Horofunction compactifications and duality

We study the global topology of the horofunction compactification of smooth manifolds with a Finsler distance. The main goal is to show, for certain classes of these spaces, that the horofunction compactification is naturally homeomorphic to the closed unit ball of the dual norm of the norm in the tangent space (at the base point) that generates the Finsler distance. We construct explicit homeomorphisms for a variety of spaces in three settings: bounded convex domains in ℂ^n with the Kobayashi distance, Hilbert geometries, and finite dimensional normed spaces. For the spaces under consideration, the horofunction boundary has an intrinsic partition into so called parts. The natural connection with the dual norm arises through the fact that the homeomorphism maps each part in the horofunction boundary onto the relative interior of a boundary face of the dual unit ball. For normed spaces the connection between the global topology of the horofunction boundary and the dual norm was suggested by Kapovich and Leeb. We confirm this connection for Euclidean Jordan algebras equipped with the spectral norm

Detecting Patches on Road Pavement Images acquired with 3D Laser Sensors using Object Detection and Deep Learning

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This paper proposes an automatic patch detection system using object detection technique. To our knowledge, this is the first time state-of-the-art object detection models Faster RCNN, and SSD MobileNet-V2 have been used to detect patches inside images acquired by LCMS. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems. The contribution of this paper are (1) an automatic pavement patch detection models for LCMS images and (2) comparative analysis of RCNN, and SSD MobileNet-V2 models for automatic patch detection

Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.https://arrow.tudublin.ie/cddpos/1016/thumbnail.jp

Learning pavement surface condition ratings through visual cues using a deep learning classification approach.

Pavement surface condition rating is an essential part of road infrastructure maintenance and asset management, and it is performed manually by the data analyst. The manual rating requires cognitive skills built through training and experience, which is quantitatively challenging and timeconsuming. This paper first analyses the complexity of the current manual visual rating system. This paper then investigates the suitability and robustness of a state-of-the-art convolutional neural network (CNN) classifier to automate the pavement surface condition index (PSCI) system used to rate pavement surfaces in Ireland. The dataset contains 3735 images of flexible asphalt pavements from Irish urban and rural environments taken from a video camera mounted in front of a van. The PSCI ratings were applied by experts using a scale of 1-10 to indicate surface conditions. The classification models are evaluated for different input pre-processing variations, image size, learning techniques, and the number of classes. Using 10 PSCI classes, the best classifier achieved a precision of 57% and a recall of 58%. Adjacent combination of classes (e.g., ratings 1 and 2 combined into a single class) to form a 5-class problem produced a classifier with a precision of 70% and recall of 77%. Given the complexity of the problem, classification using CNN holds promise as a first step towards an automated ranking system

Deep Learning Framework For Intelligent Pavement Condition Rating: A direct classification approach for regional and local roads

Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment.

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region\u27s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera\u27s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ( presence/absence detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating

Creating wheelchair-controlled video games: challenges and opportunities when involving young people with mobility impairments and game design experts

Although participatory design (PD) is currently the most acceptable and respectful process we have for designing technology, recent discussions suggest that there may be two barriers to the successful application of PD to the design of digital games: First, the involvement of audiences with special needs can introduce new practical and ethical challenges to the design process. Second, the use of non-experts in game design roles has been criticised in that participants lack skills necessary to create games of appropriate quality. To explore how domain knowledge and user involvement influence game design, we present results from two projects that addressed the creation of movement-based wheelchair-controlled video games from different perspectives. The first project was carried out together with a local school that provides education for young people with special needs, where we invited students who use wheelchairs to take part in design sessions. The second project involved university students on a game development course, who do not use wheelchairs, taking on the role of expert designers. They were asked to design concepts for wheelchair-controlled games as part of a final-year course on game design. Our results show that concepts developed by both groups were generally suitable examples of wheelchair-controlled motion-based video games, but we observed differences regarding level of detail of game concepts, and ideas of disability. Additionally, our results show that the design exercise exposed vulnerabilities in both groups, outlining that the risk of practical and emotional vulnerability needs to be considered when working with the target audience as well as expert designers

The twilight of the Liberal Social Contract? On the Reception of Rawlsian Political Liberalism

This chapter discusses the Rawlsian project of public reason, or public justification-based 'political' liberalism, and its reception. After a brief philosophical rather than philological reconstruction of the project, the chapter revolves around a distinction between idealist and realist responses to it. Focusing on political liberalism’s critical reception illuminates an overarching question: was Rawls’s revival of a contractualist approach to liberal legitimacy a fruitful move for liberalism and/or the social contract tradition? The last section contains a largely negative answer to that question. Nonetheless the chapter's conclusion shows that the research programme of political liberalism provided and continues to provide illuminating insights into the limitations of liberal contractualism, especially under conditions of persistent and radical diversity. The programme is, however, less receptive to challenges to do with the relative decline of the power of modern states

Constitutivism

A brief explanation and overview of constitutivism

Targeting Cx43 and N-Cadherin, Which Are Abnormally Upregulated in Venous Leg Ulcers, Influences Migration, Adhesion and Activation of Rho GTPases

Venous leg ulcers can be very hard to heal and represent a significant medical need with no effective therapeutic treatment currently available