OpenStreetMap (OSM), an open, crowdsourced geographic information platform, holds significant potential in fields like urban planning and resource management. Currently, most research focuses primarily on data quality...
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OpenStreetMap (OSM), an open, crowdsourced geographic information platform, holds significant potential in fields like urban planning and resource management. Currently, most research focuses primarily on data quality issues, without considering the evolution of OSM buildings. This paper employs the Markov-FLUS model to simulate and predict the expansion of OSM building data in Shenzhen. OSM building data in 2015 and 2019 were used to simulate the distribution of OSM buildings in 2023, and the distribution and completeness of OSM buildings in 2027 were subsequently simulated. The results indicate that by 2027, the growth rates of OSM buildings in Luohu and Longhua districts in Shenzhen will exceed 40%, with other areas growing by over 25%. The overall completeness of OSM buildings is projected to reach 39.99%. The simulation results can be used to identify future expansion of OSM building data in Shenzhen and support the sustainable development of OSM in the city.
Legal question answering (Legal QA) aims to provide accurate and timely answers to legal questions, significantly reducing the workload of legal professionals. This approach improves the efficiency of the judiciary an...
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Legal question answering (Legal QA) aims to provide accurate and timely answers to legal questions, significantly reducing the workload of legal professionals. This approach improves the efficiency of the judiciary and ensures prompt, professional legal assistance to the public. Currently, a major challenge is the absence of a large-scale dataset tailored for Chinese generative legal question answering. To address this, our study developed a comprehensive automatic question answering dataset for Chinese civil law, named cLegal-QA, which comprises 14,000 high-frequency questions from Chinese legal communities. This dataset spans various legal disputes and includes questions, disputes, scenarios, multiple lawyer responses, and gold-standard answers from human annotators. Additionally, we employed a generative QA model specifically designed for the cLegal-QA dataset. The results indicate that fully-supervised models, notably UniLM, T5, and BART, substantially outperform zero-shot models on this dataset, with ChatYuan being the most effective among the zero-shot models. Our analysis also reveals that answers labeled with 60-80% accuracy yield the highest efficiency. Furthermore, we evaluated the real-world performance of these models with expert validation and applied transfer learning to new civil disputes. While the QA models demonstrate commendable performance on the dataset, there is still potential for further improvement.
We report our work on evaluating performance of several MPI Allgather algorithms on Fast Ethernet. These algorithms are ring, recursive doubling, Bruck, and neighbor exchange. The first three algorithms are widely use...
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ISBN:
(纸本)0769524869
We report our work on evaluating performance of several MPI Allgather algorithms on Fast Ethernet. These algorithms are ring, recursive doubling, Bruck, and neighbor exchange. The first three algorithms are widely used today. The neighbor exchange algorithm which was recently proposed by the authors incorporates pair-wise exchange, and is expected to perform better with certain configurations, mainly when using TCP/IP over Ethernet. We tested the four algorithms on terascale Linux clusters DeepComp 6800 and DAWNING 4000A using TCP/IP over Fast Ethernet. Results show that our neighbor exchange algorithm performs the best for long messages, the ring algorithm performs the best for medium-size messages and the recursive doubling algorithm performs the best for short messages.
Fourier light field microscopy (FLFM) has emerged as a valuable tool for single-shot three-dimensional imaging largely due to its ability to reduce reconstruction artifacts and facilitate efficient parallel processing...
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The fractional Laplacian and fractional gradient are operators which play fundamental role in modeling of anomalous diffusion in d-dimensional space with the fractional exponent alpha is an element of(1,2). The princi...
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The fractional Laplacian and fractional gradient are operators which play fundamental role in modeling of anomalous diffusion in d-dimensional space with the fractional exponent alpha is an element of(1,2). The principal-value integrals are split into singular and regular parts where we avoid using any weight function for the approximation in the singularity neighborhood. The resulting approximation coefficients are calculated from optimal value of the singular domain radius which is only a function of the exponent alpha and a given grid topology. Various difference schemes are presented for the regular rectangular grids with mesh size h>0, and also for the hexagonal and the dodecahedral ones. This technique enables to evaluate the fractional operators with the approximation error O(h(4-alpha)) which is verified using testing functions with known analytical expression of their fractional Laplacian and fractional gradient. Resulting formulas can be also used for the numeric solution of the fractional partial differential equations.
This paper is to investigate the extended (2+1)-dimensional Konopelchenko–Dubrovsky equations, which can be applied to describing certain phenomena in the stratified shear flow, the internal and shallow-water waves, ...
