Relevant bibliographies by topics / Surface learning

Academic literature on the topic 'Surface learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Surface learning"

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Uma SV. "STUDENTS’ PERCEPTIONS ON CADAVERIC PAINTING AS A METHOD FOR LEARNING SURFACE ANATOMY." International Journal of Anatomy and Research 8, no. 2.3 (June 5, 2020): 7572–77. http://dx.doi.org/10.16965/ijar.2020.165.

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Chen, Ke-Wei, Laura Bear, and Che-Wei Lin. "Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks." Sensors 22, no. 6 (March 17, 2022): 2331. http://dx.doi.org/10.3390/s22062331.

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Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs’ ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.
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Zhang, Wenhe. "Surface Roughness Prediction with Machine Learning." Journal of Physics: Conference Series 1856, no. 1 (April 1, 2021): 012040. http://dx.doi.org/10.1088/1742-6596/1856/1/012040.

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Wu, Zhaohui, Lu Jiang, Qinghua Zheng, and Jun Liu. "Learning to Surface Deep Web Content." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1967–68. http://dx.doi.org/10.1609/aaai.v24i1.7779.

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We propose a novel deep web crawling framework based on reinforcement learning. The crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and submits a selected action (query) to the environment according to Q-value. Based on the framework we develop an adaptive crawling method. Experimental results show that it outperforms the state of art methods in crawling capability and breaks through the assumption of full-text search implied by existing methods.
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Isikdogan, Furkan, Alan C. Bovik, and Paola Passalacqua. "Surface Water Mapping by Deep Learning." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 11 (November 2017): 4909–18. http://dx.doi.org/10.1109/jstars.2017.2735443.

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Cheng, Jieyu, Adrian V. Dalca, Bruce Fischl, and Lilla Zöllei. "Cortical surface registration using unsupervised learning." NeuroImage 221 (November 2020): 117161. http://dx.doi.org/10.1016/j.neuroimage.2020.117161.

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Winje, Øystein, and Knut Løndal. "Bringing deep learning to the surface." Nordic Journal of Comparative and International Education (NJCIE) 4, no. 2 (July 1, 2020): 25–41. http://dx.doi.org/10.7577/njcie.3798.

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Deep learning is a key term in current educational discourses worldwide and used by researchers, policymakers, stakeholders, politicians, organisations and the media with different definitions and, consequently, much confusion about its meaning and usage. This systematic mapping review attempts to reduce this ambiguity by investigating the definitions of deep learning in 71 research publications on primary and secondary education from 1970 to 2018. The results show two conceptualisations of the term deep learning—1) meaningful learning and 2) transfer of learning—both based on cognitive learning perspectives. The term deep learning is used by researchers worldwide and is mainly investigated in the school subjects of science, languages and mathematics with samples of students between 13 and 16 years of age. Deep learning is also a prevalent term in current international education policy and national curriculum reform, thus deeply affecting the practice of teaching and learning in general education. Our review identifies a lack of studies investigating deep learning through perspectives other than cognitive learning theories and suggests that future research should emphasise applying embodied, affective, and social perspectives on learning in the wide array of school subjects, in lower primary education and in a variety of sociocultural contexts, to support the adaptation of deep learning to a general educational practice.
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Xiong, Shiyao, Juyong Zhang, Jianmin Zheng, Jianfei Cai, and Ligang Liu. "Robust surface reconstruction via dictionary learning." ACM Transactions on Graphics 33, no. 6 (November 19, 2014): 1–12. http://dx.doi.org/10.1145/2661229.2661263.

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Ologunagba, Damilola, and Shyam Kattel. "Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces." Energies 13, no. 9 (May 1, 2020): 2182. http://dx.doi.org/10.3390/en13092182.

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Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.
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T.V., Bijeesh. "Evaluation of Machine Learning Algorithms for Surface Water Delineation Using Landsat 8 Images." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (March 20, 2020): 207–16. http://dx.doi.org/10.5373/jardcs/v12i3/20201184.

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Dissertations / Theses on the topic "Surface learning"

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Guler, Riza Alp. "Learning Image-to-Surface Correspondence." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC024/document.

