Adapting Enterprise Security Approaches for Evolving Cloud Processing and Networking Models

Author(s):  
Andrew Hutchison
2021 ◽  
Vol 7 (2) ◽  
pp. 187-199
Author(s):  
Meng-Hao Guo ◽  
Jun-Xiong Cai ◽  
Zheng-Ning Liu ◽  
Tai-Jiang Mu ◽  
Ralph R. Martin ◽  
...  

AbstractThe irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.


2021 ◽  
Vol 7 (13) ◽  
pp. eabe2952
Author(s):  
Houssni Lamkaddam ◽  
Josef Dommen ◽  
Ananth Ranjithkumar ◽  
Hamish Gordon ◽  
Günther Wehrle ◽  
...  

Aerosols still present the largest uncertainty in estimating anthropogenic radiative forcing. Cloud processing is potentially important for secondary organic aerosol (SOA) formation, a major aerosol component: however, laboratory experiments fail to mimic this process under atmospherically relevant conditions. We developed a wetted-wall flow reactor to simulate aqueous-phase processing of isoprene oxidation products (iOP) in cloud droplets. We find that 50 to 70% (in moles) of iOP partition into the aqueous cloud phase, where they rapidly react with OH radicals, producing SOA with a molar yield of 0.45 after cloud droplet evaporation. Integrating our experimental results into a global model, we show that clouds effectively boost the amount of SOA. We conclude that, on a global scale, cloud processing of iOP produces 6.9 Tg of SOA per year or approximately 20% of the total biogenic SOA burden and is the main source of SOA in the mid-troposphere (4 to 6 km).


2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.


Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Edisa Lozić ◽  
Benjamin Štular

Airborne LiDAR is a widely accepted tool for archaeological prospection. Over the last decade an archaeology-specific data processing workflow has been evolving, ranging from raw data acquisition and processing, point cloud processing and product derivation to archaeological interpretation, dissemination and archiving. Currently, though, there is no agreement on the specific steps or terminology. This workflow is an interpretative knowledge production process that must be documented as such to ensure the intellectual transparency and accountability required for evidence-based archaeological interpretation. However, this is rarely the case, and there are no accepted schemas, let alone standards, to do so. As a result, there is a risk that the data processing steps of the workflow will be accepted as a black box process and its results as “hard data”. The first step in documenting a scientific process is to define it. Therefore, this paper provides a critical review of existing archaeology-specific workflows for airborne LiDAR-derived topographic data processing, resulting in an 18-step workflow with consistent terminology. Its novelty and significance lies in the fact that the existing comprehensive studies are outdated and the newer ones focus on selected aspects of the workflow. Based on the updated workflow, a good practice example for its documentation is presented.


2021 ◽  
Author(s):  
Huaqun Guo ◽  
Meng Wei ◽  
Ping Huang ◽  
Eyasu Getahun Chekole

Author(s):  
Виталий Сергеевич Ерёменко ◽  
Вера Викторовна Наумова ◽  
Алексей Андреевич Загуменнов ◽  
Станислав Владимирович Булов

Описываются подходы к организации единого рабочего пространства исследователя для обработки геологической информации. Предложены подходы по взаимодействию с внешними территориально распределенными сервисами обработки и анализа геологических данных на основе WPS-платформы и по организации интегральной платформы для доступа к интерактивным облачным сервисам обработки геологических данных. Приводятся примеры облачных сервисов и платформ и сравниваются их основные характеристики. Описывается программная реализация предложенных подходов в рамках разрабатываемой информационноаналитической системы GeologyScience.ru. Приводится общая архитектура системы, и кратко описывается каждый из ее модулей. Описываются внешние сервисы, интегрированные в разрабатываемую платформу. The purpose of the study is to create an approach for organization of unified workspace for geological data research and processing. The study proposes approaches for organization of interaction with external geographically distributed processing and analytical services of geological data based on WPS platforms. Аn approach for integral platform organization providing access to interactive cloud processing of geological data is proposed. It connects the users with modern methods of data processing and analysis. A brief description of existing cloud services and platforms and comparison of their basic features is presented. Various groups of cloud services are described depending on the type of services provided to the user. Software implementation of approaches proposed within developing informational and analytical system GeologyScience.ru is addressed. The general architecture of the system and author’s modules developed including technological solutions used are described. A brief review of the services integrated into the platform being developed now is given. The use of the developed platform for processing and analyzing of geological data allows further expanding of capabilities of information-analytical environment developers to support scientific research.


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