作者：
CIARULA, TANERADKA, VFCdr. Thomas A. Ciarula:
USN was born in Pottstown Pa. He attended Northrop University in Inglewood Calif. where he earned a bachelor of science degree in aircraft maintenance engineering in September 1971. He was selected for Officer Candidate School where he was commissioned an ensign (Aeronautical Maintenance Duty) in August 1972. Cdr. Ciarula has been assigned to VA-37
The AIMD at NAF Kadena Okinawa Japan VXE-6 and the USS Forrestal. He was the wing maintenance for training officer for Air Wing Three at NAS Chase Field in Beeville Tex. and the AIMD officer at NAS Key West Fla. In 1988 Cdr. Ciarula was assigned to the Cruise Missiles Project (PEO-CU) where he was the TASM class desk officer and the director of logistics. While at the Cruise Missiles Project he was awarded his master of science in general administration from Central Michigan University. He is currently assigned to PMA-205 in the Naval Air Systems Command. Vincent F. Neradka:received B.S. and M.S. degrees from the University of Maryland in aerospace engineering in 1964 and 1969
and an MS in technical management from the Johns Hopkins University in 1986. He joined the Johns Hopkins University Applied Physics Laboratory in 1979 working on the Vertical Launching System of the Aegis cruisers. Since that time he has participated in activities on the Phalanx program the SPS-48E radar and long term involvement with the Cruise Missile Program. On the Cruise Missile Program Mr. Neradka has focused on the specification of and testing related to environments. Simulation activities have included modeling of pneumatic systems and NASTRAN dynamic modeling. Mr. Neradka is a registered professional engineer.
This paper discusses the data that were recorded during a three-month accelerated humidity test of two Tomahawk all-up rounds (AURs), one pressurized with dry nitrogen in accordance with the current practice, and the ...
This paper discusses the data that were recorded during a three-month accelerated humidity test of two Tomahawk all-up rounds (AURs), one pressurized with dry nitrogen in accordance with the current practice, and the other left unpressurized. Temperature and humidity were recorded inside the canisters and missiles. Data analysis consisted of psychrometric calculations to determine the moisture intrusion into these missiles during the test. The work was carried out in support of a technical effort directed at changing the storage and stowage environmental protection of the Tomahawk missile.
Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP)...
详细信息
Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP) after fine-tuning. These models, however, suffer from poor robustness and lack of interpretability. We refer to pre-trained language models with knowledge injection as knowledge-enhanced pre-trained language models (KEPLMs). These models demonstrate deep understanding and logical reasoning and introduce interpretability. In this survey, we provide a comprehensive overview of KEPLMs in NLP. We first discuss the advancements in pre-trained language models and knowledge representation learning. Then we systematically categorize existing KEPLMs from three different perspectives. Finally, we outline some potential directions of KEPLMs for future research.
作者：
Xin ZhangHongzhi FengM. Shamim HossainYinzhuo ChenHongbo WangYuyu YinHangzhou Dianzi University
China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China and Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Hangzhou Dianzi University
China Department of Software Engineering
College of Computer and Information Sciences King Saud University Saudi Arabia Hangzhou Dianzi University
China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China and Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China
Action Quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the...
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Action Quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the dominance of the background in videos. Especially, the background bias tends to overshadow subtle foreground differences, which is crucial for precise action evaluation. To address the background bias issue, we propose a novel data augmentation method named Scaled Background Swap. Firstly, the background regions between different video samples are swapped to guide models focus toward the dynamic foreground regions and mitigate its sensitivity to the background during training. Secondly, the video’s foreground region is up-scaled to further enhance models’ attention to the critical foreground action information for AQA tasks. In particular, the proposed Scaled Background Swap method can effectively improve models’ accuracy and generalization by prioritizing foreground motion and swapping backgrounds. It can be flexibly applied with various video analysis models. Extensive experiments on AQA benchmarks demonstrate that Scaled Background Swap method achieves better performance than baselines. Specifically, the Spearman’s rank correlation on datasets AQA-7 and MTL-AQA reaches 0.8870 and 0.9526, respectively. The code is available at: https://***/Emy-cv/Scaled-Background Swap.
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