Arithmetic Processing Research Articles

Direct intracranial recordings in the human angular gyrus during arithmetic processing.

The role of angular gyrus (AG) in arithmetic processing remains a subject of debate. In the present study, we recorded from the AG, supramarginal gyrus (SMG), intraparietal sulcus (IPS), and superior parietal lobule (SPL) across 467 sites in 30 subjects performing addition or multiplication with digits or number words. We measured the power of high-frequency-broadband (HFB) signal, a surrogate marker for regional cortical engagement, and used single-subject anatomical boundaries to define the location of each recording site. Our recordings revealed the lowest proportion of sites with activation or deactivation within theAG compared to other subregions of the inferior parietal cortex during arithmetic processing. The few activated AG sites were mostly located at the border zones between AG and IPS, or AG and SMG. Additionally, we found that AG sites were more deactivated in trials with fast compared to slow response times. The increase or decrease of HFB within specific AG sites was the same when arithmetic trials were presented with number words versus digits and during multiplication as well as addition trials. Based on our findings, we conclude that the prior neuroimaging findings of so-called activations in the AG during arithmetic processing could have been due to group-based analyses that might have blurred the individual anatomical boundaries of AG or the subtractive nature of the neuroimaging methods in which lesserdeactivations compared to the control condition have been interpreted as "activations". Our findings offer a new perspective with electrophysiological data about the engagement of AG during arithmetic processing.

"Strength and weakness coefficient of Arabic letters"

The phonemic level of linguistics is closer to all other levels of applied sciences; As a physical phenomenon in its origin, as well as finite and countable units; Thus it is easy to describe it mathematically. I had previously had an idea based on a quantitative description of the concepts of strength and weakness in the letters of the Arabic lexicon, by calculating the ratio of strong to weak qualities for each letter and thus showing the ratio of strength, weakness and moderation. With this research in her master's thesis tagged: (Quantitative Description of the Concepts of Strength and Weakness in the Arabic Letters), she did well and completed the research, and came out with good results, which no one has ever reached. Since the purpose of the research is a quantitative representation of the strength, weakness and moderation of each letter; It seemed to me that we treat those values that we obtained for each letter (the percentage of strength and percentage of weakness) with an arithmetic process and come up with one numerical coefficient for each letter that represents the strength, weakness, or moderation of the letter. The researcher did this by making the value of the strength ratio a basis, and comparing the weakness ratio with it according to the following equation (Strength Coefficient = Strength Ratio / Weakness Ratio). Relatively speaking, if the output is greater than (1), the sound is relatively strong. But if the result is equal to (0), the letter is absolutely weak, and if the result is equal to (∞), then the letter is absolutely strong

The relationship between phonological processing and arithmetic in children with learning disabilities.

Phonological processing skills have not only been shown to be important for reading skills, but also for arithmetic skills. Specifically, previous research in typically developing children has suggested that phonological processing skills may be more closely related to arithmetic problems that are solved through fact retrieval (e.g., remembering the solution from memory) than procedural computation (e.g., counting). However, the relationship between phonological processing and arithmetic in children with learning disabilities (LDs) has not been investigated. Yet, understanding these relationships in children with LDs is especially important because it can help elucidate the cognitive underpinnings of math difficulties, explain why reading and math disabilities frequently co-occur, and provide information on which cognitive skills to target for interventions. In 63 children with LDs, we examined the relationship between different phonological processing skills (phonemic awareness, phonological memory, and rapid serial naming) and arithmetic. We distinguished between arithmetic problems that tend to be solved with fact retrieval versus procedural computation to determine whether phonological processing skills are differentially related to these two arithmetic processes. We found that phonemic awareness, but not phonological memory or rapid serial naming, was related to arithmetic fact retrieval. We also found no association between any phonological processing skills and procedural computation. These results converge with prior research in typically developing children and suggest that phonemic awareness is also related to arithmetic fact retrieval in children with LD. These results raise the possibility that phonemic awareness training might improve both reading and arithmetic fact retrieval skills. RESEARCH HIGHLIGHTS: Relationships between phonological processing and various arithmetic skills were investigated in children with learning disabilities (LDs) for the first time. We found phonemic awareness was related to arithmetic involving fact retrieval, but not to arithmetic involving procedural computation in LDs. The results suggest that phonemic awareness is not only important to skilled reading, but also to some aspects of arithmetic. These results raise the question of whether intervention in phonemic awareness might improve arithmetic fact retrieval skills.

