As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.
Abstract Artificial Intelligence (AI) is entirely coincident with the emergence of the digital computer. It was assumed from the start, some 75 years ago, that the computer had more than the required power to simulate human intelligence. This led to the speculation that ultimately computers would take over many of our own tasks which we once considered could never be modelled using machines. Here, we sketch the history and evolution of AI, note the different phases in this history, define distinctions between strong and weak AI, and emphasise the difference between generative and discriminative processes. There are many possible applications in city planning with the most suggestive and possibly the most disruptive being those related to the development of new methods for generating sustainable plans and designs. We make a key distinction between inductive and deductive AI, demonstrating these differences with methods of machine learning (ML), showing how early network methods based on the perceptron, can be linked to deductive procedures that enable us to build new models for city design. Our key illustration links urban simulation models to land cover built around geospatial data infused with ML. The aim of this paper is to chart the progress in AI and its applicability to city science and city planning from its first applications and speculate on future developments.
Cities. Urban geography, Urban groups. The city. Urban sociology
The inclusion of information regarding moves of households within the most recently published Amish directory for Wisconsin facilitates the study of migration of Amish households to specific settlements. This paper notes that while certain settlements have attracted newcomers from a wide variety of locations, others are dominated by those coming from a small number of settlements. Many of the settlements have attracted households making long distance moves. Individuals leaving settlements in the process of extinction have accounted for a large share of newcomers in some settlements, but none in others. Religious affiliation appears to be a key determinant influencing the choice of receiving settlements, displayed by comparing the spatial patterns of migration to pairs of settlements.
To achieve natural and intuitive interaction with people, HRI frameworks combine a wide array of methods for human perception, intention communication, human-aware navigation and collaborative action. In practice, when encountering unpredictable behavior of people or unexpected states of the environment, these frameworks may lack the ability to dynamically recognize such states, adapt and recover to resume the interaction. Large Language Models (LLMs), owing to their advanced reasoning capabilities and context retention, present a promising solution for enhancing robot adaptability. This potential, however, may not directly translate to improved interaction metrics. This paper considers a representative interaction with an industrial robot involving approach, instruction, and object manipulation, implemented in two conditions: (1) fully scripted and (2) including LLM-enhanced responses. We use gaze tracking and questionnaires to measure the participants' task efficiency, engagement, and robot perception. The results indicate higher subjective ratings for the LLM condition, but objective metrics show that the scripted condition performs comparably, particularly in efficiency and focus during simple tasks. We also note that the scripted condition may have an edge over LLM-enhanced responses in terms of response latency and energy consumption, especially for trivial and repetitive interactions.
Steve Benford, Eike Schneiders, Juan Pablo Martinez Avila
et al.
As robots enter the messy human world so the vital matter of safety takes on a fresh complexion with physical contact becoming inevitable and even desirable. We report on an artistic-exploration of how dancers, working as part of a multidisciplinary team, engaged in contact improvisation exercises to explore the opportunities and challenges of dancing with cobots. We reveal how they employed their honed bodily senses and physical skills to engage with the robots aesthetically and yet safely, interleaving improvised physical manipulations with reflections to grow their knowledge of how the robots behaved and felt. We introduce somatic safety, a holistic mind-body approach in which safety is learned, felt and enacted through bodily contact with robots in addition to being reasoned about. We conclude that robots need to be better designed for people to hold them and might recognise tacit safety cues among people.We propose that safety should be learned through iterative bodily experience interleaved with reflection.
