The estimation of crop emergence in potatoes by UAV RGB imagery

Abstract Background Crop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. Results In this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient ($$r^{2}$$ r2 ) of 0.96 and provide an efficient tool to evaluate emergence uniformity. Conclusions The proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency

Solid-surface vitrification is an appropriate and convenient method for cryopreservation of isolated rat follicles

<p>Abstract</p> <p>Background</p> <p>Cryopreservation of isolated follicles may be a potential option to restore fertility in young women with cancer, because it can prevent the risks of cancer transmission. Several freezing protocols are available, including slow-rate freezing, open-pulled straws vitrification (OPS) and solid-surface vitrification (SSV, a new freezing technique). The purpose of our study was to investigate the effects of these freezing procedures on viability, ultrastructure and developmental capacity of isolated rat follicles.</p> <p>Methods</p> <p>Isolated follicles from female Sprague-Dawley rats were randomly assigned to SSV, OPS and slow-rate freezing groups for cryopreservation. Follicle viability assessment and ultrastructural examination were performed after thawing. In order to study the developmental capacity of thawed follicles, we performed <it>in vitro </it>culture with a three-dimensional (3D) system by alginate hydrogels.</p> <p>Results</p> <p>Our results showed that the totally viable rate of follicles vitrified by SSV (64.76%) was slightly higher than that of the OPS group (62.38%) and significantly higher than that of the slow-rate freezing group (52.65%; <it>P </it>< 0.05). The ultrastructural examination revealed that morphological alterations were relatively low in the SSV group compared to the OPS and slow-rate freezing groups. After <it>in vitro </it>culture within a 3D system using alginate hydrogels, we found the highest increase (28.90 ± 2.21 μm) in follicle diameter in follicles from the SSV group. The estradiol level in the SSV group was significantly higher than those in the OPS and slow-rate freezing groups at the end of a 72-hr culture period (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>Our results suggest that the SSV method is an appropriate and convenient method for cryopreservation of isolated rat follicles compared with the conventional slow-rate freezing method and the OPS method.</p

A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

D&D: Learning Human Dynamics from Dynamic Camera

3D human pose estimation from a monocular video has recently seen significant improvements. However, most state-of-the-art methods are kinematics-based, which are prone to physically implausible motions with pronounced artifacts. Current dynamics-based methods can predict physically plausible motion but are restricted to simple scenarios with static camera view. In this work, we present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the laws of physics to reconstruct 3D human motion from the in-the-wild videos with a moving camera. D&D introduces inertial force control (IFC) to explain the 3D human motion in the non-inertial local frame by considering the inertial forces of the dynamic camera. To learn the ground contact with limited annotations, we develop probabilistic contact torque (PCT), which is computed by differentiable sampling from contact probabilities and used to generate motions. The contact state can be weakly supervised by encouraging the model to generate correct motions. Furthermore, we propose an attentive PD controller that adjusts target pose states using temporal information to obtain smooth and accurate pose control. Our approach is entirely neural-based and runs without offline optimization or simulation in physics engines. Experiments on large-scale 3D human motion benchmarks demonstrate the effectiveness of D&D, where we exhibit superior performance against both state-of-the-art kinematics-based and dynamics-based methods. Code is available at https://github.com/Jeffsjtu/DnDComment: ECCV 2022 (Oral

Microstructure-Empowered Stock Factor Extraction and Utilization

High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level

TransTouch: Learning Transparent Objects Depth Sensing Through Sparse Touches

Transparent objects are common in daily life. However, depth sensing for transparent objects remains a challenging problem. While learning-based methods can leverage shape priors to improve the sensing quality, the labor-intensive data collection in the real world and the sim-to-real domain gap restrict these methods' scalability. In this paper, we propose a method to finetune a stereo network with sparse depth labels automatically collected using a probing system with tactile feedback. We present a novel utility function to evaluate the benefit of touches. By approximating and optimizing the utility function, we can optimize the probing locations given a fixed touching budget to better improve the network's performance on real objects. We further combine tactile depth supervision with a confidence-based regularization to prevent over-fitting during finetuning. To evaluate the effectiveness of our method, we construct a real-world dataset including both diffuse and transparent objects. Experimental results on this dataset show that our method can significantly improve real-world depth sensing accuracy, especially for transparent objects.Comment: Accepted to the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Risk factors for high-altitude headache upon acute high-altitude exposure at 3700 m in young Chinese men: a cohort study.

BackgroundThis prospective and observational study aimed to identify demographic, physiological and psychological risk factors associated with high-altitude headache (HAH) upon acute high-altitude exposure.MethodsEight hundred fifty subjects ascended by plane to 3700 m above Chengdu (500 m) over a period of two hours. Structured Case Report Form (CRF) questionnaires were used to record demographic information, physiological examinations, psychological scale, and symptoms including headache and insomnia a week before ascending and within 24 hours after arrival at 3700 m. Binary logistic regression models were used to analyze the risk factors for HAH.ResultsThe incidence of HAH was 73.3%. Age (p =0.011), physical labor intensity (PLI) (p =0.044), primary headache history (p &lt;0.001), insomnia (p &lt;0.001), arterial oxygen saturation (SaO2) (p =0.001), heart rate (HR) (p =0.002), the Self-Rating Anxiety Scale (SAS) (p &lt;0.001), and the Epworth Sleepiness Scale (ESS) (p &lt;0.001) were significantly different between HAH and non-HAH groups. Logistic regression models identified primary headache history, insomnia, low SaO2, high HR and SAS as independent risk factors for HAH.ConclusionsInsomnia, primary headache history, low SaO2, high HR, and high SAS score are the risk factors for HAH. Our findings will provide novel avenues for the study, prevention and treatment of HAH

Defects engineering simultaneously enhances activity and recyclability of MOFs in selective hydrogenation of biomass

The development of synthetic methodologies towards enhanced performance in biomass conversion is desirable due to the growing energy demand. Here we design two types of Ru impregnated MIL-100-Cr defect engineered metal-organic frameworks (Ru@DEMOFs) by incorporating defective ligands (DLs), aiming at highly efficient catalysts for biomass hydrogenation. Our results show that Ru@DEMOFs simultaneously exhibit boosted recyclability, selectivity and activity with the turnover frequency being about 10 times higher than the reported values of polymer supported Ru towards D-glucose hydrogenation. This work provides in-depth insights into (i) the evolution of various defects in the cationic framework upon DLs incorporation and Ru impregnation, (ii) the special effect of each type of defects on the electron density of Ru nanoparticles and activation of reactants, and (iii) the respective role of defects, confined Ru particles and metal single active sites in the catalytic performance of Ru@DEMOFs for D-glucose selective hydrogenation as well as their synergistic catalytic mechanism