noun/classifier. It is argued that two parallel processes have initiated a cycle that led cvël to complete degrammaticalisation: (i) the grammaticalization of the old noun for 'thing', co(n)sa, into a wh‐word, calling for lexical replacement, and (ii) competition from the Negative Concord Item gnit. The development of covelle in Romagnol represents a well‐documented case of degrammaticalisation, whose causes and conditions can be thoroughly described and motivated within an interaction with cyclical change. [ABSTRACT FROM AUTHOR]

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

Reconfigurable logic‐in‐memory device, albeit a promising hardware platform for constructing parallel computing architectures, still suffers from high power‐consumption and low area‐efficiency arising from the circuit redundancy in silicon (Si) based technical path. 2D materials are identified as potential building blocks to significantly reduce the circuit footprints with outstanding power‐efficiency, owing to their atomically thin nature and unique electronic properties. However, the present 2D logic devices are primarily based on single channel materials with homogeneous transport polarities, limiting the device reconfigurability for multi‐functional applications. Here, 2D p–n junction based dual‐gate Gaussian‐type transistors for simplified reconfigurable logic circuits are reported. All fundamental Boolean logic operators are implemented in a single device with electrically driven reconfigurability, giving rise to an ultra‐low transistor consumption down to 13% of the traditional circuits. The device is also demonstrated as a reliable image processing unit for various computing tasks (pixel processing, comparing, and ciphering) by executing the corresponding logic operations. Moreover, in situ memory of the Boolean logics is achieved by engineering the dielectric properties of the transistor without compromising its reconfigurability. These findings provide a potential approach to achieving reconfigurable logic‐in‐memory operations at the hardware level, with significant implications for the advancement of parallel computing. [ABSTRACT FROM AUTHOR]

Summary: Recent studies have shown that a large amount of energy consumption in the communication medium occurs at the data processing plane. The data plane executes the forwarding decisions in the routers and switches. Through initial studies, the line card was identified as the most granular component of the router and later forward engine, a sub component of line card has been recognized as the most power intensive component of routers. Fundamental techniques like link rate adaption, sleep and wake‐up have been imposed to reduce the energy at line cards and forward engines. On the other hand, router's architectures undergo drastic changes like empowering data plane with parallel processing due to the rise of gigabit routers. In this paper, to increase the degree of local autonomy and better energy savings, we have adopted the parallel processing design and imposed a variable amount of traffic on different line cards. The existing parallel forward engines perform load balancing in synchronous manner. In our design, asynchronous behavior is imposed such that every line card could independently decide to wake up or turn off their forward engine based on the traffic parameters. We have done simulations and witnessed increased energy savings (average sleeping time) without compromising the completion of lookup process. [ABSTRACT FROM AUTHOR]

Habit and motivation are thought to be separate processes, with motivated behavior often considered to be goal directed, whereas habits are defined by the absence of goal‐directed control over behavior. However, there has been increasing interrogation of the binary nature of habitual versus goal‐directed behavior. Furthermore, although drug and alcohol exposure can promote the formation of habits, drug seeking itself can also be highly flexible, pointing toward the need for complex consideration of the parallel processes that drive behavior. The goal of the current study was to determine whether there was a relation between motivation—as measured by progressive ratio—and habit—as measured by contingency degradation—and whether this relation was affected by ethanol exposure history and sex. The results showed that these measures were positively correlated such that greater contingency insensitivity was associated with achieving higher break points on the progressive‐ratio task. However, this relation depended on reinforcement schedule history, ethanol exposure history, and sex. These point to potential relations between measures of habit and motivation and stress the importance of carefully parsing behavioral findings and assays. These findings are also expected to inform future substance use research, as drug history may affect these relations. [ABSTRACT FROM AUTHOR]

