SK Hynix filed to raise up to 45.45 trillion won (~$29.4B) via a Nasdaq ADR listing
近300亿美元的巨额募资，反映了 AI 算力基础设施对高带宽内存（HBM）的极端渴求。在投资者追捧 AI 存储芯片的背景下，这种规模的上市不仅是资金的角逐，更暗示着全球半导体供应链正在围绕 AI 算力需求进行深度的资本重构。
10 Matching Annotations
- Jul 2026
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thesequence.substack.com thesequence.substack.com
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- May 2026
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huggingface.co huggingface.co
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By predicting these unified tokens, it effectively leverages diverse human data to achieve state-of-the-art data efficiency and robust out-of-distribution (OOD) generalization.
这一实验结果展示了UniT在利用人类数据实现高效和鲁棒泛化方面的潜力，为数据效率和泛化能力提供了新的标准。
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- Aug 2022
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Local file Local file
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If akey is lost, this invariably means that the secureddata asset is irrevocably lost
Counterpart, be careful! If a key is lost, the secured data asset is lost
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- Dec 2020
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developers.cloudflare.com developers.cloudflare.com
- Jun 2020
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psyarxiv.com psyarxiv.com
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Im, H., & Chen, C. (2020). Social Distancing Around the Globe: Cultural Correlates of Reduced Mobility [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/b2s37
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- May 2020
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kantarainitiative.org kantarainitiative.org
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Its purpose is to decrease the reliance on privacy policies and enhance the ability for people to share and control personal information.
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www.iubenda.com www.iubenda.com
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It’s useful to remember that under GDPR regulations consent is not the ONLY reason that an organization can process user data; it is only one of the “Lawful Bases”, therefore companies can apply other lawful (within the scope of GDPR) bases for data processing activity. However, there will always be data processing activities where consent is the only or best option.
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- Apr 2020
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queue.acm.org queue.acm.org
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Want to keep your users? Just make it easy for them to leave.
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- Jan 2014
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www.dataone.org www.dataone.org
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A key component of data management is the comprehensive description of the data and contextual information that future researchers need to understand and use the data. This description is particularly important because the natural tendency is for the information content of a data set or database to undergo entropy over time (i.e. data entropy ), ultimately becoming meaningless to scientists and others [ 2 ].
I agree with the key component mentioned here, but I feel the term data entropy is an unhelpful crutch.
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www.alexandria.ucsb.edu www.alexandria.ucsb.edu
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Researchers may be underestimating the need for help using archival storage systems and dealing with attendant metadata issues.
In my mind this is a key challenge: even if people can describe what they need for themselves (that in itself is a very hard problem), what to do from the infrastructure standpoint to implement services that aid the individual researcher and also aid collaboration across individuals in the same domain, as well as across domains and institutions... in a long-term sustainable way is not obvious.
In essence... how do we translate needs that we don't yet fully understand into infrastructure with low barrier to adoption, use, and collaboration?
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