Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

Optimization of Online Learning Resource Adaptation in Higher Education through Neural Network Approaches

With the advent of the digital era, the quantity and variety of online higher education learning resources have expanded rapidly. The efficient adaptation of suitable resources to meet the needs of learners with specific requirements has become crucial for improving learning outcomes. Although current online learning resource recommendation systems have made some progress in matching resources, they still face challenges related to the inadequate integration of resource features and a superficial understanding of learners’ needs. These challenges hinder the achievement of personalized and precise matching, affecting learners’ study efficiency and the effective utilization of educational resources. This study first analyzes the importance of adapting online higher education learning resources and the limitations of existing research. Subsequently, a novel neural network optimization strategy is proposed. The research comprises two main parts. Firstly, the self-attention-convolutional neural network (SA-CNN) model is employed for the deep integration of the content features of online learning resources. This aims to enhance the comprehensiveness of resource descriptions. Secondly, a deep-metric attention model is introduced to accurately model and adapt to learners’ needs. This approach not only optimizes the feature representation of learning resources but also enhances the sensitivity and accuracy of the recommendation system towards learners’ requirements. This study is of significant importance for improving the performance of higher education online learning resource recommendation systems. It also provides new insights into the construction of personalized learning paths and ensuring the balanced allocation of educational resources

Formosan subterranean termite (Isoptera: Rhinotermitidae) soldiers regulate juvenile hormone levels and caste differentiation in workers

A caste structure is maintained in termite societies and juvenile hormone (JH) is generally regarded as the most important regulator in these termite colonies. Here, we demonstrate that the soldier caste regulates JH in workers of Coptotermes formosanus Shiraki. Worker termites (80-100 individuals) were placed in petri dishes with 0, 5, 10, or 20% soldiers. JH III titers of groups of these workers were monitored at 14, 28, 42, and 56 d. Any changes in soldier caste proportions also were noted at each sample date. On the first sample date, the JH levels in workers were similar among treatments with different initial soldier proportions, and no new soldiers were formed. Over the next three sample dates, the worker JH levels were higher for low initial soldier proportion treatments and vice versa. Concurrently, soldier formation increased with lower initial soldier proportions. JH titers in workers showed a positive and statistically significant relationship to soldier numbers until a certain soldier proportion was reached. These results provide evidence that soldier caste proportions regulate JH levels and thereby caste differentiation in workers. The means by which this regulatory mechanism may proceed is discussed. © 2005 Entomological Society of America

Efficient and Effective Deep Multi-view Subspace Clustering

Recent multi-view subspace clustering achieves impressive results utilizing deep networks, where the self-expressive correlation is typically modeled by a fully connected (FC) layer. However, they still suffer from two limitations. i) The parameter scale of the FC layer is quadratic to sample numbers, resulting in high time and memory costs that significantly degrade their feasibility in large-scale datasets. ii) It is under-explored to extract a unified representation that simultaneously satisfies minimal sufficiency and discriminability. To this end, we propose a novel deep framework, termed Efficient and Effective deep Multi-View Subspace Clustering (E$^2$MVSC). Instead of a parameterized FC layer, we design a Relation-Metric Net that decouples network parameter scale from sample numbers for greater computational efficiency. Most importantly, the proposed method devises a multi-type auto-encoder to explicitly decouple consistent, complementary, and superfluous information from every view, which is supervised by a soft clustering assignment similarity constraint. Following information bottleneck theory and the maximal coding rate reduction principle, a sufficient yet minimal unified representation can be obtained, as well as pursuing intra-cluster aggregation and inter-cluster separability within it. Extensive experiments show that E$^2$MVSC yields comparable results to existing methods and achieves state-of-the-art performance in various types of multi-view datasets

Changes in 5-HT1A Receptor Expression in the Oculomotor Nucleus in a Rat Model of Post-traumatic Stress Disorder

Post-traumatic stress disorder (PTSD) is an anxiety disorder that develops after exposure to a life-threatening traumatic experience. Mental disorder appears after the traumatic stress incident and affects the movement of the eye muscle dominated by the oculomotor nucleus, an important nuclear group of the brainstem. It has been reported that dysfunction of the neurotransmitter 5-hydroxytryptamine (5-HT) can lead to the instability of the internal environment in response to stress and plays an important role in the pathology of PTSD and that the 5-HT1A receptor (5-HT1AR) is critically involved in regulating mood and anxiety levels. In this study, the 5-HT1AR expression in the oculomotor nucleus was examined in rats with single-prolonged stress (SPS), a well established post-traumatic stress disorder animal model. Our results show that the expression of 5-HT1AR in the oculomotor nucleus neurons gradually increased 1, 4, and 7 days after exposure to SPS in comparison to the normal control group, measured by immunohistochemistry, western blotting, and reverse transcription polymerase chain reaction (RT-PCR). The expression of 5-HT1AR reached its peak 7 days after the SPS exposure and then decreased 14 days after. There is also a change in the ultrastructure in the oculomotor nucleus neuron upon SPS treatment which was observed by transmission electron microscopy. These results suggest that SPS can induce a change of the 5-HT1AR expression in the oculomotor nucleus, which may be one of the molecular mechanisms that lead to PTSD

