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

Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning

Autor: Wang, Shuhe, Wang, Guoyin, Wang, Yizhong, Li, Jiwei, Hovy, Eduard, Guo, Chen

Packing, initially utilized in the pre-training phase, is an optimization technique designed to maximize hardware resource efficiency by combining different training sequences to fit the model's maximum input length. Although it has demonstrated effe

Externí odkaz: http://arxiv.org/abs/2410.08081

BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data

Autor: Wang, Xuwu, Cui, Qiwen, Tao, Yunzhe, Wang, Yiran, Chai, Ziwei, Han, Xiaotian, Liu, Boyi, Yuan, Jianbo, Su, Jing, Wang, Guoyin, Liu, Tingkai, Chen, Liyu, Liu, Tianyi, Sun, Tao, Zhang, Yufeng, Zheng, Sirui, You, Quanzeng, Yang, Yang, Yang, Hongxia

Large language models (LLMs) have become increasingly pivotal across various domains, especially in handling complex data types. This includes structured data processing, as exemplified by ChartQA and ChatGPT-Ada, and multimodal unstructured data pro

Externí odkaz: http://arxiv.org/abs/2410.00773

Granular-ball Representation Learning for Deep CNN on Learning with Label Noise

Autor: Dai, Dawei, Zhu, Hao, Xia, Shuyin, Wang, Guoyin

In actual scenarios, whether manually or automatically annotated, label noise is inevitably generated in the training data, which can affect the effectiveness of deep CNN models. The popular solutions require data cleaning or designing additional opt

Externí odkaz: http://arxiv.org/abs/2409.03254

The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism

Autor: Song, Yifan, Wang, Guoyin, Li, Sujian, Lin, Bill Yuchen

Current evaluations of large language models (LLMs) often overlook non-determinism, typically focusing on a single output per example. This limits our understanding of LLM performance variability in real-world applications. Our study addresses this i

Externí odkaz: http://arxiv.org/abs/2407.10457