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

Magnetostratigraphy of the Lower Triassic beds from Chaohu(China) and its implications for the Induan–Olenekian stage boundary.

A magnetostratigraphic study was performed on the lower 44 m of the West Pingdingshan section near Chaohu city, (Anhui province, China) in order to provide a magnetic polarity scale for the early Triassic. Data from 295 paleomagnetic samples is integrated with a detailed biostratigraphy and lithostratigraphy. The tilt-corrected mean direction from the West Pingdingshan section, passes the reversal and fold tests. The overall mean direction after tilt correction is D=299.9º, I=18.3º (κ=305.2, α95=1.9, N=19). The inferred paleolatitude of the sampling sites (31.6ºN, 117.8ºE) is about 9.4º, consistent with the stable South China block (SCB), though the declinations indicate some 101o counter-clockwise rotations with respect to the stable SCB since the Early Triassic. Low-field anisotropy of magnetic susceptibility indicates evidence of weak strain. The lower part of the Yinkeng Formation is dominated by reversed polarity, with four normal polarity magnetozones (WP2n to WP5n), with evidence of some thinner (<0.5 m thick) normal magnetozones. The continuous magnetostratigraphy from the Yinkeng Formation, provides additional high-resolution details of the polarity pattern through the later parts of the Induan into the lowest Olenekian. The magnetostratigraphic and biostratigraphic data shows the conodont marker for the base of the Olenekian (first presence of Neospathodus waageni) is shortly prior to the base of normal magnetozone WP5n. This provides a secondary marker for mapping the base of the Olenekian into successions without conodonts. This section provides the only well-integrated study from a Tethyan section across this boundary, but problems remain in definitively relating this boundary into Boreal sections with magnetostratigraphy

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

Characteristics of calcium carbonate fouling on heat transfer surfaces under the action of electric fields

The present study examined the effect of electric fields in calcium carbonate (CaCO3) scale formation on a heat transfer surface. The effects of electric fields ranging from 0 V to 4000 V on the fouling properties of CaCO3 were investigated. Results showed that the optimal electric voltage was approximately 500 V, at which the asymptotic value of fouling resistance and the deposited weight were minimal and corresponded to 52.8 % and 61.3 % reductions, respectively, compared with the results recorded at 0 V. At higher voltages of 3000 V or 4000 V, the asymptotic value of fouling resistance and the weight of fouling deposits increased relative to those obtained at 0 V. The scanning electron microscope images of the fouling deposits obtained at 0 V showed mainly aragonites with sharp and needle-like crystal structures. The structure of CaCO3 fouling changed from aragonites to spherical vaterites as the applied voltage was increased.</p

A common reference model for environmental science research infrastructures

Independent development of research infrastructures leads to unnecessary replication of technologies and solutions whilst the lack of standard definitions makes it difficult to relate experiences in one infrastructure with those of oth-ers. The ENVRI Reference Model, www.envri.eu/rm, uses the Open Distributed Processing standard framework in order to model the "archetypical" environmental research infrastructure. The use of the ENVRI-RM to illustrate common characteristics of European ESFRI environmental infrastructures from a number of different perspectives provides a common language for and understanding of environmental research infrastructures, promote technology and solution sharing between infrastructures, and improve interoperability between implemented services

Genetic Fingerprint Concerned with Lymphatic Metastasis of Human Lung Squamous Cancer

Background and objective With the most recent introduction of microarray technology to biology, it becomes possible to perform comprehensive analysis of gene expression in cancer cell. In this study the laser microdissection technique and cDNA microarray analysis were combined to obtain accurate molecular profiles of lymphatic metastasis in patients with lung squamous cell carcinoma. Methods Primary lung squamous cancer tissues and regional lymph nodes were obtained from 10 patients who underwent complete resection of lung cancer. According to the source of lung cancer cells, the samples were classified into three groups: the primary tumor with lymphatic metastasis (TxN+, n=5), the primary tumor without lymphatic metastasis (TxN-, n=5) and matched tumor cells from metastatic lymph nodes (N+, n=5). Total RNA was extracted from laser microdissected tumor samples. Adequate RNA starting material of mRNA from primary tumor or metastatic nodes were labeled and then hybridized into the same microarray containing 6 000 known, named human genes/ESTs. After scanning, data analysis was performed using GeneSpringTM6.2. Results A total of 37 genes were found to be able to separate TxN+ from TxN-. TxN+ have higher levels of genes concerned with structural protein, signal transducer, chaperone and enzyme. TxN- have higher levels of genes coding for cell cycle regulator, transporter, signal transducer and apoptosis regulator. Interestingly, there were no differentially expressed genes between N+ and TxN+. Conclusion The acquisition of the metastatic phenotype might occur early in the development of lung squamous cancer. We raise the hypothesis that the gene-expression signature described herein is valuable to elucidate the molecular mechanisms regarding lymphatic metastasis and to look for novel therapeutic targets