Melting properties are critical for designing novel materials,especially for discovering highperformance,high-melting refractory *** measurements of these properties are extremely challenging due to their high melting...
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Melting properties are critical for designing novel materials,especially for discovering highperformance,high-melting refractory *** measurements of these properties are extremely challenging due to their high melting *** theoretical predictions are,therefore,*** of the most accurate approaches for this purpose is the ab initio free-energy approach based on density functional theory(DFT).However,it generally involves expensive thermodynamic integration using ab initio molecular dynamic *** high computational cost makes high-throughput calculations ***,we propose a highly efficient DFT-based method aided by a specially designed machine learning *** the machine learning potential can closely reproduce the ab initio phase-space distribution,even for multi-component alloys,the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation *** method achieves overall savings of computational resources by 80%compared to current *** apply the method to the high-entropy alloy TaVCrW and calculate its melting properties,including the melting temperature,entropy and enthalpy of fusion,and volume change at the melting ***,the heat capacities of solid and liquid TaVCrW are *** results agree reasonably with the CALPHAD extrapolated values.
In sparse extrinsic reward settings, reinforcement learning remains a challenge despite increasing interest in this field. Existing approaches suggest that intrinsic rewards can alleviate issues caused by reward spars...
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Sequential recommendation (SR) is designed to capture sequential patterns and a user's dynamic preferences from their historical interactions. Traditional sequential recommendation only considers a single behavior...
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The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in tra...
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The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.
As the underlying implementation technology of the current main-stream digital currency, blockchain can establish a trusted distributed system without relying on third-party trusted institutions or a privacy-protect s...
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With the development of digital currencies and 5G technology, blockchain has gained widespread attention and is being used in areas such as healthcare, industry and smart vehicles. Many security issues have also been ...
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The emerging paradigm of data products, which has become increasingly popular recently due to the rise of data meshes and data marketplaces, also poses unprecedented challenges for data management. Current data archit...
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ISBN:
(纸本)9783031683114;9783031683121
The emerging paradigm of data products, which has become increasingly popular recently due to the rise of data meshes and data marketplaces, also poses unprecedented challenges for data management. Current data architectures, namely data warehouses and data lakes, are not able to meet these challenges adequately. In particular, these architectures are not designed for a just-in-time provision of highly customized data products tailored perfectly to the needs of customers. In this paper, we therefore present a virtual data lake zone for composing tailor-made data products on demand, called LALO. LALO uses data streaming technologies to enable just-in-time composing of data products without allocating storage space in the data architecture permanently. In order to enable customers to tailor data products to their needs, LALO uses a novel mechanism that enables live adaptation of data streams. Evaluation results show that the overhead for such an adaptation is negligible. Therefore, LALO represents an efficient solution for the appropriate handling of data products, both in terms of storage space and runtime.
Continuous performance monitoring is critical for maintaining optimal performance of High-Performance Computing resources. This is especially important for technological test bed systems, in which software updates occ...
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A Low-Code Development Platform (LCDP) enables people with little or no software development training to create software applications. Unlike traditional textual programming environments, it provides tools that are mo...
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The post-processing rendered sequences improves the quality of the sequences and shortens the time of the rendering phase. However, most of the current post-processing methods for sequences are suitable for video. Dir...
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