Exploring, Engaging, Understanding in Museums

Patterns of accessibility through the space of the exhibition, connections or separations among spaces or exhibition elements, sequencing and grouping of elements, form our perceptions and shape our understanding. Through a review of several previous studies and the presentation of new work, this paper suggests that these patterns of movement form the basis of visitor understanding and that these effects can be deliberately controlled and elaborated through a closer examination of the influence of the visual and perceptual properties of an exhibition. Furthermore, it is argued that there is also a spatial discourse based on patterns of access and visibility that flows in its own right, although not entirely separate from the curatorial narrative

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

A Class of Nonperturbative Configurations in Abelian-Higgs Models: Complexity from Dynamical Symmetry Breaking

We present a numerical investigation of the dynamics of symmetry breaking in both Abelian and non-Abelian $[S U (2)]$ Higgs models in three spatial dimensions. We find a class of time-dependent, long-lived nonperturbative field configurations within the range of parameters corresponding to type-1 superconductors, that is, with vector masses ($m_v$) larger than scalar masses ($m_s$). We argue that these emergent nontopological configurations are related to oscillons found previously in other contexts. For the Abelian-Higgs model, our lattice implementation allows us to map the range of parameter space -- the values of $\beta = (m_s /m_v)^2$ -- where such configurations exist and to follow them for times t \sim \O(10^5) m^{-1}. An investigation of their properties for $\hat z$-symmetric models reveals an enormously rich structure of resonances and mode-mode oscillations reminiscent of excited atomic states. For the SU(2) case, we present preliminary results indicating the presence of similar oscillonic configurations.Comment: 21 pages, 19 figures, prd, revte

Microgravity: A Teacher's Guide With Activities in Science, Mathematics, and Technology

The purpose of this curriculum supplement guide is to define and explain microgravity and show how microgravity can help us learn about the phenomena of our world. The front section of the guide is designed to provide teachers of science, mathematics, and technology at many levels with a foundation in microgravity science and applications. It begins with background information for the teacher on what microgravity is and how it is created. This is followed with information on the domains of microgravity science research; biotechnology, combustion science, fluid physics, fundamental physics, materials science, and microgravity research geared toward exploration. The background section concludes with a history of microgravity research and the expectations microgravity scientists have for research on the International Space Station. Finally, the guide concludes with a suggested reading list, NASA educational resources including electronic resources, and an evaluation questionnaire

Visitor Use of Interpretive Facilities at Fossil Butte National Monument, Wyoming

Visitors expect to a gain high-quality outdoor experiences at any recreation site they visit. In order to support that effort, most recreation managers utilize interpretive facilities to educate and inform visitors about the site. It is important for interpretive managers to be aware of what kind of interpretive media could best be used in a given setting for the type of visitor anticipated. To accomplish this goal, it is necessary to understand visitors\u27 behavior and incorporate that understanding into the interpretive planning process. This study looks at visitor use of interpretive facilities provided at Fossil Butte National Monument, Wyoming. The primary method of data-gathering for this study was observation (participant observation and behavioral mapping). Observations were made at randomly selected times over 63 hours of observation in 1995. Data were collected at three locations: Fossil Butte visitor center, Fossil Lake self-guided trail, and roadside displays. Analysis was made using descriptive statistics for quantitative data and content analysis for qualitative data. A 13-minute audiovisual program was the most attractive interpretive facility at the visitor center. Other popular activities in the center include: examing standing displays on paleontology, asking information or directions, taking pictures, making purchases, and certain activities for children. Nature trail hiking usually was done in conjunction with use of nearby picnic facilities. More visitors in vehicles passed by the roadside display than those who stopped to read it. Based on these and other results, several recommendations are made for park managers

From Pattern Discovery to Pattern Interpretation of Semantically-Enriched Trajectory Data

The widespread use of positioning technologies ranging from GSM and GPS to WiFi devices tend to produce large-scale datasets of trajectories, which represent the movement of travelling entities. Several application domains, such as recreational area management, may benefit from analysing such datasets. However, analysis results only become truly useful and meaningful for the end user when the intrinsically complex nature of the movement data in terms of context is taken into account during the knowledge discovery process. For this reason we propose a pattern interpretation framework that consists of three main steps, namely, pattern discovery, semantic annotation and pattern analysis. The framework supports the understanding of movement patterns that were extracted using some trajectory mining algorithm. In order to demonstrate the feasibility and effectiveness of the framework, we have specifically applied it for understanding moving flock patterns in pedestrian movement. For the pattern discovery step, we have formally defined the concept of moving flock, distinguishing it from stationary flock, and developed a detection algorithm for it. A set of guidelines for setting the parameters of the algorithm is provided and a specific technique is implemented for the radius parameter.As for the semantic annotation step, we have proposed a guideline for selecting appropriate attributes for semantic enrichment of individual entities and of moving flocks. Two levels of annotation, which are at individual and pattern level, were also described. Finally, for the pattern interpretation step, we have combined the results obtained using hierarchichal clustering and decision tree classification in order to analyse the attributes of flock members and of the flocks, and the flocks themselves. The entire framework was tested on the Dwingelderveld National Park (DNP) dataset and the Delft dataset, both of which are pedestrian datasets based in the Netherlands. The DNP dataset contains records of observations on the movement of visitors in the park while the Delft dataset describes movement of the pedestrians in the city. As a result, some forms of interactions, such as certain groups of visitors following the most popular path in the park, were inferred. Furthermore, some flocks were linked with specific attractions of the park

Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility