TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models

We propose a robust and reliable evaluation metric for generative models by introducing topological and statistical treatments for rigorous support estimation. Existing metrics, such as Inception Score (IS), Frechet Inception Distance (FID), and the variants of Precision and Recall (P&R), heavily rely on supports that are estimated from sample features. However, the reliability of their estimation has not been seriously discussed (and overlooked) even though the quality of the evaluation entirely depends on it. In this paper, we propose Topological Precision and Recall (TopP&R, pronounced 'topper'), which provides a systematic approach to estimating supports, retaining only topologically and statistically important features with a certain level of confidence. This not only makes TopP&R strong for noisy features, but also provides statistical consistency. Our theoretical and experimental results show that TopP&R is robust to outliers and non-independent and identically distributed (Non-IID) perturbations, while accurately capturing the true trend of change in samples. To the best of our knowledge, this is the first evaluation metric focused on the robust estimation of the support and provides its statistical consistency under noise.Comment: Accepted to NeurIPS 202

Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires

Best-Response Dynamics, Playing Sequences, and Convergence to Equilibrium in Random Games

We analyze the performance of the best-response dynamic across all normal-form games using a random games approach. The playing sequence -- the order in which players update their actions -- is essentially irrelevant in determining whether the dynamic converges to a Nash equilibrium in certain classes of games (e.g. in potential games) but, when evaluated across all possible games, convergence to equilibrium depends on the playing sequence in an extreme way. Our main asymptotic result shows that the best-response dynamic converges to a pure Nash equilibrium in a vanishingly small fraction of all (large) games when players take turns according to a fixed cyclic order. By contrast, when the playing sequence is random, the dynamic converges to a pure Nash equilibrium if one exists in almost all (large) games.Comment: JEL codes: C62, C72, C73, D83 Keywords: Best-response dynamics, equilibrium convergence, random games, learning models in game

A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency

Although remote sensing of active fires is well-researched, their early detection has received less attention. Additionally, simple threshold approaches based on contextual statistical analysis suffer from generalization problems. Therefore, this study proposes a deep learning-based forest fire detection algorithm, with a focus on reducing detection latency, utilizing 10-min interval high temporal resolution Himawari-8 Advanced Himawari Imager. Random forest (RF) and convolutional neural network (CNN) were utilized for model development. The CNN model accurately reflected the contextual approach adopted in previous studies by learning information between adjacent matrices from an image. This study also investigates the contribution of temporal and spatial information to the two machine learning techniques by combining input features. Temporal and spatial factors contributed to the reduction in detection latency and false alarms, respectively, and forest fires could be most effectively detected using both types of information. The overall accuracy, precision, recall, and F1-score were 0.97, 0.89, 0.41, and 0.54, respectively, in the best scheme among the RF-based schemes and 0.98, 0.91, 0.63, and 0.74, respectively, in that among the CNN-based schemes. This indicated better performance of the CNN model for forest fire detection that is attributed to its spatial pattern training and data augmentation. The CNN model detected all test forest fires within an average of 12 min, and one case was detected 9 min earlier than the recording time. Moreover, the proposed model outperformed the recent operational satellite-based active fire detection algorithms. Further spatial generality test results showed that the CNN model had reliable generality and was robust under varied environmental conditions. Overall, our results demonstrated the benefits of geostationary satellite-based remote sensing for forest fire monitoring

Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea's current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined

Interspecific Variation in Seasonal Migration and Brumation Behavior in Two Closely Related Species of Treefrogs

Most amphibians migrate between flooded habitats for breeding and dry habitats for non-breeding activities, however, differences in closely related species may highlight divergent evolutionary histories. Through field surveys, Harmonic Direction Finder tracking and laboratory behavioral experiments during the wintering season, we demonstrated differences in seasonal migration and hibernation habitats between Dryophytes suweonensis and D. japonicus. We found that D. japonicus migrated toward forests for overwintering and then back to rice paddies for breeding in spring. By contrast, D. suweonensis was found to hibernate buried in the vicinity of rice paddies, its breeding habitat. We also found that the difference in migrating behavior matched with variation in microhabitat use during brumation and hibernation between the two species. Our findings highlight different ecological requirements between the two species, which may result from their segregated evolutionary histories, with speciation potentially linked to species use of a new breeding habitat. Additionally, the use of rice paddies for both breeding and hibernation may contribute to the endangered status of D. suweonensis because of the degradation of hibernation sites in winter

Mutations in DDX58, which Encodes RIG-I, Cause Atypical Singleton-Merten Syndrome

