Journal articles on the topic 'GAN adaptation'
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Feng, Chengjian, Zhaoshui He, Jiawei Wang, Qinzhuang Lin, Zhouping Zhu, Jun Lu, and Shengli Xie. "Domain adaptation with SBADA-GAN and Mean Teacher." Neurocomputing 396 (July 2020): 577–86. http://dx.doi.org/10.1016/j.neucom.2018.12.089.
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Sanabria, Andrea Rosales, Franco Zambonelli, and Juan Ye. "Unsupervised Domain Adaptation in Activity Recognition: A GAN-Based Approach." IEEE Access 9 (2021): 19421–38. http://dx.doi.org/10.1109/access.2021.3053704.
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Rahman, Aimon, M. Sohel Rahman, and M. R. C. Mahdy. "3C-GAN: class-consistent CycleGAN for malaria domain adaptation model." Biomedical Physics & Engineering Express 7, no. 5 (July 7, 2021): 055002. http://dx.doi.org/10.1088/2057-1976/ac0e74.
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Ismail, Fawad, Palash Sarker, Mohamed Mohamed, Kyekyoon Kim, and Umberto Ravaioli. "Moving mesh adaptation for Si and GaN-based power device simulation." Journal of Computational Electronics 17, no. 4 (July 24, 2018): 1621–29. http://dx.doi.org/10.1007/s10825-018-1218-5.
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Wang, Xiaoqing, Xiangjun Wang, and Yubo Ni. "Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks." Computational Intelligence and Neuroscience 2018 (July 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/7208794.
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In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset. In the process of fine-tuning, we give the unlabelled GAN generated samples distributed pseudolabels dynamically according to the current prediction probabilities. Our method can be easily applied to any existing convolutional neural networks (CNN). We demonstrate the effectiveness of our method on four facial expression recognition datasets with two CNN structures and obtain inspiring results.
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Benjdira, Bilel, Adel Ammar, Anis Koubaa, and Kais Ouni. "Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks." Applied Sciences 10, no. 3 (February 6, 2020): 1092. http://dx.doi.org/10.3390/app10031092.
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Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.
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Sajun, Ali Reza, and Imran Zualkernan. "Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning." Applied Sciences 12, no. 3 (February 7, 2022): 1718. http://dx.doi.org/10.3390/app12031718.
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Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SSL. Previous work in applying GANs to SSL are classified into pseudo-labeling/classification, encoder-based, TripleGAN-based, two GAN, manifold regularization, and stacked discriminator approaches. A quantitative and qualitative analysis of the various approaches is presented. The R3-CGAN architecture is identified as the GAN architecture with state-of-the-art results. Given the recent success of non-GAN-based approaches for SSL, future research opportunities involving the adaptation of elements of SSL into GAN-based implementations are also identified.
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Yu, Wentao, Jing Bai, and Licheng Jiao. "Background Subtraction Based on GAN and Domain Adaptation for VHR Optical Remote Sensing Videos." IEEE Access 8 (2020): 119144–57. http://dx.doi.org/10.1109/access.2020.3004495.
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Hu, Wenpeng, Ran Le, Bing Liu, Feng Ji, Jinwen Ma, Dongyan Zhao, and Rui Yan. "Predictive Adversarial Learning from Positive and Unlabeled Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7806–14. http://dx.doi.org/10.1609/aaai.v35i9.16953.
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This paper studies learning from positive and unlabeled examples, known as PU learning. It proposes a novel PU learning method called Predictive Adversarial Networks (PAN) based on GAN (Generative Adversarial Networks). GAN learns a generator to generate data (e.g., images) to fool a discriminator which tries to determine whether the generated data belong to a (positive) training class. PU learning can be casted as trying to identify (not generate) likely positive instances from the unlabeled set to fool a discriminator that determines whether the identified likely positive instances from the unlabeled set are indeed positive. However, directly applying GAN is problematic because GAN focuses on only the positive data. The resulting PU learning method will have high precision but low recall. We propose a new objective function based on KL-divergence. Evaluation using both image and text data shows that PAN outperforms state-of-the-art PU learning methods and also a direct adaptation of GAN for PU learning.
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Villalonga, Gabriel, Joost Van de Weijer, and Antonio M. López. "Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition." Sensors 20, no. 3 (January 21, 2020): 583. http://dx.doi.org/10.3390/s20030583.
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On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio ∼ 1 / 4 for new/known classes; even for more challenging ratios such as ∼ 4 / 1 , the results are also very positive.
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Litrico, Mattia, Sebastiano Battiato, Sotirios A. Tsaftaris, and Mario Valerio Giuffrida. "Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap." Journal of Imaging 7, no. 10 (September 29, 2021): 198. http://dx.doi.org/10.3390/jimaging7100198.
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This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. In the context of holistic regression, most of the real-world datasets not only exhibit a covariate (or domain) shift, but also a label gap—the target dataset may contain labels not included in the source dataset (and vice versa). We propose an approach tackling both covariate and label gap in a unified training framework. Specifically, a Generative Adversarial Network (GAN) is used to reduce covariate shift, and label gap is mitigated via label normalisation. To avoid overfitting, we propose a stopping criterion that simultaneously takes advantage of the Maximum Mean Discrepancy and the GAN Global Optimality condition. To restore the original label range—that was previously normalised—a handful of annotated images from the target domain are used. Our experimental results, run on 3 different datasets, demonstrate that our approach drastically outperforms the state-of-the-art across the board. Specifically, for the cell counting problem, the mean squared error (MSE) is reduced from 759 to 5.62; in the case of the pedestrian dataset, our approach lowered the MSE from 131 to 1.47. For the last experimental setup, we borrowed a task from plant biology, i.e., counting the number of leaves in a plant, and we ran two series of experiments, showing the MSE is reduced from 2.36 to 0.88 (intra-species), and from 1.48 to 0.6 (inter-species).
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Bonechi, Simone, Paolo Andreini, Monica Bianchini, Akshay Pai, and Franco Scarselli. "Confidence Measures for Deep Learning in Domain Adaptation." Applied Sciences 9, no. 11 (May 29, 2019): 2192. http://dx.doi.org/10.3390/app9112192.
