iGiraph: A Cost-Efficient Framework for Processing Large-Scale Graphs on Public Clouds

2016 ◽  
Author(s):  
Safiollah Heidari ◽  
Rodrigo N. Calheiros ◽  
Rajkumar Buyya
Keyword(s):  
Large Scale ◽  
Cost Efficient

scholarly journals Real-bogus classification for the Zwicky Transient Facility using deep learning

2019 ◽  
Vol 489 (3) ◽  
pp. 3582-3590 ◽  
Author(s):  
Dmitry A Duev ◽  
Ashish Mahabal ◽  
Frank J Masci ◽  
Matthew J Graham ◽  
Ben Rusholme ◽  
...  
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Large Scale ◽  
Moving Objects ◽  
False Negative ◽  
Efficient Manner ◽  
Astronomical Surveys ◽  
Comparable Performance ◽  
Cost Efficient ◽  
Initial Results

ABSTRACT Efficient automated detection of flux-transient, re-occurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present braai, a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. Braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates. We describe the open-source software tools used internally at Caltech to archive and access ZTF’s alerts and light curves (kowalski ), and to label the data (zwickyverse). We also report the initial results of the classifier deployment on the Edge Tensor Processing Units that show comparable performance in terms of accuracy, but in a much more (cost-) efficient manner, which has significant implications for current and future surveys.

scholarly journals Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

2016 ◽  
Vol 13 (5) ◽  
pp. 1387-1408 ◽  
Author(s):  
Zhen Zhang ◽  
Niklaus E. Zimmermann ◽  
Jed O. Kaplan ◽  
Benjamin Poulter
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Spatiotemporal Dynamics ◽  
Fundamental Parameter ◽  
Data Set ◽  
Dynamic Global Vegetation Model ◽  
Wetland Dynamics ◽  
Cost Efficient ◽  
Elevation Model

Abstract. Simulations of the spatiotemporal dynamics of wetlands are key to understanding the role of wetland biogeochemistry under past and future climate. Hydrologic inundation models, such as the TOPography-based hydrological model (TOPMODEL), are based on a fundamental parameter known as the compound topographic index (CTI) and offer a computationally cost-efficient approach to simulate wetland dynamics at global scales. However, there remains a large discrepancy in the implementations of TOPMODEL in land-surface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl (Lund–Potsdam–Jena Wald Schnee und Landschaft version) Dynamic Global Vegetation Model (DGVM) and quantifies uncertainties by comparing three digital elevation model (DEM) products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. In addition, we found that calibrating TOPMODEL with a benchmark wetland data set can help to successfully delineate the seasonal and interannual variation of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a river-basin scheme for aggregating the CTI, shows the best accuracy for capturing the spatiotemporal dynamics of wetlands among the three DEM products. The estimate of global wetland potential/maximum is  ∼ 10.3 Mkm2 (106 km2), with a mean annual maximum of  ∼ 5.17 Mkm2 for 1980–2010. When integrated with wetland methane emission submodule, the uncertainty of global annual CH4 emissions from topography inputs is estimated to be 29.0 Tg yr−1. This study demonstrates the feasibility of TOPMODEL to capture spatial heterogeneity of inundation at a large scale and highlights the significance of correcting maximum wetland extent to improve modeling of interannual variations in wetland area. It additionally highlights the importance of an adequate investigation of topographic indices for simulating global wetlands and shows the opportunity to converge wetland estimates across LSMs by identifying the uncertainty associated with existing wetland products.

scholarly journals A general method for rapid and cost-efficient large-scale production of 5′ capped RNA

RNA ◽  
2016 ◽  
Vol 22 (9) ◽  
pp. 1454-1466 ◽  
Author(s):  
Anna-Lisa Fuchs ◽  
Ancilla Neu ◽  
Remco Sprangers
Keyword(s):  
Large Scale ◽  
Scale Production ◽  
Large Scale Production ◽  
Cost Efficient ◽  
General Method

scholarly journals High-Throughput PCR Assays To Monitor Wolbachia Infection in the Dengue Mosquito (Aedes aegypti) and Drosophila simulans

2012 ◽  
Vol 78 (13) ◽  
pp. 4740-4743 ◽  
Author(s):  
Siu F. Lee ◽  
Vanessa L. White ◽  
Andrew R. Weeks ◽  
Ary A. Hoffmann ◽  
Nancy M. Endersby
Keyword(s):  
Aedes Aegypti ◽  
High Throughput ◽  
Large Scale ◽  
Wolbachia Infection ◽  
Drosophila Simulans ◽  
Content Type ◽  
Pcr Assays ◽  
Cost Efficient

ABSTRACTWe have developed and validated two new fluorescence-based PCR assays to detect theWolbachia wMel strain inAedes aegyptiand thewRi andwAu strains inDrosophila simulans. The new assays are accurate, informative, and cost-efficient for large-scaleWolbachiascreening.

scholarly journals Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5706
Author(s):  
Muhammad Shuaib Qureshi ◽  
Muhammad Bilal Qureshi ◽  
Muhammad Fayaz ◽  
Muhammad Zakarya ◽  
Sheraz Aslam ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Real Time ◽  
Large Scale ◽  
Smart Devices ◽  
Budget Constraints ◽  
Smart Systems ◽  
Data Intensive ◽  
Cost Efficient ◽  
And Storage

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

Meeting the Quality Assurance Challenges of the 1980s: Team Auditing by Toxicologists and QA Professionals

1984 ◽  
Vol 3 (2) ◽  
pp. 129-139 ◽  
Author(s):  
B. Kristin Hoover ◽  
Judith K. Baldwin
Keyword(s):  
Quality Assurance ◽  
Large Scale ◽  
Product Liability ◽  
Regulatory Requirements ◽  
Team Members ◽  
Short Period ◽  
Professional Isolation ◽  
Cost Efficient ◽  
Major Factors ◽  
Audit Team

The major challenge of the 1980s is to continue to provide a productive contribution to the conduct of scientific research. Major factors that may be expected to affect the ability of quality assurance to meet this challenge are economics, ever changing and expanding regulatory requirements, increasing test complexity, computerization, and continuing product liability needs. Team auditing is an effective way to utilize resources to meet the requirements imposed by these factors. This approach results in a highly cost-efficient use of personnel in order to accomplish large and difficult tasks in a short period of time. In addition, it can decrease the need for large-scale training programs and be used as a method for upgrading present staff expertise. Individual perspectives are broadened as team members learn from each other. Morale is improved as the professional isolation of working alone or in small units is overcome. If team auditing is to be successful, it must be properly planned and executed. The team must be selected, objectives established, methods defined, and time constraints determined. These factors and ways to build a cohesive audit team capable of accomplishing specific goals will be discussed in detail. In addition, useful applications of this approach will be suggested.

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