18,443 research outputs found

    A multi-band semiclassical model for surface hopping quantum dynamics

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    In the paper we derive a semiclassical model for surface hopping allowing quantum dynamical non-adiabatic transition between different potential energy surfaces in which cases the classical Born-Oppenheimer approximation breaks down. The model is derived using the Wigner transform and Weyl quantization, and the central idea is to evolve the entire Wigner matrix rather than just the diagonal entries as was done previously in the adiabatic case. The off-diagonal entries of the Wigner matrix suitably describe the non-adiabatic transition, such as the Berry connection, for avoided crossings. We study the numerical approximation issues of the model, and then conduct numerical experiments to validate the model.Comment: 29 pages, 10 figure

    Video Captioning with Guidance of Multimodal Latent Topics

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    The topic diversity of open-domain videos leads to various vocabularies and linguistic expressions in describing video contents, and therefore, makes the video captioning task even more challenging. In this paper, we propose an unified caption framework, M&M TGM, which mines multimodal topics in unsupervised fashion from data and guides the caption decoder with these topics. Compared to pre-defined topics, the mined multimodal topics are more semantically and visually coherent and can reflect the topic distribution of videos better. We formulate the topic-aware caption generation as a multi-task learning problem, in which we add a parallel task, topic prediction, in addition to the caption task. For the topic prediction task, we use the mined topics as the teacher to train a student topic prediction model, which learns to predict the latent topics from multimodal contents of videos. The topic prediction provides intermediate supervision to the learning process. As for the caption task, we propose a novel topic-aware decoder to generate more accurate and detailed video descriptions with the guidance from latent topics. The entire learning procedure is end-to-end and it optimizes both tasks simultaneously. The results from extensive experiments conducted on the MSR-VTT and Youtube2Text datasets demonstrate the effectiveness of our proposed model. M&M TGM not only outperforms prior state-of-the-art methods on multiple evaluation metrics and on both benchmark datasets, but also achieves better generalization ability.Comment: ACM Multimedia 201

    Effects of Pace and Stress on Upper-Extremity Biomechanical Responses in Sign Language Interpreters

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    Repetitive motion injuries (RMIs) are disorders of the soft tissues due to repeated exertion and excessive movement of the body. Sign language interpreters who have to move their fingers, hands, wrists and arms repeatedly are susceptible to RMIs. One of the major research voids in the studies of RMIs in sign language interpreters is the lack of quantification of biomechanical exposures. The objective of this study was to analyze the impact of pace and psychosocial stress of sign language interpreting on the biomechanical responses in a quantitative manner and compare the results with the industrial high risk benchmarks. Twelve professional sign language interpreters participated in this study with a one-half hour interpreting task. Biomechanical variables in flexion/extension and radial/ulnar planes of wrist motion in different pace and stress conditions were measured. It was found that pace has a significant positive effect on bilateral biomechanical responses while a positive stress effect was found only for the left hand. The dominant hand was significantly more physically stressed than the non-dominant hand, as indicated by wrist kinetic variables and other wrist motion variables measured in this study. In addition, wrist kinetic variables of sign language interpreting were found similar to or higher than the high risk industrial benchmarks. The results of this study proved with quantitative data that sign language interpreting is a high risk job of RMIs, requiring highly deviated wrist positions, ballistic wrist movements, and highly repetitive wrist motions. The results also shed light on how different factors may influence the biomechanical responses of sign language interpreters

    Adaptive Tag Selection for Image Annotation

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    Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-kk strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher F-score of 0.223. Moreover, by treating the underlying image annotation system as a black box, the new method can be used as an easy plug-in to boost the performance of existing systems

    The Blood AFB1-DNA Adduct Acting as a Biomarker for Predicting the Risk and Prognosis of Primary Hepatocellular Carcinoma

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    Aflatoxin B1 (AFB1) is an important carcinogen for primary hepatocellular carcinoma (PHCC). However, the values of blood AFB1-DNA adducts predicting HCC risk and prognosis have not still been clear. We conducted a hospital-based case-control study, consisting of 380 patients with pathologically diagnosed PHCC and 588 controls without any evidence of liver diseases, to elucidate the associations between the amount of AFB1-DNA adducts in the peripheral blood and the risk and outcome of HCC. All subjects had not the history of hepatitis B and C virus infection. AFB1-DNA adducts were tested using enzyme-linked immunosorbent assay. Cases with PHCC featured an increasing blood amount of AFB1-DNA adducts compared with controls (2.01 ± 0.71 vs. 0.98 ± 0.63 μmol/DNA). Increasing adduct amount significantly grew the risk of PHCC [risk values, 1.82 (1.34–2.48) and 3.82 (2.71–5.40) for medium and high adduct level, respectively]. Furthermore, compared with patients with low adduct level, these with medium or high adduct level faced a higher death and tumor-recurrence risk. These results suggest that the blood AFB1-DNA adducts may act as a potential biomarker for predicting the risk and prognosis of PHCC

    Model Law Research on Rainfall Intensity in Spray Atomization Due to Flood Discharge

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    Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
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