1,460 research outputs found

    Uniqueness of limit flow for a class of quasi-linear parabolic equations

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    We investigate the issue of uniqueness of the limit flow for a relevant class of quasi-linear parabolic equations defined on the whole space. More precisely, we shall investigate conditions which guarantee that the global solutions decay at infinity uniformly in time and their entire trajectory approaches a single steady state as time goes to infinity. Finally, we obtain a characterization of solutions which blow-up, vanish or converge to a stationary state for initial data of the form Ī»Ļ†0\lambda \varphi_0 while Ī»>0\lambda>0 crosses a bifurcation value Ī»0\lambda_0.Comment: 37 page

    Odd-parity superconductivity by competing spin-orbit coupling and orbital effect in artificial heterostructures

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    We show that odd-parity superconductivity occurs in multilayer Rashba systems without requiring spin-triplet Cooper pairs. A pairing interaction in the spin-singlet channel stabilizes the odd-parity pair-density-wave (PDW) state in the magnetic field parallel to the two-dimensional conducting plane. It is shown that the layer-dependent Rashba spin-orbit coupling and the orbital effect play essential roles for the PDW state in binary and tricolor heterostructures. We demonstrate that the odd-parity PDW state is a symmetry-protected topological superconducting state characterized by the one-dimensional winding number in the symmetry class BDI. The superconductivity in the artificial heavy-fermion superlattice CeCoIn_5/YbCoIn_5 and bilayer interface SrTiO_3/LaAlO_3 is discussed.Comment: To be published in Phys. Rev.

    Ideal Japanese Social Studies Researchers: Researcher as a Supporter for Teachersā€™ Aims Talk and their Gatekeeping

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    What is the aim which social studies researchers have in relation to research the teaching and learning of social studies and develop theories? The answers to this question would be diverse within any individuals. Although it is almost impossible for me to generalize all Japanese social studies researchersā€™ purposes for their own research, a number of Japanese researchers tended to share a similar interest for teacherā€™s aims talk or his/her good gatekeeping. The concepts of ā€œa teacher as a curricula-instructional gatekeeperā€ and ā€œaims talkā€ were introduced into Japan in 2012 when the US social studies scholar, Stephen Thorntonā€™s book Teaching Social Studies That Matters (2005) was translated into Japanese. After translated book was published, many Japanese social studies researchers began to use these terms such as, ā€œgatekeeping (gatekeeper)ā€ in Japan since 2012 (e.g., Horita, 2015; Yasuda, 2014). However, it seems that many Japanese researchers had already had a similar concern without using these terms before 2012. This studyā€™s purpose is to review some Japanese essays that are focused on gatekeeping or aims talk, and to discuss why we have been interested in these concepts for decades

    POD Network News Winter 2014

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    President\u27s Message Notes from the POD Office Conference News Election Results Committee Updates Call for Editor-POD Network News Call for Manuscripts-To Improve the Academy Journal Updates Member News Books By POD Members Breakthrough Strategies Video Series Reminders and Save-the-Dates POD Essays on Teaching Excellence Contact the Edito

    Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

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    In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics. To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at \url{https://github.com/mil-tokyo/MCD_DA}Comment: Accepted to CVPR2018 Oral, Code is available at https://github.com/mil-tokyo/MCD_D
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