Alexandru Rusu, Octavian Groza, Nicolae Popa
et al.
This study evaluates the impact of different territorial contexts on academic mobility within the framework of the Erasmus Programme, using data on Key Action 1 exchanges between 2015 and 2023. Using official EU datasets and a gravity model framework, the research investigates how economic performance, geographical distance, EU membership, AUF (Agence Universitaire de la Francophonie) regional affiliation, and state contiguity shape international academic flows. The research developed two gravity models: one aimed to measure the potential barriers to academic flows through a residuals analysis, and the second integrated territorial delineations as predictors. In both models, the core of the explanatory variable is formed by indicators describing the economic performance of states and the distance between countries. When applied, the models converge in emphasizing that the inclusion of states in different territorial configurations has a strong effect on the structuring of academic flows. This suggests that the Erasmus Programme exhibits trends of overconcentration of flows in a limited number of countries, questioning the need for a more polycentric strategy and a reshaping of the funding mechanisms. Even if the gravity models behave well, given the limited number of predictors, further studies may need to incorporate qualitative indicators for a more comprehensive evaluation of the interactions.
OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx's modern capabilities, usage, and design -- in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science's implications for urban modeling and analysis.
Saurabh Kumar, Sourav Bansal, Neeraj Agrawal
et al.
Customer care is an essential pillar of the e-commerce shopping experience with companies spending millions of dollars each year, employing automation and human agents, across geographies (like US, Canada, Mexico, Chile), channels (like Chat, Interactive Voice Response (IVR)), and languages (like English, Spanish). SOTA pre-trained models like multilingual-BERT, fine-tuned on annotated data have shown good performance in downstream tasks relevant to Customer Care. However, model performance is largely subject to the availability of sufficient annotated domain-specific data. Cross-domain availability of data remains a bottleneck, thus building an intent classifier that generalizes across domains (defined by channel, geography, and language) with only a few annotations, is of great practical value. In this paper, we propose an embedder-cum-classifier model architecture which extends state-of-the-art domain-specific models to other domains with only a few labeled samples. We adopt a supervised fine-tuning approach with isotropic regularizers to train a domain-specific sentence embedder and a multilingual knowledge distillation strategy to generalize this embedder across multiple domains. The trained embedder, further augmented with a simple linear classifier can be deployed for new domains. Experiments on Canada and Mexico e-commerce Customer Care dataset with few-shot intent detection show an increase in accuracy by 20-23% against the existing state-of-the-art pre-trained models.
Research Framework: A Legitimist representative under the Second Republic (1848-1852), Paul de Dieuleveult (1799-1867) embodied the traditional Western notable in the mid-19th century. His privileged social position marks the culmination of a social ascent begun by his father, François-Marie, in Tréguier, Côtes-du-Nord.Objectives: To examine the importance of family heritage in the Legitimist commitment of Paul de Dieuleveult and his fellow Legislative deputies.Methodology: To achieve this, we will draw on the work of our thesis (Stefanelly, 2013) and on the biographical notes of parliamentarians.Results: Paul de Dieuleveult’s commitment to the Legitimist cause was determined by his family background. His father rose socially through his medical activities, his two successive marriages, his attainment of a noble title and the exercise of local responsibilities under the Restoration. Paul belongs to this lineage. Thanks to him, he has considerable material and land assets. His marriage enables him to complete alliances with the region’s prominent families. His entry into politics in the final years of the Restoration period gave concrete expression to his legitimist commitment. The July Monarchy marked a political break, but he returned to the forefront of local political life in 1848 and became a member of parliament. During his term of office, he endeavored to build on his political base by preserving community unanimity.Conclusion: Many of his fellow Legitimists in the West, birthplace of Legitimism, are part of a family heritage. A minority of them have less marked family antecedents and have emerged socially thanks to their abilities.Contributions: The family dimension is essential to understanding the political commitment of a legitimist representative under the Second Republic, even if this is not true in all cases, and the individual psychological dimension is a factor to be taken into account.
Ethnology. Social and cultural anthropology, The family. Marriage. Woman
What counts as legitimate AI ethics labor, and consequently, what are the epistemic terms on which AI ethics claims are rendered legitimate? Based on 75 interviews with technologists including researchers, developers, open source contributors, and activists, this paper explores the various epistemic bases from which AI ethics is discussed and practiced. In the context of outside attacks on AI ethics as an impediment to "progress," I show how some AI ethics practices have reached toward authority from automation and quantification, and achieved some legitimacy as a result, while those based on richly embodied and situated lived experience have not. This paper draws together the work of feminist Anthropology and Science and Technology Studies scholars Diana Forsythe and Lucy Suchman with the works of postcolonial feminist theorist Sara Ahmed and Black feminist theorist Kristie Dotson to examine the implications of dominant AI ethics practices. By entrenching the epistemic power of quantification, dominant AI ethics practices -- employing Model Cards and similar interventions -- risk legitimizing AI ethics as a project in equal and opposite measure to which they marginalize embodied lived experience as a legitimate part of the same project. In response, I propose humble technical practices: quantified or technical practices which specifically seek to make their epistemic limits clear in order to flatten hierarchies of epistemic power.