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Cette thèse se concentre sur le développement demodèles de représentation dense d’objets 3-D àpartir d’images. L’objectif de ce travail estd’améliorer les modèles surfaciques 3-D fournispar les systèmes de vision par ordinateur, enutilisant de nouveaux éléments tirés des images,plutôt que les annotations habituellementutilisées, ou que les modèles basés sur unedivision de l’objet en différents parties.Des réseaux neuronaux convolutifs (CNNs) sontutilisés pour associer de manière dense les pixelsd’une image avec les coordonnées 3-D d’unmodèle de l’objet considéré. Cette méthodepermet de résoudre très simplement unemultitude de tâches de vision par ordinateur,telles que le transfert d’apparence, la localisationde repères ou la segmentation sémantique, enutilisant la correspondance entre une solution surle modèle surfacique 3-D et l’image 2-Dconsidérée. On démontre qu’une correspondancegéométrique entre un modèle 3-D et une imagepeut être établie pour le visage et le corpshumains
This thesis addresses the task of establishing adense correspondence between an image and a 3Dobject template. We aim to bring vision systemscloser to a surface-based 3D understanding ofobjects by extracting information that iscomplementary to existing landmark- or partbasedrepresentations.We use convolutional neural networks (CNNs)to densely associate pixels with intrinsiccoordinates of 3D object templates. Through theestablished correspondences we effortlesslysolve a multitude of visual tasks, such asappearance transfer, landmark localization andsemantic segmentation by transferring solutionsfrom the template to an image. We show thatgeometric correspondence between an imageand a 3D model can be effectively inferred forboth the human face and the human body
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Salehi, Shahin. "Machine Learning for Contact Mechanics from Surface Topography." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76531.

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Le, Jiahui. "Application of Deep-learning Method to Surface Anomaly Detection." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105240.

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In traditional industrial manufacturing, due to the limitations of science and technology, manual inspection methods are still used to detect product surface defects. This method is slow and inefficient due to manual limitations and backward technology. The aim of this thesis is to research whether it is possible to automate this using modern computer hardware and image classification of defects using different deep learning methods. The report concludes, based on results from controlled experiments, that it is possible to achieve a dice coefficient of more than 81%.
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Hamm, Simon, and sinonh@angliss edu au. "Digital Audio Video Assessment: Surface or Deep Learning - An Investigation." RMIT University. Education, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091216.154300.

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This research aims to investigate an assertion, endorsed by a range of commentators, that multimedia teaching and learning approaches encourage learners to adopt a richer, creative and deeper level of understanding and participation within the learning environment than traditional teaching and learning methods. The thesis examines this assertion by investigating one type of multimedia activity defined (for the purposes of this research) as a digital audio video assessment (DAVA). Data was collected using a constructivist epistemology, interpretative and naturalistic perspective using primarily a qualitative methodology. Three types of data collection methods were used to collect data from thirteen Diploma of Event Management students from William Angliss TAFE. Firstly, participants completed the Biggs Study Process Questionnaire (2001) which is a predictor of deep and surface learning preference. Each participant then engaged in a semi-structured interview that elicited participant's self-declared learning preferences and their approaches to completion of the DAVA. These data sources were then compared. Six factors that are critical in informing the way that the participants approached the DAVA emerged from the analysis of the data. Based on these findings it is concluded that the DAVA does not restrict, inhibit or negatively influence a participants learning preference. Learners with a pre-existing, stable learning preference are likely to adopt a learning approach that is consisten t with their preference. Participants that have a learning preference that is less stable (more flexible) may adopt either a surface or deep approach depending on the specific task, activity or assessment.
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Kidd, Joshua. "Detecting Surface Oil Using Unsupervised Learning Techniques on MODIS Satellite Data." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4098.

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The release of crude oil or other petroleum based products into marine habitats can have a devastating impact on the environment as well as the local economies that rely on these waters for commercial fishing and tourism. The Deepwater Horizon catastrophe that started on April 20th 2010 leaked an estimated 4.4 million barrels of crude oil into the Gulf of Mexico over a 3 month period threatening thousands of species and crippling the gulf coast. The National Oceanic and Atmospheric Administration (NOAA) used several satellite remote sensing technologies to manually track and predict the extent and location of oil on the surface of the gulf waters. This thesis proposes a methodology to automatically identify surface oil using an unsupervised clustering algorithm an compares the discovered regions of oil to the reports generated by NOAA during the incident. The fuzzy c-means clustering algorithm is used to partition the satellite image pixels into groups that represent either oil or not oil. A variety of MODIS data features and image analyzing techniques have been explored to produce the most accurate set of regions.
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Shah, Abhay. "Multiple surface segmentation using novel deep learning and graph based methods." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5630.

