Computer Memory - Science topic

Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain a...

The exponential growth of data in the digital age has necessitated the development of frameworks capable of efficiently handling and processing vast datasets. This paper explores the application of machine learning (ML) models within the Apache Spark ecosystem, focusing on the performance and scalability of these models in big data environments. Th...

Metalens has shown its significantly ultra-light and ultra-thin features. However, large-aperture achromatic metalens is constrained by both maximum dispersion range and computational memory. Here, we propose a fully device optimizing framework that engineers phase dispersion and amplitude transmittance to create centimeter-size achromatic metalens...

A general method to generate a centrosymmetric matrix associated with the solving of partial differential equation (PDE) on an irreducible domain by means of a linear equation system is proposed. The method applies to any PDE for which both the domain to solve and the boundary condition (BC) type accept a planar symmetry, while no conditions are re...

The object of the study is the phenomenon of an extreme increase in the time of program code execution at certain sizes of data processed by it. The problem to be solved was to verify the general nature of the phenomenon for different equipment. The evolution of modern computing technology, its RAM often takes place in an extensive way – by increas...

Previous research suggests that the MINERVA2 model can capture basic Deese/Roediger/McDermott (DRM) false recognition findings with either randomized representations or distributional semantic representations. In the current article, we extended this line of research by showing that MINERVA2 can accommodate not only basic DRM recognition findings b...

We introduce Rank1, the first reranking model trained to take advantage of test-time compute. Rank1 demonstrates the applicability within retrieval of using a reasoning language model (i.e. OpenAI's o1, Deepseek's R1, etc.) for distillation in order to rapidly improve the performance of a smaller model. We gather and open-source a dataset of more t...

Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multiple dialogues and multi-modal data sources. These unique characteristics of LLMs, beyond their large size, make their deployment more challenging during the inference stage. Specifically, (i) deploying LLMs o...

Accurate, rapid, and intelligent stored-grain insect detection and counting are important for integrated pest management (IPM). Existing stored-grain insect pest detection models are often not suitable for detecting tiny insects on the surface of grain bulks and often require high computing resources and computational memory. Therefore, this study...

In this article, we present a model for analyzing the co-occurrence count data derived from practical fields such as user–item or item–item data from online shopping platforms and co-occurring word–word pairs in sequences of texts. Such data contain important information for developing recommender systems or studying the relevance of items or words...

This study introduces a computational technique aimed at solving the auto-convolution Volterra integral equation (AVIE) and the auto-convolution Volterra integro-differential equation (AVIDE). In this approach, we use the Bernstein approximation method to estimate solutions for these equations. By leveraging the characteristics of Bernstein polynom...

Method of moments is one of the most useful approaches for radar cross‐section (RCS) simulation, allowing, that is, the computation of the scattering of real objects from 3D models. However, it is limited by computer memory and computation time. In this paper, the authors explore the question of the balance between the possible acceptable level of...

Magnetic tunnel junctions with a magnetic layer with perpendicular anisotropy are currently used in computer memories that do not require voltage sustaining. An example of layers with perpendicular magnetic anisotropy are ultrathin FeCo films on Au substrate. Here, we present an experimental and computational study of Fe$_{0.75}$Co$_{0.25}$ layers...

Memory retention mechanisms play a central role in determining the efficiency of computational architectures designed for processing extended sequences. Conventional methods for token management often impose fixed retention thresholds or rely on uniform attention weight distributions, leading to inefficient memory utilization and premature informat...

The Internet of Things (IoT) is a rapidly growing field that provides advanced solutions in various domains, including critical infrastructures. With the help of IoT, the traditional power system network can be transformed into a more effective and smarter (energy) grid. However, the security vulnerabilities related to IoT technologies have become...

A memristor, memory resistor, is a two-terminal nano device that can be made as thin as a single atom thick that has become of tremendous interest for its potential to revolutionize electronics, computing, computer architecture, and neuromorphic engineering. This thesis encompasses two major parts containing original contributions, (Part I) modelli...

Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantizati...

The increasing demand for larger and higher fidelity simulations has made Adaptive Mesh Refinement (AMR) and unstructured mesh techniques essential to focus compute effort and memory cost on just the areas of interest in the simulation domain. The distribution of these meshes over the compute nodes is often determined by balancing compute, memory,...

Sensors for the perception of multimodal stimuli—ranging from the five senses humans possess and beyond—have reached an unprecedented level of sophistication and miniaturization, raising the prospect of making man-made large-scale complex systems that can rival nature a reality. Artificial intelligence (AI) at the edge aims to integrate such sensor...

WANET is a network built in an ad hoc network where various connecting devices and moving wireless nodes make connection with the exchange valuable information and wireless medium to one another. As the size of the wireless networks and internet architectures are increasing, there is an exponential demand for data processing and sharing resources i...

Deep learning (DL) has revolutionized image classification, yet deploying convolutional neural networks (CNNs) on edge devices for real-time applications remains a significant challenge due to constraints in computation, memory, and power efficiency. This work presents an optimized implementation of VGG16 and VGG19, two widely used CNN architecture...

The parallel solver of the general synthetic iterative scheme (GSIS), as recently developed by Zhang \textit{et. al.} in Comput. Fluids 281 (2024) 106374, is an efficient method to find the solution of the Boltzmann equation deterministically. However, it consumes a significant computational memory due to the discretization of molecular velocity sp...

This research is a quantitative study with an explanatory approach, namely an approach that relies on a number of previous studies. The data used in this article are primary data that researchers obtained from Erigo employees throughout Indonesia. The data was obtained using a questionnaire method containing a number of statements strongly agree, a...

Parallel computing is the key to accelerate artificial neural networks, both in digital and analog implementations. Our research focuses on analog artificial neural networks (NN), where parallel computations are executed with voltages, charges and currents, using as the computing elements the same devices that act as memories for the raw processed...

With the emergence of new radio telescopes promising larger fields of view at lower observation frequencies (e.g., SKA), addressing direction-dependent effects (DDE) (e.g., direction-specific beam responses), polarisation leakage, and pointing errors has become all the more important. Be it through A-projection or otherwise, addressing said effects...

Background and Study. Vertical jump performance is a critical factor in volleyball, significantly influencing actions like spiking, blocking, and serving. Accurate assessment of jump height is essential for optimizing training strategies, especially at the elite level. Purpose: The aim of this study was to evaluate the validity and reliability of a...

With the rise of AI, NVIDIA GPUs have become the de facto standard for AI system design. This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs for AI model serving. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 achieves competitive performanc...

This study proposes a methodology to generalize and reduce the computational cost of sensitivity analysis for static linear elastic systems in topology optimization. The process is fully generalized by applying sensitivity analysis in conjunction with the adjoint variable method and automatic differentiation. The design sensitivities can be compute...

Comprehensive metagenomic sequence classification of diverse environmental samples faces significant computing memory challenges due to exponentially expanding genome databases. Here, we present Kun-peng, featuring a unique ordered 4GB block database design for ultra-efficient resource management, faster processing, and higher accuracy. When benchm...

The exponential growth of artificial intelligence (AI) has driven the development of increasingly large and complex models, such as those used in natural language processing and computer vision. However, scaling AI models beyond trillions of parameters has exposed significant limitations in traditional GPU-based systems, including memory bottleneck...

Modern magnetic resonance imaging (MRI) experiments require simultaneous spin and spatial dynamics treatment. This paper aims to demonstrate the possibility of simulating a large spin system for diffusion-weighted MRI experiments. The numerical simulation of diffusion MRI depends on Bloch-Torrey equations. The latter describe the behavior of spin s...

Computational Storage Drives (CSDs) integrate compute engines directly within SSDs for efficient near-data processing. With the introduction of Compute Express Link (CXL), memory expanders can similarly become Computational Memory (CM) by offloading computations. However, the integration of specific hardware accelerators in CSDs has posed substanti...

Serving Large Language Models (LLMs) efficiently has become crucial. LLMs are often served with multiple devices using techniques like data, pipeline, and tensor parallelisms. Each parallelism presents trade-offs between computation, memory, and communication overhead, making it challenging to determine the optimal parallel execution plan. Moreover...

Instruction tuning has become an important step for finetuning pretrained language models to better follow human instructions and generalize on various tasks. Nowadays, pretrained language models become increasingly larger, and full parameter finetuning is overwhelmingly costly. Therefore, Parameter Efficient Finetuning (PEFT) has arisen as a cost-...

The next generation of cosmological spectroscopic sky surveys will probe the distribution of matter across several Gigaparsecs (Gpc) or many billion light-years. In order to leverage the rich data in these new maps to gain a better understanding of the physics that shapes the large-scale structure of the cosmos, observed matter distributions must b...

The integration of Augmented Reality (AR) into mobile devices has sparked a trend in the development of mobile AR applications across diverse sectors. Nevertheless, the execution of AR tasks necessitates substantial computational, memory, and storage resources, which poses a challenge for mobile terminals with limited hardware capabilities to run A...