Analogue optical computing offers a promising approach for developing super‐fast computation techniques, benefiting from its extraordinary parallel processing capability, low energy consumption, and high integrability compared to digital electronic technologies. In this work, a reconfigurable spatiotemporal optical signal processor using the phase‐change material vanadium dioxide (VO2) to further expand the processing bandwidth and enrich the operation functions is demonstrated. As a specific example, a meta‐device is designed that can switch between a first‐order differentiator and a good reflector in the spatiotemporal domain by controlling the operation temperature. It is shown that the spatiotemporal transfer functions can be precisely engineered to meet the requirements for ideal first‐order differentiation and bright‐field imaging. This approach enables high‐accuracy spatial and temporal signal processing, as demonstrated by inspecting Gaussian signals. The theoretical evaluation determines the resolution limits of spatial and temporal edge detection. It also highlights that the device can perform spatiotemporal signal processing for pulses with complex profiles. The presented reconfigurable spatiotemporal optical signal processor holds great potential in applications such as remote sensing and fast imaging processing, among others. [ABSTRACT FROM AUTHOR]

Paul Kelly pays scant attention to the so‐called 'democratic peace' thesis that has played a major role in recent International Relations scholarship, a lacuna which we regard to be serious. Our paper investigates the normative background to the connection between democracy and peace, commonly associated with Immanuel Kant. We argue that attention should be devoted instead to the critique of international conflict found in the writings of Thomas Paine, who (unlike Kant) offers a politically grounded account of both why peace among nations is desirable and how that might be achieved. In particular, we consider Paine's views on democratic peace through the lens of his goals of eliminating the tyranny of kings and aristocrats and of ameliorating the material conditions of the poor. Paine contended that the establishment of global peace paralleled a process of global democratisation that would liberate the masses from their political and economic exploitation at the hands of a powerful few, whose interests converged in the prosecution of war. [ABSTRACT FROM AUTHOR]

In this work, degradation and mineralization of para‐nitrophenol in the aqueous medium by a mixture of two strong oxidants of persulfate and periodate have been studied. To this aim, a photo‐reactor with a volume of 1 L has been used. Optimization and modeling of the process was performed by the response surface methodology. According to the prediction of the model, the process has an ability to remove para‐nitrophenol up to 91.6% in the optimal conditions of para‐nitrophenol initial concentration of 15 mg/L, [KPS] = 50 mg/L, [KPI] = 49.7 mg/L, and pH = 5 after 40 min. Assessment of the mineralization of para‐nitrophenol under optimal conditions showed an efficiency of about 57%, while parallel processes alone (UV and UV/KPS and UV/KPI) showed lower mineralization efficiency. Also, the kinetic study showed that the results were fitted with a pseudo‐first‐order equation with a regression coefficient of 0.97 and rate constant of 0.052 (L/min). Moreover, the electrical energy consumption for the process was calculated as 110.77 (kWh/m3) in the optimum condition. In general, the results of this work showed that the use of the homogeneous hybrid AOP methods increases the efficiency of the mineralization. [ABSTRACT FROM AUTHOR]

Identifying the genetic architecture underlying adaptive traits is exceptionally challenging in natural populations. This is because associations between traits not only mask the targets of selection but also create correlated patterns of genomic divergence that hinder our ability to isolate causal genetic effects. Here, we examine the repeated evolution of components of the auxin pathway that have contributed to the replicated loss of gravitropism (i.e. the ability of a plant to bend in response to gravity) in multiple populations of the Senecio lautus species complex in Australia. We use a powerful approach which combines parallel population genomics with association mapping in a Multiparent Advanced Generation Inter‐Cross (MAGIC) population to break down genetic and trait correlations to reveal how adaptive traits evolve during replicated evolution. We sequenced auxin and shoot gravitropism‐related gene regions in 80 individuals from six natural populations (three parallel divergence events) and 133 individuals from a MAGIC population derived from two of the recently diverged natural populations. We show that artificial tail selection on gravitropism in the MAGIC population recreates patterns of parallel divergence in the auxin pathway in the natural populations. We reveal a set of 55 auxin gene regions that have evolved repeatedly during the evolution of the species, of which 50 are directly associated with gravitropism divergence in the MAGIC population. Our work creates a strong link between patterns of genomic divergence and trait variation contributing to replicated evolution by natural selection, paving the way to understand the origin and maintenance of adaptations in natural populations. [ABSTRACT FROM AUTHOR]