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In recent years, Deep Neural Networks (DNNs) have led to impressive results in a wide variety of machine learning tasks, typically relying on the existence of a huge amount of supervised data. However, in many applications (e.g., bio–medical image analysis), gathering large sets of labeled data can be very difficult and costly. Unsupervised domain adaptation exploits data from a source domain, where annotations are available, to train a model able to generalize also to a target domain, where labels are unavailable. Recent research has shown that Generative Adversarial Networks (GANs) can be successfully employed for domain adaptation, although deciding when to stop learning is a major concern for GANs. In this work, we propose some confidence measures that can be used to early stop the GAN training, also showing how such measures can be employed to predict the reliability of the network output. The effectiveness of the proposed approach has been tested in two domain adaptation tasks, with very promising results.
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Guo, Yuchen, Lan Du, and Guoxin Lyu. "SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size." Remote Sensing 13, no. 21 (October 20, 2021): 4202. http://dx.doi.org/10.3390/rs13214202.
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It is expensive and time-consuming to obtain a large number of labeled synthetic aperture radar (SAR) images. In the task of small training data size, the results of target detection on SAR images using deep network approaches are usually not ideal. In this study, considering that optical remote sensing images are much easier to be labeled than SAR images, we assume to have a large number of labeled optical remote sensing images and a small number of labeled SAR images with the similar scenes, propose to transfer knowledge from optical remote sensing images to SAR images, and develop a domain adaptive Faster R-CNN for SAR target detection with small training data size. In the proposed method, in order to make full use of the label information and realize more accurate domain adaptation knowledge transfer, an instance level domain adaptation constraint is used rather than feature level domain adaptation constraint. Specifically, generative adversarial network (GAN) constraint is applied as the domain adaptation constraint in the adaptation module after the proposals of Faster R-CNN to achieve instance level domain adaptation and learn the transferable features. The experimental results on the measured SAR image dataset show that the proposed method has higher detection accuracy in the task of SAR target detection with small training data size than the traditional Faster R-CNN.
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Bekkouch, Imad Eddine Ibrahim, Youssef Youssry, Rustam Gafarov, Adil Khan, and Asad Masood Khattak. "Triplet Loss Network for Unsupervised Domain Adaptation." Algorithms 12, no. 5 (May 8, 2019): 96. http://dx.doi.org/10.3390/a12050096.
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Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to as TripNet) harnesses the idea of a discriminator and Linear Discriminant Analysis (LDA) to push the encoder to generate domain-invariant features that are category-informative. At the same time, pseudo-labelling is used for the target data to train the classifier and to bring the same classes from both domains together. We evaluate TripNet against several existing, state-of-the-art methods on three image classification tasks: Digit classification (MNIST, SVHN, and USPC datasets), object recognition (Office31 dataset), and traffic sign recognition (GTSRB and Synthetic Signs datasets). Our experimental results demonstrate that (i) TripNet beats almost all existing methods (having a similar simple model like it) on all of these tasks; and (ii) for models that are significantly more complex (or hard to train) than TripNet, it even beats their performance in some cases. Hence, the results confirm the effectiveness of using TripNet for unsupervised domain adaptation in image classification.
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Chen, Jiawei, Yuexiang Li, Kai Ma, and Yefeng Zheng. "Generative Adversarial Networks for Video-to-Video Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3462–69. http://dx.doi.org/10.1609/aaai.v34i04.5750.
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Endoscopic videos from multicentres often have different imaging conditions, e.g., color and illumination, which make the models trained on one domain usually fail to generalize well to another. Domain adaptation is one of the potential solutions to address the problem. However, few of existing works focused on the translation of video-based data. In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains. As the frames of a video may have similar content and imaging conditions, the proposed VideoGAN has an X-shape generator to preserve the intra-video consistency during translation. Furthermore, a loss function, namely color histogram loss, is proposed to tune the color distribution of each translated frame. Two colonoscopic datasets from different centres, i.e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the performance of domain adaptation of our VideoGAN. Experimental results demonstrate that the adapted colonoscopic video generated by our VideoGAN can significantly boost the segmentation accuracy, i.e., an improvement of 5%, of colorectal polyps on multicentre datasets. As our VideoGAN is a general network architecture, we also evaluate its performance with the CamVid driving video dataset on the cloudy-to-sunny translation task. Comprehensive experiments show that the domain gap could be substantially narrowed down by our VideoGAN.
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Hartley, Zane K. J., Aaron S. Jackson, Michael Pound, and Andrew P. French. "GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit." Plant Phenomics 2021 (October 8, 2021): 1–11. http://dx.doi.org/10.34133/2021/9874597.
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3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.
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Zhang, Junbo, Shifeng Xu, Jun Sun, Dinghua Ou, Xiaobo Wu, and Mantao Wang. "Unsupervised Adversarial Domain Adaptation for Agricultural Land Extraction of Remote Sensing Images." Remote Sensing 14, no. 24 (December 12, 2022): 6298. http://dx.doi.org/10.3390/rs14246298.
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Agricultural land extraction is an essential technical means to promote sustainable agricultural development and modernization research. Existing supervised algorithms rely on many finely annotated remote-sensing images, which is both time-consuming and expensive. One way to reduce the annotation cost approach is to migrate models trained on existing annotated data (source domain) to unannotated data (target domain). However, model generalization capability is often unsatisfactory due to the limit of the domain gap. In this work, we use an unsupervised adversarial domain adaptation method to train a neural network to close the gap between the source and target domains for unsupervised agricultural land extraction. The overall approach consists of two phases: inter-domain and intra-domain adaptation. In the inter-domain adaptation, we use a generative adversarial network (GAN) to reduce the inter-domain gap between the source domain (labeled dataset) and the target domain (unlabeled dataset). The transformer with robust long-range dependency modeling acts as the backbone of the generator. In addition, the multi-scale feature fusion (MSFF) module is designed in the generator to accommodate remote sensing datasets with different spatial resolutions. Further, we use an entropy-based approach to divide the target domain. The target domain is divided into two subdomains, easy split images and hard split images. By training against each other between the two subdomains, we reduce the intra-domain gap. Experiments results on the “DeepGlobe → LoveDA”, “GID → LoveDA” and “DeepGlobe → GID” unsupervised agricultural land extraction tasks demonstrate the effectiveness of our method and its superiority to other unsupervised domain adaptation techniques.
