scholarly journals HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation

2021 ◽  
Vol 13 (11) ◽  
pp. 2076
Author(s):  
Sihan Xue ◽  
Xupu Geng ◽  
Lingsheng Meng ◽  
Ting Xie ◽  
Lei Huang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Wave Parameter ◽  
Rip Currents ◽  
Small Satellite ◽  
Interface Phenomena ◽  
Launch Site ◽  
Rich Information ◽  
The Rich ◽  
Ocean Remote Sensing ◽  
Large Observation

On 22 December 2020, HISEA-1, the first C-band SAR small satellite for ocean remote sensing, was launched from the coastal Wenchang launch site. Though small in weight, the images it produced have a high spatial resolution of 1 m and a large observation width of 100 km. The first batch of images obtained within the first week after the launch confirmed the rich information in the data, including sea ice, wind, wave, rip currents, vortexes, ships, and oil film on the sea, as well as landmark buildings. Furthermore, geometric characteristics of sea ice, wind vector, ocean wave parameter, 3D features of buildings, and some air-sea interface phenomena in dark spots could also be detected after relevant processing. All these indicate that HISEA-1 could be a reliable, remarkable, and powerful instrument for observing oceans and lands.

scholarly journals Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110407
Author(s):  
Katie Shilton ◽  
Emanuel Moss ◽  
Sarah A. Gilbert ◽  
Matthew J. Bietz ◽  
Casey Fiesler ◽  
...  
Keyword(s):  
Data Science ◽  
Methodological Approach ◽  
Digital Data ◽  
Colonial Legacy ◽  
Research Scholarship ◽  
History Of ◽  
Rich Information ◽  
The Rich ◽  
Relationship Of ◽  
The Relationship

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.

Poet as Anthropologist—“European Paideuma”

2020 ◽  
pp. 125-144
Author(s):  
Massimo Bacigalupo
Keyword(s):  
Rich Information ◽  
The Rich ◽  
The Cantos ◽  
Religious Elements

In 1939 Pound was planning a finale for The Cantos that would present religious elements. In the process he wrote a paper, “European Paideuma,” which remained unpublished because of the war. This article is reprinted here and mined for the rich information it offers about notions and particular rituals alluded to in The Cantos.

scholarly journals HIPSTER

2019 ◽  
Vol 214 ◽  
pp. 06027
Author(s):  
Adrian Bevan ◽  
Thomas Charman ◽  
Jonathan Hays
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Event Recognition ◽  
The Core ◽  
Rich Information ◽  
The Rich ◽  
Core Functionality ◽  
Python Package ◽  
Energy Physics

HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in detail.

scholarly journals Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

2020 ◽  
Vol 9 (10) ◽  
pp. 571
Author(s):  
Jinglun Li ◽  
Jiapeng Xiu ◽  
Zhengqiu Yang ◽  
Chen Liu
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Augmentation Strategies ◽  
Rich Information ◽  
The Mean ◽  
The Rich

Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively.

Mapping aboriginal tourism experiences in Taiwan: A case of the Formosan Aboriginal Culture Village

2020 ◽  
pp. 135676672095034
Author(s):  
Weisheng Chiu ◽  
Heetae Cho
Keyword(s):  
Lived Experience ◽  
Marketing Strategies ◽  
User Generated Content ◽  
Text Data ◽  
Tourism Product ◽  
Level Of Satisfaction ◽  
Aboriginal Culture ◽  
Tourist Experience ◽  
Rich Information ◽  
The Rich

Exploring tourist experience through analyzing user-generated content (UGC) has been considered as an appropriate approach for experience studies due to the rich information from the perspective of tourists. Thus, this study identified the conceptual map of individuals’ aboriginal tourism experiences by analyzing UGC, including photos and texts. A total of 206 photos and 278 reviews posted by tourists on TripAdvisor were collected and analyzed. Photo content analysis showed that aboriginal culture emerged as the most indelible experience for visitors. Analysis of text data disclosed key themes: park, tribe, car, garden, and children. Further analysis found different patterns in tourist experiences across numerous travel parties and satisfaction levels. This study explored tourists’ narratives and identified important concepts and themes of their ‘lived experience’ of aboriginal tourism. The findings of this study contribute to expanding theoretical knowledge by introducing innovative analytic techniques. Practically, this study offers a blueprint for designing the aboriginal tourism product, which can optimize the tourist experience. In addition, the differences in tourist experience with regard to travel party and level of satisfaction suggest specific marketing strategies for different segments.

Abnormal Voice Detection Algorithm Based on Semi-Supervised Co-Training Algorithm

2012 ◽  
Vol 461 ◽  
pp. 117-122 ◽  
Author(s):  
Ya Hui Zhao ◽  
Hong Li Wang ◽  
Rong Yi Cui
Keyword(s):  
Learning Strategy ◽  
Detection Algorithm ◽  
Training Algorithm ◽  
Mixed Programming ◽  
Training Samples ◽  
Rich Information ◽  
Detection Software ◽  
Voice Detection ◽  
The Rich ◽  
Validation Set

The AR-Tri-training algorithm is proposed for applying to the abnormal voice detection, and voice detection software is designed by mixed programming used Matlab and VC in this paper. Firstly, training samples are collected and the features of each sample are extracted including centroid, spectral entropy, wavelet and MFCC. Secondly, the assistant learning strategy is proposed, AR-Tri-training algorithm is designed by combining the rich information strategy. Finally, Classifiers are trained by using AR-Tri-training algorithm, and the integrated classifier is applied to voice detection. As can be drawn from the experimental results, AR-Tri-training not only removes mislabeled examples in training process, but also takes full advantage of the unlabeled examples and wrong-learning examples on validation set

scholarly journals Infrastructure sensing

2016 ◽  
Vol 6 (4) ◽  
pp. 20160023 ◽  
Author(s):  
Kenichi Soga ◽  
Jennifer Schooling
Keyword(s):  
Embedded Sensors ◽  
Wireless Sensor ◽  
Fibre Optics ◽  
Design Life ◽  
Assessment And Monitoring ◽  
Rich Information ◽  
The Rich ◽  
Construction Operation ◽  
Civil Engineering Infrastructure ◽  
Design Construction

