Mediating exchange bias by Verwey transition in CoO/Fe3O4 thin film

We report the tunability of the exchange bias effect by the first-order metal-insulator transition (known as the Verwey transition) of Fe3O4 in CoO (5 nm)/Fe3O4 (40 nm)/MgO (001) thin film. In the vicinity of the Verwey transition, the exchange bias field is substantially enhanced because of a sharp increase in magnetocrystalline anisotropy constant from high-temperature cubic to lowtemperature monoclinic structure. Moreover, with respect to the Fe3O4 (40 nm)/MgO (001) thin film, the coercivity field of the CoO (5 nm)/Fe3O4 (40 nm)/MgO (001) bilayer is greatly increased for all the temperature range, which would be due to the coupling between Co spins and Fe spins across the interface

Raman spectroscopic determination of the length, strength, compressibility, Debye temperature, elasticity, and force constant of the C-C bond in graphene

From the perspective of bond relaxation and vibration, we have reconciled the Raman shifts of graphene under the stimuli of the number-of-layer, uni-axial-strain, pressure, and temperature in terms of the response of the length and strength of the representative bond of the entire specimen to the applied stimuli. Theoretical unification of the measurements clarifies that: (i) the opposite trends of Raman shifts due to number-of-layer reduction indicate that the G-peak shift is dominated by the vibration of a pair of atoms while the D- and the 2D-peak shifts involves z-neighbor of a specific atom; (ii) the tensile strain-induced phonon softening and phonon-band splitting arise from the asymmetric response of the C3v bond geometry to the C2v uni-axial bond elongation; (iii) the thermal-softening of the phonons originates from bond expansion and weakening; and (iv) the pressure- stiffening of the phonons results from bond compression and work hardening. Reproduction of the measurements has led to quantitative information about the referential frequencies from which the Raman frequencies shift, the length, energy, force constant, Debye temperature, compressibility, elastic modulus of the C-C bond in graphene, which is of instrumental importance to the understanding of the unusual behavior of graphene

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Verwey transition in Fe3_{3}O4_{4} thin films: Influence of oxygen stoichiometry and substrate-induced microstructure

We have carried out a systematic experimental investigation to address the question why thin films of Fe$_3$O$_4$ (magnetite) generally have a very broad Verwey transition with lower transition temperatures as compared to the bulk. We observed using x-ray photoelectron spectroscopy, x-ray diffraction and resistivity measurements that the Verwey transition in thin films is drastically influenced not only by the oxygen stoichiometry but especially also by the substrate-induced microstructure. In particular, we found (1) that the transition temperature, the resistivity jump, and the conductivity gap of fully stoichiometric films greatly depends on the domain size, which increases gradually with increasing film thickness, (2) that the broadness of the transition scales with the width of the domain size distribution, and (3) that the hysteresis width is affected strongly by the presence of antiphase boundaries. Films grown on MgO (001) substrates showed the highest and sharpest transitions, with a 200 nm film having a T$_V$ of 122K, which is close to the bulk value. Films grown on substrates with large lattice constant mismatch revealed very broad transitions, and yet, all films show a transition with a hysteresis behavior, indicating that the transition is still first order rather than higher order.Comment: 9 pages, 12 figure

On the Application of Gluon to Heavy Quarkonium Fragmentation Functions

We analyze the uncertainties induced by different definitions of the momentum fraction $z$ in the application of gluon to heavy quarkonium fragmentation function. We numerically calculate the initial $g \to J / \psi$ fragmentation functions by using the non-covariant definitions of $z$ with finite gluon momentum and find that these fragmentation functions have strong dependence on the gluon momentum $\vec{k}$. As $| \vec{k} | \to \infty$, these fragmentation functions approach to the fragmentation function in the light-cone definition. Our numerical results show that large uncertainties remains while the non-covariant definitions of $z$ are employed in the application of the fragmentation functions. We present for the first time the polarized gluon to $J/\psi$ fragmentation functions, which are fitted by the scheme exploited in this work.Comment: 11 pages, 7 figures;added reference for sec.

A convex formulation for spectral shrunk clustering

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance