832 research outputs found

    Scaling Laws Governing the Elastic Properties of 3D-Graphenes

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    In this study, we have comprehensively investigated the scaling law for elastic properties of three-dimensional honeycomb-like graphenes (3D-graphenes) using hybrid neural network potential based molecular dynamics simulations and theoretical analyses. The elastic constants as functions of honeycomb hole size, denoted by the graphene wall length LL, were provided. All five independent elastic constants in the large LL limit are proportional to L1L^{-1}. The associated coefficients are combinations of two-dimensional graphene's elastic constants. High-order terms including L2L^{-2} and L3L^{-3} emerge for finite LL values. They have three origins, the distorted areas close to the joint lines of 3D-graphenes, the variation of solid angles between graphene plates, and the bending distortion of graphene plates. Significantly, the chirality becomes essential with the decreasing of LL, because the joint line structures are different between the armchair and zigzag type 3D-graphenes. Our findings provide insights into the elastic properties of graphene-based superstructures and can be used for further studies on graphene-based materials

    Quantized GAN for Complex Music Generation from Dance Videos

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    We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos. Our proposed framework takes dance video frames and human body motion as input, and learns to generate music samples that plausibly accompany the corresponding input. Unlike most existing conditional music generation works that generate specific types of mono-instrumental sounds using symbolic audio representations (e.g., MIDI), and that heavily rely on pre-defined musical synthesizers, in this work we generate dance music in complex styles (e.g., pop, breakdancing, etc.) by employing a Vector Quantized (VQ) audio representation, and leverage both its generality and the high abstraction capacity of its symbolic and continuous counterparts. By performing an extensive set of experiments on multiple datasets, and following a comprehensive evaluation protocol, we assess the generative quality of our approach against several alternatives. The quantitative results, which measure the music consistency, beats correspondence, and music diversity, clearly demonstrate the effectiveness of our proposed method. Last but not least, we curate a challenging dance-music dataset of in-the-wild TikTok videos, which we use to further demonstrate the efficacy of our approach in real-world applications - and which we hope to serve as a starting point for relevant future research

    Case report: Light-chain amyloidosis responsive to selinexor in combination with daratumumab and dexamethasone (SDd) therapy

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    The outcome of AL amyloidosis remains poor, particularly in patients with advanced organ involvement which takes long time to recovery. We conducted an observational study of two patients with AL amyloidosis treated with SDd regimen. Both patients successfully achieved significant hematological and organ responses without severe adverse events, and the time to organ response was remarkably shorter than previously reported. Notably, an over 15% reduction in interventricular septal thickness (IVST) was observed in patient#2 within 6 months. Up to now, SDd therapy has not been previously reported in AL amyloidosis and may be a promising option for these patients

    Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

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    Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

    Measurement of nuclear modification factors of gamma(1S)), gamma(2S), and gamma(3S) mesons in PbPb collisions at root s(NN)=5.02 TeV

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    The cross sections for ϒ(1S), ϒ(2S), and ϒ(3S) production in lead-lead (PbPb) and proton-proton (pp) collisions at √sNN = 5.02 TeV have been measured using the CMS detector at the LHC. The nuclear modification factors, RAA, derived from the PbPb-to-pp ratio of yields for each state, are studied as functions of meson rapidity and transverse momentum, as well as PbPb collision centrality. The yields of all three states are found to be significantly suppressed, and compatible with a sequential ordering of the suppression, RAA(ϒ(1S)) > RAA(ϒ(2S)) > RAA(ϒ(3S)). The suppression of ϒ(1S) is larger than that seen at √sNN = 2.76 TeV, although the two are compatible within uncertainties. The upper limit on the RAA of ϒ(3S) integrated over pT, rapidity and centrality is 0.096 at 95% confidence level, which is the strongest suppression observed for a quarkonium state in heavy ion collisions to date. © 2019 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.Peer reviewe
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