Memory Storage Research Articles

Data-driven non-parametric chance-constrained model predictive control for microgrids energy management using small data batches

This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven non-parametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small grid-connected microgrid with PV generation, a battery storage system, and a non-controllable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels.

An oscillatory mechanism for multi-level storage in short-term memory

Oscillatory activity is commonly observed during the maintenance of information in short-term memory, but its role remains unclear. Non-oscillatory models of short-term memory storage are able to encode stimulus identity through their spatial patterns of activity, but are typically limited to either an all-or-none representation of stimulus amplitude or exhibit a biologically implausible exact-tuning condition. Here we demonstrate a simple mechanism by which oscillatory input enables a circuit to generate persistent or sequential activity that encodes information not only in the spatial pattern of activity, but also in the amplitude of activity. This is accomplished through a phase-locking phenomenon that permits many different amplitudes of persistent activity to be stored without requiring exact tuning of model parameters. Altogether, this work proposes a class of models for the storage of information in working memory, a potential role for brain oscillations, and a dynamical mechanism for maintaining multi-stable neural representations.

Cofilin overactivation improves hippocampus-dependent short-term memory.

Many living organisms of the animal kingdom have the fundamental ability to form and retrieve memories. Most information is initially stored as short-term memory, which is then converted to a more stable long-term memory through a process called memory consolidation. At the neuronal level, synaptic plasticity is crucial for memory storage. It includes the formation of new spines, as well as the modification of existing spines, thereby tuning and shaping synaptic efficacy. Cofilin critically contributes to memory processes as upon activation, it regulates the shape of dendritic spines by targeting actin filaments. We previously found that prolonged activation of cofilin in hippocampal neurons attenuated the formation of long-term object-location memories. Because the modification of spine shape and structure is also essential for short-term memory formation, we determined whether overactivation of hippocampal cofilin also influences the formation of short-term memories. To this end, mice were either injected with an adeno-associated virus expressing catalytically active cofilin, or an eGFP control, in the hippocampus. We show for the first time that cofilin overactivation improves short-term memory formation in the object-location memory task, without affecting anxiety-like behavior. Surprisingly, we found no effect of cofilin overactivation on AMPA receptor expression levels. Altogether, while cofilin overactivation might negatively impact the formation of long-lasting memories, it may benefit short-term plasticity.

Non‐Zero and Open‐Loop Current–Voltage Characteristics in Electronic Memory Devices

AbstractThis work focuses on the non‐zero‐crossing and open‐loop current–voltage (I–V) characteristics of electronic memory devices that are studied and focused on primarily for non‐volatile memory storage applications. Gold nanoparticles‐based devices are fabricated to understand possible non‐crossing zero and open‐loop current–voltage behavior, where a non‐zero current and open loop I–V characteristics are observed at zero voltage. While other studies have attributed this behavior as a “battery effect”, this study presents an alternate perspective for non‐redox‐based charge storage memory devices. The electrical measurements clearly demonstrate that the non‐zero current and open‐loop characteristics are due to the charge trapping of the gold nanoparticles. The charge accumulation within the nanoparticle is observed to create a non‐zero potential within the device and thereby encouraging such behavior, even though the applied external voltage is zero. The longstanding mystery in deciphering if electrical measurements or the charge storage device contributes toward non‐zero property is unfurled in this article. A possible charge storage model is proposed and further verified using liquid crystals‐based two terminal devices. The presence of internal potential leads to an offset within the devices, a non‐zero current and open‐loop I–V even when the external applied voltage is zero.

Impact of ocean warming on a coral reef fish learning and memory.

Tropical ectotherms are highly sensitive to environmental warming, especially coral reef fishes, which are negatively impacted by an increase of a few degrees in ocean temperature. However, much of our understanding on the thermal sensitivity of reef fish is focused on a few traits (e.g., metabolism, reproduction) and we currently lack knowledge on warming effects on cognition, which may endanger decision-making and survival. Here, we investigated the effects of warming on learning and memory in a damselfish species, Acanthochromis polyacanthus. Fish were held at 28-28.5°C (control group), 30-30.5°C (moderate warming group) or 31.5-32°C (high warming group) for 2 weeks, and then trained to associate a blue tag (cue) to the presence of a conspecific (reward). Following 20 training trials (5 days), fish were tested for associative learning (on the following day) and memory storage (after a 5-days interval). The control group A. polyacanthus showed learning of the task and memory retention after five days, but increasing water temperature impaired learning and memory. A thorough understanding of the effects of heat stress, cognition, and fitness is urgently required because cognition may be a key factor determining animals' performance in the predicted scenario of climate changes. Knowing how different species respond to warming can lead to better predictions of future community dynamics, and because it is species specific, it could pinpoint vulnerable/resilience species.

Memory efficient data-free distillation for continual learning

Deep neural networks suffer from the catastrophic forgetting phenomenon when trained on sequential tasks in continual learning, especially when data from previous tasks are unavailable. To mitigate catastrophic forgetting, various methods either store data from previous tasks, which may raise privacy concerns, or require large memory storage. Particularly, the distillation-based methods mitigate catastrophic forgetting by using proxy datasets. However, proxy datasets may not match the distributions of the original datasets of previous tasks. To address these problems in a setting where the full training data of previous tasks are unavailable and memory resources are limited, we propose a novel data-free distillation method. Our method encodes knowledge of previous tasks into network parameter gradients by Taylor expansion, deducing a regularizer relying on gradients in network training loss. To improve memory efficiency, we design an approach to compressing the gradients in the regularizer. Moreover, we theoretically analyze the approximation error of our method. Experimental results on multiple datasets demonstrate that our proposed method outperforms the existing approaches in continual learning.

One-dimensional heat and advection-diffusion equation based on improvised cubic B-spline collocation, finite element method and Crank-Nicolson technique

In this study, we numerically investigate the solution of one-dimensional heat and advection-diffusion equation by employing improvised quartic order cubic B-spline using Crank-Nicolson method and finite element method (FEM). The numerical technique discretizes the spatial derivatives variables using Crank-Nicolson method and time derivatives using finite element method. It is demonstrated that the method is unconditionally stable with Von-Neumann process and accurate to convergence order Oh4+∆t2. The numerical results are simulated and error bounds are calculated using finite difference scheme on a piecewise uniform mesh. Finally, to support our claim, five test problems are considered and the experimental results also compared to existing methods using MATLAB as well as MATHEMATICA software tools. The error norms L2,L∞,root mean square (RMS) error with computational time and order of convergence are investigated for each example. The method is computationally efficient with less memory storage. The accuracy and efficiency of present scheme is measured in terms of absolute errors and also compared with analytical and published results in literature.

Photochargeable Semiconductors: in “Dark Photocatalysis” and Beyond

AbstractPhotochargeable semiconductors enable energy harvesting and storage in a single material. Charges separated upon absorption of photons can accumulate in highly energetic trap states if morphology, size, and chemical composition are appropriately chosen. For example, electrons can survive for several hours if hole scavengers are used to prevent their recombination with photogenerated holes, and their negative charge is balanced by positive counter‐ions. The first database of charge‐storing semiconductors is recently released, containing information from more than 50 publications within the past 40 years. Now, the database has been updated with more than 90 entries from the latest works on the topic. These materials have been largely utilized in the context of “dark photocatalysis”, that is, redox reactions enabled by photocharged semiconductors long after cessation of light irradiation. Nevertheless, a variety of further potential applications have not received enough visibility, including memory storage, steel anti‐corrosion, sensors, and micromotors. In this review, the key figures of merit of photocharged semiconductors and the empirical relationships found between them is highlighted. After showing the latest advances in dark photocatalysis, it is discussed how other application fields may benefit from these materials. For each area, promising research directions based on the findings from the database are recommended.

External Slot Relationship Memory for Multi-Domain Dialogue State Tracking

Dialogue state tracking is an essential component in multi-domain dialogue systems that aims to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample often has limitations and may introduce noise and have high time complexity issues. In this paper, we propose an external slot relation memory-based dialogue state tracking model (ER-DST). By utilizing external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Our method is evaluated on MultiWOZ 2.0 and MultiWOZ 2.1, achieving improvements of 0.23% and 0.39% over the baseline models, respectively, while reducing the complexity of the slot relationship learning component to O(n).

Determinants of Canonical Forms in Memory Storage and Object Recognition

Determinants of Canonical Forms in Memory Storage and Object Recognition

ACTIVITY IN BARREL CORTEX RELATED TO TRACE EYEBLINK CONDITIONING.

In mammals several memory systems are responsible for learning and storage of associative memory. Even apparently simple behavioral tasks, like Pavlovian conditioning, have been suggested to engage, for instance, implicit and explicit memory processes. Here we used single whisker tactile trace eyeblink conditioning (TTEBC) to investigate learning and its neuronal bases in the mouse barrel column, the primary neocortical tactile representation of one whisker. Behavioral analysis showed that conditioned responses (CR) are spatially highly restricted, they generalize from the principal whisker only to its direct neighbors. Within the respective neural representation, the principal column and its direct neighbors, spike activity showed a learning-related spike rate suppression starting during the late phase of conditioning stimulus (CS) presentation that was sustained throughout the stimulus-free trace period (Trace). Trial-by-trial analysis showed that learning-related activity was independent from the generation of eyelid movements within a trial, and set in around the steepest part of the learning curve. Optogenetic silencing of responses and their learning-related changes during CS and Trace epochs blocked CR acquisition, but not its recall after learning. Silencing during the Trace alone, which carried major parts of the learning-related changes, had no effect. In summary, we demonstrate specific barrel column spike rate plasticity during TTEBC that can be partially decoupled from the CR, the learned eye closure, a hallmark of implicit learning. Our results, thus, point to a possible role of the barrel column in contributing to other kinds of memory as well.Significance StatementAssociation learning relies on different memory systems. The Pavlovian eyeblink reflex conditioning paradigm already involves the generation of a new movement ('move the eyelid', implicit memory), and to learn the task contingencies ('know the rule of the game', explicit memory). Using this paradigm, we study the involvement of whisker-related mouse primary somatosensory cortex (S1) in different memory systems. With recording and causal stimulation techniques, we found spike plasticity in S1 during presentation of the conditioned stimulus that is causal for implicit learning. Notably, however, blocking learning-related changes during stimulus-free trace period did not impair implicit memory. We conclude that S1 activity is related to the implicit and at least one other memory system, presumably the explicit one.

Analysis of Sparse Signal Sequences under Compressive Sampling Techniques for Different Measurement Matrices

Introduction:: A more modern, extremely applicable method for signal acquisition is compression sensing. It permits effective data sampling at a rate that is significantly lower than what the Nyquist theorem suggests. Compressive sensing has a number of benefits, including a muchreduced demand for sensory devices, a smaller memory storage need, a greater data transfer rate, and significantly lower power usage. Compressive sensing has been employed in a variety of applications because of all these benefits. Neuro-signal acquisition is a domain in which compressive sensing has applications in the medical industry. Methods:: The novel methods discussed in this article are FFT-based CoSaMP (FFTCoSaMP), DCT-based CoSaMP(DCTCoSaMP) and DWT-based CoSaMP (DWTCoSaMP) based on sparse signal sequences / dictionaries by means of Transform Techniques, where CoSaMP stands for Compressive Sampling Matching Pursuit with respect to Objective Quality Assessment Algorithms like PSNR, SSIM and RMSE, where CoSaMP stands for Compressive Sampling Matching Pursuit. Results: DWTCoSaMP is giving the PSNR values of 40.26 db, for DCTCoSaMP and FFTCoSaMP, PSNR is 36.76 db and 34.76 db. For DWTCoSaMP, SSIM value is 0.8164, and for DCTCoSaMP and FTCoSaMP, SSIM 0.719 and 0.5625 respectively. Finally, for DWTCoSaMP, RMSE value is 0.442, and for DCTCoSaMP and FFTCoSaMP, SSIM 0.44 and 0.4425, respectively. Conclusion:: Among Compressed sampling techniques DWTCoSaMP, DCTCoSaMP and FFTCoSaMP discussed in this paper, DWTCoSaMP reveals the best results.

