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Integrated 3D numerical modeling during la Niña and El Niño events using Regional Ocean Modeling System (ROMS) in Makassar Strait: Preliminary study 1

Integrated 3D numerical modeling during la Niña and El Niñ...

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1st International conference on Maritime Sciences and Advanced Technology: Ocean Science and Technology Toward a Global Maritime Axis, MSAT 2017

作者： Mubarrok, S. Riza, M. Nur, A.A. Mandang, I. Physical Oceanography and Modeling Laboratory Program Study of Physics Faculty of Mathematic and Natural Sciences Mulawarman University Samarinda75123 Indonesia

This research showed the difference of thermocline layer depth during La Niña and El Niño events in the Makassar Strait, the main passage connecting the Pacific and Indian Oceans. La Niña event represented by 2011 and El Niño event represented by 2015. Three-dimensional numerical modeling using Regional Ocean Modeling System (ROMS) is used to determine ocean dynamic characteristics. The tides and atmospheric factor used as generating forces for coupled Ocean-Atmosphere-Wave-Sediment simulation model. Tidal elevation verification results from January to February 2017 showed a high confidence with RMSE = 0.148 m and MAPE = 13.51 %. Ocean circulation showed that water flows from north to south of the Makassar Strait in both conditions, El Niño and La Niña, indicating a transport from the Pacific to the Indian Oceans during those time. Two-dimensional seasonal sea surface temperature (SST) was used to determine the variability of thermocline layer depth. In general, based on simulation results, the depth of thermocline during La Niña was deeper than that during El Niño. The upper threshold of thermocline layer during El Niño phenomenon was shallower (average 27.29 m) compare to the threshold during La Niña (average 39.25 m). So do the lower threshold, during El Niño was shallower (average 160.47 m) compare to the threshold during La Niña (average 165.70 m). The thermocline thickness during El Niño (133.19 m) was found to be thicker compared to La Niña (126.43 m). © Published under licence by IOP Publishing Ltd.

关键词： Surface waters

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Fast Non-Polynomial Interpolation of Functions with Logarithmic Singularities

Fast Non-Polynomial Interpolation of Functions with Logarith...

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2018 IEEE International conference on Computational Electromagnetics, ICCEM 2018

作者： Wang, Yinkun Chen, Xiangling Li, Ying Luo, Jianshu College of Science National University of Defense Technology Changsha410073 China HPCSIP Key Laboratory Ministry of Education China College of Mathematics and Computer Science Hunan Normal University Changsha410073 China

ISBN: (纸本)9781538612415

The scattering characteristics of distributed soft targets are generally described by integral equations with singular kernels. For the purpose of fast computation of integrals of singular kernels, a fast non-polynomial interpolation is proposed in this paper for functions with logarithmic singularity. It can be executed efficiently with fast discrete cosine transforms. Interpolation errors of a manmade function, the well-known Hankel function of the first kind and the kernel-related function appeared in Hallen equation are shown as numerical examples to validate the efficiency of the proposed new interpolation. © 2018 IEEE.

关键词： Hankel functions

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Vehicle Classification Based on Deep Convolutional Neural Networks Model for Traffic Surveillance Systems 15

Vehicle Classification Based on Deep Convolutional Neural Ne...

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15th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2018

作者： Zakria Cai, Jingye Deng, Jianhua Khokhar, Muhammad Saddam Umar Aftab, Muhammad School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9781728115351

Due to growth of vehicles in urban cities, such problems are increases traffic control systems, security and crime investigation, intelligent parking and electronic toll collection. Moreover, Logistics access management system (LMS)is also a big problem in urban cities and all the issues are challenging and demands to develop effective and efficient approach for vehicle classification. Mostly, traditional vehicle classification uses hand craft feature extraction method like SIFT, Surf, HoG etc. However, these approaches are not efficient in results. This paper presents the efficient framework for vehicle classification by using Inception-v3 model for image vector features extraction that is most advance deep learning neural network, and this model is not used before for vehicle classification. After that, different classification algorithms are implemented on the feature vector. The classification conducted on three vehicle datasets and results demonstration is helpful for researchers to choose dataset with algorithm performance. © 2018 IEEE.

关键词： Vehicles

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Aladdin: Automating Release of Deep-Link APIs on Android 18

Aladdin: Automating Release of Deep-Link APIs on Android

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27th World Wide Web (WWW) conference

作者： Ma, Yun Hu, Ziniu Liu, Yunxin Xie, Tao Liu, Xuanzhe Peking Univ Key Lab High Confidence Software Technol MoE Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Microsoft Res Beijing Peoples R China Univ Illinois Champaign IL USA

ISBN: (纸本)9781450356398

Compared to the Web where each web page has a global URL for external access, a specific "page" inside a mobile app cannot be easily accessed unless the user performs several steps from the landing page of this app. Recently, the concept of "deep link" is expected to be a promising solution and has been advocated by major service providers to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier. In this paper, we present a large-scale empirical study to investigate how deep links are really adopted, over 25,000 Android apps. To our surprise, we find that deep links have quite low coverage, e.g., more than 70% and 90% of the apps do not have deep links on app stores Wandoujia and Google Play, respectively. One underlying reason is the mandatory and non-trivial manual efforts of app developers to provide APIs for deep links. We then propose the Aladdin approach along with its supporting tool to help developers practically automate the release of deep-link APIs to access locations inside their apps. Aladdin includes a novel cooperative framework by synthesizing the static analysis and the dynamic analysis while minimally engaging developers' inputs and configurations, without requiring any coding efforts or additional deployment efforts. We evaluate Aladdin with 579 popular apps and demonstrate its effectiveness and performance.

关键词： Deep link Android apps program analysis

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Lazy Persistency: a high-Performing and Write-Efficient Software Persistency Technique 18

Lazy Persistency: a High-Performing and Write-Efficient Soft...

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45th ACM/IEEE Annual International Symposium on computer Architecture (ISCA)

作者： Alshboul, Mohammad Tuck, James Solihin, Yan North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781538659847

Emerging Non-Volatile Memories (NVMs) are expected to be included in future main memory, providing the opportunity to host important data persistently in main memory. However, achieving persistency requires that programs be written with failure-safety in mind. Many persistency models and techniques have been proposed to help the programmer reason about failure-safety. They require that the programmer eagerly flush data out of caches to make it persistent. Eager persistency comes with a large overhead because it adds many instructions to the program for flushing cache lines and incurs costly stalls at barriers to wait for data to become durable. To reduce these overheads, we propose Lazy Persistency (LP), a software persistency technique that allows caches to slowly send dirty blocks to the NVMM through natural evictions. With LP, there are no additional writes to NVMM, no decrease in write endurance, and no performance degradation from cache line flushes and barriers. Persistency failures are discovered using software error detection (checksum), and the system recovers from them by recomputing inconsistent results. We describe the properties and design of LP and demonstrate how it can be applied to loop-based kernels popularly used in scientific computing. We evaluate LP and compare it to the state-of-the-art Eager Persistency technique from prior work. Compared to it, LP reduces the execution time and write amplification overheads from 9% and 21% to only 1% and 3%, respectively.

关键词： Emerging Memory Technology Memory Systems Multi-core and Parallel Architectures

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A Metaphorical Debugger Model to support deaf and hearing impaired in Java programming learning

A Metaphorical Debugger Model to support deaf and hearing im...

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Frontiers in Education (FIE) conference

作者： Marcos D. do Nascimento Francisco C. de M. B. Oliveira Anarosa A. F. Brandão Lidiane C. Silva Bruno Queiroz Eder Furtado Department of Computer Engineering Universidade de São Paulo São Paulo Brazil Department of Computer Science Universidade Estadual do Ceará Fortaleza Brazil

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This innovative practice full paper presents a Metaphorical Debugger Model (JAD) to support deaf and hearing impaired in the process of learning how to program in Java. In Brazil, 9.7 million people are deaf or hearing impaired (DHI). DHI people usually face several barriers to get proper education. During a series of Java classes conducted by our team, we verified that these difficulties were also related to the use of software that is not tailored for their impairment. We also saw that DHI students encountered many problems while developing and evolving their codes using the traditional tools, for instance, visual signs to perform tasks, can be difficult to understand, in addition, the high degree of abstraction required for the programming logic, can be an accessibility barrier for them. Thus, we decided to propose JAD aiming to provide accessible debugging for DHI. The JAD uses the concept of metaphorical interfaces, adopting appropriate symbols and signs borrowed from traffic to aid in the process of code debugging and evolution. In this paper, we present some user studies with JAD. The results suggest that Java programmers, DHI e non-DHI, had similar performance in task related to software evolution when JAD was used.