Low power reconfigurable multilevel nanophotonic devices based on Sn-doped Ge2Sb2Te5 thin films

In the past years, Ge2Sb2Te5 has been considered a promising functional material for a variety of reconfigurable multilevel devices, including photonic integrated circuits for the post-von Neumann arithmetic processing. However, despite significant advances, it is necessary to reduce the switching energy of Ge2Sb2Te5 for creation of the on-chip low power all-photonic spiking neural networks. The present work focuses on the effect of tin ion implantation on the properties of amorphous Ge2Sb2Te5 thin films, as well as on the performance of Mach-Zehnder interferometers and balanced beam splitters based on them. As a result, Sn-doping accompanied by the formation of weaker bonds in Ge2Sb2Te5 thin films is an efficient approach to significantly reduce the threshold energy of fs-laser initiated phase transitions and change the effective absorption coefficient. The possibility of using the Sn-doped Ge2Sb2Te5 thin films for fully optical multilevel reversible recording between 9 different levels (3 bits) has been demonstrated by experimental measurements of fabricated on-chip balanced beam splitters. The obtained results show that the Sn doping of Ge2Sb2Te5 layer can be used to optimize the properties of the GST225 thin films, in particular to reduce the switching energy. So, it has the potential to improve the characteristics of reconfigurable multilevel nanophotonic devices using the GST225 thin films, including fully non-volatile memory and developed on-chip low power all-photonic circuits for post-von Neumann arithmetic processing.

HEALPix Alchemy: Fast All-Sky Geometry and Image Arithmetic in a Relational Database for Multimessenger Astronomy Brokers

Efficient searches for electromagnetic counterparts to gravitational wave, high-energy neutrino, and gamma-ray burst events demand rapid processing of image arithmetic and geometry set operations in a database to cross-match galaxy catalogs, observation footprints, and all-sky images. Here we introduce HEALPix Alchemy, an open-source, pure Python implementation of a set of methods that enables rapid all-sky geometry calculations. HEALPix Alchemy is built upon HEALPix, a spatial indexing strategy that is widely used in astronomical databases as well as the native format of LIGO-Virgo-KAGRA gravitational-wave sky localization maps. Our approach leverages new multirange types built into the PostgreSQL 14 database engine. This enables fast all-sky queries against probabilistic multimessenger event localizations and telescope survey footprints. Questions such as “What are the galaxies contained within the 90% credible region of an event?” and “What is the rank-ordered list of the fields within an observing footprint with the highest probability of containing the event?” can be performed in less than a few seconds on commodity hardware using off-the-shelf cloud-managed database implementations without server-side database extensions. Common queries scale roughly linearly with the number of telescope pointings. As the number of fields grows into the hundreds or thousands, HEALPix Alchemy is orders of magnitude faster than other implementations. HEALPix Alchemy is now used as the spatial geometry engine within SkyPortal, which forms the basis of the Zwicky Transient Facility transient marshal, called Fritz.

Understanding the Effects of Transcranial Electrical Stimulation in Numerical Cognition: A Systematic Review for Clinical Translation.

Atypical development of numerical cognition (dyscalculia) may increase the onset of neuropsychiatric symptoms, especially when untreated, and it may have long-term detrimental social consequences. However, evidence-based treatments are still lacking. Despite plenty of studies investigating the effects of transcranial electrical stimulation (tES) on numerical cognition, a systematized synthesis of results is still lacking. In the present systematic review (PROSPERO ID: CRD42021271139), we found that the majority of reports (20 out of 26) showed the effectiveness of tES in improving both number (80%) and arithmetic (76%) processing. In particular, anodal tDCS (regardless of lateralization) over parietal regions, bilateral tDCS (regardless of polarity/lateralization) over frontal regions, and tRNS (regardless of brain regions) strongly enhance number processing. While bilateral tDCS and tRNS over parietal and frontal regions and left anodal tDCS over frontal regions consistently improve arithmetic skills. In addition, tACS seems to be more effective than tDCS at ameliorating arithmetic learning. Despite the variability of methods and paucity of clinical studies, tES seems to be a promising brain-based treatment to enhance numerical cognition. Recommendations for clinical translation, future directions, and limitations are outlined.

