Lower Processing Speed Research Articles

Online action proposal generation using spatio-temporal attention network

Temporal action proposal generation aims to generate temporal boundaries containing action instances. In real-time applications such as surveillance cameras, autonomous driving, and traffic monitoring, the online localization and recognition of human activities occurring in short temporal intervals are important areas of research. Existing approaches of temporal action proposal generation consider only the offline and frame-level feature aggregation along the temporal dimension. Those offline methods also generate many redundant irrelevant proposal regions in the frames as temporal boundaries. This leads to higher computational cost along with slow processing speed which is not suitable for online tasks. In this study, we propose a novel spatio-temporal attention network for online action proposal generation as opposed to existing offline proposal generation methods. Our novel proposed approach incorporates the inter-dependency between the spatial and temporal context information of each incoming video clip to generate more relevant online temporal action proposals. First, we propose a windowed spatial attention module to capture the inter-spatial relationship between the features of incoming frames. The windowed spatial network produces more robust clip-level feature representation and efficiently deals with noisy features such as occlusion or background scenes. Second, we introduce a temporal attention module to capture relevant temporal dynamic information mutually to the localized spatial information to model the long inter-frame temporal relationship since most online real life videos are untrimmed in nature. By applying these two attention modules sequentially, the novel proposed spatio-temporal network model is able to generate precise action boundaries at a particular instant of time. In addition, the model generates fewer discriminative temporal action proposals while maintaining a low computational cost and high processing speed suitable for online settings.

Gestational and childhood urinary iodine concentrations and children's cognitive function in a longitudinal mother-child cohort in rural Bangladesh.

Severe iodine deficiency adversely affects neurodevelopment; however, evidence regarding the association of non-severe deficiency and child cognitive functioning is inconclusive. This prospective mother-child cohort study was nested in a population-based nutritional supplementation trial in Bangladesh (Maternal and Infant Nutrition Interventions in Matlab [MINIMat]). Participants with data on cognitive abilities at 5 and 10 years of age (n = 1530) and at least one measurement of urinary iodine concentration (UIC) (gestational week 8, 5, and 10 years) were selected. Cognitive abilities were assessed using the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III) and Wechsler Intelligence Scale for Children (WISC-IV). UICs were measured with inductively coupled plasma mass spectrometry and thereafter adjusted for specific gravity. Median UICs in our population: (282 μg/L [pregnancy]; 406 μg/L [5 years]; 294 μg/L [10 years]) indicated that iodine intake corresponded to above 'adequate' or even 'excessive', according to the WHO classification. Maternal 'UIC <150 μg/L' was associated with lower full-scale and verbal scores at 5 and 10 years, although the associations were weakened in the fully adjusted models. A tendency of decreased verbal scores was also observed for maternal 'UIC ≥500 μg/L' but not for the corresponding child iodine category (≥300 μg/L). Child 'UIC <100 μg/L' was associated with lower processing speed (B=-3.1, 95% CI [-6.2, -0.1]; P-value = 0.041) compared with the reference group (100 μg/L≤ UIC <300 μg/L). Current findings add to the growing evidence of a causal association of early-life iodine intake with cognitive development, indicating that low iodine intake during childhood isassociated with reduced processing speed andnon-optimal gestational iodine intake is weakly associated with slightly poorer verbal development outcomes.

