The present article reports two experiments examining the impact of recollecting emotionally valenced autobiographical memories on subsequent working memory (WM) task performance. Experiment 1 found that negatively valenced recollection significantly disrupted performance on a supra-span spatial WM task. Experiment 2 replicated and extended these findings to a verbal WM task (digit recall), and found that both negative and positive autobiographical recollections had a detrimental effect on verbal WM. In addition, we observed that these disruptive effects were more apparent on early trials, immediately following autobiographical recollection. Overall, these findings show that both positive and negative affect can disrupt WM when the mood-eliciting context is based on autobiographical memories. Furthermore, these results indicate that the emotional disruption of WM can take place across different modalities of WM (verbal and visuo-spatial).
Eyewitnesses typically talk about the traumatic events that they have experienced based on their memory. This research aimed to investigate differences between emotional and factual retelling of eyewitness in terms of memory accuracy and error. Participants watched a traumatic robbery video and were instructed to recall the events in detail. Participants were divided into three retelling conditions where they: a) discussed the robbery in a factual way, b) focused on discussing their emotional response, and c) performed unrelated tasks. Results showed that eyewitnesses who talked about their emotion recalled less detailed memories and made more errors in free recall while eyewitnesses who focused on factual detail seem to be able to maintain their memory accuracy of the event.
The nonlinear conjugate gradient (CG) methods have widely been used in solving unconstrained optimization problems. They are well-suited for large-scale optimization problems due to their low memory requirements and least computational costs. In this paper, a new diagonal preconditioned conjugate gradient (PRECG) algorithm is designed, and this is motivated by the fact that a pre-conditioner can greatly enhance the performance of the CG method. Under mild conditions, it is shown that the algorithm is globally convergent for strongly convex functions. Numerical results are presented to show that the new diagonal PRECG method works better than the standard CG method.
Time stepping algorithm with spatial parallelisation is commonly used to solve time dependent partial differential equations. Computation in each time step is carried out using all processors available before sequentially advancing to the next time step. In cases where few spatial components are involved and there are relatively many processors available for use, this will result in fine granularity and decreased scalability. Naturally one alternative is to parallelise the temporal domain. Several time parallelisation algorithms have been suggested for the past two decades. One of them is the pipelined iterations across time steps. In this pipelined time stepping method, communication however is extensive between time steps during the pipelining process. This causes a decrease in performance on distributed memory environment which often has high message latency. We present a modified pipelined time stepping algorithm based on delayed pipelining and reduced communication strategies to improve overall execution time on a distributed memory environment using MPI. Our goal is to reduce the inter-time step communications while providing adequate information for the next time step to converge. Numerical result confirms that the improved algorithm is faster than the original pipelined algorithm and sequential time stepping algorithm with spatial parallelisation alone. The improved algorithm is most beneficial for fine granularity time dependent problems with limited spatial parallelisation.
Long-memory is often observed in time series data. The existence of long-memory in a data set implies that the successive data points are strongly correlated i.e. they remain persistent for quite some time. A commonly used approach in modellingthe time series data such as the Box and Jenkins models are no longer appropriate since the assumption of stationary is not satisfied. Thus, the scaling analysis is particularly suitable to be used for identifying the existence of long-memory as well as the extent of persistent data. In this study, an analysis was carried out on the observed daily mean per hour of ozone concentration that were available at six monitoring stations located in the urban areas of Peninsular Malaysia from 1998 to 2006. In order to investigate the existence of long-memory, a preliminary analysis was done based on plots of autocorrelation function (ACF) of the observed data. Scaling analysis involving five methods which included rescaled range, rescaled variance, dispersional, linear and bridge detrending techniques of scaled windowed variance were applied to estimate the hurst coefficient (H) at each station. The results revealed that the ACF plots indicated a slow decay as the number lag increased. Based on the scaling analysis, the estimated H values lay within 0.7 and 0.9, indicating the existence of long-memory in the ozone time series data. In addition, it was also found that the data were persistent for the period of up to 150 days.
This study investigates the value-at-risk (VaR) using nonlinear time-varying volatility (ARCH model) and extreme-value-theory (EVT) methodologies. Similar VaR estimation and prediction are observes under the EVT and heavy-tailed long-memory ARCH approaches. The empirical results evidence the EVT-based VaR are more accurate but only at higher quantiles. It is also found that EVT approach is able to provide a convenient framework for asymmetric properties in both the lower and upper tails which implies that the risk and reward are not equally likely for the short- and long-trading positions in Malaysian stock market.
This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics.
Pineal melatonin biosynthesis is regulated by the circadian clock located in the suprachiasmatic nucleus of the hypothalamus. Melatonin has been found to modulate the learning and memory process in human as well as in animals. Endogenous melatonin modulates the process of newly acquired information into long-term memory, while melatonin treatment has been found to reduce memory deficits in elderly people and in various animal models. However, the mechanisms mediating the enhancing effect of melatonin on memory remain elusive. This review intends to explore the possible mechanisms by looking at previous data on the effects of melatonin treatment on memory performance in rodents.
Recent evidence has exhibited dietary influence on the manifestation of different types of behavior induced by stressor tasks. The present study examined the effects of Tualang honey supplement administered with the goal of preventing or attenuating the occurrence of stress-related behaviors in male rats subjected to noise stress. Forty-eight adult male rats were randomly divided into the following four groups: i) nonstressed with vehicle, ii) nonstressed with Tualang honey, iii) stressed with vehicle, and iv) stressed with honey. The supplement was given once daily via oral gavage at 0.2 g/kg body weight. Two types of behavioral tests were performed, namely, the novel object recognition test to evaluate working memory and the forced swimming test to evaluate depressive-like behavior. Data were analyzed by a two-way analysis of variance (ANOVA) using IBM SPSS 18.0. It was observed that the rats subjected to noise stress expressed higher levels of depressive-like behavior and lower memory functions compared to the unexposed control rats. In addition, our results indicated that the supplementation regimen successfully counteracted the effects of noise stress. The forced swimming test indicated that climbing and swimming times were significantly increased and immobility times significantly decreased in honey-supplemented rats, thereby demonstrating an antidepressant-like effect. Furthermore, cognitive function was shown to be intensely affected by noise stress, but the effects were counteracted by the honey supplement. These findings suggest that subchronic exposure to noise stress induces depressive-like behavior and reduces cognitive functions, and that these effects can be attenuated by Tualang honey supplementation. This warrants further studies to examine the role of Tulang honey in mediating such effects.
Cognitive impairment and memory dysfunction following stroke diagnosis are common symptoms that significantly affect the survivors' quality of life. Stroke patients have a high potential to develop dementia within the first year of stroke onset. Currently, efforts are being exerted to assess stroke effects on the brain, particularly in the early stages. Numerous neuropsychological assessments are being used to evaluate and differentiate cognitive impairment and dementia following stroke. This article focuses on the role of available neuropsychological assessments in detection of dementia and memory loss after stroke. This review starts with stroke types and risk factors associated with dementia development, followed by a brief description of stroke diagnosis criteria and the effects of stroke on the brain that lead to cognitive impairment and end with memory loss. This review aims to combine available neuropsychological assessments to develop a post-stroke memory assessment (PSMA) scheme based on the most recognized and available studies. The proposed PSMA is expected to assess different types of memory functionalities that are related to different parts of the brain according to stroke location. An optimal therapeutic program that would help stroke patients enjoy additional years with higher quality of life is presented.
Color is a perceptual stimulus that has a significant impact on improving human emotion and memory. Studies have revealed that colored multimedia learning materials (MLMs) have a positive effect on learner's emotion and learning where it was assessed by subjective/objective measurements. This study aimed to quantitatively assess the influence of colored MLMs on emotion, cognitive processes during learning, and long-term memory (LTM) retention using electroencephalography (EEG). The dataset consisted of 45 healthy participants, and MLMs were designed in colored or achromatic illustrations to elicit emotion and that to assess its impact on LTM retention after 30-min and 1-month delay. The EEG signal analysis was first started to estimate the effective connectivity network (ECN) using the phase slope index and expand it to characterize the ECN pattern using graph theoretical analysis. EEG results showed that colored MLMs had influences on theta and alpha networks, including (1) an increased frontal-parietal connectivity (top-down processing), (2) a larger number of brain hubs, (3) a lower clustering coefficient, and (4) a higher local efficiency, indicating that color influences information processing in the brain, as reflected by ECN, together with a significant improvement in learner's emotion and memory performance. This is evidenced by a more positive emotional valence and higher recall accuracy for groups who learned with colored MLMs than that of achromatic MLMs. In conclusion, this paper demonstrated how the EEG ECN parameters could help quantify the influences of colored MLMs on emotion and cognitive processes during learning.
