Pop Music Highlighter: Marking the Emotion Keypoints

The goal of music highlight extraction is to get a short consecutive segment of a piece of music that provides an effective representation of the whole piece. In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs. The rationale behind that approach is that the highlight of a song is usually the most emotional part. This paper extends our previous work in the following two aspects. First, methodology-wise we experiment with a new architecture that does not need any recurrent layers, making the training process faster. Moreover, we compare a late-fusion variant and an early-fusion variant to study which one better exploits the attention mechanism. Second, we conduct and report an extensive set of experiments comparing the proposed attention-based methods against a heuristic energy-based method, a structural repetition-based method, and a few other simple feature-based methods for this task. Due to the lack of public-domain labeled data for highlight extraction, following our previous work we use the RWC POP 100-song data set to evaluate how the detected highlights overlap with any chorus sections of the songs. The experiments demonstrate the effectiveness of our methods over competing methods. For reproducibility, we open source the code and pre-trained model at https://github.com/remyhuang/pop-music-highlighter/.Comment: Transactions of the ISMIR vol. 1, no.

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

The Effectiveness of Corporate Advertising in a Collegiate Fitness Center

The purpose of this research is to examine the effectiveness of the corporate advertisements in a collegiate fitness center in Hong Kong. In this study, a survey questionnaire was utilized to examine whether participants’ demographic information influenced the consumers’ attitude and purchase intention toward the product. A total of 112 valid samples were collected. The result showed there was a significant difference between genders in product purchase intention. And the research also found that participants’ exercise time can make a significant difference on the attitude and purchase intention toward the products. Compared with non-sport product, the sport related products received higher scores of attitude and purchase intention from participants in the collegiate sport center. There existed some limitations (sample size, time, gender ratio) during the research process. The result indicated there is potential commercial value hidden in the Hong Kong collegiate fitness clubs

Twist-3 and Quark Mass Contributions to the Polarized Nucleon Structure Function g_2(x,Q^2)

Quark mass effects are clarified in the parton model approach to the transversely polarized nucleon structure function. The special propagator technique is employed to obtain manifestly gauge invariant results and extract the buried short-distance contributions inside the soft part after momentum factorization in the collinear expansion approach. A generalized massive special propagator for a massive quark is constructed. We identify the corresponding matrix elements of the transversely polarized structure function in deep inelastic scatterings by the massive special propagator technique.Comment: 13 pages, Revtex, a typographical error has been eliminate

Momentum Distribution for Bosons with Positive Scattering Length in a Trap

The coordinate-momentum double distribution function $\rho ({\bf r}, {\bf p}) d^{3}rd^{3}p$ is calculated in the local density approximation for bosons with positive scattering length $a$ in a trap. The calculation is valid to the first order of $a$. To clarify the meaning of the result, it is compared for a special case with the double distribution function $\rho_{w}d^{3} rd^{3}p$ of Wigner.Comment: Latex fil

RhoGDIβ-induced hypertrophic growth in H9c2 cells is negatively regulated by ZAK

We found that overexpression of RhoGDIβ, a Rho GDP dissociation inhibitor, induced hypertrophic growth and suppressed cell cycle progression in a cultured cardiomyoblast cell line. Knockdown of RhoGDIβ expression by RNA interference blocked hypertrophic growth. We further demonstrated that RhoGDIβ physically interacts with ZAK and is phosphorylated by ZAK in vitro, and this phosphorylation negatively regulates RhoGDIβ functions. Moreover, the ZAK-RhoGDIβ interaction may maintain ZAK in an inactive hypophosphorylated form. These two proteins could negatively regulate one another such that ZAK suppresses RhoGDIβ functions through phosphorylation and RhoGDIβ counteracts the effects of ZAK by physical interaction. Knockdown of ZAK expression in ZAK- and RhoGDIβ-expressing cells by ZAK-specific RNA interference restored the full functions of RhoGDIβ

Isolation of anticancer constituents from flos genkwa (Daphne genkwa Sieb.et Zucc.) through bioassay-guided procedures

BACKGROUND: Flos Genkwa (yuanhua in Chinese), the dried flower buds of Daphne genkwa Sieb.et Zucc. (Thymelaeaceae), is a traditional Chinese medicinal herb mainly used for diuretic, antitussive, expectorant, and anticancer effects. However, systematic and comprehensive studies on Flos Genkwa and its bioactivity are limited. RESULTS: After confirmation of the anti-tumor activity, the 95% ethanolic extract was subjected to successive solvent partitioning to petroleum ether, dichloromethane, n-butanol, and water soluble fractions. Each fraction was tested using the same biological activity model, and the dichloromethane fraction had the highest activity. The dichloromethane fraction was subjected to further chromatographic separation for the isolation of compounds 1–13. Among the 13 compounds, the diterpene esters (compounds 10–13) showed anticancer activity, whereas the flavonoids, lignanoids, and peptides showed moderate activity. Compound 13 was a new daphnane diterpenoid, which was named genkwanin VIII. The preliminary antitumor mechanism of yuanhuacine was studied by protein expression and cell cycle analysis in MCF-7 cancer cells. CONCLUSION: The present investigation tends to support the traditional use of Flos Genkwa for treating cancer. Through bioassay-guided fractionation and isolation techniques, the CH(2)Cl(2) fraction was determined as the active fraction of the flower buds of D. genkwa, and the anti-tumor activity was ascribable to the compounds 10–13