Bandwidth Allocation for a Revenue-Aware Network Utility Maximisation

Published versio

Performance analysis of single-link system with nonlinear equivalent capacity

Published versio

Demand management for telecommunications services

Accepted versio

Reinforcement learning for resource allocation in LEO satellite networks

Published versio

Concept-level knowledge visualization for supporting self-regulated learning

Mastery Grids is an intelligent interface that provides access to different kinds of practice content for an introductory programming course. A distinctive feature of the interface is a parallel topic-level visualization of student progress and the progress of their peers. This contribution presents an extended version of the original system that features a finegrained visualization of student knowledge on the level of the detailed concepts that are associated with the course. The student model is based on a Bayesian-network which is built using students performance history in the learning activities. Copyright held by the owner/author(s)

Wavelet probabilistic neural networks

In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independent of the number of data inputs, and in that sense, it overcomes the well-known drawback of traditional probabilistic neural networks (PNNs). Since the parameters of the proposed network are updated at a low and constant computational cost, it is particularly aimed at data stream classification and anomaly detection in off-line settings and online environments where the length of data is assumed to be unconstrained. Both synthetic and real-world datasets are used to assess the proposed WPNN. Significant performance enhancements are attained compared to state-of-the-art algorithms

COVID-19 pandemic in Panama: lessons of the unique risks and research opportunities for Latin America

The Republic of Panama has the second most unequally distributed wealth in Central America, has recently entered the list of countries affected by the COVID-19 pandemic, and has one of the largest testing rate per inhabitant in the region and consequently the highest incidence rate of COVID-19, making it an ideal location to discuss potential scenarios for assessing epidemic preparedness, and to outline research opportunities in the Region of the Americas. We address two timely important questions: What are the unique risks of COVID-19 in Panama that could help other countries in the Region be better prepared? And what kind of scientific knowledge can Panama contribute to the regional and global study of COVID-19? This paper provides suggestions about how the research community could support local health authorities plan for different scenarios and decrease public anxiety. It also presents basic scientific opportunities about emerging pandemic pathogens towards promoting global health from the perspective of a middle income countryThe Republic of Panama has the second most unequally distributed wealth in Central America, has recently entered the list of countries affected by the COVID-19 pandemic, and has one of the largest testing rate per inhabitant in the region and consequently the highest incidence rate of COVID-19, making it an ideal location to discuss potential scenarios for assessing epidemic preparedness, and to outline research opportunities in the Region of the Americas. We address two timely important questions: What are the unique risks of COVID-19 in Panama that could help other countries in the Region be better prepared? And what kind of scientific knowledge can Panama contribute to the regional and global study of COVID-19? This paper provides suggestions about how the research community could support local health authorities plan for different scenarios and decrease public anxiety. It also presents basic scientific opportunities about emerging pandemic pathogens towards promoting global health from the perspective of a middle income countr

Transcription factor Pebbled/RREB1 regulates injury-induced axon degeneration

Genetic studies of Wallerian degeneration have led to the identification of signaling molecules (e.g., dSarm/Sarm1, Axundead, and Highwire) that function locally in axons to drive degeneration. Here we identify a role for the Drosophila C2H2 zinc finger transcription factor Pebbled [Peb, Ras-responsive element binding protein 1 (RREB1) in mammals] in axon death. Loss of Peb in Drosophila glutamatergic sensory neurons results in either complete preservation of severed axons, or an axon death phenotype where axons fragment into large, continuous segments, rather than completely disintegrate. Peb is expressed in developing and mature sensory neurons, suggesting it is required to establish or maintain their competence to undergo axon death. peb mutant phenotypes can be rescued by human RREB1, and they exhibit dominant genetic interactions with dsarm mutants, linking peb/RREB1 to the axon death signaling cascade. Surprisingly, Peb is only able to fully block axon death signaling in glutamatergic, but not cholinergic sensory neurons, arguing for genetic diversity in axon death signaling programs in different neuronal subtypes. Our findings identify a transcription factor that regulates axon death signaling, and peb mutant phenotypes of partial fragmentation reveal a genetically accessible step in axon death signaling

Navigation support in complex open learner models: assessing visual design alternatives

Open Learner Models are used in modern e-learning to show system users the content of their learner models. This approach is known to prompt reflection, facilitate planning and navigation. Open Learner Models may show different levels of detail of the underlying learner model, and may structure the information differently. However, a trade-off exists between useful information and the complexity of the information. This paper investigates whether offering richer information is assessed positively by learners and results in more effective support for learning tasks. An interview pre-study revealed which information within the complex learner model is of interest. A controlled user study examined six alternative visualisation prototypes of varying complexity and resulted in the implementation of one of the designs. A second controlled study involved students interacting with variations of the visualisation while searching for suitable learning material, and revealed the value of the design alternative and its variations. The work contributes to developing complex open learner models by stressing the need to balance complexity and support. It also suggests that the expressiveness of open learner models can be improved with visual elements that strategically summarise the complex information being displayed in detail