Defining heatwave thresholds using an inductive machine learning approach

Establishing appropriate heatwave thresholds is important in reducing adverse human health consequences as it enables a more effective heatwave warning system and response plan. This paper defined such thresholds by focusing on the non-linear relationship between heatwave outcomes and meteorological variables as part of an inductive approach. Daily data on emergency department visitors who were diagnosed with heat illnesses and information on 19 meteorological variables were obtained for the years 2011 to 2016 from relevant government agencies. A Multivariate Adaptive Regression Splines (MARS) analysis was performed to explore points (referred to as "knots") where the behaviour of the variables rapidly changed. For all emergency department visitors, two thresholds (a maximum daily temperature >= 32.58 degrees C for 2 consecutive days and a heat index >= 79.64) were selected based on the dramatic rise of morbidity at these points. Nonetheless, visitors, who included children and outside workers diagnosed in the early summer season, were reported as being sensitive to heatwaves at lower thresholds. The average daytime temperature (from noon to 6 PM) was determined to represent an alternative threshold for heatwaves. The findings have implications for exploring complex heatwave-morbidity relationships and for developing appropriate intervention strategies to prevent and mitigate the health impact of heatwave

TLDR: Text Based Last-layer Retraining for Debiasing Image Classifiers

A classifier may depend on incidental features stemming from a strong correlation between the feature and the classification target in the training dataset. Recently, Last Layer Retraining (LLR) with group-balanced datasets is known to be efficient in mitigating the spurious correlation of classifiers. However, the acquisition of group-balanced datasets is costly, which hinders the applicability of the LLR method. In this work, we propose to perform LLR based on text datasets built with large language models for a general image classifier. We demonstrate that text can be a proxy for its corresponding image beyond the image-text joint embedding space, such as CLIP. Based on this, we use generated texts to train the final layer in the embedding space of the arbitrary image classifier. In addition, we propose a method of filtering the generated words to get rid of noisy, imprecise words, which reduces the effort of inspecting each word. We dub these procedures as TLDR (\textbf{T}ext-based \textbf{L}ast layer retraining for \textbf{D}ebiasing image classifie\textbf{R}s) and show our method achieves the performance that is comparable to those of the LLR methods that also utilize group-balanced image dataset for retraining. Furthermore, TLDR outperforms other baselines that involve training the last linear layer without a group annotated dataset.Comment: 19 pages, Under Revie

Classical-to-quantum convolutional neural network transfer learning

Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained a higher classification accuracy than their classical counterparts under the same training conditions in the few-parameter regime. However, the general performance of large-scale quantum models is difficult to examine because of the limited size of quantum circuits, which can be reliably implemented in the near future. We propose transfer learning as an effective strategy for utilizing small QCNNs in the noisy intermediate-scale quantum era to the full extent. In the classical-to-quantum transfer learning framework, a QCNN can solve complex classification problems without requiring a large-scale quantum circuit by utilizing a pre-trained classical convolutional neural network (CNN). We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data. The results show that transfer learning from classical to quantum CNN performs considerably better than purely classical transfer learning models under similar training conditions.Comment: 16 pages, 7 figure

Exploring Environmental Inequity in South Korea: An Analysis of the Distribution of Toxic Release Inventory (TRI) Facilities and Toxic Releases

Recently, location data regarding the Toxic Release Inventory (TRI) in South Korea was released to the public. This study investigated the spatial patterns of TRIs and releases of toxic substances in all 230 local governments in South Korea to determine whether spatial clusters relevant to the siting of noxious facilities occur. In addition, we employed spatial regression modeling to determine whether the number of TRI facilities and the volume of toxic releases in a given community were correlated with the community's socioeconomic, racial, political, and land use characteristics. We found that the TRI facilities and their toxic releases were disproportionately distributed with clustered spatial patterning. Spatial regression modeling indicated that jurisdictions with smaller percentages of minorities, stronger political activity, less industrial land use, and more commercial land use had smaller numbers of toxic releases, as well as smaller numbers of TRI facilities. However, the economic status of the community did not affect the siting of hazardous facilities. These results indicate that the siting of TRI facilities in Korea is more affected by sociopolitical factors than by economic status. Racial issues are thus crucial for consideration in environmental justice as the population of Korea becomes more racially and ethnically diverse

Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting

Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions--both direct and indirect. To confront these complexities, we introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. Specifically, we leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. Moreover, we suggest employing hierarchical signal decompositions running over the graphs to capture multiple spatial dependencies. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks. The results consistently showcase the superiority of our model, achieving an average 23\% reduction in mean squared error (MSE) compared to existing models.Comment: Temporal Graph Learning Workshop @ NeurIPS 2023, New Orleans, United State

