Veiledning og kollektiv tenking i ungdomstrinnssatsingen

Akseptert fagfellevurdert versjon (postprint).Dette er manusversjonen av artikkelen Lekang, T. (2015). Veiledning og kollektiv tenking i ungdomstrinnssatsingen. Psykologi i kommunen, 50(2), 49-58. http://www.fpkf.no/tidsskrift

Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control

Classical results in neural network approximation theory show how arbitrary continuous functions can be approximated by networks with a single hidden layer, under mild assumptions on the activation function. However, the classical theory does not give a constructive means to generate the network parameters that achieve a desired accuracy. Recent results have demonstrated that for specialized activation functions, such as ReLUs and some classes of analytic functions, high accuracy can be achieved via linear combinations of randomly initialized activations. These recent works utilize specialized integral representations of target functions that depend on the specific activation functions used. This paper defines mollified integral representations, which provide a means to form integral representations of target functions using activations for which no direct integral representation is currently known. The new construction enables approximation guarantees for randomly initialized networks for a variety of widely used activation functions

Strengthening professionalism through cooperative learning

Author's accepted version (post-print).This is an Accepted Manuscript of an article published by Taylor & Francis in Professional Development in Education on 11/10/2017, available online: http://wwww.tandfonline.com/10.1080/19415257.2017.1376223.Available from 12/04/2019.This article focuses on the processes that come into play as part of a school development project and how these processes contribute to strengthening teacher professionalism. Through processes of consciousness raising and the development of learning cultures where tacit knowledge becomes explicit and shared, and new practices are tested out and discussed, teacher professionalism is developed further. The study shows that this kind of school development work gives the teachers ownership of the development process and strengthens their consciousness about their own teaching as well as develops the learning cultures at the school. Thus, this development becomes an important way in which to reinforce the teachers’ professional knowledge, responsibility and autonomy.acceptedVersio

FDAPT: Federated Domain-adaptive Pre-training for Language Models

Combining Domain-adaptive Pre-training (DAPT) with Federated Learning (FL) can enhance model adaptation by leveraging more sensitive and distributed data while preserving data privacy. However, few studies have focused on this method. Therefore, we conduct the first comprehensive empirical study to evaluate the performance of Federated Domain-adaptive Pre-training (FDAPT). We demonstrate that FDAPT can maintain competitive downstream task performance to the centralized baseline in both IID and non-IID situations. Furthermore, we propose a novel algorithm, Frozen Federated Domain-adaptive Pre-training (FFDAPT). FFDAPT improves the computational efficiency by 12.1% on average and exhibits similar downstream task performance to standard FDAPT, with general performance fluctuations remaining less than 1%. Finally, through a critical evaluation of our work, we identify promising future research directions for this new research area.Comment: 6 page

Grid-connected cabin preheating of Electric Vehicles in cold climates – A non-flexible share of the EV energy use

The number of EVs is increasing globally. In cold climates, it is generally recommended to use electricity from the grid to preheat the EV cabin before using the car, to extend driving ranges, to ensure comfort, and for safety. A majority of such preheating sessions are happening in the morning hours during the winter, when there is also a high demand for other energy use. It is thus important to understand the power loads for grid-connected preheating of EV cabins. This work presents an experimental study, with 51 preheating sessions of five typical EV models during different outdoor temperatures. The results of the study showed that during the preheating sessions, most of the EVs had a power use of between 3 and 8 kW initially, which was reduced to about 2 to 4 kW after a 10 to 20 min initial period. For most of the sessions, the preheating lasted between 15 and 45 min. The preheating energy use was found to be up to 2 kWh for most EVs, with a maximum of 5 kWh. Multiple linear regression models were developed, to investigate the relationship between various variables and the energy use for preheating. Finally, hourly energy loads for EV cabin preheating were compared to other energy loads in apartment buildings. The power and energy loads for preheating EV cabins are affected by a number of parameters, such as the specific EV, charge point, preheating duration, temperature levels, and user habits.publishedVersio

Veiledning og kollektiv tenking i ungdomstrinnssatsingen

Akseptert fagfellevurdert versjon (postprint).Dette er manusversjonen av artikkelen Lekang, T. (2015). Veiledning og kollektiv tenking i ungdomstrinnssatsingen. Psykologi i kommunen, 50(2), 49-58. http://www.fpkf.no/tidsskrift

Rettslige rammer for vilkår etter sosialtjenesteloven §§ 20 og 20a

Oppgaven redegjør for hvilke rettslige rammer som gjelder for vilkårsstillelse til vedtak om stønad til livsopphold

Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations

Recently, Graph Neural Networks (GNNs) have shown promising performance in tasks on dynamic graphs such as node classification, link prediction and graph regression. However, few work has studied the temporal edge regression task which has important real-world applications. In this paper, we explore the application of GNNs to edge regression tasks in both static and dynamic settings, focusing on predicting food and agriculture trade values between nations. We introduce three simple yet strong baselines and comprehensively evaluate one static and three dynamic GNN models using the UN Trade dataset. Our experimental results reveal that the baselines exhibit remarkably strong performance across various settings, highlighting the inadequacy of existing GNNs. We also find that TGN outperforms other GNN models, suggesting TGN is a more appropriate choice for edge regression tasks. Moreover, we note that the proportion of negative edges in the training samples significantly affects the test performance. The companion source code can be found at: https://github.com/scylj1/GNN_Edge_Regression.Comment: 12 pages, 4 figures, 4 table

Challenges and emerging technical solutions in on-growing salmon farming

Farming of Atlantic salmon has grown rapidly from its start in the early 1970s until today, with production approaching two million tonnes. Sea cages are the dominant production system for the on-growing stage of salmon farming. It represents an effective production system with lower investment and running costs than land-based systems. The development and improvement of the sea cage farming system has been one of the most important factors for the growth of the salmon farming industry. However, during recent years certain problems related to their placement in the open marine environment have proved highly challenging, increasing operating costs and impacting on industry public relations. The problems are mainly due to parasites, diseases and escape of fish. In this article, emerging technical solutions for solving those problems are described