Pairing Li-free transition-metal-based cathodes（MX） with Li-metal anodes is an emerging trend to overcome the energy-density limitation of current rechargeable Li-ion ***,the development of practical Li-free MX cath...
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Pairing Li-free transition-metal-based cathodes（MX） with Li-metal anodes is an emerging trend to overcome the energy-density limitation of current rechargeable Li-ion ***,the development of practical Li-free MX cathodes is plagued by the existing notion of low voltage due to the long-term overlo oked voltage-tuning/phase-stability ***,we propose a p-type alloying strategy involving three voltage/phase-evolution stages,of which each of the varying trends are quantitated by two improved ligand-field descriptors to balance the above *** this,an intercalation-type 2H-V1.75Cr0.25S4cathode tuned from layered MX2family is successfully designed,which possesses an energy density of 554.3 Wh kg-1at the ele ctrode level accompanied by interfacial compatibility with sulfide solid-state ele *** propos al of this class of materials is expected to break free from scarce or high-cost transition-metal（*** and Ni） reliance in current commercial *** experiments further confirm the voltage and energy-density gains of *** strategy is not limited to specific Li-free cathodes and offers a solution to achieve high voltage and phase stability simultaneously.
To meet the increasingly complex machining needs much better, this paper proposes a 2CPU/2UPC multi-mode mechanism that can achieve the two machining processes of surface drilling and milling. The screw theory is used...
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Satisfiability Modulo Theories (SMT) has significant application in various domains. In this paper, we focus on quantifier-free Satisfiablity Modulo Real Arithmetic, referred to as SMT(RA), including both linear and n...
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Dynamic networks are data structures that represent the interactions among various entities in real-world systems, with their topology and node properties evolving over time. However, prevailing approaches typically d...
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作者：
Li, ZhilinMa, XutongHu, MengzeYan, Jun
State Key Lab. of Computer Science Ins. of Software CAS University of Chinese Academy of Sciences Beijing China
State Key Lab. of Computer Science Ins. of Software CAS Beijing China
State Key Lab. of Computer Science Ins. of Software CAS Tech. Center of Software Eng. Ins. of Software CAS University of Chinese Academy of Sciences Beijing China
Sequence Containers (SC) in the C++ Standard Template Library (STL), such as the vector, are widely used in large-scale projects for their maintainability and flexibility. However, accessing the elements in an SC is b...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
Sequence Containers (SC) in the C++ Standard Template Library (STL), such as the vector, are widely used in large-scale projects for their maintainability and flexibility. However, accessing the elements in an SC is bug-prone, as such operations will not check their boundaries during compilation or execution, which can lead to memory errors, such as buffer overflow problems. And these bugs are difficult to detect with available static analyzers, since the size of SCs and the target of iterators cannot be precisely tracked without accurate analysis of the behavior of SCs and *** address this problem, we propose a combined model of SC sizes and iterator targets by tracking them simultaneously through a set of meta-operations extracted from corresponding method calls, and report improper operation usages according to three bug patterns. We implement the approach as a static analyzer, Scasa, on the top of the Clang Static Analyzer (CSA) framework, and evaluate its effectiveness and efficiency against CSA and other state-of-the-art static analyzers on a benchmark composed of 2,230 manually created code snippets and eight popular open-source C++ projects with a lot of SC usages. The experimental results reveal that Scasa effectively identifies nearly all inherent bugs within the manual code snippets and generates 125 reports for these projects (with a time loss of 5 - 85%) where 72 of them are marked as correct with a manual revision. And to further confirm these correct reports, we also select some important ones for developers. These results show that accessing elements of SCs is bug-prone, and cooperatively tracking SC sizes and iterator targets can accurately detect these bugs with acceptable overhead. Copyright held by the owner/author(s).
Variational Autoencoders (VAEs) have gained popularity as one of the main approaches for generating diverse and high-quality synthetic images. This study examines the suitability of evaluation metrics, specifically In...
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Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we pro...
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Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.
Digital filters, a subclass of linear time-invariant systems, are widely used in signal processing and control systems. A digital filter performs mathematical operations on a sampled, discrete-time signal to reduce or...
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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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Distributed computing systems have been widely used as the amount of data grows exponentially in the era of information explosion. Job completion time (JCT) is a major metric for assessing their effectiveness. How to ...
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Distributed computing systems have been widely used as the amount of data grows exponentially in the era of information explosion. Job completion time (JCT) is a major metric for assessing their effectiveness. How to reduce the JCT for these systems through reasonable scheduling has become a hot issue in both industry and academia. Data skew is a common phenomenon that can compromise the performance of such distributed computing systems. This paper proposes SMART, which can effectively reduce the JCT through handling the data skew during the reducing phase. SMART predicts the size of reduce tasks based on part of the completed map tasks and then enforces largest-first scheduling in the reducing phase according to the predicted reduce task size. SMART makes minimal modifications to the original Hadoop with only 20 additional lines of code and is readily deployable. The robustness and the effectiveness of SMART have been evaluated with a real-world cluster against a large number of datasets. Experiments show that SMART reduces JCT by up to 6.47%, 9.26%, and 13.66% for Terasort, WordCount and InvertedIndex respectively with the Purdue MapReduce benchmarks suite (PUMA) dataset.
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