Efficient Information Processing Research Articles

Unidirectional Transmission Metasurfaces with Topological Continuity Generated from High-dimensional Design Space.

With the growing demand for nanodevices, there is a concerted effort to improve the design flexibility of nanostructures, thereby expanding the capabilities of nanophotonic devices. In this work, a Laplacian-weighted binary search (LBS) algorithm is proposed to generate a unidirectional transmission metasurface from a high-dimensional design space, offering an increased degree of design freedom. The LBS algorithm incorporates topological continuity based on the Laplacian, effectively circumventing the common issue of high structural complexity in designing high-dimensional nanostructures. As a result, metasurfaces developed using the LBS algorithm in a high-dimensional design space exhibit reduced complexity, which is advantageous for experimental fabrication. An all-dielectric metasurface with unidirectional transmission, designed from the high-dimensional space using the LBS method, demonstrated the successful application of these design principles in experiments. The metasurface exhibits high optical performance on unidirectional transmission in measurements by a high-resolution angle-resolved micro-spectra system, achieving forward transmissivity above 90% (400-700 nm) and back transmissivity below 20% (400-500 nm) within the targeted wavelength range. This work provides a feasible approach for advancing high-dimensional metasurface applications, as the LBS design method takes into account topological continuity during experimental processing. Compared to traditional direct binary search (DBS) methods, the LBS method not only improves information processing efficiency but also maintains the topological continuity of structures. Beyond unidirectional transmission, the LBS-based design method has generality and flexibility to accommodate almost all physical scenarios in metasurface design, enabling a multitude of complex functions and applications.

Low-Level Auditory Processing Correlates With Language Abilities: An ERP Study Investigating Sequence Learning and Auditory Processing in School-Aged Children.

Auditory processing and procedural learning deficits have been associated with language learning difficulties. We investigated the relationship of these skills and school-age language abilities in children with and without a history of late talking using auditory event related potentials (ERPs). Late talking (i.e., slow early language development) increases the risk of persistent language difficulties, but its causes remain unknown. Participants in this study were children with varying language abilities (n = 60). Half of the participants (n = 30) had a history of late talking. We measured procedural learning by manipulating the predictability of sine tone stimuli in a passive auditory ERP paradigm. Auditory processing was tested by examining how the presence of noise (increasing perceptual demands) affected the ERPs. Contrary to our hypotheses on auditory processing and language development, the effect of noise on ERPs did not correlate with school-age language abilities in children with or without a history of late talking. Our paradigm failed to reveal interpretable effects of predictability leaving us unable to assess the effects of procedural learning. However, better language abilities were related to weaker responses in a 75-175 ms time window, and stronger responses in a 150-250 ms time window. We suggest that the weak early responses in children with better language ability reflect efficient processing of low-level auditory information, allowing deeper processing of later, high-level auditory information. We assume that these differences reflect variation in brain maturation between individuals with varying language abilities.

The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro.

Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.

The long-term intensive gymnastic training influences functional stability and integration: A resting-state fMRI study

