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1. APARTMENT COMPOUNDS, HOUSEHOLDS, AND POPULATION IN THE ANCIENT CITY OF TEOTIHUACAN, MEXICO

   https://doi.org/10.1017/S0956536118000573  

   Smith, Michael E.; Chatterjee, Abhishek; Huster, Angela C.; Stewart, Sierra; Forest, Marion (May 2019, Ancient Mesoamerica)

   Abstract We present three new analyses of existing data from past fieldwork at Teotihuacan. First, we confirm and refine the wealth-based housing typology of Millon's Teotihuacan Mapping Project (TMP). Second, we analyze the spatial configurations of excavated compounds, using network methods to identify the size and layout of individual dwellings within walled compounds. Third, we use those results to generate the first population estimate for the city based on measurements from the TMP map. We extrapolate the average sizes of dwellings from excavated compounds to the entire sample of mapped residences as depicted on the TMP map of the city. We generate a range of population estimates, of which we suggest that 100,000 persons is the most reasonable estimate for the Xolalpan-Metepec population of Teotihuacan. These analyses show that legacy data from fieldwork long past can be used to answer research questions that are relevant and important today. 

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2. Un suburbio de Teotihuacan: Nuevas investigaciones en el barrio de Hacienda Metepec

   Forest, Marion; Somerville, Andrew (July 2023, Trace)

   This article presents the research questions and initial results of the Project Hacienda Metepec (2021-2022). Examination of the archaeological background and the recent mapping of this peripheral neighborhood of Teotihuacan allows for a discussion about its formation process, at the margin of the city. The neighborhood could have resulted from distinct urban growth processes, including planning, following the principles applied in the central sectors of Teotihuacan or, at the contrary, less planned, or unplanned sprawl. First observations of architectural elements and spatial organization suggest planning, however, the contrast between the monumentality of the neighborhood core and its relative spatial isolation remain unclear 

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3. Projecting future nitrogen inputs: are we making the right assumptions?

   https://doi.org/10.1088/1748-9326/ac6619  

   Vishwakarma, Srishti; Zhang, Xin; Mueller, Nathaniel D (May 2022, Environmental Research Letters)

   Abstract Global use of reactive nitrogen (N) has increased over the past century to meet growing food and biofuel demand, while contributing to substantial environmental impacts. Addressing continued N management challenges requires anticipating pathways of future N use. Several studies in the scientific literature have projected future N inputs for crop production under a business-as-usual scenario. However, it remains unclear how using yield response functions to characterize a given level of technology and management practices (TMP) will alter the projections when using a consistent dataset. In this study, to project N inputs to 2050, we developed and tested three approaches, namely ‘Same nitrogen use efficiency (NUE)’, ‘Same TMP’, and ‘Improving TMP’. We found the approach that considers diminishing returns in yield response functions (‘Same TMP’) resulted in 268 Tg N yr −1 of N inputs, which was 61 and 48 Tg N yr −1 higher than when keeping NUE at the current level with and without considering changes in crop mix, respectively. If TMP continue to evolve at the pace of past five decades, projected N inputs reduce to 204 Tg N yr −1 , a value that is still 59 Tg N yr −1 higher than the inputs in the baseline year 2006. Overall, our results suggest that assuming a constant NUE may be too optimistic in projecting N inputs, and the full range of projection assumptions need to be carefully explored when investigating future N budgets. 

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4. Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence

   Coskunuzer, Baris; Segovia-Dominguez, Ignacio; Chen, Yuzhou; Gel, Yulia (February 2024, Proceedings of 38th AAAI Conference on Artificial Intelligence)

   Wooldridge, Michael (Ed.)

   Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit timedependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit timedependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named Temporal MultiPersistence (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time.We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times. 

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5. Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence

   https://doi.org/10.1609/aaai.v38i10.29051  

   Coskunuzer, Baris; Segovia-Dominguez, Ignacio; Chen, Yuzhou; Gel, Yulia R (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named Temporal MultiPersistence (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times. 

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