Design, construction, maintenance and upgrading of civil engineering infrastructure requires fresh thinking to minimize use of materials, energy and labour. This can only be achieved by understanding the performance of the infrastructure, both during its construction and throughout its design life, through innovative monitoring. Advances in sensor systems offer intriguing possibilities to radically alter methods of condition assessment and monitoring of infrastructure. In this paper, it is hypothesized that the future of infrastructure relies on smarter information; the rich information obtained from embedded sensors within infrastructure will act as a catalyst for new design, construction, operation and maintenance processes for integrated infrastructure systems linked directly with user behaviour patterns. Some examples of emerging sensor technologies for infrastructure sensing are given. They include distributed fibre-optics sensors, computer vision, wireless sensor networks, low-power micro-electromechanical systems, energy harvesting and citizens as sensors.

scholarly journals Beyond comparisons of means: understanding changes in gene expression at the single-cell level

2016 ◽  
Author(s):  
Catalina A Vallejos ◽  
Sylvia Richardson ◽  
John C Marioni
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Probabilistic Approach ◽  
Embryonic Stem ◽  
Cellular Heterogeneity ◽  
Bayesian Hierarchical ◽  
False Discovery ◽  
Rich Information ◽  
The Rich

Single-cell RNA sequencing (scRNA-seq) can be used to characterise differences in gene expression patterns between pre-specified populations of cells. Traditionally, differential expression tools are restricted to the study of changes in overall expression between cell populations. However, such analyses do not take full advantage of the rich information provided by scRNA-seq. In this article, we present a Bayesian hierarchical model which can be used to study changes in expression that lie beyond comparisons of means. In particular, our method can highlight genes that undergo changes in cell-to-cell heterogeneity between the populations but whose overall expression is preserved. Evidence supporting these changes is quantified using a probabilistic approach based on tail posterior probabilities, where a probability cut-off is calibrated through the expected false discovery rate. Our method incorporates a built-in normalisation strategy and quantifies technical artefacts by borrowing information from technical spike-in genes. Control experiments validate the performance of our approach. Finally, we compare expression patterns of mouse embryonic stem cells between different stages of the cell cycle, revealing substantial differences in cellular heterogeneity.

Measurement of gluconeogenesis and mass isotopomer analysis based on [U-13C]glucose

1999 ◽  
Vol 277 (2) ◽  
pp. E199-E207 ◽  
Author(s):  
Jerry Radziuk ◽  
W.-N. Paul Lee
Keyword(s):  
Relative Rate ◽  
Tca Cycle ◽  
Whole Body ◽  
Carbon 14 ◽  
Isotopomer Analysis ◽  
Glucose Synthesis ◽  
Rich Information ◽  
The Rich ◽  
Future Work ◽  
Source Of Error

Two methods of measuring rates of gluconeogenesis based on label redistribution after the introduction of [U-13C]glucose into the whole body are examined. These methods are compared with methods previously derived for carbon-14 tracers. It is shown that the three approaches (stoichiometric, dilution, and combinatorial) are equivalent, provided the same set of assumptions are used. Barring a factor of two [see Am. J. Physiol. 270 ( Endocrinol. Metab. 33): E709–E717, 1996], the differences (∼10–15%) in the carbon-based dilutional and the molecule-based estimates of the rate of gluconeogenesis from published isotopomer data likely arise from small differences in the assumptions that concern the relative rate of label loss from the different isotopomers. The production of unlabeled substrate for glucose synthesis (phospho enolpyruvate) from the different isotopomers of lactate is shown to be a potential source of error in these methods. This error is estimated using models of the interaction of the gluconeogenetic pathway and the tricarboxylic acid (TCA) cycle and is shown to vary from negligible to 30% depending on the relative flux of the two pathways through the oxaloacetate pool. Because the estimates obtained by both methods considered are lower than is physiologically expected, some of the assumptions made may not hold. Future work will exploit the rich information content of isotopomer data to yield improved estimates.

scholarly journals Efficient Community Search over Large Directed Graph: An Augmented Index-based Approach

2020 ◽  
Author(s):  
Yankai Chen ◽  
Jie Zhang ◽  
Yixiang Fang ◽  
Xin Cao ◽  
Irwin King
Keyword(s):  
Directed Graphs ◽  
Optimal Time ◽  
Connected Subgraph ◽  
Time Cost ◽  
Large Graphs ◽  
Construction Methods ◽  
Community Search ◽  
Rich Information ◽  
The Rich ◽  
Query Algorithm

Given a graph G and a query vertex q, the topic of community search (CS), aiming to retrieve a dense subgraph of G containing q, has gained much attention. Most existing works focus on undirected graphs which overlooks the rich information carried by the edge directions. Recently, the problem of community search over directed graphs (or CSD problem) has been studied [Fang et al., 2019b]; it finds a connected subgraph containing q, where the in-degree and out-degree of each vertex within the subgraph are at least k and l, respectively. However, existing solutions are inefficient, especially on large graphs. To tackle this issue, in this paper we propose a novel index called D-Forest, which allows a CSD query to be completed within the optimal time cost. We further propose efficient index construction methods. Extensive experiments on six real large graphs show that our index-based query algorithm is up to two orders of magnitude faster than existing solutions.

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