IntroductionLong-term motor skill training has been shown to induce anatomical and functional neuroplasticity. World class gymnasts (WCGs) provide a unique opportunity to investigate the effect of long-term intensive training on neuroplasticity. Previous resting-state fMRI studies have demonstrated a high efficient information processing related to motor and cognitive functions in gymnasts compared with healthy controls (HCs). However, most research treated brain signals as static, overlooking the fact that the brain is a complex and dynamic system. In this study, we employed functional stability, a new metric based on dynamic functional connectivity (FC), to examine the impact of long-term intensive training on the functional architecture in the WCGs. MethodsWe first conducted a voxel-wise analysis of functional stability between the WCGs and HCs. Then, we applied FC density (FCD) to explore whether regions with modified functional stability were also accompanied by changes in connection patterns in the WCGs. We identified overlapping regions showing significant differences in both functional stability and FCD. Finally, we applied seed-based correlation analysis (SCA) to determine the detailed changes in connection patterns between the WCGs and HCs within these overlapping regions. ResultsCompared with the HCs, the WCGs exhibited higher functional stability in the bilateral angular gyrus (AG), bilateral inferior temporal gyrus (ITG), bilateral precentral gyrus, and right superior frontal gyrus and lower functional stability in the bilateral hippocampus, bilateral caudate, right rolandic operculum, left superior temporal gyrus, right middle frontal gyrus, right middle cingular cortex, and right precuneus than the HCs. We found that the bilateral AG and ITG not only showed higher functional stability but also increased global and long-range FCD in the WCGs relative to the HCs. The right precuneus displayed lower functional stability as well as decreased local, long-range, and global FCD in the WCGs. Both AG and ITG showed higher FC with regions in the default mode network (DMN) in the WCGs than in the HCs. ConclusionsThe increased functional stability in the AG and ITG might be associated with enhanced functional integration within the DMN in the WCGs. These findings may offer new spatiotemporal evidence for the impact of long-term intensive training on neuroplasticity.

The Role of Artificial Intellect in Today’s Banking System

The article shows the use of artificial intellect (AI) in different spheres of banking, including client servicing, risk analysis, fraud combating, process automation and decision-making. AI use in banking fosters higher efficiency and accuracy of information processing. It makes automation of standard tasks easier and gives employees wider opportunities to work at complicated and analytical goals. AI helps banks assess quickly and correctly credit risks thereby decreasing possibility of default and enhancing general security of the banking system. The author pointed out that AI improves the quality of client servicing. Chat-bots based on AI can give fast and precise answers to client’s requests and assist in problem solving. Accurate treatment of requests gives banks an opportunity to develop personalized offers and services taking into account preferences and needs of clients. However, besides numerous benefits of using AI the author puts forward problems connected with challenges and difficulties of its introduction in the banking sector. Such issues of transparency, ethics of AI use, possible risks and threats need further study as well as the development of necessary control mechanisms.

Origin of the light-induced spin currents in heavy metal/magnetic insulator bilayers.

Light-induced spin currents with the faster response is essential for the more efficient information transmission and processing. Herein, we systematically explore the effect of light illumination energy and direction on the light-induced spin currents in the W/Y3Fe5O12 heterojunction. Light-induced spin currents can be clearly categorized into two types. One is excited by the low light intensity, which mainly involves the photo-generated spin current from spin photovoltaic effect. The other is caused by the high light intensity, which is the light-thermally induced spin current and mainly excited by spin Seebeck effect. Under low light-intensity illumination, light-thermally induced temperature gradient is very small so that spin Seebeck effect can be neglected. Furthermore, the mechanism on spin photovoltaic effect is fully elucidated, where the photo-generated spin current in Y3Fe5O12 mainly originates from the process of spin precession induced by photons. These findings provide some deep insights into the origin of light-induced spin current.

The neural compensation phenomenon in schizophrenia with mild attention deficits during working memory task

BackgroundWorking memory (WM) and attention are essential cognitive processes, and their interplay is critical for efficient information processing. Schizophrenia often exhibits deficits in both WM and attention, contributing to function impairments. This study aims to investigate the neural mechanisms underlying the relationship between WM impairments and attention deficits in schizophrenia. MethodsWe assessed the functional-MRI scans of the 184 schizophrenias with different attention deficits (mild=133; severe=51) and 146 controls during an N-back WM task. We explored their whole-brain functional connectome profile by adopting the voxel-wise degree centrality (DC). Linear analysis was conducted to explore the associations among attention deficit severity, altered DC, and WM performance in patients. ResultsWe observed that all patients showed decreased DC in the pre-supplementary area (pre-SMA), and posterior cerebellum compared to the controls, and schizophrenia patients with mild attention deficits showed decreased DC in the supramarginal gyrus, insula, and precuneus compared with the other 2 groups. DC values of the detected brain regions displayed U-shaped or inverted U-shaped curves, rather than a linear pattern, in response to increasing attention deficits. The linear analysis indicated that altered DC of the pre-SMA can modulate the relationship between attention deficits and WM performance. ConclusionThe U-shaped or inverted U-shaped pattern in response to increasing attention deficits may reflect a compensation mechanism in schizophrenia with mild attention deficits. This notion is also supported by the linear analysis that schizophrenia patients with mild attention deficits can improve their WM performance by increasing the DC value of the pre-SMA.

Thermodynamic limits on neural information processing efficiency

Thermodynamic limits on neural information processing efficiency

Redefining the Association Between Memory, Mnemonics and Vocabulary Acquisition—Reviewing Paradigms in Research

The role of memory in language learning is indispensable. A child uses an extraordinary amount of memory when learning to recognise words in the language, creating rules for their use, and connecting speech to the environment and mind. Memory and learning are interdependent, as memory is the process of encoding, storing and retrieving information that is crucial to learning. The value of learning new words, along with the role of memory and mnemonic devices in the process of vocabulary acquisition, is rarely discussed. The article aims to give a comprehensive account of the word and its significance, as well as the numerous aspects that influence word learning, together with the value of learning new words and the functions of memory and mnemonic devices involved in the process of vocabulary acquisition. The various memory models, the efficient processing of information with the aid of mnemonics and how mnemonics have changed over time in studying languages and other subjects are reviewed here. The other major aspects of this study are the dual coding method and an explanation of how mnemonics operate and how mnemonics have evolved with time in studying languages and other subjects.

Handwritten Character Recognition System

Digitizing handwritten documents and enabling efficient information processing and retrieval require systems that can recognize handwritten characters. This research offers a unique approach for handwritten character detection using state-of-the-art machine learning algorithms. The proposed technique automatically extracts discriminative features from photos of handwritten characters using convolutional neural networks (CNNs). These attributes are then used by a classifier to determine which characters are related. The dataset used for training and assessment is made up of a large collection of handwritten characters gathered under various writing styles, sizes, and orientations in order to guarantee the durability and generalization power of the model. To enhance its quality and diversity, the training data is put through a rigorous preparation procedure that includes picture augmentation, noise removal, and normalization. The studies' results demonstrate how well and precisely the proposed system can recognize handwritten characters in a range of languages and writing styles. The system performs competitively compared to state-of-the-art methods and demonstrates robustness against variations in handwriting style and quality. Furthermore, the system has potential in terms of efficiency and scalability, making it suitable for real-time applications such as document digitalization, handwritten word recognition in electronic devices, and automatic form processing.

Peripheral myopic defocus signal affects the efficiency of visual information processing in myopic children.

Spectacle lenses with peripheral lenslets have shown promise for myopia control by providing peripheral myopic defocus signals. Here, we aimed to investigate the impact of prolonged exposure (>6 months) to peripheral myopic defocus on visual information processing in myopic children. The study included 30 myopic children who habitually wore spectacle lenses with highly aspherical lenslets (HAL group) and 34 children who habitually wore single-vision (SV group) spectacles. The quick contrast sensitivity function (qCSF) was used to measure contrast sensitivity (CS) under conditions of no or high noise. Both groups were tested with HAL and SV lenses. The perceptual template model was utilised to fit the contrast sensitivity function (CSF) and determine differences in information processing efficiency through internal additive noise ( ) and perceptual template gain (β). The areas under the log CSF in the SV group were significantly higher than for the HAL group in both zero-noise conditions with the SV test lens (p = 0.03) and high-noise conditions with the HAL test lens (p = 0.02). For 2 cycle per degree (cpd) stimuli, β was significantly higher in the SV group with the HAL test lens than in the HAL group (p = 0.02), while there was a trend towards a significant difference in β for 6 cpd stimuli (p = 0.07). However, there were no significant differences in between the two groups, with or without noise interference. The reduced CS observed in myopic children wearing HAL lenses for 6 months or more may be due to decreased β. This suggests that prolonged use of spectacle lenses with peripheral myopic defocus signals may compromise the central visual system's ability to process additional external noise, resulting in decreased efficiency in visual information processing.