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This paper is to investigate the extended (2+1)-dimensional Konopelchenko–Dubrovsky equations, which can be applied to describing certain phenomena in the stratified shear flow, the internal and shallow-water waves, plasmas and other fields. Painlevé analysis is passed through via symbolic computation. Bilinear-form equations are constructed and soliton solutions are derived. Soliton solutions and interactions are illustrated. Bilinear-form B?cklund transformation and a type of solutions are obtained.
Distributed software systems are becoming more and more complex *** is easy to find a huge amount of computing nodes in a nationwide or global information *** example,We Chat(Wei Xin),a well-known mobile application i...
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Distributed software systems are becoming more and more complex *** is easy to find a huge amount of computing nodes in a nationwide or global information *** example,We Chat(Wei Xin),a well-known mobile application in China,has reached a record of 650 million monthly active users in the third quarter of *** the same time,researchers are starting to talk about software systems which have billions of lines of codes[1]or can last one hundred years.
The systems engineering and robotics communities evolve in parallel, although they share common concepts and related issues. This article proposes a state-of-the-art of publications tackling both Systems Engineering (...
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ISBN:
(数字)9798331508180
ISBN:
(纸本)9798331508197
The systems engineering and robotics communities evolve in parallel, although they share common concepts and related issues. This article proposes a state-of-the-art of publications tackling both Systems Engineering (SE) and Robotics, to identify added value of Systems Engineering for Robotics, and lessons learnt for Systems Engineering from robotics use cases. In the framework of the Systems Engineering community, this synthesis of works from academics, industrial and government agencies should ease the building of SE-based seamless integrated framework for architecture, design, verification and validation of heterogeneous swarms of collaborating unmanned ground vehicles and unmanned aerial vehicles.
In the HPC area, both hardware and software move quickly. Often new hardware is developed and deployed, the corresponding software stack, including compilers and other tools, are under active development while leading...
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ISBN:
(纸本)9798400713354
In the HPC area, both hardware and software move quickly. Often new hardware is developed and deployed, the corresponding software stack, including compilers and other tools, are under active development while leading edge software developers are working to port and tune their applications, all at the same time. While the software ecosystem is in flux, one of the key challenges for users is obtaining insight into the state of implementation of key features in the programming languages and models their applications are using - whether they have been implemented, and whether the implementation conforms to the specification, especially for newly implemented features (less tested by widespread use). OpenMP is one of the most prominent shared memory programming models used for on-node programming in HPC. With the shift towards accelerators (such as GPUs and FPGAs) and heterogeneous programming OpenMP features are getting more complex. It is natural to ask whether generative AI approaches, and large language models (LLMs) in particular, can help in producing validation and verification test suites to allow users better and faster insights into the availability and correctness of OpenMP features of interest. In this work, we explore the use of ChatGPT-4 to generate a suite of tests for OpenMP features. We have chosen a set of directives and clauses, a total of 78 combinations, which first appeared in OpenMP 3.0 (released in May 2008) but are also relevant for accelerators. We prompted ChatGPT to generate tests in the C and Fortran languages, for both host (CPU) and device (accelerator). On the Summit super-computer using the GNU implementation, we found that, of the 78 generated tests 67 C tests and 43 Fortran tests compiled successfully and fewer than those executed to completion. On further analysis we show that not all generated tests are valid. We document the process, results, and provide detailed analysis regarding the quality of tests generated. With the aim of prov
Spatio-temporal traffic data collected by various sensing systems have chronic issues of missing and corruption, thus accurate data imputation and prediction have been extensively researched. Previous most methods can...
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Spatio-temporal traffic data collected by various sensing systems have chronic issues of missing and corruption, thus accurate data imputation and prediction have been extensively researched. Previous most methods can be divided into two categories: model-driven methods with physical explanations and data-driven deep learning methods. These methods all achieve promising results due to their respective advantages. However they have some limitations: model-driven methods are often linear and may not accurately model the spatiotemporal complexity, while deep learning methods often lack the physical reality making them probe to over-fitting. Inspired by both, we propose anew Convolutional-based generalized Autoregressive Tensor- Ring decomposition method (CoATR) for the completion of spatio-temporal data. CoATR not only retains the advantages of the tensor-ring (TR) decomposition model for global modeling of spatio-temporal data but also exploits the ability of deep networks to model nonlinear features. To be specific, we introduce TR decomposition to capture the global low-rankness and employ multilayer convolutional neural networks to model the global complex interactions among the TR factors for exploring the nonlinear features of the spatio-temporal data. Moreover, we design a new autoregressive network to further explore the local temporal variation in the data. Extensive experiments on a variety of common traffic datasets have validated the effectiveness and superiority of the CoATR over classical model-driven methods and other state-of-the-art data-driven deep learning methods.
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