Technical Report for Trend Prediction Based Intelligent UAV Trajectory Planning for Large-scale Dynamic Scenarios

The unmanned aerial vehicle (UAV)-enabled communication technology is regarded as an efficient and effective solution for some special application scenarios where existing terrestrial infrastructures are overloaded to provide reliable services. To maximize the utility of the UAV-enabled system while meeting the QoS and energy constraints, the UAV needs to plan its trajectory considering the dynamic characteristics of scenarios, which is formulated as the Markov Decision Process (MDP). To solve the above problem, a deep reinforcement learning (DRL)-based scheme is proposed here, which predicts the trend of the dynamic scenarios to provide a long-term view for the UAV trajectory planning. Simulation results validate that our proposed scheme converges more quickly and achieves the better performance in dynamic scenarios

Incorporating Figure Captions and Descriptive Text into Mesh Term Indexing: A Deep Learning Approach

The exponential increase of available documents online makes document classification an important application in natural language processing. The goal of text classification is to automatically assign categories to documents. Traditional text classifiers depend on features, such as, vocabulary and user-specified information which mainly relies on prior knowledge. In contrast, deep learning automatically learns effective features from data instead of adopting human-designed features. In this thesis, we specifically focus on biomedical document classification. Beyond text information from abstract and title, we also consider image and table captions, as well as paragraphs associated with images and tables, which we demonstrate to be an important feature source to our method

L2P: An Algorithm for Estimating Heavy-tailed Outcomes

Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, demand for commodities in warehouses, etc. By learning heavy-tailed distributions, "big and rare" instances (e.g., the best-sellers) will have accurate predictions. Most existing approaches are not dedicated to learning heavy-tailed distribution; thus, they heavily under-predict such instances. To tackle this problem, we introduce Learning to Place (L2P), which exploits the pairwise relationships between instances for learning. In its training phase, L2P learns a pairwise preference classifier: is instance A > instance B? In its placing phase, L2P obtains a prediction by placing the new instance among the known instances. Based on its placement, the new instance is then assigned a value for its outcome variable. Experiments on real data show that L2P outperforms competing approaches in terms of accuracy and ability to reproduce heavy-tailed outcome distribution. In addition, L2P provides an interpretable model by placing each predicted instance in relation to its comparable neighbors. Interpretable models are highly desirable when lives and treasure are at stake.Comment: 9 pages, 6 figures, 2 tables Nature of changes from previous version: 1. Added complexity analysis in Section 2.2 2. Datasets change 3. Added LambdaMART in the baseline methods, also a brief discussion on why LambdaMart failed in our problem. 4. Figure update

Does executive characteristics have any effects on firm financial distress? - Evidence from Chinese listed companies.

At present, Chinese economy is in an important period of structural adjustment, and the development of enterprises is faced with many challenges of uncertain factors and financial risks. In the fierce market competition environment, enterprises fall into financial difficulties and even go bankrupt due to improper business and development strategies. For most listed companies, the biggest advantage of the enterprise is the existence of a good executive team. Although the existing literature deals with the research on the impact of corporate governance on financial distress, most of them focus on foreign enterprises, and there is almost no research on Chinese listed companies. Therefore, this paper will take the agency theory and upper echelons theory as the theoretical basis, collect the data of listed companies from 2016 to 2020 based on the existing research basis of foreign companies and combine the actual situation of Chinese enterprises, and conduct descriptive, correlation, regression analysis and robustness test on the sorted sample data. The relationship between the characteristics of executives in corporate governance structure and financial distress is analyzed in detail. The results show that the size of the executive team is positively correlated with financial distress, while the characteristics of educational background, professional background and age are negatively correlated with financial distress. At the end of this paper, based on relevant literature research and empirical results, relevant suggestions are provided for the company to optimize the structure of the senior management team

Effect of Metformin on Lactate Metabolism in Normal Hepatocytes under High Glucose Stress in Vitro

