Hasil untuk "Recreation. Leisure"

Hyungjoo Chae, Jungsoo Park, Alan Ritter

Training autonomous web agents is fundamentally limited by the environments they learn from: real-world websites are unsafe to explore, hard to reset, and rarely provide verifiable feedback. We propose VeriEnv, a framework that treats language models as environment creators, automatically cloning real-world websites into fully executable, verifiable synthetic environments. By exposing controlled internal access via a Python SDK, VeriEnv enables agents to self-generate tasks with deterministic, programmatically verifiable rewards, eliminating reliance on heuristic or LLM-based judges. This design decouples agent learning from unsafe real-world interaction while enabling scalable self-evolution through environment expansion. Through experiments on web agent benchmarks, we show that agents trained with VeriEnv generalize to unseen websites, achieve site-specific mastery through self-evolving training, and benefit from scaling the number of training environments. Code and resources will be released at https://github.com/kyle8581/VeriEnv upon acceptance.

Hannes Rosseel, Toon van Waterschoot

Interactive acoustic auralization allows users to explore virtual acoustic environments in real-time, enabling the acoustic recreation of concert hall or Historical Worship Spaces (HWS) that are either no longer accessible, acoustically altered, or impractical to visit. Interactive acoustic synthesis requires real-time convolution of input signals with a set of synthesis filters that model the space-time acoustic response of the space. The acoustics in concert halls and HWS are both characterized by a long reverberation time, resulting in synthesis filters containing many filter taps. As a result, the convolution process can be computationally demanding, introducing significant latency that limits the real-time interactivity of the auralization system. In this paper, the implementation of a real-time multichannel loudspeaker-based auralization system is presented. This system is capable of synthesizing the acoustics of highly reverberant spaces in real-time using GPU-acceleration. A comparison between traditional CPU-based convolution and GPU-accelerated convolution is presented, showing that the latter can achieve real-time performance with significantly lower latency. Additionally, the system integrates acoustic synthesis with acoustic feedback cancellation on the GPU, creating a unified loudspeaker-based auralization framework that minimizes processing latency.

Mauricio Flores-Vargas, Enda Bates, Rachel McDonnell

Multimodal research and applications are becoming more commonplace as Virtual Reality (VR) technology integrates different sensory feedback, enabling the recreation of real spaces in an audio-visual context. Within VR experiences, numerous applications rely on the user's voice as a key element of interaction, including music performances and public speaking applications. Self-perception of our voice plays a crucial role in vocal production. When singing or speaking, our voice interacts with the acoustic properties of the environment, shaping the adjustment of vocal parameters in response to the perceived characteristics of the space. This technical report presents a real-time auralization pipeline that leverages three-dimensional Spatial Impulse Responses (SIRs) for multimodal research applications in VR requiring first-person vocal interaction. It describes the impulse response creation and rendering workflow, the audio-visual integration, and addresses latency and computational considerations. The system enables users to explore acoustic spaces from various positions and orientations within a predefined area, supporting three and five Degrees of Freedom (3Dof and 5DoF) in audio-visual multimodal perception for both research and creative applications in VR.

Gauthier Grimmer, Romain Wenger, Clément Flint et al.

The proliferation of floating anthropogenic debris in rivers has emerged as a pressing environmental concern, exerting a detrimental influence on biodiversity, water quality, and human activities such as navigation and recreation. The present study proposes a novel methodological framework for the monitoring the aforementioned waste, utilising fixed, in-situ cameras. This study provides two key contributions: (i) the continuous quantification and monitoring of floating debris using deep learning and (ii) the identification of the most suitable deep learning model in terms of accuracy and inference speed under complex environmental conditions. These models are tested in a range of environmental conditions and learning configurations, including experiments on biases related to data leakage. Furthermore, a geometric model is implemented to estimate the actual size of detected objects from a 2D image. This model takes advantage of both intrinsic and extrinsic characteristics of the camera. The findings of this study underscore the significance of the dataset constitution protocol, particularly with respect to the integration of negative images and the consideration of temporal leakage. In conclusion, the feasibility of metric object estimation using projective geometry coupled with regression corrections is demonstrated. This approach paves the way for the development of robust, low-cost, automated monitoring systems for urban aquatic environments.

Sukanya Bhattacharjee, Parag Chaudhuri

Digital preservation of the cultural heritages is one of the major applications of various computer graphics and vision algorithms. The advancement in the AR/VR technologies is giving the cultural heritage preservation an interesting spin due to its immense visualization ability. The use of these technologies to digitally recreate heritage sites and art is becoming a popular trend. A project, called Indian Digital Heritage (IDH), for recreating the heritage site of Hampi, Karnataka during Vijaynagara empire ($1336$ - $1646$ CE) has been initiated by the Department of Science and Technology (DST) few years back. Immense work on surveying the site, collecting geographical and historic information about the life in Hampi, creating 3D models for buildings and people of Hampi and many other related tasks has been undertaken by various participants of this project. A major part of this project is to make tourists visiting Hampi visualize the life of people in ancient Hampi through any handy device. With such a requirement, the mobile AR based platform becomes a natural choice for developing any application for this purpose. We contributed to the project by developing an AR based mobile application to recreate a scene from Virupaksha Bazaar of Hampi with two components - author scene with augmented virtual contents at any scale, and visualize the same scene by reaching the physical location of augmentation. We develop an interactive application for the purpose of digitally recreating ancient Hampi. Though the focus of this work is not creating any static or dynamic content from scratch, it shows an interesting application of the content created in a real world scenario.

Yuan Liao, Jorge Gil, Sonia Yeh et al.

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation.

Zirui Song, Yaohang Li, Meng Fang et al.

Large language model agents that interact with PC applications often face limitations due to their singular mode of interaction with real-world environments, leading to restricted versatility and frequent hallucinations. To address this, we propose the Multi-Modal Agent Collaboration framework (MMAC-Copilot), a framework utilizes the collective expertise of diverse agents to enhance interaction ability with application. The framework introduces a team collaboration chain, enabling each participating agent to contribute insights based on their specific domain knowledge, effectively reducing the hallucination associated with knowledge domain gaps. We evaluate MMAC-Copilot using the GAIA benchmark and our newly introduced Visual Interaction Benchmark (VIBench). MMAC-Copilot achieved exceptional performance on GAIA, with an average improvement of 6.8\% over existing leading systems. VIBench focuses on non-API-interactable applications across various domains, including 3D gaming, recreation, and office scenarios. It also demonstrated remarkable capability on VIBench. We hope this work can inspire in this field and provide a more comprehensive assessment of Autonomous agents. The anonymous Github is available at \href{https://anonymous.4open.science/r/ComputerAgentWithVision-3C12}{Anonymous Github}

Merav Parter, Asaf Petruschka, Shay Sapir et al.

We provide new algorithms for constructing spanners of arbitrarily edge- or vertex-colored graphs, that can endure up to $f$ failures of entire color classes. The failure of even a single color may cause a linear number of individual edge/vertex faults. In a recent work, Petruschka, Sapir and Tzalik [ITCS `24] gave tight bounds for the (worst-case) size $s$ of such spanners, where $s=Θ(f n^{1+1/k})$ or $s=Θ(f^{1-1/k} n^{1+1/k})$ for spanners with stretch $(2k-1)$ that are resilient to at most $f$ edge- or vertex-color faults, respectively. Additionally, they showed an algorithm for computing spanners of size $\tilde{O}(s)$, running in $\tilde{O}(msf)$ sequential time, based on the (FT) greedy spanner algorithm. The problem of providing faster and/or distributed algorithms was left open therein. We address this problem and provide a novel variant of the classical Baswana-Sen algorithm [RSA `07] in the spirit of Parter's algorithm for vertex fault-tolerant spanners [STOC `22]. In a nutshell, our algorithms produce color fault-tolerant spanners of size $\tilde{O}_k (s)$ (hence near-optimal for any fixed $k$), have optimal locality $O(k)$ (i.e., take $O(k)$ rounds in the LOCAL model), can be implemented in $O_k (f^{k-1})$ rounds in CONGEST, and take $\tilde{O}_k (m + sf^{k-1})$ sequential time. To handle the considerably more difficult setting of color faults, our approach differs from [BS07, Par22] by taking a novel edge-centric perspective, instead of (FT)-clustering of vertices; in fact, we demonstrate that this point of view simplifies their algorithms. Another key technical contribution is in constructing and using collections of short paths that are "colorful at all scales", which we call "parks". These are intimately connected with the notion of spread set-systems that found use in recent breakthroughs regarding the famous Sunflower Conjecture.

