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2022 ◽  
pp. 1-39
Author(s):  
Zhicheng Geng ◽  
Zhanxuan Hu ◽  
Xinming Wu ◽  
Luming Liang ◽  
Sergey Fomel

Detecting subsurface salt structures from seismic images is important for seismic structural analysis and subsurface modeling. Recently, deep learning has been successfully applied in solving salt segmentation problems. However, most of the studies focus on supervised salt segmentation and require numerous accurately labeled data, which is usually laborious and time-consuming to collect, especially for the geophysics community. In this paper, we propose a semi-supervised framework for salt segmentation, which requires only a small amount of labeled data. In our method, adopting the mean teacher method, we train two models sharing the same network architecture. The student model is optimized using a combination of supervised loss and unsupervised consistency loss, whereas the teacher model is the exponential moving average (EMA) of the student model. We introduce the unsupervised consistency loss to better extract information from unlabeled data by constraining the network to give consistent predictions for the input data and its perturbed version. We train and test our novel semi-supervised method on both synthetic and real datasets. Results demonstrate that our proposed semi-supervised salt segmentation method outperforms the supervised baseline when there is a lack of labeled training data.


2022 ◽  
Vol 14 (2) ◽  
pp. 363
Author(s):  
Nuerbiye Muhetaer ◽  
Ilyas Nurmemet ◽  
Adilai Abulaiti ◽  
Sentian Xiao ◽  
Jing Zhao

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 158
Author(s):  
Iulian-Alin Roșu ◽  
Dragoș-Constantin Nica ◽  
Cătălin Dumitraș ◽  
Dragoș Chitariu ◽  
Luminița Bibire ◽  
...  

In this paper, a practical application of theoretical developments found in our previous works is explored in relation to atmospheric lidar data. Multifractal structures, previously named “laminar channels”, have been identified in atmospheric profiles—these exhibit cellular and self-structuring properties, and are spatially ordered across the atmospheric profile. Furthermore, these structures have been connected to the spontaneous emergence of turbulent behavior in the calm atmospheric flow. Calculating the location and occurrence of these channels can help identify features of atmospheric evolution, such as the development of the planetary boundary layer (PBL). Employing this theoretical background to atmospheric lidar data, attempts are made to confirm this suggestion and extract information about atmospheric structure and evolution by analyzing turbulent vortex scale dynamics and scale-corresponding Lyapunov exponents that form the basis of identifying the laminar channels in atmospheric lidar profiles. A parameter named “scale laminarity index” is then introduced, which quantifies the relation between vortex scale and chaoticity throughout the profile. Finally, the algorithmic methods employed in this study are described and distributed for future use.


2021 ◽  
Author(s):  
John Miller ◽  
Guilherme Vieira da Silva ◽  
Darrell Strauss

Abstract Tropical Cyclones (TCs) with genesis in the Coral Sea, often near the east coast of Australia, present significant hazards to coastal regions in their surroundings. There has, therefore, been significant recent efforts to extract information from records of their historical tracks in order to help predict their future behaviour in the light of a changing climate. In this study, the Australian Bureau of Meteorology (BOM) database of TC tracks over the last fifty years were grouped based on K-means clustering of the maximum wind-weighted centroids. Track shape variance and track curvature (sinuosity) were assessed. Three well defined clusters of TC tracks were identified, and the results showed predominant directions of TC movement by cluster. Track sinuosity was shown to increase from east to west. Only one cluster showed a statistically significant trend (decreasing) in TC frequency. The concept of TC power dissipation index (PDI) was introduced, revealing that two of the clusters have diverging trends for PDI post 2004. The location of cyclone maximum intensity (LMI) was trended, and only one cluster showed a statistically significant trend (towards the equator) for LMI. All these findings demonstrated a clear variance in risk between the clusters and suggests that this method of cluster analysis is a useful and productive complementary tool when establishing future impacts of TCs - the method identifies divergent TC characteristics and trends at a finer scale (cluster) level which then aids in assigning specific and differing TC risk mitigation strategies to different areas of the Australian east coast.


Author(s):  
Haidee Kotze ◽  
Berit Janssen ◽  
Corina Koolen ◽  
Luka van der Plas ◽  
Gys-Walt van Egdom

Abstract This article uses the Digital Opinions on Translated Literature (dioptra-l) corpus to study readers’ perceptions of and responses to translation in a naturalistic setting, focusing on the normative constructs or cognitive-evaluative templates they use to conceptualise, evaluate and respond to translations. We answer two main questions: (1) How visible, or salient, is the fact of translation to readers reading a translated literary text, and are there differences in the degree and nature of this visibility for different languages and translation directions? (2) What are the main concepts, and emotional and evaluative parameters that readers use to describe translated literary texts, and are there differences in these concepts and parameters when considered by different translation directionalities and genres? We make use of computational methods, including collocational network analysis, keyword analysis, and sentiment analysis to extract information about the salience of translation, and the networks of emotive and evaluative language that are used around the concept of translation. This forms the basis of our proposals for particular cognitive-evaluative templates.


