Journal articles: 'Schemas augmentation'

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Lee, Harrison, Raghav Gupta, Abhinav Rastogi, Yuan Cao, Bin Zhang, and Yonghui Wu. "SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10938–46. http://dx.doi.org/10.1609/aaai.v36i10.21341.

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Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research. The Schema-Guided Dialogue (SGD) dataset introduced a paradigm for enabling models to support any service in zero-shot through schemas, which describe service APIs to models in natural language. We explore the robustness of dialogue systems to linguistic variations in schemas by designing SGD-X - a benchmark extending SGD with semantically similar yet stylistically diverse variants for every schema. We observe that two top state tracking models fail to generalize well across schema variants, measured by joint goal accuracy and a novel metric for measuring schema sensitivity. Additionally, we present a simple model-agnostic data augmentation method to improve schema robustness.

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Fearnhead, Paul, and Loukia Meligkotsidou. "Augmentation schemes for particle MCMC." Statistics and Computing 26, no. 6 (October 12, 2015): 1293–306. http://dx.doi.org/10.1007/s11222-015-9603-4.

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Meng, X.-L. "Seeking efficient data augmentation schemes via conditional and marginal augmentation." Biometrika 86, no. 2 (June 1, 1999): 301–20. http://dx.doi.org/10.1093/biomet/86.2.301.

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Fraigniaud, Pierre, Cyril Gavoille, Adrian Kosowski, Emmanuelle Lebhar, and Zvi Lotker. "Universal augmentation schemes for network navigability." Theoretical Computer Science 410, no. 21-23 (May 2009): 1970–81. http://dx.doi.org/10.1016/j.tcs.2008.12.061.

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Tufail, Ahsan Bin, Kalim Ullah, Rehan Ali Khan, Mustafa Shakir, Muhammad Abbas Khan, Inam Ullah, Yong-Kui Ma, and Md Sadek Ali. "On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer’s Disease Using Neuroimaging Modalities and Data Augmentation Methods." Journal of Healthcare Engineering 2022 (February 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/1302170.

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Alzheimer’s disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue.

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Sinha, Rashmi Sharan, Sang-Moon Lee, Minjoong Rim, and Seung-Hoon Hwang. "Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application." Electronics 8, no. 5 (May 17, 2019): 554. http://dx.doi.org/10.3390/electronics8050554.

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In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices.

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Chen, H. Z. B., R. Schober, and L. Lampe. "Two Novel Channel-Augmentation Schemes for MIMO Systems." IEEE Signal Processing Letters 14, no. 9 (September 2007): 601–4. http://dx.doi.org/10.1109/lsp.2007.896162.

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Golfarelli, Matteo, Jens Lechtenbörger, Stefano Rizzi, and Gottfried Vossen. "Schema versioning in data warehouses: Enabling cross-version querying via schema augmentation." Data & Knowledge Engineering 59, no. 2 (November 2006): 435–59. http://dx.doi.org/10.1016/j.datak.2005.09.004.

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Chindanonda, Peeranut, Vladimir Podolskiy, and Michael Gerndt. "Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads." Computers 9, no. 1 (February 14, 2020): 12. http://dx.doi.org/10.3390/computers9010012.

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Internet of Things (IoT) covers scenarios of cyber–physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of supported end nodes, as well as the frequency and volume of observations transmitted, does not change much over time. The paper addresses the challenge of adapting the capacity of the data processing part of IoT pipeline in response to dynamic workloads for centralized IoT scenarios where the quality of user experience matters, e.g., interactivity and media streaming as well as the predictive maintenance for multiple moving vehicles, centralized analytics for wearable devices and smartphones. The self-adaptation mechanism for data processing IoT infrastructure deployed in the cloud is horizontal autoscaling. In this paper we propose augmentations to the computation schemes of data processing component’s desired replicas count from the previous work; these augmentations aim to repurpose original sets of metrics to tackle the task of SLO violations minimization for dynamic workloads instead of minimizing the cost of deployment in terms of instance seconds. The cornerstone proposed augmentation that underpins all the other ones is the adaptation of the desired replicas computation scheme to each scaling direction (scale-in and scale-out) separately. All the proposed augmentations were implemented in the standalone self-adaptive agent acting alongside Kubernetes’ HPA such that limitations of timely acquisition of the monitoring data for scaling are mitigated. Evaluation and comparison with the previous work show improvement in service level achieved, e.g., latency SLO violations were reduced from 2.87% to 1.70% in case of the forecasted message queue length-based replicas count computation used both for scale-in and scale-out, but at the same time higher cost of the scaled data processor deployment is observed.

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Chen, Zhiyu. "Dataset Search and Augmentation." ACM SIGIR Forum 56, no. 1 (June 2022): 1–2. http://dx.doi.org/10.1145/3582524.3582544.

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Data has become an indispensable part of our life. However, current mainstream commercial search engines do not support specialized functions for dataset search. A dataset usually consists of both metadata and data content. Existing information retrieval models designed for Web search cannot efficiently extract semantic information inside structured datasets, even when they contain textual content. Developing new algorithms for next-generation search engines to efficiently find datasets can benefit data practitioners in their data discovery experience. In this dissertation, we consider how to effectively perform dataset search and augmentation. We start by providing an end-to-end description of a dataset search engine following the lifecycle of datasets. Our review includes web dataset acquisition techniques, dataset profiling and augmentation methods, and dataset search tasks and corresponding methods. In order to extract datasets from research articles, we present an information extraction framework to determine triples of interest which can be used for academic dataset search. We propose a feature-based method to augment tabular datasets with additional schema labels to help users and systems to better understand the datasets. We develop three methods for tabular dataset search: the first utilizes generated schema labels to enhance the search results; the second adopts pretrained language models to learn matching features; the third models the complex relations in the datasets as one or more graphs and uses graph neural networks to learn representations of queries and tables. To support dataset search in which a query is also a dataset, we propose universal dataset encoders which regard a dataset as a point set so that the encoded dataset representations can be used to search for similar datasets. Extensive experiments across multiple tasks demonstrate the superiority of our proposed methods over the state of the art. Awarded by: Lehigh University, Bethlehem, USA on 10 May 2022. Supervised by: Brian D. Davison. Available at: https://github.com/Zhiyu-Chen/Dissertation/blob/main/Dissertation_Dataset_Search.pdf.

