There is a lack of empirical evidence about global attitudes around whether and how GenAI should represent cultures. This paper assesses understandings and beliefs about culture as it relates to GenAI from a large-scale global survey. We gathered data about what culture means to different groups, and about how GenAI should approach the representation of cultural artifacts, concepts, or values. We distill working definitions of culture directly from these communities to build an understanding of its conceptual complexities and how they relate to representations in Generative AI. We survey from across parts of Europe, North and South America, Asia, and Africa. We conclude with a set of recommendations for Culture and GenAI development. These include participatory approaches, prioritizing specific cultural dimensions beyond geography, such as religion and tradition, and a sensitivity framework for addressing cultural ``redlines''.
Many natural and social science systems are described using probability distributions over elements that are related to each other: for instance, occupations with shared skills or species with similar traits. Standard information theory quantities such as entropies and $f$-divergences treat elements interchangeably and are blind to the similarity structure. We introduce a family of divergences that are sensitive to the geometry of the underlying domain. By virtue of being the Bregman divergences of structure-aware entropies, they provide a framework that retains several advantages of Kullback-Leibler divergence and Shannon entropy. Structure-aware divergences recover planted patterns in a synthetic clustering task that conventional divergences miss and are orders of magnitude faster than optimal transport distances. We demonstrate their applicability in economic geography and ecology, where structure plays an important role. Modelling different notions of occupation relatedness yields qualitatively different regionalisations of their geographic distribution. Our methods also reproduce established insights into functional $β$-diversity in ecology obtained with optimal transport methods.
Rani Mohanraj, Shuba Kumar, Sylvia Jayakumar
et al.
Polished white rice (WR), high in refined carbohydrates, the main staple in South India is associated with enhanced risk of diabetes. Brown Rice (BR), with lower glycemic load, high fibre content and micronutrients, is a healthier choice. Two hundred and twelve Persons with Diabetes (PwD) attending a tertiary diabetes care centre in a city in South India responded to a questionnaire documenting types, frequency and reasons for rice consumption, awareness and beliefs about BR. A sub-set of 10, participated in qualitative interviews, which additionally, explored the influence of traditional beliefs on and consumption patterns of rice, barriers to BR consumption and willingness to accept it in their diet. Ninety-three percent reported consuming WR with traditional usage (97 %) being the main reason for its preference. Brand image, grain size, texture and taste, of rice were other decisional considerations. Awareness about health benefits of BR was limited, with 69 % and 51 % believing it to be nutritious and helping to reduce blood sugar respectively. Appearance, texture, taste and cost were deterrents to its use. Over half agreed to switch to BR if they believed it would improve their health. Participants with a shorter duration of diabetes were more willing to change to BR. The study highlights the need to promote greater literacy regarding health benefits of BR and other forms of less polished rice. Larger trials examining the effectiveness of BR viz-a viz other types of less polished rice on blood glucose levels, metabolic factors and nutritional content among PwD are needed.
History of scholarship and learning. The humanities, Social sciences (General)
Across fields such as machine learning, social science, geography, considerable attention has been given to models that factorize a nonnegative matrix into the product of two or three matrices, subject to nonnegative or row-sum-to-1 constraints. Although these models are to a large extend similar or even equivalent, they are presented under different names, and their similarity is not well known. This paper highlights similarities among five popular models, latent budget analysis (LBA), latent class analysis (LCA), end-member analysis (EMA), probabilistic latent semantic analysis (PLSA), and nonnegative matrix factorization (NMF). We focus on an essential issue-identifiability-of these models and prove that the solution of LBA, EMA, LCA, PLSA is unique if and only if the solution of NMF is unique. We also provide a brief review for algorithms of these models. We illustrate the models with a time budget dataset from social science, and end the paper with a discussion of closely related models such as archetypal analysis.
This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A (Detailed DHC-A) of the 2020 Census. The tabulations contain statistics (counts) of demographic characteristics of the entire population of the United States, crossed with detailed races and ethnicities at varying levels of geography. The article describes the SafeTab-P algorithm, which is based on adding noise drawn to statistics of interest from a discrete Gaussian distribution. A key innovation in SafeTab-P is the ability to adaptively choose how many statistics and at what granularity to release them, depending on the size of a population group. We prove that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy (zCDP). We then describe how the algorithm was implemented on Tumult Analytics and briefly outline the parameterization and tuning of the algorithm.
William Sexton, Skye Berghel, Bayard Carlson
et al.