Training manipulation policies for humanoid robots with diverse data enhances their robustness and generalization across tasks and platforms. However, learning solely from robot demonstrations is labor-intensive, requiring expensive tele-operated data collection which is difficult to scale. This paper investigates a more scalable data source, egocentric human demonstrations, to serve as cross-embodiment training data for robot learning. We mitigate the embodiment gap between humanoids and humans from both the data and modeling perspectives. We collect an egocentric task-oriented dataset (PH2D) that is directly aligned with humanoid manipulation demonstrations. We then train a human-humanoid behavior policy, which we term Human Action Transformer (HAT). The state-action space of HAT is unified for both humans and humanoid robots and can be differentiably retargeted to robot actions. Co-trained with smaller-scale robot data, HAT directly models humanoid robots and humans as different embodiments without additional supervision. We show that human data improves both generalization and robustness of HAT with significantly better data collection efficiency. Code and data: https://human-as-robot.github.io/
The study of Features and Characteristics of Mountainous Rural Settlements (RFCMRS) is a key factor in the development of rural settlements during the urbanization process. Mountainous rural settlements, due to their unique mountainous conditions, climate, living environments, and regional culture, are among the important subjects of research for governments and the academic community worldwide. This paper, utilizing the knowledge mapping software CiteSpace (6.2.R3) for co-citation and collaboration analysis, keyword clustering, keyword time zoning, and keyword emergence, analyzes the research trajectory, key issues, and future trends of RFCMRSs. The study finds that current RFCMRS research can be categorized into the following three key issues: “implications of climate change: risks and adaptive responses”, “regional cultural heritage and economic development”, and “ecological conservation and fostering harmonious symbiosis”. Future research will focus on the following three development trends: “risk response based on climate resilience and ecological protection”, “factors of features and characteristics based on regional culture and landscape configurations”, and “human settlements based on low-carbon objectives and sustainable development principles”. Lastly, the paper proposes the following three future research suggestions: “improving the evaluation system for features and characteristics of mountainous rural settlements”, “deepening the study on the evolutionary phenomenon and mechanism for features and characteristics of mountainous rural settlements”, and “exploring the design methods for features and characteristics of mountainous rural settlements based on the concept of sustainable development”.
Reyna Vergara González, Victor Hugo Torres-Preciado, Miguel Angel Díaz Carreño
La evaluación de la política monetaria sobre la actividad económica se ha enfocado al ámbito nacional sin considerar sus efectos a nivel regional. El objetivo de este documento es estimar los efectos que un incremento no previsto de la tasa de interés tendría sobre la producción regional en México para el periodo 2000-2019. Para ello, se emplea un modelo estructural de vectores autorregresivos (SVAR) en panel. Los resultados sugieren un efecto negativo y diferenciado sobre la producción regional. Este efecto se presenta a partir del segundo trimestre, después del aumento de la tasa de interés.
Cities. Urban geography, Urban groups. The city. Urban sociology
ظهرت أهمية استخدام تكنولوجيا المعلومات والاتصالات (ICT) في الخدمات التي تقدمها المدن لساكنيها خلال العقد الماضي خاصة في الدول المتقدمة، وقد ساهم ذلك في تطور مجتمعاتها ورفع مستوى كفاءة معيشتهم. امتد هذا الاتجاه وهو تحويل المدن وخدماتها إلى مدن "ذكية" إلى جميع الدول بما فيها الدول النامية. أظهرت الدراسات أن تكلفة البنية الأساسية الذكية المرتفعة قد تعيق استخدامها في الدول النامية والفقيرة أو أنه سيقتصر استخدامها على فئات محددة من المجتمع. وحيث أن هدف المدن الذكية هو تحسين جودة حياة المواطنين فكان هناك حاجة لدراسة أساليب أو حلول لتطبيق التكنولوجيا في الدول النامية بشكل يناسب ويحسن جودة حياة مجتمعاتها. تتبع الدولة المصرية حاليا سياسات تتوافق مع اتجاه المدن الذكية على المستوى العلمي والتطبيقي، فقد تم تجهيز بنية أساسية ذكية ببعض مدن الجيل الرابع، وكذلك وضعت خطط لتقديم بعض الخدمات باستخدام تكنولوجيا المعلومات والاتصالات بالمدن القائمة. وحيث أن مصر تصنف من الدول النامية، فكان من الواجب عمل دراسة لإمكانية استفادة جميع فئات المجتمع الحضري المصري من هذه الخدمات بل ورفع مستوى معيشته من خلالها. يقترح البحث بعض الحلول الاستراتيجية والإجرائية التي تمكّن حصول جميع فئات مجتمع المدينة المصرية على الخدمات الذكية وسبل حل مشاكله باستخدام التكنولوجيا، من خلال استنباط ايجابيات وسلبيات المدينة المصرية الذكية ومجتمعاتها، وكذلك دراسة بعض التجارب الدولية المشابهة للحالة المصرية في هذا الصدد، وباستشارة المتخصصين في المجالات ذات الصلة، يضع البحث أنسب الحلول لمجتمع المدينة المصرية وأكثرها قابلية للتطبيق.