A study on the consumer's perception of front-of-pack nutrition labeling

The goal of this research is to investigate the present situation for front of pack labeling in Korea and the perception of consumers for the new system of labeling, front of pack labeling, based on the consumer survey. We investigated the number of processed foods with front of pack labeling in one retailer in Youngin-si. And we also surveyed 1,019 participants nationwide whose ages were from 20 to 49; the knowledge of nutrition labeling, the knowledge of 'front of pack labeling', and the opinion about the labeling system. The data were analyzed using SAS statistics program. The results were as follows: 13.4% of processed foods had front of pack labeling, and 16.8% of the consumers always checked the nutrition labeling, while 32.7% of the consumers seldom checked it. In addition, 44.3% of the consumers think that 'front of pack labeling' is necessary, and 58.3% of the consumers think it is important to show the percentage of daily value as a way of 'front of pack labeling'. However, 32% of the consumer think the possibility of 'front of pack labeling' is slim. Meanwhile, 58.3% of the consumers think that it is important to have the color difference according to contents. The number of favorite nutrients in the front of pack was four or five. It seems that the recognition of current nutrition labeling has the influence on the willingness of using the future 'front of pack labeling'. Along with our study, the policy for 'front of pack labeling' has to be updated and improved constantly since 'front of pack labeling' helps consumer understand nutrition facts

Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

INVESTIGATION ON EFFECTS OF ENLARGED PIPE RUPTURE SIZE AND AIR PENETRATION TIMING IN REALSCALE EXPERIMENT OF SIPHON BREAKER

To ensure the safety of research reactors, the water level must be maintained above the required height. When a pipe ruptures, the siphon phenomenon causes continuous loss of coolant until the hydraulic head is removed. To protect the reactor core from this kind of accident, a siphon breaker has been suggested as a passive safety device. This study mainly focused on two variables: the size of the pipe rupture and the timing of air entrainment. In this study, the size of the pipe rupture was increased to the guillotine break case. There was a region in which a larger pipe rupture did not need a larger siphon breaker, and the water flow rate was related to the size of the pipe rupture and affected the residual water quantity. The timing of air entrainment was predicted to influence residual water level. However, the residual water level was not affected by the timing of air entrainment. The experimental cases, which showed the characteristic of partical sweep-out mode in the separation of siphon breaking phenomenon [2], showed almost same trend of physical properties.ungraded1111Ysciescopu

Measuring Public Life Through Digital Technologies: Investigating the Use of WiFi Sensing for Enhancing Public Space

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Vibrant public spaces are an indicator of the high quality of urban life. The success or failure of urban public spaces is dependent on how people use and interact with others in those spaces. Public life researchers have primarily measured the activities and behaviors of users through direct observation methods, reporting on findings that they have witnessed for themselves. Such simple techniques still have their place and are valuable but present their own set of multi-tasking challenges. For example, the researchers may be considering several themesby nature, this approach is both labor-intensive and time-consuming. However, one digital technology, WiFi sensing, has recently been getting attention as a means for enhancing the research process by overcoming these data collection limitations. It passively detects the presence and location of a person who has a WiFi-enabled device 100 meters away around a WiFi sensor. This thesis investigates the use of WiFi sensing as an alternative observation method for measuring different aspects of public life and public space. Many urban researchers have adopted sensing technology to analyze people???s mobility in various metropolitan areas. However, their findings are also relevant to quantifying people???s number and flow rate on the move. Currently, it seems unlikely that decision-makers will leverage this data to enhance the public space. This gap seems to arise from installing sensors and obtaining basic information without systematically considering how to apply the technology and the benefits to public life. These limitations align with the critical view of smart citiessensing technology cannot transform the urban paradigm by offering new insights without moving beyond the restrictions of traditional approaches. The feasibility of WiFi sensing is explored in a series of analyses regarding (1) the spatial and temporal properties representing people???s movements and behavior, (2) the possibilities of measuring alternative key metrics of public life, and (3) the practical applications for urban planning and design. Before presenting the findings, I proposed a conceptual framework that leverages the WiFi sensing and the public life survey framework, highlighting how the two fields are associated. I conducted several data-gathering experiments for the analyses, including WiFi and ground truth data using GPS. I then assessed the properties of WiFi data focusing on the spatial accuracy to positioning people???s location. Next, I tested stay point detection from WiFi traces and examined the accuracy of the WiFi stay points with GPS stay points as ground truth. The thesis discusses the findings concerning the possibilities of WiFi sensing for public life studies in the context of a series of guided questions. First, based on our WiFi sensor network with an average sensor spacing of 50 meters, the WiFi data provides an approximate location of people within 20-30 meters accuracy at an interval of 30-second periods. WiFi sensing can also detect stationary activities, a critical metric in public life studiesHowever, the accuracy determining whether a person stays or moves from WiFi data did not achieve a high level of accuracy, with an F1 score of 0.384. The findings show that it is possible to locate people???s positions, at least at a street level, and then determine their behaviors, moving and staying patterns, with moderate accuracy. Several examples concerning the rhythm of public life and shopping-travel behavior were presented as practical case studies, helpful in urban planning and design. In this thesis, I concluded that WiFi sensing provides an approximate location of people and their staying points with moderate accuracy. However, it effectively collects and analyzes long-term data at the neighborhood scale, supplementing the weaknesses mentioned above in manual observation. The analysis method proposed in this study improves the choice and range of research methods available for urban studies, expressly incorporating new sensing data and quantitative techniques into the standard toolkit of procedures used in public life studies. Specifically, the network of WiFi sensing with multiple sensors in a neighborhood can extend our understanding of public life beyond the named project area. This monitoring system also provides insights into how design elements, amenities, and programmed events enhance the vibrancy and vitality of public spaces by offering longitudinal evidence. This work contributes to transdisciplinary research on people???s mobility, which will in turn help to improve our overall understanding of cities, how they function, and how they are used in reality.clos