Introduction: There are increasing trend in using information and communication technology to enhance the deliverance of reminiscence work for people with dementia. Thus this study aimed to explore the feasibility of Digital Memory Album (DMA) to support reminiscence work and subsequently evaluate the psychosocial benefits of the DMA system for older adults with dementia living in community. Method: This was an exploratory case study involved five participants with mild to moderate dementia and their primary caregivers. Each participant had an opportunity to review their own life history using Life Review Experience Form (LREF) with the researcher for 8 consecutive weeks in which subsequently facilitated in development of a personalised digital life story. Caregivers provided additional information to support the development of DMA. The whole process was audio recorded. The digital life story was presented in multimedia format and displayed using the DMA. The DMA was given to the participants for review. Qualitative data was collected using semi structure questions with the participants and caregivers immediately after the completion of DMA and 6 weeks after having the DMA as a gift. Results: Analysis of the audio-records and interview data indicated that DMA promoted psychosocial wellbeing which included enjoyment, comfort, stimulate long term memory and enhanced communication and social interactions with family members and friends. Caregivers expressed the DMA helped them in recollecting the past and better understanding of their loved ones. It also reoriented their focus on their loved ones from their disability to the remaining strength. Conclusion: For the first time, this study demonstrates the feasibility of using DMA in improving the psychosocial wellbeing for people with dementia in Malaysia.
Over the past nearly 35 years, there has been sporadic interest in what has commonly come to be known as the Proust phenomenon, whereby autobiographical memories are retrieved and experienced differently when evoked by odors as compared with other types of cues, such as words, images or sounds. The purpose of this review is threefold. First, we provide a detailed analysis of the methods used to investigate Proust effects. Second, we review and analyze the various findings from the literature and determine what we feel to be the most important and stable findings. Third, we provide a series of previously postulated and new hypotheses that attempt to account for the various findings. Given the early stage of research, the current review aims to provide a measure of organization to the field, as well serve as a guide for how future investigations may address the topic. We conclude with the recommendation that research in this area shift its focus from establishing the phenomenon towards explaining its causes.
One of the most consistently observed phenomena in autobiographical memory research is the reminiscence bump: a tendency for middle-aged and elderly people to access more personal memories from approximately 10-30 years of age. This systematic review (PROSPERO 2017:CRD42017076695) aimed to synthesize peer-reviewed literature pertaining to the reminiscence bump. The researchers conducted searches in nine databases for studies published between the date of inception of each database and the year 2017. Keywords used included: reminiscence, bump, peak, surge, blip, reminiscence effect, and reminiscence component. Sixty-eight quantitative studies, out of 523, met the inclusion criteria. The researchers implemented a thematic analytic technique for data extraction. Four main themes were generated: methods of memory activation/instruction for life scripts, types of memory/life scripts recalled, location of the reminiscence bump, and theoretical accounts for the bump. The two prevailing methods of memory activation implemented were the cuing method and important memories method. Three types of memories/life scripts were recalled: personal/autobiographical memory, memories for public events, and life script events. The findings illustrate differing temporal periods for the bump: approximately 10-30 years for memories for important events, approximately 5-30 years for memories that were induced by word cues, and 6-39 years for studies using life scripts. In explaining the bump, the narrative/identity account and cultural life script account received the most support.
Human cognition involves many mental processes that are highly interrelated, such as perception, attention, memory, and thinking. An important and core cognitive process is memory, which is commonly associated with the storing and remembering of environmental information. An interesting issue in memory research is on ways to enhance memory performance, and thus, remembering of information. Can colour result in improved memory abilities? The present paper highlights the relationship between colours, attention, and memory performance. The significance of colour in different settings is presented first, followed by a description on the nature of human memory. The role of attention and emotional arousal on memory performance is discussed next. The review of several studies on colours and memory are meant to explain some empirical works done in the area and related issues that arise from such studies.
In spite of extensive research conducted to study how human brain works, little is known about a special function of the brain that stores and manipulates information-the working memory-and how noise influences this special ability. In this study, Functional magnetic resonance imaging (fMRI) was used to investigate brain responses to arithmetic problems solved in noisy and quiet backgrounds.
Alzheimer's disease (AD) is a primary cause of dementia in the middle-aged and elderly worldwide. Animal models for AD are widely used to study the disease mechanisms as well as to test potential therapeutic agents for disease modification. Among the non-genetically manipulated neuroinflammation models for AD, lipopolysaccharide (LPS)-induced animal model is commonly used. This review paper aims to discuss the possible factors that influence rats' response following LPS injection. Factors such as dose of LPS, route of administration, nature and duration of exposure as well as age and gender of animal used should be taken into account when designing a study using LPS-induced memory impairment as model for AD.
Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, r.
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.
This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student -t and skewed Student -t. The stock returns' long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).