Lookahead Strategies for Sequential Monte Carlo

Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the "future" information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.Comment: Published in at http://dx.doi.org/10.1214/12-STS401 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multiband effects on the conductivity for a multiband Hubbard model

The newly discovered iron-based superconductors have attracted lots of interests, and the corresponding theoretical studies suggest that the system should have six bands. In this paper, we study the multiband effects on the conductivity based on the exact solutions of one-dimensional two-band Hubbard model. We find that the orbital degree of freedom might enhance the critical value $U_c$ of on-site interaction of the transition from a metal to an insulator. This observation is helpful to understand why undoped High-$T_c$ superconductors are usually insulators, while recently discovered iron-based superconductors are metal. Our results imply that the orbital degree of freedom in the latter cases might play an essential role.Comment: 4 pages, 5 figure

Deep Learning Based Vehicle Make-Model Classification

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.Comment: 10 pages, ICANN 2018: Artificial Neural Networks and Machine Learnin

Scattering of electromagnetic waves from a cone with conformal mapping: application to scanning near-field optical microscope

We study the response of a conical metallic surface to an external electromagnetic (EM) field by representing the fields in basis functions containing integrable singularities at the tip of the cone. A fast analytical solution is obtained by the conformal mapping between the cone and a round disk. We apply our calculation to the scattering- based scanning near-field optical microscope (s-SNOM) and successfully quantify the elastic light scattering from a vibrating metallic tip over a uniform sample. We find that the field-induced charge distribution consists of localized terms at the tip and the base and an extended bulk term along the body of the cone far away from the tip. In recent s-SNOM experiments at the visible-IR range (600nm - 1$\mu m$) the fundamental is found to be much larger than the higher harmonics whereas at THz range ($100 \mu m-3mm$) the fundamental becomes comparable to the higher harmonics. We find that the localized tip charge dominates the contribution to the higher harmonics and becomes bigger for the THz experiments, thus providing an intuitive understanding of the origin of the near-field signals. We demonstrate the application of our method by extracting a two-dimensional effective dielectric constant map from the s-SNOM image of a finite metallic disk, where the variation comes from the charge density induced by the EM field

Annealing stability of magnetic tunnel junctions based on dual MgO free layers and [Co/Ni] based thin synthetic antiferromagnet fixed system

We study the annealing stability of bottom-pinned perpendicularly magnetized magnetic tunnel junctions based on dual MgO free layers and thin fixed systems comprising a hard [Co/Ni] multilayer antiferromagnetically coupled to thin a Co reference layer and a FeCoB polarizing layer. Using conventional magnetometry and advanced broadband ferromagnetic resonance, we identify the properties of each sub-unit of the magnetic tunnel junction and demonstrate that this material option can ensure a satisfactory resilience to the 400$^\circ$C thermal annealing needed in solid-state magnetic memory applications. The dual MgO free layer possesses an anneal-robust 0.4 T effective anisotropy and suffers only a minor increase of its Gilbert damping from 0.007 to 0.010 for the toughest annealing conditions. Within the fixed system, the ferro-coupler and texture-breaking TaFeCoB layer keeps an interlayer exchange above 0.8 mJ/m$^2$, while the Ru antiferrocoupler layer within the synthetic antiferromagnet maintains a coupling above -0.5 mJ/m$^2$. These two strong couplings maintain the overall functionality of the tunnel junction upon the toughest annealing despite the gradual degradation of the thin Co layer anisotropy that may reduce the operation margin in spin torque memory applications. Based on these findings, we propose further optimization routes for the next generation magnetic tunnel junctions