Both younger and older adults have difficulty updating emotional memories

Objective. The main purpose of the study was to examine whether emotion impairs associative memory for previously seen items in older adults, as previously observed in younger adults. Method. Thirty-two younger adults and 32 older adults participated. The experiment consisted of 2 parts. In Part 1, participants learned picture–object associations for negative and neutral pictures. In Part 2, they learned picture–location associations for negative and neutral pictures; half of these pictures were seen in Part 1 whereas the other half were new. The dependent measure was how many locations of negative versus neutral items in the new versus old categories participants remembered in Part 2. Results. Both groups had more difficulty learning the locations of old negative pictures than of new negative pictures. However, this pattern was not observed for neutral items. Discussion. Despite the fact that older adults showed overall decline in associative memory, the impairing effect of emotion on updating associative memory was similar between younger and older adults.

France and the memories of ‘others’: The case of the Harkis

Historical narratives help construct social identities, which are maintained through differentiation between in-groups and "others." In this article, we contend that Fatima Besnaci-Lancou's texts, as well as her reconciliation work—in which she enjoins Beurs and Harkis' offspring to write a new, inclusive, polyphonic narrative of the Algerian War—are an example of the positive use of textually mediated identity (re)construction. Her work suggests the possibility of implementing a moderate politics of empathetic recognition of the (often migration-related) memories of "others" so as to reinforce French national belongingness.

The organization of emotionally autobiographical memories: How emotional knowledge for personal events is represented in autobiographical knowledge base?

The present study aimed to investigate the organization of autobiographical memory and to reveal how emotional knowledge for personal events is represented in autobiographical knowledge base. For these purposes, the event-cueing technique was employed (Brown & Schopflocher, 1998). Forty-six participants were provided eight retrieval cues and asked to generate a personal event related to each of them (i.e., cueing events). Following this, they responded to each cueing event by retrieving two personal episodes (i.e., cued events). The results indicated that cued events shared the life themes with cueing events, suggesting the thematic organization of autobiographical memory. We also found that the life themes of each personal episode determined types of emotional states with which they were associated. The implications for the affect and memory literature and the emotion regulation literature were discussed.

Design of associative memories using cellular neural networks

Cellular neural networks (CNNs) have locally connected neurons. This characteristic makes CNNs adequate for hardware implementation and, consequently, for their employment on a variety of applications as real-time image processing and construction of efficient associative memories. Adjustments of CNN parameters is a complex problem involved in the configuration of CNN for associative memories. This paper reviews methods of associative memory design based on CNNs, and provides comparative performance analysis of these approaches.

Naps in school can enhance the duration of declarative memories learned by adolescents

Sleep helps the consolidation of declarative memories in the laboratory, but the pro-mnemonic effect of daytime naps in schools is yet to be fully characterized. While a few studies indicate that sleep can indeed benefit school learning, it remains unclear how best to use it. Here we set out to evaluate the influence of daytime naps on the duration of declarative memories learned in school by students of 10–15 years old. A total of 584 students from 6th grade were investigated. Students within a regular classroom were exposed to a 15-min lecture on new declarative contents, absent from the standard curriculum for this age group. The students were then randomly sorted into nap and non-nap groups. Students in the nap group were conducted to a quiet room with mats, received sleep masks and were invited to sleep. At the same time, students in the non-nap group attended regular school classes given by their usual teacher (Experiment I), or English classes given by another experimenter (Experiment II). These 2 versions of the study differed in a number of ways. In Experiment I (n = 371), students were pre-tested on lecture-related contents before the lecture, were invited to nap for up to 2 h, and after 1, 2, or 5 days received surprise tests with similar content but different wording and question order. In Experiment II (n = 213), students were invited to nap for up to 50 min (duration of a regular class); surprise tests were applied immediately after the lecture, and repeated after 5, 30, or 110 days. Experiment I showed a significant ∼10% gain in test scores for both nap and non-nap groups 1 day after learning, in comparison with pre-test scores. This gain was sustained in the nap group after 2 and 5 days, but in the non-nap group it decayed completely after 5 days. In Experiment II, the nap group showed significantly higher scores than the non-nap group at all times tested, thus precluding specific conclusions. The results suggest that sleep can be used to enhance the duration of memory contents learned in school.

Design of microprogrammed controllers with address converter implemented on programmable systems with embedded memories

In the paper the improvement of a traditional structure of a microprogrammed controller with sharing codes is discussed. The idea is based on the modification of internal modules and connections of the device. Such a solution permits to reduce the number of embedded memories needed for implementation of the microprogrammed controller on programmable structures, especially FPGAs. © 2011 IEEE.

Information technology as a technical resource for the memories: memories of UNATI-Marília in the virtual environment

UNATI (Open University of the Third Age), UNESP, Marília campus, has offered subsidies for the development of this work aimed at researching the existing relationships between information mediation processes and technological devices, especially computers, assuming that reading practices and textual construction in online environments could help the “third age” population to have access to these devices, thus promoting digital inclusion in this group. Mediation was presented as an interventionist action that, by introducing an intermediate element in the learning process, causes a rupture in the ways of living and personal digital inclusion processes hitherto experienced. In the context of a workshop, we found out that there is a physical relationship between subjects and technological supports and such a contact proved to be necessary, considering that handling a computer required knowledge of procedures, thus furthering a logic of use. It turned out to be necessary to develop actions that would enable the handling of a computer so as to bring about acceptance of these supports. Accordingly, activities were developed so as to articulate reminiscent processes, memories of older adults, the writing down of such memories and the creation of a blog to bring enhanced visibility to the content produced by older people. Such actions have shown that remembering, writing down and posting can reshape not only social relations but somehow significantly promote digital inclusion among older adults.

Traumatic memories: bridging the gap between functional neuroimaging and psychotherapy

Objective: Neuroimaging studies have highlighted important issues related to structural and functional brain changes found in sufferers of psychological trauma that may influence their ability to synthesize, categorize, and integrate traumatic memories. Methods: Literature review and critical analysis and synthesis. Results: Traumatic memories are diagnostic symptoms of post-traumatic stress disorder (PTSD), and the dual representation theory posits separate memory systems subserving vivid re-experiencing (non-hippocampally dependent) versus declarative autobiographical memories of trauma (hippocampally dependent). But the psychopathological signs of trauma are not static over time, nor is the expression of traumatic memories. Multiple memory systems are activated simultaneously and in parallel on various occasions. Neural circuitry interaction is a crucial aspect in the development of a psychotherapeutic approach that may favour an integrative translation of the sensory fragments of the traumatic memory into a declarative memory system. Conclusion: The relationship between neuroimaging findings and psychological approaches is discussed for greater efficacy in the treatment of psychologically traumatized patients.

Cache-aware development of high integrity real-time systems

Cost, performance and availability considerations are forcing even the most conservative high-integrity embedded real-time systems industry to migrate from simple hardware processors to ones equipped with caches and other acceleration features. This migration disrupts the practices and solutions that industry had developed and consolidated over the years to perform timing analysis. Industry that are confident with the efficiency/effectiveness of their verification and validation processes for old-generation processors, do not have sufficient insight on the effects of the migration to cache-equipped processors. Caches are perceived as an additional source of complexity, which has potential for shattering the guarantees of cost- and schedule-constrained qualification of their systems. The current industrial approach to timing analysis is ill-equipped to cope with the variability incurred by caches. Conversely, the application of advanced WCET analysis techniques on real-world industrial software, developed without analysability in mind, is hardly feasible. We propose a development approach aimed at minimising the cache jitters, as well as at enabling the application of advanced WCET analysis techniques to industrial systems. Our approach builds on:(i) identification of those software constructs that may impede or complicate timing analysis in industrial-scale systems; (ii) elaboration of practical means, under the model-driven engineering (MDE) paradigm, to enforce the automated generation of software that is analyzable by construction; (iii) implementation of a layout optimisation method to remove cache jitters stemming from the software layout in memory, with the intent of facilitating incremental software development, which is of high strategic interest to industry. The integration of those constituents in a structured approach to timing analysis achieves two interesting properties: the resulting software is analysable from the earliest releases onwards - as opposed to becoming so only when the system is final - and more easily amenable to advanced timing analysis by construction, regardless of the system scale and complexity.

Use of shared memory in the context of embedded multi-core processors: exploration of the technology and its limits

Modern embedded systems embrace many-core shared-memory designs. Due to constrained power and area budgets, most of them feature software-managed scratchpad memories instead of data caches to increase the data locality. It is therefore programmers’ responsibility to explicitly manage the memory transfers, and this make programming these platform cumbersome. Moreover, complex modern applications must be adequately parallelized before they can the parallel potential of the platform into actual performance. To support this, programming languages were proposed, which work at a high level of abstraction, and rely on a runtime whose cost hinders performance, especially in embedded systems, where resources and power budget are constrained. This dissertation explores the applicability of the shared-memory paradigm on modern many-core systems, focusing on the ease-of-programming. It focuses on OpenMP, the de-facto standard for shared memory programming. In a first part, the cost of algorithms for synchronization and data partitioning are analyzed, and they are adapted to modern embedded many-cores. Then, the original design of an OpenMP runtime library is presented, which supports complex forms of parallelism such as multi-level and irregular parallelism. In the second part of the thesis, the focus is on heterogeneous systems, where hardware accelerators are coupled to (many-)cores to implement key functional kernels with orders-of-magnitude of speedup and energy efficiency compared to the “pure software” version. However, three main issues rise, namely i) platform design complexity, ii) architectural scalability and iii) programmability. To tackle them, a template for a generic hardware processing unit (HWPU) is proposed, which share the memory banks with cores, and the template for a scalable architecture is shown, which integrates them through the shared-memory system. Then, a full software stack and toolchain are developed to support platform design and to let programmers exploiting the accelerators of the platform. The OpenMP frontend is extended to interact with it.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

The persistence of musical memories: A descriptive study of earworms

We describe some characteristics of persistent musical and verbal retrieval episodes, commonly known as "earworms." In Study 1, participants first filled out a survey summarizing their earworm experiences retrospectively. This was followed by a diary study to document each experience as it happened. Study 2 was an extension of the diary study with a larger sample and a focus on triggering events. Consistent with popular belief, these persistent musical memories were common across people and occurred frequently for most respondents, and were often linked to recent exposure to preferred music. Contrary to popular belief, the large majority of such experiences were not unpleasant. Verbal earworms were uncommon. These memory experiences provide an interesting example of extended memory retrieval for music in a naturalistic situation.

Sleep stage II contributes to the consolidation of declarative memories

Various studies suggest that non-rapid eye movement (NREM) sleep, especially slow-wave sleep (SWS), is vital to the consolidation of declarative memories. However, sleep stage 2 (S2), which is the other NREM sleep stage besides SWS, has gained only little attention. The current study investigated whether S2 during an afternoon nap contributes to the consolidation of declarative memories. Participants learned associations between faces and cities prior to a brief nap. A cued recall test was administered before and following the nap. Spindle, delta and slow oscillation activity was recorded during S2 in the nap following learning and in a control nap. Increases in spindle activity, delta activity, and slow oscillation activity in S2 in the nap following learning compared to the control nap were associated with enhanced retention of face-city associations. Furthermore, spindles tended to occur more frequently during up-states than down-states within slow oscillations during S2 following learning versus S2 of the control nap. These findings suggest that spindles, delta waves, and slow oscillations might promote memory consolidation not only during SWS, as shown earlier, but also during S2.