Temporal Relational Reasoning in Videos

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1

Anomalous magneto-structural behavior of MnBi explained: a path towards an improved permanent magnet

Low-temperature MnBi (hexagonal NiAs phase) exhibits anomalies in the lattice constants (a, c) and bulk elastic modulus (B) below 100 K, spin reorientation and magnetic susceptibility maximum near 90 K, and, importantly for high-temperature magnetic applications, an increasing coercivity (unique to MnBi) above 180 K. We calculate the total energy and magneto-anisotropy energy (MAE) versus (a, c) using DFT+U methods. We reproduce and explain all the above anomalies. We predict that coercivity and MAE increase due to increasing a, suggesting means to improve MnBi permanent magnets.Comment: 4 pages, 5 figure

Competing Quantum Orderings in Cuprate Superconductors: A Minimal Model

We present a minimal model for cuprate superconductors. At the unrestricted mean-field level, the model produces homogeneous superconductivity at large doping, striped superconductivity in the underdoped regime and various antiferromagnetic phases at low doping and for high temperatures. On the underdoped side, the superconductor is intrinsically inhomogeneous and global phase coherence is achieved through Josephson-like coupling of the superconducting stripes. The model is applied to calculate experimentally measurable ARPES spectra.Comment: 5 pages, 4 eps included figure

Public Health Promotion: Autonomy of the Emergency Nurse Practitioner

The purpose of this paper is to examine several key issues in health care reform. From the Patient Protection and Affordable Care Act of 2010 to the cholera epidemic in Haiti, global health care reform is necessary to promote health and wellness among all nations. There is an International shortage of nurses and nursing faculty. Among the providers, it is also necessary to examine autonomy of the most up and coming nurse provider: the emergency nurse practitioner

Knowledge based cloud FE simulation of sheet metal forming processes

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques. This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions

An effective many-body theory for strongly interacting polar molecules

We derive a general effective many-body theory for bosonic polar molecules in strong interaction regime, which cannot be correctly described by previous theories within the first Born approximation. The effective Hamiltonian has additional interaction terms, which surprisingly reduces the anisotropic features of dipolar interaction near the shape resonance regime. In the 2D system with dipole moment perpendicular to the plane, we find that the phonon dispersion scales as \sqrt{|\bfp|} in the low momentum (\bfp) limit, showing the same low energy properties as a 2D charged Bose gas with Coulomb ($1/r$) interactions.Comment: Same as published version (11 pages, 2 figure