Quantifying Quantum-Mechanical Processes

The act of describing how a physical process changes a system is the basis for understanding observed phenomena. For quantum-mechanical processes in particular, the affect of processes on quantum states profoundly advances our knowledge of the natural world, from understanding counter-intuitive concepts to the development of wholly quantum-mechanical technology. Here, we show that quantum-mechanical processes can be quantified using a generic classical-process model through which any classical strategies of mimicry can be ruled out. We demonstrate the success of this formalism using fundamental processes postulated in quantum mechanics, the dynamics of open quantum systems, quantum-information processing, the fusion of entangled photon pairs, and the energy transfer in a photosynthetic pigment-protein complex. Since our framework does not depend on any specifics of the states being processed, it reveals a new class of correlations in the hierarchy between entanglement and Einstein-Podolsky-Rosen steering and paves the way for the elaboration of a generic method for quantifying physical processes

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

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 glucocorticoid-Angptl4-ceramide axis induces insulin resistance through PP2A and PKCζ.

Chronic glucocorticoid exposure is associated with the development of insulin resistance. We showed that glucocorticoid-induced insulin resistance was attenuated upon ablation of Angptl4, a glucocorticoid target gene encoding the secreted protein angiopoietin-like 4, which mediates glucocorticoid-induced lipolysis in white adipose tissue. Through metabolomic profiling, we revealed that glucocorticoid treatment increased hepatic ceramide concentrations by inducing enzymes in the ceramide synthetic pathway in an Angptl4-dependent manner. Angptl4 was also required for glucocorticoids to stimulate the activities of the downstream effectors of ceramide, protein phosphatase 2A (PP2A) and protein kinase Cζ (PKCζ). We further showed that knockdown of PP2A or inhibition of PKCζ or ceramide synthesis prevented glucocorticoid-induced glucose intolerance in wild-type mice. Moreover, the inhibition of PKCζ or ceramide synthesis did not further improve glucose tolerance in Angptl4-/- mice, suggesting that these molecules were major downstream effectors of Angptl4. Overall, our study demonstrates the key role of Angptl4 in glucocorticoid-augmented hepatic ceramide production that induces whole-body insulin resistance