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The task of automatically segmenting 3-D surfaces representing object boundaries is important in quantitative analysis of volumetric images, which plays a vital role in numerous biomedical applications. For the diagnosis and management of disease, segmentation of images of organs and tissues is a crucial step for the quantification of medical images. Segmentation finds the boundaries or, limited to the 3-D case, the surfaces, that separate regions, tissues or areas of an image, and it is essential that these boundaries approximate the true boundary, typically by human experts, as closely as possible. Recently, graph-based methods with a global optimization property have been studied and used for various applications. Sepecifically, the state-of-the-art graph search (optimal surface segmentation) method has been successfully used for various such biomedical applications. Despite their widespread use for image segmentation, real world medical image segmentation problems often pose difficult challenges, wherein graph based segmentation methods in its purest form may not be able to perform the segmentation task successfully. This doctoral work has a twofold objective. 1)To identify medical image segmentation problems which are difficult to solve using existing graph based method and develop novel methods by employing graph search as a building block to improve segmentation accuracy and efficiency. 2) To develop a novel multiple surface segmentation strategy using deep learning which is more computationally efficient and generic than the exisiting graph based methods, while eliminating the need for human expert intervention as required in the current surface segmentation methods. This developed method is possibly the first of its kind where the method does not require and human expert designed operations. To accomplish the objectives of this thesis work, a comprehensive framework of graph based and deep learning methods is proposed to achieve the goal by successfully fulfilling the follwoing three aims. First, an efficient, automated and accurate graph based method is developed to segment surfaces which have steep change in surface profiles and abrupt distance changes between two adjacent surfaces. The developed method is applied and validated on intra-retinal layer segmentation of Spectral Domain Optical Coherence Tomograph (SD-OCT) images of eye with Glaucoma, Age Related Macular Degneration and Pigment Epithelium Detachment. Second, a globally optimal graph based method is developed to attain subvoxel and super resolution accuracy for multiple surface segmentation problem while imposing convex constraints. The developed method was applied to layer segmentation of SD-OCT images of normal eye and vessel walls in Intravascular Ultrasound (IVUS) images. Third, a deep learning based multiple surface segmentation is developed which is more generic, computaionally effieient and eliminates the requirement of human expert interventions (like transformation designs, feature extrraction, parameter tuning, constraint modelling etc.) required by existing surface segmentation methods in varying capacities. The developed method was applied to SD-OCT images of normal and diseased eyes, to validate the superior segmentaion performance, computation efficieny and the generic nature of the framework, compared to the state-of-the-art graph search method.
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Ellis, David G. "Machine learning improves automated cortical surface reconstruction in human MRI studies." Thesis, University of Iowa, 2017. https://ir.uiowa.edu/etd/5465.

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Analysis of surface models reconstructed from human MR images gives re- searchers the ability to quantify the shape and size of the cerebral cortex. Increasing the reliability of automatic reconstructions would increase the precision and, therefore, power of studies utilizing cortical surface models. We looked at four different workflows for reconstructing cortical surfaces: 1) BAW + LOGIMSOS- B; 2) FreeSurfer + LOGISMOS-B; 3) BAW + FreeSurfer + Machine Learning + LOGISMOS-B; 4) Standard FreeSurfer(Dale et al. 1999). Workflows 1-3 were developed in this project. Workflow 1 utilized both BRAINSAutoWorkup(BAW)(Kim et al. 2015) and a surface reconstruction tool called LOGISMOS-B(Oguz et al. 2014). Workflow 2 added LOGISMOS-B to a custom built FreeSurfer workflow that was highly optimized for parallel processing. Workflow 3 combined workflows 1 and 2 and added random forest classifiers for predicting the edges of the cerebral cortex. These predictions were then fed into LOGISMOS-B as the cost function for graph segmentation. To compare these work- flows, a dataset of 578 simulated cortical volume changes was created from 20 different sets of MR scans. The workflow utilizing machine learning (workflow 3) produced cortical volume changes with the least amount of error when compared to the known volume changes from the simulations. Machine learning can be effectively used to help reconstruct cortical surfaces that more precisely track changes in the cerebral cortex. This research could be used to increase the power of future projects studying correlations between cortical morphometrics and neurological health.
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Fowler, Debra Anne. "Defining and determining the impact of a freshman engineering student's approach to learning (surface versus deep)." Texas A&M University, 2003. http://hdl.handle.net/1969.1/1153.

