Limited Memory Capacity Research Articles

Special major 1, 3 satisfiability logic in discrete Hopfield neural networks

<abstract> <p>Currently, the discrete Hopfield neural network deals with challenges related to searching space and limited memory capacity. To address this issue, we propose integrating logical rules into the neural network to regulate neuron connections. This approach requires adopting a specific logic framework that ensures the network consistently reaches the lowest global energy state. In this context, a novel logic called major 1,3 satisfiability was introduced. This logic places a higher emphasis on third-order clauses compared to first-order clauses. The proposed logic is trained by the exhaustive search algorithm, aiming to minimize the cost function toward zero. To evaluate the proposed model effectiveness, we compare the model's learning and retrieval errors with those of the existing non-systematic logical structure, which primarily relies on first-order clauses. The similarity index measures the similarity benchmark neuron state with the existing and proposed model through extensive simulation studies. Certainly, the major random 1,3 satisfiability model exhibited a more extensive solution space when the ratio of third-order clauses exceeds 0.7% compared to first-order clauses. As we compared the experimental results with other state-of-the-art models, it became evident that the proposed model achieved significant results in capturing the overall neuron state. These findings emphasize the notable enhancements in the performance and capabilities of the discrete Hopfield neural network.</p> </abstract>

Nameability effects and short-term memory limitations on order perception and enumeration of brief sounds.

Research has shown that perceiving the order of successive auditory stimuli could be affected by their nameability. The present research re-examined this hypothesis, using tasks requiring participants to report the order of successively presented (with no interstimulus gaps) environmental (i.e., easily named stimuli) and abstract (i.e., hard-to-name stimuli) sounds of short duration (i.e., 200 ms). Using the same sequences, we also examined the accuracy of the sounds perceived by administering enumeration tasks. Data analyses showed that accuracy in the ordering tasks was equally low for both environmental and abstract sounds, whereas accuracy in the enumeration tasks was higher for the former as compared to the latter sounds. Importantly, overall accuracy in the enumeration tasks did not reach ceiling levels, suggesting some limitations in the perception of successively presented stimuli. Overall, naming fluency seemed to affect sound enumeration, but no effects were obtained for order perception. Furthermore, an effect of each sound's location in a sequence on ordering accuracy was noted. Our results question earlier notions suggesting that order perception is mediated by stimuli's nameability and leave open the possibility that memory capacity limits may play a role.

Modelling Working Memory Capacity: Is the Magical Number Four, Seven, or Does it Depend on What You Are Counting?

Limited attentional capacity is essential to working memory. How its limit should be assessed is a debated issue. Five experiments compare Cowan's 4-units and Pascual-Leone's 7-units models of limited working memory capacity, with presentation time and attention to operative schemes as potential explanations of this discrepancy. Experiments 1a-1c used the Compound Stimuli Visual Information (CSVI) task, with long versus brief presentation. Capacity was estimated with the Bose-Einstein model, assuming a different number of attending acts in each condition. Participants' k estimates in both conditions were highly correlated and the means were not different, indicating that the same capacity is assessed in both conditions. Experiments 2 and 3 used the 5000-msec CSVI, and the Visual Array Task (VAT) in two conditions (5000- vs. 120-msec presentation). Capacity in the VAT was estimated with Morey's Bayesian method. Participants' k estimates in both VAT conditions were correlated, but the mean was higher with long presentation, suggesting that the long condition benefits from recoding or chunking. The k estimate in the CSVI correlated with the short VAT and (to a lesser degree in Exp.2) with the long VAT. The mean estimate of k in the CSVI was one unit more than in the short VAT. We conclude that the CSVI and the short VAT tap the same capacity, one unit of which in the short VAT is allocated to an operative scheme; we discuss how Cowan's and Pascual-Leone's views on limited capacity can be reconciled.