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Fontaine, Matthew C., Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian Togelius, Amy K. Hoover, and Stefanos Nikolaidis. "Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 5922–30. http://dx.doi.org/10.1609/aaai.v35i7.16740.
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Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels. While GAN generated levels are stylistically similar to human-authored examples, human designers often want to explore the generative design space of GANs to extract interesting levels. However, human designers find latent vectors opaque and would rather explore along dimensions the designer specifies, such as number of enemies or obstacles. We propose using state-of-the-art quality diversity algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to efficiently explore the latent space of a GAN to extract levels that vary across a set of specified gameplay measures. In the benchmark domain of Super Mario Bros, we demonstrate how designers may specify gameplay measures to our system and extract high-quality (playable) levels with a diverse range of level mechanics, while still maintaining stylistic similarity to human authored examples. An online user study shows how the different mechanics of the automatically generated levels affect subjective ratings of their perceived difficulty and appearance.
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Liu, Tongyu, Ju Fan, Yinqing Luo, Nan Tang, Guoliang Li, and Xiaoyong Du. "Adaptive data augmentation for supervised learning over missing data." Proceedings of the VLDB Endowment 14, no. 7 (March 2021): 1202–14. http://dx.doi.org/10.14778/3450980.3450989.
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Real-world data is dirty, which causes serious problems in (supervised) machine learning (ML). The widely used practice in such scenario is to first repair the labeled source (a.k.a. train) data using rule-, statistical- or ML-based methods and then use the "repaired" source to train an ML model. During production, unlabeled target (a.k.a. test) data will also be repaired, and is then fed in the trained ML model for prediction. However, this process often causes a performance degradation when the source and target datasets are dirty with different noise patterns , which is common in practice. In this paper, we propose an adaptive data augmentation approach, for handling missing data in supervised ML. The approach extracts noise patterns from target data, and adapts the source data with the extracted target noise patterns while still preserving supervision signals in the source. Then, it patches the ML model by retraining it on the adapted data, in order to better serve the target. To effectively support adaptive data augmentation, we propose a novel generative adversarial network (GAN) based framework, called DAGAN, which works in an unsupervised fashion. DAGAN consists of two connected GAN networks. The first GAN learns the noise pattern from the target, for target mask generation. The second GAN uses the learned target mask to augment the source data, for source data adaptation. The augmented source data is used to retrain the ML model. Extensive experiments show that our method significantly improves the ML model performance and is more robust than the state-of-the-art missing data imputation solutions for handling datasets with different missing value patterns.
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Bhandarkar, Tanvi, and A. Murugan. "Understanding Trending Variants of Generative Adversarial Networks." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 864. http://dx.doi.org/10.14419/ijet.v7i3.12.16552.
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Generative Adversarial Networks (GAN) have its major contribution to the field of Artificial Intelligence. It is becoming so powerful by paving its way in numerous applications of intelligent systems. This is primarily due to its astute prospect of learning and solving complex and high-dimensional problems from the latent space. With the growing demands of GANs, it is necessary to seek its potential and impact in implementations. In short span of time, it has witnessed several variants and extensions in image translation, domain-adaptation and other academic fields. This paper provides an understanding of such imperative GANs mutants and surveys the existing adversarial models which are prominent in their applied field.
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Huang, Lidong, Aizhong Ye, Chongjun Tang, Qingyun Duan, and Yahai Zhang. "Impact of rural depopulation and climate change on vegetation, runoff and sediment load in the Gan River basin, China." Hydrology Research 51, no. 4 (June 23, 2020): 768–80. http://dx.doi.org/10.2166/nh.2020.120.
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Abstract Climate change and rural depopulation are changing the ecological and hydrological cycles in China. Data on the normalized difference vegetation index (NDVI), temperature, precipitation, streamflow, sediment and rural population are available for the Gan River basin from 1981 to 2017. We investigated the spatio-temporal variations in climate, human activity and vegetation mainly using the Mann–Kendall test and examined their relationship using the Granger causality test. The results showed that (1) the temperature markedly increased in all seasons; (2) the precipitation increased in summer and winter but decreased in spring and autumn; (3) overall, the NDVI increased markedly during 2005–2017, but showed seasonal differences, with decreases in summer and winter and increases in spring and autumn; (4) the annual sediment transport showed a significant decreasing trend and (5) a large number of the population shifted from rural to urban areas, resulting in a decrease in the rural population between 1998 and 2018. Rural depopulation has brought about farmland abandonment, conversion of farmland to forests, which was the factor driving the recovery of the vegetation and the decrease in sediment. The results of this study can provide support for climate change adaptation and sustainable development.
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Gil-Martín, Manuel, José Antúnez-Durango, and Rubén San-Segundo. "Adaptation and Selection Techniques Based on Deep Learning for Human Activity Recognition Using Inertial Sensors." Engineering Proceedings 2, no. 1 (November 14, 2020): 22. http://dx.doi.org/10.3390/ecsa-7-08159.
