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

Molecular modeling on HIF2α-ARNT dimer destabilization caused by HIF2α V192D and/or R171A mutations

The hypoxia-inducible-factors (HIFs) are responsible for cellular adaptations to low oxygen stress by activating transcriptional programs such as erythropoiesis and angiogenesis. Because these programs are related to tumor growth and progression, HIFs have become attractive targets for cancer therapy. To function as oxygen-sensitive regulatory subunits, HIF2α must form a heterodimer with the aryl hydrocarbon receptor nuclear translocator (ARNT). Both HIF and ARNT proteins have a basic-helix-loop-helix (bHLH) domain for DNA reading in their amino-termini, followed by two tandem Per-ARNT-Sim (PAS) domain, namely PAS-A and PAS-B, for HIF-ARNT dimerization, and transactivation domains (TAD) in their carboxyl-termini. According to the recently solved HIF2α-ARNT dimer structure (not covering their TADs), there are six domain-domain interfaces including HIF2α’s bHLH with ARNT’s bHLH, HIF2α’s PAS-A with ARNT’s PAS-A, HIF2α’s PAS-B with ARNT’s PAS-A, HIF2α’s PAS-B with ARNT’s PAS-B, HIF2α’s PAS-A with HIF2α’s PAS-B, and HIF2α’s bHLH with HIF2α’s PAS-B. Structural comparison shows that HIF2α’s bHLH, PAS-A, and PAS-B domains are compactly interconnected; whereas ARNT’s bHLH, PAS-A, and PAS-B domains are linked by long flexible loops to grant structural adaptability to dimerize different bHLH-PAS proteins members. Lately, co-immunoprecipitation experiments have shown that R171A and/or V192D on HIF2α’s PAS-A domain impair HIF2α-ARNT dimerization. Herein we applied molecular dynamics simulations to investigate the structural and dynamic impact brought by these mutations. Our results conclude that these mutated amino residues, located in HIF2α’s PAS-A with HIF2α’s PAS-B interface, change the relative orientation and motion of PAS-A and PAS-B and therefore these two PAS domains are not recognizable by ARNT

IDENTIFYING GAIT ASYMMETRY USING DIGITAL SENSORS

The purpose of this study was to determine which phases and kinematics were easier to identify gait asymmetry by using digital sensors. Sixteen participants were recruited in this study. The participants were requested to walk naturally under two conditions (with or without asymmetrical load). Four digital sensor sets were attached on 4 limbs to collect kinematics data. The results showed that only the AS1 of Medial-Later acceleration of upper limb on the stance phase significantly different between unloading and loading conditions; on the lower limb were AS1 of Superior-Inferior acceleration and Flex/Extension angular velocity on the swing phase. The digital sensors that attach on upper and lower limbs both can detect gait asymmetry, but the asymmetrical phase and kinematics are different on upper and lower limbs

Optimization of multi-model ensemble forecasting of typhoon waves

Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles). The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting