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We use computational modeling and formal analysis techniques to study temporal behavior of a discrete logical model of the naïve T cell differentiation. The model is analyzed formally and automatically by performing temporal logic queries via statistical model checking. While the model can be verified and then further explored using Monte Carlo simulations, model checking allows for much more efficient...
Due to the tremendous advances in GPS and location-based web services, highly available spatiotemporal trajectory data poses an important challenge - knowledge discovery from trajectories. Knowledge discovery tasks on trajectory big data such as classification, clustering and outlier detection require a dedicated data model, which can support various utility functions and provide a robust object-relational...
For many macromolecular systems the accurate sampling of the relevant regions on the potential energy surface cannot be obtained by a single, long Molecular Dynamics (MD) trajectory. New approaches are required to promote more efficient sampling. We present the design and implementation of the Extensible Toolkit for Advanced Sampling and analYsis (Ex-TASY) for building and executing advanced sampling...
Scientific discovery and analysis are increasingly computational and data-driven. While scripting languages, such as Python, R and Perl, are the means of choice of the majority of scientists to encode and run their data analysis, scripts are generally not amenable to reuse or reproducibility. Scripts do rarely get reused or even shared with third party scientists. We argue in this paper that the reproducibility...
This paper presents a data mining technique for qualitative analysis of Hodgkin-Huxley model of cell excitability. Such problem cannot be solved analytically. Therefore we apply Monte-Carlo techniques for the generation of model parameters, and use data mining algorithm for classification of learning tuples obtained. As a result we attain a decision tree capable of classifying the excitability depending...
Given the difficulty of developing physics-based degradation process models in practice, data-driven prognostics approaches are preferred in several industrial applications. Among data-driven approaches, one can distinguish between (i) degradation-based approaches that predict the future evolution of the equipment degradation and (ii) direct Remaining Useful Life (RUL) prediction approaches which...
With the rapid development of Chinese air transportation industry, air traffic becomes increasingly congested thus further worsen the delay problem. As an appealing countermeasure, the Network-wide Flight Trajectories Planning (NFTP) could provide a pre-tactical solution which reduces airspace congestion and flight delay by optimizing the 4D trajectories of all flights in the entire airspace. Notwithstanding,...
This paper presents a new data-driven air traffic modelling and analysis technique that can support operational risk analysis for unmanned aircraft integration. The proposed technique exploits advances in computer vision to autonomously extract and analyse the spatial distribution of arbitrary traffic densities, which can provide the foundation for quantitative and tailored risk assessments. The framework...
This paper describes a nonlinear Model Predictive Control (MPC) algorithm for a distributed parameter thermal system (a long duct). For prediction a specially designed neural model of the process is used. The model consists of a set of local neural sub-models, which calculate temperatures for a number of predefined locations of sensors, and a neural interpolator, which calculates the temperature for...
In recent years, privacy preserving trajectory data publishing has gained widespread attention. Aiming at the trajectory anonymity issues in publishing trajectory data of moving target, using the inherent uncertainty of the trajectory acquisition system, and based on the case that the uncertain threshold of trajectory is variable in practical applications, the traditional (k, δ)-anonymous model was...
With the wide availability of GPS devices in our lives, massive amounts of object movement data have been collected from various moving object targets, such as mobile devices, animals, and vehicles. In the last decade, Moving Object Databases (MOD) have attracted many researchers. Analyzing such data has deep implications in many areas, such as ecological study and traffic control. In this study,...
This paper proposes a framework for tracking multiple fluorescent objects in 2D + time video-microscopy. We present a novel batch-processing track-before-detect multiple object tracking approach based on a spatio-temporal marked point process model of ellipses. Our approach takes into account events such as births, deaths, splits and merges of objects which are motivated by the biological and physical...
In this work we present a data-driven method for the reconstruction of dynamical systems from noisy and incomplete observation sequences. The key idea is to benefit from the availability of representative datasets of trajectories of the system of interest. These datasets provide an implicit representation of the dynamics of this system, in contrast to the explicit knowledge of the dynamical model...
We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states — virtual place fields — in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence...
The paper considers model of complexation of measurement and information systems for the processing of data streams to provide information management systems. It allows to control the production system in case of boundary modes and emergencies.
Many closed-loop, continuous-control brain-machine interface (BMI) architectures rely on decoding via a linear readout of noisy population neural activity. However, recent work has found that decomposing neural population activity into correlated and uncorrelated variability reveals that improvements in cursor control coincide with the emergence of correlated neural variability. In order to address...
Kinematic and dynamic models are used to create simplified, yet accurate representations of reality. In application to biological systems, there is often a choice on what level of complexity is appropriate for the model. This paper introduces a structured method for obtaining an accurate model that can represent the sit-to-stand motion and reproduce the associated contact forces in the standing phase...
Synthetic aperture radar (SAR) raw signal simulation is very useful for validating SAR system design parameters, for testing the effectiveness of different processing algorithms, and for other applications. If a nominal sensor rectilinear line of flight is assumed, frequency domain raw signal simulation can be used to generate the raw data with high efficiency compared with time domain simulation...
We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping and visible views. As we all know that there are some kinds of information hidden in the non-overlapping fields always. In this paper, we will mine time dependence data so that we can analyze the crowd anomaly detection from time dimension's angle. Firstly, we have to preprocess the real scene using...
Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernel-based methods, which can provide very good performance for nonlinear problems. In this work, we apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to...
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