Modular Arithmetic CPM for SDR Platforms

This brief presents a new Continuous Phase Modulation (CPM) module for multi- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> transmission based on modular arithmetic processing. It is well known that CPM systems are sensitive to changes in the modulation index, which is implied when fixed-point format is used in the hardware implementation. In this scenario, modular arithmetic allows a more accurate signal processing regarding SQNR performance. This proposal has been shown to improve performance compared to other state-of-the-art methods, even when the modulation indices do not have an exact representation in a given fixed-point format. Due to its highly reconfigurable architecture, this hardware block is suitable for Software Defined Radio (SDR) platforms. A complete digital architecture is also provided. Implementation results show that the new proposal have clear advantages in terms of hardware resources and operating frequency against other CPM transmitters in open-literature.

Cichlids and stingrays can add and subtract \u2018one\u2019 in the number space from one to five

The numerical understanding of cichlids and stingrays was examined regarding addition and subtraction abilities within the number space of one to five. Experiments were conducted as two-alternative forced-choice experiments, using a delayed matching to sample technique. On each trial, fish had to perform either an addition or subtraction, based on the presentation of two-dimensional objects in two distinct colors, with the color signaling a particular arithmetic process. Six cichlids and four stingrays successfully completed training and recognized specific colors as symbols for addition and subtraction. Cichlids needed more sessions than stingrays to reach the learning criterion. Transfer tests showed that learning was independent of straightforward symbol memorization. Individuals did not just learn to pick the highest or lowest number presented based on the respective color; instead, learning was specific to adding or subtracting ‘one’. Although group results were significant for both species in all tests, individual results varied. Addition was learned more easily than subtraction by both species. While cichlids learned faster than stingrays, and more cichlids than stingrays learned the task, individual performance of stingrays exceeded that of cichlids. Previous studies have provided ample evidence that fish have numerical abilities on par with those of other vertebrate and invertebrate species tested, a result that is further supported by the findings of the current study.

Effect of Obesity on Arithmetic Processing in Preteens With High and Low Math Skills: An Event-Related Potentials Study.

Preadolescence is an important period for the consolidation of certain arithmetic facts, and the development of problem-solving strategies. Obese subjects seem to have poorer academic performance in math than their normal-weight peers, suggesting a negative effect of obesity on math skills in critical developmental periods. To test this hypothesis, event-related potentials (ERPs) were collected during a delayed-verification math task using simple addition and subtraction problems in obese [above 95th body mass index (BMI) percentile] and non-obese (between 5th and 90th BMI percentile) preteens with different levels of math skill; thirty-one with low math skills (14 obese, mean BMI = 26.40, 9.79 years old; 17 non-obese, BMI = 17.45, 9.76 years old) and thirty-one with high math skills (15 obese, BMI = 26.90, 9.60 years old; 16 non-obese, BMI = 17.13, 9.63 years old). No significant differences between weight groups were observed in task accuracy regardless of their mathematical skill level. For ERPs, electrophysiological differences were found only in the subtraction condition; participants with obesity showed an electrophysiologic pattern associated with a reduced ability to allocate attention resources regardless of their math skill level, these differences were characterized by longer P300 latency than their normal-weight peers. Moreover, the participants with obesity with high math skills displayed hypoactivity in left superior parietal lobule compared with their normal-weight peers. Additionally, obese preteens with low math skills displayed smaller arithmetic N400 amplitude than non-obese participants, reflecting difficulties in retrieving visual, semantic, and lexical information about numbers. We conclude that participants with obesity are less able than their normal-weight peers to deploy their attention regardless of their behavioral performance, which seems to have a greater effect on obese participants with low math skills because they also show problems in the retrieval of solutions from working memory, resulting in a delay in the development of mathematical skills.

Neuronal codes for arithmetic rule processing in the human brain

Arithmetic is a cornerstone of scientifically and technologically advanced human culture, but its neuronal mechanisms are poorly understood. Calculating with numbers requires temporary maintenance and manipulation of numerical information according to arithmetic rules. We explored the brain mechanisms involved in simple arithmetic operations by recording single-neuron activity from the medial temporal lobe of human subjects performing additions and subtractions. We found abstract and notation-independent codes for addition and subtraction in neuronal populations. The neuronal codes of arithmetic in different brain areas differed drastically. Decoders applied to time-resolved recordings demonstrate a static code in hippocampus based on persistently rule-selective neurons, in contrast to a dynamic code in parahippocampal cortex originating from neurons carrying rapidly changing rule information. The implementation of abstract arithmetic codes suggests different cognitive functions for medial temporal lobe regions in arithmetic.

Fact retrieval or compacted counting in arithmetic—A neurophysiological investigation of two hypotheses.

There is broad consensus on the assumption that adults solve single-digit multiplication problems almost exclusively by fact retrieval from memory. In contrast, there has been a long-standing debate on the cognitive processes involved in solving single-digit addition problems. This debate has evolved around two theoretical accounts. Proponents of a fact-retrieval account postulate that these are also solved through fact retrieval, whereas proponents of a compacted-counting account propose that solving very small additions (with operands between 1 and 4) involves highly automatized and unconscious compacted counting. In the present electroencephalography (EEG) study, we put these two accounts to the test by comparing neurophysiological correlates of solving very small additions and multiplications. Forty adults worked on an arithmetic production task involving all (nontie) single-digit additions and multiplications. Afterward, participants completed trial-by-trial strategy self-reports. In our EEG analyses, we focused on induced activity (event-related synchronization/desynchronization, ERS/ERD) in three frequency bands (theta, lower alpha, upper alpha). Across all frequency bands, we found higher evidential strength for similar rather than different neurophysiological processes accompanying the solution of very small addition and multiplication problems. In the alpha bands, evidence for similarity was even stronger when operand-1-problems were excluded. In two additional analyses, we showed that ERS/ERD can differentiate between self-reported problem-solving strategies (retrieval vs. procedure) and between very small n × 1 and n + 1 problems, demonstrating its high sensitivity to cognitive processes in arithmetic. The present findings support a fact-retrieval account, suggesting that both very small additions and multiplications are solved through fact retrieval. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Research on Embedded Controller of Hydraulic Vibrating Table Based on DSP

Background: The vibration control loop is the key technology adopt to improve the control performance of the vibration table, which is set outside of the hydraulic vibration table servo control loop. However, the huge number of signal processing work prompts high demands on the calculation ability of the vibration controller. One kind of multi-CPU embedded vibration controller constructed by Digital Signal Processor (DSP) is proposed considering the working principle of the hydraulic vibration table and the Power Spectrum Density (PSD) reproduction process. The embedded controller consists of an acquisition unit, a calculation unit, and a monitoring unit distributes vibration control tasks to the different processing unit to realize distributed algorithm calculations. Every processing unit uses dual-port memory to accomplish data interaction between each other. The development of the embedded controller provides a benchmark engineering case for the design of the hydraulic vibration table vibration controller. Objective: This article focuses on the development of the multi-CPU embedded vibration controller and the distributed calculations. Meanwhile, the power spectrum density experiment is carried out to verify the performance of the hydraulic vibration embedded controller. Methods: 1) The structure of the hydraulic vibration table control system is given, that is, two closed-loop controls. The bandwidth of the system is further broadened by the vibration control of the outer loop. Besides, the accuracy of vibration control is also improved. Then, the development needs of the vibration controller are put forward according to the detailed process of the power spectrum density replication. 2) An arithmetic processing unit is formed by using TI C2000 series DSP to calculate a large number of signal processing and a signal acquisition unit at high speed. In order to improve signal processing efficiency, the signal acquisition unit is used to perform preprocessing calculations (data acquisition and filtering) and vibration control calculations in a distributed manner. 3) Processing speed is further improved by taking full advantage of DSP software sources include lots of library functions and optimized assembly library functions. 4) The friendly operation of the controller and the safety monitoring of the experiment process are realized by the industrial computer served as the human-computer interaction unit. 5) Multi-CPU data sharing is achieved through using dual-port RAM to realize. Results: Through experiments, the developed embedded controller is fully estimated. The experiment shows that the developed hydraulic vibration table can realize real-time vibration control. Concerning the acceleration power spectrum density reproduction experiment, 256 acceleration response samples are calculated, and the update time is 4ms. The tracking accuracy of the timedomain waveform is controlled within 0.3%. Conclusion: The use of the developed embedded controller with signal conditioning equipment can achieve real-time control of the hydraulic vibration table, but the performance of the embedded controller can be promoted in advance, and the performance improvement of the hydraulic vibration table embedded controller can be studied from the following aspects: 1) The Fourier calculation is executed by the acquisition unit to share the calculation workload of the calculation unit; 2) The computing unit uses a signal processor chip with better performance, although this will bring development difficulties; 3) The monitoring computer can use an embedded controller with superior performance instead of an industrial computer to reduce the size, improve the performance; 4) The DSP real-time operating system should be used, and the task scheduling of vibration control experiments should be optimized.

