Memory Set Research Articles

Toward Collective Memory Reconstruction as Epistemic Activism

The United States, alongside other Western democracies, is in search of a usable past. Collective memory in the United States has persistently distorted or whitewashed its past, resulting in a distinct kind of (socially sanctioned) ignorance of the present. Collective memory reconstruction can thus be understood as “epistemic activism,” targeting an “epistemology of ignorance,” borrowing and expanding key concepts from the work of Charles Mills and José Medina. In this article I begin to defend an ethical practice of collective memory reconstruction as epistemic activism. I first outline a qualified understanding of “collective memory” that survives philosophical skepticism. I then draw on Paul Ricœur’s critical phenomenology of abuses of memory and analyze collective memory distortions of the US Civil War and the US struggle for civil rights. I suggest that a reconstructed democratic collective memory will be a set of plural and dynamic collective memories, rather than a homogeneous and static memory. I end by outlining some consequences that follow from this conclusion.

Continual Learning-Based Fast Beamforming Adaptation in Downlink MISO Systems

Beamforming is an effective way to improve spectrum efficiency in multi-antenna systems. However, conventional iterative algorithms suffering from high computational delay renders beamforming solutions outdated and deep learning (DL) based realtime solutions suffer from the “task mismatch” problem, leading to severe performance deterioration. To resolve these issues, this letter proposes a beamforming adaptation scheme, named continual learning based beamforming neural network (CL-BNN), which can continuously learn and optimize the downlink beamforming in dynamic environment. The proposed CL-BNN scheme is model-driven and uses domain/expert knowledge to reduce training complexity. By combining our customized selection criteria into the structure of the CL-BNN, which selects the appropriate samples from the existing data and newly received data into a memory set for model training, taking into account the objective function, such that the tradeoff between maintaining performance of the trained task and adapting to the new task is achieved. Simulation results show that the proposed CL-BNN scheme achieves good adaptability in dynamic environments with low complexity.

Naga Writings in English – Coming into One’s Own

In the past decade, Nagaland has witnessed a marked rise in publications of writings in English. The establishment of homegrown publishing houses have contributed significantly to this increase. This paper will attempt to historicize publishing in Nagaland and undertake a critical survey of published writings in English from Nagaland. Through these trajectories, I argue that a gendered tradition of orality has been transposed into writing and the notion that only Naga women are writing literature fails to account for the whole range of writings in English from Nagaland. Women writers insert themselves into this realm of public discourse through their literary works by negotiating both traditional patriarchal structures in which they are embedded as well as the demands of the world literary market. Creative writing is therefore, a space patriarchy “allows” them to engage the violence and trauma that characterized our neo-colonial encounter with the Indian state. The younger generation of writers, many of whom grew up after the ceasefire came into place, demonstrate either a desire to forget or display a new set of memories that seemingly circumvent the conflict and violence that characterized life before the 1990s. I examine self-publishing as a mode of mediating state censorship and the aesthetic demands of the world literary system. I argue that there is apathy on the part of national and international publishers toward representations of contemporary Naga experiences that deviate from or contravene cherished and commercially viable stereotypes of Nagas as exotic, festive natives; dangerous violent headhunters and/or insurgents. Naga writers who have successfully crossed over to the world literary market are ones who cater to these two aesthetic paradigms. In terms of subject matter and language, the current crop of emerging writers are questioning the canonical indoctrination of our colonial past as well as mainstream taste for conflict and magic realism cultivated as representative of Naga literature in English. New Naga writers are producing works that reject English (British) figures of speech and even sacrosanct rules of grammar, representing instead a colloquial Naga English that both disrupts and creates new narrative expectations. Finally, I contend that new Naga writers in English and Naga publishers are part of a emergent literary culture negotiating between insularity and a desire to demonstrate global cultural currency.

