397 research outputs found

    Ractive MD Simulation on the Formation of Amorphous Alumina Layer Using Atomic Layer Deposition (ALD)

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    In this study, a systematic study has been performed by using the large-scale classical reactive molecular dynamics (MD) simulations to model the Atomic Layer Deposition (ALD) processes that generated tan ultra-thin and sub-nano meter amorphous alumina. The ALD process employed both water pulse and (Trimethyl-Aluminum) TMA precursors deposited onto the surface of an aluminum wetting layer. The study varied the sizes of the substrate and the concentrations of water/hydroxide precursors with a range of operating temperature to design the most favorable configurations for the subsequent TMA precursors to add onto. The role of crystallographic orientation of the Al wetting layer was also investigated and compared. Advantages and limitations in using the reactive interatomic potentials of ReaxFF were identified and correlated with the observations obtained from the MD simulations

    Study on Space and Identity in Wide Sargasso Sea

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    Jean Rhys’s Wide Sargasso Sea mainly tells the tragic fate of the white Creole woman Antoinette Cosway, dealing with problems of identity and inequality arising from French and British colonisation in the Caribbean. This novel serves not only as a narrative of personal tragedy but also as a spatially oriented exploration where the space is instrumental in shaping characters and reflecting colonial history. Based on the Space Theory and Homi K. Bhabha’s Postcolonial Theory, this paper endeavours to trace Antoinette’s journey across three significant spaces, explore how these spaces impact her identity and self-reconstruction under the patriarchal and racial oppression, and reveal the complex interactions between space and Antoinette’s identity in the novel, aiming to break down the binary oppositions in colonial discourse and understand the multiplicity and fluidity of identity

    Calibrating "Cheap Signals" in Peer Review without a Prior

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    Peer review lies at the core of the academic process, but even well-intentioned reviewers can still provide noisy ratings. While ranking papers by average ratings may reduce noise, varying noise levels and systematic biases stemming from ``cheap'' signals (e.g. author identity, proof length) can lead to unfairness. Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information. We ask reviewers to predict others' scores and use these predictions for calibration. Assuming reviewers adjust their predictions according to the noise, we demonstrate that the calibrated score results in a more robust ranking compared to average ratings, even with varying noise levels and biases. In detail, we show that the error probability of the calibrated score approaches zero as the number of reviewers increases and is significantly lower compared to average ratings when the number of reviewers is small

    Occurrence and spatial distribution of antibiotic resistance genes in the Bohai Sea and Yellow Sea areas, China

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    Rapid development of Bohai and Yellow Sea Economic Rim has led to the concern of emerging contamination of marine environments. This study investigated the spatial distribution of antibiotic resistance genes (ARGs) in Bohai and Yellow Sea areas. A large scale sampling from Bohai Sea, Yellow Sea and the major cities along the coastline from the mouth of Yalu River to the Yangtze River was performed. The spatial distribution of target ARGs based on the absolute abundances was in the trend of river water coastal water > the Bohai Sea > the Yellow Sea, inshore > offshore and inner bay > bay mouth. The total absolute abundances of selected ARGs in the coastal waters (1.23 x 10(4)-3.94 x 10(5) copies/mL) were about 1-4 orders of magnitude higher than those in the sea (21.1-8.00 x 10(3) copies/mL). The abundances of ARGs fluctuated greatly in the Yellow Sea and the coastal areas. Sulfonamide resistance genes hold the highest abundances in the Bohai and Yellow Sea (up to 2.13 x 10(3) copies/mL of still and 6.23 x 10(3) copies/mL of sul2), followed by tetracycline and quinolone resistance genes, while qnrA hold the highest abundances in coastal areas (up to 3.66 x 10(5) copies/mL). The distribution coefficients of target genes between sediments and corresponding water samples were more than 1.0 in the majority of different aquatic systems. According to the principle component analysis and redundancy analysis, water samples collected from the sea clustered together while those from the coastal zone and rivers were separated. Ammonium and nitrate played important roles in the distribution and variation of ARGs. Co-occurrence network analysis revealed that the potential multi-antibiotics resistant bacteria were detected with higher abundances in the Yellow Sea than in the Bohai Sea. These observations provided a comprehensive new insight into the pollution status of ARGs in the Bohai and Yellow Sea areas. (C) 2019 Elsevier Ltd. All rights reserved

    Brain-inspired bodily self-perception model that replicates the rubber hand illusion

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    At the core of bodily self-consciousness is the perception of the ownership of one's body. Recent efforts to gain a deeper understanding of the mechanisms behind the brain's encoding of the self-body have led to various attempts to develop a unified theoretical framework to explain related behavioral and neurophysiological phenomena. A central question to be explained is how body illusions such as the rubber hand illusion actually occur. Despite the conceptual descriptions of the mechanisms of bodily self-consciousness and the possible relevant brain areas, the existing theoretical models still lack an explanation of the computational mechanisms by which the brain encodes the perception of one's body and how our subjectively perceived body illusions can be generated by neural networks. Here we integrate the biological findings of bodily self-consciousness to propose a Brain-inspired bodily self-perception model, by which perceptions of bodily self can be autonomously constructed without any supervision signals. We successfully validated our computational model with six rubber hand illusion experiments on platforms including a iCub humanoid robot and simulated environments. The experimental results show that our model can not only well replicate the behavioral and neural data of monkeys in biological experiments, but also reasonably explain the causes and results of the rubber hand illusion from the neuronal level due to advantages in biological interpretability, thus contributing to the revealing of the computational and neural mechanisms underlying the occurrence of the rubber hand illusion.Comment: 32 pages, 10 figures and 1 tabl

    Building Program Vector Representations for Deep Learning

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    Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

    Many-body higher-order topological invariant for CnC_n-symmetric insulators

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    Higher-order topological insulators in two spatial dimensions display fractional corner charges. While fractional charges in one dimension are known to be captured by a many-body bulk invariant, computed by the Resta formula, a many-body bulk invariant for higher-order topology and the corresponding fractional corner charges remains elusive despite several attempts. Inspired by recent work by Tada and Oshikawa, we propose a well-defined many-body bulk invariant for CnC_n symmetric higher-order topological insulators, which is valid for both non-interacting and interacting systems. Instead of relating them to the bulk quadrupole moment as was previously done, we show that in the presence of CnC_n rotational symmetry, this bulk invariant can be directly identified with quantized fractional corner charges. In particular, we prove that the corner charge is quantized as e/ne/n with CnC_n symmetry, leading to a Zn\mathbb{Z}_n classification for higher-order topological insulators in two dimensions.Comment: 12 pages, 7 figures, references update
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