We present a data-driven machine-learning approach for modeling space-time socioeconomic dynamics. Through coarse-graining fine-scale observations, our modeling framework simplifies these complex systems to a set of tractable mechanistic relationships -- in the form of ordinary differential equations -- while preserving critical system behaviors. This approach allows for expedited 'what if' studies and sensitivity analyses, essential for informed policy-making. Our findings, from a case study of Baltimore, MD, indicate that this machine learning-augmented coarse-grained model serves as a powerful instrument for deciphering the complex interactions between social factors, geography, and exogenous stressors, offering a valuable asset for system forecasting and resilience planning.
Salim I. Amoukou, Tom Bewley, Saumitra Mishra
et al.
We introduce a novel approach for detecting distribution shifts that negatively impact the performance of machine learning models in continuous production environments, which requires no access to ground truth data labels. It builds upon the work of Podkopaev and Ramdas [2022], who address scenarios where labels are available for tracking model errors over time. Our solution extends this framework to work in the absence of labels, by employing a proxy for the true error. This proxy is derived using the predictions of a trained error estimator. Experiments show that our method has high power and false alarm control under various distribution shifts, including covariate and label shifts and natural shifts over geography and time.
Rapti Siriwardane-de Zoysa, Vani Sreekantha, David Mwambari
et al.
Refusal remains a core concern in processes of research across the sciences. Drawing on previous anthropological theorisations, this paper contemplates on the manifold ‘arts’ of refusal during ethnographic research praxis, drawing on diverse thematic experiences and contexts across coastal India, Malaysia, and Uganda. We argue for a concerted engagement with refusal as more than an act of withholding co-operation and as an expression of resistance. While recognizing refusal as a locally situated and historically contingent sensibility, we ask in what other ways might the more generative qualities of refusal be explored, paying particular attention to the performative nature of refusal itself that may entrench as much as reverse power differentials in the ‘field’. Drawing on decolonial and post-development epistemologies and diverse experiences as scholars situated and working across different geographies and disciplines, we explore the many entanglements, articulations, and enactments that remain ubiquitous in everyday ethnographic research praxis through several thematic angles. These include the negotiation of uneven (and often violent) forms of research collaboration and co-optation, the enactment of benevolent sexism as an ‘ethics of care’, and embodied practices such as silence(-ing), together with play and humour in participants’ critiques of scientific truth-telling. While illustrating subtler manifestations of refusal across ethnographic research-based encounters, we also contemplate pedagogical practices of un/learning (to ‘read’) and to teach the arts of identifying and productively working with the many appearances of refusal – both manifest and less visible.
Kyeong-Tae Lee, Garance Perrois, Hyun-Sung Yang
et al.
This study was carried out to determine the levels of resistance and resilience of kelp forests to large-scale physical disturbances. Our study site, Seongsan, Jeju Island, was impacted by super typhoon ‘Hinnamnor’. Before the typhoon, Seongsan had shown high ecosystem stability. Our results indicated that the ecological stability of a kelp forest facing a severe typhoon is strongly linked to the prevailing environmental conditions. Although typhoon impact resulted in a significant loss of brown macroalgae canopy, <i>Ecklonia cava</i> remained dominant within the kelp forest community. Resistance and resilience levels strongly depended on water temperature and movement and presence of turf-forming algae. Hence, hydrodynamic and biological factors strongly influence the overall stability of a kelp forest. We also report the first occurrences of a scleractinian coral species (i.e., <i>Montipora millepora</i>) at Seongsan, which became visible after canopy loss following the typhoon. Our findings provide valuable ecological information about the benthic community of kelp-dominated ecosystems and are essential to mitigate the impacts of expected climate change-driven rises in seawater temperature and the frequency of super typhoons.
Grishin Igor, Selivanov Victor, Rudenko Marina
et al.
It is generally accepted that UAVs - unmanned aerial vehicles, otherwise known as drones, are used only for military purposes. This is a misconception: since the 60s of the last century, Russian and American specialists have been building unmanned UAVs not only for the armies of their countries, but also for peaceful purposes. The purpose of the article is to study the possibilities and progress in the development of drones for civil and needs. In preparing and writing the article, such research methods as general scientific methods of historical and logical, abstract and concrete, analysis and synthesis, comparisons and analogies were used. The main result of the study is the conclusion that unmanned aerial vehicles can be successfully used for civilian purposes, and not just for military purposes. Drones are now actively used for agricultural and environmental purposes. They are called “eco-drones”. They are no different from ordinary ones; the prefix is designed to emphasize their purely peaceful, scientific purpose.
Cameron Trotter, Filipa Peleja, Dario Dotti
et al.
There is high demand for online fashion recommender systems that incorporate the needs of the consumer's body shape. As such, we present a methodology to classify human body shape from a single image. This is achieved through the use of instance segmentation and keypoint estimation models, trained only on open-source benchmarking datasets. The system is capable of performing in noisy environments owing to to robust background subtraction. The proposed methodology does not require 3D body recreation as a result of classification based on estimated keypoints, nor requires historical information about a user to operate - calculating all required measurements at the point of use. We evaluate our methodology both qualitatively against existing body shape classifiers and quantitatively against a novel dataset of images, which we provide for use to the community. The resultant body shape classification can be utilised in a variety of downstream tasks, such as input to size and fit recommendation or virtual try-on systems.
Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, these task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.
While American forensic anthropologists often state that they estimate ancestry, is that what they are really estimating? Although typological terminology, the oids, was replaced with continental terminology, the change was linguistic rather than substantive. The American population is comprised largely of immigrants. Genetic data suggests a high degree of admixture within American population groups. Further, data from documented skeletal collections suggest that Americans have undergone secular changes. Our paper addresses the uniqueness of the American population as compared to ancestral continental and geographic origin groups to address what it is that forensic anthropologists are really estimating, ancestry or something else? We conclude, based on uniqueness of American population groups, that what forensic anthropologists are estimating is best described as affinity, a term that indicates similarity and is not exclusively attached to definitions of race and ethnicity.
This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions into machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society's most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench "bias," are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI's long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.
Viraj Prabhu, Ramprasaath R. Selvaraju, Judy Hoffman
et al.
Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies. We investigate the effectiveness of unsupervised domain adaptation (UDA) of such models across geographies at closing this performance gap. To do so, we first curate two shifts from existing datasets to study the Geographical DA problem, and discover new challenges beyond data distribution shift: context shift, wherein object surroundings may change significantly across geographies, and subpopulation shift, wherein the intra-category distributions may shift. We demonstrate the inefficacy of standard DA methods at Geographical DA, highlighting the need for specialized geographical adaptation solutions to address the challenge of making object recognition work for everyone.
Dermatoglyphics is an integral part of the biological anthropology related to the studies of the population polymorphism and geographical variability of Homo sapiens. V.V. Bunak presented the dermatoglyphic methods in the chapter XI of "Anthropometry" too compact, the text needs some technical explanations, which was the purpose of this work. Materials and methods. We have combined the comments to the XI chapter of "Anthropometry" with a summary of the main concepts and terms of the accepted descriptive dermatoglyphic methodology. Results and discussion. The presentation of the dermatoglyphic methods in the XI chapter of "Anthropometry" contains 1) general remarks on technical equipment, 2) technique for obtaining prints, 3) topography of the relief and some dermatoglyphic traits. The main accent in the presentation of the method is on obtaining correct prints of the ridge skin for their further transcription and use in morphological analysis. The descriptive method of dermatoglyphics of the hand is partially present; the main attention is devoted to the explanation of general concepts (epidermal lines, radiant, triradius, type of pattern), the designations of features in the text and in the figures are minimal. There is practically no description of the dermatoglyphics of the soles. There are some terminological discrepancies in the description of some traits of the palms. Conclusion. The technique for obtaining prints of the skin of the palms and soles in "Anthropometry" by V.V. Bunak is still relevant today, however, the descriptive part is presented in fragments. Numerous data on dermatoglyphics had not yet been systematized in sufficient detail by the time "Anthropometry" was published, and an abbreviated form of the methodology was justified. The notes we have cited to the text of chapter XI of "Anthropometry" are specifying.
Quan Ho singing is a unique cultural practice originated in Bac Ninh, Northern Vietnam. Various aspects of this custom from historical values to cultural impacts have been thoroughly examined. There is, however, no comprehensive study explores Quan Ho under the scope of urban planning and architecture. In recent years, Bac Ninh has been increasingly urbanized, leading to changes in multiple elements of Quan Ho culture, especially cultural space. This paper focuses on such cultural space, presents publicly available data, and collects data along with qualitative, quantitative and comparative analysis to address the effects of urbanization on Quan Ho cultural space. The paper considers characteristics, factors and changing trends of this cultural space, therefore confirms how transformation of Quan Ho cultural space is unavoidable. The paper also addresses planning and architectural requirements to harmonize between preservation and development. Among these requirements, the paper discusses in detail proper programming to optimize green spaces and limit over-urbanization, as well as planning original Quan Ho villages based on two distinctive models: adaptive preservation and development of new facilities on the old foundation. Proper landuse is suggested to build new living space to accommodate the evolving Quan Ho culture in modern society.
Sorin Grigorescu, Mihai Zaha, Bogdan Trasnea
et al.
Forest roads in Romania are unique natural wildlife sites used for recreation by countless tourists. In order to protect and maintain these roads, we propose RovisLab AMTU (Autonomous Mobile Test Unit), which is a robotic system designed to autonomously navigate off-road terrain and inspect if any deforestation or damage occurred along tracked route. AMTU's core component is its embedded vision module, optimized for real-time environment perception. For achieving a high computation speed, we use a learning system to train a multi-task Deep Neural Network (DNN) for scene and instance segmentation of objects, while the keypoints required for simultaneous localization and mapping are calculated using a handcrafted FAST feature detector and the Lucas-Kanade tracking algorithm. Both the DNN and the handcrafted backbone are run in parallel on the GPU of an NVIDIA AGX Xavier board. We show experimental results on the test track of our research facility.