The JCMT BISTRO Survey: The Magnetic Fields of the IC 348 Star-forming Region

Autor: Choi, Youngwoo, Kwon, Woojin, Pattle, Kate, Arzoumanian, Doris, Bourke, Tyler L., Hoang, Thiem, Hwang, Jihye, Koch, Patrick M., Sadavoy, Sarah, Bastien, Pierre, Furuya, Ray, Lai, Shih-Ping, Qiu, Keping, Ward-Thompson, Derek, Berry, David, Byun, Do-Young, Chen, Huei-Ru Vivien, Chen, Wen Ping, Chen, Mike, Chen, Zhiwei, Ching, Tao-Chung, Cho, Jungyeon, Choi, Minho, Choi, Yunhee, Coudé, Simon, Chrysostomou, Antonio, Chung, Eun Jung, Dai, Sophia, Debattista, Victor, Di Francesco, James, Diep, Pham Ngoc, Doi, Yasuo, Duan, Hao-Yuan, Duan, Yan, Eswaraiah, Chakali, Fanciullo, Lapo, Fiege, Jason, Fissel, Laura M., Franzmann, Erica, Friberg, Per, Friesen, Rachel, Fuller, Gary, Gledhill, Tim, Graves, Sarah, Greaves, Jane, Griffin, Matt, Gu, Qilao, Han, Ilseung, Hasegawa, Tetsuo, Houde, Martin, Hull, Charles L. H., Inoue, Tsuyoshi, Inutsuka, Shu-ichiro, Iwasaki, Kazunari, Jeong, Il-Gyo, Johnstone, Doug, Karoly, Janik, Könyves, Vera, Kang, Ji-hyun, Lacaille, Kevin, Law, Chi-Yan, Lee, Chang Won, Lee, Hyeseung, Lee, Chin-Fei, Lee, Jeong-Eun, Lee, Sang-Sung, Li, Dalei, Li, Di, Li, Guangxing, Li, Hua-bai, Lin, Sheng-Jun, Liu, Hong-Li, Liu, Tie, Liu, Sheng-Yuan, Liu, Junhao, Longmore, Steven, Lu, Xing, Lyo, A-Ran, Mairs, Steve, Matsumura, Masafumi, Matthews, Brenda, Moriarty-Schieven, Gerald, Nagata, Tetsuya, Nakamura, Fumitaka, Nakanishi, Hiroyuki, Ngoc, Nguyen Bich, Ohashi, Nagayoshi, Onaka, Takashi, Park, Geumsook, Parsons, Harriet, Peretto, Nicolas, Priestley, Felix, Pyo, Tae-Soo, Qian, Lei, Rao, Ramprasad, Rawlings, Jonathan, Rawlings, Mark, Retter, Brendan, Richer, John, Rigby, Andrew, Saito, Hiro, Savini, Giorgio, Seta, Masumichi, Sharma, Ekta, Shimajiri, Yoshito, Shinnaga, Hiroko, Soam, Archana, Kang, Miju, Kataoka, Akimasa, Kawabata, Koji, Kemper, Francisca, Kim, Jongsoo, Kim, Shinyoung, Kim, Gwanjeong, Kim, Kyoung Hee, Kim, Mi-Ryang, Kim, Kee-Tae, Kim, Hyosung, Kirchschlager, Florian, Kirk, Jason, Kobayashi, Masato I. N., Kusune, Takayoshi, Kwon, Jungmi, Tamura, Motohide, Tang, Ya-Wen, Tang, Xindi, Tomisaka, Kohji, Tsukamoto, Yusuke, Viti, Serena, Wang, Hongchi, Wang, Jia-Wei, Wu, Jintai, Xie, Jinjin, Yang, Meng-Zhe, Yen, Hsi-Wei, Yoo, Hyunju, Yuan, Jinghua, Yun, Hyeong-Sik, Zenko, Tetsuya, Zhang, Guoyin, Zhang, Yapeng, Zhang, Chuan-Peng, Zhou, Jianjun, Zhu, Lei, de Looze, Ilse, André, Philippe, Dowell, C. Darren, Eden, David, Eyres, Stewart, Falle, Sam, Gouellec, Valentin J. M. Le, Poidevin, Frédérick, van Loo, Sven

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and

Externí odkaz: http://arxiv.org/abs/2411.01960

Highly tunable moir\'e superlattice potentials in twisted hexagonal boron nitrides

Autor: Han, Kwanghee, Cho, Minhyun, Kim, Taehyung, Kim, Seung Tae, Kim, Suk Hyun, Park, Sang Hwa, Yang, Sang Mo, Watanabe, Kenji, Taniguchi, Takashi, Menon, Vinod, Kim, Young Duck

Moir\'e superlattice of twisted hexagonal boron nitride (hBN) has emerged as an advanced atomically thin van der Waals interfacial ferroelectricity platform. Nanoscale periodic ferroelectric moir\'e domains with out-of-plane potentials in twisted hBN

Externí odkaz: http://arxiv.org/abs/2410.22593

Resource-Efficient Medical Report Generation using Large Language Models

Autor: Abdullah, Hamza, Ameer, Kim, Seong Tae

Medical report generation is the task of automatically writing radiology reports for chest X-ray images. Manually composing these reports is a time-consuming process that is also prone to human errors. Generating medical reports can therefore help re

Externí odkaz: http://arxiv.org/abs/2410.15642

LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic Pathologies

Autor: Hamza, Ameer, Abdullah, Ahn, Yong Hyun, Lee, Sungyoung, Kim, Seong Tae

Generating Natural Language Explanations (NLEs) for model predictions on medical images, particularly those depicting thoracic pathologies, remains a critical and challenging task. Existing methodologies often struggle due to general models' insuffic

Externí odkaz: http://arxiv.org/abs/2410.04749

PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond

Autor: Lee, Jongseo, Ahn, Geo, Kim, Seong Tae, Choi, Jinwoo

For automatic human figure drawing (HFD) assessment tasks, such as diagnosing autism spectrum disorder (ASD) using HFD images, the clarity and explainability of a model decision are crucial. Existing pixel-level attribution-based explainable AI (XAI)

Externí odkaz: http://arxiv.org/abs/2409.18260