The importance of using (ICT) has emerged over the past decade, this has contributed in developing the societies and raise their quality of life, especially in developed countries. The transformation of cities and their services into "smart" has spread all over the world, including developing countries. Studies have shown that the high cost of smart infrastructure may either hinder its use in developing and poor countries or limit its use to specific levels of society. Therefore, there was a need to come up with solutions to apply new technologies in developing countries that suit and improve the quality of life of their societies.
Egypt currently is setting policies in order to be executed in smart cities. Smart infrastructure has been equipped in some new cities, and some services were transformed to be smart in existing cities. Since Egypt is classified as a developing country, it was necessary to study the possibility of benefiting all levels of the Egyptian urban society from these services and raising their quality of life.
The research suggests some strategic and procedural solutions that enable all levels of Egyptian cities communities to acquire the smart services and solve their problems using technology.
By extracting the pros and cons of the Egyptian city and its communities, as well as studying some international experiences similar to the Egyptian case, that used technology to solve their citizens problems, in addition of consulting specialists in related fields, the research sets the most appropriate and applicable solutions for the Egyptian smart city that suit their communities.
Cities. Urban geography, Urbanization. City and country
While volunteers are widespread in U.S. local food systems, they have not been the focus of data collection or research. As a result, we have limited understanding of the perspectives and motivations of local food volunteers. In this reflective essay, I describe my insights from volunteering in Alexandria, Virginia. The two initiatives that I focus on were to establish a “Power of Produce” program at my local farmers market and an after-school culinary and gardening program at a private elementary school. The former program I have been able to sustain for three years (as of this writing), whereas the latter program was discontinued after two eight-week sessions. In this essay, I describe my motivations in conceptualizing and organizing these programs. I also describe challenges I encountered due to inexperience or capacity constraints as a volunteer. I conclude by discussing how additional research that examines the roles and motivations of volunteers could be valuable.
Yuchong Zhang, Miguel Vasco, Mårten Björkman
et al.
This paper presents findings from an exploratory needfinding study investigating the research current status and potential participation of the competitions on the robotics community towards four human-centric topics: safety, privacy, explainability, and federated learning. We conducted a survey with 34 participants across three distinguished European robotics consortia, nearly 60% of whom possessed over five years of research experience in robotics. Our qualitative and quantitative analysis revealed that current mainstream robotic researchers prioritize safety and explainability, expressing a greater willingness to invest in further research in these areas. Conversely, our results indicate that privacy and federated learning garner less attention and are perceived to have lower potential. Additionally, the study suggests a lack of enthusiasm within the robotics community for participating in competitions related to these topics. Based on these findings, we recommend targeting other communities, such as the machine learning community, for future competitions related to these four human-centric topics.
Conveying human goals to autonomous systems (AS) occurs both when the system is being designed and when it is being operated. The design-step conveyance is typically mediated by robotics and AI engineers, who must appropriately capture end-user requirements and concepts of operations, while the operation-step conveyance is mediated by the design, interfaces, and behavior of the AI. However, communication can be difficult during both these periods because of mismatches in the expectations and expertise of the end-user and the roboticist, necessitating more design cycles to resolve. We examine some of the barriers in communicating system design requirements, and develop an augmentation for applied cognitive task analysis (ACTA) methods, that we call robot task analysis (RTA), pertaining specifically to the development of autonomous systems. Further, we introduce a top-down view of an underexplored area of friction between requirements communication -- implied human expectations -- utilizing a collection of work primarily from experimental psychology and social sciences. We show how such expectations can be used in conjunction with task-specific expectations and the system design process for AS to improve design team communication, alleviate barriers to user rejection, and reduce the number of design cycles.
