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

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

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

Virtual replica of a towing tank experiment to determine the Kelvin half-angle of a ship in restricted water

The numerical simulation of ship flows has evolved into a highly practical approach in naval architecture. In typical virtual towing tanks, the principle of Galilean relativity is invoked to maintain the ship as fixed, while the surrounding water is prescribed to flow past it. This assumption may be identified, at least partly, as being responsible for the wide-scale adoption of computational solutions within practitioners' toolkits. However, it carries several assumptions, such as the levels of inlet turbulence and their effect on flow properties. This study presents an alternative virtual towing tank, where the ship is simulated to advance over a stationary fluid. To supplement the present work, the free surface disturbance is processed into Fourier space to determine the Kelvin half-angle for an example case. The results suggest that it is possible to construct a fully unsteady virtual towing tank using the overset method, without relying on Galilean relativity. Differences between theoretical and numerical predictions for the Kelvin half-angle are predominantly attributed to the assumptions used by the theoretical method. The methods presented in this work can potentially be used to validate free-surface flows, even when one does not have access to experimental wave elevation data