AUGUR 发表于 2025-3-27 00:52:33
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Rent and Technological Change in the Extractive Industries,gricultural commodities, the combination of Malthusian population theory with the insights of Ricardo on rent seemed to ensure that any benefits from technological change would be dissipated as growing population necessitated the cultivation of worse and worse land. Food prices would thus be risingAromatic 发表于 2025-3-27 12:13:59
The U.S.-Japan Semiconductor Trade Agreement in the Globalization of Imperfect and Dynamic Competit- which has existed despite the industry’s globalization and the increasing importance of U.S.Japan interfirm strategic alliances -- in the context of imperfect and dynamic competition among private firms that either seek government intervention to their advantage or confront such intervention to th新字 发表于 2025-3-27 15:32:35
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Free Versus Controlled Competition,num, a strong growth of employment, and profits of firms are 50 to 100 per cent higher than in economy B. In this latter economy, employment hardly rises, notwithstanding an average real growth of the gross product similar to that of economy A, and a higher level of investment.Exaggerate 发表于 2025-3-27 22:06:17
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Bo Carlsson causing the model to be biased towards the source domain. Recent works in SSDA show that aligning only the labeled target samples with the source samples potentially leads to incomplete domain alignment of the target domain to the source domain. In our approach, to align the two domains, we leveragBRACE 发表于 2025-3-28 07:18:47
Gunnar Eliassonty. As the cornerstone of a smart parking system is the ability to detect occupancy, traditional methods use the classification backbone to predict spots from a manual labeled grid. This is time-consuming and loses the system’s scalability. Additionally, most of the approaches use deep learning mode摄取 发表于 2025-3-28 13:29:09
G. N. von Tunzelmannover time. Dynamic network architectures are one of the existing techniques developed to handle the varying computational load in real-time deployments. Here we introduce LeAF (Legacy Augmentation for Flexible inference), a novel paradigm to augment the key-phases of a pre-trained DNN with alternati