Miracle-dz/UniFAR-FTUs huggingface dataset:
Single marker analysis to associate SSR marker to yield component traits in interspecific hybridization between Helianthus annuus L. and Helianthus argophyllus Torr. & A. Gray

In the present investigation sunflower marker-trait association were studied for eight yield component traits using sixteen polymorphic SSR markers with a set of 123 BC1F1 population of the cross CMS 38B × HEL 153/83. Association of mean performance of BC1F2 families of corresponding BC1F1 individuals and with corresponding marker score were assessed for single marker analysis by using student’s t test in SPSS version 16. A total of fourteen markers gave significant association with at least one of the eight traits studied. Most of the markers were found to be related to more than one trait. The markers ORS 1146 was found to be associated with three traits viz., days to 50% flowering, head diameter and oil content. This indicates that the same gene is controlling the expression of these characters. Some of the markers were associated to only one trait. Moreover, phenotypically these characters have more association with each other. Hence these markers may be useful for marker assisted breeding programme on further validation in different genetic backgrounds.
A New Exponentially Fitted Numerical Integration Scheme for Solving Singularly Perturbed Two Point Boundary Value Problems

: This article is concerned with an exponentially fitted numerical integration method based on uniform mesh for solving singularly perturbed two point boundary value problems. Exact and approximate rule of integration with finite difference approximation of first derivatives are used to derive a three term scheme. Theory of singular perturbation is used to introduce a fitting factor in the derived scheme. Thomas algorithm is employed to solve the resulting tridiagonal system of equations. Convergence of the proposed method is also analyzed. Solutions of several linear and nonlinear example problems are presented in terms of maximum absolute errors (MAE) to show the applicability of the proposed scheme. It is easily observed that the proposed method is able to approximate the solution very well.
3D Face Reconstruction System Based on Deep Learning and Sparse Face Model

3D face reconstruction technology is very popular in the digital image processing area. The method of 3D face reconstruction based on single image faces many challenges such as: (i) depth information is lacked due to the input of 2D image; (ii) the statistical face model is not accurate; and (iii) many current difficulties can be solved by using new technique like deep learning. In order to get an accurate and efficient 3D face reconstruction result, a new 3D face reconstruction algorithm based on deep learning and sparse 3D face model is proposed in this paper. Deep learning is exploited to find out the statistical property of 3D human face. And sparse 3D face model is applied to improve algorithm efficiency. Experiments under different conditions are performed to prove the accuracy and robustness of our proposed algorithm.
Long-run Effects of a Disaster: Measuring the Economic Impacts of the Kobe Earthquake

In 1995, the Great Hanshin-Awaji Earthquake, a.k. a. Kobe Earthquake, occurred in the second largest economic region of Japan, and its economic damages were accounted around 10 trillion yen (Okuyama et al., 1999). A catastrophic event in this magnitude would have surely created some long-run impacts on the regional economy as well as on the surrounding regions. In addition, the recovery and reconstruction activities would have affected the economic structure of the region and interdependencies between regions. While these long-run economic impacts are considered to be sizable, few studies have conducted to empirically measure or estimate such impacts, due to the significant noises in economic data disturbed by macroeconomic fluctuations and to the lack of detailed economic data on the damages and losses caused by the earthquake. At the same time, several cross-country and/or cross-hazard studies, for example Skidmore and Toya (2002) and Cuaresma et al. (2008), have been carried out to analyze whether or not long-run economic implications of disasters actually materialize, while their results are somewhat mixed. This paper presents an empirical investigation of long-run economic impacts of the Kobe earthquake, using econometric models.
O N S AMPLING E RROR IN B ATCH A CTION -V ALUE P REDICTION A LGORITHMS

Estimating a policy’s action-values is a fundamental aspect of reinforcement learning. In this work, we study the application of TD methods for learning action-values in an offline setting with a fixed batch of data. Motivated by recent work (Pavse et al., 2020), we observe that a fixed batch of offline data may contain two forms of distribution shift: the data may be collected from a different behavior policy than the target policy (off-policy data) and the empirical distribution of the data may differ from the sampling distribution of the data (sampling error). In this work, we focus on the second problem by analyzing the sampling error that arises due to variance in sampling from a finite-sized batch of data in the on-policy offline RL setting. We study how action-value learning algorithms suffer from this sampling error by considering their so-called certainty-equivalence estimates (Sutton, 1988; Pavse et al., 2020). We prove that each algorithm uses its certainty-equivalence estimates of the policy and transition dynamics to converge to its respective fixedpoint. We then empirically evaluate each algorithm’s performance by measuring the mean-squared value error on Gridworld. Ultimately, we find that by reducing sampling error, an algorithm can produce significantly accurate action-value estimations.
Modified Method to Remove Haze with Enhancement of Light in an Impervious Manner