This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B (Detailed DHC-B) as part of the 2020 Census. The tabulations contain household statistics about household type and tenure iterated by the householder's detailed race, ethnicity, or American Indian and Alaska Native tribe and village at varying levels of geography. We describe the algorithmic strategy which is based on adding noise from a discrete Gaussian distribution and show that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy. We discuss how the implementation of the SafeTab-H codebase relies on the Tumult Analytics privacy library. We also describe the theoretical expected error properties of the algorithm and explore various aspects of its parameter tuning.
Do social networks and peer influence shape major life decisions in polarized settings? We explore this question by examining how peers influenced the allegiances of West Point cadets during the American Civil War. Leveraging quasi-random variations in the proportion of cadets from Free States, we analyze how cadets' decisions about which army to join depended on the composition of their peers. We have three main findings. First, there was a strong and significant peer effect: a higher proportion of classmates from Free States significantly increased the likelihood that cadets from Slave States joined the Union Army. Second, the peer effect varies with geography, most notably with the slave population share in cadets' home states or counties, and with cadets' own slave ownership in 1860. Third, peer effects were amplified by shared experiences such as having served together in the Mexican-American War, continuous military service, and belonging to the same cohort, suggesting that sustained interaction is important.
ABSTRACT The field of soil science has seen significant advancements in recent years, largely due to the integration of computational tools and statistical methods. Among these resources, the programming language R has emerged as a powerful and versatile platform for soil scientists, aiding in a spectrum of tasks from data analysis and modeling to visualization. Nonetheless, the broader trends and specific patterns of R usage in soil research have not been thoroughly documented. Our study investigated the prevalence of R and its package usage in 25,888 research articles published in 10 leading soil science journals over a decade, from 2014 to 2023. A considerable number of these articles, 7899 (or 30.5%), named R as their primary data analysis tool. The use of R has followed a steady linear growth pattern, rising from 13.9% in 2014 to 46.5% in 2023. The most commonly used R packages were “vegan,” “ggplot2,” “lme4,” “nlme,” and “randomForest,” with each journal showcasing unique research focuses, resulting in varying frequencies of R package applications across different publications. Furthermore, there was a notable increase in the average number of R packages used per article throughout the study period. This research highlights the pivotal role of R, armed with its robust statistical and visualization capabilities, in enabling soil scientists to conduct comprehensive analyses and gain in‐depth insights into the complex dimensions of soil science.
The relevance of addressing the understanding of the geography of religion as a section of religious studies is on the one hand due to the growing popularity of this discipline in the modern scientific space, and on the other its insufficient conceptualisation. The high interest in the geography of religion on the part of the scientific community is caused by its focus on the comprehension of theoretical and practical problems related to the spatial characteristics of religious life. Nowadays, the study of territorial aspects of the coexistence of different religious traditions acquires a special urgency due to the combination of globalisation trends and the desire to preserve authentic identity. Historically, the problem field and scientific apparatus of the geography of religion has been formed with reliance on theoretical developments and methodological tools of philosophy and various scientific disciplines, which supports terminological and methodological polyphony within this discipline and hinders its conceptualisation. The aim of this work is to identify possible grounds for attributing the geography of religion to the sections of religious studies. To achieve this goal the following tasks were solved: the origins of the formation of geography of religion were revealed; the approach to the formation of geography of religion from the perspective of geographical science was analysed; the influence of philosophy of religion on the methodological foundations of geography of religion was shown; the main vectors of interrelation between geography of religion and religious studies were determined. The research is limited to the European tradition of studying the relationship between religion and geography. The study is based on the texts of the classics of philosophical thought, as well as the works of Russian and foreign scholars devoted to the comprehension of the geography of religion as a scientific discipline. To reconstruct the thinkers' position on the issues raised, to identify their dependence on the intellectual context and to compare them, such methods as historical and philosophical analysis of the text, discourse analysis and comparative analysis were used. The result of the study was the substantiation of the conclusion about the possibility and expediency of attributing the geography of religion to the branches of religious studies. Such positioning of geography of religion does not contradict the modern understanding of the problem field and tasks of this discipline as they are formulated within the framework of geographical science, and at the same time allows us to identify promising research strategies in interaction with various religious studies disciplines such as: philosophy of religion, history of religion, anthropology of religion, sociology of religion and psychology of religion.