Energy efficiency of electronic digital processors is primarily limited by the energy consumption of electronic communication and interconnects. The industry is almost unanimously pushing towards replacing both long-haul, as well as local chip interconnects, using optics to drastically increase efficiency. In this paper, we explore what comes after...

As the network continues to become more complex due to the increased number of devices and ubiquitous connectivity, the trend is shifting from a centralized implementation to decentralization. Similarly, strategies to secure networks are increasingly leaning towards decentralization for its potential to enhance security in future networks with the...

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we pro...

Review A Lightweight Approach to Understand Forest Roads for New Nnergy Vehicles Luping Wang 1,*, Yuan Feng 1, Shanshan Wang 2, and Hui Wei 3 1 Laboratory of 3D Scene Understanding and Visual Navigation, School of Mechanical Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai 200093, China 2 Intel Asia-Pac...

In this paper, we study the thermodynamic cost associated with erasing a static random access memory. By combining the stochastic thermodynamics framework of electronic circuits with machine learning-based optimization techniques, we show that it is possible to erase an electronic random access memory at arbitrarily fast speed and finite heat dissi...

Image processing algorithms continue to demand higher performance from computers. However, computer performance is not improving at the same rate as before. In response to the current challenges in enhancing computing performance, a wave of new technologies and computing paradigms is surfacing. Among these, memristors stand out as one of the most p...

Large-scale 3D photoacoustic (PA) imaging has become increasingly important for both clinical and pre-clinical applications. Limited by cost and system complexity, only systems with sparsely-distributed sensors can be widely implemented, which desires advanced reconstruction algorithms to reduce artifacts. However, high computing memory and time co...

span lang="EN-US">In this research, we use several machine learning methods and feature selection to process social media data, namely restaurant reviews. The selection feature used is a combination of information gain (IG) and adaptive boosting (AdaBoost) which is used to see its effect on the classification performance evaluation value of machin...

The focus of condensed matter physics has shifted in the past decade from understanding the properties of only one class of materials, such as perovskites and conductors, to natural materials with many degrees of freedom and the consequences of these degrees. Therefore, there is an interest in understanding the transport, magnetic, and superconduct...

The search is based on the preliminary transformation of matrices or adjacency lists traditionally used in the study of graphs into projections cleared of redundant information (refined) followed by the selection of the desired shortest paths. Each projection contains complete information about all the shortest paths from its base (angle vertex) an...

The advent of wurtzite ferroelectrics is enabling a new generation of ferroelectric devices for computer memory that has the potential to bypass the von Neumann bottleneck, due to their robust polarization and silicon compatibility. However, the microscopic switching mechanism of wurtzites is still undetermined due to the limitations of density fun...

In the Monte Carlo thermal-physical calculations for nuclear reactors, the precise and effective transfer of data between different meshes is a difficult issue of thermal and physical coupling. Converting two separate meshes and transferring the data is an exceptionally difficult and complex task within the conventional nuclear thermal-physics coup...

Deep convolutional neural networks (CNNs) are widely used in computer vision and have achieved significant performance for image classification tasks. Overfitting is a general problem in deep learning models that inhibit the generalization capability of deep models due to the presence of noise, the limited size of the training data, the complexity...

Anthropogenic pressure on Ukraine's ecosystems has been high for the past 110 years, and its destructive influence has been increasing in some regions throughout this time [1-4]. These phenomena always reached their culmination during the military operations that took place on the territory of our state and the consequences initiated by them [1, 2]...

This document is a corrected, revised, and expanded version of an earlier RG document: https://www.researchgate.net/publication/334233647_Computer_Memories_A_History_Revision_2. At 68 pages, this is a substantial increase in the size of the original document, which was only 23 pages. I was so surprised at how many people viewed my original document...

As the field of multi-agent reinforcement learning (MARL) progresses towards larger and more complex environments, achieving strong performance while maintaining memory efficiency and scalability to many agents becomes increasingly important. Although recent research has led to several advanced algorithms, to date, none fully address all of these k...

Functional dependency analysis is an important field of data science, where the goal is to determine the relationships between different data attributes and attribute sets in a given data set. This can lead to gaining valuable information about the data that is often not evident through surface-level analysis. In previous work, the authors proposed...

Thermally induced oscillatory rarefied gas flow inside a two-dimensional rectangular cavity is investigated based on the hybrid macro-/mesoscopic scheme. The effects of the Knudsen (Kn) numbers and the oscillation frequency of lid temperature on the flow parameters are analyzed. The Shakhov model equation is solved numerically based on the mesoscop...

Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on stacking fewer GNN layers compared to SuperGlue. This...

We introduce the Embedded Computational Framework of Memory (eCFM), a model that integrates structured semantic word representations with an instance-based memory model to account for the influence of semantic information in verbal short-term memory. The eCFM combines principles from the episodic MINERVA 2 model and the semantic Latent Semantic Ana...

This paper proposes an alternative implementation of the MENT algorithm, an exact maximum-entropy algorithm used to infer a phase space distribution from its projections. A key step in the MENT algorithm is to compute the distribution's projections via numerical integration. In this approach, the run time scales quickly with the phase space dimensi...

The very rapid development of information technology over time has felt the benefits of most people in the fields of education, health, entertainment, information sources, the world of business and communication without limitations of place and time. The internet is one of the latest communication information technologies, the internet has almost u...

Scientific computing applications, such as computational fluid dynamics and climate modeling, typically rely on 64-bit double-precision floating-point operations, which are extremely costly in terms of computation, memory, and energy. While the machine learning community has successfully utilized low-precision computations, scientific computing rem...

Emerging applications such as blockchain, autonomous vehicles, healthcare, federated learning, self-consistent large language models (LLMs), and multi-agent LLMs increasingly rely on the reliable acquisition and provision of data from external sources. Multi-component networks, which supply data to the applications, are defined as data provision ne...

Purpose The 3D U-Net deep neural network structure is widely employed for dose prediction in radiotherapy. However, the attention to the network depth and its impact on the accuracy and robustness of dose prediction remains inadequate. Methods 92 cervical cancer patients who underwent Volumetric Modulated Arc Therapy (VMAT) are geometrically augme...

The rise of Large Language Models (LLMs) has revolutionized healthcare and life sciences, enabling breakthroughs in clinical decision-making, drug discovery, and personalized medicine. However, real-world deployment of these models in resource-constrained settings remains challenging due to the high computational, memory, and latency demands. Recen...

This paper aims to extend the concept of Neutrosophic Hypersoft Matrix (NHSM) theory. NHSM is the matrix representation of a Neutrosophic Hypersoft Set (NHSS), where NHSS is the combination of a Neutrosophic set and a Hypersoft set. An NHSS can be stored in computer memory using the matrix notion, which is very useful and applicable. Based on NHSM,...

Human gesture recognition is a very important tool in human-computer or human-robot interaction. In many cases, such algorithms may need to be executed on systems with limited computational capabilities, due to size or weight constraints, introducing restrictions that can impede gesture recognition performance. This paper proposes a gesture recogni...

In the era of Industry 4.0, the prominence of 3D printing as a pivotal manufacturing technology has greatly expanded, particularly within the domain of additive manufacturing (AM). Among the thriving research applications tailored for integration with AM, topology optimization (TO) has emerged as a resounding success. Given the prerequisite of TO f...

The increasing usage of Large Language Models (LLMs) has resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users. Consequently, throughput (under reasonable latency constraints) has emerged as a key metric that determines serving systems' performance. To boost t...

For no-insulation (NI) high-temperature superconducting (HTS) coils, a 3D electromagnetic model, which is fast and accurate, conducive to establish, and straightforward to multi-physics coupling, is still required. This paper introduces a polygon-anisotropic-resistivity (PAR) method for 3D FEM electromagnetic simulations of NI HTS coils. This model...

The blending of 2D materials and polymers promote the heterosis in flexible electronics, including senses, computing, memory and logic circuits.

Non-Markovian dynamics, where future states depend on accumulated histories rather than solely on the present moment, offer groundbreaking insights into multiverse theory, spacetime manipulation, and quantum complexity. Traditional Markovian models assume a memoryless progression of states, which can be limiting when applied to complex systems like...

Inspection of defects in pipelines can be materialized by measuring ultrasonic guided waves the properties of which are conventionally analyzed with three-dimensional finite-element methods (FEM). They require complicated geometric discretization and memory consumption in a single analysis, thus are clumsy and limited to be used for field fast anal...

We propose, analyze, and test a penalty projection-based robust efficient and accurate algorithm for the Uncertainty Quantification (UQ) of the time-dependent Magnetohydrodynamic (MHD) flow problems in convection-dominated regimes. The algorithm uses the Elsässer variables formulation and discrete Hodge decomposition to decouple the stochastic MHD...

