Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 202

Rhus verniciflua Stokes against Advanced Cancer: A Perspective from the Korean Integrative Cancer Center

Active anticancer molecules have been searched from natural products; many drugs were developed from either natural products or their derivatives following the conventional pharmaceutical paradigm of drug discovery. However, the advances in the knowledge of cancer biology have led to personalized medicine using molecular-targeted agents which create new paradigm. Clinical benefit is dependent on individual biomarker and overall survival is prolonged through cytostatic rather than cytotoxic effects to cancer cell. Therefore, a different approach is needed from the single lead compound screening model based on cytotoxicity. In our experience, the Rhus verniciflua stoke (RVS) extract traditionally used for cancer treatment is beneficial to some advanced cancer patients though it is herbal extract not single compound, and low cytotoxic in vitro. The standardized RVS extract's action mechanisms as well as clinical outcomes are reviewed here. We hope that these preliminary results would stimulate different investigation in natural products from conventional chemicals

Estimation of Genetic Parameters by Single-Trait and Multi-Trait Models for Carcass Traits in Hanwoo Cattle

Hanwoo breed is preferred in South Korea because of the high standards in marbling and the palatability of its meat. Numerous studies have been conducted and are ongoing to increase the meat production and quality in this beef population. The aim of this study was to estimate and compare genetic parameters for carcass traits using BLUPF90 software. Four models were constructed, single trait pedigree model (STPM), single-trait genomic model (STGM), multi-trait pedigree model (MTPM), and multi-trait genomic model (MTGM), using the pedigree, phenotype, and genomic information of 7991 Hanwoo cattle. Four carcass traits were evaluated: Back fat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Heritability estimates of 0.40 and 0.41 for BFT, 0.33 and 0.34 for CWT, 0.36 and 0.37 for EMA, and 0.35 and 0.38 for MS were obtained for the single-trait pedigree model and the multi-trait pedigree model, respectively, in Hanwoo. Further, the genomic model showed more improved results compared to the pedigree model, with heritability of 0.39 (CWT), 0.39 (EMA), and 0.46 (MS), except for 0.39 (BFT), which may be due to random events. Utilization of genomic information in the form of single nucleotide polymorphisms (SNPs) has allowed more capturing of the variance from the traits improving the variance components

Responses of Korean Buddhism to the Ethos of Contemporary Korea: Three Discourses in the Wake of Modernization

The revival of Buddhism in Korea began in the 20th century as the nation suffered a downfall from the colonization of the Japanese Imperialists. In this chaotic time of social turmoil, transformation into a modern nation resulted not from a natural flow of events but rather from an articulation through a series of discourses on Korean identity. The modernization process in Korea was precipitated by the Japanese colonialism, thereby adding to the complexity during the time of social transformation. In this paper, we have reviewed the three major discourses of Korean Buddhism in the wake of modernization. The following discourses were attempts to deal with the problems faced by the Buddhist community during modernization: the discourse on secularity and social participation, the discourse on modernity centering on the issue of modifying precepts, and the discourse on identity contemplating the originality of Korean Buddhism. The fact that the old controversies concerning precepts continue even to this day in Korea might be regarded as a proof of the vibrant dynamics of contemporary Korean Buddhism. Accordingly, the next unavoidable discourse regarding Korean Buddhism would be on whether and how it can adapt itself to contemporary society, along with what part it will play in the forthcoming society

Kernel excess mass test for multimodality

In this paper we propose a new statistical procedure for testing the multimodality of an underlying distribution. Peter Hall developed an innovative idea of calibrating the null distribution for the excess mass test statistic using the empirical distribution function. We find that the qualitative characteristics of a smooth underlying distribution function on the number of modes is barely preserved in the excess mass functional by the non-smooth empirical distribution function. Instead of the empirical distribution function, we propose to use a kernel distribution function estimator. We derive the limiting distribution of the resulting test statistic under strong unimodality, based on which we apply the calibration idea to the proposed test statistic to obtain a cut-off value. Our numerical study suggests that the calibrated kernel excess mass test has greater power than other existing methods. We also illustrate the use of the proposed method in a case study in astronomy which supports an assumption on a physical property of minor planets in the solar system.OAIID:RECH_ACHV_DSTSH_NO:T201816872RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A079053CITE_RATE:.385DEPT_NM:통계학과EMAIL:[email protected]_YN:YY

Analysis of long period variable stars with nonparametric tests for trend detection

Abstract In astronomy the study of variable stars -i.e., stars characterized by showing significant variation in their brightness over time -has made crucial contributions to our understanding of many fields, from stellar birth and evolution to the calibration of the extragalactic distance scale. In this paper, we develop a method for analyzing multiple, (pseudo)-periodic time series with the goal of detecting temporal trends in their periods. We allow for non-stationary noise and for clustering among the various time series. We apply this method to the long-standing astronomical problem of identifying variables stars whose regular brightness fluctuations have periods that change over time. The results of our analysis show that such changes can be substantial, raising the possibility that astronomers' estimates of galactic distances can be refined. Two significant contributions of our approach, relative to existing methods for this problem, are as follows: 1. The method is nonparametric, making minimal assumptions about both the temporal trends themselves but also the covariance structure of the non-stationary noise. 2. Our proposed test has higher power than existing methods. The test is based on inference for a high-dimensional Normal mean, with control of the False Discovery Rate to account for multiplicity. We present theory and simulations to demonstrate the performance of our method relative. We also analyze data from the American Association of Variable Star Observers and find a monotone relationship between mean period and strength of trend similar to that identified b

Intentional Electromagnetic Interference Source Reconstruction for Automotive Simulation

An intentional electromagnetic interference (IEMI) source has been reconstructed by using the dipole moment. The automotive simulations with the equivalent dipole can accurately characterize the costly IEMI experiments. Based on the measurement, the least square method is applied in source reconstruction. Importing the reconstructed source and the full-scale car model in simulation tool, fields inside the car are obtained. By comparing the simulation result with the experiment data, the automotive simulation model for IEMI is validated

Nonparametric inference for interval data using kernel methods

Symbolic data have become increasingly popular in the era of big data. In this paper, we consider density estimation and regression for interval-valued data, a special type of symbolic data, common in astronomy and official statistics. We propose kernel estimators with adaptive bandwidths to account for variability of each interval. Specifically, we derive cross-validation bandwidth selectors for density estimation and extend the Nadaraya–Watson estimator for regression with interval data. We assess the performance of the proposed methods in comparison with existing kernel methods by extensive simulation studies and real data analysis.</p

NOON-state interference in the frequency domain

Abstract The examination of entanglement across various degrees of freedom has been pivotal in augmenting our understanding of fundamental physics, extending to high dimensional quantum states, and promising the scalability of quantum technologies. In this paper, we demonstrate the photon number path entanglement in the frequency domain by implementing a frequency beam splitter that converts the single-photon frequency to another with 50% probability using Bragg scattering four-wave mixing. The two-photon NOON state in a single-mode fiber is generated in the frequency domain, manifesting the two-photon interference with two-fold enhanced resolution compared to that of single-photon interference, showing the outstanding stability of the interferometer. This successful translation of quantum states in the frequency domain will pave the way toward the discovery of fascinating quantum phenomena and scalable quantum information processing