Multiple Error Correction in Redundant Residue Number Systems: A Modified Modular Projection Method with Maximum Likelihood Decoding

Error detection and correction codes based on redundant residue number systems are powerful tools to control and correct arithmetic processing and data transmission errors. Decoding the magnitude and location of a multiple error is a complex computational problem: it requires verifying a huge number of different possible combinations of erroneous residual digit positions in the error localization stage. This paper proposes a modified correcting method based on calculating the approximate weighted characteristics of modular projections. The new procedure for correcting errors and restoring numbers in a weighted number system involves the Chinese Remainder Theorem with fractions. This approach calculates the rank of each modular projection efficiently. The ranks are used to calculate the Hamming distances. The new method speeds up the procedure for correcting multiple errors and restoring numbers in weighted form by an average of 18% compared to state-of-the-art analogs.

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Aim:The aim of this study is to assess the neuropsychological profiles of chronic schizophrenia and alcohol-dependent subjects.Materials and Methods:This hospital-based cross-sectional study included 30 chronic schizophrenia patients, 30 alcohol-dependent patients and 30-matched normal controls. Demographic and clinical data were collected on a self-designed proforma. Positive and Negative Syndrome Scale (PANSS) and Severity of Alcohol Dependence Questionnaire (SADQ-C) were administered to chronic schizophrenia and alcohol-dependent patients, respectively. The AIIMS Comprehensive Neuropsychological Battery in Hindi (Adult Form) was used to assess neuropsychological dysfunctions.Results:Neuropsychological dysfunctions were found in 83.3% of chronic schizophrenia patients, 36.7% alcohol dependents and none of the normal subjects. In comparison to normal subjects, schizophrenia patients had significantly more dysfunctions in neuropsychological-domains such as motor, tactile, visual, receptive and expressive speech, reading, writing, arithmetic, memory, and intellectual processes. A significant positive correlation was found between the PANSS total score and T scores of most of the clinical scales except motor and visual scales; the PANSS general psychopathology score and T scores of most of the clinical scales except motor visual and pathognomonic scales; the PANSS negative score and T scores of most of the clinical scales except visual scale; and the PANSS positive score and T scores of receptive speech, arithmetic, and memory scales. In comparison to normal subjects, the alcohol dependents had significantly more dysfunctions in neuropsychological-domains such as motor, tactile, visual, receptive and expressive speech, reading, writing, arithmetic, and memory. A significant positive correlation was found between the SADQ total scale and T scores of clinical scales such as expressive speech, writing, arithmetic, intellectual processes, left hemisphere, and total battery scales.Conclusions:Neuropsychological dysfunction was significantly more common and severe in chronic schizophrenia patients than in alcohol-dependent patients. In comparison to alcohol dependents, the chronic schizophrenia patients had more dysfunctions in neuropsychological-domains such as tactile, arithmetic, memory, and intellectual processes.

