computational narrative is a complex field. While the computational processing of narratives has been tackled from different perspectives, the literature has focused on the analysis of a main plot, even if it is compo...
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This work systematically investigates the impact of nitric acid leaching treatment with various concentrations (1-6 mol/L) on the chemical composition and structural characteristics of coal samples by utilizing elemen...
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Pre-trained vision-language models like CLIP have shown powerful zero-shot inference ability via image-text matching and prove to be strong few-shot learners in various downstream tasks. However, in real-world scenari...
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Unmanned Aerial Vehicles (UAVs) are increasingly recognized for their potential to revolutionize emergency response communications and localization, especially when traditional infrastructure is damaged or non-existen...
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Classification of data streams has become an important and active field of study. The primary characteristics of data streams are a large quantity of incoming data, rapid arrival rate, and changes in their nature and ...
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Deep learning (DL) has gained great success in recent years, leading to state-of-the-art performance in research community and industrial fields like computer vision and natural language processing. One of the reasons...
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Deep learning (DL) has gained great success in recent years, leading to state-of-the-art performance in research community and industrial fields like computer vision and natural language processing. One of the reasons for this success is the huge amount parameters adopted in DL models. However, it is impractical to train a moderately large model with a large number of parameters on a typical single device. Thus, It is necessary to train DL models in clusters with distributed training algorithms. However, traditional distributed training algorithms are usually sub-optimal and highly customized, which owns the drawbacks to train large-scale DL models in varying computing clusters. To handle the above problem, researchers propose auto-parallelism, which is promising to train large-scale DL models efficiently and practically in various computing clusters. In this survey, we perform a broad and thorough investigation on challenges, basis, and strategy searching methods of auto-parallelism in DL training. First, we abstract basic parallelism schemes with their communication cost and memory consumption in DL training. Further, we analyze and compare a series of current auto-parallelism works and investigate strategies and searching methods which are commonly used in practice. At last, we discuss several trends in auto-parallelism which are promising in further research.
The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to ...
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The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.
This work presents a novel volumetric parameterization technique along with the continuous adjoint method to support gradient-based CFD shape optimization of turbomachinery stages. The proposed parameterization retain...
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This work presents a novel volumetric parameterization technique along with the continuous adjoint method to support gradient-based CFD shape optimization of turbomachinery stages. The proposed parameterization retains axisymmetry and periodicity by acting on a transformed coordinate system. The same volumetric model controls the shape and the computational volume mesh in a seamless manner, avoiding the additional use of a mesh deformation tool. Moreover, it is differentiated to compute mesh sensitivities (i.e., derivatives of nodal coordinates with respect to the design variables) and is combined with the flow and continuous adjoint, multi-row solvers of the in-house PUMA software. Flow field solutions in successive rows communicate based on the mixing plane approach;the development of continuous adjoint to the latter is also presented in this article. The adjoint to the turbulence model and distance-from-the-wall (Hamilton-Jacobi) equations are solved, increasing the accuracy of the computed sensitivity derivatives. All these tools run on modern GPUs, accelerating both flow/adjoint solutions and shape/mesh manipulations. The capabilities of these tools are demonstrated in the shape optimization of the rotor blades of the MT1 high-pressure, transonic, turbine stage, aiming at maximum stage isentropic efficiency with constraints on stage reaction and inlet capacity.
The improvement of consensus algorithms has greatly enhanced the performance of consortium blockchain, making it possible to be applied in large-scale network scenarios such as finance, healthcare and supply chain man...
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The integration of deep learning with conventional structured light center extraction techniques improves the accuracy of extracting structural gold centers. The method is divided into three steps. The initial step in...
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