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When an engineering student attends four or five years of college to become a professional engineer one makes the assumption that they approach this learning process in such a way to gain the most knowledge possible. The purpose of this study is to measure the learning approach (deep versus surface) of first-year engineering students, test the impact of two interventions (journaling and learning strategy awareness) on increasing the deep approach to learning, and determine the relationship of the approach to learning on retention within an engineering program. The study was conducted using a quantitative self-reporting instrument to measure surface and deep learning at the beginning and end of the first and second semesters of the freshman year in an engineering program. Retention was measured as the continuous enrollment of a student in the second semester of the first-year engineering program. Results indicate that the first-year engineering students have a slightly higher level of the deep approach to learning than a surface approach to learning when they begin college. However, the results also indicate that the deep approach to learning decreased during the first semester and during the second semester of their freshman year. A student's approach to learning can be impacted by their prior knowledge, the teaching context, the institutional context or the motivation of the student. Results surrounding the learning strategies intervention also indicate that the first-year engineering students do not possess the strong learning strategies that are anticipated from students accepted into an engineering program with stringent application requirements. Finally, results indicate that a deep approach to learning appears to have a positive relationship and a surface approach to learning appears to have a negative relationship to retention in an engineering program. This study illustrates that incorporating learning theory and the use of current learning strategy measurements contributes to the understanding of a freshman engineering student's approach to learning. The understanding of the engineering student's approach to learning benefits faculty in establishing curriculum and pedagogical design. The benefit to the student is in understanding more about themselves as a learner.
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Niskanen, M. (Matti). "A visual training based approach to surface inspection." Doctoral thesis, University of Oulu, 2003. http://urn.fi/urn:isbn:9514270673.

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Abstract Training a visual inspection device is not straightforward but suffers from the high variation in material to be inspected. This variation causes major difficulties for a human, and this is directly reflected in classifier training. Many inspection devices utilize rule-based classifiers the building and training of which rely mainly on human expertise. While designing such a classifier, a human tries to find the questions that would provide proper categorization. In training, an operator tunes the classifier parameters, aiming to achieve as good classification accuracy as possible. Such classifiers require lot of time and expertise before they can be fully utilized. Supervised classifiers form another common category. These learn automatically from training material, but rely on labels that a human has set for it. However, these labels tend to be inconsistent and thus reduce the classification accuracy achieved. Furthermore, as class boundaries are learnt from training samples, they cannot in practise be later adjusted if needed. In this thesis, a visual based training method is presented. It avoids the problems related to traditional training methods by combining a classifier and a user interface. The method relies on unsupervised projection and provides an intuitive way to directly set and tune the class boundaries of high-dimensional data. As the method groups the data only by the similarities of its features, it is not affected by erroneous and inconsistent labelling made for training samples. Furthermore, it does not require knowledge of the internal structure of the classifier or iterative parameter tuning, where a combination of parameter values leading to the desired class boundaries are sought. On the contrary, the class boundaries can be set directly, changing the classification parameters. The time need to take such a classifier into use is small and tuning the class boundaries can happen even on-line, if needed. The proposed method is tested with various experiments in this thesis. Different projection methods are evaluated from the point of view of visual based training. The method is further evaluated using a self-organizing map (SOM) as the projection method and wood as the test material. Parameters such as accuracy, map size, and speed are measured and discussed, and overall the method is found to be an advantageous training and classification scheme.
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Westell, Jesper. "Multi-Task Learning using Road Surface Condition Classification and Road Scene Semantic Segmentation." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157403.

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Understanding road surface conditions is an important component in active vehicle safety. Estimations can be achieved through image classification using increasingly popular convolutional neural networks (CNNs). In this paper, we explore the effects of multi-task learning by creating CNNs capable of simultaneously performing the two tasks road surface condition classification (RSCC) and road scene semantic segmentation (RSSS). A multi-task network, containing a shared feature extractor (VGG16, ResNet-18, ResNet-101) and two taskspecific network branches, is built and trained using the Road-Conditions and Cityscapes datasets. We reveal that utilizing task-dependent homoscedastic uncertainty in the learning process improvesmulti-task model performance on both tasks. When performing task adaptation, using a small set of additional data labeled with semantic information, we gain considerable RSCC improvements on complex models. Furthermore, we demonstrate increased model generalizability in multi-task models, with up to 12% higher F1-score compared to single-task models.
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Books on the topic "Surface learning"

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Individuals, groups, and organizations beneath the surface: An introduction. London: Karnac, 2006.

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Limbird, A. Surface mine reclamation: Learning from natural revegetation of abandoned mine spoils. S.l: s.n, 1989.

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Laube, Pascal. Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29017-7.

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Kuznecova, Irina, and Mihail Prohorov. Educational research project in physics based on open data. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1242226.

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One of the forms of teaching physics in high schools with a natural science specialization and in the junior courses of universities can be an educational research project. The use of modern open scientific data makes it possible to make the project interesting, modern, relevant and multidisciplinary. The implementation of such a project allows the student to understand some areas of modern scientific research and the relationship between various natural sciences. Direct comparison of the project results with published fundamental research and discussion of the differences obtained are possible. As the first example of a training project, the determination of the frequency of asteroids and large meteorites (of the Tunguska and Chelyabinsk class) falling to Earth by counting craters on the surface of the Moon and Mercury is considered. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in natural science specialties: physics, astronomy, geography, geology, soil science, biology, etc—, and students of engineering and technical specialties of full-time and distance learning.
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Dascano, Mark. Microsoft Surface Heaphones: Learning the Essentials. Independently Published, 2018.

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Dascano, Mark. Surface Laptop 2: Learning the Essentials. Independently Published, 2018.

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Sinyangwe, Michael. Science of Artificial Intelligence - Mastering the Learning Surface. Independently Published, 2019.

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Petty, Ray. On the Surface of Things : Learning Through Print Making. Hodder & Stoughton Educational Division, 1999.

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Burns, Roy, and Joey Farris. One Surface Learning: Applying Rhythmic Patterns to the Drumset. Alfred Publishing Company, 1999.

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Stapley, Lionel. Individuals, Groups, and Organizations Beneath the Surface: An Introduction. Karnac Books, 2006.

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Book chapters on the topic "Surface learning"

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Rodway, Joelle. "Getting beneath the surface." In Networks For Learning, 172–93. Abingdon, Oxon; New York, NY: Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315276649-11.

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Rusu, Radu Bogdan. "Surface and Object Class Learning." In Springer Tracts in Advanced Robotics, 109–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35479-3_8.

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Zhao, Fenqiang, Zhengwang Wu, Li Wang, Weili Lin, Shunren Xia, Dinggang Shen, and Gang Li. "Unsupervised Learning for Spherical Surface Registration." In Machine Learning in Medical Imaging, 373–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7_38.

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Stamp, Mark. "Spatial response surface sampling." In Introduction to Machine Learning with Applications in Information Security, 357–74. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003264873-20.

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Hong, Yoonmi, Sahar Ahmad, Ye Wu, Siyuan Liu, and Pew-Thian Yap. "Vox2Surf: Implicit Surface Reconstruction from Volumetric Data." In Machine Learning in Medical Imaging, 644–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87589-3_66.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Kernel Fisher Envelope Surface for Pattern Recognition." In Intelligent Control and Learning Systems, 101–17. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_7.

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AbstractIt is found that the batch process is more difficultly monitored compared with the continuous process, due to its complex features, such as nonlinearity, non-stable operation, unequal production cycles, and most variables only measured at the end of batch. Traditional methods for batch process, such as multiway FDA (Chen 2004) and multi-model FDA (He et al. 2005), cannot solve these issues well. They require complete batch data only available at the end of a batch. Therefore, the complete batch trajectory must be estimated real time, or alternatively only the measured values at the current moment are used for online diagnosis. Moreover, the above approaches do not consider the problem of inconsistent production cycles.
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Ferri, César, José Hernández-Orallo, and Miguel Angel Salido. "Volume under the ROC Surface for Multi-class Problems." In Machine Learning: ECML 2003, 108–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39857-8_12.