Contextual valence is an important dimension during value‐based decision‐making. Previous research has revealed behavioral and neural asymmetries between the gain context and the loss context. The present event‐related potential study investigated the effects of contextual valence on neural dynamics underlying magnitude and time, two important reward dimensions, during feedback evaluation. Forty‐two participants performed a simple guessing task in which they experienced both a gain context wherein high or low rewards were delivered immediately or six months later, and a loss context wherein high or low losses were delivered in the same way. Results showed that in the gain context, time and magnitude information were processed in a parallel way during the time windows of the reward positivity (RewP) and the P3. In the loss context, however, time and magnitude information were processed in a serial way such that time information was encoded during the RewP and P3 periods, whereas magnitude information was not tracked until the time window of the late positive potential. Our findings suggest that the neural dynamics underlying time and magnitude information are distinct between the gain and loss contexts, thus providing a novel perspective for the well‐known gain–loss asymmetry. Our findings add a new perspective on the gain‐loss asymmetry by examining the influences of contextual valence on neural dynamics underlying magnitude and time information. We provide evidence that time and magnitude are encoded in a parallel way in a gain context but in a serial way (time‐first‐magnitude‐second) in a loss context during feedback evaluation. [ABSTRACT FROM AUTHOR]

We introduce a framework for applying sensitivity analysis to a set of potential hybrid microgrid design options, from which a decision maker can select the most preferred one. In contrast to optimization‐centric models that define an objective and produce a single solution, our goal is to empower the decision maker by providing both a range of options and accessible information that enables the decision maker to easily assess their relative upsides and downsides. Moreover, our decoupled approach allows our framework to be paired with any existing model capable of generating a set of potential hybrid microgrid designs. We introduce metrics for computing risk, stemming from power deficits, over a variety of scenarios relating to weather conditions, power demand fluctuations, and extended time horizons. These factors which are inherently uncertain are all important in military operational contexts. By introducing a design of experiments method for sensitivity analysis, we are able to implement parallel processing and maintain computational tractability. [ABSTRACT FROM AUTHOR]

Summary: Big data needs to have scalable storage platforms as well as parallel processing environments. Hadoop is preferable because it is an open‐source software which works in a distributed, parallel processing environment and no need of any special hardware. In recent developments of Hadoop, security was introduced as optional. A symmetric block cipher advanced encryption standard (AES) is used for encryption. A 128 MB block of data needs 8 million encryptions. A better way of hiding the data from unauthorized access is preferable. Steganography, a more contemporary security technology than cryptography, is the art and science of hiding subconscious information in hidden item while exposing opponents their existence. The cover is an image, and the secret information may be a photo, music, video, or text. A double layer hexcode substituted–color space transformed (HSCT) steganography with 3 band splitting of data ensures confidentiality of the data and one time pad key generated with client's ID provides authentication also. Considering the volume of data, steganography concept is much faster than AES or any other cipher. Hiding the data inside the random images will indulge in fast reconstruction of data when needed for processing using either MapReduce or tools like spark. [ABSTRACT FROM AUTHOR]

