SPI-GAN: Distilling Score-based Generative Models with Straight-Path Interpolations

Score-based generative models (SGMs) are a recently proposed paradigm for deep generative tasks and now show the state-of-the-art sampling performance. It is known that the original SGM design solves the two problems of the generative trilemma: i) sampling quality, and ii) sampling diversity. However, the last problem of the trilemma was not solved, i.e., their training/sampling complexity is notoriously high. To this end, distilling SGMs into simpler models, e.g., generative adversarial networks (GANs), is gathering much attention currently. We present an enhanced distillation method, called straight-path interpolation GAN (SPI-GAN), which can be compared to the state-of-the-art shortcut-based distillation method, called denoising diffusion GAN (DD-GAN). However, our method corresponds to an extreme method that does not use any intermediate shortcut information of the reverse SDE path, in which case DD-GAN fails to obtain good results. Nevertheless, our straight-path interpolation method greatly stabilizes the overall training process. As a result, SPI-GAN is one of the best models in terms of the sampling quality/diversity/time for CIFAR-10, CelebA-HQ-256, and LSUN-Church-256

Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.Comment: Accepted at IEEE BigData 202

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page

Effects of a Herbal Medicine, Yukgunja-Tang, on Functional Dyspepsia Patients Classified by 3-Dimensional Facial Measurement: A Study Protocol for Placebo-Controlled, Double-Blind, Randomized Trial

Introduction. Functional dyspepsia (FD), a common upper gastrointestinal disease, is difficult to manage because of the limitations of current conventional treatments. Yukgunja-tang (YGJT) is widely used to treat FD in clinical practice in Korea, Japan, and China. However, YGJT significantly improves few symptoms of FD. In Korean medicine, FD is a well-known functional gastric disease that shows difference in the effect of herbal medicine depending on constitution or type of Korean medicine diagnosis. This study aims to investigate the efficacy of YGJT on FD patients classified by 3-dimensional facial measurement using a 3-dimensional facial shape diagnostic system (3-FSDS). Methods. A placebo-controlled, double-blind, randomized, two-center trial will be performed to evaluate the efficacy of YGJT on FD patients. Eligible subjects will be initially classified as two types by 3-dimensional facial measurement using the 3-FSDS. Ninety-six subjects (48 subjects per each type) will be enrolled. These subjects will be randomly allocated into treatment or control groups in a 2 : 1 ratio. YGJT or placebo will be administered to each group during the 8-week treatment period. The primary outcome is total dyspepsia symptom scale, and the secondary outcomes include single dyspepsia symptom scale, proportion of responders with adequate symptom relief, visual analog scale, Nepean dyspepsia index-Korean version, functional dyspepsia-related quality of life, and spleen qi deficiency questionnaire. Discussion. This is the first randomized controlled trial to assess the efficacy of the YGJT on FD patients classified by 3-dimensional facial measurement. We will compare the treatment effect of the YGJT on FD patients classified as two types using the 3-FSDS. The results of this trial will help the FD patients improve the symptoms and quality of life effectively and provide objective evidence for prescribing the YGJT to FD patients in clinical practice. Trial Registration. This trial is registered with Clinical Research Information Service Identifier: KCT0001920, 15 May, 2016

Low-temperature formation of epitaxial graphene on 6H-SiC induced by continuous electron beam irradiation

It is observed that epitaxial graphene forms on the surface of a 6H-SiC substrate by irradiating electron beam directly on the sample surface in high vacuum at relatively low temperature (similar to 670 degrees C). The symmetric shape and full width at half maximum of 2D peak in the Raman spectra indicate that the formed epitaxial graphene is turbostratic. The gradual change of the Raman spectra with electron beam irradiation time increasing suggests that randomly distributed small grains of epitaxial graphene form first and grow laterally to cover the entire irradiated area. The sheet resistance of epitaxial graphene film is measured to be similar to 6.7 k Omega/sq.open4

Learning from Noisy Labels for MIMO Detection With One-Bit ADCs

This paper presents a data detection method for multiple-input multiple-output systems with one-bit analog-to-digital converters. The basic idea is to learn the likelihood function of the system from training samples. To this end, a training data generation strategy is first proposed, which labels a one-bit received signal with a symbol index determined by channel-based data detection. This strategy requires no extra training overhead beyond pilot symbols for channel estimation, but leads to noisy labels due to data detection errors. For accurate learning from the noisy labels, an expectation-maximization algorithm is also developed. This algorithm learns both the likelihood function and the transition probability from each noisy label to a true label. Numerical results demonstrate that the presented method performs similar to the optimal maximum likelihood detection. IEEE11Nsciescopu

Localized Energy-Aware Broadcast Protocol for Wireless Networks with Directional Antennas

Abstract. We consider broadcast protocols in wireless networks that have limited energy and computation resources. The well-known algorithm, DBIP (Directional Broadcast Incremental Power), which exploits "Incremental Power" philosophy for wireless networks with directional antenna to construct broadcasting tree, provides very good results in terms of energy savings. Unfortunately, its computation is centralized, as the source node needs to know the entire topology of the network. Mobility of nodes or frequent changes in the node activity status (from "active" to "passive" and vice-versa) may cause global changes in topology which must be propagated throughout the network for any centralized solution. This may results in extreme and un-acceptable communication overhead. In this paper, we propose and evaluate a localized energyefficient broadcast protocol, Localized Directional Broadcast Incremental Power Protocol (LDBIP), which employs distributed location information and computation to construct broadcast trees. In the proposed method, a source node sets up spanning tree with its local neighborhood position information and includes certain hops relay information in packet. Directional antennas are used for transmitting broadcast packet, and the transmission power is adjusted for each transmission to the minimal necessary for reaching the particular neighbor. Relay nodes will consider relay instructions received to compute their own local neighborhood spanning tree and then rebroadcasts. Experimental results verify that this new protocol shows similar performance with DBIP in static wireless networks, and better performance in mobile scenarios.

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Prostate cancer is the fourth most common cause of cancer in men in Korea, and there has been a rapid increase in cases. In the present study, we constructed a risk prediction model for prostate cancer using representative data from Korea. Participants who completed health examinations in 2009, based on the Korean National Health Insurance database, were eligible for the present study. The crude and adjusted risks were explored with backward selection using the Cox proportional hazards model to identify possible risk variables. Risk scores were assigned based on the adjusted hazard ratios, and the standardized points for each risk factor were proportional to the β-coefficient. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability was assessed by plotting the mean predicted probability against the mean observed probability of prostate cancer. Among the candidate predictors, age, smoking intensity, body mass index, regular exercise, presence of type 2 diabetes mellitus, and hypertension were included. Our risk prediction model showed good discrimination (c-statistic: 0.826, 95% confidence interval: 0.821–0.832). The relationship between model predictions and actual prostate cancer development showed good correlation in the calibration plot. Our prediction model for individualized prostate cancer risk in Korean men showed good performance. Using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding prostate cancer screening