Multiple User-Class Dynamic Stochastic Assignment for a Route Guidance Strategy

Traffic information systems have become a major issue in many countries as a modern technology for alleviating traffic congestion in urban areas. Pre-trip or en-route real-time travel information regarding traffic conditions can enhance driversâ knowledge of the situation in road networks and may assist in driversâ decisions such as the choice of departure time, route, and destination. In facts, several papers have shown that traffic information yields benefits to drivers such as travel-time reduction and the avoidance of traffic accidents, among others. In considering the potential benefits of alternative driver information systems, it is also necessary to evaluate the potential of adverse impacts that improved information may have. Ben-Akiva et al. (1991) explained this phenomenon in terms of three elements: oversaturation; overreaction; and concentration. Among them, overreaction and concentration are the principal causes of adverse effects. Overreaction occurs when driversâ reactions to traffic information cause congestion to transfer from one road to another. It may also generate fluctuations in road usage. Overreaction may occur if drivers respond too sensitively to information on current traffic conditions. Concentration may occur when drivers choose a specific route in a very short period. In order to implement the strategies of an Intelligent Transportation System (ITS), it is necessary to predict the temporal evolution of the traffic pattern on a congested transportation network, where travel demands and travel costs vary over time and space. For urban areas, dynamic models are mainly considered as they describe how commuters adjust their travel decisions concerning routes and departure times. Moreover, to model the impact of information provision by an ITS, it is necessary to develop a multi-class model given there are different classes of users in a transportation network, who respond in differing ways to traffic information. In this chapter, a multiple-user-class dynamic stochastic assignment (MDSA) model is introduced to reflect drivers who have varying perceptual errors and varying dynamic traffic behaviors. MDSA is an extended version of a static single-user-class assignment. The driver's route-choice mechanism is based on his/her past experience of the road traffic conditions during prior days of travel. Some information-provision strategies that are involved in a route-guidance system are also introduced for the effective use of the systems

RPLKG: Robust Prompt Learning with Knowledge Graph

Large-scale pre-trained models have been known that they are transferable, and they generalize well on the unseen dataset. Recently, multimodal pre-trained models such as CLIP show significant performance improvement in diverse experiments. However, when the labeled dataset is limited, the generalization of a new dataset or domain is still challenging. To improve the generalization performance on few-shot learning, there have been diverse efforts, such as prompt learning and adapter. However, the current few-shot adaptation methods are not interpretable, and they require a high computation cost for adaptation. In this study, we propose a new method, robust prompt learning with knowledge graph (RPLKG). Based on the knowledge graph, we automatically design diverse interpretable and meaningful prompt sets. Our model obtains cached embeddings of prompt sets after one forwarding from a large pre-trained model. After that, model optimizes the prompt selection processes with GumbelSoftmax. In this way, our model is trained using relatively little memory and learning time. Also, RPLKG selects the optimal interpretable prompt automatically, depending on the dataset. In summary, RPLKG is i) interpretable, ii) requires small computation resources, and iii) easy to incorporate prior human knowledge. To validate the RPLKG, we provide comprehensive experimental results on few-shot learning, domain generalization and new class generalization setting. RPLKG shows a significant performance improvement compared to zero-shot learning and competitive performance against several prompt learning methods using much lower resources

BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}Comment: Accepted to CVPR 202

Optoelectronic properties of poly(fluorene) co‐polymer light‐emitting devices on a plastic substrate *

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92131/1/1.2150379.pd

Evaluation of Mineral Carbonation of Asbestos-Tex and Analysis of Airborne Asbestos Concentrations

Asbestos is a human carcinogen that causes diseases, such as lung cancer and malignant mesothelioma. In Korea, approximately 1.23 × 109 kg of asbestos raw materials was imported for about 30 years. More than 80% of this were used as building material, such as interior materials and ceiling materials. Among the manufactured asbestos-containing materials, the closest product to the human body is asbestos-tex, used as ceiling material. In this study, asbestos contained in asbestos-tex was transformed into a shape that is physically safe for the human body through mineral carbonation and the results were verified through the analysis of airborne asbestos concentrations. We found that asbestos-tex powder in a buffer solution at 100 °C and at partial CO2 pressures of greater than 10 MPa transformed its constituent chrysotile asbestos moiety ((Mg3Si2O5(OH)4) into magnesite (MgCO3). Consequently, the needle-shaped asbestos fibers (diameters ≤ 3 µm) were converted to an angular rod-shaped mineral (diameters > 5 µm) that is safe for humans

Use of Smart Card Data to Define Public Transit Use in Seoul, South Korea

In South Korea, use of the smart card to pay public transit fares has grown since its introduction in 1996. The proportion of smart card use in Seoul, South Korea, is more than 90% for buses and 75% for Metro. In 2004, the Seoul metropolitan government introduced a new smart card system that has a distance-based fare system, which requires the input of detailed user data, such as boarding time and Global Positioning System–based vehicle location. To investigate the reliability of smart card data, the number of users of every Metro station in Seoul, gathered from smart card data, was directly compared with data obtained from the Seoul Metro Company. By using two simple manipulations to include daily variations and the number of cash users, smart card data appear to be statistically similar to the surveyed data obtained from the Seoul Metro Company. By analyzing the line-specific proportions of smart card use rather than the average smart card use, the accuracy of the results is improved. From the results, it can be seen that smart card data show potential as a basis for describing the characteristics of public transit users, such as the number of transfers, boarding time, hourly trip distribution of the number of trips for different transit modes, and travel time distribution for all transit modes and user types