Adaptive DCTNet for Audio Signal Classification

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.Comment: International Conference of Acoustic and Speech Signal Processing (ICASSP). New Orleans, United States, March, 201

Cavity-Assisted Dynamical Spin-Orbit Coupling in Cold Atoms

We consider ultracold atoms subjected to a cavity-assisted two-photon Raman transition. The Raman coupling gives rise to effective spin-orbit interaction which couples atom's center-of-mass motion to its pseudospin degrees of freedom. Meanwhile, the cavity photon is dynamically affected by the atom. This feedback between atom and photon leads to a dramatic modification of the atomic dispersion relation, and further leads to dynamical instability of the system. We propose to detect the change of cavity photon number as a direct way to demonstrate dynamical instability.Comment: 5 pages, 5 figure

Density oscillations in trapped dipolar condensates

We investigated the ground state wave function and free expansion of a trapped dipolar condensate. We find that dipolar interaction may induce both biconcave and dumbbell density profiles in, respectively, the pancake- and cigar-shaped traps. On the parameter plane of the interaction strengths, the density oscillation occurs only when the interaction parameters fall into certain isolated areas. The relation between the positions of these areas and the trap geometry is explored. By studying the free expansion of the condensate with density oscillation, we show that the density oscillation is detectable from the time-of-flight image.Comment: 7 pages, 9 figure

Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications

Networked video applications, e.g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth. In this paper, we have developed a Quality Enhancement Network (QENet) to reduce the video compression artifacts, leveraging the spatial and temporal priors generated by respective multi-scale convolutions spatially and warped temporal predictions in a recurrent fashion temporally. We have integrated this QENet as a standard-alone post-processing subsystem to the High Efficiency Video Coding (HEVC) compliant decoder. Experimental results show that our QENet demonstrates the state-of-the-art performance against default in-loop filters in HEVC and other deep learning based methods with noticeable objective gains in Peak-Signal-to-Noise Ratio (PSNR) and subjective gains visually

Properties of a coupled two species atom-heteronuclear molecule condensate

We study the coherent association of a two-species atomic condensate into a condensate of heteronuclear diatomic molecules, using both a semiclassical treatment and a quantum mechanical approach. The differences and connections between the two approaches are examined. We show that, in this coupled nonlinear atom-molecule system, the population difference between the two atomic species plays a significant role in the ground-state stability properties as well as in coherent population oscillation dynamics.Comment: 7 pages, 4 figure

A Simplified Latent Semantic Indexing Approach for Multi-Linguistic Information Retrieval

Latent Semantic Indexing (LSI) approach provides a promising solution to overcome the language barrier between queries and documents, but unfortunately the high dimensions of the training matrix is computationally prohibitive for its key step of Singular Value Decomposition (SVD). Based on the semantic parallelism of the multi-linguistic training corpus we prove in this paper that, theoretically if the training term-by-document matrix can appear in either of two symmetry forms, strong or weak, the dimension of the matrix under decomposition can be reduced to the size of a monolingual matrix. The retrieval accuracy will not deteriorate in such a simplification. And we also discuss what these two forms of symmetry mean in the context of multi-linguistic information retrieval. Although in real world data the term-by-document matrices are not naturally in either symmetry form, we suggest a way to make them appear more symmetric in the strong form by means of word clustering and term weighting. A real data experiment is also given to support our method of simplification.