Hasil untuk "History America"

B. Thompson, Robert D. Brown, S. Amin‐Hanjani et al.

William Cronon

D. Mcadam

J. Clifford, G. Marcus

M. Harris, K. Flegal, C. Cowie et al.

Homi K. Bhabha

K. Stamatelou, M. E. Francis, C. Jones et al.

Paul Wong, R. Takai

P. Curtin

R. Nash

Rafael Rojas, Josefina Zoraida Vázquez, Clara E. Lida et al.

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Nuzhat Nuari Khan Rivu, Md Kamrujjaman, Asif Iqbal

This study utilizes mathematical models to assess progress toward achieving the UNAIDS 90-90-90 and 95-95-95 targets aimed at managing and eradicating HIV/AIDS. It contrasts stochastic and deterministic models, focusing on their utility in optimizing public health strategies. Stochastic models account for real-world unpredictability, offering more realistic insights compared to deterministic approaches. The 95-95-95 targets aim for 95\% of people living with HIV to know their status, 95\% of those diagnosed to receive antiretroviral therapy (ART), and 95\% of those on ART to achieve viral suppression. These benchmarks are critical for reducing transmission and improving health outcomes. This analysis establishes the basic reproduction number ($R_0$) to guide interventions and examines the stability of disease-free and endemic equilibria, providing a foundation for applying optimal control strategies to minimize HIV prevalence effectively and cost-efficiently. Moreover, the data for this study was sourced from the official UNAIDS website, focusing on North America. An innovative feature of this study is the application of the Stochastic method, which enhances model accuracy and operational efficiency in simulating HIV transmission under various interventions. This research offers actionable insights for policymakers and contributes to global efforts to achieve the 95-95-95 targets by 2030, advancing the fight against HIV/AIDS.

D. H. Bennett, M. Kazin

Lars Hertel, Neil Daftary, Fedor Borisyuk et al.

We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM). Such architectures can significantly improve recommendation quality, but usually incur high latency cost necessitating infrastructure upgrades or very small Transformer models. An important part of user history modeling is early fusion of the candidate item and various methods have been studied. We revisit early fusion and compare concatenation of the candidate to each history item against appending it to the end of the list as a separate item. Using the latter method, allows us to reformulate the recently proposed amortized history inference algorithm M-FALCON \cite{zhai2024actions} for the case of DLRM models. We show via experimental results that appending with cross-attention performs on par with concatenation and that amortization significantly reduces inference costs. We conclude with results from deploying this model on the LinkedIn Feed and Ads surfaces, where amortization reduces latency by 30\% compared to non-amortized inference.

J. Crowley, L. Kerber

G. Hodgson

T. McCarty, J. Miller

Xiang Li, Ge Wu, Lingfeng Yang et al.

New knowledge originates from the old. The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models. In this survey, we comprehensively review and summarize the topic--``Historical Learning: Learning Models with Learning History'', which learns better neural models with the help of their learning history during its optimization, from three detailed aspects: Historical Type (what), Functional Part (where) and Storage Form (how). To our best knowledge, it is the first survey that systematically studies the methodologies which make use of various historical statistics when training deep neural networks. The discussions with related topics like recurrent/memory networks, ensemble learning, and reinforcement learning are demonstrated. We also expose future challenges of this topic and encourage the community to pay attention to the think of historical learning principles when designing algorithms. The paper list related to historical learning is available at \url{https://github.com/Martinser/Awesome-Historical-Learning.}

Michael Pitts

The economic and social dimensions of the Mexico-U.S. border make up a topic frequently discussed within the field of North American Studies. Such studies typically concern the exploitative labor and immigration policies of the United States, which negatively shape the experiences of Mexican laborers crossing the border in search of better wages and work opportunities. Such research, however, often overlooks the power of cultural products such as film to comment upon these topics. As the following analysis of the Mexican film Sleep Dealer (2008) illustrates, film and, more specifically, cyberpunk cinema act as important vehicles through which complex commentaries on the socioeconomic conditions of the Mexico-U.S. border may be developed and presented.

Zeerak Talat, Anne Lauscher

Machine learning and NLP require the construction of datasets to train and fine-tune models. In this context, previous work has demonstrated the sensitivity of these data sets. For instance, potential societal biases in this data are likely to be encoded and to be amplified in the models we deploy. In this work, we draw from developments in the field of history and take a novel perspective on these problems: considering datasets and models through the lens of historical fiction surfaces their political nature, and affords re-configuring how we view the past, such that marginalized discourses are surfaced. Building on such insights, we argue that contemporary methods for machine learning are prejudiced towards dominant and hegemonic histories. Employing the example of neopronouns, we show that by surfacing marginalized histories within contemporary conditions, we can create models that better represent the lived realities of traditionally marginalized and excluded communities.