Evaluating Feature Attribution Methods for Electrocardiogram

The performance of cardiac arrhythmia detection with electrocardiograms(ECGs) has been considerably improved since the introduction of deep learning models. In practice, the high performance alone is not sufficient and a proper explanation is also required. Recently, researchers have started adopting feature attribution methods to address this requirement, but it has been unclear which of the methods are appropriate for ECG. In this work, we identify and customize three evaluation metrics for feature attribution methods based on the characteristics of ECG: localization score, pointing game, and degradation score. Using the three evaluation metrics, we evaluate and analyze eleven widely-used feature attribution methods. We find that some of the feature attribution methods are much more adequate for explaining ECG, where Grad-CAM outperforms the second-best method by a large margin.Comment: 5 pages, 3 figures. Code is available at https://github.com/SNU-DRL/Attribution-EC

Have M&A delistings negatively impacted U.S. capital markets? Evidence from the effect on industry peer firms

We provide evidence of negative information spillovers associated with delistings from mergers and acquisitions (M&A delistings), a key factor in the long-term decline in the number of publicly listed firms in the U.S. Specifically, we show that M&A delistings are associated with a decrease in the quality of analysts’ information environment (increased absolute forecast errors and dispersion) for targets’ industry peer firms; these results are stronger when targets are larger, and for public targets than for private targets. Additional tests, including a falsification test using non-completed M&As, suggest that our results are robust to endogeneity concerns arising from industry-level shocks

Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing

In the past few years, contrastive learning has played a central role for the success of visual unsupervised representation learning. Around the same time, high-performance non-contrastive learning methods have been developed as well. While most of the works utilize only two views, we carefully review the existing multi-view methods and propose a general multi-view strategy that can improve learning speed and performance of any contrastive or non-contrastive method. We first analyze CMC's full-graph paradigm and empirically show that the learning speed of $K$-views can be increased by $_{K}\mathrm{C}_{2}$ times for small learning rate and early training. Then, we upgrade CMC's full-graph by mixing views created by a crop-only augmentation, adopting small-size views as in SwAV multi-crop, and modifying the negative sampling. The resulting multi-view strategy is called ECPP (Efficient Combinatorial Positive Pairing). We investigate the effectiveness of ECPP by applying it to SimCLR and assessing the linear evaluation performance for CIFAR-10 and ImageNet-100. For each benchmark, we achieve a state-of-the-art performance. In case of ImageNet-100, ECPP boosted SimCLR outperforms supervised learning

The Mothers and Children’s Environmental Health (MOCEH) study

The MOCEH study is a prospective hospital- and community-based cohort study designed to collect information related to environmental exposures (chemical, biological, nutritional, physical, and psychosocial) during pregnancy and childhood and to examine how exposure to environmental pollutants affects growth, development, and disease. The MOCEH network includes one coordinating center, four local centers responsible for recruiting pregnant women, and four evaluation centers (a nutrition center, bio-repository center, neurocognitive development center, and environment assessment center). At the local centers, trained nurses interview the participants to gather information regarding their demographic and socioeconomic characteristics, complications related to the current gestation period, health behaviors and environmental factors. These centers also collect samples of blood, placenta, urine, and breast milk. Environmental hygienists measure each participant’s level of exposure to indoor and outdoor pollutants during the pre- and postnatal periods. The participants are followed up through delivery and until the child is 5 years of age. The MOCEH study plans to recruit 1,500 pregnant women between 2006 and 2010 and to perform follow-up studies on their children. We expect this study to provide evidence to support the hypothesis that the gestational environment has an effect on the development of diseases during adulthood. We also expect the study results to enable evaluation of latency and age-specific susceptibility to exposure to hazardous environmental pollutants, evaluation of growth retardation focused on environmental and genetic risk factors, selection of target environmental diseases in children, development of an environmental health index, and establishment of a national policy for improving the health of pregnant women and their children

UMineAR: Mobile-Tablet-Based Abandoned Mine Hazard Site Investigation Support System Using Augmented Reality

Conventional mine site investigation has difficulties in fostering location awareness and understanding the subsurface environment; moreover, it produces a large amount of hardcopy data. To overcome these limitations, the UMineAR mobile tablet application was developed. It enables users to rapidly identify underground mine objects (drifts, entrances, boreholes, hazards) and intuitively visualize them in 3D using a mobile augmented reality (AR) technique. To design UMineAR, South Korean georeferenced standard-mine geographic information system (GIS) databases were employed. A web database system was designed to access via a tablet groundwater-level data measured every hour by sensors installed in boreholes. UMineAR consists of search, AR, map, and database modules. The search module provides data retrieval and visualization options/functions. The AR module provides 3D interactive visualization of mine GIS data and camera imagery on the tablet screen. The map module shows the locations of corresponding borehole data on a 2D map. The database module provides mine GIS database management functions. A case study showed that the proposed application is suitable for onsite visualization of high-volume mine GIS data based on geolocations; no specialized equipment or skills are required to understand the underground mine environment. UMineAR can be used to support abandoned-mine hazard site investigations