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Deep learning techniques have been widely applied to Human Activity Recognition (HAR), but a specific challenge appears when HAR systems are trained and tested with different subjects. This paper describes and evaluates several techniques based on deep learning algorithms for adapting and selecting the training data used to generate a HAR system using accelerometer recordings. This paper proposes two alternatives: autoencoders and Generative Adversarial Networks (GANs). Both alternatives are based on deep neural networks including convolutional layers for feature extraction and fully-connected layers for classification. Fast Fourier Transform (FFT) is used as a transformation of acceleration data to provide an appropriate input data to the deep neural network. This study has used acceleration recordings from hand, chest and ankle sensors included in the Physical Activity Monitoring Data Set (PAMAP2) dataset. This is a public dataset including recordings from nine subjects while performing 12 activities such as walking, running, sitting, ascending stairs, or ironing. The evaluation has been performed using a Leave-One-Subject-Out (LOSO) cross-validation: all recordings from a subject are used as testing subset and recordings from the rest of the subjects are used as training subset. The obtained results suggest that strategies using autoencoders to adapt training data to testing data improve some users’ performance. Moreover, training data selection algorithms with autoencoders provide significant improvements. The GAN approach, using the generator or discriminator module, also provides improvement in selection experiments.
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Lasloum, Tariq, Haikel Alhichri, Yakoub Bazi, and Naif Alajlan. "SSDAN: Multi-Source Semi-Supervised Domain Adaptation Network for Remote Sensing Scene Classification." Remote Sensing 13, no. 19 (September 27, 2021): 3861. http://dx.doi.org/10.3390/rs13193861.
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We present a new method for multi-source semi-supervised domain adaptation in remote sensing scene classification. The method consists of a pre-trained convolutional neural network (CNN) model, namely EfficientNet-B3, for the extraction of highly discriminative features, followed by a classification module that learns feature prototypes for each class. Then, the classification module computes a cosine distance between feature vectors of target data samples and the feature prototypes. Finally, the proposed method ends with a Softmax activation function that converts the distances into class probabilities. The feature prototypes are also divided by a temperature parameter to normalize and control the classification module. The whole model is trained on both the unlabeled and labeled target samples. It is trained to predict the correct classes utilizing the standard cross-entropy loss computed over the labeled source and target samples. At the same time, the model is trained to learn domain invariant features using another loss function based on entropy computed over the unlabeled target samples. Unlike the standard cross-entropy loss, the new entropy loss function is computed on the model’s predicted probabilities and does not need the true labels. This entropy loss, called minimax loss, needs to be maximized with respect to the classification module to learn features that are domain-invariant (hence removing the data shift), and at the same time, it should be minimized with respect to the CNN feature extractor to learn discriminative features that are clustered around the class prototypes (in other words reducing intra-class variance). To accomplish these maximization and minimization processes at the same time, we use an adversarial training approach, where we alternate between the two processes. The model combines the standard cross-entropy loss and the new minimax entropy loss and optimizes them jointly. The proposed method is tested on four RS scene datasets, namely UC Merced, AID, RESISC45, and PatternNet, using two-source and three-source domain adaptation scenarios. The experimental results demonstrate the strong capability of the proposed method to achieve impressive performance despite using only a few (six in our case) labeled target samples per class. Its performance is already better than several state-of-the-art methods, including RevGrad, ADDA, Siamese-GAN, and MSCN.
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Chamorro-Atalaya, Omar, Guillermo Morales-Romero, Adrián Quispe-Andía, Nicéforo Trinidad-Loli, Beatriz Caycho-Salas, Cesar León-Velarde, and Sofía Gamarra-Mendoza. "Distance Education and Student Satisfaction Regarding the Pedagogical Support Services Provided in Virtual Teaching-learning Environments." International Journal of Emerging Technologies in Learning (iJET) 16, no. 20 (October 25, 2021): 255. http://dx.doi.org/10.3991/ijet.v16i20.24559.
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The objective of this article is to know the satisfaction of students with online pedagogical support services, generated by the context of distance education due to the COVID-19 pandemic, which have been provided with various technologi-cal tools. During the development of the research, it was determined that there is a satisfaction with the pedagogical support of 61.2%, being the indicators of health services and welfare university, those that present the highest satisfaction both with 62.3%, on the other hand, are the indicators registrations and license plates, and psychopedagogical services those that present a greater dissatisfaction of 32.7 and 32.2, respectively. Although the pedagogical support provided by the university must continue to be improved, a slight improvement has been deter-mined with the previous semester in which the adaptation of online services be-gan, since dissatisfaction on the part of students decreased by 9.18%.
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Van den Broeck, Wouter A. J., Toon Goedemé, and Maarten Loopmans. "Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning." Remote Sensing 14, no. 23 (November 22, 2022): 5911. http://dx.doi.org/10.3390/rs14235911.
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Historical land cover (LC) maps are an essential instrument for studying long-term spatio-temporal changes of the landscape. However, manual labelling on low-quality monochromatic historical orthophotos for semantic segmentation (pixel-level classification) is particularly challenging and time consuming. Therefore, this paper proposes a methodology for the automated extraction of very-high-resolution (VHR) multi-class LC maps from historical orthophotos under the absence of target-specific ground truth annotations. The methodology builds on recent evolutions in deep learning, leveraging domain adaptation and transfer learning. First, an unpaired image-to-image (I2I) translation between a source domain (recent RGB image of high quality, annotations available) and the target domain (historical monochromatic image of low quality, no annotations available) is learned using a conditional generative adversarial network (GAN). Second, a state-of-the-art fully convolutional network (FCN) for semantic segmentation is pre-trained on a large annotated RGB earth observation (EO) dataset that is converted to the target domain using the I2I function. Third, the FCN is fine-tuned using self-annotated data on a recent RGB orthophoto of the study area under consideration, after conversion using again the I2I function. The methodology is tested on a new custom dataset: the ‘Sagalassos historical land cover dataset’, which consists of three historical monochromatic orthophotos (1971, 1981, 1992) and one recent RGB orthophoto (2015) of VHR (0.3–0.84 m GSD) all capturing the same greater area around Sagalassos archaeological site (Turkey), and corresponding manually created annotations (2.7 km² per orthophoto) distinguishing 14 different LC classes. Furthermore, a comprehensive overview of open-source annotated EO datasets for multiclass semantic segmentation is provided, based on which an appropriate pretraining dataset can be selected. Results indicate that the proposed methodology is effective, increasing the mean intersection over union by 27.2% when using domain adaptation, and by 13.0% when using domain pretraining, and that transferring weights from a model pretrained on a dataset closer to the target domain is preferred.
