889 resultados para Learn-to-learn
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Peer reviewed
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General note: Title and date provided by Bettye Lane.
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General note: Title and date provided by Bettye Lane.
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Insecticide treated bed nets and indoor residual spraying are the most widely used vector control methods in Africa. The World Health Organization now recommends four classes of insecticides for use against adult mosquitoes in public health programs. Of these four classes of insecticides, pyrethroids have become the insecticides of choice in treating mosquito bed nets and in the use of indoor spraying to prevent malaria transmission. Pyrethroids are not only used in malaria control but also in agriculture to protect against pest insects. This concurrent use of pyrethroids in vector control and protection of crops from pests in agriculture may exert selection pressure on mosquito larval population and induce resistance to this class of insecticides. The main objective of our study was to explore the role of agricultural chemicals and the response of mosquitoes to pyrethroids in an area of high malaria transmission.
We used a cross-sectional study design. This was a two-step study involving both mosquitoes and human subjects. In this study, we collected larvae growing in breeding sites affected by different agricultural practices. We used purposive sampling to identify active mosquito breeding sites and then interviewed households adjacent to those breeding sites to learn about their agricultural practices that might influence the response of mosquitoes to pyrethroids. We also performed secondary analysis of larval data from a previous case-control study by Obala et al.
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Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.
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Distributed Computing frameworks belong to a class of programming models that allow developers to
launch workloads on large clusters of machines. Due to the dramatic increase in the volume of
data gathered by ubiquitous computing devices, data analytic workloads have become a common
case among distributed computing applications, making Data Science an entire field of
Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,
a sequence of operations they wish to apply on this dataset, and some constraint they may have
related to their work (performances, QoS, budget, etc). However, it is actually extremely
difficult, without domain expertise, to perform data science. One need to select the right amount
and type of resources, pick up a framework, and configure it. Also, users are often running their
application in shared environments, ruled by schedulers expecting them to specify precisely their resource
needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and
profiling are hard, high dimensional problems that block users from making the right
configuration choices and determining the right amount of resources they need. Paradoxically, the
system is gathering a large amount of monitoring data at runtime, which remains unused.
In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit
monitoring data to learn about workloads, and process user requests into a tailored execution
context. In this work, we study different techniques that have been used to make steps toward
such system awareness, and explore a new way to do so by implementing machine learning
techniques to recommend a specific subset of system configurations for Apache Spark applications.
Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight
the complexity in choosing the best one for a given workload.
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Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.
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Background: Healthcare worldwide needs translation of basic ideas from engineering into the clinic. Consequently, there is increasing demand for graduates equipped with the knowledge and skills to apply interdisciplinary medicine/engineering approaches to the development of novel solutions for healthcare. The literature provides little guidance regarding barriers to, and facilitators of, effective interdisciplinary learning for engineering and medical students in a team-based project context. Methods: A quantitative survey was distributed to engineering and medical students and staff in two universities, one in Ireland and one in Belgium, to chart knowledge and practice in interdisciplinary learning and teaching, and of the teaching of innovation. Results: We report important differences for staff and students between the disciplines regarding attitudes towards, and perceptions of, the relevance of interdisciplinary learning opportunities, and the role of creativity and innovation. There was agreement across groups concerning preferred learning, instructional styles, and module content. Medical students showed greater resistance to the use of structured creativity tools and interdisciplinary teams. Conclusions: The results of this international survey will help to define the optimal learning conditions under which undergraduate engineering and medicine students can learn to consider the diverse factors which determine the success or failure of a healthcare engineering solution.
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We investigated whether children’s inhibitory control is associated with their ability to produce irregular verb forms as well as learn from corrective feedback following their use of an over-regularized form. Forty-eight 3.5 to 4.5 year old children were tested on the irregular past tense and provided with adult corrective input via models of correct use or recasts of errors following ungrammatical responses. Inhibitory control was assessed with a three-item battery of tasks that required suppressing a prepotent response in favor of a non-canonical one. Results showed that inhibitory control was predictive of children’s initial production of irregular forms and not associated with their post-feedback production of irregulars. These findings show that children’s executive functioning skills may be a rate-limiting factor on their ability to produce correct forms, but might not interact with their ability to learn from input in this domain. Findings are discussed in terms of current theories of past-tense acquisition and learning from input more broadly.
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Ageing of the population is a worldwide phenomenon. Numerous ICT-based solutions have been developed for elderly care but mainly connected to the physiological and nursing aspects in services for the elderly. Social work is a profession that should pay attention to the comprehensive wellbeing and social needs of the elderly. Many people experience loneliness and depression in their old age, either as a result of living alone or due to a lack of close family ties and reduced connections with their culture of origin, which results in an inability to participate actively in community activities (Singh & Misra, 2009). Participation in society would enhance the quality of life. With the development of information technology, the use of technology in social work practice has risen dramatically. The aim of this literature review is to map out the state of the art of knowledge about the usage of ICT in elderly care and to figure out research-based knowledge about the usability of ICT for the prevention of loneliness and social isolation of elderly people. The data for the current research comes from the core collection of the Web of Science and the data searching was performed using Boolean? The searching resulted in 216 published English articles. After going through the topics and abstracts, 34 articles were selected for the data analysis that is based on a multi approach framework. The analysis of the research approach is categorized according to some aspects of using ICT by older adults from the adoption of ICT to the impact of usage, and the social services for them. This literature review focused on the function of communication by excluding the applications that mainly relate to physical nursing. The results show that the so-called ‘digital divide’ still exists, but the older adults have the willingness to learn and utilise ICT in daily life, especially for communication. The data shows that the usage of ICT can prevent the loneliness and social isolation of older adults, and they are eager for technical support in using ICT. The results of data analysis on theoretical frames and concepts show that this research field applies different theoretical frames from various scientific fields, while a social work approach is lacking. However, a synergic frame of applied theories will be suggested from the perspective of social work.