Enhancing Multiple Sequence Alignment with Genetic Algorithms: A Bioinformatics Approach in Biomedical Engineering

This study aimed to create a genetic information processing technique for the problem of multiple alignment of genetic sequences in bioinformatics. The objective was to take advantage of the computer hardware's capabilities and analyze the results obtained regarding quality, processing time, and the number of evaluated functions. The methodology was based on developing a genetic algorithm in Java, which resulted in four different versions: Gp1, Gp2, Gp3 and Gp4 . A set of genetic sequences were processed, and the results were evaluated by analyzing numerical behavior profiles. The research found that algorithms that maintained diversity in the population produced better quality solutions, and parallel processing reduced processing time. It was observed that the time required to perform the process decreased, according to the generated performance profile. The study concluded that conventional computer equipment can produce excellent results when processing genetic information if algorithms are optimized to exploit hardware resources. The computational effort of the hardware used is directly related to the number of evaluated functions. Additionally, the comparison method based on the determination of the performance profile is highlighted as a strategy for comparing the algorithm results in different metrics of interest, which can guide the development of more efficient genetic information processing techniques.

Mixed anxiety-depressive disorder in Parkinson's disease associated with worse resting state functional response to deep brain stimulation of subthalamic nucleus

BackgroundParkinson's disease (PD), even though generally perceived as a dominantly motor disorder, is associated with a wide range of non-motor symptoms, including mixed anxiety-depressive disorder (MADD). ObjectivesThe aim of the presented study was to determine whether deep brain stimulation (DBS) of the subthalamic nucleus (STN) brings the functional characteristics of non-motor networks closer to the condition detected in healthy population and whether pre-DBS presence of MADD in PD patients was associated with different reaction to this therapeutic modality. MethodsResting-state fMRI signature elicited by STN DBS activation and deactivation in 81 PD patients was compared against healthy controls, with the focus on measures of efficiency of information processing and localised subnetwork differences. ResultsWhile all the MRI metrics showed statistically significant differences between PD patients in DBS OFF condition and healthy controls, none were detected in such a comparison against DBS ON condition. Furthermore, in the post-DBS evaluation, PD patients with MADD in the pre-DBS stage showed no differences in depression scales compared to pre-DBS psychiatrically intact PD patients, but still exhibited lower DBS-related connectivity in a subnetwork encompassing anterior and posterior cingulate, dorsolateral prefrontal and medial temporal cortices. ConclusionsSTN DBS improved all the metrics of interest towards the healthy state, normalising the resting-state MRI signature of PD. Furthermore, pre-DBS presence of MADD, even though clinically silent at post-DBS MRI acquisition, was associated with lower DBS effect in areas highly relevant for depression. This finding points to a possibly latent nature of post-DBS MADD, calling for caution in further follow-up of these patients.

Realization of skyrmion shift register

The big data explosion demands novel data storage technology. Among many different approaches, solitonic racetrack memory devices hold great promise for accommodating nonvolatile and low-power functionalities. As representative topological solitons, magnetic skyrmions are envisioned as potential information carriers for efficient information processing. While their advantages as memory and logic elements have been vastly exploited from theoretical perspectives, the corresponding experimental efforts are rather limited. These challenges, which are key to versatile skyrmionic devices, will be studied in this work. Through patterning concaved surface topography with designed arrays of indentations on standard Si/SiO2 substrates, we demonstrate that the resultant non-flat energy landscape could lead to the formation of hexagonal and square skyrmion lattices in Ta/CoFeB/MgO multilayers. Based on these films, one-dimensional racetrack devices are subsequently fabricated, in which a long-distance deterministic shifting of skyrmions between neighboring indentations is achieved at room temperature. Through separating the word line and the bit line, a prototype shift register device, which can sequentially generate and precisely shift complex skyrmionic data strings, is presented. The deterministic writing and long-distance shifting of skyrmionic bits can find potential applications in transformative skyrmionic memory, logic as well as the in-memory computing devices.