Remotely administered computerized cognitive test battery with older adults

AbstractBackgroundComputerized cognitive assessments are an efficient and sensitive method of identifying cognitive impairments in aging and cognitively impaired populations. The current study examines the feasibility and reliability of remote, home testing with a new tool, the California Cognitive Assessment Battery (CCAB).MethodThe CCAB runs on a remote tablet platform (Figure 1) and includes telemedical interaction with an examiner via video chat (Figure 2). Examiners administer a comprehensive set of cognitive tests, including verbal, visual, memory, and processing speed tasks, as well as psychological questionnaires (Figure 3). The tasks are scored automatically, including consensus automated speech recognition (CASR) for the transcription of responses on verbal tasks (e.g., list‐learning memory tasks, picture description, verbal fluency). For the current study, the CCAB was administered remotely to 185 healthy older military veterans (aged 60–89; 18% women) in their homes, with testing sessions under video and audio supervision and assistance. The battery was administered three times within one week to assess test‐retest reliability.ResultReaction times, word onset latency, response accuracy, error types, and speech samples were collected and analyzed. Participant experience was generally favorable: 98% of participants completed all three testing sessions, with 99% of all tests in those sessions completed successfully. Additionally, after a mid‐year software and examiner procedure reformulation of a small subset of CCAB tests, there was a 42% decrease in test failures and a 6% decrease in repeated tests. Individual test times were overall highly uniform across participants (Figure 4), with an entire CCAB battery having low daily test time variability. Also, compared to home noise levels, recorded speech levels were sufficient to support CASR transcript generation for scoring verbal tasks (Figure 5). Finally, in a subset of 50 participants, similar test battery characteristics were found when a CCAB session was repeated later under laboratory conditions.ConclusionThe CCAB shows promise as an objective, reliable automated digital assessment tool to evaluate cognitive functioning in adults both at home and in the laboratory or clinic.

Parameters tuning of multi-model database based on deep reinforcement learning

As we all know, the performance of database management system is directly linked to a vast array of knobs, which control various aspects of system operation, ranging from memory and thread counts settings to I/O optimization. Improper settings of configuration parameters are shown to have detrimental effects on performance, reliability and availability of the overall database management system. This is also true for multi-model databases, which use a single platform to support multiple data models. Existing approaches for automatic DBMS knobs tuning are not directly applicable to multi-model databases due to the diversity of multi-model database instances and workloads. Firstly, in cloud environment, they have difficulty adapting to changing environments and diverse workloads. Secondly, they rely on large-scale high-quality training samples that are difficult to obtain. Finally, they focus primarily on throughput metrics, ignoring tuning requirements for resource utilization. Therefore, in this paper, we propose a multi-model database configuration parameters tuning solution named MMDTune. It selects influential parameters, recommends the optimal configurations in a high-dimensional continuous space. For different workloads, the TD3 algorithm is improved to generate reasonable parameter adjustment plans according to the internal state of the multi-model databases. We conduct extensive experiments under 5 different workloads on real cloud databases to evaluate MMDTune. Experimental results show that MMDTune adapts well to a new hardware environment or workloads, and significantly outperforms the representative tuning tools, such as OtterTune, CDBTune.

Estimating Likelihood of Dementia in the Absence of Diagnostic Data: A Latent Dementia Index in 10 Genetically Informed Studies.

Epidemiological research on dementia is hampered by differences across studies in how dementia is classified, especially where clinical diagnoses of dementia may not be available. We apply structural equation modeling to estimate dementia likelihood across heterogeneous samples within a multi-study consortium and use the twin design of the sample to validate the results. Using 10 twin studies, we implement a latent variable approach that aligns different tests available in each study to assess cognitive, memory, and functional ability. The model separates general cognitive ability from components indicative of dementia. We examine the validity of this continuous latent dementia index (LDI). We then identify cut-off points along the LDI distributions in each study and align them across studies to distinguish individuals with and without probable dementia. Finally, we validate the LDI by determining its heritability and estimating genetic and environmental correlations between the LDI and clinically diagnosed dementia where available. Results indicate that coordinated estimation of LDI across 10 studies has validity against clinically diagnosed dementia. The LDI can be fit to heterogeneous sets of memory, other cognitive, and functional ability variables to extract a score reflective of likelihood of dementia that can be interpreted similarly across studies despite diverse study designs and sampling characteristics. Finally, the same genetic sources of variance strongly contribute to both the LDI and clinical diagnosis. This latent dementia indicator approach may serve as a model for other research consortia confronted with similar data integration challenges.

Lactate-mediated neural plasticity genes emerged during the evolution of memory systems

The ability to record experiences and learning is present to different degrees in several species; however, the complexity and diversity of memory processes are cognitive function features that differentiate humans from other species. Lactate has recently been discovered to act as a signaling molecule for neuronal plasticity linked to long-term memory. Because lactate is not only an energy substrate for neurons but also a signaling molecule for plasticity (Magistretti and Allaman in Nat Rev Neurosci 19:235–249, 2018. https://doi.org/10.1038/nrn.2018.19), it is of particular interest to understand how and when memory-related genes and lactate-mediated neural plasticity (LMNP) genes emerged and evolved in humans. To understand the evolutionary origin and processes of memory and LMNP genes, we first collected information on genes related to memory and LMNP from the literature and then conducted a comparative analysis of these genes. We found that the memory and LMNP genes have different origins, suggesting that these genes may have become established gradually in evolutionarily and functional terms and not at the same time. We also found that memory and LMNP systems have a similar evolutionary history, having been formed with the gradual participation of newly emerging genes throughout their evolution. We propose that the function of LMNP as a signaling process may be evolutionarily associated with memory systems through an unidentified system that is linked by 13 common genes between memory and LMNP gene sets. This study provides evolutionary insight into the possible relationship between memory and the LMNP systems that deepens our understanding of the evolution of memory systems.

Exploring Synchronous Page Fault Handling

The advance of nonvolatile memory in storage technology has presented challenges in redefining the ways in handling the main memory and the storage. This work is motivated by the strong demands in effective handling of page faults over ultralow-latency storage devices. In particular, we propose synchronous and asynchronous prefetching strategies to satisfy process executions with different memory demands in supporting of synchronous page fault handling. An adaptive CPU scheduling strategy is also proposed to cope with the needs of processes in maintaining their working sets in the main memory. Six representative benchmarks and applications were evaluated. It was shown that our strategy can effectively save 12.33% of the total execution time and reduce 13.33% of page faults, compared to the conventional demand paging strategy with nearly no sacrificing of process fairness.

Balanced softmax cross-entropy for incremental learning with and without memory

When incrementally trained on new classes, deep neural networks are subject to catastrophic forgetting which leads to an extreme deterioration of their performance on the old classes while learning the new ones. Using a small memory containing few samples from past classes has shown to be an effective method to mitigate catastrophic forgetting. However, due to the limited size of the replay memory, there is a large imbalance between the number of samples for the new and the old classes in the training dataset resulting in bias in the final model. To address this issue, we propose to use the Balanced Softmax Cross-Entropy and show that it can be seamlessly combined with state-of-the-art approaches for class-incremental learning in order to improve their accuracy while also potentially decreasing the computational cost of the training procedure. We further extend this approach to the more demanding class-incremental learning without memory setting and achieve competitive results with memory-based approaches. Experiments on the challenging ImageNet, ImageNet-Subset, and CIFAR100 benchmarks with various settings demonstrate the benefits of our approach.

Performance degradation prediction model of rolling bearing based on self-checking long short-term memory network

The prediction of performance degradation is of great significance for the health monitoring of rolling bearings. When predicting the performance degradation trend for the whole-life data of bearings, there are problems such as long prediction time, high cost and single evaluation criteria for prediction effect, which hinder the accuracy of degradation prediction. To this end, a self-checking long and short-term memory (Sc-LSTM) prediction model is proposed in this paper for predicting the performance degradation trend of bearings. First, Relation was used as a performance degradation indicator and improved using the mean and normalization methods. This approach can effectively alleviate the problems of long forecasting time and high cost. Secondly, the long and short-term memory (LSTM) sets up many different prediction schemes and proposes a test layer by segmental prediction and comparison to select the appropriate test index σ. According to the robust stability of the self-checking method and the high accuracy of LSTM nonlinear prediction, an Sc-LSTM performance degradation prediction model is established. The model introduces the prediction effect assessment of prediction pass rate η to make up for the problem of inadequate assessment of prediction effect by a single error. Finally, using the Sc-LSTM model, relation is used to perform segmental predictive analysis and experimental validation of the overall prediction. Experiments show that the model can effectively improve the prediction accuracy and is feasible in the prediction of bearing performance degradation.

Context Binding in Visual Working Memory Is Reflected in Bilateral Event-Related Potentials, But Not in Contralateral Delay Activity.

Successful retrieval of a specific item from visual working memory (VWM) depends on the binding of that item to its unique context. Recent functional magnetic resonance imaging studies of VWM manipulating memory set homogeneity have identified an important role for the intraparietal sulcus in context binding, independent of any role in representing stimulus identity. The current study explored whether the contralateral delay activity (CDA), which is an event-related potential (ERP) component derived from posterior electrodes that tracks the amount of information held in VWM, might also be sensitive to context-binding demands. In experiment 1, human participants performed lateralized delayed recognition with memory sets containing one, three, or five items that were drawn from the same category (orientations: "homogeneous") or from different categories (orientation, color, and luminance: "heterogeneous"). Because the location and identity of the memory probe indicated the item to be retrieved, homogeneous trials placed higher context-binding demands. VWM capacity was higher in heterogeneous trials. ERPs contralateral (contra) and ipsilateral (ipsi) to the remembered stimuli were higher for homogeneous trials, but these differences were removed in the contra - ipsi subtraction that produced the CDA. In experiment 2, human participants performed lateralized delayed recall with memory sets of one or three items (homogeneous or heterogeneous). Behavior was superior for three-item heterogeneous trials than for homogeneous trials, with modeling revealing context-binding errors in the latter. Bilateral ERPs and CDA results replicated experiment 1. These results support that the CDA tracks the number of object files engaged by VWM and establish that it is not sensitive to context-binding demands.

Soundscapes of Mostar: Space and Art Beyond the Divided City

ABSTRACT The ‘spatial turn’ in peace research has primarily highlighted the visuality of the spaces in which peace takes place. In this article, however, we argue that ‘sound’ can challenge rigid visual markers, which are particularly prominent in divided cities. Drawing on a sound story from a larger archive, we investigate how a sound artist re-imagines the divided city of Mostar by mobilizing a set of sonic memories. Our sonic reading of the Partisan’s Cemetery traces the spatial transformation of the city, which disrupts visual representations of space as divided and presents alternative spatial imaginations.

“What Blood Unites, the Sea Shall Not Divide”: Bohemian Day at the World's Columbian Exposition of 1893

“What Blood Unites, the Sea Shall Not Divide”: Bohemian Day at the World's Columbian Exposition of 1893

Философская концептуализация «memory studies» в рамках социо-гуманитарных наук

The purpose of the article is to present a systematic study of the genesis of memory through the most significant philosophical concepts and ideas. Therefore, the phenomenon of memory in socio-humanitarian knowledge in the article assumes a philosophical study of the results of social and humanitarian sciences, as well as their interdisciplinary nature in understanding the most important aspects of memory. The philosophical study of the phenomenon of memory sets the task of clarifying the subject area of research as an object marked by this concept throughout its development. The study of the phenomenon of memory explains our attention to the history of its perception in philosophy, clarifying the stages of the subject organization of the field of memory, i.e. such areas of research and subjects that later turned into a research source for the development of the philosophy of memory. The stated approach allows us to explain the main categories that define the categorical context, i.e. what is designated by the problem of “memory”, how and which subject area of memory research is represented in its genesis. At the same time, the philosophical interpretation of the concept of memory requires the study of methodological approaches and views that help philosophers and researchers to represent the distinctive meanings of memory concepts defined in various philosophical or sociohumanitarian studies, and how this representation of memory served the practical activity of science. The analysis of the genesis of the research direction “memory studies” and the understanding of memory as an integral system contains the proposed classification of the structural stages of philosophical theory in understanding the nature of this phenomenon.

Do Objects (Re)produce Home among International Migrants?