Effect of V and Ni addition on the Ti-Pt shape memory alloys for high temperature applications

Currently, high temperature shape memory alloys (HTSMA) are in great demand for their development and commercialization. The titanium- platinum (TiPt) alloy is the most attractive and promising HTSMA for aerospace and automobile applications due to its high transformation temperature of above 1300 K. The binary TiPt alloy has been found to show a limited shape memory capacity due to a lower critical stress needed for slip deformation rather than martensitic transformation. Incorporating alloying elements has been recommended as a method to enhance both the mechanical properties and shape memory effect of the binary TiPt alloy but may alter the transformation temperature. In this study, the effect of vanadium (V) and nickel (Ni) on the B2 TiPt alloy is investigated using first principle approach. The structural and elastic properties of the alloys are determined. The addition of both V and Ni was found to be stabilizing the cubic beta phase with a positive tetragonal shear C՛ observed. In addition, the density of states of the structures are presented.

Silencing eavesdroppers: SVM unveiling physical layer threats

Frothy Disturbance Intrusion Detection Systems (FIDS) can help detect and prevent security attacks using the Support Vector Machine (SVM) algorithm. Recognizing the importance of FIDS in protecting various domains linked to the internet, focus lies on adapting traditional intrusion detection methods for the landscape, which faces challenges such as resource constraints and limited memory and battery capacity. This study entails the creation of a lightweight attack detection technique that utilizes a supervised machine learning-based FIDS using the SVM algorithm. Simulations are used to demonstrate the usefulness of the proposed SVM-based FIDS classifier, which employs a combination of two or three complex features and achieves satisfactory classification accuracy and detection time. This strategy has the ability to enhance application security by effectively addressing the particular.

Advanced Chinese EFL Learners’ Performance on Request

This study aims to investigate the performance of advanced Chinese EFL learners in making requests in different situational contexts. Request, as a sub type of directives, refers to attempts made by a speaker to persuade or dissuade the hearer from performing some kind of action for the benefit of the speaker (Ellis, 2008). According to Brown and Levinson’s (1987) politeness theory, making a request is considered a face-threatening act since it involves imposing one’s will on the hearer. Consequently, indirect request strategies are generally preferred by speakers as they help mitigate any potential imposition on the hearer’s face. However, this is not always the case. Brown and Levinson (ibid.) argue that the specific semantic formula used by speakers depends on contextual factors such as power dynamics between speaker and hearer, social distance between them, and size of imposition involved. In other words, making requests requires language learners to consider various aspects including linguistic form, communicative function, and situational context; thus placing significant cognitive burden on them due to their limited working memory capacity. As a result, breakdowns in communication often occur when EFL learners attempt to make requests.

IMGA: Efficient In-Memory Graph Convolution Network Aggregation With Data Flow Optimizations

Aggregating features from neighbor vertices is a fundamental operation in Graph Convolution Network (GCN). However, the sparsity in graph data creates poor spatial and temporal locality, causing dynamic and irregular memory access patterns and limiting the performance of aggregation on the Von Neumann architecture. The emerging processing-in-memory (PIM) architecture is based on emerging non-volatile memory (NVM), like Spin-orbit torque Magnetic RAM (SOT-MRAM), and demonstrates promising prospects in alleviating the Von Neumann bottleneck. However, the limited memory capacity of PIM medium still incurs non-negligible data movements between PIM architecture and external memory. To solve this challenge, we propose a SOT-MRAM based in-memory computing architecture, called IMGA, for efficient in-situ graph aggregation. Specifically, we design adaptive data flow management strategies that reuse vertex data in MRAM when processing graphs of different scales and adopt edge data as the control signal source to utilize the graph’s structural information. A reordering optimization strategy leveraging hardware-software co-design principle is proposed to further reduce the costly data movement. Experimental results demonstrate that IMGA achieves an average 2523x and 21x speedup, and 1.03E+6 and 1.04E+3 energy efficiency compared with CPU and GPU, respectively.

31 Examining the Mechanisms of Verbal Working Memory Capacity Consumption in Monolingual Spanish-Speaking Individuals

Objective:Converging evidence across languages suggests that the word length effect (WLE; rate of number of syllables, phonemes, or pronunciation times per word) significantly contributes to estimates of verbal working memory (WM) capacity limits in the storage phase, but not in the manipulation phase (i.e., word length effect decay), of WM. Direct examination of the WLE on verbal WM performance within monolingual Spanish-speakers has not been reported. We investigated the psychophysical mechanisms of capacity consumption in Spanish-speakers across three syllabic word length rates to clarify the relative contributions of the WLE to storage (digit span forward) versus manipulation (digit span backward) memory phases within one language of monolingual speakers.Participants and Methods:Monolingual Spanish-speaking adults (N = 84) born in Latin American countries and age 18-65 completed testing over Zoom. Inclusion criteria required proficiency in the Spanish-language; exclusion criteria were bilingualism, multilingualism, TONI-4 IQ < 85, or history of head injury/LOC. A within-group design measured the WLE across three cognitive load conditions in the forward and backward directions of the digit span test varying in Spanish syllabic word length: the Mexican WAIS-IV Digit Span Test (“Standard Load”), and two modified measures with either a ∼20% decrease (“Low Load”) or ∼20% increase (“High Load”) in total syllables/digit relative to the Standard Load.Results:A reverse WLE was observed on syllable accuracy percentage task performance (p < 0.01), such that longer word length led to higher capacity limits during storage WM. A WLE, not decay, was found on both raw score (p < .001) and syllable accuracy percentage (p < 0.01) task performances during manipulation WM, where longer word length led to lower capacity limits.Conclusions:The reverse WLE was attributed to higher-order, executive-function cognitive strategies (such as chunking) that superseded negative word length effects. A larger syllabic discrepancy during manipulation WM could have superseded executive-function strategies, rendering a traditional WLE. Our study contributed more precise capacity estimates and clearer understanding of successful WM performance within monolingual, Latin American-born Spanish-speakers, helping to reduce cultural disparities in neurocognitive and neuropsychological research. Future studies may extend these findings to examine how WM capacity resources can be harnessed to improve memory strategies in clinically-applied settings with Spanish-speaking populations.

Enhancing energy efficiency in cloud scaling: A DRL-based approach incorporating cooling power

The rapid growth of cloud computing significantly boosts energy usage, driven mainly by CPU operations and cooling. While cloud scaling efficiently allocates resources for changing workloads, current energy-driven methods often prioritize energy metrics combined with throughput, execution time, or SLA compliance, neglecting cooling power’s influence on energy consumption. To bridge this gap, we propose a deep reinforcement learning (DRL)-based autoscaler that considers cooling power as a critical factor for decision-making. Our approach employs DRL to dynamically adjust cloud resources, aiming to maximize energy efficiency and meet performance objectives. DRL, unlike RL, uses neural networks to handle the extensive state–action space in cloud scaling, overcoming the challenge of limited memory capacity for storing Q-values. In this study, we evaluate the performance of our proposed solution through a simulation-based experiment. We compare the performance of the proposed DRL-based autoscalers against an RL-based autoscaler. Our findings indicate that the DDQN-based autoscaler consistently outperforms other algorithms by maintaining optimal Power Usage Effectiveness (PUE) levels and improving task execution speed during high workloads. In contrast, the DQN-based autoscaler excels at sustaining optimal PUE levels during lower task loads, with a faster convergence rate at a scaling factor of 2 compared to scaling factor 1.

Development of scalable GPU-based direct whole-core depletion calculation methods

GPU (Graphical Processing Unit) acceleration methods are introduced to the depletion solver and the cross section (XS) calculation routines of the direct whole core calculation code, nTRACER, for scalable and superior computing performance. Extensive GPU acceleration besides transport solvers is necessary to resolve the performance degradation in depletion calculations caused by the newly generated nuclides in depleted fuel regions and the poor scalability. A matrix exponential solver employing the Gauss-Seidel-based Chebyshev rational approximate method is implemented to reduce branch divergence and load imbalance in GPU computing in the solution of the Bateman equation. A non-zero element major storage scheme is developed to optimize memory access efficiency on GPUs by exploiting the same sparsity pattern of the depletion matrix in all the depletion regions. XS data are serialized for effective massive parallelization of XS calculation on GPUs. A block decomposition scheme for XS calculation is implemented to reduce the global memory requirement due to the limited memory capacity of commercial GPUs. It is demonstrated that nTRACER can complete a direct whole core depletion calculation for a realistic core in a few hours on a moderate-sized GPU-based heterogeneous computing platform after extensive GPU porting to all the essential routines.

T2S-MAKEP and T2T-MAKEP: A PUF-based Mutual Authentication and Key Exchange Protocol for IoT devices

Nowadays, more constrained devices are becoming connected, building an extensive Internet of Things (IoT) network, but suffering from many security issues. In particular, authentication has become a severe research challenge for IoT systems. Furthermore, confidentiality, integrity, and availability are considered the core underpinnings of information security in general. Unfortunately, deploying conventional authentication protocols for IoT devices in practice is challenging for two main reasons. First, IoT devices have limited memory capacity, processing power, and energy resources. Second, these protocols store secret keys in the IoT devices’ volatile memory, making them vulnerable to physical attacks. Luckily, Physical Unclonable Functions (PUF) has emerged as promising low-cost security primitive. A PUF eliminates the need to store secret keys in device memory, making it a potential alternative to deploying more secure and low-cost authentication protocol schemes for IoT systems. Thing-to-Thing (T2T) or direct connection between IoT devices represents a promising technique to enable things to communicate directly without the need for a trusted third party. This paper proposes two novel lightweight Mutual Authentication and Key Exchange Protocols (MAKEP) for IoT devices using PUF. The first scheme, called T2S-MAKEP, ensures secure communication for Thing-to-Server (T2S). The second, called T2T-MAKEP, allows two endpoints of resource-constrained IoT devices, each with an embedded PUF circuit, to communicate securely. Both proposed protocols, T2T-MAKEP and T2S-MAKEP, allow for robust authentication without storing any information on the device’s memory and simultaneously establish the session key exchange. Our proposed protocols have been verified and validated using the automatic security analysis checker, Verifpal.

An Ultrasonic RF Acquisition System for Plant Stems Based on Labview Double Layer Multiple Triggering.

Ultrasound is widely used in medical and engineering inspections due to its non-destructive and easy-to-use characteristics. However, the complex internal structure of plant stems presents challenges for ultrasound testing. The density and thickness differences in various types of stems can cause different attenuation of ultrasonic signal propagation and the formation of different echo locations. To detect structural changes in plant stems, it is crucial to acquire complete ultrasonic echo RF signals. However, there is currently no dedicated ultrasonic RF detection equipment for plant stems, and some ultrasonic acquisition equipment has limited memory capacity that cannot store a complete echo signal. To address this problem, this paper proposes a double-layer multiple-timing trigger method, which can store multiple trigger sampling memories to meet the sampling needs of different plant stems with different ultrasonic echo locations. The method was tested in experiments and found to be effective in acquiring complete ultrasonic RF echo signals for plant stems. This approach has practical significance for the ultrasonic detection of plant stems.

Remembering in the Digital World: Autobiographical Memory in Social Media

The use of social media continues to increase in modern cultures in recent years. This new context leads to creating a virtual self, which somewhat differs from the real self. Further, social relations are set and maintained predominantly in this new context. There is no doubt that social media does not only affect individuals' social relations but also their cognitive skills by pulling them into new situations that they are unfamiliar with. In this new context, content coded in social media can be remembered in real life, and content coded in real life can be remembered in social media. This new context, where mostly real-life stories are shared, is likely to strongly affect the autobiographical memory processes of individuals. Past research suggests that social media affects autobiographical memory processes both directly (phenomenological characteristics, functions) and indirectly through cognitive processes (eg, attention, working memory, transitive memory). Studies about its indirect effects indicate that people with limited attention and working memory capacity have difficulty processing the high number of stimuli offered by social media. Given that the long-term memory capacity is not limited, it is plausible to expect that some of the contents are encoded into the memory; however, problems may occur in the storage and retrieval processes. Studies about the direct effect of social media show that the phenomenological characteristics (number of details, accuracy, emotional content) and functions (self, social, directing, therapeutic) of events experienced or shared on social media differ from real-life events. This theoretical review discusses the effects of social media use on cognitive processes related to memory processes and more specifically on the phenomenological and functional characteristics of autobiographical memory. To recognize and prevent potential psychological issues that may emerge in relation to this new setting, it appears essential to comprehend how social media affects autobiographical memory, which is essential for self-perception.

Instructional load induces functional connectivity changes linked to task automaticity and mnemonic preference

Learning new rules rapidly and effectively via instructions is ubiquitous in our daily lives, yet the underlying cognitive and neural mechanisms are complex. Using functional magnetic resonance imaging we examined the effects of different instructional load conditions (4 vs. 10 stimulus-response rules) on functional couplings during rule implementation (always 4 rules). Focusing on connections of lateral prefrontal cortex (LPFC) regions, the results emphasized an opposing trend of load-related changes in LPFC-seeded couplings. On the one hand, during the low-load condition LPFC regions were more strongly coupled with cortical areas mostly assigned to networks such as the fronto-parietal network and the dorsal attention network. On the other hand, during the high-load condition, the same LPFC areas were more strongly coupled with default mode network areas. These results suggest differences in automated processing evoked by features of the instruction and an enduring response conflict mediated by lingering episodic long-term memory traces when instructional load exceeds working memory capacity limits. The ventrolateral prefrontal cortex (VLPFC) exhibited hemispherical differences regarding whole-brain coupling and practice-related dynamics. Left VLPFC connections showed a persistent load-related effect independent of practice and were associated with ‘objective’ learning success in overt behavioral performance, consistent with a role in mediating the enduring influence of the initially instructed task rules. Right VLPFC's connections, in turn, were more susceptible to practice-related effects, suggesting a more flexible role possibly related to ongoing rule updating processes throughout rule implementation.

Cyber-Physical Simulation

Abstract: Integration of the internet into entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called the internet of things. However .the ubiquitous and wide range iot networks make them prone to cyber attacks. One of the main types of attack is a denial of service, where the attacker floods the network with the large volume of data to prevent nodes from using the services. An intrusion detection mechanism is considered a chief source of protection for information and communication technology. However, conventional intrusion detection methods need to be modified and improved for application to the iot owing to certain limitations, such as resource-constraints devices, the limited memory and battery capacity nodes, and specific protocol stacks.

Cloud-Based Artificial Intelligence Framework for Battery Management System

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.

Real-time detection of track fasteners based on object detection and FPGA

An accurate and fast fasteners detection is of great significance for improving the inspection efficiency of railway tracks. However, the task is also challenging due to the limited memory and processing capacity of the embedded maintenance systems. To address these challenges, in this paper, we presents a solution that can be deployed on the high-speed operation vehicles, including You Only Look Once version 2 (YOLOv2) network and Field Programmable Gate Array (FPGA) HW-based acceleration strategy. The algorithm proposed in this paper is implemented completely on FPGA, and the hardware acceleration strategy such as loop pipelining, array partition and ping-pong buffer optimization is applied to get the highest performance. The hardware implementation results show that the improved YOLOv2 detection network and fastener detection system are superior to the existing embedded maintenance system in processing time and hardware resources. The proposed systems can detect a track image in 41 ms, which is much faster than the existing embedded maintenance system.

Partial recall: Implications for the discrete slot limit of working memory capacity.

The nature of working memory capacity (WMC) has been a highly contested topic among cognitive scientists. Some advocate for the discrete nature of this construct, fixed to a set number of independent slots, each capable of storing a single chunk of bound information. Others advocate for a continuous limit, guided by a pool of immediately available resources spent across the to-be-remembered items. To understand the nature of WMC, it was first essential to separate capacity from other factors, such as performance consistency, which may impact overall WM performance. Recent work by Schor et al., (2020, Psychonomic Bulletin & Review, 27[5], 1006-1013) has provided a method for separating these constructs within a single visual array task. The present study used this statistical model to extract partial information, defined as accurate recall of a correct color, but not location, at a rate greater than expected through guessing. The successful memory of this information would demonstrate that capacity does not rely on the existence of empty slots, which discrete slot model advocates argue, are necessary for successful storage and recall of items. The present study found that participants were able to successfully recall partial information at a rate significantly greater than chance, but not beyond the individual working memory capacity limit. These findings help provide additional support for the discrete resource slot model, while simultaneously casting doubt on its strong object slot model alternative.

Two types of redundancy in multimedia learning: a literature review.

Regarding the redundancy effect in multimedia learning environments, more consistency is needed in the theoretical assumptions and investigation of this effect. Current research lacks a comprehensive account of different redundant scenarios in which materials facilitate or inhibit learning and provides little conceptual guidance on how learning processes are affected by different types of redundancy. Theoretical assumptions refer to redundancy as a contentual overlap of information provided by the learning material; in this case, processing duplicated information strains the learners' limited cognitive capacities. Other assumptions refer to the role of processing limitations in working memory channels, including separate processing for visual and verbal information. In this case, an ineffective combination of sources leads to an overload of the limited working memory capacity. This paper reviews empirical research on the redundancy effect (63 studies) and classifies two types of redundancy: (1) content redundancy, and (2) working memory channel redundancy. From an instructional psychology perspective, the analyses reveal four different implementations of redundant scenarios: (1) adding narration to visualizations, (2) adding written text to visualizations, (3) adding written text to narration, and (4) adding written text to narrated visualizations. Regarding the effects of the two redundancy types within these scenarios, analyses indicate positive effects of content redundancy (affected by learners' prior knowledge), negative effects of working memory channel redundancy (regarding visualizations and written text), and positive effects of working memory channel redundancy (regarding narration and written text). Moreover, results point to factors that might moderate the effect of redundancy and illustrate interactions with existing multimedia effects. Overall, this review provides an overview of the state of empirical research and reveals that the consideration of both redundancy types provides further explanations in this field of research.

HCALA: Hyperelliptic curve-based anonymous lightweight authentication scheme for Internet of Drones

A drone is often called an ”Unmanned Aerial Vehicle” or UAV. It is utilized in various civilian and military applications, including agriculture, surveillance, and delivery of packages. A promising idea for enhancing the safety and quality of drone flight is to build the Internet of Drones (IoD), in which drones are used to collect sensitive data, which is then communicated in real-time to external user (Ui) through the Ground Station Server (GSS). Before deployment, the GSS and all drones are registered with Control Room (CR), a central authority, which is a trusted authority. To ensure secure and reliable communications, an efficient and secure authentication scheme is needed to enable users and drones to authenticate each other and share a session key. Furthermore, because drones generally have small batteries and limited memory capacity, efficient and lightweight security techniques are suitable for them. Many schemes to secure IoD environments have been proposed recently; however, some were proven as insecure, and some degraded efficiency. In this work, we focus on developing a novel blockchain-based authentication scheme, called HCALA, on protecting the communication between an external user and drone utilizing Hyperelliptic Curve Cryptography (HECC). To evaluate the viability and efficacy of HCALA, We employ the extensively used Random Oracle Model (ROM) and formal security verification using a software tool called AVISPA, which is used to validate the internet security protocols. HCALA is also examined by utilizing informal security analysis techniques, demonstrating that the proposed protocol can withstand several well-known active and passive adversary attacks. It also shows that HCALA is more efficient regarding different parameters, according to the performance comparison. Compared to previous similar schemes, the security and functionality aspects are improved, and the computation, communication costs, and energy consumption are reduced.