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Shah, Abhay, Michael D. Abramoff, and Xiaodong Wu. "Simultaneous Multiple Surface Segmentation Using Deep Learning." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 3–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67558-9_1.

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Zhang, Wen, and Yalin Wang. "Geometric Brain Surface Network for Brain Cortical Parcellation." In Graph Learning in Medical Imaging, 120–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35817-4_15.

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Gritsenko, Andrey, Zhiyu Sun, Stephen Baek, Yoan Miche, Renjie Hu, and Amaury Lendasse. "Deformable Surface Registration with Extreme Learning Machines." In Proceedings in Adaptation, Learning and Optimization, 304–16. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01520-6_28.

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Conference papers on the topic "Surface learning"

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Harnum, Monalisa, Ali Asmar, Yerizon, and Fridgo Tasman. "The influence of online learning barriers and students’ learning independence on the learning outcomes of class VIII students of SMPN 1 V Koto Kampung Dalam." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122480.

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Dewimarni, Syelfia, and Rizalina. "Android-based statistical learning media design." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122349.

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Fitriani, Yosa, Yerizon, I. Made Arnawa, Ali Asmar, and Nurul Afifah Rusyda. "Development of mathematical learning tools based on discovery learning to improve problem-solving skills in class XI." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122396.

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Afrizal, Ahmad Fauzan, and Ronal Rifandi. "The influence of learning motivation and independence on students' mathematics learning outcomes in SMA UNP laboratory development." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122432.

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Filda, Dara, and Armiati. "Designing hypothetical learning trajectory based on realistic mathematics education in learning reflection using motif of batik Riau." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122417.

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Rahayu, Ninik Puspita, and Ahmad Fauzan. "The influence of motivation and independence of learning on the mathematics student learning outcomes of class VIII SMPN 12 Padang." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122435.

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Yendra, Novi, Ahmad Fauzan, and Beni Junedi. "Development of problem based learning (PBL) learning tools to improve mathematical problem solving ability of class VIII SMP/MTs students." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122385.

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Fitriani, Silvia, Ali Asmar, and I. Made Arnawa. "Development of learning tools based on learning cycle 7E assisted by mind mapping for problem solving for junior high school students." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122562.

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Yerizon, Nilma Wulandari Wahyuni,, and Fridgo Tasman. "The influence of teachers’ learning methods and teaching methods on mathematics learning achievements of class VII students of SMP Hikmah Padang Panjang." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122383.

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Yandri, Tricky, Ahmad Fauzan, Yerizon, and Nurul Afifah Rusyda. "The effect of student learning methods and teacher teaching methods on mathematics learning achievements of students grade VIII at SMPN 2 Pasaman Barat." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0122421.

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Reports on the topic "Surface learning"

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Zouabe, J., M. Zavarin, and H. Wainwright. Machine Learning in Environmental Chemistry: Application to Surface Complexation Modeling. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1669226.

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Geza, Mangistu, T. Tesfa, Liangping Li, and M. Qiao. Toward Hybrid Physics -Machine Learning to improve Land Surface Model predictions. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769785.

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Mishra, Umakant, and Sagar Gautam. Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1891184.

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Levy, Brian. How ‘Soft Governance’ Can Help Improve Learning Outcomes. Research on Improving Systems of Education (RISE), February 2023. http://dx.doi.org/10.35489/bsg-rise-ri_2023/053.