Objective: To study the effect of metformin on lactate metabolism in hepatocytes in vitro under high glucose stress. Method: LO2 hepatocytes was cultured in vitro, hepatocytes were randomly divided into blank control group, 25 mmol/L glucose solution, 27 mmol/L glucose solution, 29 mmol/L glucose solution, 31 mmol/L glucose solution, 33 mmol/L glucose solution, 35 mmol/L glucose solution treatment group, after determining the optimal concentration as 31 mmol/L, use 30 mmol/L metformin solution, and then divided into blank control group, normal hepatocytes + the optimal concentration of glucose solution, normal hepatocytes + metformin solution , normal hepatocytes+. The optimal concentration of glucose solution normal hepatocytes + metformin solution, calculate the number of hepatocytes on cell count plate respectively in the 12 h, 24 h, 48 h, and use the lactic acid kit to determine the lactic acid value of the cell culture medium of normal liver cells + optimal concentration glucose solution and normal liver cells + optimal concentration glucose solution + metformin solution at 12 h, 24 h, and 48 h, respectively. Results: There was no significant change in the lactic acid concentration but significant increase in the number of surviving hepatocytes in the high-glycemic control group compared with that in the high-glycemic control group without metformin. Conclusions: Metformin has no significant effect on lactic acid metabolism of hepatocytes under high glucose stress in vitro, and has a protective effect on hepatocytes under high glucose stress. Based on this, it is preliminarily believed that metformin is not the direct factor leading to diabetic lactic acidosis

Different Concentrations of Doxycycline in Swine Manure Affect the Microbiome and Degradation of Doxycycline Residue in Soil

Antibiotic residues that enter the soil through swine manure could disturb the number, community structure and functions of microbiota which could also degrade antibiotics in soil. Five different concentrations of doxycycline (DOX) incorporated into swine manure were added to soil to explore the effects of DOX on microbiota in soil and degradation itself. The results showed that the soil microbiome evolved an adaptation to the soil containing DOX by generating resistance genes. Moreover, some of the organisms within the soil microbiome played crucial roles in the degradation of DOX. The average degradation half-life of DOX in non-sterile groups was 13.85 ± 0.45 days, which was significantly shorter than the 29.26 ± 0.98 days in the group with sterilized soil (P < 0.01), indicating that the soil microbiome promoted DOX degradation. DOX addition affected the number of tetracycline resistance genes, depending on the type of gene and the DOX concentration. Among these genes, tetA, tetM, tetW, and tetX had significantly higher copy numbers when the concentration of DOX was higher. In contrast, a lower concentration of DOX had an inhibitory effect on tetG. At the same time, the microbial compositions were affected by the initial concentration of DOX and the different experimental periods. The soil chemical indicators also affected the microbial diversity changes, mainly because some microorganisms could survive in adversity and become dominant bacterial groups, such as the genera Vagococcus and Enterococcus (which were associated with electrical conductivity) and Caldicoprobacter spp. (which were positively correlated with pH). Our study mainly revealed soil microbiota and DOX degradation answered differently under variable concentrations of DOX mixed with swine manure in soil

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset RenderIH for interacting hands with accurate and diverse pose annotations. The dataset contains 1M photo-realistic images with varied backgrounds, perspectives, and hand textures. To generate natural and diverse interacting poses, we propose a new pose optimization algorithm. Additionally, for better pose estimation accuracy, we introduce a transformer-based pose estimation network, TransHand, to leverage the correlation between interacting hands and verify the effectiveness of RenderIH in improving results. Our dataset is model-agnostic and can improve more accuracy of any hand pose estimation method in comparison to other real or synthetic datasets. Experiments have shown that pretraining on our synthetic data can significantly decrease the error from 6.76mm to 5.79mm, and our Transhand surpasses contemporary methods. Our dataset and code are available at https://github.com/adwardlee/RenderIH.Comment: Accepted by ICCV 202