Pierre Lemaire, Angelo Furno, Stefania Rubrichi et al.

Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by individuals. Such information is invaluable for monitoring large urban areas and supporting the implementation of decision-making services. When analyzed in real time, NSD can enable the early detection of critical urban events, including fires, large accidents, stampedes, terrorist attacks, and sports and leisure gatherings, especially if these events significantly impact mobile phone network activity in the affected areas. This paper presents empirical evidence that advanced NSD can detect anomalies in mobile traffic service consumption, attributable to critical urban events, with fine spatial and temporal resolutions. We introduce two methodologies for real-time anomaly detection from multivariate time series extracted from large-scale NSD, utilizing a range of algorithms adapted from the state-of-the-art in unsupervised machine learning techniques for anomaly detection. Our research includes a comprehensive quantitative evaluation of these algorithms on a large-scale dataset of NSD service consumption for the Paris region. The evaluation uses an original dataset of documented critical or unusual urban events. This dataset has been built as a ground truth basis for assessing the algorithms performance. The obtained results demonstrate that our framework can detect unusual events almost instantaneously and locate the affected areas with high precision, largely outperforming random classifiers. This efficiency and effectiveness underline the potential of NSD-based anomaly detection in significantly enhancing emergency response strategies and urban planning.

Luís Branquinho, Pedro Forte, Ronaldo V. Thomatieli-Santos et al.

Changing the date of the FIFA World Cup Qatar 2022 may represent a factor to consider for the expected performance of participating players. This was due to fixture congestion at the start of the season and expected weather conditions during the competition. Thus, the main purpose of this brief report was to critically analyze the potential impact of changing the competition date and weather conditions on players’ performance. In addition, a brief description about the performance during the World Cup is also provided. For the research, the Web of Science, PubMed and SPORTDiscus databases were accessed using the primary keywords FIFA World Cup and World Soccer Cup associated with the secondary keywords match running performance, fixture congestion, fatigue and weather conditions. After applying inclusion and exclusion criteria, 52 articles were considered for analysis. The results seem to indicate that although changes were expected due to the modifications made (i.e., the competition date and scheduling congestion), the performance of the players seems not to have been affected in terms of the analyzed indicators. Furthermore, it seems possible to identify some patterns in the behavior of the teams that reached the most advanced stages of the competition.

Courtney R. Chaaban, Jeffrey A. Turner, Darin A. Padua

The optimal set of return to sport (RTS) tests after anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) remains elusive. Many athletes fail to pass current RTS test batteries, fail to RTS, or sustain secondary ACL injuries if they do RTS. The purpose of this review is to summarize current literature regarding functional RTS testing after ACLR and to encourage clinicians to have patients “think” (add a secondary cognitive task) outside the “box” (in reference to the box used during the drop vertical jump task) when performing functional RTS tests. We review important criteria for functional tests in RTS testing, including task-specificity and measurability. Firstly, tests should replicate the sport-specific demands the athlete will encounter when they RTS. Many ACL injuries occur when the athlete is performing a dual cognitive-motor task (e.g., attending to an opponent while performing a cutting maneuver). However, most functional RTS tests do not incorporate a secondary cognitive load. Secondly, tests should be measurable, both through the athlete’s ability to complete the task safely (through biomechanical analyses) and efficiently (through measures of performance). We highlight and critically examine three examples of functional tests that are commonly used for RTS testing: the drop vertical jump, single-leg hop tests, and cutting tasks. We discuss how biomechanics and performance can be measured during these tasks, including the relationship these variables may have with injury. We then discuss how cognitive demands can be added to these tasks, and how these demands influence both biomechanics and performance. Lastly, we provide clinicians with practical recommendations on how to implement secondary cognitive tasks into functional testing and how to assess athletes’ biomechanics and performance.

Gan Zhang, Wenjun Zhu

The physical and economic damages of hurricanes can acutely affect employment and the well-being of employees. However, a comprehensive understanding of these impacts remains elusive as many studies focused on narrow subsets of regions or hurricanes. Here we present an open-source dataset that serves interdisciplinary research on hurricane impacts on US employment. Compared to past domain-specific efforts, this dataset has greater spatial-temporal granularity and variable coverage. To demonstrate potential applications of this dataset, we focus on the short-term employment disruptions related to hurricanes during 1990-2020. The observed county-level employment changes in the initial month are small on average, though large employment losses (>30%) can occur after extreme storms. The overall small changes partly result from compensation among different employment sectors, which may obscure large, concentrated employment losses after hurricanes. Additional econometric analyses concur on the post-storm employment losses in hospitality and leisure but disagree on employment changes in the other industries. The dataset also enables data-driven analyses that highlight vulnerabilities such as pronounced employment losses related to Puerto Rico and rainy hurricanes. Furthermore, predictive modeling of short-term employment changes shows promising performance for service-providing industries and high-impact storms. In the examined cases, the nonlinear Random Forests model greatly outperforms the multiple linear regression model. The nonlinear model also suggests that more severe hurricane hazards projected by physical models may cause more extreme losses in US service-providing employment. Finally, we share our dataset and analytical code to facilitate the study and modeling of hurricane impacts in a changing climate.

Dawon Oh, In Kwon Park

COVID-19 significantly has changed the lifestyle in the urban areas. Urban parks reemerged as a savior of leisure activities and social joints under strict social-distancing measures. Thus, there have been significant changes in the thresholds of service distances of urban parks: People have become more willing to visit parks in farther areas. This paper aims to examine the difference between before and after the COVID-19 pandemic by applying a gravity model. We examine variations in the service areas of urban parks, dependig on the accessibility and design components of the park, using a dataset consisting of the park visitor's 'origin (home)' and 'destination (park)'. This LBD (Location-based Big Data) provides the home location of the urban park visitor. The data was constructed by SK Telecom, using individual smartphone signal data on a daily basis. Adjusted coefficients are estimated by OLS(ordinary least squares) with cluster-robust standard errors to compare the difference between 2019 and 2020. Contrary to a common belief, the transit accessibility of the park plays a more significant role than the physical traits of each parks. Accessibility itself determines a lot of the threshold distance of the park visit. While previous studies have identified the factors influencing the reaching distance of park services, this study also attempts to determine how the effects of the factors have changed due to the COVID-19 pandemic. As proven in this study, the marginal effects of those factors vary before and after the pandemic. By identifying the factors that determine the distance to visit in urban parks, it is possible to see which factors should be more focused on in planning small parks for residents in the neighborhood or large parks for more visitors from the entire region.

Justin P Cooke, Matthew F Campbell, Edward B Steager et al.