ABSTRACT Ordinary objects can play a significant role in the making and reproduction of home, as an emplaced set of emotions, memories and relationships, among international migrants. Based on qualitative research with Ecuadorians and Peruvians in Britain, Italy and Spain, we show how certain objects, by virtue of their evocative power, help migrants to transform their dwelling places into homely environments (home making), and/or retain connections with what used to be home for them (home reproduction). We develop a framework on the potential of objects for home making and reproduction along four lines: embodying migrants’ collective backgrounds and identities; affording migrants to feel at home; encapsulating the memories and symbols of former homes, households, and significant relationships; eliciting connections with settings and events that meant ‘home’ over their life course. Such functions hold a promise to guide comparative research on migration-related materialities and on migrant transnational homemaking.

The role of cognitive control in the SNARC effect: A review.

The spatial-numerical association of response codes (SNARC) effect, in which people respond to small numbers faster with the left hand and to large numbers faster with the right hand, is a popular topic in cognitive psychology. Some well-known theoretical accounts explaining this effect include the mental number line model, polarity correspondence principle, dual-route model, and working memory account. However, these fail to explain the finding that the size of the SNARC effect is modulated by cognitive control. Here, we propose a new account-a cognitive control-based view of the SNARC effect. This view argues that the SNARC effect is fundamentally determined by cognitive control in resolving conflicts during stimulus-response mapping. Several subcomponents of cognitive control, such as working memory, mental or task set shifting, inhibition control, and conflict adaptation, can easily modulate the SNARC effect. The cognitive control-based view can account for the flexible SNARC effect observed in diverse task situations while providing new insight into its mechanism.

ECiS: Encryption prior to compression for digital image security with reduced memory

Digital images are a popular source of information and play an important role in several applications. However, the large amount of available data in the form of images causes serious security concerns. Although digital images are widely available, the storage of these images requires a large amount of data. Compression is an efficient way to reduce image size and efficiently utilize network resources. In this study, a joint encryption and compression technique, namely, ECiS, is developed to solve the two major issues with digital images outlined above. To achieve high security, we encrypt an image by using DNA, SHA-256, and a chaotic-based encryption technique. To reduce the bandwidth or storage space demand, we compress the encrypted image by using a compression technique based on zero memory set partitioned embedded block (ZM-SPECK). The strength of our ECiS technique is performing the analysis by applying several standard tests, including key analysis, statistical analysis, differential analysis, and time cost evaluation on a USC-SIPI dataset. Furthermore, the extensive evaluations on the dataset demonstrate that the proposed technique is secure and has a lower memory and encryption overhead than similar techniques.

Deep Learning Technique to Identify the Malicious Traffic in Fog based IoT Networks

The network of devices known as the Internet of Things (IoT) consists of hardware with sensors and software. These devices communicate and exchange data through the internet. IoT device-based data exchanges are often processed at cloud servers. Since the number of edge devices and quantity of data exchanged is increasing, massive latency-related concerns are observed. The answer to these issues is fog computing technology. Fog computing layer is introduced between the edge devices and cloud servers. Edge devices can conveniently access data from the fog servers. Security of fog layer devices is a major concern. As it provides easy access to different resources, it is more vulnerable to different attacks. In this paper, a deep learning-based intrusion detection approach called Multi-LSTM Aggregate Classifier is proposed to identify malicious traffic for the fog-based IoT network. The MLAC approach contains a set of long short-term memory (LSTM) modules. The final outcomes of these modules are aggregated using a Random Forest to produce the final outcome. Network intrusion dataset UNSW-NB15 is used to evaluate performance of the MLAC technique. For binary classification accuracy of 89.40% has been achieved using the proposed deep learning-based MLAC model.

Mind wandering and contextual binding

ABSTRACT In a memory-scanning task, participants were shown two rows of items followed by a cue indicating which row was the relevant memory set. The target item could be in the relevant set (a positive target), in the irrelevant set (a negative lure target), or an item not seen on the trial (a negative control target). After each block of trials, participants indicated the extent to which they were on or off task. Consistent with prior research, lure targets were more difficult to discriminate from positive targets than were control targets. However, this effect was larger when participants reported being off task. Our interpretation is that lures are more difficult to reject when mind wandering because less attention is devoted to the process of binding items to an appropriate spatial context.

Integrative genome‐wide chromatin accessibility and transcriptome profiling of diffuse large B‐cell lymphoma

Integrative genome‐wide chromatin accessibility and transcriptome profiling of diffuse large B‐cell lymphoma