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Titlebook: Organisiertes Misstrauen und ausdifferenzierte Kontrolle; Zur Soziologie der P Martin Wei?mann Book‘‘‘‘‘‘‘‘ 2023 Der/die Herausgeber bzw. d

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樓主: 熱愛
41#
發(fā)表于 2025-3-28 18:28:32 | 只看該作者
42#
發(fā)表于 2025-3-28 21:37:18 | 只看該作者
43#
發(fā)表于 2025-3-29 01:26:41 | 只看該作者
Martin Wei?mannbile applications also need to satisfy the requirements of low latency, low storage and low consumption. To fulfill above objectives, we aim to propose a new deep learning compression algorithm. We conduct comprehensive experiments to compare the proposed light-weight model with other standard state
44#
發(fā)表于 2025-3-29 04:49:15 | 只看該作者
45#
發(fā)表于 2025-3-29 11:04:13 | 只看該作者
Martin Wei?mannbile applications also need to satisfy the requirements of low latency, low storage and low consumption. To fulfill above objectives, we aim to propose a new deep learning compression algorithm. We conduct comprehensive experiments to compare the proposed light-weight model with other standard state
46#
發(fā)表于 2025-3-29 14:50:33 | 只看該作者
Martin Wei?mannbile applications also need to satisfy the requirements of low latency, low storage and low consumption. To fulfill above objectives, we aim to propose a new deep learning compression algorithm. We conduct comprehensive experiments to compare the proposed light-weight model with other standard state
47#
發(fā)表于 2025-3-29 15:55:17 | 只看該作者
s is calculated. An energy-hole alleviating algorithm is ultimately proposed based on the energy analysis in WSN. Finally, experimental results validate the efficiency and effectiveness of the proposed algorithm in energy-hole detection and mitigation.
48#
發(fā)表于 2025-3-29 21:59:45 | 只看該作者
Martin Wei?manns is calculated. An energy-hole alleviating algorithm is ultimately proposed based on the energy analysis in WSN. Finally, experimental results validate the efficiency and effectiveness of the proposed algorithm in energy-hole detection and mitigation.
49#
發(fā)表于 2025-3-30 00:33:41 | 只看該作者
Martin Wei?mannbile applications also need to satisfy the requirements of low latency, low storage and low consumption. To fulfill above objectives, we aim to propose a new deep learning compression algorithm. We conduct comprehensive experiments to compare the proposed light-weight model with other standard state
50#
發(fā)表于 2025-3-30 06:31:20 | 只看該作者
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