376 research outputs found
Building Detection from Very High Resolution Remotely Sensed Imagery Using Deep Neural Networks
Get PDFThe past decades have witnessed a significant change in human societies with a fast pace and rapid urbanization. The boom of urbanization is contributed by the influx of people to the urban area and comes with building construction and deconstruction. The estimation of both residential and industrial buildings is important to reveal and demonstrate the human activities of the regions. As a result, it is essential to effectively and accurately detect the buildings in urban areas for urban planning and population monitoring. The automatic building detection method in remote sensing has always been a challenging task, because small targets cannot be identified in images with low resolution, as well as the complexity in the various scales, structure, and colours of urban buildings. However, the development of techniques improves the performance of the building detection task, by taking advantage of the accessibility of very high-resolution (VHR) remotely sensed images and the innovation of object detection methods.
The purpose of this study is to develop a framework for the automatic detection of urban buildings from the VHR remotely sensed imagery at a large scale by using the state-of-art deep learning network. The thesis addresses the research gaps and difficulties as well as the achievements in building detection. The conventional hand-crafted methods, machine learning methods, and deep learning methods are reviewed and discussed. The proposed method employs a deep convolutional neural network (CNN) for building detection. Two input datasets with different spatial resolutions were used to train and validate the CNN model, and a testing dataset was used to evaluate the performance of the proposed building detection method. The experiment result indicates that the proposed method performs well at both building detection and outline segmentation task with a total precision of 0.92, a recall of 0.866, an F1-score of 0.891. In conclusion, this study proves the feasibility of CNN on solving building detection challenges using VHR remotely sensed imagery
Toward an Attention-Based Model of CEO and Innovation
No full textCEOs are primarily responsible for managing technological innovation as the key source of competitive advantage for science-based and technological intensive industries. Thus, studying the effects of CEOs on innovation is an important stream of research in strategic management. However, research on this topic has not reached consistent conclusions because of both theoretical and empirical limitations. To address the limitations and advance knowledge, this research builds an attention-based model that integrates the upper echelons perspective and the attention-based view of the firm to explain and predict how CEOs influence technological innovation through their attentional perspective. This model conceptualizes a CEO’s attention as a multidimensional construct that has three independent dimensions – attention breadth, attention focus, and attention heterogeneity. Further, to capture the complexity of technological innovation, this model includes three distinct dimensions – innovation productivity, innovation approach, and innovation quality. This attention-based model offers three unique advancements. First, this model probes into the substance of information-processing by examining a CEO’s attention, which is one of the most crucial cognitive mechanisms. Second, this model explicates the mechanism and process that underlie relationships between CEO demographic characteristics and technological innovation. Finally, this model presents a holistic yet parsimonious conceptual framework that guides future research by theorizing the causality between the three dimensions of managerial attention and the three dimensions of technological innovation.Management, Department o
AU-SWCNTS-HF Schottky diodes fabricated by dielectrophoresis
No full textIn this paper we report a single-walled carbon nanotubes (SWCNTs) Schottky diodes fabricated by DEP technique. The device was made of semiconducting SWCNTs (s-SWCNTs) contacting with asymmetric-work-function metal electrodes of Au and Hf, and the properties were measured and analyzed in detail. The results show that our device has a good rectifying characteristic and could be tuned by a back gate voltage. Above 220K, thermionic emission is the dominant transport mechanism, while tunneling begins to lead below 220K. And the Schottky barrier height was calculated to be 0.48eV. ? 2014 IEEE.EI
Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with Machine Learning Algorithms
No full textSalinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline–alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline–alkali land quality and plan regional agricultural development
INVESTIGATING THE ROLES OF WASP IN MEGAKARYOCYTES AND PATHOGENESIS OF THROMBOCYTOPENIA IN WISKOTT- ALDRICH SYNDROME USING ISOGENIC PLURIPOTENT STEM CELL MODELS
No full textCRISPR-Cas9-induced double-strand breaks disrupt maintenance of epigenetic information.
No full textBackgroundCRISPR-Cas9 genome editing enables precise genetic modifications by introducing targeted DNA double-strand breaks (DSBs). While Cas9-induced DSBs are known to cause unintended on-target mutations, their impact on the epigenetic landscape remains unexplored.ResultsHere, we investigate how Cas9-induced DSBs affect DNA methylation patterns in human embryonic stem cells (hESCs). We induce DSBs at differentially methylated regions of imprinted genomic loci and perform high-coverage, long-read native DNA sequencing to simultaneously obtain genetic variant and base-resolution methylation data in a haplotype-resolved manner. Our findings reveal that DSBs cause significant changes in DNA methylation at target sites through mechanisms including homologous recombination, large structural variations, or defective methylation maintenance during DNA repair. Notably, these epigenetic changes can occur either together with or independently of genetic alterations. Beyond imprinted loci, Cas9-induced DSBs significantly disrupt DNA methylation patterns of the MLH1 epimutation alleles in colorectal cancer cells, and hypermethylated heterochromatin loci in hESCs. Clonal analysis indicates that the aberrant methylation changes are stable during in vitro passaging. Intriguingly, significant changes in DNA methylation levels are also detected around endogenous deletions in unedited genomic regions, suggesting that methylation alterations are not unique to Cas9 nuclease activity but represent a general outcome of DSB repair in human cells.ConclusionsThis study underscores the importance of assessing and mitigating unintended epigenetic consequences in genome editing applications, as such changes can profoundly affect gene regulation and cellular function.We thank the members of the Li laboratory for their helpful discussions and Jinna Xu for administrative support. We thank Drs. Y. Huang and Y. Takahashi for critical reading of an early draft of the manuscript. We thank KAUST Bioscience Core Lab for providing material support in nanopore sequencing experiments.
The research of the Li laboratory was supported by the KAUST Office of Sponsored Research (OSR) under award numbers BAS/1/1080-01-01. This work was financially supported in part by funding from King Abdullah University of Science and Technology (KAUST) – KAUST Center of Excellence for Smart Health (KCSH), under award number 5932 (ML)
Let's explore with a divided team! The effects of top management team demographic faultlines on technological exploration
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