2021 ◽  
pp. 1-20
Author(s):  
Silvia Restrepo ◽  
Enrique ter Horst ◽  
Juan Diego Zambrano ◽  
Laura H. Gunn ◽  
German Molina ◽  
...  

This manuscript builds on a novel, automatic, freely-available Bayesian approach to extract information in abstracts and titles to classify research topics by quartile. This approach is demonstrated for all N= 149,129 ISI-indexed publications in biological sciences journals during 2017. A Bayesian multinomial inverse regression approach is used to extract rankings of topics without the need of a pre-defined dictionary. Bigrams are used for extraction of research topics across manuscripts, and rankings of research topics are constructed by quartile. Worldwide and local results (e.g., comparison between two peer/aspirational research institutions in Colombia) are provided, and differences are explored both at the global and local levels. Some topics persist across quartiles, while the relevance of others is quartile-specific. Challenges in sustainable development appear as more prevalent in top quartile journals across institutions, while the two Colombian institutions favour plant and microorganism research. This approach can reduce information inequities, by allowing young/incipient researchers in biological sciences, especially within lower income countries or universities with limited resources, to freely assess the state of the literature and the relative likelihood of publication in higher impact journals by research topic. This can also serve institutions of higher education to identify missing research topics and areas of competitive advantage.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruchika Mittal ◽  
Gauri Srivastava ◽  
Deepak Ganjewala

Abstract Monoterpenes, a class of isoprenoid compounds, are extensively used in flavor, fragrance, perfumery, and cosmetics. They display many astonishing bioactive properties of biological and pharmacological significance. All monoterpenes are derived from universal precursor geranyl diphosphate. The demand for new monoterpenoids has been increasing in flavor, fragrances, perfumery, and pharmaceuticals. Chemical methods, which are harmful for human and the environment, synthesize most of these products. Over the years, researchers have developed alternative methods for the production of newer monoterpenoids. Microbial biotransformation is one of them, which relied on microbes and their enzymes. It has produced many new desirable commercially important monoterpenoids. A growing number of reports reflect an ever-expanding scope of microbial biotransformation in food and aroma industries. Simultaneously, our knowledge of the enzymology of monoterpene biosynthetic pathways has been increasing, which facilitated the biotransformation of monoterpenes. In this article, we have covered the progress made on microbial biotransformation of commercial monoterpenes with a brief introduction to their biosynthesis. We have collected several reports from authentic web sources, including Google Scholar, Pubmed, Web of Science, and Scopus published in the past few years to extract information on the topic.


2021 ◽  
Author(s):  
Luis Flores Horgue ◽  
Alexis Assens ◽  
Leon Fodoulian ◽  
Leonardo Marconi Archinto ◽  
Joel Tuberosa ◽  
...  

Sensory adaptation is critical to extract information from a changing world. Taking advantage of the extensive parallel coding lines present in the olfactory system, we explored the potential variations of neuronal identities before and after olfactory experience. We found that at rest, the transcriptomic profiles of olfactory sensory neuron populations are already highly divergent, specific to the olfactory receptor they express, and are surprisingly associated with the sequence of these latter. These divergent profiles further evolve in response to the environment, as odorant exposure leads to massive reprogramming via the modulation of transcription. Adenylyl cyclase 3, but not other main elements of the olfactory transduction cascade, plays a critical role in this activity-induced transcriptional adaptation. These findings highlight a broad range of sensory neuron identities that are present at rest and that adapt to the experience of the individual, thus providing a novel layer of complexity to sensory coding.


2021 ◽  
Vol 8 (11) ◽  
pp. 419-422
Author(s):  
Senthil Kumaran M ◽  
Bedanta Sarma ◽  
Arun Kumar S

The increasing demand to dispose of the cases swiftly, police often resort to third-degree methods to extract information from the individual; and in the process violate the fundamental rights to life and personal liberty stated under article 21 of the constitution of India. With the development of science and technology quickly eliciting the information is possible by adopting methods of polygraph, brain mapping, and narco analysis. In the past various experts, committees and judgements in courts have recommended these technologies to be used. Though there is a demand, it also raises serious legal, ethical and medical issues. Through this article we attempted to analyze the issues from various angles, and should take steps in the future to implement them. Keywords: Deception Detection Test (DDT), polygraph, brain mapping, narco analysis.


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