Empirical Formulation of Highway Traffic Flow Prediction Objective Function Based on Network Topology

Accurate Highway road predictions are necessary for timely decision making by the transport authorities. In this paper, we propose a traffic flow objective function for a highway road prediction model. The bi-directional flow function of individual roads is reported considering the net inflows and outflows by a topological breakdown of the highway network. Further, we optimise and compare the proposed objective function for constraints involved using stacked long short-term memory (LSTM) based recurrent neural network machine learning model considering different loss functions and training optimisation strategies. Finally, we report the best fitting machine learning model parameters for the proposed flow objective function for better prediction accuracy.Peer reviewe

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

The development of local solar irradiance for outdoor computer graphics rendering

Atmospheric effects are approximated by solving the light transfer equation, LTE, of a given viewing path. The resulting accumulated spectral energy (its visible band) arriving at the observer’s eyes, defines the colour of the object currently on the line of sight. Due to the convenience of using a single rendering equation to solve the LTE for daylight sky and distant objects (aerial perspective), recent methods had opt for a similar kind of approach. Alas, the burden that the real-time calculation brings to the foil had forced these methods to make simplifications that were not in line with the actual world observation. Consequently, the results of these methods are laden with visual-errors. The two most common simplifications made were: i) assuming the atmosphere as a full-scattering medium only and ii) assuming a single density atmosphere profile. This research explored the possibility of replacing the real-time calculation involved in solving the LTE with an analytical-based approach. Hence, the two simplifications made by the previous real-time methods can be avoided. The model was implemented on top of a flight simulator prototype system since the requirements of such system match the objectives of this study. Results were verified against the actual images of the daylight skies. Comparison was also made with the previous methods’ results to showcase the proposed model strengths and advantages over its peers

Leveraging Relational Structure through Message Passing for Modelling Non-Euclidean Data

Modelling non-Euclidean data is difficult since objects for comparison can be formed of different numbers of constituent parts with different numbers of relations between them, and traditional (Euclidean) methods are non-trivial to apply. Message passing enables such modelling by leveraging the structure of the relations within a (or between) given object(s) in order to represent and compare structure in a vectorized form of fixed dimensions. In this work, we contribute novel message passing techniques that improve state of the art for non-Euclidean modelling in a set of specifically chosen domains. In particular, (1) we introduce an attention-based structure-aware global pooling operator for graph classification and demonstrate its effectiveness on a range of chemical property prediction benchmarks, we also show that our method outperforms state of the art graph classifiers in a graph isomorphism test, and demonstrate the interpretability of our method with respect to the learned attention coefficients. (2) We propose a style similarity measure for Boundary Representations (B-Reps) that leverages the style signals in the second order statistics of the activations in a pre-trained (unsupervised) 3D encoder, and learns their relative importance to an end-user through few-shot learning. Our approach differs from existing data-driven 3D style methods since it may be used in completely unsupervised settings. We show quantitatively that our proposed method with B-Reps is able to capture stronger style signals than alternative methods on meshes and point clouds despite its significantly greater computational efficiency. We also show it is able to generate meaningful style gradients with respect to the input shape. (3) We introduce a novel message passing-based model of computation and demonstrate its effectiveness in expressing the complex dependencies of biological systems necessary to model life-like systems and tracing cell lineage during cancerous tumour growth, and demonstrate the improvement over existing methods in terms of post-analysis

A fully coupled computational fluid dynamics – agent-based model of atherosclerotic plaque development: Multiscale modeling framework and parameter sensitivity analysis

Background: Peripheral Artery Disease (PAD) is an atherosclerotic disorder that leads to impaired lumen patency through intimal hyperplasia and the build-up of plaques, mainly localized in areas of disturbed flow. Computational models can provide valuable insights in the pathogenesis of atherosclerosis and act as a predictive tool to optimize current interventional techniques. Our hypothesis is that a reliable predictive model must include the atherosclerosis development history. Accordingly, we developed a multiscale modeling framework of atherosclerosis that replicates the hemodynamic-driven arterial wall remodeling and plaque formation. Methods: The framework was based on the coupling of Computational Fluid Dynamics (CFD) simulations with an Agent-Based Model (ABM). The CFD simulation computed the hemodynamics in a 3D artery model, while 2D ABMs simulated cell, Extracellular Matrix (ECM) and lipid dynamics in multiple vessel cross-sections. A sensitivity analysis was also performed to evaluate the oscillation of the ABM output to variations in the inputs and to identify the most influencing ABM parameters. Results: Our multiscale model qualitatively replicated both the physiologic and pathologic arterial configuration, capturing histological-like features. The ABM outputs were mostly driven by cell and ECM dynamics, largely affecting the lumen area. A subset of parameters was found to affect the final lipid core size, without influencing cell/ECM or lumen area trends. Conclusion: The fully coupled CFD-ABM framework described atherosclerotic morphological and compositional changes triggered by a disturbed hemodynamics

Granger Causality-based Information Fusion Applied to Electrical Measurements from Power Transformers.

In the immediate future, with the increasing presence of electrical vehicles and the large increase in the use of renewable energies, it will be crucial that distribution power networks are managed, supervised and exploited in a similar way as the transmission power systems were in previous decades. To achieve this, the underlying infrastructure requires automated monitoring and digitization, including smart-meters, wide-band communication systems, electronic device based-local controllers, and the Internet of Things. All of these technologies demand a huge amount of data to be curated, processed, interpreted and fused with the aim of real-time predictive control and supervision of medium/low voltage transformer substations. Wiener–Granger causality, a statistical notion of causal inference based on Information Fusion could help in the prediction of electrical behaviour arising from common causal dependencies. Originally developed in econometrics, it has successfully been applied to several fields of research such as the neurosciences and is applicable to time series data whereby cause precedes effect. In this paper, we demonstrate the potential of this methodology in the context of power measures for providing theoretical models of low/medium power transformers. Up to our knowledge, the proposed method in this context is the first attempt to build a data-driven power system model based on G-causality. In particular, we analysed directed functional connectivity of electrical measures providing a statistical description of observed responses, and identified the causal structure within data in an exploratory analysis. Pair-wise conditional G-causality of power transformers, their independent evolution in time, and the joint evolution in time and frequency are discussed and analysed in the experimental section.This work was partly supported by the MINECO/ FEDER under the RTI2018- 098913-B100 project. The authors would like to acknowledge the support of 370 CDTI (Centro para el Desarrollo Tecnologico Industrial, Ministerio de Cien cia, Innovacion y Universidades and FEDER, SPAIN) under the PASTORA project (Ref.: ITC-20181102). and to thank the companies within the PAS TORA consortium: Endesa, Ayesa, Ormaz´abal and Ingelectus. We would like to thank the reviewers for their thoughtful comments and efforts towards im 375 proving our manuscript. Finally, JM Gorriz would like to thank Dr G´omez Exp´osito for his helpful advice and comments