关键词： Visualization Java Tools Debugging programming Auditory system Task analysis

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Cross-Subject Emotion Recognition Using Deep Adaptation Networks 1

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25th International conference on Neural Information Processing (ICONIP)

作者： Li, He Jin, Yi-Ming Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong Univ Ctr Brain Comp & Machine Intelligence Dept Comp Sci & Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Shanghai Educ Commiss Intelligent Interac 800 Dong Chuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr 800 Dong Chuan Rd Shanghai 200240 Peoples R China

ISBN: (数字)9783030042219

ISBN: (纸本)9783030042219;9783030042202

Affective models based on EEG signals have been proposed in recent years. However, most of these models require subject-specific training and generalize worse when they are applied to new subjects. This is mainly caused by the individual differences across subjects. While, on the other hand, it is time-consuming and high cost to collect subject-specific training data for every new user. How to eliminate the individual differences in EEG signals for implementation of affective models is one of the challenges. In this paper, we apply Deep adaptation network (DAN) to solve this problem. The performance is evaluated on two publicly available EEG emotion recognition datasets, SEED and SEED-IV, in comparison with two baseline methods without domain adaptation and several other domain adaptation methods. The experimental results indicate that the performance of DAN is significantly superior to the existing methods.

关键词： Affective brain-computer interface Emotion recognition EEG Deep neural network Domain adaptation

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An Analysis on Retention Error Behavior and Power Consumption of Recent DDR4 DRAMs

An Analysis on Retention Error Behavior and Power Consumptio...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Mathew, Deepak M. Schultheis, Martin Rheinlaender, Carl C. Sudarshan, Chirag Weis, Christian Wehn, Norbert Jung, Matthias Univ Kaiserslautern Kaiserslautern Germany Fraunhofer Inst Expt Software Engn IESE Kaiserslautern Germany

ISBN: (纸本)9783981926309

DRAM technology is scaling aggressively that results ill high leakage power, worse data retention time behavior, and large process variations. Due to these process variations, vendors provide large guard bands on various DRAM currents and timing specifications that are over pessimistic. Detailed knowledge on the DRAM retention behavior and currents for the average case allow to improve memory system performance and energy efficiency of specific applications by moving away from worst case behavior. In this paper, we present an advanced measurement platform to investigate off-the-shelf DDR4 DRAMs' retention behavior, and to precisely measure various DRAM currents (IDDs and WIN) at a wide range of operating temperatures. Error Checking and Correction (ECC) schemes are popular in correcting randomly scattered single bit errors. Since retention failures also occur randomly, ECCs can be used to improve DRAM retention behavior. Therefore, for the first time, we show the influence of ECC on the retention behavior of recent DDR4 DRAMs, and how it varies across various DRAM architectures considering detailed structure of the DRAM (true-cell devices / mixed-cell devices).

关键词： Random access memory Temperature measurement Current measurement Error correction codes Power demand Temperature distribution computer architecture

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Semantic Segmentation-Based Building Footprint Extraction Using Very high-Resolution Satellite Images and Multi-Source GIS Data

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REMOTE SENSING 2019年第4期11卷 403-403页

作者： Li, Weijia He, Conghui Fang, Jiarui Zheng, Juepeng Fu, Haohuan Yu, Le Tsinghua Univ Dept Earth Syst Sci Key Lab Earth Syst Modeling Minist Educ Beijing 100084 Peoples R China JCGCS Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci Beijing 100084 Peoples R China Tencent Shenzhen 518000 Peoples R China Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China

Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE conference on computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net-based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net-based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.

关键词： building extraction deep learning semantic segmentation data fusion high-resolution satellite images GIS data

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Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures 18

Parallel <i>k</i>-means Clustering of Geospatial Data Sets U...

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18th IEEE International conference on Data Mining Workshops (ICDMW)

作者： Mills, Richard Tran Sripathi, Vamsi Kumar, Jitendra Sreepathi, Sarat Hoffman, Forrest M. Hargrove, William W. Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Intel Corp Data Ctr Grp Hillsboro OR USA Oak Ridge Natl Lab Div Environm Sci POB 2008 Oak Ridge TN 37831 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN USA US Forest Serv So Res Stn USDA Asheville NC USA

ISBN: (纸本)9781538692882

The increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery and mining of weather, climate, ecological, and other geoscientific data sets fused from disparate sources. Many of the standard tools used on individual workstations are impractical for the analysis and synthesis of data sets of this size;however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of parallelism available in state-of-the-art high-performance computing platforms can enable such analysis. Here, we describe pKluster, an open-source tool we have developed for accelerated k-means clustering of geospatial and geospatiotemporal data, and discuss algorithmic modifications and code optimizations we have made to enable it to effectively use parallel machines based on novel CPU architectures-such as the Intel Knights Landing Xeon Phi and Skylake Xeon processors-with many cores and hardware threads, and employing significant single instruction, multiple data (SIMD) parallelism. We outline some applications of the code in ecology and climate science contexts and present a detailed discussion of the performance of the code for one such application, LiDAR-derived vertical vegetation structure classification.

关键词： Instruction sets Parallel processing Acceleration computer architecture Clustering algorithms Geoscience

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