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Scibilia, B., A. Kobi, R. Chassagnon, and A. Barreau. "Minimal Design Augmentation Schemes to Resolve Complex Aliasing in Industrial Experiments." Quality Engineering 14, no. 4 (June 18, 2002): 523–29. http://dx.doi.org/10.1081/qen-120003554.

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Guo, Hongyu, and Yongyi Mao. "Interpolating Graph Pair to Regularize Graph Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7766–74. http://dx.doi.org/10.1609/aaai.v37i6.25941.

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We present a simple and yet effective interpolation-based regularization technique, aiming to improve the generalization of Graph Neural Networks (GNNs) on supervised graph classification. We leverage Mixup, an effective regularizer for vision, where random sample pairs and their labels are interpolated to create synthetic images for training. Unlike images with grid-like coordinates, graphs have arbitrary structure and topology, which can be very sensitive to any modification that alters the graph's semantic meanings. This posts two unanswered questions for Mixup-like regularization schemes: Can we directly mix up a pair of graph inputs? If so, how well does such mixing strategy regularize the learning of GNNs? To answer these two questions, we propose ifMixup, which first adds dummy nodes to make two graphs have the same input size and then simultaneously performs linear interpolation between the aligned node feature vectors and the aligned edge representations of the two graphs. We empirically show that such simple mixing schema can effectively regularize the classification learning, resulting in superior predictive accuracy to popular graph augmentation and GNN methods.

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Abdalla, Peshraw Ahmed, Abdalbasit Mohammed Qadir, Omed Jamal Rashid, Karwan M. Hama Rawf, Ayub O. Abdulrahman, and Bashdar Abdalrahman Mohammed. "Deep Transfer Learning Networks for Brain Tumor Detection: The Effect of MRI Patient Image Augmentation Methods." International Journal of Electronics and Communications Systems 2, no. 2 (December 25, 2022): 39–48. http://dx.doi.org/10.24042/ijecs.v2i2.14815.

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The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant. To identify brain tumors, this research examines how three deep learning networks are affected by conventional data augmentation methods, including MobileNetV2, VGG19, and DenseNet201. The findings showed that before and after utilizing approaches, picture augmentation schemes significantly affected the networks. The accuracy of MobileNetV2, which was originally 85.33%, was then enhanced to 96.88%. The accuracy of VGG19, which was 77.33%, was then enhanced to 95.31%, and DenseNet201, which was originally 82.66%, was then enhanced to 93.75%. The models' accuracy percentage engagement change is 13.53%, 23.25%, and 23.25%, respectively. Finally, the conclusion showed that applying data augmentation approaches improves performance, producing models far better than those trained from scratch.

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Li, Juntao, Lisong Qiu, Bo Tang, Dongmin Chen, Dongyan Zhao, and Rui Yan. "Insufficient Data Can Also Rock! Learning to Converse Using Smaller Data with Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6698–705. http://dx.doi.org/10.1609/aaai.v33i01.33016698.

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Recent successes of open-domain dialogue generation mainly rely on the advances of deep neural networks. The effectiveness of deep neural network models depends on the amount of training data. As it is laboursome and expensive to acquire a huge amount of data in most scenarios, how to effectively utilize existing data is the crux of this issue. In this paper, we use data augmentation techniques to improve the performance of neural dialogue models on the condition of insufficient data. Specifically, we propose a novel generative model to augment existing data, where the conditional variational autoencoder (CVAE) is employed as the generator to output more training data with diversified expressions. To improve the correlation of each augmented training pair, we design a discriminator with adversarial training to supervise the augmentation process. Moreover, we thoroughly investigate various data augmentation schemes for neural dialogue system with generative models, both GAN and CVAE. Experimental results on two open corpora, Weibo and Twitter, demonstrate the superiority of our proposed data augmentation model.

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Dou, Longxu, Yan Gao, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, and Jian-Guang Lou. "MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 12745–53. http://dx.doi.org/10.1609/aaai.v37i11.26499.

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Text-to-SQL semantic parsing is an important NLP task, which facilitates the interaction between users and the database. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under various settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

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Rejitha, R. S., R. Suja, P. G. Jairaj, and C. R. Rajalakshmi. "Capacity Augmentation of Water Supply Schemes—a Case Study of Varkala Scheme." Water Conservation Science and Engineering 7, no. 1 (January 18, 2022): 23–32. http://dx.doi.org/10.1007/s41101-021-00123-y.

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Anderson, Jason, Sherman Lo, Andrew Neish,, and Todd Walter. "Authentication of Satellite-Based Augmentation Systems with Over-the-Air Rekeying Schemes." NAVIGATION: Journal of the Institute of Navigation 70, no. 3 (2023): navi.595. http://dx.doi.org/10.33012/navi.595.

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Covington, M. F., E. Krupinski, R. J. Avery, and P. H. Kuo. "Classification Schema of Symptomatic Enterogastric Reflux Utilizing Sincalide Augmentation on Hepatobiliary Scintigraphy." Journal of Nuclear Medicine Technology 42, no. 3 (July 17, 2014): 198–202. http://dx.doi.org/10.2967/jnmt.114.141168.

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Moyo, Thembani, Alain Y. Kibangou, and Walter Musakwa. "Societal context-dependent multi-modal transportation network augmentation in Johannesburg, South Africa." PLOS ONE 16, no. 4 (April 8, 2021): e0249014. http://dx.doi.org/10.1371/journal.pone.0249014.

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In most developing countries, formal and informal transportation schemes coexist without effective and smart integration. In this paper, the authors show how to leverage opportunities offered by formal and informal transportation schemes to build an integrated multi-modal network. Precisely, the authors consider integration of rickshaws to a bus-train network, by taking into account accessibility and societal constraints. By modelling the respective networks with weighted graphs, a graph augmentation problem is solved with respect to a composite cost taking into account constraints on the use of rickshaws. The solution, is based on finding a minimum cost spanning tree of a merged graph. The method is applied in the South African context, in the city of Johannesburg where rickshaws are not yet a significant part of the transportation system. The implications of the study reveal that using non-motorised transportation services is a viable option of improving mobility in the city. The composite cost introduced herein could be used for new routing algorithm including societal, environmental, architectural contexts and commuter experiences through rating.

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Bean, Nigel, and Guy Latouche. "Approximations to quasi-birth-and-death processes with infinite blocks." Advances in Applied Probability 42, no. 4 (December 2010): 1102–25. http://dx.doi.org/10.1239/aap/1293113153.

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The numerical analysis of quasi-birth-and-death processes rests on the resolution of a matrix-quadratic equation for which efficient algorithms are known when the matrices have finite order, that is, when the number of phases is finite. In this paper we consider the case of infinitely many phases from the point of view of theoretical convergence of truncation and augmentation schemes, and we develop four different methods. Two methods rely on forced transitions to the boundary. In one of these methods, the transitions occur as a result of the truncation itself, while in the other method, they are artificially introduced so that the augmentation may be chosen to be as natural as possible. Two other methods rely on forced transitions within the same level. We conclude with a brief numerical illustration.

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Bean, Nigel, and Guy Latouche. "Approximations to quasi-birth-and-death processes with infinite blocks." Advances in Applied Probability 42, no. 04 (December 2010): 1102–25. http://dx.doi.org/10.1017/s0001867800004547.

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The numerical analysis of quasi-birth-and-death processes rests on the resolution of a matrix-quadratic equation for which efficient algorithms are known when the matrices have finite order, that is, when the number of phases is finite. In this paper we consider the case of infinitely many phases from the point of view of theoretical convergence of truncation and augmentation schemes, and we develop four different methods. Two methods rely on forced transitions to the boundary. In one of these methods, the transitions occur as a result of the truncation itself, while in the other method, they are artificially introduced so that the augmentation may be chosen to be as natural as possible. Two other methods rely on forced transitions within the same level. We conclude with a brief numerical illustration.

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Lang, Shinan, Guiqiang Li, Yi Liu, Wei Lu, Qunying Zhang, and Kun Chao. "A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows." Remote Sensing 15, no. 19 (September 28, 2023): 4756. http://dx.doi.org/10.3390/rs15194756.

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To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network’s learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming.

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Asif, Rao Muhammad, Jehangir Arshad, Mustafa Shakir, Sohail M. Noman, and Ateeq Ur Rehman. "Energy Efficiency Augmentation in Massive MIMO Systems through Linear Precoding Schemes and Power Consumption Modeling." Wireless Communications and Mobile Computing 2020 (September 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/8839088.

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Massive multiple-input multiple-output or massive MIMO system has great potential for 5th generation (5G) wireless communication systems as it is capable of providing game-changing enhancements in area throughput and energy efficiency (EE). This work proposes a realistic and practically implementable EE model for massive MIMO systems while a general and canonical system model is used for single-cell scenario. Linear processing schemes are used for detection and precoding, i.e., minimum mean squared error (MMSE), zero-forcing (ZF), and maximum ratio transmission (MRT/MRC). Moreover, a power dissipation model is proposed that considers overall power consumption in uplink and downlink communications. The proposed model includes the total power consumed by power amplifier and circuit components at the base station (BS) and single antenna user equipment (UE). An optimal number of BS antennas to serve total UEs and the overall transmitted power are also computed. The simulation results confirm considerable improvements in the gain of area throughput and EE, and it also shows that the optimum area throughput and EE can be realized wherein a larger number of antenna arrays at BS are installed for serving a greater number of UEs.

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Zheng, Zewei, Keyu Yan, Shuaixian Yu, Bing Zhu, and Ming Zhu. "Path following control for a stratospheric airship with actuator saturation." Transactions of the Institute of Measurement and Control 39, no. 7 (January 21, 2016): 987–99. http://dx.doi.org/10.1177/0142331215625770.

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This paper proposes two different path following control schemes for a stratospheric airship with actuator saturation. Each of the control schemes consists of a guidance loop and an attitude control loop. In both schemes, guidance laws are designed according to the line-of-sight guidance-based path following principle. In the first control scheme, a robust H∞ controller without constraints is designed based on the planar model of a stratospheric airship to stabilize path-following errors. The input constraints are then addressed by using a regional [Formula: see text]-based model recovery anti-windup compensator, which prevents the unconstrained controller from misbehaving in the constrained closed loop with anti-windup augmentation and ensures the systematic stability. In the second control scheme, model predictive control is applied to guarantee the path-following of the closed-loop system and explicitly address the magnitude and rate of rudders of the stratospheric airship. Theoretical results are illustrated by numerical simulations where both closed-loop systems are capable of following their desired paths and the constraints on control inputs are satisfied.

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Lasniari, Sarah, Jasril Jasril, Suwanto Sanjaya, Febi Yanto, and Muhammad Affandes. "Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra." Jurnal Sistem Komputer dan Informatika (JSON) 3, no. 4 (June 30, 2022): 450. http://dx.doi.org/10.30865/json.v3i4.4167.

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Beef is an example of an animal protein-rich food. The consumption of meat in Indonesia is increasing year after year, in tandem with the country's growing population. Many traders purposefully combine beef and pork in order to maximize profits. With the naked eye, it's difficult to tell the difference between pork and beef. In Muslim-majority countries, the assurance of halal meat is crucial. This study uses Deep Learning with the Convolutional Neural Network (CNN) method and ResNet-50 with data augmentation to classify images of beef and pork. The original meat picture databases contain 457 images, however following the data augmentation process, there are 2742 images in total, divided into three classes. The distribution of training and test data is 90 percent:10 percent in the comparison test scenario between the two original data schemes and supplemented data. With an average of 87.64 % accuracy, 87.59 % recall, and 90.90 % precision, the Confusion Matrix is the best classification performance model. There was no evidence of overfitting based on observations from the visualization of the training and testing process.

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Drewes, J. E., and S. J. Khan. "Contemporary design, operation, and monitoring of potable reuse systems." Journal of Water Reuse and Desalination 5, no. 1 (August 14, 2014): 1–7. http://dx.doi.org/10.2166/wrd.2014.148.

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Water scarcity driven by population growth, lack of conventional supplies, and climate change impacts have resulted in increasing interest worldwide in drinking water augmentation using treated wastewater effluents. Potable reuse can occur indirect or direct, but is also practiced in many places as ‘de facto reuse’, where upstream wastewater discharge occurs to drinking water supplies. With this increasing recognition of potable reuse, there is very limited guidance and standardization for proper design and operation of potable reuse schemes that is protective of public health. This study provided guidance on contemporary approaches for the design, operation, and monitoring of potable reuse schemes, including source water characterization and source control approaches; linking water quality treatment performance goals to health risks; risk mitigation strategies including the design principles of multiple barriers for microbial and chemical contaminants; assessing system reliability and fail-safe design approaches; and, finally, monitoring strategies for process performance and compliance.

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Mondal, Nepal C., and Surendra N. Das. "Evaluation and augmentation of safe drinking water supply schemes in Salur Mandal, Vizainagaram district, Andhra Pradesh." Journal of the Geological Society of India 80, no. 1 (July 2012): 57–64. http://dx.doi.org/10.1007/s12594-012-0118-8.

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LIU, J. S., W. H. WONG, and A. KONG. "Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes." Biometrika 81, no. 1 (March 1, 1994): 27–40. http://dx.doi.org/10.1093/biomet/81.1.27.

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Khan, Rahim, Qiang Yang, Ahsan Bin Tufail, Alam Noor, and Yong-Kui Ma. "Classification of Digital Modulated COVID-19 Images in the Presence of Channel Noise Using 2D Convolutional Neural Networks." Wireless Communications and Mobile Computing 2021 (July 12, 2021): 1–15. http://dx.doi.org/10.1155/2021/5539907.

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The wireless environment poses a significant challenge to the propagation of signals. Different effects such as multipath scattering, noise, degradation, distortion, attenuation, and fading affect the distribution of signals adversely. Deep learning techniques can be used to differentiate among different modulated signals for reliable detection in a communication system. This study aims at distinguishing COVID-19 disease images that have been modulated by different digital modulation schemes and are then passed through different noise channels and classified using deep learning models. We proposed a comprehensive evaluation of different 2D Convolutional Neural Network (CNN) architectures for the task of multiclass (24-classes) classification of modulated images in the presence of noise and fading. It is used to differentiate between images modulated through Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16- and 64-Quadrature Amplitude Modulation and passed through Additive White Gaussian Noise, Rayleigh, and Rician channels. We obtained mixed results under different settings such as data augmentation, disharmony between batch normalization (BN), and dropout (DO), as well as lack of BN in the network. In this study, we found that the best performing model is a 2D-CNN model using disharmony between BN and DO techniques trained using 10-fold cross-validation (CV) with a small value of DO before softmax and after every convolution and fully connected layer along with BN layers in the presence of data augmentation, while the least performing model is the 2D-CNN model trained using 5-fold CV without augmentation.

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Teng, Xiao, Tuo Wang, Xiang Zhang, Long Lan, and Zhigang Luo. "Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method." Complexity 2020 (February 17, 2020): 1–8. http://dx.doi.org/10.1155/2020/6737951.

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Stock trend prediction refers to predicting future price trend of stocks for seeking profit maximum of stock investment. Although it has aroused broad attention in stock markets, it is still a tough task not only because the stock markets are complex and easily volatile but also because real short-term stock data is so limited that existing stock prediction models could be far from perfect, especially for deep neural networks. As a kind of time-series data, the underlying patterns of stock data are easily influenced by any tiny noises. Thus, how to augment limited stock price data is an open problem in stock trend prediction, since most data augmentation schemes adopted in image processing cannot be brutally used here. To this end, we devise a simple yet effective time-sensitive data augmentation method for stock trend prediction. To be specific, we augment data by corrupting high-frequency patterns of original stock price data as well as preserving low-frequency ones in the frame of wavelet transformation. The proposed method is motivated by the fact that low-frequency patterns without noisy corruptions do not hurt the true patterns of stock price data. Besides, a transformation technique is proposed to recognize the importance of the patterns at varied time points, that is, the information is time-sensitive. A series of experiments carried out on a real stock price dataset including 50 corporation stocks verify the efficacy of our data augmentation method.

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Laursen, T. A., and V. G. Oancea. "Automation and Assessment of Augmented Lagrangian Algorithms for Frictional Contact Problems." Journal of Applied Mechanics 61, no. 4 (December 1, 1994): 956–63. http://dx.doi.org/10.1115/1.2901586.

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In recent work, the augmented Lagrangian approach to large deformation frictional contact problems has been advocated for finite element calculations. The resulting algorithms have been shown to facilitate accurate constraint enforcement in a robust manner, while providing an effective technique for removal of the nonsymmetry associated with most frictional constitutive laws. In this work, we examine automation schemes for the algorithm, such that multipliers can be determined to a specified degree of accuracy without user intervention. The performance of the resulting automatic augmentation algorithm is then examined, providing important information about practical attributes of the procedure.

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Khodorovich, Olga, Vladimir Solodkii, Alena Kalinina-Masri, Karen Sarkisian, Tatiana Sherstneva, Viktoriia Kleshneva, and Liia Kanakhina. "Breast cancer in patients after augmentation with implants." Problems in oncology 67, no. 4 (September 4, 2021): 518–24. http://dx.doi.org/10.37469/0507-3758-2021-67-4-518-524.

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Relevance. Currently, there is no definite answer to the question of the cause of breast cancer, since it is a systemic and multifactorial disease. Given that the number of aesthetic operations on the mammary glands using endoprostheses only increases every year, new cases of detection of malignant diseases do not decrease. Some researchers are beginning to speak out about the possible connection of augmentation mammoplasty in the anamnesis with the subsequent occurrence of breast cancer due to inadequate clinical and instrumental examination and ignoring some of its components (for example, the mammographic examination). Introduction. Taking into account the analysis of the literature and the presented clinical examples, the possible reasons for the connection of augmentation mammoplasty in the anamnesis with the subsequent detection of a malignant neoplasm are analyzed. Materials and methods. We analyzed domestic and foreign literature and described two clinical examples with authentic documentation based on the results of the examination and treatment. The article describes in detail the schemes of drug treatment and descriptions of the results of morphological examination of the surgical material. The types and results of surgical treatment with reliable photos are also presented. Results. Given the complexity of the diagnostic stage in patients with breast endoprosthesis in the described clinical examples, family history collected in sufficient detail, cytogenetic studies conducted, we should talk about the likely underdiagnosis during preventive examinations. Conclusion. The multifactorial and systematic nature of such a disease as breast cancer suggests that perhaps an intensive increase in the detectability of the above-mentioned against the background of previously performed aesthetic surgery would be the simplest solution to the problem. In this category of patients, anamnesis should be collected in more detail and carefully, and the entire complex of clinical and instrumental examinations, including mammography, magnetic resonance and ultrasound examinations, should be performed in order to fully diagnose and develop further patient management tactics.

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Kanel-Belov, Alexei, Jie-Tai Yu, and Andrey Elishev. "On the augmentation topology of automorphism groups of affine spaces and algebras." International Journal of Algebra and Computation 28, no. 08 (December 2018): 1449–85. http://dx.doi.org/10.1142/s0218196718400040.

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We study topological properties of Ind-groups [Formula: see text] and [Formula: see text] of automorphisms of polynomial and free associative algebras via Ind-schemes, toric varieties, approximations, and singularities. We obtain a number of properties of [Formula: see text], where [Formula: see text] is the polynomial or free associative algebra over the base field [Formula: see text]. We prove that all Ind-scheme automorphisms of [Formula: see text] are inner for [Formula: see text], and all Ind-scheme automorphisms of [Formula: see text] are semi-inner. As an application, we prove that [Formula: see text] cannot be embedded into [Formula: see text] by the natural abelianization. In other words, the Automorphism Group Lifting Problem has a negative solution. We explore close connection between the above results and the Jacobian conjecture, as well as the Kanel-Belov–Kontsevich conjecture, and formulate the Jacobian conjecture for fields of any characteristic. We make use of results of Bodnarchuk and Rips, and we also consider automorphisms of tame groups preserving the origin and obtain a modification of said results in the tame setting.

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Chiacchio, Pasquale, Stefano Chiaverini, Lorenzo Sciavicco, and Bruno Siciliano. "Closed-Loop Inverse Kinematics Schemes for Constrained Redundant Manipulators with Task Space Augmentation and Task Priority Strategy." International Journal of Robotics Research 10, no. 4 (August 1991): 410–25. http://dx.doi.org/10.1177/027836499101000409.

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Hakim, Dewi Ananta, Ade Jamal, Anto Satriyo Nugroho, Ali Akbar Septiandri, and Budi Wiweko. "Embryo Grading after In Vitro Fertilization using YOLO." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 13, no. 3 (November 25, 2022): 137. http://dx.doi.org/10.24843/lkjiti.2022.v13.i03.p01.

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In vitro fertilization is an implementation of Assistive Reproductive Technology. This technology can produce embryos outside the mother's womb by manipulating gametes outside the human body. The success rate of in vitro fertilization is the selection of good-grading embryos. In this study, the authors used Yolo Version 3 to perform object detection objectively by introducing grades for each embryo image. The author uses an embryo image sourced from the Indonesian Medical Education and Research Institute with information on the quality of the embryo. In this study, the author separated the data into two schemes. The first scheme separates data into training data of 70%, 15% validation data, and 15% for testing data. The second scheme uses a Stratified K-Fold Cross-Validation with a fold value =3. In training, the writer configures the values ??of Max Batches=6000, Steps=4800,5400, Batch=64, and Subdivision=16 by doing image augmentation (saturation=1.5, exposure=1.5, hue=0.1, jitter=0.3, random=1). For each of the obtained mAP (Mean Average Precision) values ??for data separation schemes, one is 100.00% in the 6000th iteration, while for the two-data separation scheme, the highest mAP is 97.33%.% in the fold=3 and 5000th iteration. It means that both separation schemes are sufficient in terms of mAP.

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Acción, Álvaro, Francisco Argüello, and Dora B. Heras. "A New Multispectral Data Augmentation Technique Based on Data Imputation." Remote Sensing 13, no. 23 (November 30, 2021): 4875. http://dx.doi.org/10.3390/rs13234875.

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Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out.

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Yang, Dongxu, Yadong Liu, Zongtan Zhou, Yang Yu, and Xinbin Liang. "Decoding Visual Motions from EEG Using Attention-Based RNN." Applied Sciences 10, no. 16 (August 14, 2020): 5662. http://dx.doi.org/10.3390/app10165662.

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The main objective of this paper is to use deep neural networks to decode the electroencephalography (EEG) signals evoked when individuals perceive four types of motion stimuli (contraction, expansion, rotation, and translation). Methods for single-trial and multi-trial EEG classification are both investigated in this study. Attention mechanisms and a variant of recurrent neural networks (RNNs) are incorporated as the decoding model. Attention mechanisms emphasize task-related responses and reduce redundant information of EEG, whereas RNN learns feature representations for classification from the processed EEG data. To promote generalization of the decoding model, a novel online data augmentation method that randomly averages EEG sequences to generate artificial signals is proposed for single-trial EEG. For our dataset, the data augmentation method improves the accuracy of our model (based on RNN) and two benchmark models (based on convolutional neural networks) by 5.60%, 3.92%, and 3.02%, respectively. The attention-based RNN reaches mean accuracies of 67.18% for single-trial EEG decoding with data augmentation. When performing multi-trial EEG classification, the amount of training data decreases linearly after averaging, which may result in poor generalization. To address this deficiency, we devised three schemes to randomly combine data for network training. Accordingly, the results indicate that the proposed strategies effectively prevent overfitting and improve the correct classification rate compared with averaging EEG fixedly (by up to 19.20%). The highest accuracy of the three strategies for multi-trial EEG classification achieves 82.92%. The decoding performance for the methods proposed in this work indicates they have application potential in the brain–computer interface (BCI) system based on visual motion perception.

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Liang, Xitong, Samuel Livingstone, and Jim Griffin. "Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models." Entropy 25, no. 9 (September 8, 2023): 1310. http://dx.doi.org/10.3390/e25091310.

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Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) approach can be employed to jointly sample models and coefficients, but the effective design of the trans-dimensional jumps of RJMCMC can be challenging, making it hard to implement. Alternatively, the marginal likelihood can be derived conditional on latent variables using a data-augmentation scheme (e.g., Pólya-gamma data augmentation for logistic regression) or using other estimation methods. However, suitable data-augmentation schemes are not available for every generalised linear model and survival model, and estimating the marginal likelihood using a Laplace approximation or a correlated pseudo-marginal method can be computationally expensive. In this paper, three main contributions are presented. Firstly, we present an extended Point-wise implementation of Adaptive Random Neighbourhood Informed proposal (PARNI) to efficiently sample models directly from the marginal posterior distributions of generalised linear models and survival models. Secondly, in light of the recently proposed approximate Laplace approximation, we describe an efficient and accurate estimation method for marginal likelihood that involves adaptive parameters. Additionally, we describe a new method to adapt the algorithmic tuning parameters of the PARNI proposal by replacing Rao-Blackwellised estimates with the combination of a warm-start estimate and the ergodic average. We present numerous numerical results from simulated data and eight high-dimensional genetic mapping data-sets to showcase the efficiency of the novel PARNI proposal compared with the baseline add–delete–swap proposal.

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SIETTOS, CONSTANTINOS I., IOANNIS G. KEVREKIDIS, and DIMITRIOS MAROUDAS. "COARSE BIFURCATION DIAGRAMS VIA MICROSCOPIC SIMULATORS: A STATE-FEEDBACK CONTROL-BASED APPROACH." International Journal of Bifurcation and Chaos 14, no. 01 (January 2004): 207–20. http://dx.doi.org/10.1142/s0218127404009193.

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We present and illustrate a feedback control-based framework that enables microscopic/stochastic simulators to trace their "coarse" bifurcation diagrams, characterizing the dependence of their expected dynamical behavior on parameters. The framework combines the so-called "coarse time stepper" and arc-length continuation ideas from numerical bifurcation theory with linear dynamic feedback control. An augmented dynamical system is formulated, in which the bifurcation parameter evolution is linked with the microscopic simulation dynamics through feedback laws. The augmentation stably steers the system along both stable and unstable portions of the open-loop bifurcation diagram. The framework is illustrated using kinetic Monte Carlo simulations of simple surface reaction schemes that exhibit both coarse regular turning points and coarse Hopf bifurcations.

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Gu, Geonmo, Byungsoo Ko, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, and Minchul Shin. "Towards Light-Weight and Real-Time Line Segment Detection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 726–34. http://dx.doi.org/10.1609/aaai.v36i1.19953.

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Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). We design an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods. To maintain competitive performance with a light-weight network, we present novel training schemes: Segments of Line segment (SoL) augmentation, matching and geometric loss. SoL augmentation splits a line segment into multiple subparts, which are used to provide auxiliary line data during the training process. Moreover, the matching and geometric loss allow a model to capture additional geometric cues. Compared with TP-LSD-Lite, previously the best real-time LSD method, our model (M-LSD-tiny) achieves competitive performance with 2.5% of model size and an increase of 130.5% in inference speed on GPU. Furthermore, our model runs at 56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices, respectively. To the best of our knowledge, this is the first real-time deep LSD available on mobile devices.

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Chen, Ran, Jing Zhao, Xueqi Yao, Sijia Jiang, Yingting He, Bei Bao, Xiaomin Luo, Shuhan Xu, and Chenxi Wang. "Generative Design of Outdoor Green Spaces Based on Generative Adversarial Networks." Buildings 13, no. 4 (April 20, 2023): 1083. http://dx.doi.org/10.3390/buildings13041083.

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Generative Adversarial Networks (GANs) possess a significant ability to generate novel images that adhere to specific guidelines across multiple domains. GAN-assisted generative design is a design method that can automatically generate design schemes without the constraints of human conditions. However, more research on complex objects with weak regularity, such as parks, is required. In this study, parks were selected as the research object, and we conducted our experiment as follows: (1) data preparation and collection; (2) pre-train the two neural network, then create the design layout generation system and the design plan generation system; (3) realize the data augmentation and enhanced hundred level dataset to thousand level dataset; (4) optimized training; (5) test the optimized training model. Experimental results show that (1) the machine learning model can acquire specific park layout patterns, quickly generate well-laid-out plan layout plans, and create innovative designs that differ from the human designer’s style within reasonable limits; (2) GAN-driven data augmentation methods can significantly improve the generative ability of algorithms, reduce generative pressure, and achieve better generative results; (3) pix2pix is prone to mode collapse, and CycleGAN has fixed rule errors in expressing certain design elements; and (4) GAN has the ability to mine design rules in the same way as humans.

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BINDER, ROBERT V., and JEFFREY J. P. TSAI. "KB/RMS: AN INTELLIGENT ASSISTANT FOR REQUIREMENT DEFINITION." International Journal on Artificial Intelligence Tools 01, no. 04 (December 1992): 503–22. http://dx.doi.org/10.1142/s021821309200003x.

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In this paper, we present a conceptual framework and a system model for an intelligent assistant for requirement definition, KB/RMS. The requirement definition process is characterized by the Requirement Context Model. Informal and formal methods for requirement definition are considered in the light of this model, which serves as the logical schema for the KB/RMS database. We summarize conventional and knowledge-based system support for requirement definition. The use of natural language processing, a semantic model of the problem and solution spaces, domain and technology models, inference driven augmentation, validation, and verification of the semantic model is discussed. Finally, we present the production of design representations from the augmented semantic model.

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Ligrani, Phil. "Heat Transfer Augmentation Technologies for Internal Cooling of Turbine Components of Gas Turbine Engines." International Journal of Rotating Machinery 2013 (2013): 1–32. http://dx.doi.org/10.1155/2013/275653.

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To provide an overview of the current state of the art of heat transfer augmentation schemes employed for internal cooling of turbine blades and components, results from an extensive literature review are presented with data from internal cooling channels, both with and without rotation. According to this survey, a very small number of existing investigations consider the use of combination devices for internal passage heat transfer augmentation. Examples are rib turbulators, pin fins, and dimples together, a combination of pin fins and dimples, and rib turbulators and pin fins in combination. The results of such studies are compared with data obtained prior to 2003 without rotation influences. Those data are comprised of heat transfer augmentation results for internal cooling channels, with rib turbulators, pin fins, dimpled surfaces, surfaces with protrusions, swirl chambers, or surface roughness. This comparison reveals that all of the new data, obtained since 2003, collect within the distribution of globally averaged data obtained from investigations conducted prior to 2003 (without rotation influences). The same conclusion in regard to data distributions is also reached in regard to globally averaged thermal performance parameters as they vary with friction factor ratio. These comparisons, made on the basis of such judgment criteria, lead to the conclusion that improvements in our ability to provide better spatially-averaged thermal protection have been minimal since 2003. When rotation is present, existing investigations provide little evidence of overall increases or decreases in overall thermal performance characteristics with rotation, at any value of rotation number, buoyancy parameter, density ratio, or Reynolds number. Comparisons between existing rotating channel experimental data and the results obtained prior to 2003, without rotation influences, also show that rotation has little effect on overall spatially-averaged thermal performance as a function of friction factor.

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Osipov, Sergey, Andrey Rogalev, Nikolay Rogalev, Igor Shevchenko, and Andrey Vegera. "Asymmetric Method of Heat Transfer Intensification in Radial Channels of Gas Turbine Blades." Inventions 7, no. 4 (December 7, 2022): 117. http://dx.doi.org/10.3390/inventions7040117.

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Loop and semi-loop cooling schemes are widely used for the high-temperature gas turbine blades. In such schemes, the mid-chord airfoil parts are traditionally cooled by radial channels with ribbed walls. The blades with a small specific span, or “short” blades, have different heat flux amounts on pressure and suction sides, which results in a temperature difference in these sides of 100–150 °K. This difference causes thermal stresses and reduces the long-term strength margins. This paper presents a new method of heat transfer intensification in the ribbed radial cooling channels. The method is based on air streams’ injection through holes in the ribs that split channels. The streams are directed along the walls into the stagnation zones behind the ribs. The results of a 3D coolant flow simulation with ANSYS CFX code show the influence of the geometry parameters upon the channel heat transfer asymmetry. In the Reynolds number within a range of 6000–20,000, the method provides the heat transfer augmentation difference by up to 40% on the opposite channel walls. Test results presented in the criteria relations form allow for the calculation of mean the heat transfer coefficient along the channel length.

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Turchenkov, Dmitry Alexandrovich, and Mihail Alexandrovich Turchenkov. "Analysis of simplifications of numerical schemes for Langevin equation, effect of variations in the correlation of augmentations." Computer Research and Modeling 4, no. 2 (June 2012): 325–38. http://dx.doi.org/10.20537/2076-7633-2012-4-2-325-338.

