Degeneracy of Ground State in Two-dimensional Electron-Lattice System

We discuss the ground state of a two dimensional electron-lattice system described by a Su-Schrieffer-Heeger type Hamiltonian with a half-filled electronic band, for which it has been pointed out in the previous paper [J. Phys. Soc. Jpn. 69 (2000) 1769-1776] that the ground state distortion pattern is not unique in spite of a unique electronic energy spectrum and the same total energy. The necessary and sufficient conditions to be satisfied by the distortion patterns in the ground state are derived numerically. As a result the degrees of degeneracy in the ground state is estimated to be about $N^{N/4}$ for $N \gg 1$ with $N$ the linear dimension of the system.Comment: 2pages, 2figure

Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi

Slow Atomic Motion in Zr-Ti-Cu-Ni-Be Metallic Glasses Studied by NMR

Nuclear magnetic resonance is used for the first time to detect slow atomic motion in metallic glasses, specifically, Be motion in Zr-Ti-Cu-Ni-Be bulk metallic glasses. The observations are not consistent with the vacancy-assisted and interstitial diffusion mechanisms and favor the spread-out free volume fluctuation mechanism for Be diffusion. Comparison with the results of Be diffusion measured by elastic backscattering the NMR results also indicates that the energy barriers for short- and long-range Be motion are the same

Measuring and analysing vibration motors in insoles via accelerometers

Purpose: Falling is a major public health concern among elderly people, and they often cause serious injuries1,2. They most frequently occur during walking and are associated with the chronic deterioration in the neuromuscular and sensory systems, as well as with ankle muscle weakness and lower endurance of these muscles to fatigue1,3. Vibrating insoles, providing a subsensory mechanical noise signal to the plantar side of the feet, may improve balance in healthy young and older people and in patients with stroke or diabetic neuropathy4. The object of this study is to find the most suitable vibrator to put into the insole which can effectively improve the balance control of the elderlies. Method: We choose three different vibration actuators (micro vibration motor, brushless motor and eccentric motor) with two different weights on the insole. First, we put three same motors and two accelerometers on the insole, as shown in Figure1, then attach another layer on both side of the insole. Second, connect the motors to the power supply and the accelerometer to NI PXI-1033 spectrum analyzer which is used to collect the accelerometers' data. At last, using Fast Fourier Transform (FFT) to analyze and compare the results to see which motor is the most stable and suitable to put into the insole. Results & Discussion: The results showed that the most stable one is the brushless motor. The reason why the frequency is stable is that the relationship between voltage and frequency is linear, and the error is small through continuous measurements. On the other hand, when a person weight 55 kg stands on the insole, the frequency isn't affected by the weight. These two results appear very similar to each other, as shown in Figure 2. According to the result, we use the brushless motor to be our vibrator in the insole, and hope this will help the elderlies improve their balance control ability more efficiency

Magnetic circular dichroism from the impurity band in III-V diluted magnetic semiconductors

The magnetic circular dichroism of III-V diluted magnetic semiconductors, calculated within a theoretical framework suitable for highly disordered materials, is shown to be dominated by optical transitions between the bulk bands and an impurity band formed from magnetic dopant states. The theoretical framework incorporates real-space Green's functions to properly incorporate spatial correlations in the disordered conduction band and valence band electronic structure, and includes extended and localized electronic states on an equal basis. Our findings reconcile unusual trends in the experimental magnetic circular dichroism in III-V DMSs with the antiferromagnetic p-d exchange interaction between a magnetic dopant spin and its host.Comment: 5 pages, 4 figure

SATMC: Spectral Energy Distribution Analysis Through Markov Chains

We present the general purpose spectral energy distribution (SED) fitting tool SED Analysis Through Markov Chains (SATMC). Utilizing Monte Carlo Markov Chain (MCMC) algorithms, SATMC fits an observed SED to SED templates or models of the user's choice to infer intrinsic parameters, generate confidence levels and produce the posterior parameter distribution. Here we describe the key features of SATMC from the underlying MCMC engine to specific features for handling SED fitting. We detail several test cases of SATMC, comparing results obtained to traditional least-squares methods, which highlight its accuracy, robustness and wide range of possible applications. We also present a sample of submillimetre galaxies that have been fitted using the SED synthesis routine GRASIL as input. In general, these SMGs are shown to occupy a large volume of parameter space, particularly in regards to their star formation rates which range from ~30-3000 M_sun yr^-1 and stellar masses which range from ~10^10-10^12 M_sun. Taking advantage of the Bayesian formalism inherent to SATMC, we also show how the fitting results may change under different parametrizations (i.e., different initial mass functions) and through additional or improved photometry, the latter being crucial to the study of high-redshift galaxies.Comment: 17 pages, 11 figures, MNRAS accepte

Measurement of teicoplanin by liquid chromatography-tandem mass spectrometry:development of a novel method

Teicoplanin is an antibiotic used for the treatment of endocarditis, osteomyelitis, septic arthritis and methicillin-resistant Staphylococcus aureus. Teicoplanin is emerging as a suitable alternative antibiotic to vancomycin, where their trough serum levels are monitored by immunoassay routinely. This is the first report detailing the development of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for measuring teicoplanin in patients' serum

COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation

The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel-level supervisions for a text detection dataset (i.e. where only bounding-box annotations are available) are generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which provides pixel-level supervisions for the COCO-Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed dataset can be used instead of synthetic data, allowing us to use only a fraction of the training samples and significantly improving the performances