Spin moment over 10-300 K and delocalization of magnetic electrons above the Verwey transition in magnetite

In order to probe the magnetic ground state, we have carried out temperature dependent magnetic Compton scattering experiments on an oriented single crystal of magnetite (Fe$_3$O$_4$), together with the corresponding first-principles band theory computations to gain insight into the measurements. An accurate value of the magnetic moment $\mu_S$ associated with unpaired spins is obtained directly over the temperature range of 10-300K. $\mu_S$ is found to be non-integral and to display an anomalous behavior with the direction of the external magnetic field near the Verwey transition. These results reveal how the magnetic properties enter the Verwey energy scale via spin-orbit coupling and the geometrical frustration of the spinel structure, even though the Curie temperature of magnetite is in excess of 800 K. The anisotropy of the magnetic Compton profiles increases through the Verwey temperature $T_v$ and indicates that magnetic electrons in the ground state of magnetite become delocalized on Fe B-sites above $T_v$.Comment: 5 pages, 5 figures, to appear in Journal of Physics and Chemistry of Solid

High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models

Learning in deep models using Bayesian methods has generated significant attention recently. This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference, while maintaining a measure of uncertainty in the model parameters. Stochastic gradient MCMC algorithms (SG-MCMC) are a family of diffusion-based sampling methods for large-scale Bayesian learning. In SG-MCMC, multivariate stochastic gradient thermostats (mSGNHT) augment each parameter of interest, with a momentum and a thermostat variable to maintain stationary distributions as target posterior distributions. As the number of variables in a continuous-time diffusion increases, its numerical approximation error becomes a practical bottleneck, so better use of a numerical integrator is desirable. To this end, we propose use of an efficient symmetric splitting integrator in mSGNHT, instead of the traditional Euler integrator. We demonstrate that the proposed scheme is more accurate, robust, and converges faster. These properties are demonstrated to be desirable in Bayesian deep learning. Extensive experiments on two canonical models and their deep extensions demonstrate that the proposed scheme improves general Bayesian posterior sampling, particularly for deep models.Comment: AAAI 201

Band dependent emergence of heavy quasiparticles in CeCoIn5

We investigate the low temperature (T $<$ 2 K) electronic structure of the heavy fermion superconductor CeCoIn5 (T$_c$ = 2.3 K) by angle-resolved photoemission spectroscopy (ARPES). The hybridization between conduction electrons and f-electrons, which ultimately leads to the emergence of heavy quasiparticles responsible for the various unusual properties of such materials, is directly monitored and shown to be strongly band dependent. In particular the most two-dimensional band is found to be the least hybridized one. A simplified multiband version of the Periodic Anderson Model (PAM) is used to describe the data, resulting in semi-quantitative agreement with previous bulk sensitive results from de-Haas-van-Alphen measurements.Comment: 6 pages, 3 figure

Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements

Emotion evoked by an advertisement plays a key role in influencing brand recall and eventual consumer choices. Automatic ad affect recognition has several useful applications. However, the use of content-based feature representations does not give insights into how affect is modulated by aspects such as the ad scene setting, salient object attributes and their interactions. Neither do such approaches inform us on how humans prioritize visual information for ad understanding. Our work addresses these lacunae by decomposing video content into detected objects, coarse scene structure, object statistics and actively attended objects identified via eye-gaze. We measure the importance of each of these information channels by systematically incorporating related information into ad affect prediction models. Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International Conference on Multimodal Interaction, Boulder, CO, US