The Geometric Construction of WZW Effective Action in Non-commutative Manifold

By constructing close one cochain density ${\Omega^1}_{2n}$ in the gauge group space we get WZW effective Lagrangian on high dimensional non-commutative space.Especially consistent anomalies derived from this WZW effective action in non-commutative four-dimensional space coincides with those by L.Bonora etc.Comment: 9 pages, latex, no figure

Inferring Document Readability by Integrating Eye Movement Features

Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract high-level features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition

Time-varying Sliding Mode Controls in Rigid Spacecraft Attitude Tracking

AbstractTo solve the problem of attitude tracking of a rigid spacecraft with an either known or measurable desired attitude trajectory, three types of time-varying sliding mode controls are introduced under consideration of control input constraints. The sliding surfaces of the three types initially pass arbitrary initial values of the system, and then shift or rotate to reach predetermined ones. This way, the system trajectories are always on the sliding surfaces, and the system work is guaranteed to have robustness against parameter uncertainty and external disturbances all the time. The controller parameters are optimized by means of genetic algorithm to minimize the index consisting of the weighted index of squared error (ISE) of the system and the weighted penalty term of violation of control input constraint. The stability is verified with Lyapunov method. Compared with the conventional sliding mode control, simulation results show the proposed algorithm having better robustness against inertia matrix uncertainty and external disturbance torques

Detecting regions of interest using eye tracking for CBIR

Identifying Regions of Interest (ROIs) in images has been shown an effective way to enhance the performance of Content Based Image Retrieval (CBIR). Most existing ROI identification methods are based on salience detection, and the identified ROIs may not be the regions that users are really interested in. While manual selection of ROIs can directly reflect users’ interests, it puts extra cognitive overhead to users. To alleviate these limitations, in this paper, we propose a novel eye-tracking based method to detect ROIs for CBIR, in an unobtrusive way. Experimental results have demonstrated that our model performed effectively compared with various state of the art methods

Full-spectrum thermal analysis in twisted bilayer graphene

It was recently reported that a magic angle, i.e. 1.1º, exists in twisted bilayer graphene which could lead to intrinsic unconventional superconductivity. Variations of the twisting angle between different graphene layers could lead to altered electronic band structures, which gives the peculiar superconductivity phenomenon. Effects of twisting angles on different properties of bilayer graphene need to be comprehensively investigated in order to fully understand its mechanism. In this work, classical molecular dynamics simulations are performed to calculate the interfacial thermal resistance (R) at twisting angles from 0º to 359º. Due to the symmetric structures of the honeycomb lattice, only angles from 0º to 60º is needed but the full spectrum is explored to generate the complete picture of R with . It was reported the interfacial thermal resistance changes periodically with twisting angle, with the smallest R values at every 60º starting from 0º and the largest values at every 60º starting from 30º. Phonon density of states and radial distribution functions are calculated to explain the predicted results. Effects of temperature and single-direction, bi-direction tensile strains on the calculated interfacial thermal resistance are also studied. Results in this work contribute to the fundamental understanding of the thermal properties in twisted bilayer graphene and provide reasonable guidelines to its applications in thermal management devices

Data processing system of continuous temperature measurement for liquid steel

Six different methods of accurate measurement of the temperature of liquid steel in real time are analyzed. Using a comparison and analysis, a fast and accurate mathematical model is obtained.Проведен анализ шести различных методов точного измерения температуры жидкой стали в реальном времени. С помощью сравнения и анализа результатов получена быстрая и точная математическая модель.Проведено аналіз шести різних методів точного вимірювання температури рідкої сталі в реальному часі. . За допомогою порівняння і аналізу результатів отримана швидка і точна математична модель

Molecular dynamics and machine learning in catalysts

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts

Manipulating Multiple Order Parameters via Oxygen Vacancies: The case of Eu0.5Ba0.5TiO3-{\delta}

Controlling functionalities, such as magnetism or ferroelectricity, by means of oxygen vacancies (VO) is a key issue for the future development of transition metal oxides. Progress in this field is currently addressed through VO variations and their impact on mainly one order parameter. Here we reveal a new mechanism for tuning both magnetism and ferroelectricity simultaneously by using VO. Combined experimental and density-functional theory studies of Eu0.5Ba0.5TiO3-{\delta}, we demonstrate that oxygen vacancies create Ti3+ 3d1 defect states, mediating the ferromagnetic coupling between the localized Eu 4f7 spins, and increase an off-center displacement of Ti ions, enhancing the ferroelectric Curie temperature. The dual function of Ti sites also promises a magnetoelectric coupling in the Eu0.5Ba0.5TiO3-{\delta}.Comment: Accepted by Physical Review B, 201