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  1. 14041

    Multi-Task Learning for Real-Time BSIM-CMG Parameter Extraction of NSFETs With Multiple Structural Variations by Seunghwan Lee, Seungjoon Eom, Jinsu Jeong, Junjong Lee, Sanguk Lee, Hyeok Yun, Yonghwan Ahn, Rock-Hyun Baek

    Published 2024-01-01
    “…The proposed method was evaluated using a 1.4 nm node NSFET and eight additional NSFETs with <inline-formula> <tex-math notation="LaTeX">$L_{\mathrm {g}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {ns}}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$W_{\mathrm {ns}}$ </tex-math></inline-formula> variations of 1, 0.5, and 5 nm, respectively, from the baseline values of 12, 5, and 25 nm at the 1.4 nm node. …”
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  2. 14042

    Subnanomolar MAS-related G protein-coupled receptor-X2/B2 antagonists with efficacy in human mast cells and disease models by Ghazl Al Hamwi, Mohamad Wessam Alnouri, Sven Verdonck, Piotr Leonczak, Shaswati Chaki, Stefan Frischbutter, Pavel Kolkhir, Michaela Matthey, Constantin Kopp, Marek Bednarski, Yvonne K. Riedel, Daniel Marx, Sophie Clemens, Vigneshwaran Namasivayam, Susanne Gattner, Dominik Thimm, Katharina Sylvester, Katharina Wolf, Andreas E. Kremer, Steven De Jonghe, Daniela Wenzel, Magdalena Kotańska, Hydar Ali, Piet Herdewijn, Christa E. Müller

    Published 2025-04-01
    “…Here, we present a multi-disciplinary approach involving chemistry, biology, and computational science, resulting in the development of a small-molecule MRGPRX2 antagonist (PSB-172656, 3-ethyl-7,8-difluoro-2-isopropylbenzo[4,5]imidazo [1,2-a] pyrimidin-4(1H)-one) based on a fragment screening hit. …”
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  7. 14047

    Sieving with Streaming Memory Access by Ziyu Zhao, Jintai Ding, Bo-Yin Yang

    Published 2025-03-01
    “…The above is corroborated by the results from our efficient CPU-based BGJ implementation in an optimized framework, which saves about 40% RAM footprint and is ≥ 24.5x more efficient gate-count-wise compared to the Ducas–Stevens–van Woerden 2021 4-GPU implementation, which like most prior sieving-based SVP computations is a HK3 (Herold–Kirshanova 2017) sieve. …”
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