TT-depth-optimized Quantum Search with Quantum Data-access Machine

Quantum search algorithms offer a remarkable advantage of quadratic reduction in query complexity using quantum superposition principle. However, how an actual architecture may access and handle the database in a quantum superposed state has been largely unexplored so far; the quantum state of data was simply assumed to be prepared and accessed by a black-box operation -- so-called quantum oracle, even though this process, if not appropriately designed, may adversely diminish the quantum query advantage. Here, we introduce an efficient quantum data-access process, dubbed as quantum data-access machine (QDAM), and present a general architecture for quantum search algorithm. We analyze the runtime of our algorithm in view of the fault-tolerant quantum computation (FTQC) consisting of logical qubits within an effective quantum error correction code. Specifically, we introduce a measure involving two computational complexities, i.e. quantum query and $T$-depth complexities, which can be critical to assess performance since the logical non-Clifford gates, such as the $T$ (i.e., $\pi/8$ rotation) gate, are known to be costliest to implement in FTQC. Our analysis shows that for $N$ searching data, a QDAM model exhibiting a logarithmic, i.e., $O(\log{N})$, growth of the $T$-depth complexity can be constructed. Further analysis reveals that our QDAM-embedded quantum search requires $O(\sqrt{N} \times \log{N})$ runtime cost. Our study thus demonstrates that the quantum data search algorithm can truly speed up over classical approaches with the logarithmic $T$-depth QDAM as a key component.Comment: 13 pages, 8 figures / Comment welcom

Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping

Milk thistle, Silybum marianum (L.), is a well-known medicinal plant used for the treatment of liver diseases due to its high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds, which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosomes using only image analysis is difficult, and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using the Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets, and the precise predictions were manually selected and used as new labeled images for retraining the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for retraining the model, allowed the production of sufficient labeled data for retraining. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image-based high-throughput phenotyping in ImageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits

Prediction of Host-Specific Genes by Pan-Genome Analyses of the Korean Ralstonia solanacearum Species Complex

The soil-borne pathogenic Ralstonia solanacearum species complex (RSSC) is a group of plant pathogens that is economically destructive worldwide and has a broad host range, including various solanaceae plants, banana, ginger, sesame, and clove. Previously, Korean RSSC strains isolated from samples of potato bacterial wilt were grouped into four pathotypes based on virulence tests against potato, tomato, eggplant, and pepper. In this study, we sequenced the genomes of 25 Korean RSSC strains selected based on these pathotypes. The newly sequenced genomes were analyzed to determine the phylogenetic relationships between the strains with average nucleotide identity values, and structurally compared via multiple genome alignment using Mauve software. To identify candidate genes responsible for the host specificity of the pathotypes, functional genome comparisons were conducted by analyzing pan-genome orthologous group (POG) and type III secretion system effectors (T3es). POG analyses revealed that a total of 128 genes were shared only in tomato-non-pathogenic strains, 8 genes in tomato-pathogenic strains, 5 genes in eggplant-non-pathogenic strains, 7 genes in eggplant-pathogenic strains, 1 gene in pepper-non-pathogenic strains, and 34 genes in pepper-pathogenic strains. When we analyzed T3es, three host-specific effectors were predicted: RipS3 (SKWP3) and RipH3 (HLK3) were found only in tomato-pathogenic strains, and RipAC (PopC) were found only in eggplant-pathogenic strains. Overall, we identified host-specific genes and effectors that may be responsible for virulence functions in RSSC in silico. The expected characters of those genes suggest that the host range of RSSC is determined by the comprehensive actions of various virulence factors, including effectors, secretion systems, and metabolic enzymes

The Effect of Personal Value on CSV (Creating Shared Value)

The purpose of this study is to reveal the effect of personal value as a part of creating shared value (CSV). We extracted factors of personal value through a literature review. Personal value consists of social commitment, self-actualization, goal setting, and solidarity. Self-actualization is the universal motivation of the individual, goal setting is the basis for the occurrence of action, and solidarity is the relationship factor that defends competition and personalization. This study was conducted on three hypotheses. Hypothesis 1 is that self-actualization will have an effect on CSV. Hypothesis 2 is that goal setting will have an effect on CSV. Hypothesis 3 is that solidarity will have an effect on CSV. The proxy of CSV is social commitment. We examine the effects of these personal values on CSV by surveying 557 university students. This study applied the regression model to test the hypotheses. The empirical results are as follows. CSV increases when we are more self-actualized. Goal setting positively affects CSV. CSV goes up as we have many relationships with organizations and are more cooperative in work. This study suggests the important elements of personal values in a university setting for CSV, and enables setting the direction of the education by setting the index of the attitude to increase the value of the individual in CSV

The effect of personal value on CSV (creating shared value)

