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pro vyhledávání: '"Lee, At"'
Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer interface technology leverages this brain activity to generate external c
Externí odkaz:
http://arxiv.org/abs/2411.09243
Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an innovative communication tool. Human communication accompanies various f
Externí odkaz:
http://arxiv.org/abs/2411.09211
Autor:
Liu, Chun-Fan, Shang, Hsien, Johnstone, Doug, Ai, Tsung-Han, Lee, Tsz Ming, Krasnopolsky, Ruben, Hirano, Naomi, Dutta, Somnath, Hsu, Shih-Ying, López-Vázquez, Jesús Alejandro, Liu, Sheng-Yuan, Liu, Tie, Tatematsu, Ken'ichi, Zhang, Qizhou, Rawlings, Mark G., Eden, David, Ren, Zhiyuan, Sanhueza, Patricio, Kwon, Woojin, Lee, Chang Won, Kuan, Yi-Jehng, Bandopadhyay, Somdeb, Väisälä, Miikka S., Lee, Chin-Fei, Das, Indrani
The Atacama Large Millimeter/submillimeter Array Survey of Orion Planck Galactic Cold Clumps (ALMASOP) reveals complex nested morphological and kinematic features of molecular outflows through the CO (J = 2 - 1) and SiO (J = 5 - 4) emission. We chara
Externí odkaz:
http://arxiv.org/abs/2411.08827
Autor:
Han, Cheongho, Udalski, Andrzej, Bond, Ian A., Lee, Chung-Uk, Gould, Andrew, Albrow, Michael D., Chung, Sun-Ju, Hwang, Kyu-Ha, Jung, Youn Kil, Ryu, Yoon-Hyun, Shvartzvald, Yossi, Shin, In-Gu, Yee, Jennifer C., Yang, Hongjing, Zang, Weicheng, Cha, Sang-Mok, Kim, Doeon, Kim, Dong-Jin, Kim, Seung-Lee, Lee, Dong-Joo, Lee, Yongseok, Park, Byeong-Gon, Pogge, Richard W., Mróz, Przemek, Szymański, Michał K., Skowron, Jan, Poleski, Radosław, Soszyński, Igor, Pietrukowicz, Paweł, Kozłowski, Szymon, Rybicki, Krzysztof A., Iwanek, Patryk, Ulaczyk, Krzysztof, Wrona, Marcin, Gromadzki, Mariusz, Mróz, Mateusz J., Abe, Fumio, Barry, Richard, Bennett, David P., Bhattacharya, Aparna, Fujii, Hirosame, Fukui, Akihiko, Hamada, Ryusei, Hirao, Yuki, Silva, Stela Ishitani, Itow, Yoshitaka, Kirikawa, Rintaro, Koshimoto, Naoki, Matsubara, Yutaka, Miyazaki, Shota, Muraki, Yasushi, Olmschenk, Greg, Ranc, Clément, Rattenbury, Nicholas J., Satoh, Yuki, Sumi, Takahiro, Suzuki, Daisuke, Tomoyoshi, Mio, Tristram, Paul J., Vandorou, Aikaterini, Yama, Hibiki, Yamashita, Kansuke
We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain us
Externí odkaz:
http://arxiv.org/abs/2411.09096
Autor:
Shi, Xihang, Lee, Wen Wei, Karnieli, Aviv, Lohse, Leon Merten, Gorlach, Alexey, Wong, Lee Wei Wesley, Saldit, Tim, Fan, Shanhui, Kaminer, Ido, Wong, Liang Jie
Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for molding the generation and flow of light. The use of nanoscale and atomic features to design light sources and
Externí odkaz:
http://arxiv.org/abs/2411.09019
This paper presents a bimanual haptic display based on collaborative robot arms. We address the limitations of existing robot arm-based haptic displays by optimizing the setup configuration and implementing inertia/friction compensation techniques. T
Externí odkaz:
http://arxiv.org/abs/2411.07402
Eye gaze is considered a promising interaction modality in extende reality (XR) environments. However, determining selection intention from gaze data often requires additional manual selection techniques. We present a Bayesian-based machine learning
Externí odkaz:
http://arxiv.org/abs/2411.06726
Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplicity, fine-tuning entails several troublesome engineering choices, such
Externí odkaz:
http://arxiv.org/abs/2411.06710
Neural additive model (NAM) is a recently proposed explainable artificial intelligence (XAI) method that utilizes neural network-based architectures. Given the advantages of neural networks, NAMs provide intuitive explanations for their predictions w
Externí odkaz:
http://arxiv.org/abs/2411.06367
Generative retrieval has recently emerged as a new alternative of traditional information retrieval approaches. However, existing generative retrieval methods directly decode docid when a query is given, making it impossible to provide users with exp
Externí odkaz:
http://arxiv.org/abs/2411.05572