Singleton-Merten syndrome (SMS) is an autosomal-dominant multi-system disorder characterized by dental dysplasia, aortic calcification, skeletal abnormalities, glaucoma, psoriasis, and other conditions. Despite an apparent autosomal-dominant pattern of inheritance, the genetic background of SMS and information about its phenotypic heterogeneity remain unknown. Recently, we found a family affected by glaucoma, aortic calcification, and skeletal abnormalities. Unlike subjects with classic SMS, affected individuals showed normal dentition, suggesting atypical SMS. To identify genetic causes of the disease, we performed exome sequencing in this family and identified a variant (c.1118A>C [p.Glu373Ala]) of DDX58, whose protein product is also known as RIG-I. Further analysis of DDX58 in 100 individuals with congenital glaucoma identified another variant (c.803G>T [p.Cys268Phe]) in a family who harbored neither dental anomalies nor aortic calcification but who suffered from glaucoma and skeletal abnormalities. Cys268 and Glu373 residues of DDX58 belong to ATP-binding motifs I and II, respectively, and these residues are predicted to be located closer to the ADP and RNA molecules than other nonpathogenic missense variants by protein structure analysis. Functional assays revealed that DDX58 alterations confer constitutive activation and thus lead to increased interferon (IFN) activity and IFN-stimulated gene expression. In addition, when we transduced primary human trabecular meshwork cells with c.803G>T (p.Cys268Phe) and c.1118A>C (p.Glu373Ala) mutants, cytopathic effects and a significant decrease in cell number were observed. Taken together, our results demonstrate that DDX58 mutations cause atypical SMS manifesting with variable expression of glaucoma, aortic calcification, and skeletal abnormalities without dental anomalies

Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis

New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis

Examining the Moderating Effect of Mindfulness on the Relationship between Soldiers’ Perceived Stress and Hopelessness

Mindfulness is a type of meditation in which one consciously pays attention to being fully present in the moment. Research has shown that mindfulness can lower anxiety, stress, and hopelessness. This fact may also apply to people in special circumstances, such as those in the military. Therefore, we examined the relationship between perceived stress, mindfulness, and hopelessness among military soldiers. Specifically, we verified the moderating effect of mindfulness on the relationship between perceived stress and mindfulness. We surveyed 309 Korean military soldiers and a total of 257 data were analyzed through descriptive statistical analysis, correlation analysis, and regression analysis. Our results showed that perceived stress, mindfulness, and hopelessness are interrelated, and that mindfulness moderated the influence of perceived stress on hopelessness. In other words, the lower the level of mindfulness, the greater the hopelessness when the perceived stress increased. This study suggests that conducting mindfulness training for soldiers can benefit soldiers’ adaptation to military life

Machine learning apporaches to wildfire detection using geostationary satellite data

More than 60% of the areas in South Korea consists of forests with rugged terrains. Thus, when wildfires occur, they can rapidly spread out. In addition, most wildfires are caused by anthropogenic factors, which are unpredictable. Early detection of wildfires can reduce the related damage. This study proposed a novel ensemble approach for detecting wildfires using Himawari-8 satellite data and machine learning in South Korea. Himawari-8 Advanced Himawari Imager (AHI) is the geostationary satellite sensor operated by the Japan Meteorological Agency. AHI collects data at 16 bands from visible to infrared at 500 m – 2 km resolution covering from East Asia to Australia every 10 minutes. In-situ wildfire data provided by the Korea Forest Service were used as reference data. The proposed approach combines a threshold-based algorithm and random forest machine learning. The threshold-based algorithm first detects potential wildfire areas and then random forest is adopted to remove false alarms from the identified potential wildfire areas. The 3.85 band widely used in wildfire detection was used in the first step in the threshold-based algorithm. After the first thresholding step, three conditions based on the characteristics of fire and non-fire pixels were used as a second thresholding step. The results from the threshold-based algorithm are fed into random forest machine learning. At first, all band reflectance, brightness temperature, ratio and difference values were used as input variables to random forest. Based on the relative variable importance identified by random forest, a total of 26 variables were finally determined as input variables in the random forest model for wildfire detection. Since time series data with a 10 min interval were used, not only detecting wildfires, but also monitoring them was conducted. In particular, how early the proposed approach identified wildfires was examined. Results showed that the proposed algorithm detected wildfires much better than the existing ones, especially for small-scale wildfires. Among total 65 reference cases, 50 cases are detected within 22 minutes except one case. In addition, false alarm rates found in the existing algorithms (e.g., MODIS hot spots), were greatly reduced when using the proposed approach