Results 261 to 270 of about 3,692,894 (287)

Flexible Sensors for Robotics Tactile Perception: A Review

Advanced Robotics Research, EarlyView.
Flexible tactile sensing for robotics is reviewed through four interconnected dimensions. Physical mechanisms include piezoresistive, capacitive, piezoelectric, triboelectric, iontronic, and optical sensing. Structural design includes bioinspired, defect‐based, and MEMS‐based tactile systems.
Yu Song, Ying Chen, Yihao Chen, Xue Feng
wiley   +1 more source

Bio‐Inspired Object Retrieval via Proximity‐Guided Adaptive Thigmotactic Exploration and Neuromorphic Recognition

Advanced Robotics Research, EarlyView.
Sparse active‐infrared proximity sensing is coupled to an adaptive thigmotactic controller for camera‐inaccessible object retrieval. After grasping, receptor‐informed encoding transforms force, temperature, and wrist force–torque signals into spike trains for spiking neural network recognition.
Fengyi Wang, Nitish Thakor, Gordon Cheng
wiley   +1 more source

TacVerse: A Multisensor Dataset and Benchmark for Cross‐Sensor Vision‐Based Tactile Perception

Advanced Robotics Research, EarlyView.
TacVerse provides a controlled benchmark of 106 800 tactile images from seven vision‐based tactile sensors across shape classification, grating classification, and force regression. Direct cross‐sensor transfer reveals substantial sensor‐shift degradation, with grating and force perception more affected than shape recognition.
Lan Wei, Gurmeher Khurana, Sirine Bhouri, Wenhao Hong, Zeyuan Xin, Qingzheng Cong, Wen Fan, Yanzheng Xiang, Dandan Zhang   +8 more
wiley   +1 more source

From RNA design to delivery: Computational strategies for functional RNA therapeutics. [PDF]

Bioact Mater
Wang Y, Tong S, Wang X, Jin X, Tan D, Lu Y.   +5 more
europepmc   +1 more source

Interactive neural machine translation [PDF]

Computer Speech and Language, 2017
Despite the promising results achieved in last years by statistical machine translation, and more precisely, by the neural machine translation systems, this technology is still not error-free. The outputs of a machine translation system must be corrected by a human agent in a post-editing phase.
Alvaro Peris, Miguel Domingo, Francisco Casacuberta   +2 more
exaly   +4 more sources

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Multilingual Neural Machine Translation

Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts, 2020
The advent of neural machine translation (NMT) has opened up exciting research in building multilingual translation systems i.e. translation models that can handle more than one language pair. Many advances have been made which have enabled (1) improving translation for low-resource languages via transfer learning from high resource languages; and (2 ...
Raj Dabre, Chenhui Chu, Anoop Kunchukuttan   +2 more
openaire   +1 more source

Combining Translation Memory with Neural Machine Translation

Proceedings of the 6th Workshop on Asian Translation, 2019
In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT
Akiko Eriguchi, Spencer Rarrick, Hitokazu Matsushita   +2 more
openaire   +2 more sources

A Survey of Multilingual Neural Machine Translation

ACM Computing Surveys, 2021
Anoop Kunchukuttan
exaly