“Not Like Baghdad” – The Looting and Protection of Egypt’s Treasures

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The Poetics of Piracy: Emulating Spain in English Literature, by Barbara Fuchs (2013)

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Los "antiguos peruanos" de la antropología americana: la arqueología de los Andes del siglo XIX como nexo de investigación global

Between 1820 and 1920, American anthropologists acquired more human remains of Andean origin than those of any other individual population worldwide. This article explains why in 1965 in the Smithsonian Institution’s National Museum of Natural History, anthropologists represented the growth of all humanity using 160 Andean skulls. Using archival sources, it argues that the excavation and interpretation of Andean ancestors in Peru prior to 1900 was foundational to the development of Americanist anthropology as a whole, and should be understood as the necessary preface to the more famous development of archaeology in Peru under Uhle and Tello. Exploration of this history is necessary in light of recent efforts in the United States to return or repatriate ancestral remains to source communities.Entre 1820 y 1920, los antropólogos estadounidenses adquirieron más restos humanos de origen andino que los de cualquier otra población individual del mundo. Este artículo explica por qué en 1965, en el Museo Nacional de Historia Natural de la Smithsonian Institution, los antropólogos representaron el crecimiento de toda la humanidad utilizando 160 cráneos andinos. Basándose en fuentes de archivo, se argumenta que la excavación e interpretación de los antepasados andinos en Perú antes de 1900 fue fundacional para el desarrollo de la antropología americanista en su conjunto, y debe entenderse como el prefacio necesario para el desarrollo más famoso de la arqueología en Perú bajo Max Uhle y Julio César Tello. La exploración de esta historia es necesaria a la luz de los recientes esfuerzos en Estados Unidos por devolver o repatriar restos ancestrales a las comunidades de origen

Erratum: Author Correction: Identification of genes required for eye development by high-throughput screening of mouse knockouts.

[This corrects the article DOI: 10.1038/s42003-018-0226-0.]

Using AI libraries for Incompressible Computational Fluid Dynamics

Recently, there has been a huge effort focused on developing highly efficient open source libraries to perform Artificial Intelligence (AI) related computations on different computer architectures (for example, CPUs, GPUs and new AI processors). This has not only made the algorithms based on these libraries highly efficient and portable between different architectures, but also has substantially simplified the entry barrier to develop methods using AI. Here, we present a novel methodology to bring the power of both AI software and hardware into the field of numerical modelling by repurposing AI methods, such as Convolutional Neural Networks (CNNs), for the standard operations required in the field of the numerical solution of Partial Differential Equations (PDEs). The aim of this work is to bring the high performance, architecture agnosticism and ease of use into the field of the numerical solution of PDEs. We use the proposed methodology to solve the advection-diffusion equation, the non-linear Burgers equation and incompressible flow past a bluff body. For the latter, a convolutional neural network is used as a multigrid solver in order to enforce the incompressibility constraint. We show that the presented methodology can solve all these problems using repurposed AI libraries in an efficient way, and presents a new avenue to explore in the development of methods to solve PDEs and Computational Fluid Dynamics problems with implicit methods.Comment: 24 pages, 6 figure

Contact with Beach Sand and Risk of Illness

Background: Recently, numerous studies of fecal contamination of beach sand have shown that beach sand can harbor higher concentrations of fecal indicator organisms than nearby recreational waters. Although fecal pathogens have also been isolated from beach sand, the risk of illness associated with beach sand contact and fecal indicator organism concentrations in sand is unclear. Methods: During 2003-2005 and 2007, beach visitors at 7 U.S. beaches were enrolled in the study and asked about sand contact the day of their beach visit. Ten to 12 days later participants were telephoned to answer questions about health symptoms experienced since the visit. At 2 study beaches in 2007, beach sand was analyzed for concentrations of the fecal indicators Enterococcus, Bacteroides, B. thetaiotaomicron, and F+-specific coliphage. Results: We completed a total of 27,365 interviews at 4 freshwater and 3 marine water beaches. Sand contact was strongly associated with age, water contact, and beach. After controlling for age, sex, water contact, race/ethnicity, and beach, digging in the sand was positively associated with gastrointestinal (GI) illness (aIPR=1.14; 95% CI 1.02-1.26) and diarrhea (aIPR=1.20; 95% CI 1.05-1.36). The point estimate was slightly stronger between being buried in the sand and GI illness (aIPR=1.22; 95% CI 1.04-1.42) and diarrhea (aIPR=1.23; 95% CI 1.01-1.51), respectively. Similar effects were observed among nonswimmers digging in sand for GI illness (aIPR = 1.26; 95% CI = 1.03-1.55) and diarrhea (aIPR = 1.26; 95% CI = 0.98-1.62). Stronger associations were observed among those getting sand in their mouth for GI illness (aIPR=1.82; 95% CI 1.19-2.78) and diarrhea (aIPR=1.65; 95% CI = 0.96-2.84). Non-enteric illnesses were not consistently associated with sand contact. Variation was observed in beach specific results suggesting site-specific factors may be important in the risk of illness following sand exposure. At 2 marine beaches 144 sand samples were analyzed for fecal indicators and 4,999 interviews were completed. A molecular measure of Enterococcus in sand (qPCR CCE/g) was positively associated with GI illness among those digging in sand (aOR per log increase in qPCR CCE/g=1.45; 95% CI 1.05-2.01) and buried in the sand (aOR = 3.12; 95% CI 1.08-9.05). The relationship between other sand fecal indicator measures with GI illness was not consistent. Conclusions: Contact with beach sand was positively associated with enteric illness at beach sites but there was variability in the effect by beach. This study demonstrated a positive relationship between sand contact activities and GI illness as a function of microbial sand quality

GAN for time series prediction, data assimilation and uncertainty quantification

We propose a new method in which a generative adversarial network (GAN) is used to quantify the uncertainty of forward simulations in the presence of observed data. Previously, a method has been developed which enables GANs to make time series predictions and data assimilation by training a GAN with unconditional simulations of a high-fidelity numerical model. After training, the GAN can be used to predict the evolution of the spatial distribution of the simulation states and observed data is assimilated. In this paper, we describe the process required in order to quantify uncertainty, during which no additional simulations of the high-fidelity numerical model are required. These methods take advantage of the adjoint-like capabilities of generative models and the ability to simulate forwards and backwards in time. Set within a reduced-order model framework for efficiency, we apply these methods to a compartmental model in epidemiology to predict the spread of COVID-19 in an idealised town. The results show that the proposed method can efficiently quantify uncertainty in the presence of measurements using only unconditional simulations of the high-fidelity numerical model.Comment: arXiv admin note: text overlap with arXiv:2105.0772