Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

Value Discount of Business Groups Surrounding the Asia Financial Crisis: Evidence from Korean Chaebols

Asian Financial Crisis, Business Group, Chaebol, Diversification, Firm Value

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

We study contextual linear bandit problems under uncertainty on features; they are noisy with missing entries. To address the challenges from the noise, we analyze Bayesian oracles given observed noisy features. Our Bayesian analysis finds that the optimal hypothesis can be far from the underlying realizability function, depending on noise characteristics, which is highly non-intuitive and does not occur for classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. We thus propose an algorithm aiming at the Bayesian oracle from observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page

Temperature dependence of Mott transition in VO_2 and programmable critical temperature sensor

The temperature dependence of the Mott metal-insulator transition (MIT) is studied with a VO_2-based two-terminal device. When a constant voltage is applied to the device, an abrupt current jump is observed with temperature. With increasing applied voltages, the transition temperature of the MIT current jump decreases. We find a monoclinic and electronically correlated metal (MCM) phase between the abrupt current jump and the structural phase transition (SPT). After the transition from insulator to metal, a linear increase in current (or conductivity) is shown with temperature until the current becomes a constant maximum value above T_{SPT}=68^oC. The SPT is confirmed by micro-Raman spectroscopy measurements. Optical microscopy analysis reveals the absence of the local current path in micro scale in the VO_2 device. The current uniformly flows throughout the surface of the VO_2 film when the MIT occurs. This device can be used as a programmable critical temperature sensor.Comment: 4 pages, 3 figure

SOLiDzipper: A High Speed Encoding Method for the Next-Generation Sequencing Data

Background Next-generation sequencing (NGS) methods pose computational challenges of handling large volumes of data. Although cloud computing offers a potential solution to these challenges, transferring a large data set across the internet is the biggest obstacle, which may be overcome by efficient encoding methods. When encoding is used to facilitate data transfer to the cloud, the time factor is equally as important as the encoding efficiency. Moreover, to take advantage of parallel processing in cloud computing, a parallel technique to decode and split compressed data in the cloud is essential. Hence in this review, we present SOLiDzipper, a new encoding method for NGS data. Methods The basic strategy of SOLiDzipper is to divide and encode. NGS data files contain both the sequence and non-sequence information whose encoding efficiencies are different. In SOLiDzipper, encoded data are stored in binary data block that does not contain the characteristic information of a specific sequence platform, which means that data can be decoded according to a desired platform even in cases of Illumina, Solexa or Roche 454 data. Results The main calculation time using Crossbow was 173 minutes when 40 EC2 nodes were involved. In that case, an analysis preparation time of 464 minutes is required to encode data in the latest DNA compression method like G-SQZ and transmit it on a 183 Mbit/s bandwidth. However, it takes 194 minutes to encode and transmit data with SOLiDzipper under the same bandwidth conditions. These results indicate that the entire processing time can be reduced according to the encoding methods used, under the same network bandwidth conditions. Considering the limited network bandwidth, high-speed, high-efficiency encoding methods such as SOLiDzipper can make a significant contribution to higher productivity in labs seeking to take advantage of the cloud as an alternative to local computing. Availability http://szipper.dinfree.com . Academic/non-profit: Binary available for direct download at no cost. For-profit: Submit request for for-profit license from the web-site