449 research outputs found
Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students
In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies.
To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on).
The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts.
This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution.
The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials.
The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world
Modelling complexity and redundancy in endocytic actin polymerisation
Actin is one of the most ubiquitous proteins of life and can form filaments which play crucial roles in a wide range of processes from cell division to intracellular trafficking. Formation of these filament networks is tightly controlled using a wide array of protein types, chief among them being nucleators. Nucleators facilitate the unfavourable first steps of filament formation and thus their regulation dictates when and where filamentous networks are produced. The central proline-rich region of Las17 (yeast homologue of human WASp) is thought to nucleate actin “mother filaments” at the endocytic sites. Arp2/3 – a potent nucleator activated by Las17 – can branch these mother filaments. Proline also constitutes the core binding region of SH3 domains which leaves the nucleating region of Las17 open to competitive regulation. Eleven Las17-binding SH3 domains are recruited to yeast endocytic sites. Five of these bind via a tandem of domains (three SH3s in Sla1 and two SH3s in Bzz1). We hypothesise that this “cloud” of SH3 domains can regulate the access of actin to the proline-rich region of Las17. However, the high number of proteins and interactions involved renders a purely experimental approach challenging.
Throughout this thesis, two agent-based models are built (one being a progression of the other) to test the veracity of our regulatory cloud hypothesis. Binding affinities were experimentally obtained to build the model, demonstrate the power of avidity conferred through tandem SH3 binding, and refine our Las17 nucleating mechanism. We identify that the weak interactions of the SH3 cloud can combine in effect – particularly complemented by the tandem binding of Sla1 and Bzz1 – to define a window of Las17 nucleating activity. This work suggests how endocytic SH3 domains can regulate endocytic progression whilst also furthering our understanding of the relatively unexplored nucleating mechanisms employed by Las1
Educator Professional Development as Rhetorical Situation
Teacher effectiveness is recognized as the most prominent in-school influencer of student learning, and professional development (PD) of in-service educators is seen as vital to improving teachers’ effectiveness throughout their careers. Professional development is often studied atheoretically and with a linear view in which PD providers deliver instruction and teachers receive and apply that instruction as it was delivered to them. By casting them as passive, blank-slate receivers and automatic appliers of the PD, this view obscures the complexities of teachers’ role in PD. Examining educator PD through the lens of rhetoric, and viewing the PD experience as a rhetorical situation, allows us to tease apart the highly connected ecology of roles and text(s) present within any PD situation. Understanding more about the roles teachers take in PD–as PD provider or receiver, and as rhetorical audience and rhetor–opens up opportunities for engaging educators fully in their own and one another’s development.
This collective case study of four educators used interviews and collection and analysis of PD-related Twitter activity in order to discover how the participants embrace, resist, and shift between the roles of PD receiver and provider and the roles of rhetorical audience and rhetor. The resulting study demonstrates that rhetoric acts as a rich lens for bringing to light the ways educators bring their own expertise and experiences to PD activities, make a number of complex choices within those activities for both their own enrichment and the enrichment of others involved, and embrace methods of PD, such as using social media platforms, that give them full access to all roles. The conclusion of this dissertation offers three tools for use by readers: 1) the rhetorical lens constructed in this project and used to view PD as a rhetorical situation; 2) a set of recommendations for educators who wish to seek PD using social media, including both composition methods to try and mindsets for shifting between the rhetorical roles available through social media; and 3) a set of recommendations for those offering PD to educators, with an emphasis on accounting for the complexities of their roles as learners with their own expertise, as audience members with an audience’s inherent power, and as potential rhetors when given access to the role
Ensemble and continual federated learning for classifcation tasks
Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have been successfully applied to real use cases. The vast majority of work in federated learning assumes static datasets and relies on the use of deep neural networks. However, in real world problems, it is common to have a continual data stream, which may be non stationary, leading to phenomena such as concept drift. Besides, there are many multi-device applications where other, non-deep strategies are more suitable, due to their simplicity, explainability, or generalizability, among other reasons. In this paper we present Ensemble and Continual Federated Learning, a federated architecture based on ensemble techniques for solving continual classification tasks. We propose the global federated model to be an ensemble, consisting of several independent learners, which are locally trained. Thus, we enable a flexible aggregation of heterogeneous client models, which may differ in size, structure, or even algorithmic family. This ensemble-based approach, together with drift detection and adaptation mechanisms, also allows for continual adaptation in situations where data distribution changes over time. In order to test our proposal and illustrate how it works, we have evaluated it in different tasks related to human activity recognition using smartphonesOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has received financial support from AEI/FEDER (European Union) Grant Number PID2020-119367RB-I00, as well as the ConsellerĂa de Cultura, EducaciĂłn e Universitade of Galicia (accreditation ED431G-2019/04, ED431G2019/01, and ED431C2018/29), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S
Jornadas Nacionales de InvestigaciĂłn en Ciberseguridad: actas de las VIII Jornadas Nacionales de InvestigaciĂłn en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de InvestigaciĂłn en Ciberseguridad (8ÂŞ. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernizaciĂłn tecnolĂłxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry
United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation.
This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews
A Multi-Agent Architecture for An Intelligent Web-Based Educational System
An intelligent educational system must constitute an adaptive system built on multi-agent system architecture. The multi-agent architecture component provides self-organization, self-direction, and other control functionalities that are crucially important for an educational system. On the other hand, the adaptiveness of the system is necessary to provide customization, diversification, and interactional functionalities. Therefore, an educational system architecture that integrates multi-agent functionality [50] with adaptiveness can offer the learner the required independent learning experience. An educational system architecture is a complex structure with an intricate hierarchal organization where the functional components of the system undergo sophisticated and unpredictable internal interactions to perform its function. Hence, the system architecture must constitute adaptive and autonomous agents differentiated according to their functions, called multi-agent systems (MASs). The research paper proposes an adaptive hierarchal multi-agent educational system (AHMAES) [51] as an alternative to the traditional education delivery method. The document explains the various architectural characteristics of an adaptive multi-agent educational system and critically analyzes the system’s factors for software quality attributes
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest satellite communication (SatCom) missions are characterized by a
fully reconfigurable on-board software-defined payload, capable of adapting
radio resources to the temporal and spatial variations of the system traffic.
As pure optimization-based solutions have shown to be computationally tedious
and to lack flexibility, machine learning (ML)-based methods have emerged as
promising alternatives. We investigate the application of energy-efficient
brain-inspired ML models for on-board radio resource management. Apart from
software simulation, we report extensive experimental results leveraging the
recently released Intel Loihi 2 chip. To benchmark the performance of the
proposed model, we implement conventional convolutional neural networks (CNN)
on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy,
precision, recall, and energy efficiency for different traffic demands. Most
notably, for relevant workloads, spiking neural networks (SNNs) implemented on
Loihi 2 yield higher accuracy, while reducing power consumption by more than
100 as compared to the CNN-based reference platform. Our findings point
to the significant potential of neuromorphic computing and SNNs in supporting
on-board SatCom operations, paving the way for enhanced efficiency and
sustainability in future SatCom systems.Comment: currently under review at IEEE Transactions on Machine Learning in
Communications and Networkin
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