Text Summarization Based on Semantic Similarity

In the contemporary information age, the sheer volume of textual data poses a significant challenge for efficient comprehension and utilization. This project endeavors to address this challenge by developing a Text Summarization System grounded in semantic similarities. The primary goal is to create a robust and intuitive tool that extracts key information from large textual datasets, offering users a concise and meaningful summary. The proposed system employs advanced Natural Language Processing (NLP) techniques to analyze the semantic relationships within the text. Rather than relying solely on syntactic structures, the model identifies and leverages semantic similarities, such as shared concepts, themes, and contextual relationships, to distill the essential content. This approach enhances the summarization process by ensuring that the generated summaries reflect a deeper understanding of the underlying semantics, thereby capturing the core meaning of the text. Throughout the development of this project, the B.Tech student will delve into the intricacies of semantic analysis, exploring techniques to recognize and prioritize key concepts. The system's effectiveness will be evaluated through rigorous testing on diverse textual datasets, assessing its ability to generate coherent and relevant summaries across various domains. This project not only contributes to the field of NLP but also has practical applications in information retrieval, document summarization, and content curation. By providing an innovative solution to the challenges of information overload, the Text Summarization System based on semantic similarities offers a valuable tool for enhancing efficiency in information processing and decision-making. Index terms Text Summarization, Semantic Similarities, Natural Language Processing (NLP), Semantic Analysis, Information Retrieval, Document Summarization, Content Curation, Information Overload, Decision Making, Textual Data Analysis, Key Concept Recognition, Conceptual Relationships, Syntactic Structures, Semantic Understanding, Textual Datasets Evalution.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Quantitative data processing of whole service value chain for power grid internal simulation market

The internal simulation market system can stimulate employee initiative, reduce costs and improve information processing efficiency. However, the complexity of the internal simulation market poses a challenge to computing resources. Efficient data processing techniques are crucial for internal simulation market systems. In this paper, the internal simulation market model and the value chain theory are first put forward. Second, the internal simulation market construction within power grid enterprises is proposed. Then we propose a data mining-based collaborative fusion and processing method for multi-value chain quantitative data in power grid internal simulation markets to conduct data processing, including data fusion, abnormal data elimination and data dimensionality reduction, thereby improving the accuracy of information processing and data sharing. Finally, we validate the superior performance of the proposed method through simulations.

Brain-inspired computing with fluidic iontronic nanochannels

The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy-to-fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarization, our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarization, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in silico classification with a simple readout function. Our results represent a significant step toward realizing the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain.

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks

ABSTRACT Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

Multi-association evidential feature selection and its application to identifying schizophrenia

Granular Computing (GrC)-based feature selection can remove redundant features from a massive amount of data and improve the efficiency of information processing. However, the existing method of neighborhood-based information granule only considers the distance between samples, ignoring other significant relationships existing between them. To fill this gap, this paper proposes a novel feature selection approach based on two-step multi-association neighborhood evidence entropy. This approach is constructed in three phases. Firstly, adaptive k value corresponding to each sample in sparse representation method is determined. Sparse correlation and distance measure are fused to form a multi-association information granule. Then, the samples in the multi-association information granule are estimated and weak-related information is removed to constitute a two-step multi-association information granule. Secondly, sparse correlation information is processed using Dempster-Shafer evidence theory, and a new credibility-based function is developed. In addition, the credibility is used to construct a novel neighborhood evidence entropy, which can effectively reflect the uncertainty of data. Thirdly, the proposed neighborhood evidence entropy is applied to assess the importance of features. As a result, several vital features are selected. The experimental results on twelve datasets demonstrate that the effectiveness of the proposed method is superior to other algorithms in construction of information granules and classification accuracy, respectively. Finally, the proposed method is applied to the selection of brain regions in schizophrenia. It can effectively analyze the lesions of schizophrenia and improve the prediction of the disorder. The code is available at https://github.com/fxx-Aurora/TMAE-FS/tree/main.