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Sit down and read on: working memory and long-term memory particle-verb processing

Particle verbs (e.g., look up) are lexical items for which particle and verb share a single lexical entry. Using event-related brain potentials, we examined working memory and long-term memory involvement in particle-verb processing. Dutch participants read sentences with head verbs that allow zero, two, or more than five particles to occur downstream. Additionally, sentences were presented for which the encountered particle was semantically plausible, semantically implausible, or forming a non-existing particle verb. An anterior negativity was observed at the verbs that potentially allow for a particle downstream relative to verbs that do not, possibly indexing storage of the verb until the dependency with its particle can be closed. Moreover, a graded N400 was found at the particle (smallest amplitude for plausible particles and largest for particles forming non-existing particle verbs), suggesting that lexical access to a shared lexical entry occurred at two separate time points.

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Moments to remember

The pervasive use of media at current-day festivals thoroughly impacts how these live events are experienced, anticipated, and remembered. This empirical study examined event-goers’ live media practices – taking photos, making videos, and in-the-moment sharing of content on social media platforms – at three large cultural events in the Netherlands. Taking a practice approach (Ahva 2017; Couldry 2004), the author studied online and offline event environments through extensive ethnographic fieldwork: online and offline observations, and interviews with 379 eventgoers. Analysis of this research material shows that through their live media practices eventgoers are continuously involved in mediated memory work (Lohmeier and Pentzold 2014; Van Dijck 2007), a form of live storytelling thatrevolves around how they want to remember the event. The article focuses on the impact of mediated memory work on the live experience in the present. It distinguishes two types of mediatised experience of live events: live as future memory and the experiential live. The author argues that memory is increasingly incorporated into the live experience in the present, so much so that, for many eventgoers, mediated memory-making is crucial to having a full live event experience. The article shows how empirical research in media studies can shed new light on key questions within memory studies.

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Model-predictive space heating control for energy flexibility – a case study using a long short-term memory neural network surrogate model and a genetic optimization algorithm

This paper presents a case study where a model predictive control (MPC) logic is developed for energy flexible operation of a space heating system in an educational building. A Long Short-Term Memory Neural Network (LSTM) surrogate model is trained on the output of an EnergyPlus building simulation model. This LSTM model is used within an MPC framework where a genetic algorithm is used to optimize setpoint sequences. The EnergyPlus model is used to validate the performance of the control logic. The MPC approach leads to a substantial reduction in energy consumption (7%) and energy costs (13%) with improved comfort performance. Additional energy costs savings are possible (7–16%) if a sacrifice in indoor thermal comfort is accepted. The presented method is useful for developing MPC systems in the design stages where measured data is typically not available. Additionally, this study illustrates that LSTM models are promising for MPC for buildings.

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Mapping an Emerging Field: Local Memory Websites

Online platforms for collecting local memories are often claimed to be a driving force of empowerment for individuals, groups and the community as a whole. Long term online participation especially plays a key role in the claims for empowerment on group and community level. However, the present research on local memory websites lacks empirical data to substantiate these claims and leaves aside questions about their wider presence, the way they are organized and how their particular structure and affordances enable online participation. To address these issues, we develop six analytical dimensions in order to analyse a comprehensive number of such sites, examining in particular their organizational and online participatory features. On the basis of a cross-sectional design including 80 cases from the Netherlands, the United Kingdom and various other countries, we show three types of websites can be distinguished, namely residential, institutional and associational. In addition, we find that the expectancy of online participation is maximized not only by organizational aspects that fosterautonomy, but also by characteristics that enlarge the sense of authenticity. Our findings also show a limited number of cases with a considerable level of online participation, which offers the empirical data for analysis in terms of empowerment on group and community level.Nevertheless, we conclude that in most cases the organizational characteristics and participatory affordances of the websites are not sufficient to produce empowerment on all levels.