Regionalism and the Asia-Pacific Economy: Perspectives and Challenges

The current trends of regional initiatives for economic cooperation in the Asia-Pacific seem favorable, but there remain some problems, such as trade imbalances. that make us uncertain about whether such cooperation will lead to the harmonization of national interests of the diverse economies in the region. It is necessary, therefore, for the Asia-Pacific countries to think seriously about harmonization and coordination of their policies and pinpoint guidelines and initiatives to achieve a better policy to imporve upon the current situation. Of particular important is the common interst these countries share in keeping international trade as open as possible, within the framework of the GATT principle

Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest–Ordinary Kriging Model

The accurate prediction of soil contamination in abandoned mining areas is necessary to address their environmental risks. This study employed a combined model of machine learning and geostatistics to predict the spatial distribution of soil contamination using heavy metal data collected in an abandoned metal mine. An exploratory data analysis was used to identify patterns in the collected data, the root mean squared error (RMSE) and coefficient of determination (R2) were used to verify the predicted values, and the model was validated using K-fold cross-validation. The prediction results were produced as a map by applying hyperparameter tuning to Random Forest (RF) and Ordinary Kriging (OK) through GridSearchCV using optimal parameter selections. Furthermore, the prediction residuals of the RF model were calculated, and the RF prediction map and OK interpolation results of the RF prediction residuals were summarized to construct an RF–OK prediction map. The RMSE and R2 values for the RF, OK, and RF–OK interpolation models were 66.214, 65.101, and 52.884 mg/kg and 0.867, 0.871, and 0.915, respectively. In addition, the optimization results with the minimum RMSE and maximum R2 were obtained through hyperparameter tuning. The proposed RF–OK hybrid model demonstrated superior prediction performance compared to the individual models

A Review of Smartphone Applications for Solar Photovoltaic Use: Current Status, Limitations, and Future Perspectives

Smartphones and tablets can be effectively used in the solar photovoltaic (PV) energy field for different purposes because of their versatile capabilities incorporating hardware and software functionalities. These multifarious capabilities enable new approaches for measuring and visualizing data that are seldom available in conventional computing platforms. In this study, 100 accessible smartphone applications (apps) developed in the solar PV energy sector were investigated. The apps were categorized based on their main function as follows: computation of sun position, PV system optimal settings, PV site investigation, potential assessment of PV systems, environmental and economic assessment of PV systems, monitoring and control of PV systems, and education and learning for PV system. Each of these categories was further divided based on principal features or functions. Exemplary apps were chosen for each category and their characteristics and usefulness were investigated. Moreover, the apps for roof or rooftops and those that require built-in or external sensors were organized and analyzed according to their topic and functionality. Limitations regarding app implementation in solar PV and implications for future improvement as an alternative solar design tools were discussed. This study has significance in that it has first presented the current applicability and future perspectives of solar PV smartphone apps. Furthermore, they can be effectively used by the energy prosumers as an analysis tool for energy design due to evolving smartphone sensor technologies current opportunity factors

Methods for Converting Monthly Total Irradiance Data into Hourly Data to Estimate Electric Power Production from Photovoltaic Systems: A Comparative Study

Hourly irradiance values are essential data to reasonably estimate the electric power production (EPP) from a photovoltaic (PV) system. Worldwide monthly irradiance data are available from meteorological observation satellites; however, adequate hourly data are not widely available in developing countries or rural areas where PV systems are needed most. Aiming to supply such data, this study compared three different methods (i.e., sunshine hours mean, the SOLPOS algorithm, and the Duffie and Beckman algorithm) to convert the monthly accumulated irradiance data into hourly irradiance data. The monthly accumulated irradiance data at 11 sites in the United States and Korea, acquired from the National Renewable Energy Laboratory, were converted into hourly irradiance data by employing the three methods. The converted hourly data were entered into the System Advisor Model to estimate the monthly total EPP values (henceforth, EPPs) from the PV systems. Each estimated EPP value was compared with those analyzed from the measured hourly data (regarded as the reference values in this study). After considering the errors between the EPPs estimated from the converted hourly irradiance data and measured using the hourly irradiance data, the simulation results with identical PV capacities indicated that the SOLPOS algorithm was the most appropriate conversion method