In deep learning based stock trading strategy models, most of the research just use simple convolutional neural networks (CNN) to process stock data. But task-specific neural network structures have ...been proposed extensively, and their effectiveness has been demonstrated in computer vision (CV) and natural language processing (NLP) tasks. In this paper, we proposed a multi-scale convolutional neural feature extraction network (MS-CNN) for stock data, which can better extract stock trend features and thus make better decisions. The network structure inspired by the human stock trading model: in human behavior, we do not only look at a single set of stock data, but rather combine all the stock data, such as opening, closing, and trading volume, to make a comprehensive judgment. And humans will consider the current stock trend on different time scales, such as 3-Day Line and 5-Day Line. This is consistent with the two-dimensional convolution kernels commonly used in CV tasks, so we used convolution kernels of 3×3 and 5×5 in the network with two-dimensional convolution size and constructed a novel network structure for stock data. With double deep Q networks (DDQN) algorithm, we get the best performance for our network. The experimental results show that we can obtain high yield on the datasets of Dow Jones (DJI), AAPLE (AAPL), and General Electric (GE).
Deep reinforcement learning (DRL) has attracted strong interest since AlphaGo beat human professionals, and its applications in stock trading are widespread. In this paper, an enhanced stock trading ...strategy called Dual Action and Dual Environment Deep Q-Network (DADE-DQN) for profit and risk reduction is proposed. Our approach incorporates several key highlights. First, to achieve a better balance between exploration and exploitation, a dual-action selection and dual-environment mechanism are incorporated into our DQN framework. Second, our approach optimizes the utilization of storage transitions by utilizing independent replay memories and performing dual mini-batch updates, leading to faster convergence and more efficient learning. Third, a novel deep network structure that incorporates Long Short-Term Memory (LSTM) and attention mechanisms is introduced, thereby improving the network’s ability to capture essential features and patterns. In addition, an innovative feature selection method is presented to efficiently enhance the input data by utilizing mutual information to identify and eliminate irrelevant features. Evaluation on six datasets shows that our DADE-DQN algorithm outperforms multiple DRL-based strategies (TDQN, DQN-Pattern, DQN-Vanilla) and traditional strategies (B&H, S&H, MR, TF). For example, on the KS11 dataset, the DADE-DQN strategy has achieved an impressive cumulative return of 79.43% and a Sharpe ratio of 2.21, outperforming all other methods. These experimental results demonstrate the performance of our approach in enhancing stock trading strategies.
Advancements in machine learning have led to an increased interest in applying deep reinforcement learning techniques to investment decision-making problems. Despite this, existing approaches often ...rely solely on single-scaling daily data, neglecting the importance of multi-scaling information, such as weekly or monthly data, in decision-making processes. To address this limitation, a multi-scaling convolutional neural network for reinforcement learning-based stock trading, termed multi-scaling convolutional neural network SARSA (state, action, reward, state, action), is proposed. Our method utilizes a multi-scaling convolutional neural network to obtain multi-scaling features of daily and weekly financial data automatically. This involves using a convolutional neural network with several filter sizes to perform a multi-scaling extraction of temporal features. Multiple-scaling feature mining allows agents to operate over longer time scaling, identifying low stock positions on the weekly line and avoiding daily fluctuations during continuous declines. This mimics the human approach of considering information at varying temporal and spatial scaling during stock trading. We further enhance the network’s robustness by adding an average pooling layer to the backbone convolutional neural network, reducing overfitting. State, action, reward, state, action, as an on-policy reinforcement learning method, generates dynamic trading strategies that combine multi-scaling information across different time scaling, while avoiding dangerous strategies. We evaluate the effectiveness of our proposed method on four real-world datasets (Dow Jones, NASDAQ, General Electric, and AAPLE) spanning from 1 January 2007 to 31 December 2020, and demonstrate its superior profits compared to several baseline methods. In addition, we perform various comparative and ablation tests in order to demonstrate the superiority of the proposed network architecture. Through these experiments, our proposed multi-scaling module yields better results compared to the single-scaling module.
Wheat yield and grain protein content (GPC) are two main optimization targets for breeding and cultivation. Remote sensing provides nondestructive and early predictions of yield and GPC, ...respectively. However, whether it is possible to simultaneously predict yield and GPC in one model and the accuracy and influencing factors are still unclear. In this study, we made a systematic comparison of different deep learning models in terms of data fusion, time-series feature extraction, and multitask learning. The results showed that time-series data fusion significantly improved yield and GPC prediction accuracy with
values of 0.817 and 0.809. Multitask learning achieved simultaneous prediction of yield and GPC with comparable accuracy to the single-task model. We further proposed a two-to-two model that combines data fusion (two kinds of data sources for input) and multitask learning (two outputs) and compared different feature extraction layers, including RNN (recurrent neural network), LSTM (long short-term memory), CNN (convolutional neural network), and attention module. The two-to-two model with the attention module achieved the best prediction accuracy for yield (
= 0.833) and GPC (
= 0.846). The temporal distribution of feature importance was visualized based on the attention feature values. Although the temporal patterns of structural traits and spectral traits were inconsistent, the overall importance of both structural traits and spectral traits at the postanthesis stage was more important than that at the preanthesis stage. This study provides new insights into the simultaneous prediction of yield and GPC using deep learning from time-series proximal sensing, which may contribute to the accurate and efficient predictions of agricultural production.
In this paper, a two-step iteration method was established which can be viewed as a generalisation of the existing modulus-based methods for vertical linear complementarity problems. The convergence ...analysis of the proposed method is presented, which can enlarge the convergence domain of the parameter matrix compared to the recent results. Numerical examples show that the proposed method is efficient with the two-step technique and confirm the improvement of the theoretical results.
Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve ...the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO
2
concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.
Recently, the algorithmic trading of financial assets is rapidly developing with the rise of deep learning. In particular, deep reinforcement learning, as a combination of deep learning and ...reinforcement learning, stands out among many approaches in the field of decision-making because of its high performance, strong generalization, and high fitting ability. In this paper, we attempt to propose a hybrid method of recurrent reinforcement learning (RRL) and deep learning to figure out the algorithmic trading problem of determining the optimal trading position in the daily trading activities of the stock market. We adopt deep neural network (DNN), long short-term memory neural network (LSTM), and bidirectional long short-term memory neural network (BiLSTM) to automatically extract higher-level abstract feature information from sequential trading data, respectively, and then generate optimal trading strategies by interacting with the environment in a reinforcement learning framework. In particular, the BiLSTM consisting of two LSTM models with opposite directions is able to make full use of the information from both directions in attempting to capture more effective information. In experiments, the daily data of Dow Jones, S&P500, and NASDAQ (from Jan-01, 2005 to Dec-31, 2020) are applied to verify the performance of the newly proposed DNN-RL, LSTM-RL, and BiLSTM-RL trading systems. Experimental results show that the proposed methods significantly outperform the benchmark methods, such as RRL and Buy and Hold, with higher scalability and better robustness. Especially, BiLSTM-RL performs better than other methods.
RNA binding proteins (RBPs) have the potential for transcriptional regulation in sepsis-induced liver injury, but precise functions remain unclear. Our aim is to conduct a genome-wide expression ...analysis of RBPs and illuminate changes in the regulation of alternative splicing in sepsis-induced liver injury. RNA-seq data on "sepsis and liver" from the publicly available NCBI data set was analyzed, and differentially expressed RBPs and alternative splicing events (ASEs) in the healthy and septic liver were identified. Co-expression analyses of sepsis-regulated RBPs and ASEs were performed. Models of sepsis were established to validate hepatic RBP gene expression patterns with different treatments. Pairwise analysis of gene expression profiles of sham, cecum ligation puncture (CLP), and CLP with dichloroacetate (CLPDCA) mice allowed 1208 differentially expressed genes (DEGs), of which 800 were up-regulated and 408 down-regulated, to be identified. DEGs were similar in both Sham and CLPDCA mice. The KEGG analysis showed that up-regulated genes as being involved in cytokine-cytokine receptor interaction and IL-17 signaling pathway and down-regulated genes in metabolic pathways. Differences in lipid metabolism-related alternative splicing events, including A3SS, were also found in CLP and CLPDCA compared with sham mice. Thirty-seven RBPs, including S100a11, Ads2, Fndc3b, Fn1, Ddx28, Car2, Cisd1, and Ptms, were differentially expressed in CLP mice and the regulated alternative splicing genes(RASG) with the RBP shown to be enriched in lipid metabolic and oxidation-reduction-related processes by GO functional analysis. In KEEG analysis the RASG mainly enriched in metabolic pathway. The models of sepsis were constructed with different treatment groups, and S100a11 expression in the CLP group found to be higher than in the sham group, a change that was reversed by DCA. The alternative splicing ratio of Srebf1 and Cers2 decreased compared with the sham group increased after DCA treatment. Abnormal profiles of gene expression and alternative splicing were associated with sepsis-induced liver injury. Unusual expression of RBPs, such as S100a11, may regulate alternative splicing of lipid metabolism-associated genes, such as Srebf1 and Cers2, in the septic liver. RBPs may constitute potential treatment targets for sepsis-induced liver injury.
•A new deep-learning-based individual stoma tracking pipeline was proposed.•The circadian rhythm of stomata opening was first reported from video data.•Smaller stomata not only respond faster but ...also had longer closure time at night.
Plant stomata are essential channels for gas exchange between plants and the environment. The infrared gas-exchange system has greatly accelerated the studies of stomatal conductance (gs). Nevertheless, due to the lack of in-situ monitoring techniques, the behavior of stomata themselves remains poorly understood, especially in nocturnal environmental conditions. Here, a deep-learning-based stoma tracking pipeline (StomataTracker) was first proposed to continuously monitor stoma traits from unprecedentedly long-term, continuous, and non-destructive video data. Compared to the semi-automatic method (ImageJ), the open-source StomataTracker could greatly improve the extraction efficiency from 207 s to 1.47 s of stomatal traits, including stomatal area, perimeter, length, and width. The R2 adjusted of the four stomatal traits ranged from 0.620 to 0.752. In addition, the rhythm of wheat stomata opening in a completely dark environment was first reported from long-term video data. The closed time of stoma at night was negatively correlated with stomatal traits, and the R ranged from −0.583 to −0.855. The heterogeneity of stomatal behavior also highlighted that smaller stomata have the rhythm pattern of longer closure time at night. Overall, our study provides a novel perspective for stomatal study, and it is conducive to accelerating the application of stomatal circadian rhythm in wheat breeding.
Traumatic brain injury (TBI) can cause non-neurological injuries to other organs such as the intestine. Newer studies have shown that paracellular hyperpermeability is the basis of intestinal barrier ...dysfunction following TBI. Ischemia–reperfusion injury, inflammatory response, abnormal release of neurotransmitters and hormones, and malnutrition contribute to TBI-induced intestinal barrier dysfunction. Several interventions that may protect intestinal barrier function and promote the recovery of TBI have been proposed, but relevant studies are still limited. This review is to clarify the established mechanisms of intestinal barrier dysfunction following TBI and to describe the possible strategies to reduce or prevent intestinal barrier dysfunction.
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