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
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 countriesâ news coverage on climate change. Keywords: Climate change; UK; China; newspaper; Copenhage
T1Ď-based fibril-reinforced poroviscoelastic constitutive relation of human articular cartilage using inverse finite element technology
BackgroundMapping of T1Ď 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 T1Ď 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 T1Ď value from MR maps. A T1Ď-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 T1Ď and the volume fraction of the hydrated solid matrix in the T1Ď-based FRPE constitutive relation. Compared with the common FRPE constitutive relation (i.e., without T1Ď), the T1Ď-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 T1Ď-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 T1Ď 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
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., ``''). 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
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The uncertainty analysis of the MODIS GPP product in global maize croplands
Gross primary productivity (GPP) is very important in the global carbon cycle. Currently, the newly released estimates of 8-day GPP at 500 m spatial resolution (Collection 6) are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Science Team for the global land surface via the improved light use efficiency (LUE) model. However, few studies have evaluated its performance. In this study, the MODIS GPP products (GPPMOD) were compared with the observed GPP (GPPEC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPPMOD was underestimated by 6%â58% across sites. Nevertheless, after incorporating the parameters of the calibrated LUE, the measurements of meteorological variables and the reconstructed Fractional Photosynthetic Active Radiation (FPAR) into the GPPMOD algorithm in steps, the accuracies of GPPMOD estimates were improved greatly, albeit to varying degrees. The differences between the GPPMOD and the GPPEC were primarily due to the magnitude of LUE and FPAR. The underestimate of maize cropland LUE was a widespread problem which exerted the largest impact on the GPPMOD algorithm. In American and European sites, the performance of the FPAR exhibited distinct differences in capturing vegetation GPP during the growing season due to the canopy heterogeneity. In addition, at the DE-Kli site, the GPPMOD abruptly produced extreme low values during the growing season because of the contaminated FPAR from a continuous rainy season. After correcting the noise of the FPAR, the accuracy of the GPPMOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPPMOD, especially for the maize crop ecosystem, to maintain food security and better understand global carbon cycle
Preparation of titanium by electrochemical reduction of titanium dioxide powder in molten SrCl2-KCl
Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis
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
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 walletsâ 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
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 TiAlC-coated Zircaloy-4
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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