Variational approach to renormalized phonon in momentum-nonconserving nonlinear lattices

A previously proposed variational approach for momentum-conserving systems [J. Liu et.al., Phys. Rev. E 91, 042910 (2015)] is extended to systematically investigate general momentum-nonconserving nonlinear lattices. Two intrinsic identities characterizing optimal reference systems are revealed, which enables us to derive explicit expressions for optimal variational parameters. The resulting optimal harmonic reference systems provide information for the band gap as well as the dispersion of renormalized phonons in nonlinear lattices. As a demonstration, we consider the one-dimensional \phi^?4 lattice. By combining the transfer integral operator method, we show that the phonon band gap endows a simple power-law temperature dependence in the weak stochasticity regime where predicted dispersion is reliable by comparing with numerical results. In addition, an exact relation between ensemble averages of the \phi^?4 lattice in the whole temperature range is found, regardless of the existence of the strong stochasticity threshold.Comment: 8 pages, 3 figure

Triggering waves in nonlinear lattices: Quest for anharmonic phonons and corresponding mean free paths

Guided by a stylized experiment we develop a self-consistent anharmonic phonon concept for nonlinear lattices which allows for explicit "visualization." The idea uses a small external driving force which excites the front particles in a nonlinear lattice slab and subsequently one monitors the excited wave evolution using molecular dynamics simulations. This allows for a simultaneous, direct determination of the existence of the phonon mean free path with its corresponding anharmonic phonon wavenumber as a function of temperature. The concept for the mean free path is very distinct from known prior approaches: the latter evaluate the mean free path only indirectly, via using both, a scale for the phonon relaxation time and yet another one for the phonon velocity. Notably, the concept here is neither limited to small lattice nonlinearities nor to small frequencies. The scheme is tested for three strongly nonlinear lattices of timely current interest which either exhibit normal or anomalous heat transport

Examining Accumulated Emotional Traits in Suicide Blogs With an Emotion Topic Model

Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limits the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people’s daily writings (i.e. Blogs), and examining these emotional traits which are predictive of suicidal behaviors. A complex emotion topic (CET) model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i.e., accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and non-suicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors

Managers\u27 occupational stress in China : the role of self-efficacy

The role of self-efficacy, an individual difference variable, in occupational stress research is seldom discussed, and is even rarely examined in Chinese societies. This study investigates the relationships between stressors, managerial self-efficacy (MSE) and work-related strains (job satisfaction, physical strain, and psychological strain). A total of 450 enterprise managers in eight cities of the People\u27s Republic of China completed a battery of structured questionnaires. The results of the study generally support that total stressors was negatively related to job satisfaction, physical strain, and psychological strain. Furthermore, MSE was statistically significantly related to strains in that respondents with high levels of MSE reported higher levels of job satisfaction, lower levels of physical strain and psychological strain than did those with low MSE. Related to the moderating effects of MSE on stressor-strain relationship, only significant moderating effect was found in predicting physical strain, as demonstrated by a series of hierarchical regressions while controlling for age, tenure, and position levels and educational levels

A combined cepstral distance method for emotional speech recognition

Affective computing is not only the direction of reform in artificial intelligence but also exemplification of the advanced intelligent machines. Emotion is the biggest difference between human and machine. If the machine behaves with emotion, then the machine will be accepted by more people. Voice is the most natural and can be easily understood and accepted manner in daily communication. The recognition of emotional voice is an important field of artificial intelligence. However, in recognition of emotions, there often exists the phenomenon that two emotions are particularly vulnerable to confusion. This article presents a combined cepstral distance method in two-group multi-class emotion classification for emotional speech recognition. Cepstral distance combined with speech energy is well used as speech signal endpoint detection in speech recognition. In this work, the use of cepstral distance aims to measure the similarity between frames in emotional signals and in neutral signals. These features are input for directed acyclic graph support vector machine classification. Finally, a two-group classification strategy is adopted to solve confusion in multi-emotion recognition. In the experiments, Chinese mandarin emotion database is used and a large training set (1134 + 378 utterances) ensures a powerful modelling capability for predicting emotion. The experimental results show that cepstral distance increases the recognition rate of emotion sad and can balance the recognition results with eliminating the over fitting. And for the German corpus Berlin emotional speech database, the recognition rate between sad and boring, which are very difficult to distinguish, is up to 95.45%

Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure