Images crowdsourcing of mobile devices can be applied to many real-life application scenarios. However, this type of scenario application often faces issues such as the limitation of bandwidth, insufficient storage space, and the processing capability of CPU. These lead to only a few photos that can be crowdsourced. Therefore, it is a great challenge to use a limited number of resources to select photos and make it possible to cover the target area maximally. In this paper, the geographic and geometric information of the photo called data-unit is used to cover the target area as much as possible. Compared with traditional content-based image delivery methods, the network delay and computational costs can be greatly reduced. In the case of resource constraints, this paper uses the utility of photos to measure the coverage of the target area, and improves a photo utility calculation method based on data-unit. In the meantime, this paper proposes the minimum selection problem of images under the coverage requirements, and designs a selection algorithm based on greedy strategies. Compared with other traditional random selection algorithms, the results prove the effectiveness and superiority of the minimum selection algorithm.
Open Set Domain Adaptation (OSDA) aims to reduce the variation across domains while distinguishing between known samples and unknown samples. However, existing OSDA methods are based on deep neural network classifiers to separate unknown samples, which leads the network to produce overconfident predictions and fails to establish the boundary between the known and the unknown. We propose an Energy-based Open Set Domain Adaptation method (EOS). Specifically, EOS is a new two-stage approach of separation followed by alignment. We use an energy-based anomaly detection strategy as a separation method for unknown samples, transforming the traditional K-way classification task into a K+1-dimensional classifier that uses an additional dimension to model the uncertainty of Out-of-distribution (OOD) samples. Then, we use a coarse-to-fine separation method to continuously adjust the separation results and add the exact separation results as weights in the alignment process, weighing their importance to the feature distribution alignment. In the alignment phase we also optimize our separation network module at the same time, so that the module can be better adapted with invariant features. We have done experiments on the standard ground Office-31, Office-Home, and VisDA-2017 benchmarks, and the results show that our approach outperforms our competitors in most cases.
In the field of computational fluid dynamics (CFD), smoothed particle hydrodynamics (SPH) is very suitable for simulating problems with large deformation, free surface flow and other types of flow scenarios. However, traditional smoothed particle hydrodynamics methods suffer from the problem of high computation complexity, which constrains their application in scenarios with accuracy requirements. DualSPHysics is an excellent smoothed particle hydrodynamics software proposed in academia. Based on this tool, this paper presents a largescale parallel smoothed particle hydrodynamics framework: parallelDualSPHysics, which can solve the simulation of large-scale free surface flow. First, an efficient domain decomposition algorithm is proposed. And the data structure of DualSPHysics in a parallel framework is reshaped. Secondly, we proposed a strategy of overlapping computation and communication to the parallel particle interaction and particle update module, which greatly improves the parallel efficiency of the smoothed particle hydrodynamics method. Finally, we also added the pre-processing and post-processing modules to enable parallelDualSPHysics to run in modern high performance computers. In addition, a thorough evaluation shows that the 3 to 120 million particles tested can still maintain more than 90% computing efficiency, which demonstrates that the parallel strategy can achieve superior parallel efficiency.
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