Sergio. Martín Serrano, Rubén Izquierdo, Iván García Daza
et al.
In the field of autonomous driving research, the use of immersive virtual reality (VR) techniques is widespread to enable a variety of studies under safe and controlled conditions. However, this methodology is only valid and consistent if the conduct of participants in the simulated setting mirrors their actions in an actual environment. In this paper, we present a first and innovative approach to evaluating what we term the behavioural gap, a concept that captures the disparity in a participant's conduct when engaging in a VR experiment compared to an equivalent real-world situation. To this end, we developed a digital twin of a pre-existed crosswalk and carried out a field experiment (N=18) to investigate pedestrian-autonomous vehicle interaction in both real and simulated driving conditions. In the experiment, the pedestrian attempts to cross the road in the presence of different driving styles and an external Human-Machine Interface (eHMI). By combining survey-based and behavioural analysis methodologies, we develop a quantitative approach to empirically assess the behavioural gap, as a mechanism to validate data obtained from real subjects interacting in a simulated VR-based environment. Results show that participants are more cautious and curious in VR, affecting their speed and decisions, and that VR interfaces significantly influence their actions.
Laura Stegner, David Porfirio, Laura M. Hiatt
et al.
End-user development (EUD) represents a key step towards making robotics accessible for experts and nonexperts alike. Within academia, researchers investigate novel ways that EUD tools can capture, represent, visualize, analyze, and test developer intent. At the same time, industry researchers increasingly build and ship programming tools that enable customers to interact with their robots. However, despite this growing interest, the role of EUD within HRI is not well defined. EUD struggles to situate itself within a growing array of alternative approaches to application development, such as robot learning and teleoperation. EUD further struggles due to the wide range of individuals who can be considered end users, such as independent third-party application developers, consumers, hobbyists, or even employees of the robot manufacturer. Key questions remain such as how EUD is justified over alternate approaches to application development, which contexts EUD is most suited for, who the target users of an EUD system are, and where interaction between a human and a robot takes place, amongst many other questions. We seek to address these challenges and questions by organizing the first End-User Development for Human-Robot Interaction (EUD4HRI) workshop at the 2024 International Conference of Human-Robot Interaction. The workshop will bring together researchers with a wide range of expertise across academia and industry, spanning perspectives from multiple subfields of robotics, with the primary goal being a consensus of perspectives about the role that EUD must play within human-robot interaction.
Urban informal settlements or slums are among the most vulnerable places to climate-change-related health risks. Yet, little data exist documenting environmental and human health vulnerabilities in slums or how to move research to action. Citizen science, where residents co-define research objectives with professionals, collect and analyze data, and help translate findings into ameliorative actions, can help fill data gaps and contribute to more locally relevant climate justice interventions. This paper highlights a citizen-science, climate justice planning process in the Mukuru informal settlement of Nairobi, Kenya. We describe how residents, non-governmental organizations and academics partnered to co-create data-gathering processes and generated evidence to inform an integrated, climate justice strategy called the Mukuru Special Planning Area, Integrated Development Plan. The citizen science processes revealed that <1% of residents had access to a private in-home toilet, and 37% lacked regular access to safe and affordable drinking water. We found that 42% of households were subject to regular flooding, 39% reported fair or poor health, and 40% reported a child in the household was stunted. These and other data were used in a community planning process where thousands of residents co-designed improvement and climate change adaptation strategies, such as flood mitigation, formalizing roads and pathways with drainage, and a water and sanitation infrastructure plan for all. We describe the participatory processes used by citizen scientists to generate data and move evidence into immediate actions to protect human health and a draft a long-range, climate justice strategy. The processes used to create the Mukuru Special Planning Area redevelopment plan suggest that participatory, citizen-led urban science can inform local efforts for health equity and global goals of climate justice.