Mediators of cognitive impairment in cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) is associated with cognitive decline. CAA has diverse impacts on brain structure and function; however, the brain lesions that mediate the association of CAA with cognition are not understood well. To determine the degree to which CAA neuroimaging biomarkers mediate the association of CAA with cognitive dysfunction. We analyzed cross-sectional data of patients with probable CAA and controls without cognitive impairment from the Functional Assessment of Vascular Reactivity study. Neuropsychological tests were grouped into domains of memory, executive function, and processing speed. Candidate CAA neuroimaging biomarkers were pre-specified based on prior literature, consisting of white matter hyperintensity volume, peak width of skeletonized mean diffusivity (PSMD) on diffusion tensor magnetic resonance imaging (MRI), cerebrovascular reactivity (CVR), cortical thickness, and cortical thickness in a meta-region of interest typically affected by Alzheimer's disease (AD). Cognitive scores and neuroimaging markers were standardized and reported in relation to values in controls. Mediation analysis was used to estimate the total effect of CAA on cognition and the proportion of the total effect that was mediated by neuroimaging biomarkers, controlling for age, sex, and education. There were 131 participants (67 CAA and 64 controls). Mean age was 72.1 ± 7.7 years, and 54.2% were women. As expected, compared to controls, CAA was associated with lower cognition. In mediation analyses, CAA had direct unmediated effects of 48%, 46%, and 52% on all three cognitive domains. The association of CAA with memory was partially mediated by CVR and PSMD, accounting for 18% and 36% of the total effect of CAA. The association of CAA with executive function was partially mediated by PSMD and mean cortical thickness in the AD meta-region of interest (ROI), accounting for 33% and 31% of the total effect of CAA. The association of CAA with processing speed was partially mediated by CVR and PSMD, accounting for 8% and 34% of the total effect of CAA. Among CAA participants, the presence of cortical superficial siderosis was associated with lower processing speed. Altered white matter diffusivity (i.e. PSMD), CVR, and atrophy, taken together, account for about half the effect of CAA on cognition.

A cross-lagged prospective network analysis of depression and anxiety and cognitive functioning components in midlife community adult women.

Scar theory proposes that heightened depression and anxiety precede and predict worse cognitive functioning outcomes, whereas the vulnerability theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged prospective network analyses to facilitate causal inferences in understanding the relations between psychopathology and cognitive functioning components. Racially-diverse midlife women (n = 1816) participated in the Study of Women's Health Across the Nation at two time-points, spanning one year apart. Five psychopathology (anxiety severity, depressed mood, somatic symptoms, positive affect, interpersonal problems) and four cognitive functioning nodes (working memory (WM), processing speed (PS), facial recognition (FCR), and verbal memory (VRM)) were assessed. All analyses adjusted for age, menopausal status, estradiol, and follicle-stimulating hormones. Contemporaneous networks yielded notable inverse between-node relations (edges) between interpersonal problems and reduced FCR and PS, and between depressed mood and lower FCR, VRM, or PS. Nodes that had the highest likelihood to bridge other constructs were positive affect, anxiety severity, WM, and VRM. Temporal networks produced edges consistent with the scar (v. vulnerability) hypotheses. Higher somatic symptoms were related to reduced PS and WM, and greater depressed mood was correlated with lower future PS and WM. Also, higher anxiety severity coincided with decreased future PS and WM. Greater positive affect was associated with stronger future PS, FCR, and WM. Also, positive affect had the strongest relations with other nodes. Findings suggest the importance of targeting symptoms and cognitive functioning simultaneously.

Health-Related Quality of Life of Colombian children and adolescents with mild Traumatic Brain Injury

Little is known about the impact of mild traumatic brain injury (TBI) on the functioning of children in Latin America, especially regarding a broad construct called health-related quality of life (HRQoL). The objective of this study was to explore the effects of mild TBI on the HRQoL of Colombian children and adolescents, considering possible cognitive and behavioral sequelae derived from TBI. A case-control study was conducted in which 30 children and adolescents with mild TBI and their parents and a group of 30 healthy subjects participated. The results show that participants with mild TBI have an HRQoL similar to the general population of the same age and sex. They report a lower HRQoL than their parents in the Autonomy and Parents dimension. At the cognitive level, the group with mild TBI has a lower processing speed and less work done in selective and sustained attention tasks; at the behavioral level, they exhibit more symptoms of anxiety, depression, withdrawal and social problems.