Particle tracking has several important applications for solute transport studies in aquifer systems. Travel time distribution at observation points, particle coordinates in time and streamlines are some practical results providing information of expected transport patterns and interaction with boundary conditions. However, flow model complexity and simultaneous displacement of multiple particle groups leads to rapid increase of computational requirements. MODPATH is a particle tracking engine for MODFLOW models and source code displays potential for parallel processing of particles. This article addresses the implementation of this feature with the OpenMP library. Two synthetic aquifer applications are employed for performance tests on a desktop computer with increasing number of particles. Speed up analysis shows that dynamic thread scheduling is preferable for highly heterogeneous flows, providing processing adaptivity to the presence of slow particles. In simulations writing particles position in time, thread exclusive output files lead to higher speed up factors. Results show that above a threshold number of particles, simulation runtimes become independent of flow model grid complexity and are controlled by the large number of particles, then parallel processing reduces simulation runtimes for the particle tracking model MODPATH. Article impact statement: Parallel particles processing for MODPATH using the OpenMP library [ABSTRACT FROM AUTHOR]

Java streams are receiving the attention of developers targeting the Java virtual machine (JVM) as they ease the development of data‐processing logic, while also favoring code extensibility and maintainability through a concise and declarative style based on functional programming. Recent studies aim to shedding light on how Java developers use streams. However, they consider only small sets of applications and mainly apply manual code inspection and static analysis techniques. As a result, the large‐scale dynamic analysis of stream processing remains an open research question. In this article, we present the first large‐scale empirical study on the use of streams in Java code exercised via unit tests. We present stream‐analyzer, a novel dynamic program analysis (DPA) that collects runtime information and key metrics, which enable a fine‐grained characterization of sequential and parallel stream processing. We use a fully automatic approach to massively apply our DPA for the analysis of open‐source software projects hosted on GitHub. Our findings advance the understanding of the use of Java streams. Both the scale of our analysis and the profiling of dynamic information enable us to confirm with more confidence the outcome highlighted at a smaller scale by related work. Moreover, our study reports the popularity of many features of the Stream API and highlights multiple findings about runtime characteristics unique to streams, while also revealing inefficient stream processing and stream misuses. Finally, we present implications of our findings for developers of the Stream API, tool builders and researchers, and educators. [ABSTRACT FROM AUTHOR]

The immense increase of unstructured data require novel computing systems that can process the input data with low power and parallel processing. This functionality is similar to that of human brains that are composed of numerous neurons, synapses, and their complex connections. To mimic the functionality of the human brain with an electronic device, the resistive switching device and crossbar array has attracted considerable attention for artificial synaptic devices and integrated systems, respectively. For this purpose, the self‐rectifying resistive switching cell based on the Si:ZrOx thin film is developed and its reliability characteristics are tested. Four achievements are highlighted in this study. 1) The retention characteristic is improved by the adoption of TaOx thin film as an oxygen reservoir layer. 2) The asymmetric electrodes can make the self‐rectifying resistive cell (SRC) have sufficient rectifying characteristic. 3) The linearity of conductance update has a dominant effect on the inference performance compared to that of the conductance range variation. 4) The device of the interface‐type resistive switching shows a high enough device yield in the crossbar array device and exhibits reliable multiply‐and‐accumulate operations in the crossbar array to mimic the human brain‐inspired computing system. [ABSTRACT FROM AUTHOR]

Finding the centrality measures of nodes in a graph is a problem of fundamental importance due to various applications from social networks, biological networks, and transportation networks. Given the large size of such graphs, it is natural to use parallelism as a recourse. Several studies show how to compute the various centrality measures of nodes in a graph on parallel architectures, including multi‐core systems and GPUs. However, as these graphs evolve and change, it is pertinent to study how to update the centrality measures on changes to the underlying graph. In this article, we show novel parallel algorithms for updating the betweenness‐ and closeness‐centrality values of nodes in a dynamic graph. Our algorithms process a batch of updates in parallel by extending the approach of handling a single update for betweenness‐ and closeness‐centrality. For the latter, we also introduce techniques based on traversals of the block‐cut tree of a graph. Besides, our algorithms incorporate mechanisms to exploit the structural properties of graphs for enhanced performance. We implement our algorithms on two parallel architectures: an Intel 24‐core CPU and an Nvidia Tesla V100 GPU. To the best of our knowledge, we are the first to show GPU algorithms for the above two problems. In addition, we conduct detailed experiments to study the impact of various parameters associated with our algorithms and their implementation. Our results on a collection of real‐world graphs indicate that our algorithms achieve a significant speedup over corresponding state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

We analyze the protocol of the bitcoin blockchain by using the PRISM probabilistic model checker. In particular, we (i) extend PRISM with the ledger data type, (ii) model the behavior of the key participants in the protocol—the miners—and (iii) describe the whole protocol as a parallel composition of processes. The probabilistic analysis of the model highlights how forks happen and how they depend on specific parameters of the protocol, such as the difficulty of the cryptopuzzle and the network communication delays. Our results confirm that considering transactions in blocks at depth larger than 5 as confirmed is reasonable because the majority of miners have consistent blockchains up‐to that depth with probability of almost 1. We also study the behavior of networks with churn miners, which may leave the network and rejoin afterwards, and with different topologies. [ABSTRACT FROM AUTHOR]

The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever‐expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent‐based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed‐initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively. [ABSTRACT FROM AUTHOR]

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developed N‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N‐SDM was built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N‐SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied in N‐SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach. N‐SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N‐SDM is openly available on the GitHub repository https://github.com/N‐SDM/N‐SDM. [ABSTRACT FROM AUTHOR]

One of the major issues in systems biology is developing control theory for gene regulatory networks (GRNs). Particularly, an important objective is to develop therapeutic intervention strategies to alter regulatory dynamics of GRNs to avoid undesired or diseased states. Several optimal control strategies have been developed to find therapeutic intervention strategies for small (or medium) sized GRNs modeled as probabilistic Boolean control networks (PBCNs). However, due to the humongous nature of GRNs, we require an optimal control strategy that scales to large systems without posing any constraints on the network dynamics. In this article, we formulate the infinite horizon discounted cost optimal control problem by leveraging the Markov decision process (MDP) based PBCN structure to model GRNs. Further, we design a stationary optimal control strategy, thereby avoiding the states associated with a disease. An augmented state space is defined to facilitate the control design method. By exploiting Kullback–Leibler (KL) divergence and the log transformation of the cost function referred to as the desirability function, the optimal control is devised using the path integral (PI) approach. We propose a sampling‐based technique for the approximation of the PI and hence the optimal control of PBCNs. The sampling‐based strategy is amicable to parallel implementation, thereby addressing the optimal control problem of large‐scale PBCNs. Finally, the viability of the results in the article is shown by some illustrative examples. [ABSTRACT FROM AUTHOR]

High‐performance artificial muscle is always the pursuit of researchers for robotics. Herein, a bionic artificial muscle is reported called "ExoMuscle" mimicking the sarcomere in skeletal muscle with a bio‐inspired structure to contract "myofilaments" enabling the artificial muscle to mimic the architecture of muscle such as parallel, fusiform, convergent, and pennation and beyond the performance of skeletal muscle. The reported actuators excel in various aspects compared with skeletal muscle including actuation stress (0.41–0.9 MPa), strain (50%), optimal length, velocity‐independence output, power density (10.94 kW kg−1), and efficiency (69.11%). With its own adjustable pennation architecture, it achieves variable actuation stress up to 0.9 MPa meanwhile maintaining high efficiency. Furthermore, ExoMuscle highly conforms to the anatomical complexity of the human body to cooperate with skeletal muscles closely opening the door for bio‐robotics, especially wearable robots. [ABSTRACT FROM AUTHOR]

In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two‐phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones. [ABSTRACT FROM AUTHOR]

