Video Question Answering via Attribute-Augmented Attention Network Learning

Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.Comment: Accepted for SIGIR 201

First-principles study, fabrication and characterization of (Zr0.25Nb0.25Ti0.25V0.25)C high-entropy ceramic

The formation possibility of a new (Zr0.25Nb0.25Ti0.25V0.25)C high-entropy ceramic (ZHC-1) was first analyzed by the first-principles calculations and thermodynamical analysis and then it was successfully fabricated by hot pressing sintering technique. The first-principles calculation results showed that the mixing enthalpy of ZHC-1 was 5.526 kJ/mol and the mixing entropy of ZHC-1 was in the range of 0.693R-1.040R. The thermodynamical analysis results showed that ZHC-1 was thermodynamically stable above 959 K owing to its negative mixing Gibbs free energy. The experimental results showed that the as-prepared ZHC-1 (95.1% relative density) possessed a single rock-salt crystal structure, some interesting nanoplate-like structures and high compositional uniformity from nanoscale to microscale. By taking advantage of these unique features, compared with the initial metal carbides (ZrC, NbC, TiC and VC), it showed a relatively low thermal conductivity of 15.3 + - 0.3 W/(m.K) at room temperature, which was due to the presence of solid solution effects, nanoplates and porosity. Meanwhile, it exhibited the relatively high nanohardness of 30.3 + - 0.7 GPa and elastic modulus of 460.4 + - 19.2 GPa and the higher fracture toughness of 4.7 + - 0.5 MPa.m1/2, which were attributed to the solid solution strengthening mechanism and nanoplate pullout and microcrack deflection toughening mechanism.Comment: 49 pages,6 figures, 4 table

Numerical methods of characterizing symmetry protected topological states in one dimension

In this dissertation, we use numerical methods to study one dimensional symmetry protected topological (SPT) phases. We focus on the density matrix renormalization group (DMRG) methods and explore the machine learning methods. We investigated different SPT phases in the context of interactions and disorders. The application of machine learning methods reveals new insights into the topological phases. We begin by studying the Z3 parafermionic chain, the simplest generalization of the Kitaev p-wave wire. The quantum entanglement diagnostics we performed allow us to determine phase boundaries, and the nature of the phase transitions. An intervening incommensurate phase is found between the topological and trivial phases. We locate and characterize a putative tricritical point in the phase diagram where the three above mentioned phases meet at a single point. The phase diagram is predicted to contain a Lifshitz type transition which we con rm using entanglement measures. As another generalization of the Kitaev p-wave wire, we study the interacting inversion symmetric superconductor. We introduce interaction and inversion symmetry and preserve its original time-reversal, particle-hole and chiral symmetry. The symmetries indicates a Z2 classification. We study the quantum entanglement, teleportation and fractional Josephson effects of this system. The ground state of the topological phase is a condensation of four electrons instead of cooper-pairs. While there is a nonzero teleportation for cooper-pairs, the teleportation of one electron is suppressed. The inversion symmetry restricts the edge modes of the system to be cooper-pairs other than two uncorrelated electrons. It is also proved by the 2 pi periodicity in the fractional Josephson effects. At last we apply machine learning methods for classification of SPT phases when strong disorder is present. The entanglement spectrum is used as features to train the random forest model. We do the training using the data generated from a small fraction in the parameter space. The model can give high accuracy predictions to other regions in the phase space. It is even able to make correct predictions to system in a different symmetry class. A detailed analysis of the model indicates that it is able to capture the degeneracy in the entanglement spectrum