Diffusive versus displacive contact plasticity of nanoscale asperities: Temperature- and velocity-dependent strongest size

We predict a strongest size for the contact strength when asperity radii of curvature decrease below ten nanometers. The reason for such strongest size is found to be correlated with the competition between the dislocation plasticity and surface diffusional plasticity. The essential role of temperature is calculated and illustrated in a comprehensive asperity size-strengthtemperature map taking into account the effect of contact velocity. Such a map should be essential for various phenomena related to nanoscale contacts such as nanowire cold welding, self-assembly of nanoparticles and adhesive nano-pillar arrays, as well as the electrical, thermal and mechanical properties of macroscopic interfaces

Raman fingerprint of semi-metal WTe2 from bulk to monolayer

Tungsten ditelluride (WTe2), a layered transition-metal dichalcogenide (TMD), has recently demonstrated an extremely large magnetoresistance effect, which is unique among TMDs. This fascinating feature seems to be correlated with its special electronic structure. Here, we report the observation of 6 Raman peaks corresponding to the A_2^4, A_1^9, A_1^8, A_1^6, A_1^5 and A_1^2 phonons, from the 33 Raman-active modes predicted for WTe2. This provides direct evidence to distinguish the space group of WTe2 from that of other TMDs. Moreover, the Raman evolution of WTe2 from bulk to monolayer is clearly revealed. It is interesting to find that the A_2^4 mode, centered at ~109.8 cm-1, is forbidden in a monolayer, which may be attributable to the transition of the point group from C2v (bulk) to C2h (monolayer). Our work characterizes all observed Raman peaks in the bulk and few-layer samples and provides a route to study the physical properties of two-dimensional WTe2.Comment: 19 pages, 4 figures and 2 table

Mandarin Singing Voice Synthesis Based on Harmonic Plus Noise Model and Singing Expression Analysis

The purpose of this study is to investigate how humans interpret musical scores expressively, and then design machines that sing like humans. We consider six factors that have a strong influence on the expression of human singing. The factors are related to the acoustic, phonetic, and musical features of a real singing signal. Given real singing voices recorded following the MIDI scores and lyrics, our analysis module can extract the expression parameters from the real singing signals semi-automatically. The expression parameters are used to control the singing voice synthesis (SVS) system for Mandarin Chinese, which is based on the harmonic plus noise model (HNM). The results of perceptual experiments show that integrating the expression factors into the SVS system yields a notable improvement in perceptual naturalness, clearness, and expressiveness. By one-to-one mapping of the real singing signal and expression controls to the synthesizer, our SVS system can simulate the interpretation of a real singer with the timbre of a speaker.Comment: 8 pages, technical repor

Affective Music Information Retrieval

Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio