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In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success of recent deep learning methods for diffusion, we find that they often underexplore the cascade structure. We consider a cascade as not merely a sequence...
Big data has been revolutionizing individualized medicine, improving the results of diagnostic imaging, genetic testing, and by providing frameworks for electronic health record sharing and analysis. In this paper we present a nextstep in personalized data-driven health by demonstrating the capability of predictive individualized models to model peak postprandial plasma glucose concentrations. Past...
This paper presents an approach to the formation control problem for autonomous non-holonomic mobile robots using a Non-linear Model Predictive Formation Control (NMPFC) algorithm that employs a leader-follower scheme. An explanation of the the NMPFC problem is given, after which the results of both simulations and experiments with a three robot formation employing the developed algorithm are discussed...
This paper presents predictive models based on dynamic recurrent neural networks DRNNs with short term delay units to predict daily solar radiation intensity. The proposed approach aims to evaluate the daily global solar radiation using simple recurrent neural networks (SRNNs) with meteorological data. First, we present a reference model based on a feed-forward multilayer perceptron (MLP), then we...
Background: An increasing research effort has devoted to just-in-time (JIT) defect prediction. A recent study by Yang et al. at FSE'16 leveraged individual change metrics to build unsupervised JIT defect prediction model. They found that many unsupervised models performed similarly to or better than the state-of-the-art supervised models in effort-aware JIT defect prediction. Goal: In Yang et al.'s...
The article describes one of the Data Assimilation methods application based on the Kalman filter application. The application in forecasting the Ensemble Kalman Filter is shown by the Lorenz attractor model. Errors were taken into account in carrying out the experiment for both the model and the forecasting. The effect of model and filter parameters on the forecasting quality were estimated. As a...
Today's mobile users want faster data and more reliable services. The next generation of wireless networks 5G promises to deal with this, and more. In this context, to enable ultra-short response times, fast relocation of service instances between edge nodes and reduce migration time its required to cope with user mobility to guarantee the (QoE). In this new paradigm called 5G cellular systems, the...
The telecommunication community is driven to find new frequency above 10 GHz because of exponential increase in user demands for higher data capacity which means higher bandwidth. Rain attenuation is very significant at frequency bands of 28-30 GHz. The USM-UKM model proposes an accurate rain attenuation prediction at frequency bands between 12-13 GHz. This paper proposes a simple enhancement of the...
Pedestrian trajectory prediction is important in various applications such as driverless vehicles, social robots, intelligent tracking systems and space planning. Existing methods focus on analysing the influence of neighbours but ignore the effect of the intended destinations of pedestrians which also plays a key role in route planning. In this paper, we propose a novel two- stage trajectory prediction...
In functional genomics, small interfering RNA (siRNA) can be used to knockdown gene expression. Usually, a target gene has numerous potential siRNAs, but their efficiencies of gene silencing often varies. Thus, for a successful RNA interference (RNAi), selecting the most effective siRNA is a critical step. Despite various computational algorithms have been developed, the efficacy prediction accuracy...
Four enhanced machine learning models were used to predict obesity in high school students by focusing on both risk and protective factors: binary logistic regression; improved decision tree (IDT); weighted k-nearest neighbor (KNN); and artificial neural network (ANN). Nine health-related behaviors from the 2015 Youth Risk Behavior Surveillance System (YRBSS) for the state of Tennessee were used as...
Cardiovascular disease (CVD) caused by atherosclerosis is one of the major causes of death world-wide. Currently, diverse machine learning models have been applied to disease prediction and classification. However, most of them tend to focus on the performance of the algorithm and neglect the underlying variables for patients in different carotid atherosclerotic stages. In this paper, we propose a...
Multiple publications have indicated that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns remain unresolved. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation,...
Recent studies show that drug-disease associations provide important information for drug discovery and drug repositioning. Wet experimental identification of drug-disease associations is time-consuming and labor-intensive. Therefore, the development of computational methods that predict drug-disease associations is an urgent task. In this paper, we propose a novel computational method named NTSIM,...
Accurate wind speed prediction is vital for improving the efficiency and reliability of wind power system operation. In this paper, a novel structure of forecasting method, the State Transition ANN model (T-ANN), is proposed for hourly wind speed forecasting. In the proposed method, the ‘state’ is determined to describe the characteristic of the wind speed time series. Mapping relationship between...
In this paper, constrained model predictive control (MPC) based on parallel neural network optimization is proposed to apply to pulse width modulation (PWM) rectifier and improve power quality. An decoupled model of three-phase rectifier in abc coordinates is built. Then, the constrained MPC method is proposed. This method breaks the limits of predictive control with finite set and without constraints...
In this paper, based on quaternion and Euler angles, an attitude control algorithm is proposed for pitching and rolling of quadrotor aircraft. In addition, the target tracking algorithm of quadrotor aircraft is designed by using the collected video information and color feature recognition. The system is based on the homemade quadrotor aircraft, using gyroscope, accelerometer as the original measurement...
Load forecasting is the basis of the design and implementation of the control strategy of the combined cooling heating and power (CCHP) system, and the precision affects the comprehensive energy efficiency of the system directly. In this paper, the gray relational analysis method is used to indicate the strong coupling relationship among the loads of heating, cooling and electricity in the system...
We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek. In contrast to prior work in trajectory forecasting, our algorithm, DARKO, goes further to reason about semantic states (will I pick up an object?), and future goal states that are far in terms of both space and time....
Free-head 3D gaze tracking outputs both the eye location and the gaze vector in 3D space, and it has wide applications in scenarios such as driver monitoring, advertisement analysis and surveillance. A reliable and low-cost monocular solution is critical for pervasive usage in these areas. Noticing that a gaze vector is a composition of head pose and eyeball movement in a geometrically deterministic...
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