VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting

Zero-Shot Object Counting (ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars. To deal with ZSOC, preceding studies proposed a two-stage pipeline: discovering exemplars and counting. However, there remains a challenge of vulnerability to error propagation of the sequentially designed two-stage process. In this work, an one-stage baseline, Visual-Language Baseline (VLBase), exploring the implicit association of the semantic-patch embeddings of CLIP is proposed. Subsequently, the extension of VLBase to Visual-language Counter (VLCounter) is achieved by incorporating three modules devised to tailor VLBase for object counting. First, Semantic-conditioned Prompt Tuning (SPT) is introduced within the image encoder to acquire target-highlighted representations. Second, Learnable Affine Transformation (LAT) is employed to translate the semantic-patch similarity map to be appropriate for the counting task. Lastly, the layer-wisely encoded features are transferred to the decoder through Segment-aware Skip Connection (SaSC) to keep the generalization capability for unseen classes. Through extensive experiments on FSC147, CARPK, and PUCPR+, the benefits of the end-to-end framework, VLCounter, are demonstrated.Comment: Accepted to AAAI 2024. Code is available at https://github.com/Seunggu0305/VLCounte

Impact of Wide-Base Tires on Pavements: A National Study

This paper summarizes a multi-year effort comparing the new-generation wide-base tires (NG-WBT) and dual-tire assembly from a holistic point of view. The tires were compared considering not only pavement damage but also environmental impact. Numerical modeling, prediction methods, experimental measurements, and life-cycle assessment were combined to provide recommendations about the use of NG-WBT. A finite element (FE) approach considering variables that are usually omitted in the conventional analysis of flexible pavement was used for modeling pavement structures combining layer thickness, material properties, tire load, tire-inflation pressure, and pavement type (interstate and low volume). A prediction tool, ICT-Wide, was developed based on an artificial neural network to obtain critical pavement responses in cases excluded from the FE analysis matrix. Based on the bottom-up fatigue cracking, permanent deformation, and international roughness index, the life-cycle energy consumption, cost, and green-house gas emissions were estimated. To make this research useful for state departments of transportation and practitioners, a modification to AASHTOware is proposed to account for NG-WBT. The revision is based on two adjustment factors, one accounting for the discrepancy between the AASHTOware approach and the FE model of this study, and the other addressing the impact of NG-WBT. Although greater pavement damage may result from NG-WBT, for the analyzed cases, the extra pavement damage may be outweighed by the environmental benefits when NG-WBT market penetration is considered

Pavement Rehabilitation Strategy Course Development

Pavement rehabilitation and preservation treatments have become standard practice for state and local transportation agencies. The ultimate goals include maintaining a safe and reliable level of service for all users, maximizing pavement service life, and optimizing budget allocations for infrastructure construction projects. The essential key to meet these goals requires transportation agencies to identify the right treatment for the right pavement at the right time. Based on the manuals of Bureau of Design and Environment (BDE) within the Illinois Department of Transportation (IDOT), training course materials were developed in this project. The goals of the training course are to enhance the understanding of fundamental concepts of pavement rehabilitation and preservation strategies, establish consistent practices following the guidance manuals and minimize potential errors by selecting appropriate treatments. The training course is designed to be completed in one-and-a-half days, covering seven blocks: (1) Introduction, (2) Preservation and Rehabilitation Definitions, (3) Distresses, (4) Condition Rating Survey, (5) Testing, (6) Treatments, and (7) Selection Guidelines. The final completed deliverables of this project include PowerPoint slides for in-class instruction, a stand-alone online platform, and review and final examination questions for the initial three blocks.IDOT-R27-170Ope

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing–structure–property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure–property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni–Co–Mn cathode materials illustrates M3I3’s approach to creating libraries of multiscale structure–property–processing relationships. We end with a future outlook toward recent developments in the field of M3I3

Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Development of mechanistic rolling resistance model for pavement life cycle assessment

As of 2017, the transportation sector accounted for the largest share (29 percent) of the total greenhouse gas (GHG) emissions in the U.S. Nearly 82 percent of the transportation GHG emissions results from the operation of on-road vehicles. An average American drives 26.9 miles per day and nearly 65 percent of freight by weight is transported by trucks, a demand that makes building and maintaining an adequate roadway infrastructure crucial for the national economy. The construction of roadway infrastructure consumes large amount of resources and energy; therefore, mitigating emissions has been a keen interest of the transportation industry to achieve sustainable transportation. Life cycle assessment (LCA) quantifies the environmental impacts of a product. It has been shown that a major component of pavement LCA results from rolling resistance (RR) between vehicle-pavement interaction. This study focuses primarily on the development of mechanistic RR model to capture the impact of excess fuel consumption (EFC) and dynamic wheel load (DWL) caused by pavement roughness. The study begins with the development of regional life cycle impact assessment (LCIA) databases and models for Illinois. The LCIA databases were regionalized based on inventory data obtained from questionnaire surveys and simulations; the databases were supplemented with default models in commercial databases and modified to add spatial and temporal proximity. Due to the nature of large infrastructure construction projects, great amounts of materials and equipment need to be hauled during a pavement’s service life. Based on the multi-parameter hauling model developed in the study, considering the effect of the hauling truck driving cycle may increase the contribution of hauling by up to 4% in the life cycle global warming potential (GWP). A mechanistic multi-degree-of-freedom tractor-trailer model captures not only complex truck dynamic motion excited by roughness, but also the excess fuel consumption (EFC) caused by road roughness and speed. In general, the truck EFC increases with IRI and speed prior to 65 mph. At speeds over 65 mph, the EFC begins to decrease because of a sharp increase in aerodynamic drag force. Finally, vehicle traveling on rough pavement produces dynamic wheel loads (DWLs) that result in additional pavement damage that may lead to a premature pavement failure. This study adopted a tractor-trailer model developed at the Illinois Center for Transportation (ICT) and three-dimensional (3-D) pavement finite element (FE) models coupled with 3-D tire contact stresses to examine the effect of DWLs on pavement performance. The Mechanistic Empirical Pavement Design Guide (MEPDG)’s approach was employed to predict pavement performance that identifies pavement maintenance schedule. Based on the maintenance schedule, an LCA case study was carried out for thin and thick asphalt concrete (AC) pavement sections. The result of the case study indicates that the impact of DWLs may produce probabilistic LCA results for both pavement sections. In addition, high extreme DWLs tend to cause shorter pavement service lives that trigger more frequent rehabilitation treatments, making the maintenance stage contribution high. However, more frequent rehabilitation treatments tend to keep the overall pavement roughness levels low, significantly reducing the use stage impact caused by roughness-induced RR. This study contributes to a more accurate estimation of pavement environmental impacts caused by roughness-induced DWLs through the development of regional LCIA databases, hauling, mechanistic tractor-trailer, and pavement performance prediction models. The results of the study may be used in the transportation industry’s decision-making process to mitigate transportation-related fuel consumption and GHG emissions.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

Development of mechanistic rolling resistance model for pavement life cycle assessment

As of 2017, the transportation sector accounted for the largest share (29 percent) of the total greenhouse gas (GHG) emissions in the U.S. Nearly 82 percent of the transportation GHG emissions results from the operation of on-road vehicles. An average American drives 26.9 miles per day and nearly 65 percent of freight by weight is transported by trucks, a demand that makes building and maintaining an adequate roadway infrastructure crucial for the national economy. The construction of roadway infrastructure consumes large amount of resources and energy; therefore, mitigating emissions has been a keen interest of the transportation industry to achieve sustainable transportation. Life cycle assessment (LCA) quantifies the environmental impacts of a product. It has been shown that a major component of pavement LCA results from rolling resistance (RR) between vehicle-pavement interaction. This study focuses primarily on the development of mechanistic RR model to capture the impact of excess fuel consumption (EFC) and dynamic wheel load (DWL) caused by pavement roughness. The study begins with the development of regional life cycle impact assessment (LCIA) databases and models for Illinois. The LCIA databases were regionalized based on inventory data obtained from questionnaire surveys and simulations; the databases were supplemented with default models in commercial databases and modified to add spatial and temporal proximity. Due to the nature of large infrastructure construction projects, great amounts of materials and equipment need to be hauled during a pavement’s service life. Based on the multi-parameter hauling model developed in the study, considering the effect of the hauling truck driving cycle may increase the contribution of hauling by up to 4% in the life cycle global warming potential (GWP). A mechanistic multi-degree-of-freedom tractor-trailer model captures not only complex truck dynamic motion excited by roughness, but also the excess fuel consumption (EFC) caused by road roughness and speed. In general, the truck EFC increases with IRI and speed prior to 65 mph. At speeds over 65 mph, the EFC begins to decrease because of a sharp increase in aerodynamic drag force. Finally, vehicle traveling on rough pavement produces dynamic wheel loads (DWLs) that result in additional pavement damage that may lead to a premature pavement failure. This study adopted a tractor-trailer model developed at the Illinois Center for Transportation (ICT) and three-dimensional (3-D) pavement finite element (FE) models coupled with 3-D tire contact stresses to examine the effect of DWLs on pavement performance. The Mechanistic Empirical Pavement Design Guide (MEPDG)’s approach was employed to predict pavement performance that identifies pavement maintenance schedule. Based on the maintenance schedule, an LCA case study was carried out for thin and thick asphalt concrete (AC) pavement sections. The result of the case study indicates that the impact of DWLs may produce probabilistic LCA results for both pavement sections. In addition, high extreme DWLs tend to cause shorter pavement service lives that trigger more frequent rehabilitation treatments, making the maintenance stage contribution high. However, more frequent rehabilitation treatments tend to keep the overall pavement roughness levels low, significantly reducing the use stage impact caused by roughness-induced RR. This study contributes to a more accurate estimation of pavement environmental impacts caused by roughness-induced DWLs through the development of regional LCIA databases, hauling, mechanistic tractor-trailer, and pavement performance prediction models. The results of the study may be used in the transportation industry’s decision-making process to mitigate transportation-related fuel consumption and GHG emissions.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste