Generation of magnetic skyrmions through pinning effect

Based on analytical estimation and lattice simulation, a proposal is made that magnetic skyrmions can be generated through the pinning effect in 2D chiral magnetic materials, in absence of an external magnetic field or magnetic anisotropy. In our simulation, stable magnetic skyrmions can be generated in the pinning areas. The properties of the skyrmions are studied for various values of ferromagnetic exchange strength and the Dzyaloshinskii-Moriya interaction strength.Comment: 22 pages, 15 figure

Shape of a skyrmion

We propose a method of determining the shape of a two-dimensional magnetic skyrmion, which can be parameterized as the position dependence of the orientation of the local magnetic moment, by using the expansion in terms of the eigenfunctions of the Schr\"{o}dinger equation of a harmonic oscillator. A variational calculation is done, up to the next-to-next-to-leading order. This result is verified by a lattice simulation based on Landau-Lifshitz-Gilbert equation. Our method is also applied to the dissipative matrix in the Thiele equation as well as two interacting skyrmions in a bilayer system.Comment: 25 pages, 11 figure

Deterministic endless collective evolvement in active nematics

We propose a simple deterministic dynamic equation and reveal the mechanism of large-scale endless evolvement of spatial density inhomogeneity in active nematic. We determine the phase regions analytically. The interplay of density, magnitude of nematic order, and nematic director is crucial for the long-wave-length instability and the emergence of seemingly fluctuated collective motions. Ordered nematic domains can absorb particles, grow and divide endlessly. The present finding extends our understanding of the large-scale and seemingly fluctuated organization in active fluids.Comment: 5 pages, 3 figure

Effective Attraction Interactions between Like-charge Macroions Bound to Binary Fluid Lipid Membranes

Using integral equation theory of liquids to a binary mixed fluid lipid membrane, we study the membrane-mediated interactions between the macroions and the redistribution of neutral and charged lipids due to binding macroions. We find that when the concentration of binding macroions is infinitely dilute, the main contribution to the attractive potential between macroions is the line tension between neutral and charged lipids of the membrane, and the bridging effect also contributes to the attraction. As the relative concentration of charged lipids is increased, we observe a repulsive - attractive - repulsive potential transition due to the competition between the line tension of lipids and screened electrostatic macroion-macroion interactions. For the finite concentration of macroions, the main feature of the attraction is similar to the infinite dilution case. However, due to the interplay of formation of charged lipid - macroion complexes, the line tension of redistributed binary lipids induced by single macroion is lowered in this case, and the maximum of attractive potential will shift toward the higher values of the charged lipid concentration

Predictive Learning: Using Future Representation Learning Variantial Autoencoder for Human Action Prediction

The unsupervised Pretraining method has been widely used in aiding human action recognition. However, existing methods focus on reconstructing the already present frames rather than generating frames which happen in future.In this paper, We propose an improved Variantial Autoencoder model to extract the features with a high connection to the coming scenarios, also known as Predictive Learning. Our framework lists as following: two steam 3D-convolution neural networks are used to extract both spatial and temporal information as latent variables. Then a resample method is introduced to create new normal distribution probabilistic latent variables and finally, the deconvolution neural network will use these latent variables generate next frames. Through this possess, we train the model to focus more on how to generate the future and thus it will extract the future high connected features. In the experiment stage, A large number of experiments on UT and UCF101 datasets reveal that future generation aids Prediction does improve the performance. Moreover, the Future Representation Learning Network reach a higher score than other methods when in half observation. This means that Future Representation Learning is better than the traditional Representation Learning and other state- of-the-art methods in solving the human action prediction problems to some extends

Construct order parameter from the spectra of mutual information

In this paper, we try to establish a connection between a quantum information concept, i.e. the mutual information, and the conventional order parameter in condensed matter physics. We show that a non-vanishing mutual information at a long distance means the existence of long-range order. By analyzing the entanglement spectra of the reduced density matrix that are used to calculate the mutual information, we show how to find the local order operator used to identify various phases with long-rang order.Comment: 7 page

A spin chain with spiral orders: perspectives of quantum information and mechanical response

In this paper, we study the ground state of a one-dimensional exactly solvable model with a spiral order. While the model's energy spectra is the same as the one-dimensional transverse field Ising model, its ground state manifests spiral order with various periods. The quantum phase transition from a spiral-order phase to a paramagnetic phase is investigated in perspectives of quantum information science and mechanics. We show that the modes of the ground-state fidelity and its susceptibility can tell the change of periodicity around the critical point. We study also the spin torsion modulus which defines the coefficient of the potential energy stored under a small rotation. We find that at the critical point, it is a constant; while away from the critical point, the spin torsion modulus tends to zero.Comment: 7 pages, 6 figure

HRank: A Path based Ranking Framework in Heterogeneous Information Network

Recently, there is a surge of interests on heterogeneous information network analysis. As a newly emerging network model, heterogeneous information networks have many unique features (e.g., complex structure and rich semantics) and a number of interesting data mining tasks have been exploited in this kind of networks, such as similarity measure, clustering, and classification. Although evaluating the importance of objects has been well studied in homogeneous networks, it is not yet exploited in heterogeneous networks. In this paper, we study the ranking problem in heterogeneous networks and propose the HRank framework to evaluate the importance of multiple types of objects and meta paths. Since the importance of objects depends upon the meta paths in heterogeneous networks, HRank develops a path based random walk process. Moreover, a constrained meta path is proposed to subtly capture the rich semantics in heterogeneous networks. Furthermore, HRank can simultaneously determine the importance of objects and meta paths through applying the tensor analysis. Extensive experiments on three real datasets show that HRank can effectively evaluate the importance of objects and paths together. Moreover, the constrained meta path shows its potential on mining subtle semantics by obtaining more accurate ranking results.Comment: 12 pages, 11 figure

Density Matrix Spectra and Order Parameters in the 1D Extended Hubbard Model

Without any knowledge of the symmetry existing in the system, we derive the exact forms of the order parameters which show long-range correlation in the ground state of the one-dimensional extended Hubbard model using a quantum information approach. Our work demonstrates that the quantum information approach can help us to find the explicit form of the order parameter, which cannot be derived systematically via traditional methods in the condensed matter theory.Comment: 9 pages, 14 figure

Fidelity susceptibilities in the one-dimensional extended Hubbard model

We investigated the fidelity susceptibility in the one-dimension (1D) Hubbard model and the extended Hubbard model at half-filling via the density matrix renormalization group. From the numerical results, we argue that in the 1D Hubbard model, the fidelity susceptibility shows a divergence at two points which is infinitesimally close to the critical point while it is always extensive exactly at the critical point. For the extended Hubbard model, we found that for a properly chosen driving parameter, the fidelity susceptibility is able to reveal the quantum phase transitions between the PS (phase separation)-superconducting, superconducting-CDW (charge-density wave), CDW-SDW(spin-density wave), SDW-PS, CDW-BOW (bond-order wave), and the BOW-SDW phases.Comment: 11 pages, 16 figure