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Raiyn, Jamal. "Developing Vehicle Locations Strategy on Urban Road." Transport and Telecommunication Journal 18, no. 4 (December 20, 2017): 253–62. http://dx.doi.org/10.1515/ttj-2017-0022.

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Abstract Various forecasting schemes have been proposed to manage urban road traffic data, which is collected by different sources such as, videos cameras, sensors and mobile phone services. However, these are not sufficient for the purpose because of their limited coverage and high costs of installation and maintenance. This paper describes urban road congestion as a resource assignment problem in urban areas, in which vehicles are assigned to available sections of road. In order to accomplish this and reduce road congestion, an estimation of the vehicle location is needed. Different strategies for estimating location have been proposed, such as the use of Wi-Fi and cellular systems, and GPS/GNSS. In this process, accuracy plays an important role. Therefore, to increase the accuracy of the primary GNSS system, an augmentation system is considered.

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Asif, Rao Muhammad, Mustafa Shakir, Ateeq Ur Rehman, Muhammad Shafiq, Rehan Ali Khan, and Wali Ullah Khan. "Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems." Journal of Sensors 2022 (June 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/7205687.

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Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DL transmissions. Conclusively, it can be summarized that some complex computations of MMSE channel estimators can enhance the average sum of SE per cell as per the results verified in this model.

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Li, Shuangli, Jingbo Zhou, Tong Xu, Dejing Dou, and Hui Xiong. "GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4541–49. http://dx.doi.org/10.1609/aaai.v36i4.20377.

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Recently many efforts have been devoted to applying graph neural networks (GNNs) to molecular property prediction which is a fundamental task for computational drug and material discovery. One of major obstacles to hinder the successful prediction of molecular property by GNNs is the scarcity of labeled data. Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs. However, the fundamental property of a molecule could be altered with the augmentation method (like random perturbation) on molecular graphs. Whereas, the critical geometric information of molecules remains rarely explored under the current GNN and GCL architectures. To this end, we propose a novel graph contrastive learning method utilizing the geometry of the molecule across 2D and 3D views, which is named GeomGCL. Specifically, we first devise a dual-view geometric message passing network (GeomMPNN) to adaptively leverage the rich information of both 2D and 3D graphs of a molecule. The incorporation of geometric properties at different levels can greatly facilitate the molecular representation learning. Then a novel geometric graph contrastive scheme is designed to make both geometric views collaboratively supervise each other to improve the generalization ability of GeomMPNN. We evaluate GeomGCL on various downstream property prediction tasks via a finetune process. Experimental results on seven real-life molecular datasets demonstrate the effectiveness of our proposed GeomGCL against state-of-the-art baselines.

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Nordquist, William D., and David J. Krutchkoff. "Part III: Crystalline Fluorapatite-Coated Hydroxyapatite; Potential Use as a Bacteriostatic Agent for Both Pre-Implant Cases and Retreatment of Infected Implant Sites: A Report of 4 Cases." Journal of Oral Implantology 37, no. 1 (February 1, 2011): 43–51. http://dx.doi.org/10.1563/aaid-joi-d-10-00165.

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Abstract Four cases of peri-implant bone loss associated with undiagnosed necrotic pulps of adjacent teeth are reported. In two cases, bone was obliterated along sinus tracts (fistulas) that coursed between the implant and adjacent tooth. Endodontic treatment was completed on the adjacent teeth concurrent with periapical surgery to seal the tooth apex. The sinus tract (fistula) was excised, and the implant plus tooth surfaces were treated with a combination of concentrated citric acid and 4.3% sodium fluoride solutions. The third case involved peri-implant surgery with endodontic treatment on the adjacent tooth. A fourth case represented an infected socket augmentation which was surgically treated, augmented with microcrystalline fluorapatite in the range of a 300 µm to 400 µm cluster, and allowed to heal for 4 months followed by a trephine bone biopsy and subsequent analysis for bone growth around the fluoridated nonceramic microcrystalline hydroxyapatite (HA). An augmentation procedure employing fluoridated of resorbable HA was then completed. Histologic analysis showed healthy bone regeneration suggesting that therapeutic fluoride treatment and resultant fluorapatite were helpful in inhibiting reinfection following surgical treatment. All 4 infected implant sites were successfully managed and retained using the aforementioned treatment schemes, and there was no evidence of posttreatment infection in any of the 4 cases. It is proposed that fluoride treatment, through the use of 4.3% sodium fluoride and/or fluoridated hydroxyapatite (fluorapatite), shows promise as an adjunctive treatment component in inhibiting peri-implant infection and reinfection when managing ailing or failing implants.

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Wilson, Greg, and H. Tuba Özkan-Haller. "Ensemble-Based Data Assimilation for Estimation of River Depths." Journal of Atmospheric and Oceanic Technology 29, no. 10 (October 1, 2012): 1558–68. http://dx.doi.org/10.1175/jtech-d-12-00014.1.

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Abstract A method is presented for estimating bathymetry in a river, based on observations of depth-averaged velocity during steady flow. The estimator minimizes a cost function that combines known information in the form of a prior estimate and measured data (including measurement noise). State augmentation is used to relate the measured variable (velocity) to the unknown parameter (bathymetry). Specifically, the unknown consists of deviations in depth about a known along-channel mean. Verification of the method is performed using a simple 1D channel geometry as well as for two real-world reaches. In all cases, the verification is based on nominal river depths of 3–10 m, channel widths of 50–100 m, and Froude numbers much less than one. Further tests are performed to assess the usefulness of various observation types and sampling schemes for this type of estimation.

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Journal articles on the topic 'Schemas augmentation'

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