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Xiao, Jie. "Research on Super-Resolution Relationship Extraction and Reconstruction Methods for Images Based on Multimodal Graph Convolutional Networks." Mathematical Problems in Engineering 2022 (September 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/1016112.
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This study constructs a multimodal graph convolutional network model, conducts an in-depth study on image super-resolution relationship extraction and reconstruction methods, and constructs a model of image super-resolution relationship extraction and reconstruction methods based on multimodal graph convolutional networks. In this study, we study the domain adaptation model algorithm based on chart convolutional networks, which constructs a global relevance graph based on all samples using pre-extracted features and performs distribution approximation of sample features in two domains using a diagram convolutional neural network with maximum mean difference loss; with this approach, the model effectively preserves the structural information among the samples. In this study, several comparison experiments are designed based on the COCO and VG datasets; the image space information-based and knowledge graph-based target detection and recognition models substantially improve recognition performance over the baseline model. The super-pixel-based target detection and recognition model can also effectively reduce the number of floating-point operations and the complexity of the model. In this study, we propose a multiscale GAN-based image super-resolution reconstruction algorithm. Aiming at the problems of detail loss or blurring in the reconstruction of detail-rich images by SRGAN, it integrates the idea of the Laplace pyramid to complete the task of multiscale reconstruction of images through staged reconstruction. It incorporates the concept of a discriminative network with patch GAN to effectively improve the recovery effect of graph details and improve the reconstruction quality of images. Using Set5, Set14, BSD100, and Urban100 datasets as test sets, experimental analysis is conducted from objective and subjective evaluation metrics to effectively validate the performance of the improved algorithm proposed in this study.
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Song, Qing-Hua, Takao Kobayashi, Takayuki Hosoi, and Jong-Chol Cyong. "Effects of Traditional Chinese Medicines on Murine Bone Metabolism in a Microgravity Environment." American Journal of Chinese Medicine 31, no. 05 (January 2003): 739–49. http://dx.doi.org/10.1142/s0192415x03001363.
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We investigated the effects of three traditional Chinese medicine prescriptions on changes of bone metabolism in mice, using a gravity device to produce a microgravity environment. We found that Hochu-ekki-to (TJ-41) and Hachimi-jio-gan (TJ-7) suppress the increase in the ratio of serum Ca/P and the increase of calcium in urine. Moreover, TJ-41 and Shin-bu-to (TJ-30) reversed the increase of alkaline phosphatase activity (ALP), and TJ-41 also reversed the decrease of estradiol in the serum. The mechanism may be that the traditional Chinese medicines increased estradiol, causing the decrease of ALP, which induced the changes of Ca and P in serum, leading to a decreased excretion of Ca in urine. In this study, TJ-41 was effective in every parameter while TJ-7 and TJ-30 was effective on some parameters, showing that traditional Chinese medicines have specificities in the space environment. In conclusion, this study suggests that some traditional Chinese medicines may be beneficial for adaptation to a space environment.
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Sylvain, Tristan, Pengchuan Zhang, Yoshua Bengio, R. Devon Hjelm, and Shikhar Sharma. "Object-Centric Image Generation from Layouts." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2647–55. http://dx.doi.org/10.1609/aaai.v35i3.16368.
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We begin with the hypothesis that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes with multiple objects well. Our layout-to-image-generation method, which we call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a novel Scene-Graph Similarity Module (SGSM). The SGSM learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We also propose changes to the conditioning mechanism of the generator that enhance its object instance-awareness. Apart from improving image quality, our contributions mitigate two failure modes in previous approaches: (1) spurious objects being generated without corresponding bounding boxes in the layout, and (2) overlapping bounding boxes in the layout leading to merged objects in images. Extensive quantitative evaluation and ablation studies demonstrate the impact of our contributions, with our model outperforming previous state-of-the-art approaches on both the COCO-Stuff and Visual Genome datasets. Finally, we address an important limitation of evaluation metrics used in previous works by introducing SceneFID -- an object-centric adaptation of the popular Fréchet Inception Distance metric, that is better suited for multi-object images.
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Yakushin, Sergei B., Theodore Raphan, and Bernard Cohen. "Gravity-Specific Adaptation of the Angular Vestibuloocular Reflex: Dependence on Head Orientation With Regard to Gravity." Journal of Neurophysiology 89, no. 1 (January 1, 2003): 571–86. http://dx.doi.org/10.1152/jn.00287.2002.
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The gain of the vertical angular vestibuloocular reflex (aVOR) was adaptively altered by visual-vestibular mismatch during rotation about an interaural axis, using steps of velocity in three head orientations: upright, left-side down, and right-side down. Gains were decreased by rotating the animal and visual surround in the same direction and increased by visual and surround rotation in opposite directions. Gains were adapted in one head position (single-state adaptation) or decreased with one side down and increased with the other side down (dual-state adaptation). Animals were tested in darkness using sinusoidal rotation at 0.5 Hz about an interaural axis that was tilted from horizontal to vertical. They were also sinusoidally oscillated from 0.5 to 4 Hz about a spatial vertical axis in static tilt positions from yaw to pitch. After both single- and dual-state adaptation, gain changes were maximal when the monkeys were in the position in which the gain had been adapted, and the gain changes progressively declined as the head was tilted away from that position. We call this gravity-specific aVOR gain adaptation. The spatial distribution of the specific aVOR gain changes could be represented by a cosine function that was superimposed on a bias level, which we called gravity-independent gain adaptation. Maximal gravity-specific gain changes were produced by 2–4 h of adaptation for both single- and dual-state adaptations, and changes in gain were similar at all test frequencies. When adapted while upright, the magnitude and distribution of the gravity-specific adaptation was comparable to that when animals were adapted in side-down positions. Single-state adaptation also produced gain changes that were independent of head position re gravity particularly in association with gain reduction. There was no bias after dual-state adaptation. With this difference, fits to data obtained by altering the gain in separate sessions predicted the modulations in gain obtained from dual-state adaptations. These data show that the vertical aVOR gain changes dependent on head position with regard to gravity are continuous functions of head tilt, whose spatial phase depends on the position in which the gain was adapted. From their different characteristics, it is likely that gravity-specific and gravity-independent adaptive changes in gain are produced by separate neural processes. These data demonstrate that head orientation to gravity plays an important role in both orienting and tuning the gain of the vertical aVOR.
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Hasan, S. M. Kamrul, and Cristian A. Linte. "Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning." Applied Sciences 12, no. 23 (November 28, 2022): 12163. http://dx.doi.org/10.3390/app122312163.
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Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model—namely, combine-all in semi-supervised learning (CqSL)—to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured L2 similarity (SL2SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly.
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Lee, Qian-Yo, Chiyang James Chou, Ming-Xuan Lee, and Yen-Chun Lee. "Detecting the Knowledge Domains of Compound Semiconductors." Micromachines 13, no. 3 (March 20, 2022): 476. http://dx.doi.org/10.3390/mi13030476.
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The development of compound semiconductors (CS) has received extensive attention worldwide. This study aimed to detect and visualize CS knowledge domains for quantifying CS research patterns and emerging trends through a scientometric review based on the literature between 2011 and 2020 by using CiteSpace. The combined dataset of 24,622 bibliographic records were collected through topic searches and citation expansion to ensure adequate coverage of the field. While research in “solar cell” and “perovskite tandem” appears to be the two most distinctive knowledge domains in the CS field, research related to thermoelectric materials has grown at a respectable pace. Most notably, the deep connections between “thermoelectric material” and “III-Sb nanowire (NW)” research have been demonstrated. A rapid adaptation of black phosphorus (BP) field-effect transistors (FETs) and gallium nitride (GaN) transistors in the CS field is also apparent. Innovative strategies have focused on the opto-electronics with engineered functionalities, the design, synthesis and fabrication of perovskite tandem solar cells, the growing techniques of Sb-based III–V NWs, and the thermal conductivity of boron arsenide (BAs). This study revealed how the development trends and research areas in the CS field advance over time, which greatly help us to realize its knowledge domains.
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Ferreira, André, Ricardo Magalhães, Sébastien Mériaux, and Victor Alves. "Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network." Applied Sciences 12, no. 10 (May 11, 2022): 4844. http://dx.doi.org/10.3390/app12104844.
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Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session. Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. To overcome these challenges, an adaptation of the alpha Generative Adversarial Networks (GANs) architecture was used to test its ability to generate realistic 3D MRI scans of the rat brain in silico. As far as the authors are aware, this was the first time a GAN-based approach was used to generate synthetic MRI data of the rat brain. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test. The last two tests proved the realism and applicability of the generated scans to real problems. Therefore, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans, and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation.
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Ali, Muhaddisa Barat, Irene Yu-Hua Gu, Mitchel S. Berger, Johan Pallud, Derek Southwell, Georg Widhalm, Alexandre Roux, Tomás Gomez Vecchio, and Asgeir Store Jakola. "Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas." Brain Sciences 10, no. 7 (July 18, 2020): 463. http://dx.doi.org/10.3390/brainsci10070463.
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Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.
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Castro-Vázquez, Genaro. "Cultural Scripts Underpinning Prostate Cancer-Literacy in Japan." American Journal of Men's Health 16, no. 1 (January 2022): 155798832210766. http://dx.doi.org/10.1177/15579883221076658.
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In a country where cancer has been dubbed a “national disease” ( kokumin bio) that mostly affects Japanese men, this article presents a reading of the cultural scripts underneath prostate cancer—one of the “Western type of cancers” ( ōbeigata no gan). The reading is grounded in an adaptation of the “sexual scripting theory,” the construct of cancer-literacy, and the analysis of 3,092 newspaper reports published from 2005 to 2020, in three Japanese newspapers with the largest circulation in the country. The analysis is presented in line with three axes: cancer-self, cancer-biopedagogy, and cancer-economics to indicate that a cancer-self largely entails the subjectivity of a Westernized, married, heterosexual man who undergoes andropause, needs to understand what bladder somatics is, and depends on his family and the feminization of care to cope with cancer. The chances to prevent and/or survive the disease chiefly hinge on adopting a form of cancer-biopedagogy, which entails a composite entanglement of knowledge and health-related practices underpinned by the ethnicization of cancer through the consumption of “traditional food” ( washoku) and the assumption that turning into a “healthy self” is determined by Japanese ethnic traits. Cancer-economics is concerned with costs of testing and treatments, health care insurance policies, and food and dietary supplements that serve to commodify a cancer-self who deals with prostate and urinary-related issues.
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Jacquot, Sophie, and Tommaso Vitale. "Adaptation et réflexivité." Gouvernement et action publique 4, no. 4 (2014): 35. http://dx.doi.org/10.3917/gap.144.0035.
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Shankar, Prabhat, Masatoshi Nishikawa, and Tatsuo Shibata. "2P273 Gain Noise Relation in Adaptation Networks(24. Mathematical biology,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S204. http://dx.doi.org/10.2142/biophys.53.s204_2.
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Salam, Aziz, and Zhulmaydin Chairil Fachrussyah. "ADAPTASI DAN INOVASI TEKNOLOGI PERAHU NELAYAN DAN ALAT TANGKAP TRADISIONAL DI TELUK TOMINI." Marine Fisheries : Journal of Marine Fisheries Technology and Management 12, no. 1 (October 6, 2021): 101–11. http://dx.doi.org/10.29244/jmf.v12i1.32940.
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The development of boat technology and traditional fishing gear, which is still predominantly used by fishing communities in Indonesia, appears to be running slowly. This study aims to determine the adaptations and technological innovations that have been applied in fishing communities for the development of capture fisheries technology in Tomini Gulf. Data and information about fishing boats and fishing gear in the form of types, functions, sizes, structures, pictures, raw materials, and changes in shape, changes in function, as well as adaptations and technological innovations that occur are obtained by field surveys conducted in fishing villages. Other data were obtained through in-depth interviews with fishermen and community leaders using interview grid instruments. Adaptations and innovations that occur in fishing boats include the use of bodi hull types on ketinting (long-tail outboard engine) boats and the use of fiberglass and PVC (Polyvinyl chloride) pipe as substitutes for wood and bamboo as raw materials for hull and outrigger buoys. The lampu suntik (waterproof lamp of LED sircuit in a used syringe plastic tube) is an innovation made on the totabito squid fishing gear. Adaptation and innovation in fisheries technology occurs part-by-part (partial) and requires proving success to gain wide acceptance.
Keywords: adaptation; innovation; techology; fishing boat; fishing gear; traditional.
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Ilchmann, A., and E. P. Ryan. "On gain adaptation in adaptive control." IEEE Transactions on Automatic Control 48, no. 5 (May 2003): 895–99. http://dx.doi.org/10.1109/tac.2003.811276.
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ILCHMANN, ACHIM, and DAVID H. OWENS. "Exponential Stabilization using Nondifferential Gain Adaptation." IMA Journal of Mathematical Control and Information 7, no. 4 (1990): 339–49. http://dx.doi.org/10.1093/imamci/7.4.339.
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Gegenfurtner, K., A. Schutz, and M. Schneider. "Saccadic gain adaptation follows perceived position." Journal of Vision 8, no. 6 (April 8, 2010): 917. http://dx.doi.org/10.1167/8.6.917.
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Shelhamer, Mark, and Richard A. Clendaniel. "Context-specific adaptation of saccade gain." Experimental Brain Research 146, no. 4 (August 29, 2002): 441–50. http://dx.doi.org/10.1007/s00221-002-1199-1.
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Vail, Alexander L., and Mark I. McCormick. "Metamorphosing reef fishes avoid predator scent when choosing a home." Biology Letters 7, no. 6 (June 8, 2011): 921–24. http://dx.doi.org/10.1098/rsbl.2011.0380.
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Most organisms possess anti-predator adaptations to reduce their risk of being consumed, but little is known of the adaptations prey employ during vulnerable life-history transitions when predation pressures can be extreme. We demonstrate the use of a transition-specific anti-predator adaptation by coral reef fishes as they metamorphose from pelagic larvae to benthic juveniles, when over half are consumed within 48 h. Our field experiment shows that naturally settling damselfish use olfactory, and most likely innate, predator recognition to avoid settling to habitat patches manipulated to emit predator odour. Settlement to patches emitting predator odour was on average 24–43% less than to control patches. Evidence strongly suggests that this avoidance of sedentary and patchily distributed predators by nocturnal settlers will gain them a survival advantage, but also lead to non-lethal predator effects: the costs of exhibiting anti-predator adaptations. Transition-specific anti-predator adaptations, such as demonstrated here, may be widespread among organisms with complex life cycles and play an important role in prey population dynamics.
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Gimmon, Yoav, Americo A. Migliaccio, Christopher J. Todd, William V. C. Figtree, and Michael C. Schubert. "Simultaneous and opposing horizontal VOR adaptation in humans suggests functionally independent neural circuits." Journal of Neurophysiology 120, no. 4 (October 1, 2018): 1496–504. http://dx.doi.org/10.1152/jn.00134.2018.
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The healthy vestibulo-ocular reflex (VOR) ensures that images remain on the fovea of the retina during head rotation to maintain stable vision. VOR behavior can be measured as a summation of linear and nonlinear properties although it is unknown whether asymmetric VOR adaptation can be performed synchronously in humans. The purpose of the present study is twofold. First, examine whether the right and left VOR gains can be synchronously adapted in opposing directions. Second, to investigate whether the adaptation context transfers between both sides. Three separate VOR adaptation sessions were randomized such that the VOR was adapted Up-bilaterally, Down-bilaterally, or Mixed (one side up, opposite side down). Ten healthy subjects completed the study. Subjects were tested while seated upright, 1 meter in front of a wall in complete dark. Each subject made active (self-generated) head impulse rotations for 15 min while viewing a gradually increasing amount of retinal slip. VOR training demand changed by 10% every 90 s. The VOR changed significantly for all training conditions. No significant differences in the magnitude of VOR gain changes between training conditions were found. The human VOR can be simultaneously driven in opposite directions. The similar magnitude of VOR gain changes across training conditions suggests functionally independent VOR circuits for each side of head rotation that mediate simultaneous and opposing VOR adaptations. NEW & NOTEWORTHY Our results indicate that humans have the adaptive capacity for concurrent and opposing directions of vestibulo-ocular reflex (VOR) motor learning. Context specificity of VOR adaptation is dependent on the error signal being unilateral or bilateral, which we illustrate via a lack of VOR gain transfer using unique adaptive demands.
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Cullingham, Catherine I., Janice E. K. Cooke, and David W. Coltman. "Effects of introgression on the genetic population structure of two ecologically and economically important conifer species: lodgepole pine (Pinus contorta var. latifolia) and jack pine (Pinus banksiana)." Genome 56, no. 10 (October 2013): 577–85. http://dx.doi.org/10.1139/gen-2013-0071.
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Forest trees exhibit a remarkable range of adaptations to their environment, but as a result of frequent and long-distance gene flow, populations are often only weakly differentiated. Lodgepole and jack pine hybridize in western Canada, which adds the opportunity for introgression through hybridization to contribute to population structure and (or) adaptive variation. Access to large sample size, high density SNP datasets for these species would improve our ability to resolve population structure, parameterize introgression, and separate the influence of demography from adaptation. To accomplish this, 454 transcriptome reads for lodgepole and jack pine were assembled using Newbler and MIRA, the assemblies mined for SNPs, and 1536 SNPs were selected for typing on lodgepole pine, jack pine, and their hybrids (N = 536). We identified population structure using both Bayesian clustering and discriminate analysis of principle components. Introgressed SNP loci were identified and their influence on observed population structure was assessed. We found that introgressed loci resulted in increased differentiation both within lodgepole and jack pine populations. These findings are timely given the recent mountain pine beetle population expansion in the hybrid zone, and will facilitate future studies of adaptive traits in these ecologically important species.