The paper critically reviews communicative and agonistic planning theories from the viewpoint of a systemic turn in deliberative democracy theory. While the approach reveals complementarities between the theories, it also argues that each theory is vulnerable to criticism because of an ‘institutional gap’. The theories are found to complement each other in addressing planning conflicts at different dimensions. Communicative planning theory deals with conflicts between different stakeholders’ interests in planning processes. Agonistic planning theory, in turn, concentrates on conflicts from a more ontological dimension, related to the (implicit) conflict between hegemonic and marginalized discourses and related identity-forming processes of inclusion and exclusion in planning policies and governance. The institutional gap of communicative planning theory is argued to reside in its focus on situational deliberation that largely ignores the institutional dimension of rules and norms of democratic conduct. Agonistic pluralism, in turn, does engage with the dimension of democratic institutions, but in an overly critical manner, making it difficult for agonistic planning theory to address the dynamic interplay between institutional reconfiguration and policy stabilization in planning. This is argued to be the institutional gap of agonistic planning theory. The paper calls for further work in the field of planning theory to incorporate a systemic approach to deliberative democracy and thereby tap into the dialectics of institutional and situational dimensions of planning.
Cities. Urban geography, Urbanization. City and country
Parag Khanna, Elmira Yadollahi, Mårten Björkman
et al.
Despite great advances in what robots can do, they still experience failures in human-robot collaborative tasks due to high randomness in unstructured human environments. Moreover, a human's unfamiliarity with a robot and its abilities can cause such failures to repeat. This makes the ability to failure explanation very important for a robot. In this work, we describe a user study that incorporated different robotic failures in a human-robot collaboration (HRC) task aimed at filling a shelf. We included different types of failures and repeated occurrences of such failures in a prolonged interaction between humans and robots. The failure resolution involved human intervention in form of human-robot bidirectional handovers. Through such studies, we aim to test different explanation types and explanation progression in the interaction and record humans.
By incorporating ergonomics principles into the task allocation processes, human-robot collaboration (HRC) frameworks can favour the prevention of work-related musculoskeletal disorders (WMSDs). In this context, existing offline methodologies do not account for the variability of human actions and states; therefore, planning and dynamically assigning roles in human-robot teams remains an unaddressed challenge.This study aims to create an ergonomic role allocation framework that optimises the HRC, taking into account task features and human state measurements. The presented framework consists of two main modules: the first provides the HRC task model, exploiting AND/OR Graphs (AOG)s, which we adapted to solve the allocation problem; the second module describes the ergonomic risk assessment during task execution through a risk indicator and updates the AOG-related variables to influence future task allocation. The proposed framework can be combined with any time-varying ergonomic risk indicator that evaluates human cognitive and physical burden. In this work, we tested our framework in an assembly scenario, introducing a risk index named Kinematic Wear.The overall framework has been tested with a multi-subject experiment. The task allocation results and subjective evaluations, measured with questionnaires, show that high-risk actions are correctly recognised and not assigned to humans, reducing fatigue and frustration in collaborative tasks.
Juliana Jansen Ferreira, Vinícius Segura, Joana G. R. Souza
et al.
Generative models are a powerful tool in AI for material discovery. We are designing a software framework that supports a human-AI co-creation process to accelerate finding replacements for the ``forever chemicals''-- chemicals that enable our modern lives, but are harmful to the environment and the human health. Our approach combines AI capabilities with the domain-specific tacit knowledge of subject matter experts to accelerate the material discovery. Our co-creation process starts with the interaction between the subject matter experts and a generative model that can generate new molecule designs. In this position paper, we discuss our hypothesis that these subject matter experts can benefit from a more iterative interaction with the generative model, asking for smaller samples and ``guiding'' the exploration of the discovery space with their knowledge.