Approximation-based homomorphic encryption for secure and efficient blockchain-driven watermarking service

<p>Homomorphic encryption has been widely used to preserve the privacy of watermarking process on blockchain-driven watermarking services. It offers transparent and traceable encrypted watermarking without revealing sensitive data such as original images or watermark data to the public. Nevertheless, the existing works suffer from enormous memory storage and extensive computing power. This study proposed an approximation-based homomorphic encryption for resource-efficient encrypted watermarking without sacrificing watermarking quality. We demonstrated the efficiency of the Cheon-Kim-Kim-Son (CKKS) encrypted watermarking process using discrete cosine transform-singular value decomposition (DCT-SVD) embedding. The evaluation results showed that it could preserve the watermarking quality similar to non-encrypted watermark embedding, even after geometrical and filtering attacks. Compared to existing homomorphic encryption, such as Brakerski-Gentry-Vaikuntanathan (BFV) encryption, it has superior performance regarding resource utilization and watermarking quality preservation.</p>

Unimodal load selectively reduces recruitment of sensory cortices for working memory storage

Unimodal load selectively reduces recruitment of sensory cortices for working memory storage

Hopfield model with planted patterns: A teacher-student self-supervised learning model

While Hopfield networks are known as paradigmatic models for memory storage and retrieval, modern artificial intelligence systems mainly stand on the machine learning paradigm. We show that it is possible to formulate a teacher-student self-supervised learning problem with Boltzmann machines in terms of a suitable generalization of the Hopfield model with structured patterns, where the spin variables are the machine weights and patterns correspond to the training set's examples. We analyze the learning performance by studying the phase diagram in terms of the training set size, the dataset noise and the inference temperature (i.e. the weight regularization). With a small but informative dataset the machine can learn by memorization. With a noisy dataset, an extensive number of examples above a critical threshold is needed. In this regime the memory storage limits become an opportunity for the occurrence of a learning regime in which the system can generalize.

The organizational principles of impression formation

Trait impressions about others are a fundamental tool to navigate the rich social environment and yet a unitary model of its organizational principles is still lacking. The statistical properties of impression formation observed in previous studies are akin to processes that govern information encoding and storage in memory, suggesting similar cognitive and computational mechanisms. Here, in 2,780 participants, impression formation has been formalized with a computational model representing three organizational principles of memory (temporal, semantic and valence-related). The model specifically captured two main patterns of impression formation: (1) a negative valence effect that makes negative impressions loom longer than positive ones; (2) an interaction effect between the temporal and valence content that endorses more negative impressions when negative information is met first. This work shows that mechanisms of information encoding, storage and retrieval interact in ways that explain biased impression formation about social partners, thereby providing quantitative evidence for those mechanisms in individuals’ impressions of others’ social qualities. We discuss the implications of these results for social impressions in different, real-world contexts, and suggest how the proposed model might be extended to capture other kinds of effects, from negativity bias and pessimism to social discrimination.

Disentangling Effects of Memory Storage and Inter-articulator Coordination on Generalization in Speech Motor Sequence Learning.

Generalization in motor control is the extent to which motor learning affects movements in situations different than those in which it originally occurred. Recent data on orofacial speech movements indicates that motor sequence learning generalizes to novel syllable sequences containing phonotactically illegal, but previously practiced, consonant clusters. Practicing an entire syllable, however, results in even larger performance gains compared to practicing just its clusters. These patterns of generalization could reflect language-general changes in phonological memory storage and/or inter-articulator coordination during motor sequence learning. To disentangle these factors, we conducted two experiments in which talkers intensively practiced producing novel syllables containing illegal onset and coda clusters over two consecutive days. During the practice phases of both experiments, we observed that, through repetition, talkers gradually produced the syllables with fewer errors, indicative of learning. After learning, talkers were tested for generalization to single syllables (Experiment 1) or syllable pairs (Experiment 2) that overlapped to varying degrees with the practiced syllables. Across both experiments, we found that performance improvements from practicing syllables with illegal clusters partially generalized to novel syllables that contained those clusters, but performance was more error prone if the clusters occurred in a different syllable position (onset versus coda) as in practice, demonstrating that inter-articulator coordination is contextually sensitive. Furthermore, changing the position of a cluster was found to be more deleterious to motor performance during the production of the second syllables in syllable pairs, which required talkers to store more phonological material in memory prior to articulation, compared to single syllables. This interaction effect reveals a complex interplay between memory storage and inter-articulator coordination on generalization in speech motor sequence learning.