Online determination of a cutter's health status is crucial for the attainment of condition‐based automated tool change in computer numerically controlled (CNC) machining. Due to the impracticalities associated with direct condition measurements, data‐based modeling of monitoring signals provides a viable practical route. However, the highly noisy and redundant nature of the associated data impacts negatively on model's accuracy and typically calls for additional initial preprocessing before modeling. Additionally, the long sequential data entails widely varying condition distributions exhibited by different cutters, even from the same batch on similar machining parameters, posing a challenge to model generalization. An end‐to‐end model has thus been developed to work directly on unprocessed data to establish global sensitive features from varying distributions for online tool wear estimation in CNC machining. The model utilizes three main functional blocks. First, a data denoising and feature selection block automatically processes raw multisensor data directly, dispensing with scaling or preprocessing of inputs as conventionally done. Each sensor channel's independence is preserved at initial processing ensuring complementary information from different sensors is utilized while simultaneously minimizing existing redundancies. The weighted denoised data is then processed through a transformer encoder block for determination of global dependencies in the time‐series sequence, regardless of the time‐step position. The learned features are then fed to an upper supervised learning block for association with the monitored wear condition. The developed model works directly on raw noisy data irrespective of scaling differences, saving on preprocessing computational cost. The global associations extracted on long sequences by the transformer‐encoder allow for model generalization to varying wear distributions. The parallel processing structure of all channels ensures complementary information is utilized minimizing unforeseen model bias. The model's performance as evaluated on experimental milling data and further comparison with other reported models on same dataset shows attainment of comparable state‐of‐art results. [ABSTRACT FROM AUTHOR]

Several multimedia applications have recently been implemented on mobile devices, including digital image compression, video compression, and audio processing. Furthermore, Artificial Intelligence (AI) processing has grown in popularity, necessitating the execution of large amounts of data in mobile devices. Therefore, the processing core in a mobile device requires high performance, programmability, and versatility. Multimedia apps for mobile devices typically comprise repeated arithmetic and table‐lookup coding operations. A Content Addressable Memory‐based massive‐parallel SIMD matriX core (CAMX) is presented to increase the processing speed of both operations on a processing core. The CAMX serves as a CPU core accelerator for mobile devices. The CAMX supports high‐parallel processing and is equipped with two CAM modules for high‐speed repeated arithmetic and table‐lookup coding operations. The CAMX has great performance, programmability, and versatility on mobile devices because it can handle logical, arithmetic, search, and shift operations in parallel. This paper shows that the CAMX can process parallel repeated arithmetic and table‐lookup coding operations; single‐precision floating‐point arithmetic can calculate 1024 entries in 5613 clock cycles in parallel without embedding a dedicated floating‐point arithmetic unit. This clock cycle using two's complement‐reduced floating‐point addition implementation decreases 59% than the implementation of straight‐forward floating‐point addition. The implementation of straight‐forward floating‐point additions is improved as two's complement instruction reduced algorithms. Thus, this paper proposes an instruction reduction architecture by modulating the CAMX to directly access the data in the left and right CAM modules from the preserve register. The CAMX has achieved high performance, programmability, and versatility by not embedding a dedicated processing unit. Moreover, assuming the CAMX processes at an operating frequency of 0.1, 0.5, 1.0, or 1.5 GHz, it can process floating‐point additions above approximately 4500 parallelized data, with better performance than an ARM core using NEON and Vector Floating‐Point (VFP). In addition, related works executed by software instruction, dedicated floating‐point arithmetic unit, or both and the CAMX are compared while assuming the same operation frequency. From this result, the CAMX which has 128‐bit and 1024‐entry CAM modules achieves higher performance than the related works executed by only software instructions and by combining software instructions and a dedicated floating‐point arithmetic unit. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Increased error‐related negativity (ERN), a measure of error monitoring, has been suggested as a biomarker of obsessive–compulsive disorder (OCD). Additional insight into error monitoring is possible using time‐frequency decomposition of electroencephalographic (EEG) data, as it allows disentangling the brain's parallel processing of information. Greater error‐related theta is thought to reflect an error detection signal, while delta activity may reflect more elaborative post‐detection processes (i.e., strategic adjustments). Recent investigations show that decreased error‐related alpha may index attentional engagement following errors; additionally, increases and decreases in error‐related beta could reflect motor inhibition and motor preparation, respectively. However, time‐frequency dynamics of error monitoring in OCD are largely unknown. The present study examined time‐frequency theta, delta, alpha and beta power in early adolescents with OCD using a data‐driven, cluster‐based approach. The aim was to explore electrocortical measures of error monitoring in early adolescents with (n = 27, 15 females) and without OCD (n = 27, 14 females) during an arrowhead version of the flanker task while EEG activity was recorded. Results indicated that the OCD group was characterized by increased ERN and error‐related theta, as well as reduced error‐related beta power decrease (i.e., greater power) compared to participants without OCD. Greater error‐related beta explained variance in OCD over and above the ERN and error‐related theta. By examining separate time‐frequency measures, the present study provides novel insights into the dynamics of error monitoring, suggesting that pediatric OCD may be characterized by enhanced error monitoring (i.e., greater theta power) and post‐error inhibition (i.e., reduced beta power decrease). The present study provided novel insights into the dynamics of error monitoring by capitalizing on both event‐related potentials and time‐frequency measures. The results suggest that pediatric OCD may be characterized by enhanced error monitoring (i.e., greater ERN and theta power) and post‐error inhibition, and reduced motor preparation (i.e., reduced beta power decrease). Overall, these results are promising as examining error‐related time‐frequency measures could enhance clinical utility. [ABSTRACT FROM AUTHOR]

