Mechanically induced pseudo-magnetic fields in the excitonic fine structures of droplet epitaxial quantum dots

We present numerical investigations based on the Luttinger-Kohn four-band $k \cdot p$ theory and, accordingly, establish a quantitatively valid model of the excitonic fine structures of droplet epitaxial GaAs/AlGaAs quantum dots under uni-axial stress control. In the formalisms, stressing a photo-excited quantum dot is equivalent creating a pseudo-magnetic field that is directly coupled to the pseudo-spin of the exciton doublet and tunable to tailor the polarized fine structure of exciton. The latter feature is associated with the valence-band-mixing of exciton that is especially sensitive to external stress in inherently unstrained droplet epitaxial GaAs/AlGaAs quantum dots and allows us to mechanically design and prepare any desired exciton states of QD photon sources prior to the photon generation.Comment: 7 figure

Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201