@misc{LuisZhuCostaetal., author = {Luis, Leonel and Zhu, Hong and Costa, Jo{\~a}o and Valls-Sol{\´e}, Josep and Brandt, Thomas and Zhou, Wu and Schneider, Erich}, title = {Reply to the commentary on luis et al. "spontaneous plugging of the horizontal semicircular canal with reversible canal dysfunction and recovery of vestibular evoked myogenic potentials}, series = {Otology \& Neurotology}, volume = {35}, journal = {Otology \& Neurotology}, number = {2}, issn = {1531-7129}, pages = {379 -- 383}, language = {en} } @misc{YiWuXietal., author = {Yi, Chao and Wu, Shunxiang and Xi, Bin and Ming, Daodong and Zhang, Yisong and Zhou, Zhenwen}, title = {Terrorist Video Detection System Based on Faster R-CNN and LightGBM}, series = {CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering}, journal = {CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering}, isbn = {978-1-4503-7772-0}, doi = {10.1145/3424978.3425121}, pages = {1 -- 8}, abstract = {Nowadays the mobile phone has become an indispensable tool in the lives of many people. While facilitating people's lives, it also provides criminals with a very important tool for spreading the terrorist video. Traditional manual detection of the terrorist video has the problem of low accuracy and inefficiency. To address the issue, this paper proposes a terrorist video detection system based on Light Gradient Boosting Machine (LightGBM) and Faster Region-based Convolutional Neural Network (Faster R-CNN) for mobile phone forensics system, which is used to quickly detect whether there is a terrorist video in the suspect's mobile phone. The system uses a multi-model method for detection, which includes preliminary detection and deep detection in two stages. Experimental research shows that it can effectively and accurately detect terrorist videos in mobile phones, thereby helping criminal investigation personnel to quickly grasp criminal evidence and provide some clues for the detection of the case.}, language = {en} } @misc{WenShaikhSteueretal., author = {Wen, Shuyu and Shaikh, Mohd Saif and Steuer, Oliver and Prucnal, Slawomir and Grenzer, J{\"o}rg and H{\"u}bner, Ren{\´e} and Turek, Marcin and Pyszniak, Krzysztof and Reiter, Sebastian and Fischer, Inga Anita and Georgiev, Yordan M. and Helm, Manfred and Wu, Shaoteng and Luo, Jun-Wei and Zhou, Shengqiang and Berenc{\´e}n, Yonder}, title = {Room-temperature extended short-wave infrared GeSn photodetectors realized by ion beam techniques}, series = {Applied Physics Letters}, volume = {123}, journal = {Applied Physics Letters}, number = {8}, issn = {0003-6951}, doi = {10.1063/5.0166799}, pages = {1 -- 7}, abstract = {GeSn alloys hold great promise as high-performance, low-cost, near- and short-wavelength infrared photodetectors with the potential to replace the relatively expensive and currently market-dominant InGaAs- and InSb-based photodetectors. In this Letter, we demonstrate room-temperature GeSn pn photodetectors fabricated by a complementary metal-oxide-semiconductor compatible process, involving Sn and P ion implantation and flash-lamp annealing prior to device fabrication. The fabrication process enables the alloying of Ge with Sn at concentrations up to 4.5\% while maintaining the high-quality single-crystalline structure of the material. This allows us to create Ge0.955Sn0.045 pn photodetectors with a low dark current density of 12.8 mA/cm2 and a relatively high extended responsivity of 0.56 A/W at 1.71 μm. These results pave the way for the implementation of a cost-effective, scalable, and CMOS-compatible short-wavelength infrared detector technology.}, language = {en} }