7,205 research outputs found

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures

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    Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation. Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents. In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    An Exploration of the Suitability of Pharmacy Education in Saudi Arabia to Prepare Graduates to Meet Healthcare Needs: a Mixed-Methods Study

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    The key role of pharmacists within the health system, particularly in optimising safe, responsible and effective use of medicines, underpins the demand for a highly skilled and competent workforce. Therefore, developing the capacity of pharmacists to attain and maintain essential competencies relevant to the population’s health needs is required to ensure a high standard of patient care, thereby helping to improve patient and population health. In Saudi Arabia, little evidence exists regarding the assessment of national educational programmes’ structure and outcomes. Moreover, no national competency framework exists for pharmacists in any sector or stage of practice. In the absence of such core quality elements to inform pharmacy education assessment and development, the extent to which pharmacy schools in Saudi Arabia prepare competent pharmacists to address societal needs from pharmacy services is unclear. Therefore, this study aimed to explore the extent to which pharmacy education can prepare competent pharmacists to address the healthcare needs for pharmacy practice in Saudi Arabia. An exploratory sequential mixed methods research design was used to address the aim of this study in three phases: individual interviews and focus groups were employed with a purposively selected sample of pharmacy policy makers, pharmacists and the public to explore societal healthcare needs and the roles required of pharmacists to meet those needs; a national online survey of pharmacists and an online nominal group consensus method of pharmacy experts were used to identify competencies considered essential to develop a profession-wide national foundation level competency framework; and a case study in which curriculum mapping of two purposively selected Doctor of Pharmacy (PharmD) curricula was used to assess the extent to which the current pharmacy programme in Saudi Arabia meets the identified competencies of the developed national competency framework. Based on qualitative and quantitative analyses of societal healthcare needs, pharmacists’ roles, core competencies and curricular contents within the local context of Saudi Arabia, findings showed that there is a mismatch between initial education and real practice needs and expectations. While the country’s current needs from pharmacists are to optimise health system capacity and increase access to primary care services and medicines expertise in community pharmacies, the study indicated local education is product-oriented with a focus of curricular content and experiential training opportunities in most schools on preparing future pharmacists for hospital pharmacy practice. The study also identified several gaps between current initial education programmes and the competencies required to practise the expected roles, suggesting that current initial education might not prepare the students sufficiently to provide the full range of quality pharmaceutical services as per the country’s pharmacy practice needs. The study provided a new understanding of graduates’ readiness to practise as per the country’s pharmacy practice needs, the quality of educational programmes and pharmacists' professional development opportunities in Saudi Arabia. Findings maybe used to inform the development of competency-based education and maximise graduates’ capacity to deliver and develop pharmaceutical services effectively to best meet societal healthcare needs in Saudi Arabia

    Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks

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    The commercial use of Machine Learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes Intellectual Property Protection (IPP) of trained models a pressing issue. Unlike other domains that can build on a solid understanding of the threats, attacks and defenses available to protect their IP, the ML-related research in this regard is still very fragmented. This is also due to a missing unified view as well as a common taxonomy of these aspects. In this paper, we systematize our findings on IPP in ML, while focusing on threats and attacks identified and defenses proposed at the time of writing. We develop a comprehensive threat model for IP in ML, categorizing attacks and defenses within a unified and consolidated taxonomy, thus bridging research from both the ML and security communities

    Exploring Potential Domains of Agroecological Transformation in the United States

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    There is now substantial evidence that agroecology constitutes a necessary pathway towards socially just and ecologically resilient agrifood systems. In the United States, however, agroecology remains relegated to the margins of research and policy spaces. This dissertation explores three potential domains of agroecological transformation in the US. Domains of transformation are sites of contestation in which agroecology interfaces with the industrial agrifood system; these material and conceptual spaces may point to important pathways for scaling agroecology. To explore this concept, I examine formal agroecology education (Chapter 1), extension services and statewide discourses around soil health (Chapter 2), and models of farmland access not based on private property (Chapter 3). While these constitute three distinct topics, I seek to demonstrate that they are linked by similar forces that enable and constrain the extent to which these domains can be sites of agroecological transformation. First, I use case study methodology to explore the evolution of an advanced undergraduate agroecology course at the University of Vermont. I examine how course content and pedagogy align with a transformative framing of agroecology as inherently transdisciplinary, participatory, action-oriented, and political. I find that student-centered pedagogies and experiential education on farms successfully promote transformative learning whereby students shift their understanding of agrifood systems and their role(s) within them. In my second chapter, I zoom out to consider soil health discourses amongst farmers and extension professionals in Vermont. Using co-created mental models and participatory analysis, I find that a singular notion of soil health based on biological, chemical, and physical properties fails to capture the diverse ways in which farmers and extension professionals understand soil health. I advocate for a principles-based approach to soil health that includes social factors and may provide a valuable heuristic for mobilizing knowledge towards agroecology transition pathways. My third chapter, conducted in collaboration with the national non-profit organization Agrarian Trust, considers equitable farmland access. Through semi-structured interviews with 13 farmers and growers across the US, I explore both farmer motivations for engaging with alternative land access models (ALAMs) and the potential role(s) these models may play within broader transformation processes. I argue that ALAMs constitute material and conceptual ‘third spaces’ within which the private property regime is challenged and new identities and language around land ownership can emerge; as such, ALAMs may facilitate a (re)imagining of land-based social-ecological relationships. I conclude the dissertation by identifying conceptual and practical linkages across the domains explored in Chapters 1-3. I pay particular attention to processes that challenge neoliberal logics, enact plural ways of knowing, and prefigure just futures. In considering these concepts, I apply an expansive notion of pedagogy to explore how processes of teaching and (un)learning can contribute to cultivating foundational capacities for transition processes