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Mapping an Emerging Field: Local Memory Websites

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Stroke survivors' long-term participation in paid employment

BACKGROUND:Knowledge on long-term participation is scarce for patients with paid employment at the time of stroke. OBJECTIVE:Describe the characteristics and the course of participation (paid employment and overall participation) in patients who did and did not remain in paid employment. METHODS:Patients with paid employment at the time of stroke completed questions on work up to 30 months after starting rehabilitation, and the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P, Frequency, Restrictions and Satisfaction scales) up to 24 months. Baseline characteristics of patients with and without paid employment at 30 months were compared using Fisher’s Exact Tests and Mann-Whitney U Tests. USER-P scores over time were analysed using Linear Mixed Models. RESULTS:Of the 170 included patients (median age 54.2 interquartile range 11.2 years; 40% women) 50.6% reported paid employment at 30 months. Those returning to work reported at baseline more working hours, better quality of life and communication, were more often self-employed and in an office job. The USER-P scores did not change statistically significantly over time. CONCLUSION:About half of the stroke patients remained in paid employment. Optimizing interventions for returning to work and achieving meaningful participation outside of employment seem desirable.

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Stroke survivors' long-term participation in paid employment

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Gaining insights into dwelling characteristics using machine learning for policy making on nearly zero-energy buildings with the use of smart meter and weather data

Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.

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Gaining insights into dwelling characteristics using machine learning for policy making on nearly zero-energy buildings with the use of smart meter and weather data

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Collective Empowerment through Local Memory Websites: Balancing between group interest and common good

The research in this dissertation explores the social significance of local memory websites. Local memory websites offer local residents a platform where they collect and share memories about particular places or experiences in their neighbourhoods and districts. Following a systematic review and a broad field study, a narrative approach is developed to study collective levels of empowerment within the ‘Memory of East’ and the ‘Memory of West’, both in Amsterdam. Two empirical questions steer a double case study: 1) ‘How does the organizational development influence the online dynamics?’ and 2) ‘What collective empowerment do the online dynamics express?’ With its stronger social capital, the Memory of East is more likely to resist official memory intuitions, commercial popular culture and local politics than the Memory of West. On the other hand, with its more inclusive character, the Memory of West is more representative for the broad cultural backgrounds of its inhabitants than the Memory of East. These findings are shown to be related to five organizational continuums on which both websites are plotted to indicate their crucial organizational differences. Apart from a claim about the theoretical value of this model, it is illustrated how it functions as a discursive tool for the core groups behind both websites.

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Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture

A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.

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Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture

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The development of the MIBBO

Offering physical activities matching with the preferences of residents in long-term care facilities could increase compliance and contribute to client-centered care. A measure to investigate meaningful activities by using a photo-interview has been developed (“MIBBO”). In two pilot studies including 133 residents living on different wards in long-term care facilities, feasibility, most chosen activities, and consistency of preferences were investigated. It was possible to conduct the MIBBO on average in 30 min with the majority (86.4%) of residents. The most frequently chosen activities were: gymnastics and orchestra (each 28%), preparing a meal (31%), walking (outside, 33%), watering plants (38%), and feeding pets (40%). In a retest one week after the initial interview 69.4% agreement of chosen activities was seen. The MIBBO seems a promising measure to help health care professionals in identifying residents’ preferred activities. Future research should focus on the implementation of the tailored activity plan, incorporating it into the daily routine.

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Products 867

Sit down and read on: working memory and long-term memory particle-verb processing

Moments to remember

Model-predictive space heating control for energy flexibility – a case study using a long short-term memory neural network surrogate model and a genetic optimization algorithm

Mapping an Emerging Field: Local Memory Websites

Stroke survivors' long-term participation in paid employment

Gaining insights into dwelling characteristics using machine learning for policy making on nearly zero-energy buildings with the use of smart meter and weather data

Collective Empowerment through Local Memory Websites: Balancing between group interest and common good

Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture

The development of the MIBBO

Projects 2

AM-SMART: Additive Manufacturing of Smart Bone Implants

Knowledge and Creativity European University (KreativEU)

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Products 867

Sit down and read on: working memory and long-term memory particle-verb processing

Moments to remember

Model-predictive space heating control for energy flexibility – a case study using a long short-term memory neural network surrogate model and a genetic optimization algorithm

Mapping an Emerging Field: Local Memory Websites

Stroke survivors' long-term participation in paid employment

Gaining insights into dwelling characteristics using machine learning for policy making on nearly zero-energy buildings with the use of smart meter and weather data

Collective Empowerment through Local Memory Websites: Balancing between group interest and common good

Sentiment polarity classification of corporate review data with a bidirectional Long-Short Term Memory (biLSTM) neural network architecture

The development of the MIBBO

Projects 2

AM-SMART: Additive Manufacturing of Smart Bone Implants

Knowledge and Creativity European University (KreativEU)