Abstract The estuarine plastisphere, a novel ecological habitat in the Anthropocene, has garnered global concerns. Recent geochemical evidence has pointed out its potential role in influencing nitrogen biogeochemistry. However, the biogeochemical significance of the plastisphere and its mechanisms regulating nitrogen cycling remain elusive. Using 15N- and 13C-labelling coupled with metagenomics and metatranscriptomics, here we unveil that the plastisphere likely acts as an underappreciated nitrifying niche in estuarine ecosystems, exhibiting a 0.9 ~ 12-fold higher activity of bacteria-mediated nitrification compared to surrounding seawater and other biofilms (stone, wood and glass biofilms). The shift of active nitrifiers from O2-sensitive nitrifiers in the seawater to nitrifiers with versatile metabolisms in the plastisphere, combined with the potential interspecific cooperation of nitrifying substrate exchange observed among the plastisphere nitrifiers, collectively results in the unique nitrifying niche. Our findings highlight the plastisphere as an emerging nitrifying niche in estuarine environment, and deepen the mechanistic understanding of its contribution to marine biogeochemistry.
With the evolution of content on the web and the Internet, there is a need for cyberspace that can be used to work, live, and play in digital worlds regardless of geography. The Metaverse provides the possibility of future Internet and represents a future trend. In the future, the Metaverse will be a space where the real and the virtual are combined. In this article, we have a comprehensive survey of the compelling Metaverse. We introduce computer technology, the history of the Internet, and the promise of the Metaverse as the next generation of the Internet. In addition, we briefly introduce the related concepts of the Metaverse, including novel terms like trusted Metaverse, human-intelligence Metaverse, personalized Metaverse, AI-enabled Metaverse, Metaverse-as-a-service, etc. Moreover, we present the challenges of the Metaverse such as limited resources and ethical issues. We also present Metaverse's promising directions, including lightweight Metaverse and autonomous Metaverse. We hope this survey will provide some helpful prospects and insightful directions about the Metaverse to related developments.
The escalating food insecurity in Africa, caused by factors such as war, climate change, and poverty, demonstrates the critical need for advanced early warning systems. Traditional methodologies, relying on expert-curated data encompassing climate, geography, and social disturbances, often fall short due to data limitations, hindering comprehensive analysis and potential discovery of new predictive factors. To address this, this paper introduces "HungerGist", a multi-task deep learning model utilizing news texts and NLP techniques. Using a corpus of over 53,000 news articles from nine African countries over four years, we demonstrate that our model, trained solely on news data, outperforms the baseline method trained on both traditional risk factors and human-curated keywords. In addition, our method has the ability to detect critical texts that contain interpretable signals known as "gists." Moreover, our examination of these gists indicates that this approach has the potential to reveal latent factors that would otherwise remain concealed in unstructured texts.
American democracy is currently heavily reliant on plurality in single-member districts, or PSMD, as a system of election. But public perceptions of fairness are often keyed to partisan proportionality, or the degree of congruence between each party's share of the the vote and its share of representation. PSMD has not tended to secure proportional outcomes historically, partially due to gerrymandering, where line-drawers intentionally extract more advantage for their side. But it is now increasingly clear that even blind PSMD is frequently disproportional, and in unpredictable ways that depend on local political geography. In this paper we consider whether it is feasible to bring PSMD into alignment with a proportionality norm by targeting proportional outcomes in the design and selection of districts. We do this mainly through a close examination of the "Freedom to Vote Test," a redistricting reform proposed in draft legislation in 2021. We find that applying the test with a proportionality target makes for sound policy: it performs well in legal battleground states and has a workable exception to handle edge cases where proportionality is out of reach.
Liz Izhikevich, Manda Tran, Michalis Kallitsis
et al.
Cloud computing has dramatically changed service deployment patterns. In this work, we analyze how attackers identify and target cloud services in contrast to traditional enterprise networks and network telescopes. Using a diverse set of cloud honeypots in 5~providers and 23~countries as well as 2~educational networks and 1~network telescope, we analyze how IP address assignment, geography, network, and service-port selection, influence what services are targeted in the cloud. We find that scanners that target cloud compute are selective: they avoid scanning networks without legitimate services and they discriminate between geographic regions. Further, attackers mine Internet-service search engines to find exploitable services and, in some cases, they avoid targeting IANA-assigned protocols, causing researchers to misclassify at least 15\% of traffic on select ports. Based on our results, we derive recommendations for researchers and operators.
Vitória Ribeiro Gomes Maria, Figueiredo Ferreira Giulia, Ferreira de Araújo Daniele
et al.
