1,673 research outputs found

    Measurement of Anomalous Diffusion Using Recurrent Neural Networks

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    Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement (MSD) with time has an exponent different from one. We show that recurrent neural networks (RNN) can efficiently characterize anomalous diffusion by determining the exponent from a single short trajectory, outperforming the standard estimation based on the MSD when the available data points are limited, as is often the case in experiments. Furthermore, the RNN can handle more complex tasks where there are no standard approaches, such as determining the anomalous diffusion exponent from a trajectory sampled at irregular times, and estimating the switching time and anomalous diffusion exponents of an intermittent system that switches between different kinds of anomalous diffusion. We validate our method on experimental data obtained from sub-diffusive colloids trapped in speckle light fields and super-diffusive microswimmers.Comment: 6 pages, 4 figures. Supplemental material available as separate file in the Ancillary Files sectio

    How to make modal shift from road to rail possible in the European transport market, as aspired to in the EU Transport White Paper 2011

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    The total demand for freight transport in Europe has increased significantly in recent decades, but most of it has been handled by road transport. To fulfil the modal shift targets set in the EU White Paper 2011, it will be necessary to double rail’s market share from today’s 18 %, by 2050. Translating this into reality means rail will have to handle 3 to 4 times the cargo volume it does today. With this in mind, the paper develops a vision of an efficient rail freight system in 2050. Methodology To achieve the above objective, the research applies literature survey and group discussion methodology and applying a system approach. Keeping on board the EU Transport White Paper 2011 modal shift targets, as well as future freight demand and customer requirements, the current research attempts to answer the following three critical questions: -How can rail offer the quality of service that will attract customers and fulfil the targets? - How can rail offer its customers a price that is competitive with road? - How can rail offer the capacity to meet the increased demand from modal shift

    Dense 3D Object Reconstruction from a Single Depth View

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    In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.Comment: TPAMI 2018. Code and data are available at: https://github.com/Yang7879/3D-RecGAN-extended. This article extends from arXiv:1708.0796

    Preparing HPC Applications for the Exascale Era: A Decoupling Strategy

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    Production-quality parallel applications are often a mixture of diverse operations, such as computation- and communication-intensive, regular and irregular, tightly coupled and loosely linked operations. In conventional construction of parallel applications, each process performs all the operations, which might result inefficient and seriously limit scalability, especially at large scale. We propose a decoupling strategy to improve the scalability of applications running on large-scale systems. Our strategy separates application operations onto groups of processes and enables a dataflow processing paradigm among the groups. This mechanism is effective in reducing the impact of load imbalance and increases the parallel efficiency by pipelining multiple operations. We provide a proof-of-concept implementation using MPI, the de-facto programming system on current supercomputers. We demonstrate the effectiveness of this strategy by decoupling the reduce, particle communication, halo exchange and I/O operations in a set of scientific and data-analytics applications. A performance evaluation on 8,192 processes of a Cray XC40 supercomputer shows that the proposed approach can achieve up to 4x performance improvement.Comment: The 46th International Conference on Parallel Processing (ICPP-2017

    Exploring Application Performance on Emerging Hybrid-Memory Supercomputers

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    Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of "fast" and "slow" memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201
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