Postoperative Radiotherapy and N2 Non-small Cell Lung Cancer Prognosis: A Retrospective Study Based on Surveillance, Epidemiology, and End Results Database

The purpose of this study is to clarify the significance of postoperative radiotherapy for N2 lung cancer. This study aimed to investigate the effect of postoperative radiotherapy on the survival and prognosis of patients with N2 lung cancer. Data from 12,000 patients with N2 lung cancer were extracted from the Surveillance, Epidemiology, and End Results database (2004-2012). Age at disease onset and 5-year survival rates were calculated. Survival curves were plotted using the Kaplan-Meier method. The univariate log-rank test was performed. Multivariate Cox regression were used to examine factors affecting survival. Patients’ median age was 67 years (mean 66.46 ± 10.03). The 5-year survival rate was 12.55%. Univariate analysis revealed age, sex, pathology, and treatment regimen as factors affecting prognosis. In multivariate analysis, when compared to postoperative chemotherapy, postoperative chemoradiotherapy was better associated with survival benefits (hazard ratio [HR]= 0.85, 95% confidence interval [CI]: 0.813-0.898, P <0.001). Propensity score matching revealed that patients who had received postoperative chemoradiotherapy had a better prognosis than did patients who had received postoperative chemotherapy (HR=0.869, 95% CI: 0.817-0.925, P <0.001). Female patients and patients aged <65 years had a better prognosis than did their counterparts. Patients with adenocarcinoma had a better prognosis than did patients with squamous cell carcinoma. Moreover, prognosis worsened with increasing disease T stage. Patients who had received postoperative chemoradiotherapy had a better prognosis than did patients who had received postoperative chemotherapy. Postoperative radiotherapy was an independent prognostic factor in this patient group

Psychometric properties of the Chinese version of the oncology nurses health behaviors determinants scale: a cross-sectional study

ObjectiveTo test the validity and reliability of the Oncology Nurses Health Behaviors Determinants Scale (HBDS-ON) in oncology nurses, the Chinese version was developed.MethodsThe Brislin double translation-back translation approach was employed to forward translation, back translation, synthesis, cross-cultural adaptation, and pre-survey, resulting in the first Chinese version of the Oncology Nurses Health Behaviors Determinants Scale (HBDS-ON). A convenience sample technique was used to select 350 study participants in Liaoning, Shandong, and Jiangsu, China, who satisfied the inclusion and exclusion criteria, to assess the validity and reliability of the scale.ResultsThe Chinese version of the Oncology Nurses Health Behaviors Determinants Scale (HBDS-ON) had six subscales (perceived threat, perceived benefits, perceived barriers, self-efficacy, cues to action, and personal protective equipment availability and accessibility), including 29 items. The average scale level was 0.931, and the content validity level of the items varied from 0.857 to 1.000. Each Cronbach’s α coefficient had an acceptable internal consistency reliability range of 0.806 to 0.902. X2/df = 1.667, RMSEA = 0.044, RMR = 0.018, CFI = 0.959, NFI = 0.905, TLI = 0.954, and IFI = 0.960 were the model fit outcomes in the validation factor analysis. All of the model fit markers fell within reasonable bounds.ConclusionThe Chinese version of the Oncology Nurses Health Behaviors Determinants Scale (HBDS-ON) has good reliability and validity and can be used as a tool to assess the influencing factors of chemotherapy exposure for oncology nurses in China

Development and Molecular Cytogenetic Identification of a New Wheat–Psathyrostachys huashanica Keng Translocation Line Resistant to Powdery Mildew

Psathyrostachys huashanica Keng, a wild relative of common wheat with many desirable traits, is an invaluable source of genetic material for wheat improvement. Few wheat–P. huashanica translocation lines resistant to powdery mildew have been reported. In this study, a wheat–P. huashanica line, E24-3-1-6-2-1, was generated via distant hybridization, ethyl methanesulfonate (EMS) mutagenesis, and backcross breeding. A chromosome karyotype of 2n = 44 was observed at the mitotic stage in E24-3-1-6-2-1. Genomic in situ hybridization (GISH) analysis revealed four translocated chromosomes in E24-3-1-6-2-1, and P. huashanica chromosome-specific marker analysis showed that the alien chromosome fragment was from the P. huashanica 4Ns chromosome. Moreover, fluorescence in situ hybridization (FISH) analysis demonstrated that reciprocal translocation had occurred between the P. huashanica 4Ns chromosome and the wheat 3D chromosome; thus, E24-3-1-6-2-1 carried two translocations: T3DS·3DL-4NsL and T3DL-4NsS. Translocation also occurred between wheat chromosomes 2A and 4A. At the adult stage, E24-3-1-6-2-1 was highly resistant to powdery mildew, caused by prevalent pathotypes in China. Further, the spike length, numbers of fertile spikelets, kernels per spike, thousand-kernel weight, and grain yield of E24-3-1-6-2-1 were significantly higher than those of its wheat parent 7182 and addition line 24-6-3-1. Thus, this translocation line that is highly resistant to powdery mildew and has excellent agronomic traits can be used as a novel promising germplasm for breeding resistant and high-yielding cultivars