Dynamics of fluctuations in a quantum system

"\textit{The noise is the signal}"[R. Landauer, Nature \textbf{392}, 658 (1998)] emphasizes the rich information content encoded in fluctuations. This paper assesses the dynamical role of fluctuations of a quantum system driven far from equilibrium, with laser-aligned molecules as a physical realization. Time evolutions of the expectation value and the uncertainty of a standard observable are computed quantum mechanically and classically. We demonstrate the intricate dynamics of the uncertainty that are strikingly independent of those of the expectation value, and their exceptional sensitivity to quantum properties of the system. In general, detecting the time evolution of the fluctuations of a given observable provides information on the dynamics of correlations in a quantum system.Comment: 6 pages, 2 figure

RELATIONSHIP BETWEEN THE KINEMATICS OF THE TRUNK AND LOWER EXTREMITIES AND BALL VELOCITY DURING THE OVERHAND THROW IN MALE CANOE POLO PLAYERS

The purpose of this study was to examine the relationship between the motion of the trunk and lower extremities and ball velocity during the overhand throw in canoe polo. Fifteen male national canoe polo team players participated in this study. The overhand throwing motion was captured using a three dimensional motion analysis system. Kinematic and temporal parameters in the trunk and lower extremities were measured and analyzed. Results indicated that five variables were associated with variations in ball velocity. Specifically, as ball velocity increased, canoe polo players showed an increased maximal angular velocity in trunk-tilt sideways, upper torso rotation, and right knee flexion. In addition, the right knee flexion range of motion and time to maximum right knee flexion angular velocity increased as ball velocity increased

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