Joint Cuts and Matching of Partitions in One Graph

As two fundamental problems, graph cuts and graph matching have been investigated over decades, resulting in vast literature in these two topics respectively. However the way of jointly applying and solving graph cuts and matching receives few attention. In this paper, we first formalize the problem of simultaneously cutting a graph into two partitions i.e. graph cuts and establishing their correspondence i.e. graph matching. Then we develop an optimization algorithm by updating matching and cutting alternatively, provided with theoretical analysis. The efficacy of our algorithm is verified on both synthetic dataset and real-world images containing similar regions or structures

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk

Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories,or display cumulative long term prognostics, in an intuitive and easily interpretable manner.Postprin

Highway Lighting Test Bed on INDOT Facility (Off-Roadway)

According to the National Highway Traffic Safety Administration (NHTSA), during 2016 there were 7,277,000 vehicle crashes nationally. Among them, approximately 70% happened during the daytime and around 30% of crashes occurred during the nighttime. There were 11,375 nighttime fatal crashes that account for about 48% of total fatal crashes (23,714). Given the fact that only 25%–33% of the vehicle miles traveled (VMT) occur at night, the above statistics indicate that the nighttime crash fatality rate is much higher and nighttime crashes are usually more severe compared to daytime crashes. Providing lighting on roadways is one of the proven safety countermeasures for preventing crashes and reducing fatalities. In particular, lighting at roadway intersections can reduce vehicle crashes by 10% to 26%. Currently, to conduct lighting field testing, INDOT is using several in-service highways, intersections, interchanges, and rest areas. These locations require traffic control and lane closures, which raises safety concerns and causing inconvenience to the public. In addition to the cost and safety concerns, during the evaluation period the new luminaires being tested actually functioned as lighting sources in place of the existing luminaires that were removed in order to install the new luminaires. This means that the new luminaries were used for roadway lighting at the test sites even before they were proven to meet the roadway lighting requirements. To eliminate traffic control and potential safety concerns, it was proposed to create test beds for field evaluating and to verify the performance of new lighting technologies and luminaires in a controlled, standard setting. Through this study, two lighting test bed facilities were designed and constructed. Illuminance values of installed luminaires were manually measured by a remotely controlled electric cart and drone. The measured illuminance values were analyzed and the analysis indicated that the efficiency of illuminance measurement can be significantly improved by automated methods. An illuminance data repository model was developed to be an effective tool that can greatly facilitate data input and storage process. The use of this model will further increase the productivity of illuminance measurement at the lighting test beds

A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS-ABC Algorithm

This study analyzes the impact of Industry 4.0 and SARS-CoV-2 on the manufacturing industry, in which manufacturing entities are faced with insufficient resources and uncertain services; however, the current study does not fit this situation well. A multi-service composition for complex manufacturing tasks in a cloud manufacturing environment is proposed to improve the utilization of manufacturing service resources. Combining execution time, cost, energy consumption, service reliability and availability, a quality of service (QoS) model is constructed as the evaluation standard. A hybrid search algorithm (VS–ABC algorithm) based on the vortex search algorithm (VS) and the artificial bee colony algorithm (ABC) is introduced and combines the advantages of the two algorithms in search range and calculation speed. We take the customization production of automobiles as an example, and the case study shows that the VS–ABC algorithm has better applicability compared with traditional vortex search and artificial bee colony algorithms

Bringing modern machine learning into clinical practice through the use of intuitive visualization and human-computer interaction

