室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future

Reciprocatory magnetic reconnection in a coronal bright point

Coronal bright points (CBPs) are small-scale and long-duration brightenings in the lower solar corona. They are often explained in terms of magnetic reconnection. We aim to study the sub-structures of a CBP and clarify the relationship among the brightenings of different patches inside the CBP. The event was observed by the X-ray Telescope (XRT) aboard the Hinode spacecraft on 2009 August 22$-$23. The CBP showed repetitive brightenings (or CBP flashes). During each of the two successive CBP flashes, i.e., weak and strong flashes which are separated by $\sim$2 hr, the XRT images revealed that the CBP was composed of two chambers, i.e., patches A and B. During the weak flash, patch A brightened first, and patch B brightened $\sim$2 min later. During the transition, the right leg of a large-scale coronal loop drifted from the right side of the CBP to the left side. During the strong flash, patch B brightened first, and patch A brightened $\sim$2 min later. During the transition, the right leg of the large-scale coronal loop drifted from the left side of the CBP to the right side. In each flash, the rapid change of the connectivity of the large-scale coronal loop is strongly suggestive of the interchange reconnection. For the first time we found reciprocatory reconnection in the CBP, i.e., reconnected loops in the outflow region of the first reconnection process serve as the inflow of the second reconnection process.Comment: 13 pages, 8 figure

Enhancing Stock Movement Prediction with Adversarial Training

This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11% relative improvements on average w.r.t. accuracy, validating the usefulness of adversarial training for stock prediction task.Comment: IJCAI 201

Outcomes of treatment of sudden deafness using different Protocols: a retrospective analysis of 104 cases

AbstractObjectiveTo compare different treatment protocols for sudden deafness(SD), for the purpose of identifying an appropriate approach to SD.MethodsA total of 104 patients with diagnosis of sudden hearing loss treated from Jan 2006 to December 2008 were included in this study, of which 31 received the typical pharmaceutical treatment(group I), 40 received the typical pharmaceutical treatment plus polarized liquid(Group II) and 33 received the hyperbaric oxygen in addition to the treatment included in Group II (Group III).ResultsThe total improvement rate(67.74%, 62.50% and 75.76% for Groups I, II and III respectively) was not statistically different between the three groups (P>0.05).ConclusionThe three treatment protocols are similar when judged by the treatment outcomes in SD, neither being superior to the others. The two important factors that appear to influence treatment outcomes are the audiogram pattern and duration of hearing loss before seeking treatment. Patients with upsloping or peak–type audiograms and treated within 7 days from the onset have better prognosis than others

A regioselectivity switch in Pd-catalyzed hydroallylation of alkynes.

By exploiting the reactivity of a vinyl-Pd species, we control the regioselectivity in hydroallylation of alkynes under Pd-hydride catalysis. A monophosphine ligand and carboxylic acid combination promotes 1,5-dienes through a pathway involving isomerization of alkynes to allenes. In contrast, a bisphosphine ligand and copper cocatalyst favor 1,4-dienes via a mechanism that involves transmetalation. Our study highlights how to access different isomers by diverting a common organometallic intermediate

IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner. However, most of them consider all semantics equally and thus align them uniformly, regardless of their diverse complexities. In fact, semantics are diverse (i.e. involving different kinds of semantic concepts), and humans usually follow a latent structure to combine them into understandable languages. It may be difficult to optimally capture such sophisticated correspondences in existing methods. In this paper, to address such a deficiency, we propose an Iterative Matching with Recurrent Attention Memory (IMRAM) method, in which correspondences between images and texts are captured with multiple steps of alignments. Specifically, we introduce an iterative matching scheme to explore such fine-grained correspondence progressively. A memory distillation unit is used to refine alignment knowledge from early steps to later ones. Experiment results on three benchmark datasets, i.e. Flickr8K, Flickr30K, and MS COCO, show that our IMRAM achieves state-of-the-art performance, well demonstrating its effectiveness. Experiments on a practical business advertisement dataset, named \Ads{}, further validates the applicability of our method in practical scenarios.Comment: 9 pages; Accepted by CVPR202