14,134 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

    GeoYCSB: A Benchmark Framework for the Performance and Scalability Evaluation of Geospatial NoSQL Databases

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    The proliferation of geospatial applications has tremendously increased the variety, velocity, and volume of spatial data that data stores have to manage. Traditional relational databases reveal limitations in handling such big geospatial data, mainly due to their rigid schema requirements and limited scalability. Numerous NoSQL databases have emerged and actively serve as alternative data stores for big spatial data. This study presents a framework, called GeoYCSB, developed for benchmarking NoSQL databases with geospatial workloads. To develop GeoYCSB, we extend YCSB, a de facto benchmark framework for NoSQL systems, by integrating into its design architecture the new components necessary to support geospatial workloads. GeoYCSB supports both microbenchmarks and macrobenchmarks and facilitates the use of real datasets in both. It is extensible to evaluate any NoSQL database, provided they support spatial queries, using geospatial workloads performed on datasets of any geometric complexity. We use GeoYCSB to benchmark two leading document stores, MongoDB and Couchbase, and present the experimental results and analysis. Finally, we demonstrate the extensibility of GeoYCSB by including a new dataset consisting of complex geometries and using it to benchmark a system with a wide variety of geospatial queries: Apache Accumulo, a wide-column store, with the GeoMesa framework applied on top

    Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

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    The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.Comment: Under Review at ACM Computing Survey

    Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles

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    This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Impact of language skills and system experience on medical information retrieval

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    What works to reduce equality gaps for disabled students in higher education: Rapid Combined Evidence Review

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    This evidence review explores what works to reduce equality gaps for disabled students in higher education (HE) through an extensive systematic review of the academic literature, engagement with expert stakeholders, and analysis of institutional data

    Pharmacists’ perceptions of pharmaceutical services in asthma inhaled medication in Surabaya: A mixed-method study

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    Understanding pharmacists’ perceptions is important because pharmacists play an important role in asthma management. Asthma treatment is divided into controller and reliever, and most asthma treatments are administered via the inhalation route. This study aimed to determine pharmacists’ perceptions of pharmaceutical services in asthma inhaled medication in pharmacies in Surabaya. This research is a mixed-method research with an explanatory sequential research design in pharmacies in the city of Surabaya, East Java, from September to November 2017. Sample collection used purposive sampling and was analyzed descriptively. The research sample was 53 pharmacists. Most of the respondents had a high level (41.5%) based on the total questionnaire on the role of pharmacists in pharmaceutical services in treatment with asthma relievers and controller inhalers, while only a few respondents had a very high level (5%). Pharmacists also feel their role in aspects of community pharmacy, such as their role in increasing asthma patient compliance and their role in improving asthma patient control. This can be seen in pharmacists who motivate patients to keep taking the medication so that this can improve asthma patient compliance in using asthma drugs
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