340 research outputs found

    Biased or balanced? ----A study of British and Chinese newspaper coverage of climate change in the 2009 United Nations Climate Change Conference

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    Nowadays, climate science communication is an issue not only discussed within a country but more like a global urgent task that needs wide perspective. This study investigated the prevalence of news frames in Climate change news coverage from the Guardian and China Daily during the 2009 United Nations Climate Change Conference in Copenhagen. Five news frames, attribution of responsibility, human interest, economic consequences, morality and conflict were applied to 59 Chinese news stories and 71 British news articles. By applying deductive framing analysis, the results showed that with slight variation, the proportion and pattern of reports on Copenhagen Climate Summit were similar across the two countries. This outcome extends the knowledge of developing and developed countries’ news coverage on climate change. Keywords: Climate change; UK; China; newspaper; Copenhage

    T1ρ-based fibril-reinforced poroviscoelastic constitutive relation of human articular cartilage using inverse finite element technology

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    BackgroundMapping of T1ρ relaxation time is a quantitative magnetic resonance (MR) method and is frequently used for analyzing microstructural and compositional changes in cartilage tissues. However, there is still a lack of study investigating the link between T1ρ relaxation time and a feasible constitutive relation of cartilage which can be used to model complicated mechanical behaviors of cartilage accurately and properly.MethodsThree-dimensional finite element (FE) models of ten in vitro human tibial cartilage samples were reconstructed such that each element was assigned by material-level parameters, which were determined by a corresponding T1ρ value from MR maps. A T1ρ-based fibril-reinforced poroviscoelastic (FRPE) constitutive relation for human cartilage was developed through an inverse FE optimization technique between the experimental and simulated indentations.ResultsA two-parameter exponential relationship was obtained between the T1ρ and the volume fraction of the hydrated solid matrix in the T1ρ-based FRPE constitutive relation. Compared with the common FRPE constitutive relation (i.e., without T1ρ), the T1ρ-based FRPE constitutive relation indicated similar indentation depth results but revealed some different local changes of the stress distribution in cartilages.ConclusionsOur results suggested that the T1ρ-based FRPE constitutive relation may improve the detection of changes in the heterogeneous, anisotropic, and nonlinear mechanical properties of human cartilage tissues associated with joint pathologies such as osteoarthritis (OA). Incorporating T1ρ relaxation time will provide a more precise assessment of human cartilage based on the individual in vivo MR quantification

    RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding

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    Knowledge graph (KG) reasoning is an important problem for knowledge graphs. It predicts missing links by reasoning on existing facts. Knowledge graph embedding (KGE) is one of the most popular methods to address this problem. It embeds entities and relations into low-dimensional vectors and uses the learned entity/relation embeddings to predict missing facts. However, KGE only uses zeroth-order (propositional) logic to encode existing triplets (e.g., ``Alice is Bob's wife."); it is unable to leverage first-order (predicate) logic to represent generally applicable logical \textbf{rules} (e.g., ``∀x,y ⁣:x is y’s wife→y is x’s husband\forall x,y \colon x ~\text{is}~ y\text{'s wife} \rightarrow y ~\text{is}~ x\text{'s husband}''). On the other hand, traditional rule-based KG reasoning methods usually rely on hard logical rule inference, making it brittle and hardly competitive with KGE. In this paper, we propose RulE, a novel and principled framework to represent and model logical rules and triplets. RulE jointly represents entities, relations and logical rules in a unified embedding space. By learning an embedding for each logical rule, RulE can perform logical rule inference in a soft way and give a confidence score to each grounded rule, similar to how KGE gives each triplet a confidence score. Compared to KGE alone, RulE allows injecting prior logical rule information into the embedding space, which improves the generalization of knowledge graph embedding. Besides, the learned confidence scores of rules improve the logical rule inference process by softly controlling the contribution of each rule, which alleviates the brittleness of logic. We evaluate our method with link prediction tasks. Experimental results on multiple benchmark KGs demonstrate the effectiveness of RulE

    Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis

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    We propose a novel Edge guided Generative Adversarial Network (EdgeGAN) for photo-realistic image synthesis from semantic layouts. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to two largely unresolved challenges. First, the semantic labels do not provide detailed structural information, making it difficult to synthesize local details and structures. Second, the widely adopted CNN operations such as convolution, down-sampling and normalization usually cause spatial resolution loss and thus are unable to fully preserve the original semantic information, leading to semantically inconsistent results (e.g., missing small objects). To tackle the first challenge, we propose to use the edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. Further, to preserve the semantic information, we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout. Extensive experiments on two challenging datasets show that the proposed EdgeGAN can generate significantly better results than state-of-the-art methods. The source code and trained models are available at https://github.com/Ha0Tang/EdgeGAN.Comment: 40 pages, 29 figure

    Towards Understanding the Adoption and Social Experience of Digital Wallet Systems

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    For millions around the globe, digital wallets are replacing cash and credit cards. These services support user-to-user payments, and add a social component to transactions. However, there is little understanding of the key factors behind digital wallets’ rapid growth in US (Venmo) and China (WeChat Pay). What are the factors that led to their success? How social relationships play a role in their adoption? We conduct a mixed methods study, using a comprehensive survey (N=879) and semi-structured interviews (N=41) to explore the interplay of the two roles of these digital wallets, i.e., a payment system and a social platform. Our analysis suggests that the network effect does benefit their adoption and retention, but through different mechanisms. In return, transaction activities performed in digital wallets help strengthen existing social ties. We also present design implications for future social payment services

    Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

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    The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned \textit{semantics} of language tokens do the most heavy lifting during the reasoning process. Different from human's symbolic reasoning process, the semantic representations of LLMs could create strong connections among tokens, thus composing a superficial logical chain. To test our hypothesis, we decouple semantics from the language reasoning process and evaluate three kinds of reasoning abilities, i.e., deduction, induction and abduction. Our findings reveal that semantics play a vital role in LLMs' in-context reasoning -- LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic or counter-commonsense reasoning tasks by leveraging in-context new knowledge. The surprising observations question whether modern LLMs have mastered the inductive, deductive and abductive reasoning abilities as in human intelligence, and motivate research on unveiling the magic existing within the black-box LLMs. On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities. Code is available at {\url{https://github.com/XiaojuanTang/ICSR}}

    Deposition, characterization and high-temperature steam oxidation behavior of single-phase Ti2_{2}AlC-coated Zircaloy-4

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    Oxidation of single-phase and dense Ti2AlC coatings with or without a 500 nm TiC diffusion barrier deposited on Zircaloy-4 by annealing of nanoscale multilayer stacks between 800 °C and 1200 °C in high-temperature steam was investigated. Coatings without TiC barrier formed a duplex scale: outer θ-Al2O3 rich layer mixed with TiO2 and inner porous TiO2 layer; correspondingly, a triple-layered scale (θ-Al2O3 + TiO2/θ-Al2O3/TiO2) grew on coatings with barrier at 800 °C. The TiC barrier suppresses the rapid diffusion of Al into the substrate, contributing to improved performance and longer life of Ti2AlC/TiC coatings. However, both coatings demonstrated low protection effect from 1000 °C in steam
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