Foreword Special Issue on Spintronics-Devices and Circuits

It is our great pleasure to introduce this Special Issue on Spintronics-Devices and Circuits to the IEEE Transactions on Electron Devices readership. This issue features research that addresses the important and timely topics related to spintronic devices, circuits, and new architectures for low power applications. Spintronics is one of the emerging fields for the next-generation nano-electronic devices that offer solutions for memory and energy-efficient circuits and systems for data-intensive processing applications. Recent advances have expanded this technology to the entire microelectronics community in the domains of sensors, memories, oscillators, quantum information, processors, and computer architecture. These advancements in spintronics have shown great promise for stochastic and neuromorphic computing, in-memory computing, and data security applications. However, these applications are constrained by the challenges related to low spin efficiency, device reliability, high power consumption, low processing speed, and large area overhead. This Special Issue has been organized to bring together researchers and experts to address these technological challenges.

Energy-Efficient Advanced Data Encryption System Using Spin-Based Computing-in-Memory Architecture

Spintronic-based computing-in-memory (CiM) architecture has emerged as one of the efficient solutions to eliminate the latency/bandwidth bottleneck of conventional von-Neumann architecture. Voltage-controlled spin–orbit torque (SOT) memory offers ultralow power and high-speed operation among the various spintronic memories. In this article, advanced encryption standard (AES) system within CiM architecture using voltage-controlled SOT device has been presented. The entire encryption process is performed within the high-density spintronic-based memory array to achieve low power and high processing speed. The reconfigurable logic operations and random key generation for AES are achieved by using a single voltage-controlled SOT device within the memory array. The results show that the proposed architecture is 96%, 52.1%, and 14% more efficient in terms of energy consumption, throughput, and area, respectively, when compared with one of the most efficient SOT-based AES systems.

Effect of age, cerebral infarcts, vasculopathy and haemoglobin on cognitive function, in Tanzanian children with sickle cell anaemia.

Developmental difficulties in many cognitive domains are common in children with sickle cell anaemia (SCA). Children with stroke are most affected but delayed or atypical cognitive function has been reported in children with SCA and silent infarcts (SCI), vasculopathy, and normal brain MRI. However, very few studies of cognition have been conducted in Africa, a continent with 75% of the SCA burden. We therefore investigated cognitive profiles in Tanzanian children with SCA and examined the impact of age, SCI, vasculopathy, and haemoglobin concentration (Hb). Children aged 6-16 years with and without SCA were eligible for this cross-sectional study. Cognitive assessment was performed using Raven's Matrices, assessing fluid, non-verbal intelligence and subtests from the Wechsler Intelligence Scales for Children (WISC-IV), assessing processing speed (PS), perceptual reasoning (PR), and working memory (WM) as these tests are less culture-bound. Magnetic resonance imaging (MRI) and angiography (MRA) were also completed to assess the presence of SCI and vasculopathy. Hb was collected in both SCA children and their non-SCA siblings. Seventy-three children with SCA and 71 healthy siblings (Meanages 11.9, SD=2.8 and 11.1, SD=2.9 years respectively) were recruited. Compared with healthy siblings, children with SCA had lower PS (Meandiff 7.35 points; p=.002). Older children had higher performance scores on all tests in relation to their ages. Lowest cognitive scores were observed on the PS subtest, where patients with SCI (SCI+) had lowest mean values as compared to children with no SCI (SCI-) and healthy siblings (i.e., SCI+<SCI-<healthy siblings, p=.028). On post-hoc analysis the difference was between SCI+ and healthy siblings SCI+<non-SCA siblings (p=.015); there was no difference between SCI+ and SCI- patient groups. PS was significantly lower in SCA patients with no vasculopathy as compared to healthy siblings. The mean difference from healthy siblings was -8.352 and -0.752 points for VASC- and VASC+respectively (p=.004). There was a significant positive effect of Hb on PSI (p=.001) in both patients and controls and a trend level significant positive effect of Hb on PR (p=.050) and WM (p=.051). In this Tanzanian study, cognitive performance was reduced in children with SCA with or without SCI on MRI or vasculopathy. Cognitive performance improved with increasing age. Lower Hb was associated with lower cognitive performance in both patients with SCA and their non-SCA siblings. SCI and vasculopathy do not appear to have an impact on cognitive function.

Deep Neural Network Based Reconciliation for CV-QKD

High-speed reconciliation is indispensable for supporting the continuous-variable quantum key distribution (CV-QKD) system to generate the secure key in real-time. However, the error correction process’s high complexity and low processing speed limit the reconciliation speed. Therefore, reconciliation has also become the bottleneck of system performance. In this paper, we proposed a high-speed reconciliation scheme that uses the deep neural network to optimize the decoding process of the low-density parity-check (LDPC) code. We first introduced a network structure of decoding implementation based on the deep neural network, which can be applied to decoding algorithms of parallel strategy and significantly reduce the decoding complexity. Subsequently, we proposed two improved decoding algorithms based on this structure, named linear fitting algorithm and deep neural network-assisted decoding algorithm. Finally, we introduced a high-speed reconciliation scheme based on the CPU-GPU hybrid platform. Simulation results show that the proposed reconciliation scheme reduces the complexity and enables us to realize the high-speed CV-QKD system. Furthermore, the improved decoding algorithm can also reduce the FER, thereby increasing the secret key rate.

Ultrafast and large-gap microwelding of glass substrates by selective absorption of continuous-wave laser into transiently excited electrons

Glass-to-glass welding using ultrashort laser pulses is attracting attention. However, the low processing speed and the requirement for a small air gap between glass substrates have impeded its use in industry. In this study, we achieve ultrafast microwelding of glass substrates via the selective absorption of a continuous-wave laser into transiently excited electrons using an ultrashort laser pulse. This method increases the processing speed by a factor of 500 compared with the conventional method, and the allowable gap between the substrates by a factor of four, while maintaining a welding strength similar to that of conventional methods.

Transtorno Depressivo Maior: Um Estudo Comparativo sobre Cognição Socioemocional e Funções Executivas

Abstract The present study aimed to assess socioemotional cognition and executive functions in patients with unipolar Major Depressive Disorder. The sample included 22 patients between 36 and 93 years of age (M = 59.32; SD = 12.89) and 23 patients between 30 and 81 years of age (M = 63.00; SD = 13.56) controls. In addition to demographic data, symptoms of anxiety and depression, empathy, theory of mind, recognition of emotions, inhibitory control, cognitive flexibility and phonemic verbal fluency were obtained. There was no statistical difference between the groups regarding age and education. Patients had significantly more anxiety, depression and personal distress than controls. Individuals with more severe depressive symptoms had a lower processing speed than the others.

Association of cognitive performance with clinical staging in schizophrenia spectrum disorders: a prospective 6-year follow-up study

BackgroundClinical staging has been developed to capture the large heterogeneity in schizophrenia spectrum disorders. Including cognitive performance in the staging model may improve its clinical validity. Moreover, cognitive functioning could predict transition across stages. However, current evidence of the association between cognition and clinical staging is inconsistent. Therefore, we aim to assess whether cognitive parameters are associated with clinical stages in a large sample of patients with schizophrenia spectrum disorders and to identify cognitive markers at baseline that are associated with stage-transition at three and six-year follow-up. MethodsWe applied the staging model of Fusar-Poli et al. (2017) in 927 patients with non-affective psychotic disorders, assessed at baseline, and after three and six-year follow-up. Cognitive performance was assessed with a standard test battery. Generalized linear mixed models were used to analyze associations of cognitive performance with staging and stage-transition at follow-up. ResultsFindings showed that higher stages of illness were significantly associated with lower processing speed (F = 3.688, p = 0.025) and deficits in working memory (F = 6.365, p = 0.002) across assessments. No associations between cognitive parameters at baseline and stage-transition at three- and six-year follow-up were found. ConclusionWe conclude that processing speed and working memory were modestly associated with higher stages of illness in schizophrenia spectrum disorders, thereby slightly improving its clinical validity. However, associations were small and we found no evidence for predictive validity.

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

Correlation Of Functional Disorders In Cerebral Blood Flow With Cognitive Style Of Freshmen Students

In 97 healthy male freshmen students, we studied the parameters of arterial blood flow intensity (index of vascular resistance RI, amplitude-frequency index AFI), venous outflow (venous outflow index VOI), and tone and elasticity of cerebral vessels (diastolic index DIA, dicrotic index DIC, maximum blood velocity of fast filling in the artery Vmax, average blood velocity of slow filling in the artery Vav). Separately, we assessed the effectiveness of such mental operations as classification and analysis and studied the cognitive styles. In 35.9% of male freshmen, we revealed the functional dystonia of cerebral vessels with a pronounced inter-lacunar redistribution of blood flow to the region of internal carotid arteries. Their cognitive style had low rigidity, flexible control, strong cognitive automation, and low information processing speed. The cognitive style in freshmen without dystonic disorders was characterized by a balance of rigidity and flexibility of thinking, with a medium information processing speed.

What is a processing speed weakness? Importance of cognitive ability when defining processing speed in a child psychiatric population

ABSTRACT There is growing evidence that processing speed (PS) deficits in youth with neuropsychiatric conditions are associated with functional difficulties. However, there is no consistent definition of slower PS; specifically, whether slower PS should be defined as a discrepancy from same-aged peers (normative weakness) or as an intrapersonal deficit relative to overall cognitive ability (relative weakness). In a sample of clinically-referred youth, we calculated slower PS both ways and examined the impact on adaptive, academic, and psychopathology outcomes in relation to different levels of cognitive ability. Significant PS x cognitive ability interactions were found on adaptive and academic outcomes. A norm-based weakness in PS (PSI Standard Score <85) was associated with lower adaptive skills and lower academic skills regardless of cognitive ability. In the above average cognitive ability group, relatively lower PS (PSI >15 point difference from VCI) was associated with significantly lower academic performance. No significant associations were found for general psychopathology. Results suggest a normative weakness in PS impacts functional outcomes interactively and differently with level of general cognitive ability. Data suggest that higher cognitive ability may be somewhat protective from the impact of normatively weak PS on adaptive outcomes; however, youth across all abilities with normatively weak PS showed weaker academic performance. Second, children with high cognitive abilities and relatively weak PS showed discrepant performance compared to comparison group. Implications and areas for future research are discussed.

Design and Implementation of Nanotechnology QCA Geometric Greedy Router

This paper presents an optimized geometric greedy router (GGR) based on quantum dot cellular automata (QCA) technology. The proposed structure of GGR is based on a spanning tree of the network. This type of communication does not require an IP address. It uses only local information and can be used in many communication devices. In this paper, we first describe the principal components of the router and then we present their QCA architecture. The QCA technology is the most likely alternative to replace conventional circuits (CMOS) due to their very low power consumption and high processing speed. To consider integration with other complex circuit, we have utilized QCA clock-phase-based technique for the proposed design architecture. The results obtained using the QCA designer tool exhibit the superiority of the presented architecture over the existing designs. The proposed structure shows a reduction of 30% reduction in occupied space. The power dissipation rate of the proposed design is analyzed by QCAPro tool to approve its reliability.

Effect of processing speed on wear and corrosion behavior of novel MoS2 and CeO2 reinforced hybrid aluminum matrix composites fabricated by friction stir processing

The extensive use of aluminum matrix composites as structural material urges it to possess good surface properties such as wear and corrosion resistance. To achieve that, mixture of MoS 2 and CeO 2 powder was reinforced in Al7075 alloy through friction stir processing with different processing speed and multi-pass approach. The microstructural image analysis by optical microscope and scanning electron microscope revealed reduction of matrix grain size and improvement of particle distribution for lower processing speed and multi-pass processing. The elemental analysis indicated well dispersion of reinforcement particles in matrix along with the presence of compounds of aluminum alloy. The microhardness and tensile strength were increased with reduction of processing speed and increase of number of pass. The friction coefficient and the wear loss were also low for composites produced with low processing speed and multi-pass. A combined abrasive and adhesive wear was observed in all the processed composites. With increase of processing speed and number of pass, the corrosion behavior of the composites in 3.5% NaCl solution deteriorated. The composite produced with 30 mm/min and 2 pass exhibited best particle distribution, microhardness and wear resistance. However, the composite fabricated with 30 mm/min and 1 pass yielded best corrosion resistance. • Novel hybrid aluminum matrix composites developed by friction stir processing • Microstructure of the fabricated composites analyzed • Microhardness distribution measured • Wear mechanism and friction coefficient variation analyzed and effect of processing speed on wear loss discussed • Corrosion behavior and effect of process parameters on corrosion parameters discussed