In peripheral countries, the lack of adequate urban planning associated with natural dynamics intensifies the existing vulnerabilities of the environment, causing physical and material losses. Therefore, this research aims to discuss the potential use of Environmental Protection Areas as a tool to drive urban growth with a low-impact development, helping to mitigate urban floods and bringing nature into the city landscape. The municipality of Maricá, located in the metropolitan region of Rio de Janeiro, Brazil, is taken as a case study. The method proposed to drive the regional environmental planning and management can be described as a three-stage method coupled with the adapted SWOT Matrix, following: the diagnosis, the prognosis, and the action plan. This process points to the definition of a Hydrological Interest Area that would allow not only the restoration of local vegetation and a better interaction of the population with the watercourses, but also the recovery of areas that have been gradually impacted by the urban expansion. The method presented in this research allows its application in different urban contexts, once it has the objective of recognizing the strengths, weaknesses, opportunities, and threats to allow the elaboration of sustainable actions and guidelines.
<p>Accurate measurements of ice flow are essential to predict future changes in
glaciers and ice caps. Glacier displacement can in principle be measured on
the large scale by cross-correlation of satellite images. At weekly to
monthly scales, the expected displacement is often of the same order as the
noise for the commonly used satellite images, complicating the retrieval of
accurate glacier velocity. Assessments of velocity changes on short timescales and over complex areas such as mountain ranges are therefore still
lacking but are essential to better understand how glacier dynamics are
driven by internal and external factors. In this study, we take advantage of
the wide availability and redundancy of satellite imagery over the western
Pamirs to retrieve glacier velocity changes over 10 d intervals for 7 years
and for a wide range of glacier geometry and dynamics. Our results reveal
strong seasonal trends. In spring/summer, we observe velocity increases of
up to 300 % compared to a slow winter period. These accelerations clearly
migrate upglacier throughout the melt season, which we link to changes in
subglacial hydrology efficiency. In autumn, we observe glacier accelerations
that have rarely been observed before. These episodes are primarily confined
to the upper ablation zone with a clear downglacier migration. We suggest
that they result from glacier instabilities caused by sudden subglacial
pressurization in response to (1) supraglacial pond drainage and/or (2) gradual closure of the hydrological system. Our 10 d resolved measurements
allow us to characterize the short-term response of glaciers to changing
meteorological and climatic conditions.</p>
Abstract Gangs are geographically oriented social entities as evidenced by the display of cardinal points in their graffiti, the use of neighborhood namesakes, a focus on territoriality as their raison d'être, as well as in the way they are policed and legally cordoned. Despite gang members' real and imagined penchant for transgressive place‐making and demarcation, it has been sociologists and criminologists, not geographers, who have produced the lion's share of spatially nuanced research on gangs. In this article, I provide a review of the social scientific literature on gangs, concluding with a call for how to make the discipline of geography more inclusive for gang researchers who possess real‐world experience with assertive place‐making practices.
Jing Zhang, Xiaojuan Cheng, Peter W. Fritsch
et al.
Species diversity is high in the Himalaya-Hengduan Mountains, particularly at the edges characterized by deep ravines and “sky islands”. Studies focused on sky-island species are sparse and the patterns observed in response to both geographic and climatic factors are inconsistent. Here phylogeographic and phylogenetic analyses of <i>Gaultheria nummularioides</i>, a species originating in the late Pliocene with its main distribution in the Himalaya-Hengduan Mountains, were conducted to reveal the pattern of genetic dynamics in response to physical geography, glacial fluctuations, and monsoons. We found that in this species genetic variation is higher among populations than within populations, with a significant phylogeographic boundary between the central Himalaya and the eastern Himalaya and the Hengduan Mountains. We also found a high incidence of private alleles, possibly associated with strong habitat isolation. The phylogeographic pattern recovered is consistent with populations in glacial refugia that have experienced expansion after glaciation. The divergence times of most haplotypes coincide with the time of the weakening of the Asian monsoon in these regions. Models of geographic range size showed a significant decrease from the Last Interglacial through the Last Glacial Maximum to the Current, and a predicted increase from the Current to the year 2070. Our study provides insights for understanding speciation among sky islands in this region.
Tropospheric delay is one of the important factors affecting GNSS positioning accuracy, and there are different ways to deal with the multiple measurement situations. In short baseline measurements, the difference method is commonly used to eliminate tropospheric errors. However, it cannot be used in long baseline measurements or complex weather since it still has great influences on precision measurement after difference calculation. Therefore, modelling method is usually used to reduce tropospheric delay. As it is well known, there are three types of commonly used tropospheric delay correction models, which are suitable for different situations. When any model is used to solve the tropospheric delay in a large scale, there is always an error between the model value and the actual one. In order to investigate the applicability of the three models in different atmospheric conditions, we actually used the measured meteorological data provided by IGS (International GNSS Service) stations as a reference, and then calculated the ZTD (Zenith Tropospheric Delay) with the different models, including Hopfield model, Saastamoinen model and Black model. The calculation results indicate that Saastamoinen model is the most robust and practical model.