Micro aerial vehicles are making a large impact in applications such as search-and-rescue, package delivery, and recreation. Unfortunately, these diminutive drones are currently constrained to carrying small payloads, in large part because they use propellers optimized for larger aircraft and inviscid flow regimes. Fully realizing the potential of emerging microflyers requires next-generation propellers that are specifically designed for low-Reynolds number conditions and that include new features advantageous in highly viscous flows. One aspect that has received limited attention in the literature is the addition of roughness to propeller blades as a method of reducing drag and increasing thrust. To investigate this possibility, we used large eddy simulation to conduct a numerical investigation of smooth and rough propellers. Our results indicate that roughness produces a 2% increase in thrust and a 5% decrease in power relative to a baseline smooth propeller operating at the same Reynolds number of Rec = 6500, held constant by rotational speed. We corroborated our numerical findings using thrust-stand-based experiments of 3D-printed propellers identical to those of the numerical simulations. Our study confirms that surface roughness is an additional parameter within the design space for micro-propellers that will lead to unprecedented drone efficiencies and payloads.

Mat D. Duerden

Abstract This article, adapted from the 2019 Butler Lecture at the NRPA National Research session, provides an overview of the origins, development, and potential future trajectory of the field of leisure research. Economic and societal trends of the last few decades may signal the need for the field to intentionally transition its focus on leisure as a context to leisure as an experience. More specifically the field needs to actively consider how insights gained from 60 years of research on leisure experiences can be applied to contexts beyond leisure. To illustrate the possibility of such a shift, lessons learned from transitioning a traditional recreation management department to a department of experience design and management are shared.

Myroslava Hrytsak, Dariya Popovych, Nataliya Badiuk et al.

Background. Earlier we found that the newly created sulfate-chloride sodium-magnesium drinking mineral waters of Truskavets’ spa have similar neuroendocrine and metabolic effects on healthy old female rats significantly different from daily water. The aim of this study is to elucidate the effects of these mineral waters on the neuroendocrine status and metabolism of these animals. Materials and Methods. Experiment was performed on 50 healthy female Wistar rats. Animals of the first group remained intact, using tap water from drinking ad libitum. Rats of the control group for 6 days injected a tap water through the tube at a dose of 1,5 mL/100 g of body mass. The rats of the main groups received the water "Myroslava" and "Khrystyna". The day after the completion of the drinking course in all rats, at first, assessed the state of autonomous regulation by parameters of the HRV. The plasma levels of the hormones of adaptation were determined: corticosterone, triiodothyronine and testosterone (by the ELISA) as well as electrolytes: calcium, magnesium, phosphates, chloride, sodium and potassium; nitric metabolites: creatinine, urea, uric acid, medium molecular polypeptides, bilirubin; lipid peroxidation products and antioxidant enzymes, as well as cholesterol, amylase and glucose. Most of the listed parameters of metabolism were also determined in daily urine. In the adrenals the thickness of glomerular, fascicular, reticular and medullar zones was measured. Results. To identify exactly those parameters, the set of which all four groups of animals differ significantly from each other, the information field of the registered parameters was subjected to discriminant analysis. The program included in the model 8 endocrine and 16 metabolic parameters, information about which is condensed into three roots. The first root reflects directly the SOD and corticosterone and inversely the reticular zone as well as plasma uric acid and glucose. The second root contains information about Nap/Kp ratio, natrihistia, amylasemia, magnesiumuria as well as inversely about kaliemia. The third root reflects directly the triiodothyronine, parathyroid activity, plasma Ca, natriuria and chloriduria as well as urine malondyaldehide. Inversely displays the root information about the testosterone, Ku/Nau ratio, glomerular zone, plasma katalase and Na as well as uricosuria and amylasuria. In the information space of the three discriminant roots, all four groups are quite clearly distinguished. Classification accuracy is 94% (three errors). Conclusion. The newly created sulfate-chloride sodium-magnesium drinking mineral waters of Truskavets resort have specific endocrine and metabolic effects on healthy old female rats with weekly use. This provides a basis for preclinical studies.

Nidhi Mishra, Gauri Tulsulkar, Hanhui Li et al.

There are considerable advancements in medical health care in recent years, resulting in rising older population. As the workforce for such a population is not keeping pace, there is an urgent need to address this problem. Having robots to stimulating recreational activities for older adults can reduce the workload for caretakers and give them time to address the emotional needs of the elderly. In this paper, we investigate the effects of the humanoid social robot Nadine as an activity host for the elderly. This study aims to analyse if the elderly feels comfortable and enjoy playing game/activity with the humanoid robot Nadine. We propose to evaluate this by placing Nadine humanoid social robot in a nursing home as a caretaker where she hosts bingo game. We record sessions with and without Nadine to understand the difference and acceptance of these two scenarios. We use computer vision methods to analyse the activities of the elderly to detect emotions and their involvement in the game. We envision that such humanoid robots will make recreational activities more readily available for the elderly. Our results present positive enforcement during recreational activity, Bingo, in the presence of Nadine.

Alec J. Linot, Michael D. Graham

Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to attractors that exist on finite-dimensional manifolds. We present a data-driven reduced order modeling method that capitalizes on this fact by finding the coordinates of this manifold and finding an ordinary differential equation (ODE) describing the dynamics in this coordinate system. The manifold coordinates are discovered using an undercomplete autoencoder -- a neural network (NN) that reduces then expands dimension. Then the ODE, in these coordinates, is approximated by a NN using the neural ODE framework. Both of these methods only require snapshots of data to learn a model, and the data can be widely and/or unevenly spaced. We apply this framework to the Kuramoto-Sivashinsky for different domain sizes that exhibit chaotic dynamics. With this system, we find that dimension reduction improves performance relative to predictions in the ambient space, where artifacts arise. Then, with the low-dimensional model, we vary the training data spacing and find excellent short- and long-time statistical recreation of the true dynamics for widely spaced data (spacing of ~0.7 Lyapunov times). We end by comparing performance with various degrees of dimension reduction, and find a "sweet spot" in terms of performance vs. dimension.

Enrique Orduna-Malea

Dot-science was launched in 2015 as a new academic top-level domain (TLD) aimed to provide 'a dedicated, easily accessible location for global Internet users with an interest in science'. The main objective of this work is to find out the general scholarly usage of this top-level domain. In particular, the following three questions are pursued: usage (number of web domains registered with the dot-science), purpose (main function and category of websites linked to these web domains), and impact (websites' visibility and authority). To do this, 13,900 domain names were gathered through ICANN's Domain Name Registration Data Lookup database. Each web domain was subsequently categorized, and data on web impact were obtained from Majestic's API. Based on the results obtained, it is concluded that the dot-science top-level domain is scarcely adopted by the academic community, and mainly used by registrar companies for reselling purposes (35.5% of all web domains were parked). Websites receiving the highest number of backlinks were generally related to non-academic websites applying intensive link building practices and offering leisure or even fraudulent contents. Majestic's Trust Flow metric has been proved an effective method to filter reputable academic websites. As regards primary academic-related dot-science web domain categories, 1,175 (8.5% of all web domains registered) were found, mainly personal academic websites (342 web domains), blogs (261) and research groups (133). All dubious content reveals bad practices on the Web, where the tag 'science' is fundamentally used as a mechanism to deceive search engine algorithms.

Nan Jiang, Kuiran Wang, Xiaoke Peng et al.

Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. With this, monitoring the operation status of UAVs is crucially important. In this work, we consider the task of tracking UAVs, providing rich information such as location and trajectory. To facilitate research on this topic, we propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes. The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs. Furthermore, the advancement of addressing research challenges in Anti-UAV can help the design of anti-UAV systems, leading to better surveillance of UAVs. Besides, a novel approach named dual-flow semantic consistency (DFSC) is proposed for UAV tracking. Modulated by the semantic flow across video sequences, the tracker learns more robust class-level semantic information and obtains more discriminative instance-level features. Experimental results demonstrate that Anti-UAV is very challenging, and the proposed method can effectively improve the tracker's performance. The Anti-UAV benchmark and the code of the proposed approach will be publicly available at https://github.com/ucas-vg/Anti-UAV.