In cartography imaging technology, the images capturing while foggy weathers are complicated for object detection to a certain extent. GPS and navigation systems are moreover comes under same group for facing such problems. We presented an approach to analyze and remove haze in a frame and enhance light for every pixel. Larger size and numerous data images with colour are accepted. In real time execution, the contrast enhancement is achievable through refine atmospheric light, dark channel prior and transmission map. The main aim for our method to improve accuracy and to less the execution time to complete contrast enhancement for identifying objects while navigation.
Increasing Time Efficiency of Change over Process on Solid Product using SMED (Single Minute Exchange of Dies) Method in Pharmaceutical Industry

Over time change over is a major problem that is often encountered in manufacturing industries. Efforts that can be made to overcome the length of change-over time, an industry will increase batch size to meet consumer demand. Fast change over time is the key to reducing large batch production and improving distribution channels. This research uses the Single Minute Exchange Of Dies (SMED) method which is a systematic method to reduce change over time so that it can reduce batch size to be more flexible to market demand and reduce the increase in storage of medicinal products that are very closely related to expiration time (expired time ) which can harm consumers and the pharmaceutical industry, especially in this study. The process of monitoring changeover time in the wet granulation room is done by making a logbook that lists the time of start and end of change over which must be filled by the operator. Based on the monitoring, the change-over time in the wet granulation room in each drug preparation is different, with SMED the maximum changeover time is 120 minutes, where only product B has a faster time of 88 minutes.
Targeting hhh -Transducin Repeat–Containing Protein E3 Ubiquitin Ligase Augments the Effects of Antitumor Drugs on Breast Cancer Cells

B -Transducin repeat–containing proteins ( B -TrCP) serve as substrate recognition component of E3 ubiquitin ligases that control stability of important regulators of cell cycle and signal transduction. B -TrCP function is essential for the induction of nuclear factor K B transcriptional activities, which play a key role in proliferation and survival of cancer cells and are often constitutively up-regulated in human breast cancers. Here we show that inhibition of B -TrCP either by RNAi approach or by forced expression of a dominant-negative B -TrCP mutant suppresses growth and survival of human breast cancer cells. In addition, inhibition of B -TrCP augments the antiproliferative effects of anticancer drugs such as doxorubicin, tamoxifen, and paclitaxel on human mammary tumor cells. These data provide the proof of principle that targeting B -TrCP might be beneficial for anticancer therapies. (Cancer Res 2005; 65(5): 1904-8)
Evaluating the impact of lowering blood alcohol concentration limits for drivers on

Background Drink-driving is an important risk factor for road traffic accidents (RTAs) which cause high levels of morbidity and mortality globally. Lowering the permitted blood alcohol concentration (BAC) for drivers is a common public health intervention enacted in countries and jurisdictions across the world. In Scotland, on 5th December 2014, the BAC limit for drivers was reduced from 0·08 g/dL to 0·05 g/dL. This study evaluated the impact of this change on RTA and alcohol consumption outcomes. Methods We employed an observational, comparative interrupted time series design using data from the whole of the intervention (Scotland) and control (England & Wales) groups for the period January 2013 to December 2016. Weekly counts of RTAs were obtained from police accident records and weekly off-trade and four-weekly on-trade alcohol consumption was estimated from market research data. We used data from automated traffic counters as denominators to calculate RTA rates. We estimated the effect of the intervention on RTA and alcohol consumption outcomes using negative binomial panel regression and seasonal autoregressive integrated moving average models, respectively. Findings The change in drink-drive legislation was associated with a 1% increase in total RTA rates in Scotland (rate ratio (RR) 1·01; 95% CI 0·94, 1·08; p=0·77) after adjustment for seasonality and underlying temporal trend. When expressed relative to England & Wales, where the intervention did not happen, the association was estimated as a 7% increase in total RTA rates (RR 1·07; 95% CI 0·98, 1·17; p=0·10). Similar findings were observed for serious/fatal RTAs and single vehicle night-time RTAs. For alcohol consumption, in Scotland, the change in legislation was associated with a 0·3% decrease (relative change) for consumption measured by per capita off-trade sales (-0·3%;-1·7%, 1·1%; p=0·71) and a 0·7% decrease in per capita on-trade sales (-0·7%;-0·8%, -0·5%; p<0.001). Interpretation Lowering the BAC limit to 0·05 g/dL from 0·08 g/dL in Scotland was not associated with a reduction in RTAs, but was associated with a small reduction in per capita on-trade alcohol sales. One plausible explanation is that the legislative change was not suitably enforced, for example with random breath testing measures. Our findings suggest that changing the legal BAC limit in isolation does not improve RTA outcomes. These findings have significant policy implications internationally as several countries and jurisdictions consider a similar reduction in BAC limit. Funding This project was funded by the NIHR Public Health Research Programme (project number PHR 14/186/58). Registration ISRCTN registry (ISRCTN38602189), date applied 02/05/17, date assigned 27/06/17 https://doi.org/10.1186/ISRCTN38602189
CAUSAL RELATIONSHIP BETWEEN BITCOIN PRICE VOLATILITY AND TRADING VOLUME: ROLLING WINDOW APPROACH

This study investigates the causal relationship between price volatility and trading volume for bitcoin which is the first cryptocurrency. Data are daily and cover the period starting from December 27, 2013 to March 3, 2019. Price volatility series was produced by using EGARCH model. The Toda-Yamamoto causality test was applied under rolling window approach. According to the Granger causality test, there is a strong causal relationship running from the trading volume to the price volatility. There also exists a causality running from price volatility to volume. But this causality is not statistically strong. At the same time, a positive and significant contemporaneous correlation was found between the two variables. Both findings support the sequential information arrival hypothesis for the bitcoin market.
Calibration Transfer for Predicting Lake-Water pH from near Infrared Spectra of Lake Sediments

With near infrared spectra of lake sediment samples, it is possible to predict lake-water pH using a partial least squares (PLS) calibration model. The calibration models have large model and prediction residuals and it is important to understand the residuals. All the sediment samples were measured on five different instruments. This allows the use of each of the five instruments as master with the other four as field instruments. When using more than one near infrared instrument, prediction becomes unreliable unless calibration transfer is used. A number of techniques including Savitzky–Golay Transform (SGT), finite impulse response (FIR), piecewise direct standardisation (PDS), orthogonal scatter correction (OSC) and wavelet transform (WT) were compared. The quality of the predictions was expressed as root mean square error of prediction (RMSEP), but the calibration transfer methods were also compared on practical usefulness. In these data, the OSC filtering worked best and gave adequate calibration transfer results.
Near- versus Mid-Infrared Diffuse Reflectance Spectroscopy for the Quantitative Determination of the Composition of Forages and By-Products

The objective of this work was to explore the relative value of near- and mid-infrared diffused reflectance spectroscopy in determining the composition of forages and by-products. Sixty-seven samples consisting of 15 alfalfa, 16 tall fescue and 15 orchard grass hays, 10 corn stovers and 11 wheat straws at various stages of maturity were examined by diffuse reflectance using a scanning monochromator (1100–2500 nm), a Fourier near infrared spectrometer (10,000–4000 cm−1, 4 and 16 cm−1 resolution, neat and 5% sample in KBr) and a Fourier mid-infrared spectrometer (4000–400 cm−1, 4 and 16 cm−1 resolution, neat and 5% sample in KBr). Samples were analysed chemically and spectroscopically for fibres, in vitro digestibility, crude protein, nitrobenzene oxidation products and various measures of lignin content. The results showed that diffuse mid-infrared reflectance spectroscopy can perform as well as, and sometimes better than, diffuse near infrared spectroscopy in determining the composition of forages and by-products. In addition, Fourier near infrared spectroscopy did not perform as well as either near infrared using a scanning monochromator or the Fourier mid-infrared spectrometer. Finally, diluting samples with KBr was not beneficial for either Fourier based determinations. Additional work with more diverse data sets and various Fourier instrument configurations will be needed to further define the limits and usefulness of Fourier transform near- and mid-infrared spectroscopy in the determination of forage and by-product composition.
Role of pollution and weather indicators in the COVID-19 outbreak: A brief study on Delhi, India

The present study examines the impact of environment pollution indicators and weather indicators on the COVID-19 outbreak in the capital city of India. In this study, we hypothesize that certain weather conditions with an atmosphere having high content of air pollutants, might impact the transmission of COVID-19, in addition to the direct human to human diffusion. The Kendall and Spearman rank correlation tests were chosen as an empirical methodology to conduct the statistical analysis. In this regard, we compiled a daily dataset of COVID-19 cases (Confirmed, Recovered, Deceased), Weather indicators (Temperature and relative humidity) and pollution indicators (PM 2.5, PM 10, NO2, CO, and SO2) in Delhi state of India. The effects of each parameter within three time frames of same day, 7 days ago, and 14 days ago are evaluated. This study reveal a significant correlation between the transmission of COVID-19 outbreaks and the atmospheric pollutants with a combination of specific climatic conditions. The findings of this research will help the policymakers to identify risky geographic areas and enforce timely preventive measures.
Silicone-based Capacitive E-skin for Exteroception and Proprioception

Thin and imperceptible soft skins that can detect internal deformations as well as external forces, can go a long way to address perception and control challenges in soft robots. However, decoupling proprioceptive and exteroceptive stimuli is a challenging task. In this paper, we present a silicone-based, capacitive E-skin for exteroception and proprioception (SCEEP). This soft and stretchable sensor can perceive stretch as along with touch at 100 different points via its 100 tactels. In this paper, we present a novel algorithm that decouples global strain from local indentations due to external forces. The soft skin is 10.1cm in length and 10cm in width and can be used to accurately measure the global strain of up to 25% with an error of under 3%; while at the same time, can determine the amplitude and position of local indentations. This is a step towards a fully soft electronic skin that can act as a proprioceptive sensor to measure internal states while measuring external forces.
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding. However, the effective use of information from different sources requires an accurate calibration between the sensors involved, which usually implies a tedious and burdensome process. We present a method to calibrate the extrinsic parameters of any pair of sensors involving LiDARs, monocular or stereo cameras, of the same or different modalities. The procedure is composed of two stages: first, reference points belonging to a custom calibration target are extracted from the data provided by the sensors to be calibrated, and second, the optimal rigid transformation is found through the registration of both point sets. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups. In order to assess the performance of the proposed method, a novel evaluation suite built on top of a popular simulation framework is introduced. Experiments on the synthetic environment show that our calibration algorithm significantly outperforms existing methods, whereas real data tests corroborate the results obtained in the evaluation suite. Open-source code is available at https://github.com/beltransen/velo2cam_calibration.
First-Order Problem Solving through Neural MCTS based Reinforcement Learning

The formal semantics of an interpreted first-order logic (FOL) statement can be given in Tarskian Semantics or a basically equivalent Game Semantics. The latter maps the statement and the interpretation into a two-player semantic game. Many combinatorial problems can be described using interpreted FOL statements and can be mapped into a semantic game. Therefore, learning to play a semantic game perfectly leads to the solution of a specific instance of a combinatorial problem. We adapt the AlphaZero algorithm so that it becomes better at learning to play semantic games that have different characteristics than Go and Chess. We propose a general framework, Persephone, to map the FOL description of a combinatorial problem to a semantic game so that it can be solved through a neural MCTS based reinforcement learning algorithm. Our goal for Persephone is to make it tabula-rasa, mapping a problem stated in interpreted FOL to a solution without human intervention.
Moving Sum Data Segmentation for Stochastic Processes Based on Invariance

The segmentation of data into stationary stretches also known as multiple change point problem is important for many applications in time series analysis as well as signal processing. Based on strong invariance principles, we analyse data segmentation methodology using moving sum (MOSUM) statistics for a class of regime-switching multivariate processes where each switch results in a change in the drift. In particular, this framework includes the data segmentation of multivariate partial sum, integrated diffusion and renewal processes even if the distance between change points is sublinear. We study the asymptotic behaviour of the corresponding change point estimators, show consistency and derive the corresponding localisation rates which are minimax optimal in a variety of situations including an unbounded number of changes in Wiener processes with drift. Furthermore, we derive the limit distribution of the change point estimators for local changes - a result that can in principle be used to derive confidence intervals for the change points.
Downflowing umbral flashes as evidence of standing waves in sunspot umbrae

Context. Umbral flashes are sudden brightenings commonly visible in the core of some chromospheric lines. Theoretical and numerical modeling suggests that they are produced by the propagation of shock waves. According to these models and early observations, umbral flashes are associated with upflows. However, recent studies have reported umbral flashes in downflowing atmospheres. Aims. We aim to understand the origin of downflowing umbral flashes. We explore how the existence of standing waves in the umbral chromosphere impacts the generation of flashed profiles. Methods. We performed numerical simulations of wave propagation in a sunspot umbra with the code MANCHA. The Stokes profiles of the Ca II 8542 Å line were synthesized with the NICOLE code. Results. For freely propagating waves, the chromospheric temperature enhancements of the oscillations are in phase with velocity upflows. In this case, the intensity core of the Ca II 8542 Å atmosphere is heated during the upflowing stage of the oscillation. However, a different scenario with a resonant cavity produced by the sharp temperature gradient of the transition region leads to chromospheric standing oscillations. In this situation, temperature fluctuations are shifted backward and temperature enhancements partially coincide with the downflowing stage of the oscillation. In umbral flash events produced by standing oscillations, the reversal of the emission feature is produced when the oscillation is downflowing. The chromospheric temperature keeps increasing while the atmosphere is changing from a downflow to an upflow. During the appearance of flashed Ca II 8542 Å cores, the atmosphere is upflowing most of the time, and only 38% of the flashed profiles are associated with downflows. Conclusions. We find a scenario that remarkably explains the recent empirical findings of downflowing umbral flashes as a natural consequence of the presence of standing oscillations above sunspot umbrae.
M\v{e}j P\v{r}ehled / Be informed

The amount of information available to the general public is enormous, and it is challenging to extract meaningful and reliable content. The availability of news sources and their trustability are the biggest problems for selecting the proper authority. Moreover, news providers’ inability to publish good news on social media creates a gap for young people to access trustworthy information. This article describes an automated system that can extract the essential news and publish them in a different form on social media networks.