Children with Autism Spectrum Disorder of All Ages, Levels of Symptom Severity and General Cognitive Ability Display Low Processing Speed Index Scores Warranting Special Educational Assistance

The processing speed index (PSI) of the Wechsler intelligence scale for children (WISC-IV) has been found to predict a child's level of academic functioning. The consistently reported PSI weakness in children with autism spectrum disorder (ASD) therefore warrants special assistance and attempts at compensation for the disadvantages associated with these children's low PSI. We investigated the association of PSI scores with age, general cognitive ability [as measured by full-scale IQ (FSIQ)], symptom severity and discrepancy between the WISC-IV indices verbal comprehension (VCI) and perceptual reasoning (PRI) in 101 school children with ASD. The PSI weakness in children with ASD was not related to age, FSIQ, VCI-PRI discrepancy or any of the symptom measures. These findings suggest that school children with ASD independent of their age, level of cognitive ability, VCI-PRI profile and most notably independent of their symptom severity should be entitled to special assistance and compensation in educational settings.

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

AbstractAttack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer deployment time, poor communication range, and reduced energy, H‐WSNs are subjected to difficult implementation and have their own limitations. To tackle these issues, we have presented a hybrid deep learning model using convolutional neural network and long short term memory (HDMCL) for attack detection in H‐WSN. This research is divided into three steps: preprocessing, dimensionality reduction, and classification (attack detection). At first, the raw input data (patient's health data) is preprocessed using the one‐hot encoding method. Next, the modified Huber independent component analysis based squirrel search algorithm (MHICA‐SSA) effectively reduces the data dimensionality in which the MHICA‐SSA is the amalgamation of both modified Huber independent component analysis and squirrel search algorithm. The novel algorithm designed overcomes the complexities associated with existing techniques such as the curse of dimensionality problem, improves the parameter interpretation, minimizes the time and storage space, minimizes space complexity, and enhances the attack detection accuracy of the convolutional neural network‐based long short‐term memory (CNN‐LSTM). After that, the deep learning CNN‐LSTM model is utilized for normal, black hole, and gray hole attack detection. The NS2.34 network simulator implements the proposed work thereby the proposed work efficiency is validated using different performance measures such as packet delivery ratio, false alarm rate, network lifetime, energy consumption, throughput, attack detection time, and rate. The proposed work performance is evaluated using different existing methods such as fish swarm optimization based particle swarm optimization, intelligent opportunistic routing algorithm, Jensen‐Shannon divergence‐based independent component analysis, and radio frequency identification based WSN. The throughput of the proposed work is increased up to 3.2 times and the network lifetime is increased up to 4 times when compared to the existing techniques.

A fast evidential approach for stock forecasting

Within the framework of evidence theory, the confidence functions of different information can be combined into a combined confidence function to solve uncertain problems. The Dempster combination rule is a classic method of fusing different information. This paper proposes a similar confidence function for the time point in the time series. The Dempster combination rule can be used to fuse the growth rate of the last time point, and finally a relatively accurate forecast data can be obtained. Stock price forecasting is a concern of economics. The stock price data is large in volume, and more accurate forecasts are required at the same time. The classic methods of time series, such as ARIMA, cannot balance forecasting efficiency and forecasting accuracy at the same time. In this paper, the fusion method of evidence theory is applied to stock price prediction. Evidence theory deals with the uncertainty of stock price prediction and improves the accuracy of prediction. At the same time, the fusion method of evidence theory has low time complexity and fast prediction processing speed.