    A direct-laser-written heart-on-a-chip platform for generation and stimulation of engineered heart tissues

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    In this dissertation, we first develop a versatile microfluidic heart-on-a-chip model to generate 3D-engineered human cardiac microtissues in highly-controlled microenvironments. The platform, which is enabled by direct laser writing (DLW), has tailor-made attachment sites for cardiac microtissues and comes with integrated strain actuators and force sensors. Application of external pressure waves to the platform results in controllable time-dependent forces on the microtissues. Conversely, oscillatory forces generated by the microtissues are transduced into measurable electrical outputs. After characterization of the responsivity of the transducers, we demonstrate the capabilities of this platform by studying the response of cardiac microtissues to prescribed mechanical loading and pacing. Next, we tune the geometry and mechanical properties of the platform to enable parametric studies on engineered heart tissues. We explore two geometries: a rectangular seeding well with two attachment sites, and a stadium-like seeding well with six attachment sites. The attachment sites are placed symmetrically in the longitudinal direction. The former geometry promotes uniaxial contraction of the tissues; the latter additionally induces diagonal fiber alignment. We systematically increase the length for both configurations and observe a positive correlation between fiber alignment at the center of the microtissues and tissue length. However, progressive thinning and “necking” is also observed, leading to the failure of longer tissues over time. We use the DLW technique to improve the platform, softening the mechanical environment and optimizing the attachment sites for generation of stable microtissues at each length and geometry. Furthermore, electrical pacing is incorporated into the platform to evaluate the functional dynamics of stable microtissues over the entire range of physiological heart rates. Here, we typically observe a decrease in active force and contraction duration as a function of frequency. Lastly, we use a more traditional ?TUG platform to demonstrate the effects of subthreshold electrical pacing on the rhythm of the spontaneously contracting cardiac microtissues. Here, we observe periodic M:N patterns, in which there are ? cycles of stimulation for every ? tissue contractions. Using electric field amplitude, pacing frequency, and homeostatic beating frequencies of the tissues, we provide an empirical map for predicting the emergence of these rhythms

    Computational methods for 3D imaging of neural activity in light-field microscopy

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    Light Field Microscopy (LFM) is a 3D imaging technique that captures spatial and angular information from light in a single snapshot. LFM is an appealing technique for applications in biological imaging due to its relatively simple implementation and fast 3D imaging speed. For instance, LFM can help to understand how neurons process information, as shown for functional neuronal calcium imaging. However, traditional volume reconstruction approaches for LFM suffer from low lateral resolution, high computational cost, and reconstruction artifacts near the native object plane. Therefore, in this thesis, we propose computational methods to improve the reconstruction performance of 3D imaging for LFM with applications to imaging neural activity. First, we study the image formation process and propose methods for discretization and simplification of the LF system. Typical approaches for discretization are performed by computing the discrete impulse response at different input locations defined by a sampling grid. Unlike conventional methods, we propose an approach that uses shift-invariant subspaces to generalize the discretization framework used in LFM. Our approach allows the selection of diverse sampling kernels and sampling intervals. Furthermore, the typical discretization method is a particular case of our formulation. Moreover, we propose a description of the system based on filter banks that fit the physics of the system. The periodic-shift invariant property per depth guarantees that the system can be accurately described by using filter banks. This description leads to a novel method to reduce the computational time using singular value decomposition (SVD). Our simplification method capitalizes on the inherent low-rank behaviour of the system. Furthermore, we propose rearranging our filter-bank model into a linear convolution neural network (CNN) that allows more convenient implementation using existing deep-learning software. Then, we study the problem of 3D reconstruction from single light-field images. We propose the shift-invariant-subspace assumption as a prior for volume reconstruction under ideal conditions. We experimentally show that artifact-free reconstruction (aliasing-free) is achievable under these settings. Furthermore, the tools developed to study the forward model are exploited to design a reconstruction algorithm based on ADMM that allows artifact-free 3D reconstruction for real data. Contrary to traditional approaches, our method includes additional priors for reconstruction without dramatically increasing the computational complexity. We extensively evaluate our approach on synthetic and real data and show that our approach performs better than conventional model-based strategies in computational time, image quality, and artifact reduction. Finally, we exploit deep-learning techniques for reconstruction. Specifically, we propose to use two-photon imaging to enhance the performance of LFM when imaging neurons in brain tissues. The architecture of our network is derived from a sparsity-based algorithm for reconstruction named Iterative Shrinkage and Thresholding Algorithm (ISTA). Furthermore, we propose a semi-supervised training based on Generative Adversarial Neural Networks (GANs) that exploits the knowledge of the forward model to achieve remarkable reconstruction quality. We propose efficient architectures to compute the forward model using linear CNNs. This description allows fast computation of the forward model and complements our reconstruction approach. Our method is tested under adverse conditions: lack of training data, background noise, and non-transparent samples. We experimentally show that our method performs better than model-based reconstruction strategies and typical neural networks for imaging neuronal activity in mammalian brain tissue. Our approach enjoys both the robustness of the model-based methods and the reconstruction speed of deep learning.Open Acces
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