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McElligott, J. G., M. Weiser, and R. Baker. "Effect of temperature on the normal and adapted vestibulo-ocular reflex in the goldfish." Journal of Neurophysiology 74, no. 4 (October 1, 1995): 1463–72. http://dx.doi.org/10.1152/jn.1995.74.4.1463.
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1. The vestibulo-ocular reflex, a sensorimotor process, operates in a similar manner for homeothermic (mammals) and poikilothermic (fish) animals. However, individual physiological, biochemical, and/or pharmacological thermolabile processes that underlie the operation of this reflex could alter the operation of this reflex in a poikilotherm. The object of this study was to determine what aspects of the vestibulo-ocular reflex are affected by temperature changes naturally experienced by a poikilothermic animal, the goldfish. 2. Experiments were conducted on the visuovestibulo-(Vis-VOR) and vestibulo-ocular reflex (VOR) during normal operation as well as during the acquisition (learning) and retention (memory) phases of adaptive gain change. These studies were carried out at temperatures to which goldfish had been acclimated over several weeks and after rapid (< 5 min) shifts from this acclimation temperature. 3. Normal sinusoidal Vis-VOR and VOR gains before adaptation were found to be independent of the acclimation temperature over a wide range. Acute temperature changes of up to 10 degrees C either above or below a 20 degrees C acclimation temperature (Ac degree C = 20 degrees C) did not significantly modify normal visual and/or vestibular oculomotor reflex gains. 4. Surprisingly, slight reductions in temperature, as small as 2.5 degrees C, noticeably reduced Vis-VOR and VOR gain adaptations. Both short (3 h) and intermediate (up to 48 h) term reflex modifications were affected. Loss of adaptation was observed 10 degrees C below the acclimation temperature (Ac - 10 degrees C); however, return to the original temperature immediately restored most (60-100%) of the previously acquired Vis-VOR and VOR gain changes. In contrast, elevation of temperature up to 10 degrees C above the acclimation temperature (Ac + 10 degrees C) did not alter either increases or decreases in the adapted Vis-VOR or VOR gain. 5. A decrease in temperature reduced the magnitude of an adapted VOR gain increase and elevated the magnitude of an adapted gain decrease, thus returning the VOR gain back toward its normal control gain before adaptation. Because both increases and decreases in VOR gain were affected by the same temperature reduction, the cold effect was not a generalized reflex suppression, but inactivation of a process responsible for maintaining VOR adaptation. 6. During the acquisition phase, the time course and magnitude of adaptive VOR gain increases at temperatures acutely set 8-10 degrees C below the acclimation temperature were similar to those obtained at the acclimation temperature. Because the same temperature decrease inactivated retention of adapted VOR gain changes, the neuronal processes underlying the acquisition and the retention phases of Vis-VOR or VOR adaptation are suggested to differ qualitatively. 7. With the use of velocity step stimuli, both the adapted dynamic (< 100 ms) and sustained (> 100 ms) components of VOR adaptation were reduced by cooling. This effect on the dynamic component demonstrates an alteration in the shortest latency pathway through the vestibular nucleus and indicates that one thermosensitive site resides in the brain stem. 8. These results also show that, over a wide range of temperatures (20 +/- 10 degrees C), the neuronal processing that is responsible for the normal operation of the visuovestibulo- and/or vestibulo-ocular reflex and for the retention of reflex adaptation functions by separate physiological processes within the same brain stem and cerebellar circuitry. 9. We conclude that temperature exhibits a unique, and unexpected, state-dependent effect on sensorimotor regulation and adaptation for periods up to 48 h. Temperature does not alter normal VOR or the acquisition phase of an adapted gain change. (ABSTRACT TRUNCATED)
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Mammano, Fabrizio, Caroline Petit, and François Clavel. "Resistance-Associated Loss of Viral Fitness in Human Immunodeficiency Virus Type 1: Phenotypic Analysis of Protease andgag Coevolution in Protease Inhibitor-Treated Patients." Journal of Virology 72, no. 9 (September 1, 1998): 7632–37. http://dx.doi.org/10.1128/jvi.72.9.7632-7637.1998.
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ABSTRACT We have studied the phenotypic impact of adaptative Gag cleavage site mutations in patient-derived human immunodeficiency virus type 1 (HIV-1) variants having developed resistance to the protease inhibitor ritonavir or saquinavir. We found that Gag mutations occurred in a minority of resistant viruses, regardless of the duration of the treatment and of the protease mutation profile. Gag mutations exerted only a partial corrective effect on resistance-associated loss of viral fitness. Reconstructed viruses with resistant proteases displayed multiple Gag cleavage defects, and in spite of Gag adaptation, several of these defects remained, explaining the limited corrective effect of cleavage site mutations on fitness. Our data provide clear evidence of the interplay between resistance and fitness in HIV-1 evolution in patients treated with protease inhibitors.
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Nordstrom, K., I. M. de Miguel, and D. C. O'Carroll. "Rapid contrast gain reduction following motion adaptation." Journal of Experimental Biology 214, no. 23 (November 9, 2011): 4000–4009. http://dx.doi.org/10.1242/jeb.057539.
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Ordaz, Patricio, and Alex Poznyak. "‘KL’-gain adaptation for attractive ellipsoid method." IMA Journal of Mathematical Control and Information 32, no. 3 (February 10, 2014): 447–69. http://dx.doi.org/10.1093/imamci/dnt046.
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Langlois, Frédéric, and Robert Ladouceur. "Adaptation of a GAD treatment for hypochondriasis." Cognitive and Behavioral Practice 11, no. 4 (September 2004): 393–404. http://dx.doi.org/10.1016/s1077-7229(04)80056-7.
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Harris, Robert A., David C. O'Carroll, and Simon B. Laughlin. "Contrast Gain Reduction in Fly Motion Adaptation." Neuron 28, no. 2 (November 2000): 595–606. http://dx.doi.org/10.1016/s0896-6273(00)00136-7.
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