Non-invasive mapping of cortical categorization function by repetitive navigated transcranial magnetic stimulation

Over the past years navigated repetitive transcranial magnetic stimulation (nrTMS) had become increasingly important for the preoperative examination and mapping of eloquent brain areas. Among other applications it was demonstrated that the detection of neuropsychological function, such as arithmetic processing or face recognition, is feasible with nrTMS. In order to investigate the mapping of further brain functions, this study aims to investigate the cortical mapping of categorization function via nrTMS. 20 healthy volunteers purely right-handed, with German as mother tongue underwent nrTMS mapping using 5 Hz/10 pulses. 52 cortical spots spread over each hemisphere were stimulated. The task consisted of 80 pictures of living and non-living images, which the volunteers were instructed to categorize while the simulation pulses were applied. The highest error rates for all errors of all subjects were observed in the left hemisphere’s posterior middle frontal gyrus (pMFG) with an error rate of 60%, as well as in the right pMFG and posterior supra marginal gyrus (pSMG) (45%). In total the task processing of non-living objects elicited more errors in total, than the recognition of living objects. nrTMS is able to detect cortical categorization function. Moreover, the observed bihemispheric representation, as well as the higher error incidence for the recognition of non-living objects is well in accordance with current literature. Clinical applicability for preoperative mapping in brain tumor patients but also in general neuroscience has to be evaluated as the next step.

Overlapping Neuronal Population Responses in the Human Parietal Cortex during Visuospatial Attention and Arithmetic Processing.

Engagement of posterior parietal cortex (PPC) in visuospatial attention and arithmetic processing has been extensively documented using neuroimaging methods. Numerous studies have suggested a close connection between visuospatial attention and arithmetic processing. However, the extant evidence in humans stems from neuroimaging methods that have relied on group analyses without much knowledge about the profile of neurophysiological engagement within localized neuronal populations at the individual brain level. Hence, it has remained unclear if the overlap of two functions in the PPC is the product of averaging, or they truly stem from a common profile of activity within the same neuronal populations in the human PPC. In the current study, we leveraged the anatomical precision and high signal-to-noise ratio of intracranial electrocorticography and probed the engagement of discrete PPC neuronal populations in seven neurosurgical patients (n = 179 total PPC sites covered; 26 sites on average per individual participant). We aimed to study the extent of parietal activations within each individual brain during visuospatial attention versus arithmetic tasks and the profile of electrophysiological responses within a given recording site during these tasks. Our findings indicated that about 40% of PPC sites did not respond to either visuospatial attention or arithmetic stimuli-or episodic memory conditions that were used as an adjunct control condition. Of those that were activated during either visuospatial attention or arithmetic conditions, a large majority showed overlapping responses during both visuospatial attention and arithmetic conditions. Most interestingly, responses during arithmetic processing were greatest in sites along the intraparietal sulcus region showing preference to contralateral, instead of ipsilateral, visual probes in the visuospatial attention task. Our results provide novel data about the relationship between numerical and spatial orientation at the neuronal population level and shed light on the complex functional organization of the PPC that could not be attained with noninvasive methods.

Automatic Face Emotion Recognition With the Aid of Probability-Based Bird Swarm-Trained Neural Network

This paper intends to develop a novel FER model, which consists of four stages: (1) face detection, (2) feature extraction, (3) dimension reduction, and (4) classification. In this context, the face detection is done using Viola Jones method (VJ). It is the first object recognition model to offer better recognition rates in real-time. Further, features extraction techniques like local binary pattern (LBP) and discrete wavelet transform (DWT) are used for extracting the features from face detected images. Moreover, the dimension reduction of features is done using principal component analysis (PCA), which is an arithmetical process that exploits an orthogonal transformation to exchange a group of annotations of probably interrelated constraints. The classification procedure is performed using neural network (NN), with the new training algorithm called bird swarm algorithm, which is modified based on probability and hence termed as probability-based BSA (P-BSA).

