作者：
BAITIS, AEAPPLEBEE, TrMCNAMArA, TMA. Erich Baitis:
a native of Germany came to the David W. Taylor Naval Ship R&D Center in 1957 as a cooperative student/trainee and received his B.S. degree in physics from Virginia Polytechnic Institute. As a 32-year-old naval architect in 1971 he received both the Vietnam Honor Service Medal and the Navy's Meritorious Civilian Service Award for his eight months as liaison with the Vietnamese Navy's ferro-cement program. As head of the Seakeeping and Stabilization Group of the Surface Ship Dynamics Branch his work has led to the development of a new standard Ship Motion Computer Program and the application of ship motions to ship habitability operability and survivability problems. A major area of this work has been the ship-aircraft interface which is particularly sensitive to ship motions wind and other environmental factors. He is a member of the American Society of Naval Engineers and was awarded the Solberg Award for 1982 “in recognition of significant engineering research and development contributions in the area of improved helicopter operations from a ship in a seaway.” Terrence A. Applebee:is currently a naval architect at the David W. Taylor Naval Ship R&D Center in the Surface Ship Dynamics Branch. he came to the Center after earning a B.S. degree in ocean engineering from Florida Institute of Technology in 1973. Since that time
he has worked in the areas of seakeeping performance evaluation ship-helicopter interfacing and human factor considerations. He is a member of the American Society of Naval Engineers and the Society of Naval Architects and Marine Engineers. Thomas M. McNamara:is an employee of the John Hopkins University Applied Physics Laboratory in the ocean data acquisition program. From 1979 to 1983
he worked at David W. Taylor Naval Ship R&D Center in the Surface Ship Dynamics Branch. His expertise has focused on the development of computer models for human factor evaluations as well as motion stabilization systems. He has participated in the development of advanced stabilizat
The FFG 7/LAMPS MK III Operator Guidance Manual (OGM) was developed for all FFG-7 class frigates which are not fin stabilized or are operating with the fins off. The OGM was developed to assis the ship operators of th...
The FFG 7/LAMPS MK III Operator Guidance Manual (OGM) was developed for all FFG-7 class frigates which are not fin stabilized or are operating with the fins off. The OGM was developed to assis the ship operators of the FFG-7 class in choosing ship speed and heading combinations which will minimize ship motion-related problems during various phases of the LAMPS deployment. Crew safety and performance were major concerns in the development of the OGM. This paperreviews the effect of human factors on ship operations during helicopterrecovery, maintenance, and transit to and from the hangar.
The Shipboarddata Multiplex System (SdMS) is a general purpose information transfer system directed toward fulfilling the internal data Intercommunication requirements of a variety of naval combatant ships and submar...
The Shipboarddata Multiplex System (SdMS) is a general purpose information transfer system directed toward fulfilling the internal data Intercommunication requirements of a variety of naval combatant ships and submarines in the 1980–1990 time frame. The need for a modern data transfer system of the size and capability of SdMS has been increase in unison with the sophistication of shipboard electronic equipment and the associated magnitude of equipment-to-equipment signal traffic. Instead of the miles of unique cabling that must be specifically designed for each ship, SdMS will meet information transfer needs with general-purpose multiplex cable that will be Installed according to a standard plan that does not vary with changes to the ship's electronics suite. Perhaps the greatest impact of SdMS will be the decoupling of ship subsystems from each other and from the ship. Standard multiplex interfaces will avoid the cost anddelay of modifying subsystems to make them compatible. The ability to wire a new ship according to a standard multiplex cable plan, long before the ship subsystems are fully defined, frees both the ship and the subsystems to develop at their own pace, will allow compression of the development schedules and will provide ships with more advanced subsystems. This paperdescribes the SdMS system currently being developed by the U.S. Navy.
Multilingual Language Models (MLLMs) such as mBErT, XLM, XLM-r, etc. have emerged as a viable option for bringing the power of pretraining to a large number of languages. Given their success in zero-shot transfer lear...
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Multilingual Language Models (MLLMs) such as mBErT, XLM, XLM-r, etc. have emerged as a viable option for bringing the power of pretraining to a large number of languages. Given their success in zero-shot transfer learning, there has emerged a large body of work in (i) building bigger MLLMs covering a large number of languages (ii) creating exhaustive benchmarks covering a wider variety of tasks and languages for evaluating MLLMs (iii) analysing the performance of MLLMs on monolingual, zero-shot cross-lingual and bilingual tasks (iv) understanding the universal language patterns (if any) learnt by MLLMs and (v) augmenting the (often) limited capacity of MLLMs to improve their performance on seen or even unseen languages. In this survey, we review the existing literature covering the above broad areas of research pertaining to MLLMs. Based on our survey, we recommend some promising directions of future research.
In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto...
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In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, andreaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning anddeep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains.
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