Dielectric Properties of a Phenanthrene-Like Nanoisland B14A10: Monte Carlo Study

The dielectric properties of a monolayer phenanthrene-like nanoisland of formula B[Formula: see text]A[Formula: see text] are examined in this research using Monte Carlo simulations. We evaluate the behaviors of the electric hysteresis loops and blocking temperature under the exchange coupling, crystalline field and temperature effects. The multi-loop hysteresis behavior examined in this research may have beneficial applications in nanotechnology and multi-state memory storage. Besides, the results found in the Phenanthrene-like nanoisland are in good agreement with several theoretical and experimental results.

The effect of working memory load on inattentional deafness during aeronautical decision-making

Operating an aircraft requires pilots to handle a significant amount of multi-modal information, which creates a high working memory load. Detecting auditory alarms in this high-load scenario is crucial for aviation safety. According to cognitive control load theory, an increase in working memory load may enhance distractor interference, resulting in improved detection sensitivity for task-irrelevant stimuli. Therefore, understanding the effect of working memory load on auditory alarm detection is of particular interest in aviation safety research. The studies were designed to investigate the effect of storage load and executive function load of working memory on auditory alarm detection during aeronautical decision-making through three experiments. In Experiment 1 and 2, participants performed an aeronautical decision-making task while also detecting an auditory alarm during the retention interval of a working memory task (visual-spatial, visual-verbal and auditory-verbal). In Experiment 3, participants were required to detect an auditory alarm while performing the 2-back and 3-back aeronautical decision-making tasks. Experiment 1 found that the auditory alarm sensitivity was higher in conditions of low visual-spatial working memory storage load compare to high load conditions. Experiment 2 found that a high storage load of visual-verbal working memory reduced auditory alarm sensitivity but auditory-verbal working memory load did not. Experiment 3 found that, unlike storage load, auditory alarm sensitivity was stronger under high executive function load relative to low executive function load. These findings show that working memory storage load and executive function load have different effects on auditory alarm sensitivity. The relationship between executive function and auditory alarm sensitivity supports cognitive control load theory, while the impact of the storage function on auditory alarm sensitivity does not adhere to this theory.

A memphotoristor for high-efficiency temporal vision processing

A photodetector with history-dependent dynamical responses to optical inputs could offer an essential breakthrough in performing temporal vision processing and time-series prediction without the requirement for sophisticated circuitry and undesired high energy consumption. Till now, memristor dynamics and its effective modulation with photo illumination have been utilized to mimic bio-inspired vision processing. Despite significant effort, it is still challenging to process real-time analogue temporal optical information with high accuracy, which requires developing a photonic counterpart of the electric-triggered memristor. Here, we report on the development of a Ga2O3-based proof-of-concept memphotoristor, in which memristive dynamics were stimulated with ultraviolet photon flux rather than conventional voltage. Specifically, a distinct hysteresis loop opening appeared in the cyclic photocurrent-ultraviolet intensity curves, where the magnitude of the loop opening depends on the photon flux. Additionally, light-illumination-induced fading memory, nonlinearity, and temporal dynamics were successfully utilized to demonstrate in-sensor reservoir computation and time-series prediction with an accuracy of 98.86%. Our research will pave the way to developing a wide range of cutting-edge optoelectronics for various applications, such as photosensors, photonic memory storage, and neuromorphic vision sensing of objects in real time.