System-Level Simulation for Integrated Phase-Change Photonics

Conventional computing systems are limited in performance by the well-known von Neumann bottleneck, arising from the physical separation of processor and memory units. The use of electrical signals in such systems also limits computing speeds and introduces significant energy losses. There is thus a pressing need for unconventional computing approaches, ones that can exploit the high bandwidths/speeds and low losses intrinsic to photonics. A promising platform for such a purpose is that offered by integrated phase-change photonics. Here, chalcogenide phase-change materials are incorporated into standard integrated photonics devices to deliver wide-ranging computational functionality, including non-volatile memory and fast, low-energy arithmetic and neuromorphic processing. We report the development of a compact behavioral model for integrated phase-change photonic devices, one which is fast enough to allow system level simulations to be run in a reasonable timescale with basic computing resources, while also being accurate enough to capture the key operating characteristics of real devices. Moreover, our model is readily incorporated with commercially available simulation software for photonic integrated circuits, thereby enabling the design, simulation and optimization of large-scale phase-change photonics systems. We demonstrate such capabilities by exploring the optimization and simulation of the operating characteristics of two important phase-change photonic systems recently reported, namely a spiking neural network system and a matrix-vector photonic crossbar array (photonic tensor core). Results show that use of our behavioral model can significantly facilitate the design and optimization at the system level, as well as expediting exploration of the capabilities of novel phase-change computing architectures.

Investigating Language and Domain-General Processing in Neurotypicals and Individuals With Aphasia - A Functional Near-Infrared Spectroscopy Pilot Study.

Brain reorganization patterns associated with language recovery after stroke have long been debated. Studying mechanisms of spontaneous and treatment-induced language recovery in post-stroke aphasia requires a network-based approach given the potential for recruitment of perilesional left hemisphere language regions, homologous right hemisphere language regions, and/or spared bilateral domain-general regions. Recent hardware, software, and methodological advances in functional near-infrared spectroscopy (fNIRS) make it well-suited to examine this question. fNIRS is cost-effective with minimal contraindications, making it a robust option to monitor treatment-related brain activation changes over time. Establishing clear activation patterns in neurotypical adults during language and domain-general cognitive processes via fNIRS is an important first step. Some fNIRS studies have investigated key language processes in healthy adults, yet findings are challenging to interpret in the context of methodological limitations. This pilot study used fNIRS to capture brain activation during language and domain-general processing in neurotypicals and individuals with aphasia. These findings will serve as a reference when interpreting treatment-related changes in brain activation patterns in post-stroke aphasia in the future. Twenty-four young healthy controls, seventeen older healthy controls, and six individuals with left hemisphere stroke-induced aphasia completed two language tasks (i.e., semantic feature, picture naming) and one domain-general cognitive task (i.e., arithmetic) twice during fNIRS. The probe covered bilateral frontal, parietal, and temporal lobes and included short-separation detectors for scalp signal nuisance regression. Younger and older healthy controls activated core language regions during semantic feature processing (e.g., left inferior frontal gyrus pars opercularis) and lexical retrieval (e.g., left inferior frontal gyrus pars triangularis) and domain-general regions (e.g., bilateral middle frontal gyri) during hard versus easy arithmetic as expected. Consistent with theories of post-stroke language recovery, individuals with aphasia activated areas outside the traditional networks: left superior frontal gyrus and left supramarginal gyrus during semantic feature judgment; left superior frontal gyrus and right precentral gyrus during picture naming; and left inferior frontal gyrus pars opercularis during arithmetic processing. The preliminary findings in the stroke group highlight the utility of using fNIRS to study language and domain-general processing in aphasia.

QCA based design of cost-efficient code converter with temperature stability and energy efficiency analysis

Quantum dot Cellular Automata (QCA) are a prominent nanotechnology that is widely employed in digital circuits and systems. In comparison to complementary metal–oxide semiconductor (CMOS) technology, QCA is a remarkable and challenging alternative with many attractive aspects such as fast execution and low power use. To accomplish all arithmetic processes, code converters are the fundamental unit of information change. Most of the converters, out of many designed for far in QCA, did not considered temperature stability, energy dissipation which may contribute to propagate as a non-operational circuit in various temperatures. This paper proposes a novel concept for a binary-to-gray code and a BCD-to-Excess 3 code converter based on QCA. The promised structure diminishes the number of cells, area utilized to make the design cost effective. The fundamental purpose of this research is to give a temperature stable and energy efficient converter design. The operability of the introduced design is verified with the QCADesigner2.0.3 tool and the temperature stability factor and energy waste are evaluated with QCADesignerE2.0.3.