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On the surface, global gains in educating children have been remarkable. Access has expanded enormously. So, too, has knowledge about ‘best practices’—both education-sector-specific knowledge about how students learn and successful teachers teach, and knowledge about ‘best practice’ arrangements for governing education systems. Yet the combination of access and knowledge has not translated into broad-based gains in learning outcomes. Why? In seeking to address this question, a useful point of departure is the 2018 Learning World Development Report’s distinction between proximate and underlying causes of learning shortfalls. Proximate causes include the skills and motivations of teachers, the quality of school management, the available of other inputs used in schools, and the extent to which learners come to school prepared to learn. Underlying these are the governance arrangements through which these inputs are deployed. Specialist knowledge on the proximate drivers of learning outcomes can straightforwardly be applied in countries where governance works well. However, in countries where the broader governance context is less supportive, specialist sector-specific interventions to support learning are less likely to add value. In these messy governance contexts, knowledge about the governance and political drivers of policymaking and implementation can be an important complement to sector-specific expertise. To help uncover new ways of improving learning outcomes (including in messy governance contexts), the Research on Improving Systems of Education (RISE) Programme has championed a broad-ranging, interdisciplinary agenda of research. RISE was organised around a variety of thematic and country-focused research teams that probed both proximate and underlying determinants of learning. As part of the RISE work programme, a political economy team commissioned studies on the politics of education policy adoption (the PET-A studies) for twelve countries (Chile, Egypt, Ethiopia, India, Indonesia, Kenya, Nigeria, Pakistan, Peru, South Africa, Tanzania and Vietnam). A December 2022 RISE synthesis of the individual country studies1 laid out and applied a framework for systematically assessing how political and institutional context influences learning outcomes—and used the results to suggest some ‘good fit’ soft governance entry points for improving learning outcomes across a variety of different contexts. This insight note elaborates on the synthesis paper’s argument and its practical implications.
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Lei, Yu. Wireless 3D Nanorod Composite Arrays based High Temperature Surface-Acoustic-Wave Sensors for Selective Gas Detection through Machine Learning Algorithms (Final Report). Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1579515.

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Hodgdon, Taylor, Anthony Fuentes, Brian Quinn, Bruce Elder, and Sally Shoop. Characterizing snow surface properties using airborne hyperspectral imagery for autonomous winter mobility. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42189.

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With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aeriel Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.
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Badrinarayan, Aneesha. Performance assessments in college admission: Designing an effective and equitable process. Learning Policy Institute, May 2022. http://dx.doi.org/10.54300/150.937.

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At the same time that many colleges and universities are seeking new ways to more equitably admit and support students, a growing number of schools and districts are using performance assessments to prepare for and monitor deeper learning in high school. Performance assessments measure students’ knowledge, skills, and abilities by asking students to use them in the real-world contexts in which they are required. Student performance on well-designed assessments provides a reflection of deeper learning practices and offers rigorous and equitable ways to surface important academic and nonacademic knowledge and skills.
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Douglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41222.

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The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.
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Cook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree, and Ronald Treminio. Landform identification in the Chihuahuan Desert for dust source characterization applications : developing a landform reference data set. Engineer Research and Development Center (U.S.), October 2022. http://dx.doi.org/10.21079/11681/45644.

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ERDC-Geo is a surface erodibility parameterization developed to improve dust predictions in weather forecasting models. Geomorphic landform maps used in ERDC-Geo link surface dust emission potential to landform type. Using a previously generated southwest United States landform map as training data, a classification model based on machine learning (ML) was established to generate ERDC-Geo input data. To evaluate the ability of the ML model to accurately classify landforms, an independent reference landform data set was created for areas in the Chihuahuan Desert. The reference landform data set was generated using two separate map-ping methodologies: one based on in situ observations, and another based on the interpretation of satellite imagery. Existing geospatial data layers and recommendations from local rangeland experts guided site selections for both in situ and remote landform identification. A total of 18 landform types were mapped across 128 sites in New Mexico, Texas, and Mexico using the in situ (31 sites) and remote (97 sites) techniques. The final data set is critical for evaluating the ML-classification model and, ultimately, for improving dust forecasting models.
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Chang, Michael Alan, Alejandra Magana, Bedrich Benes, Dominic Kao, and Judith Fusco. Driving Interdisciplinary Collaboration through Adapted Conjecture Mapping: A Case Study with the PECAS Mediator. Digital Promise, May 2022. http://dx.doi.org/10.51388/20.500.12265/156.

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In this report, we demonstrate how an interdisciplinary team of computer science and learning sciences researchers utilize an adapted conjecture mapping tool during a collaborative problem-solving session. The session is documented through an edited “Dialogue” format, which captures the process of conjecture map construction and subsequent reflection. We find that creating the conjecture map collaboratively surfaces a key tension: while learning sciences theory often highlights the nuanced and complex relational nature of learning, even the most cutting-edge computing techniques struggle to discern these nuances. Articulating this tension proved to be highly generative, enabling the researchers to discuss how considering impacted community members as a critical “part of the solution” may lead to a socio-technical tool which supports desired learning outcomes, despite limitations in learning theory and technical capability. Ultimately, the process of developing the conjecture map directed researchers towards a precise discussion about how they would need to engage impacted community members (e.g., teachers) in a co-design process.
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