The purpose of this study is to reveal the effect of personal value as a part of creating shared value (CSV). We extracted factors of personal value through a literature review. Personal value consists of social commitment, self-actualization, goal setting, and solidarity. Self-actualization is the universal motivation of the individual, goal setting is the basis for the occurrence of action, and solidarity is the relationship factor that defends competition and personalization. This study was conducted on three hypotheses. Hypothesis 1 is that self-actualization will have an effect on CSV. Hypothesis 2 is that goal setting will have an effect on CSV. Hypothesis 3 is that solidarity will have an effect on CSV. The proxy of CSV is social commitment. We examine the effects of these personal values on CSV by surveying 557 university students. This study applied the regression model to test the hypotheses. The empirical results are as follows. CSV increases when we are more self-actualized. Goal setting positively affects CSV. CSV goes up as we have many relationships with organizations and are more cooperative in work. This study suggests the important elements of personal values in a university setting for CSV, and enables setting the direction of the education by setting the index of the attitude to increase the value of the individual in CSV

Comparison of Precipitable Water Vapor Observations by GPS, Radiosonde and NWP Simulation

Precipitable water vapor (PWV) derived from a numerical weather prediction (NWP) model were compared to observations derived from ground-based Global Positioning System (GPS) receivers. The model data compared were from the Weather Research and Forecasting (WRF) model short-range forecasts on nested grids. The numerical experiments were performed by selecting the cloud microphysics schemes and for the comparisons, the Changma period of 2008 was selected. The observational data were derived from GPS measurements at 9-sites in South Korea over a 1-month period, in the middle of June-July 2008. In general, the WRF model demonstrated considerable skill in reproducing the temporal and spatial evolution of the PWV as depicted by the GPS estimations. The correlation between forecasts and GPS estimates of PWV depreciated slowly with increasing forecast times. Comparing simulations with a resolution of 18 km and 6 km showed no obvious PWV dependence on resolution. Besides, GPS and the model PWV data were found to be in quite good agreement with data derived from radiosondes. These results indicated that the GPS-derived PWV data, with high temporal and spatial resolution, are very useful for meteorological applications

Analysis on Characteristics of Radiosonde Bias Using GPS Precipitable Water Vapor

As an observation instrument of the longest record of tropospheric water vapor, radiosonde data provide upper-air pressure (geopotential height), temperature, humidity and wind. However, the data have some well-known elements related to inaccuracy. In this article, radiosonde precipitable water vapor (PWV) at Sokcho observatory was compared with global positioning system (GPS) PWV during each summertime of year 2007 and 2008 and the biases were calculated. As a result, the mean bias showed negative values regardless of the rainfall occurrence. In addition, on the basis of GPS PWV, the maximum root mean square error (RMSE) was 5.67 mm over the radiosonde PWV

Analysis on GPS PWV Effects as an Initial Input Data of NWP Model

The Precipitable Water Vapor (PWV) from GPS with high resolution in terms of time and space might reduce the limitations of the numerical weather prediction (NWP) model for easily variable phenomena, such as precipitation and cloud. We have converted to PWV from Global Positioning System (GPS) data of Korea Astronomy and Space Science Institute (KASI) and Ministry of Maritime Affairs & Fisheries (MOMAF). First of all, we have selected the heavy rainfall case of having a predictability limitation in time and space due to small-scale motion. In order to evaluate the effect for GPS PWV, we have executed the sensitivity experiment with PWV from GPS data over Korean peninsula in the Weather Research & Forecasting 3-Dimensional Variational (WRF-3DVAR). We have also suggested the direction of further research for an improvement of the predictability of NWP model on the basis of this case

Short-Term Comparison of Several Solutinos of Elliptic Relative Motion

Recently introduced, several explicit solutions of relative motion between neighboring elliptic satellite orbits are reviewed. The performance of these solutions is compared with an analytic solution of the general linearized equation of motion. The inversion solution by the Hill-Clohessy-Wiltshire equations is used to produce the initial condition of numerical results. Despite the difference of the reference orbit, the relative motion with the relatively small eccentricity shows the similar results on elliptic case and circular case. In case of the 'chief' satellite with the relatively large eccentricity, HCW equation with the circular reference orbit has relatively larger error than other elliptic equation of motion does

An Analysis of the Effect on the Data Processing of Korea GPS Network by the Absolute Phase Center Variations of GPS Antenna

The International GNSS Service (IGS) has prepared for a transition from the relative phase center variation (PCV) to the absolute PCV, because the terrestrial scale problem of the absolute PCV was resolved by estimating the PCV of the GPS satellites. Thus, the GPS data will be processed by using the absolute PCV which will be an IGS standard model in the near future. It is necessary to compare and analyze the results between the relative PCV and the absolute PCV for the establishment of the reliable processing strategy. This research analyzes the effect caused by the absolute PCV via the GPS network data processing. First, the four IGS stations, Daejeon, Suwon, Beijing and Wuhan, are selected to make longer baselines than 1000 km, and processed by using the relative PCV and the absolute PCV to examine the effect of the antenna raydome. Beijing and Wuhan stations of which the length of baselines are longer than 1000 km show the average difference of 1.33 cm in the vertical component, and 2.97 cm when the antenna raydomes are considered. Second, the 7 permanent GPS stations among the total 9 stations, operated by Korea Astronomy and Space Science Institute, are processed by applying the relative PCV and the absolute PCV, and their results are compared and analyzed. An insignificant effect of the absolute PCV is shown in Korea regional network with the average difference of 0.12 cm in the vertical component