Hasil untuk "Communities. Classes. Races"

Sudadi Sudadi, Slamet Minardi, Ongko Cahyono et al.

Counseling and Practices of Tayurdapot Cultivation for Farmers Group “Abdi Dalem Klebengan”, Kadipiro Village, Banjarsari, Surakarta. Citizens association (RW) 17 as a partner in this community service activity is one of the RWs in Kelurahan Kadipiro, Banjarsari District, Surakarta City which has a farmer group, namely the Farmer Group (Poktan) "Abdi Dalem Klebengan" which until now has been very active in empowering the community in agriculture. Even though it is located in the city area, Poktan "Abdi Dalem Klebengan" realizes that agricultural activities are one of the sectors that can help maintain the community's economy. Therefore, Poktan "Abdi Dalem Klebengan" is very active in trying to make the people in their area, namely RT 01 RW 17, work on agriculture around their settlements, including in the yards of their respective houses by working on potted plants to help the family's economy. One type of plant that is easy to cultivate in a pot is a vegetable plant (Plant vegetables in a Pot-Tayurdapot). This service activity aims to awaken and maintain the spirit of farming around the house for members of the Poktan "Abdi Dalem Klebengan" in particular and the community of RT 01 RW 17 Ngipang, Kadipiro, Banjarsari, Surakarta in general. Activities are carried out by the method of socialization activities and demonstrations of Tayurdapot cultivation practices in synergy with the Poktan "Abdi Dalem Klebengan". Through this effort, it is hoped that the families of the group members and the community of RT 01 RW 17 will be helped by their need for vegetables that can reduce daily shopping costs. Poktan "Abdi Dalem Klebengan" also produces organic fertilizer from its livestock business so that it can synergize with this service activity through its help organic fertilizer as a planting medium for tayurdapot for residents of RT 01 RW 17. This service activity is mono-year, but is expected to continue in the following year through other productive activities such as tabulampot, livestock business, fish rearing and guidance on the production of organic fertilizers and liquid biofertilizers.

Adriana Diaconu, Marta Pappalardo

This article examines the responses elicited by experiments addressing complex issues in housing. It explores how such experiments are implemented and their ability to facilitate or hinder dialogue beyond their initiators, decision-makers, and urban and real estate development professionals. The first theoretical part looks at the various definitions and typologies of urban experimentation, which reveal seemingly contradictory aspects regarding their implementation, resulting in an analytical grid. We then apply this grid to three housing development projects: two experiments on the energy transition and the third on metropolitan hospitality. This enables us to observe how these experiments are positioned between contradictory dynamics regarding their implementation, objectives and the actors involved. Our findings indicate that, while the creation of support and the enrollment of new participants are the most sought-after tools for addressing dissent in the cases studied, the processes for forming alliances diverge. Our perspective finally helps us understand the specific features of experiments conducted in housing projects that also aim to transform living practices.

Rosario Mumare, Franco Maximiliano Santacroce, Gabriel Cacopardo

El presente artículo contribuye al campo de conocimiento sobre procesos proyectuales y espacio público en barrios populares. A partir de esta experiencia, ubicada en una plaza del barrio Caribe de la ciudad de Mar del Plata y llevada a cabo entre julio del 2023 y mayo del 2024, se propone una discusión del concepto de flexibilidad, asociado a la disciplina del proyecto espacial arquitectónico, considerando la perspectiva de una red de actores involucrados. La articulación entre vecinos, actores académicos y sectores referentes a las políticas públicas y el contexto territorial en el que se inserta la experiencia, aportan otros modos de entender y accionar el proyecto del espacio público desde prácticas interactivas inter-actorales que abren caminos para la sostenibilidad y apropiación del espacio público. La metodología incluye un análisis del sitio con fuerte anclaje territorial por parte de los autores, actores analíticos y parte activa de la experiencia, lo que implica una base pormenorizada de datos con distintos registros de documentación gráfica y fotografías y mapas georreferenciados. Por otro lado, se aplicaron metodologías diversas de trabajo colectivo en el barrio y en aulas, con talleres, asambleas y reuniones vecinales con progresivas aproximaciones al proyecto. Este trabajo aporta también conocimientos y metodologías de estrategias participativas que pueden ser útiles para políticas públicas en barrios populares, al accionar modelos de co-creación entre comunidad vecinal, instituciones académicas y actores estatales.

Maitham F. AL-Sunni, Hassan Almubarak, Katherine Horng et al.

We present Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a real-time adaptive control framework for autonomous racing that addresses the challenge of rapidly changing tire-surface interactions. Unlike existing approaches requiring substantial data collection or offline training, LLA-MPC employs a model bank for immediate adaptation without a learning period. It integrates two key mechanisms: a look-back window that evaluates recent vehicle behavior to select the most accurate model and a look-ahead horizon that optimizes trajectory planning based on the identified dynamics. The selected model and estimated friction coefficient are then incorporated into a trajectory planner to optimize reference paths in real-time. Experiments across diverse racing scenarios demonstrate that LLA-MPC outperforms state-of-the-art methods in adaptation speed and handling, even during sudden friction transitions. Its learning-free, computationally efficient design enables rapid adaptation, making it ideal for high-speed autonomous racing in multi-surface environments.

Jim O'Connor, Nicholas Lorentzen, Gary B. Parker et al.

This paper investigates the development of high-performance racing controllers for a newly implemented racing mode within the Xpilot-AI platform, utilizing the Neuro Evolution of Augmenting Topologies (NEAT) algorithm. By leveraging NEAT's capability to evolve both the structure and weights of neural networks, we develop adaptive controllers that can navigate complex circuits under the challenging space simulation physics of Xpilot-AI, which includes elements such as inertia, friction, and gravity. The racing mode we introduce supports flexible circuit designs and allows for the evaluation of multiple agents in parallel, enabling efficient controller optimization across generations. Experimental results demonstrate that our evolved controllers achieve up to 32% improvement in lap time compared to the controller's initial performance and develop effective racing strategies, such as optimal cornering and speed modulation, comparable to human-like techniques. This work illustrates NEAT's effectiveness in producing robust control strategies within demanding game environments and highlights Xpilot-AI's potential as a rigorous testbed for competitive AI controller evolution.

Yassine Kebbati, Andreas Rauh, Naima Ait-Oufroukh et al.

This paper addresses autonomous racing by introducing a real-time nonlinear model predictive controller (NMPC) coupled with a moving horizon estimator (MHE). The racing problem is solved by an NMPC-based off-line trajectory planner that computes the best trajectory while considering the physical limits of the vehicle and circuit constraints. The developed controller is further enhanced with a learning extension based on Gaussian process regression that improves model predictions. The proposed control, estimation, and planning schemes are evaluated on two different race tracks. Code can be found here: https://github.com/yassinekebbati/GP_Learning-based_MPC_with_MHE

David Tan

Standard real options theory predicts delay in exercising the option to invest or deploy when extreme asset volatility or technological uncertainty are present. However, in the current race to develop artificial general intelligence (AGI), sovereign actors are exhibiting behaviors contrary to theoretical predictions: the US and China are accelerating AI investment despite acknowledging the potential for catastrophic failure from AGI misalignment. We resolve this puzzle by formalizing the AGI race as a continuous-time preemption game with endogenous existential risk. In our model, the cost of failure is no longer bounded only by the sunk cost of investment (I), but rather a systemic ruin parameter (D) that is correlated with development velocity and shared globally. As the disutility of catastrophe is embedded in both players' payoffs, the risk term mathematically cancels out of the equilibrium indifference condition. This creates a "suicide region" in the investment space where competitive pressures force rational agents to deploy AGI systems early, despite a negative risk-adjusted net present value. Furthermore, we show that "warning shots" (sub-existential disasters) will fail to deter AGI acceleration, as the winner-takes-all nature of the race remains intact. The race can only be halted if the cost of ruin is internalized, making safety research a prerequisite for economic viability. We derive the critical private liability threshold required to restore the option value of waiting and propose mechanism design interventions that can better ensure safe AGI research and socially responsible deployment.

Valerii Serpiva, Artem Lykov, Artyom Myshlyaev et al.

RaceVLA presents an innovative approach for autonomous racing drone navigation by leveraging Visual-Language-Action (VLA) to emulate human-like behavior. This research explores the integration of advanced algorithms that enable drones to adapt their navigation strategies based on real-time environmental feedback, mimicking the decision-making processes of human pilots. The model, fine-tuned on a collected racing drone dataset, demonstrates strong generalization despite the complexity of drone racing environments. RaceVLA outperforms OpenVLA in motion (75.0 vs 60.0) and semantic generalization (45.5 vs 36.3), benefiting from the dynamic camera and simplified motion tasks. However, visual (79.6 vs 87.0) and physical (50.0 vs 76.7) generalization were slightly reduced due to the challenges of maneuvering in dynamic environments with varying object sizes. RaceVLA also outperforms RT-2 across all axes - visual (79.6 vs 52.0), motion (75.0 vs 55.0), physical (50.0 vs 26.7), and semantic (45.5 vs 38.8), demonstrating its robustness for real-time adjustments in complex environments. Experiments revealed an average velocity of 1.04 m/s, with a maximum speed of 2.02 m/s, and consistent maneuverability, demonstrating RaceVLA's ability to handle high-speed scenarios effectively. These findings highlight the potential of RaceVLA for high-performance navigation in competitive racing contexts. The RaceVLA codebase, pretrained weights, and dataset are available at this http URL: https://racevla.github.io/

Thea Rabe, Heidi Mogstad

Studies on citizen-led support for migrants in Europe have paid increased attention to history and temporality. This article analyses Norwegian citizen humanitarians as agents of history who use the past to intervene in the present and extend themselves into the future. The analysis relies on long-term fieldwork, interviews and digital observations of ‘citizen humanitarians’ involved in informal aid and solidarity practices with illegalised migrants in Europe. We demonstrate how collective memories and family histories from World War II provide meaning and legitimacy to their humanitarian actions, including unlawful acts. The citizen humanitarians mobilise ‘post-holocaust morality’ to draw symbolic parallels between the persecution of Jews and present-day treatment of migrants in Europe and define good and evil in their time. Historical comparisons and identifications with rescuers and resistance movements further enable citizen humanitarians to position themselves on ‘the right side of history’. The article argues that our informants, who are ‘ordinary’ Norwegian citizens, partake in symbolic narrations of contemporary European border policies as a potential new cultural trauma. While highlighting some risks and limitations, we show that collective memories of war and rescue can nourish political critique and subversive humanitarianism. We also demonstrate the analytical value of attending to humanitarian actors’ historical consciousness and engagements with the past and future.

Dalila García Hernández, Salvador Adame Martínez, Carlos Alberto Pérez Ramírez et al.

La sociedad actual enfrenta una susceptibilidad que demerita la compleja construcción de la percepción del riesgo ante situaciones que, por su frecuencia, se normalizan. Estas se interiorizan hasta dejar de ser consideradas como negativas o peligrosas, ya que forman parte de la cotidianidad. Por ello, el objetivo de este artículo es analizar la transformación de la realidad desde los principios de la antropología, sugiriendo cómo la carga moral se presenta en la percepción que cada sistema social tiene sobre el riesgo. Esto se explica a través de la revisión y análisis derivados del mapeo sistemático disponible sobre la percepción de las inundaciones. El enfoque del estudio es analítico-reflexivo, a partir de la argumentación de los aspectos claves que inciden en la transformación del entorno. La aceptación o rechazo generado mediante el ejercicio de la percepción, independientemente del grado de vulnerabilidad que la sociedad ha concebido. Este análisis se centra en el contexto del sureste mexicano, donde se destaca cómo las inundaciones pueden generar pérdidas socioeconómicas de alto impacto. Desde la antropología, se logra profundizar en el argumento de la dinámica social real mediante, ejercicios analíticos. Esto se plantea a partir de la necesidad de responder a la pregunta: ¿Cómo se desarrolla la percepción del riesgo en un escenario de vulnerabilidad real, vinculándola con la incidencia de la realidad empírica?

Nicolas Baumann, Edoardo Ghignone, Cheng Hu et al.

Head-to-head racing against opponents is a challenging and emerging topic in the domain of autonomous racing. We propose Predictive Spliner, a data-driven overtaking planner that learns the behavior of opponents through Gaussian Process (GP) regression, which is then leveraged to compute viable overtaking maneuvers in future sections of the racing track. Experimentally validated on a 1:10 scale autonomous racing platform using Light Detection and Ranging (LiDAR) information to perceive the opponent, Predictive Spliner outperforms State-of-the-Art (SotA) algorithms by overtaking opponents at up to 83.1% of its own speed, being on average 8.4% faster than the previous best-performing method. Additionally, it achieves an average success rate of 84.5%, which is 47.6% higher than the previous best-performing method. The method maintains computational efficiency with a Central Processing Unit (CPU) load of 22.79% and a computation time of 8.4 ms, evaluated on a Commercial off-the-Shelf (CotS) Intel i7-1165G7, making it suitable for real-time robotic applications. These results highlight the potential of Predictive Spliner to enhance the performance and safety of autonomous racing vehicles. The code for Predictive Spliner is available at: https://github.com/ForzaETH/predictive-spliner.

Saikrishna Badrinarayanan, Osonde Osoba, Miao Cheng et al.

AI fairness measurements, including tests for equal treatment, often take the form of disaggregated evaluations of AI systems. Such measurements are an important part of Responsible AI operations. These measurements compare system performance across demographic groups or sub-populations and typically require member-level demographic signals such as gender, race, ethnicity, and location. However, sensitive member-level demographic attributes like race and ethnicity can be challenging to obtain and use due to platform choices, legal constraints, and cultural norms. In this paper, we focus on the task of enabling AI fairness measurements on race/ethnicity for \emph{U.S. LinkedIn members} in a privacy-preserving manner. We present the Privacy-Preserving Probabilistic Race/Ethnicity Estimation (PPRE) method for performing this task. PPRE combines the Bayesian Improved Surname Geocoding (BISG) model, a sparse LinkedIn survey sample of self-reported demographics, and privacy-enhancing technologies like secure two-party computation and differential privacy to enable meaningful fairness measurements while preserving member privacy. We provide details of the PPRE method and its privacy guarantees. We then illustrate sample measurement operations. We conclude with a review of open research and engineering challenges for expanding our privacy-preserving fairness measurement capabilities.

Adrian Remonda, Nicklas Hansen, Ayoub Raji et al.

Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars operating close to the limit of handling has been limited by the high costs of vehicle acquisition and management, as well as the limited physics accuracy of open-source simulators. In this paper, we propose a racing simulation platform based on the simulator Assetto Corsa to test, validate, and benchmark autonomous driving algorithms, including reinforcement learning (RL) and classical Model Predictive Control (MPC), in realistic and challenging scenarios. Our contributions include the development of this simulation platform, several state-of-the-art algorithms tailored to the racing environment, and a comprehensive dataset collected from human drivers. Additionally, we evaluate algorithms in the offline RL setting. All the necessary code (including environment and benchmarks), working examples, datasets, and videos are publicly released and can be found at: https://assetto-corsa-gym.github.io

Valerii Serpiva, Aleksey Fedoseev, Sausar Karaf et al.

This paper presents the OmniRace approach to controlling a racing drone with 6-degree of freedom (DoF) hand pose estimation and gesture recognition. To our knowledge, it is the first-ever technology that allows for low-level control of high-speed drones using gestures. OmniRace employs a gesture interface based on computer vision and a deep neural network to estimate a 6-DoF hand pose. The advanced machine learning algorithm robustly interprets human gestures, allowing users to control drone motion intuitively. Real-time control of a racing drone demonstrates the effectiveness of the system, validating its potential to revolutionize drone racing and other applications. Experimental results conducted in the Gazebo simulation environment revealed that OmniRace allows the users to complite the UAV race track significantly (by 25.1%) faster and to decrease the length of the test drone path (from 102.9 to 83.7 m). Users preferred the gesture interface for attractiveness (1.57 UEQ score), hedonic quality (1.56 UEQ score), and lower perceived temporal demand (32.0 score in NASA-TLX), while noting the high efficiency (0.75 UEQ score) and low physical demand (19.0 score in NASA-TLX) of the baseline remote controller. The deep neural network attains an average accuracy of 99.75% when applied to both normalized datasets and raw datasets. OmniRace can potentially change the way humans interact with and navigate racing drones in dynamic and complex environments. The source code is available at https://github.com/SerValera/OmniRace.git.

Ross Gruetzemacher, Shahar Avin, James Fox et al.

We present insights from "Intelligence Rising", a scenario exploration exercise about possible AI futures. Drawing on the experiences of facilitators who have overseen 43 games over a four-year period, we illuminate recurring patterns, strategies, and decision-making processes observed during gameplay. Our analysis reveals key strategic considerations about AI development trajectories in this simulated environment, including: the destabilising effects of AI races, the crucial role of international cooperation in mitigating catastrophic risks, the challenges of aligning corporate and national interests, and the potential for rapid, transformative change in AI capabilities. We highlight places where we believe the game has been effective in exposing participants to the complexities and uncertainties inherent in AI governance. Key recurring gameplay themes include the emergence of international agreements, challenges to the robustness of such agreements, the critical role of cybersecurity in AI development, and the potential for unexpected crises to dramatically alter AI trajectories. By documenting these insights, we aim to provide valuable foresight for policymakers, industry leaders, and researchers navigating the complex landscape of AI development and governance.