Ferroelectric materials, characterized by their high dielectric constants, piezoelectricity, and pyroelectricity, find numerous applications in electronic products such as computer memory, highly sensitive sensors, and sonar devices. Additionally, they hold potential applications in fields like solid-state refrigeration, with their performance ofte...

Zn ²⁺ doped Ni 0.3 Zn x Co 0.7-x Fe 2 O 4 (0.3 ≥ x ≥ 0.7) spinel nanoparticles were synthesized via Sol–gel-auto combustion methods using EDTA and citric acid as fuel. XRD, Raman spectroscopy, FE-SEM, and EDX demonstrated that samples possessed a well-crystalline cubic spinel structure. Both crystallite size and the lattice parameter values increas...

Integration of optical sensors with memristors can establish the bridge between photosensing and memory devices for Internet of Things (IoT) based applications. This paper presents the realization of integrated sensing and computing memory (ISCM) devices using tungsten disulfide (WS2) and their application for neuromorphic computing. The ISCM devic...

The spin transfer tunnel magnetic tunnel junction (STT-MTJ) has been widely used in computers, memory, and other fields because of its nonvolatility, low power consumption, and high capacity for integration, attracting significant attention in recent years. Building an accurate and efficient magnetic tunnel junction (MTJ) behavior model is necessar...

Computer science is a basic science. It is the science that makes knowledge and its other achievements possible. The study of computer science involves the systematic study of processes to help obtain, represent, process, store, communicate, and access information. This is done by analyzing the feasibility, structure, expression, and mechanization...

Internet of Things (IoT) devices are going to be the primary data source in smart cities, often generating and communicating critical, sensitive, and private data that face threats of confidentiality breaches due to the rise of quantum computers. Even though NIST has approved post-quantum cryptography (PQC) schemes that promise to protect from quan...

Key message Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Abstract Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common...

In this paper, we propose a novel framework for video anomaly detection that employs dual memory modules for both normal and anomaly patterns. By maintaining separate memory modules, one for normal patterns and one for anomaly patterns, our approach captures a broader range of video data behaviors. By exploring separate memory modules for normal an...

Aligning future system design with the ever-increasing compute needs of large language models (LLMs) is undoubtedly an important problem in today's world. Here, we propose a general performance modeling methodology and workload analysis of distributed LLM training and inference through an analytical framework that accurately considers compute, memo...

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the o...

Water is an irreplaceable commodity with a high economic value. Today, water scarcity is the biggest challenge in the world, and the crises arising from lack of freshwater resources are serious threats to sustainable environmental development and human health and welfare. As the problems grow in complexity and dimensions, it becomes less possible t...

Relational and logical methods of knowledge representation play a key role in creating a mathematical basis for information systems. Predicate algebra and predicate operators are among the most effective tools for describing information in detail. These tools make it easy to formulate formalized information, create database queries, and simulate hu...

The main goal of this work is to design an optimized sensor-fault identification and diagnostic system for the Internet of Things (IoT) and Cyber-Physical Systems (CPS). The challenge is to accomplish this task within the sensors’ limited computing, memory, and energy capabilities. More importantly, identifying errors is time-sensitive, even though...

Research Article International Journal of Advanced Technology and Engineering Exploration, Vol 11(115) ISSN (Print): 2394-5443 ISSN (Online): 2394-7454 http://dx.doi.org/10.19101/IJATEE.2024.111100083 Comparative analysis of potato blight diseases BARI-72 and BARI-73 using a simplified convolutional neural network method Md. Ashikur Rahman Khan1* J...

In this research we apply several machine learning methods and word embedding features to process social media data, specifically comments on the Disney Plus Hotstar application. The word embedding features used include Word2Vec, GloVe, and FastText. Our aim is to evaluate the impact of these features on the classification performance of machine le...

Data Structures! This book is your guide to understanding the fundamental building blocks of efficient and effective computer programs. Data, the lifeblood of any program, needs organization to be useful. Data structures provide the organization and tools to store, access, manipulate, and search for information in a computer's memory. Mastering dat...

Time-series classification (TSC) has been widely utilized across various domains, including brain-computer interfaces (BCI) for emotion recognition through electroencephalogram (EEG) signals. However, traditional methods often struggle to capture the complex emotional patterns present in EEG data. Recent advancements in encoding techniques have pro...

Modern data analysis typically involves the fitting of a statistical model to data, which includes estimating the model parameters and their precision (standard errors) and testing hypotheses based on the parameter estimates. Linear mixed models (LMMs) fitted through likelihood methods have been the foundation for data analysis for well over a quar...