Zobrazeno 1 - 10
of 12 114
pro vyhledávání: '"A. Naganuma"'
Autor:
L. Xu, S. Fukushima, S. Sobanska, K. Murata, A. Naganuma, L. Liu, Y. Wang, H. Niu, Z. Shi, T. Kojima, D. Zhang, W. Li
Publikováno v:
Atmospheric Chemistry and Physics, Vol 20, Pp 14321-14332 (2020)
Tracing the aging progress of soot particles during transport is highly challenging. An Asian dust event could provide an ideal opportunity to trace the continuous aging progress of long-range-transported soot particles. Here, we collected individual
Externí odkaz:
https://doaj.org/article/e1b182cf70d2415cb223046b614443e3
Autor:
Inoue, Shuhei, Chin, Kah Wuy, Uno, Shinsuke, Kohno, Kotaro, Niwa, Yuka, Naganuma, Toyo, Yamamura, Ryosuke, Watanabe, Kazuki, Takekoshi, Tatsuya, Oshima, Tai
We established a design method for a Magic-T with a single-layer dielectric/metal structure suitable for both wideband and multi-element applications for millimeter and submillimeter wave imaging observations. The design method was applied to a Magic
Externí odkaz:
http://arxiv.org/abs/2405.03919
Autor:
Kimura, Masanari, Naganuma, Hiroki
The key factor in implementing machine learning algorithms in decision-making situations is not only the accuracy of the model but also its confidence level. The confidence level of a model in a classification problem is often given by the output vec
Externí odkaz:
http://arxiv.org/abs/2405.00442
In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications. Traditional methodologies for annotating datasets for NER models are challenged by
Externí odkaz:
http://arxiv.org/abs/2404.01334
Autor:
Yoshida, Kotaro, Naganuma, Hiroki
Machine learning models traditionally assume that training and test data are independently and identically distributed. However, in real-world applications, the test distribution often differs from training. This problem, known as out-of-distribution
Externí odkaz:
http://arxiv.org/abs/2401.17541
Autor:
Shukuri, Kotaro, Ishigaki, Ryoma, Suzuki, Jundai, Naganuma, Tsubasa, Fujimoto, Takuma, Kawakubo, Daisuke, Shuzo, Masaki, Maeda, Eisaku
Utilizing Large Language Models (LLMs) facilitates the creation of flexible and natural dialogues, a task that has been challenging with traditional rule-based dialogue systems. However, LLMs also have the potential to produce unexpected responses, w
Externí odkaz:
http://arxiv.org/abs/2312.13715
Autor:
Adachi, Hayato, Endo, Ryuusuke, Shinya, Hikari, Naganuma, Hiroshi, Ono, Tomoya, Uemoto, Mitsuharu
In our previous work, we synthesized a metal/2D material heterointerface consisting of $L1_0$-ordered iron-palladium (FePd) and graphene (Gr) called FePd(001)/Gr. This system has been explored by both experimental measurements and theoretical calcula
Externí odkaz:
http://arxiv.org/abs/2308.02171
This paper proposes a novel spatiotemporal (ST) fusion framework for satellite images, named Robust Optimization-based Spatiotemporal Fusion (ROSTF). ST fusion is a promising approach to resolve a trade-off between the temporal and spatial resolution
Externí odkaz:
http://arxiv.org/abs/2308.00500
In out-of-distribution (OOD) generalization tasks, fine-tuning pre-trained models has become a prevalent strategy. Different from most prior work that has focused on advancing learning algorithms, we systematically examined how pre-trained model size
Externí odkaz:
http://arxiv.org/abs/2307.08187
This paper proposes a method for properly sampling graph signals under smoothness priors. Unlike many existing approaches that assume bandlimited graph signals, our method designs a sampling operator for graph signals that are not necessarily bandlim
Externí odkaz:
http://arxiv.org/abs/2306.14634