Vernon Grant, Deborah Mekiana, Jacques Philip

Physical activity (PA), sleep, and weight are important factors for youth health. However, data about these factors are unknown in youth living in isolated Alaska Native communities. This study aims to assess PA, sleep, height and weight in elementary through high school students living in Anaktuvuk Pass. Fourteen children (<12) and 24 youths (12–20) volunteered to participate in this study. PA and sleep data were collected with actigraphy. Height and weight were assessed with standard procedures. Demographics were collected via survey. Results show that 10.53% and 18.42% of participants were overweight and obese, respectively. Average bedtime was 00:15 am and wake time 08:23 am. Total sleep time was 498.21 min. Participants averaged 477.64 min in sedentary activity, 297.29 min in light activity, 150.66 min in moderate activity, and 18.05 min in vigorous activity. Adjusted models suggest that high school students engage in significantly more sedentary activity, and significantly less light, moderate, and vigorous activity compared to those in middle and elementary school. All students engaged in less moderate and vigorous activity on the weekend compared to the weekday. Data suggest that as children age they become more sedentary. Future studies should focus on increasing daily PA in high school students while considering other obesogenic factors.

Andrei Boiarov, Dmitry Bleklov, Pavlo Bredikhin et al.

This paper presents RaceLens, a novel application utilizing advanced deep learning and computer vision models for comprehensive analysis of racing photos. The developed models have demonstrated their efficiency in a wide array of tasks, including detecting racing cars, recognizing car numbers, detecting and quantifying car details, and recognizing car orientations. We discuss the process of collecting a robust dataset necessary for training our models, and describe an approach we have designed to augment and improve this dataset continually. Our method leverages a feedback loop for continuous model improvement, thus enhancing the performance and accuracy of RaceLens over time. A significant part of our study is dedicated to illustrating the practical application of RaceLens, focusing on its successful deployment by NASCAR teams over four seasons. We provide a comprehensive evaluation of our system's performance and its direct impact on the team's strategic decisions and performance metrics. The results underscore the transformative potential of machine intelligence in the competitive and dynamic world of car racing, setting a precedent for future applications.

Drew Hanover, Antonio Loquercio, Leonard Bauersfeld et al.

Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms which can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground which in turn increases productivity and further strengthens their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.

Ubaidillah, Nazlia

Pule village topography is a hilly area that has economic potential if it is managed well. The functions of it are as water absorption and earthquake defense, besides that the hilly area also can be as ecotourism for the alternative strategy of economic enhancement and for ecosystem education. This research-based service aims to empower the community economy and apply the education that is integrated with nature through participatory method related to the goals achieved. The first result, officially opened the ecotourism with the views of Kekep Hills. The second, the forming of community-based tourism (Pokdarwis) as the management of ecotourism. The third, the education that is integrated of nature becomes the educational and fun media. It was created by using ecotourism pattern to grow the awareness of conservation for childhood

Dariusz Rajchel, Anna Rajchel

W strukturze przestrzennej miast oprócz terenów mieszkaniowych, usługowych, przemysłowych czy rekreacyjno-wypoczynkowych występują tereny poprzemysłowe, które wymagają określonych prac umożliwiających zmianę ich przeznaczenia. Na obszarach poprzemysłowych najczęściej wprowadza się funkcję usługową, np. handlową, czy mieszkaniową, np. lofty, ale występują również rejony, które mogą być przekształcone w teren zieleni z funkcją rekreacyjno-wypoczynkowo-sportową. Takim terenem jest Kamionka Piast w Opolu. Jest to obszar po byłej cementowni zlokalizowany w centralnej części miasta w pobliżu osiedli mieszkaniowych, terenów sportowych z infrastrukturą oświatową, zakładami naprawy taboru kolejowego. Celem artykułu było zaprezentowanie działań podjętych przez miasto Opole związanych z rewitalizacją, rekultywacją oraz remediacją zdegradowanego terenu poprzemysłowego i przekształceniem go w jedno z atrakcyjniejszych miejsc Opola pod kątem wypoczynkowym, rekreacyjnym i przyrodniczym. Ponieważ prace związane z ożywieniem kamionki były wykonywane w ramach projektu, w artykule odwołano się do dokumentacji projektowej, aktów prawnych, lokalnych dokumentów strategicznych dotyczących rewitalizacji miejsc zdegradowanych, polskich i zagranicznych opracowań na ten temat. Kamionka Piast po rewitalizacji jest jednym z atrakcyjniejszych miejsc Opola pełniącym funkcję rekreacyjną, wypoczynkową, sportową, edukacyjną. Powstanie nowych osiedli, zwłaszcza mikroapartamentów, cieszy się dużą popularnością wśród studentów. Zachowanie cennych gatunków roślin i zwierząt oraz zabezpieczenie terenu przed degradacją wpłynęło na poprawę przestrzeni i krajobrazu.