Recently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep‐learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep‐learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep‐learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator‐scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type‐based operator‐scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling‐based operator‐scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively. [ABSTRACT FROM AUTHOR]

This paper explores cardiac electrophysiological simulations of the monodomain equations and introduces a novel subcycling time integration algorithm to exploit the structure of the ionic model. The aim of this work is to improve upon the efficiency of parallel cardiac monodomain simulations by using our subcycling algorithm in the computation of the ionic model to handle the local sharp changes of the solution. This will reduce the turnaround time for the simulation of basic cardiac electrical function on both idealized and patient‐specific geometry. Numerical experiments show that the proposed approach is accurate and also has close to linear parallel scalability on a computer with more than 1000 processor cores. Ultimately, the reduction in simulation time can be beneficial in clinical applications, where multiple simulations are often required to tune a model to match clinical measurements. [ABSTRACT FROM AUTHOR]

Strain sensors have attracted tremendous attention in healthcare monitoring and human–computer interaction due to their great potential in intelligent equipment. However, conventional strain sensors cannot possess high sensitivity, wide stretchable range, and low limit of detection (LoD) at the same time. Here, an ultrasensitive wearable strain sensor over a broad range based on the simple parallel connection architecture of Ir‐nanoparticles‐modified carbon nanotubes (Ir NPs@CNTs) and two Pt layers using a reticular patterned polymer substrate (Dragon Skin 30, (DS)) is reported. It exhibits an extremely high gauge factor of 13 590, broader strain range up to 98%, lower LoD of 0.02% strain, faster response time of 134 ms, and long‐term durability above 18 000 cycles. The mechanisms of performance improvement are proposed based on the synergistic and hierarchical effects of the combined sandwich structures by parallel connection on patterned DS, including geometric effect, crack formation/propagation in parallel grooves, and tunneling‐based charge carriers between IrNPs and IrNPs/CNTs. By introducing a metal nanolayer, the sensor also shows heat sensitivity at various environmental temperatures. The wearable sensors have been utilized for human‐motion detection with improved comprehensive performance, indicating their promising applications in flexible electronics and artificial intelligent fields. [ABSTRACT FROM AUTHOR]

Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay‐sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited‐processor fog nodes (UPFN) and limited‐processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP‐hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near‐optimal solution bounded by 83OPT+ϵ28mα has been found. m is the number of fog nodes, and α is the upper bound on the Lipschitz constant of the delay function. Another one is the application‐driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]