The increasing trend of systematic collection of medical data (diagnoses, hospital admission emergencies, blood test results, scans, etc) by healthcare providers offers an unprecedented opportunity for the application of modern data mining, pattern recognition, and machine learning algorithms. The ultimate aim is invariably that of improving outcomes, be it directly or indirectly. Notwithstanding the successes of recent research efforts in this realm, a major obstacle of making the developed models usable by medical professionals (rather than computer scientists or statisticians) remains largely unaddressed. Yet, a mounting amount of evidence shows that the ability to understand and easily use novel technologies is a major factor governing how widely adopted by the target users (doctors, nurses, and patients, amongst others) they are likely to be. In this work we address this technical gap. In particular, we describe a portable, web-based interface that allows healthcare professionals to interact with recently developed machine learning and data driven prognostic algorithms. Our application interfaces a statistical disease progression model and displays its predictions in an intuitive and readily understandable manner. Different types of geometric primitives and their visual properties (such as size or colour) are used to represent abstract quantities such as probability density functions, the rate of change of relative probabilities, and a series of other relevant statistics which the heathcare professional can use to explore patients’ risk factors or provide personalized, evidence and data driven incentivization to the patient.Publisher PDFPeer reviewe

Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide

The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed for pavement design by the Indiana Department of Transportation (INDOT) since 2009 and has generated efficient pavement designs with a lower cost. It has been demonstrated that the success of MEPDG implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Vehicular traffic loading is one of the key factors that may cause not only pavement structural failures, such as fatigue cracking and rutting, but also functional surface distresses, including friction and smoothness. In particular, truck load spectra play a critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to update the traffic design input module, and thus to improve the current INDOT pavement design procedures. Efforts were made to reclassify truck traffic categories to accurately account for the specific axle load spectra on two-lane roads with low truck traffic and interstate routes with very high truck traffic. The traffic input module was updated with the most recent data to better reflect the axle load spectra for pavement design. Vehicle platoons were analyzed to better understand the truck traffic characteristics. The unclassified vehicles by traffic recording devices were examined and analyzed to identify possible causes of the inaccurate data collection. Bus traffic in the Indiana urban areas was investigated to provide additional information for highway engineers with respect to city streets as well as highway sections passing through urban areas. New equivalent single axle load (ESAL) values were determined based on the updated traffic data. In addition, a truck traffic data repository and visualization model and a TABLEAU interactive visualization dashboard model were developed for easy access, view, storage, and analysis of MEPDG related traffic data

Highway Lighting Test Bed on INDOT Facility (Off-Roadway)

SPR-4442According to the National Highway Traffic Safety Administration (NHTSA), during 2016 there were 7,277,000 vehicle crashes nationally. Among them, approximately 70% happened during the daytime and around 30% of crashes occurred during the nighttime. There were 11,375 nighttime fatal crashes that account for about 48% of total fatal crashes (23,714). Given the fact that only 25%\u201333% of the vehicle miles traveled (VMT) occur at night, the above statistics indicate that the nighttime crash fatality rate is much higher and nighttime crashes are usually more severe compared to daytime crashes. Providing lighting on roadways is one of the proven safety countermeasures for preventing crashes and reducing fatalities. In particular, lighting at roadway intersections can reduce vehicle crashes by 10% to 26%. Currently, to conduct lighting field testing, INDOT is using several in-service highways, intersections, interchanges, and rest areas. These locations require traffic control and lane closures, which raises safety concerns and causing inconvenience to the public. In addition to the cost and safety concerns, during the evaluation period the new luminaires being tested actually functioned as lighting sources in place of the existing luminaires that were removed in order to install the new luminaires. This means that the new luminaries were used for roadway lighting at the test sites even before they were proven to meet the roadway lighting requirements. To eliminate traffic control and potential safety concerns, it was proposed to create test beds for field evaluating and to verify the performance of new lighting technologies and luminaires in a controlled, standard setting. Through this study, two lighting test bed facilities were designed and constructed. Illuminance values of installed luminaires were manually measured by a remotely controlled electric cart and drone. The measured illuminance values were analyzed and the analysis indicated that the efficiency of illuminance measurement can be significantly improved by automated methods. An illuminance data repository model was developed to be an effective tool that can greatly facilitate data input and storage process. The use of this model will further increase the productivity of illuminance measurement at the lighting test beds