Spatio-temporal structures in sheared polymer systems

We investigate spatio-temporal structures in sheared polymer systems by solving a time-dependent Ginzburg-Landau model in two dimensions. (i) In polymer solutions above the coexistence curve, crossover from linear to nonlinear regimes occurs with increasing the shear rate. In the nonlinear regime the solution behaves chaotically with large-amplitude composition fluctuations. A characteristic heterogeneity length is calculated in the nonlinear regime. (ii) We also study dynamics of shear-band structures in wormlike micellar solutions under the condition of fixed stress. The average shear rate exhibits large temporal fluctuations with occurrence of large disturbances in the spatial structures.Comment: 16pages, 10figures, to be published in Physica

Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval

Deep cross-modal learning has successfully demonstrated excellent performance in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics should be taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Data in different modalities are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study that uses deep architectures for learning the temporal correlation between audio and lyrics. A pre-trained Doc2Vec model followed by fully-connected layers is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) We propose an end-to-end network to learn cross-modal correlation between audio and lyrics, where feature extraction and correlation learning are simultaneously performed and joint representation is learned by considering temporal structures. ii) As for feature extraction, we further represent an audio signal by a short sequence of local summaries (VGG16 features) and apply a recurrent neural network to compute a compact feature that better learns temporal structures of music audio. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval

Temporal structures for Fast and Slow Speech Rate

The rhythmic component in speech synthesis often remains rather rudimentary, despite recent major efforts in the modeling of prosodic models. The European COST Action 258 has identified this problem as one of the next challenges for speech synthesis. This paper is a contribution to a new, promising approach that was tested on a French temporal model

Temporal Variability and Stability in Infant-Directed Sung Speech: Evidence for Language-specific Patterns.

In this paper, sung speech is used as a methodological tool to explore temporal variability in the timing of word-internal consonants and vowels. It is hypothesized that temporal variability/stability becomes clearer under the varying rhythmical conditions induced by song. This is explored crosslinguistically in German – a language that exhibits a potential vocalic quantity distinction – and the non-quantity languages French and Russian. Songs by non-professional singers, i.e. parents that sang to their infants aged 2 to 13 months in a non-laboratory setting, were recorded and analyzed. Vowel and consonant durations at syllable contacts of trochaic word types with ¦CVCV or ¦CVːCV structure were measured under varying rhythmical conditions. Evidence is provided that in German non-professional singing, the two syllable structures can be differentiated by two distinct temporal variability patterns: vocalic variability (and consonantal stability) was found to be dominant in ¦CVːCV structures whereas consonantal variability (and vocalic stability) was characteristic for ¦CVCV structures. In French and Russian, however, only vocalic variability seemed to apply. Additionally, findings suggest that the different temporal patterns found in German were also supported by the stability pattern at the tonal level. These results point to subtle (supra) segmental timing mechanisms in sung speech that affect temporal targets according to the specific prosodic nature of the language in question

Modeling sequences and temporal networks with dynamic community structures

In evolving complex systems such as air traffic and social organizations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks' large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.Comment: 15 Pages, 6 figures, 2 table