Audio Time-Scale Modification with Temporal Compressing Networks

We proposed a novel approach in the field of time-scale modification on audio signals. While traditional methods use the framing technique, spectral approach uses the short-time Fourier transform to preserve the frequency during temporal stretching. TSM-Net, our neural-network model encodes the raw audio into a high-level latent representation. We call it Neuralgram, in which one vector represents 1024 audio samples. It is inspired by the framing technique but addresses the clipping artifacts. The Neuralgram is a two-dimensional matrix with real values, we can apply some existing image resizing techniques on the Neuralgram and decode it using our neural decoder to obtain the time-scaled audio. Both the encoder and decoder are trained with GANs, which shows fair generalization ability on the scaled Neuralgrams. Our method yields little artifacts and opens a new possibility in the research of modern time-scale modification. The audio samples can be found on https://ernestchu.github.io/tsm-net-demo

Direct Counting Analysis on Network Generated by Discrete Dynamics

A detail study on the In-degree Distribution (ID) of Cellular Automata is obtained by exact enumeration. The results indicate large deviation from multiscaling and classification according to ID are discussed. We further augment the transfer matrix as such the distributions for more complicated rules are obtained. Dependence of In-degree Distribution on the lattice size have also been found for some rules including R50 and R77.Comment: 8 pages, 11 figure

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

Cellular Ability to Sense Spatial Gradients in the Presence of Multiple Competitive Ligands

Many eukaryotic and prokaryotic cells can exhibit remarkable sensing ability under small gradient of chemical compound. In this study, we approach this phenomenon by considering the contribution of multiple ligands to the chemical kinetics within Michaelis-Menten model. This work was inspired by the recent theoretical findings from Bo Hu et al. [Phys. Rev. Lett. 105, 048104 (2010)], our treatment with practical binding energies and chemical potential provides the results which are consistent with experimental observations.Comment: 5 pages, 4 figure

Role of Bell Singlet State in the Suppression of Disentanglement

The stability of entanglement of two atoms in a cavity is analyzed in this work. By studying the general Werner states we clarify the role of Bell-singlet state in the problem of suppression of disentanglement due to spontaneous emission. It is also shown explicitly that the final amount of entanglement depends on the initial ingredients of the Bell-singlet state.Comment: 5 pages, 2 figures, accepted by Phys. Rev.