SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmentation branch is based on a fully convolutional network, which has been proved effective in image segmentation task, and the optical flow branch takes advantage of the FlowNet model. The unified framework is trained iteratively offline to learn a generic notion, and fine-tuned online for specific objects. Extensive experiments on both the video object segmentation and optical flow datasets demonstrate that introducing optical flow improves the performance of segmentation and vice versa, against the state-of-the-art algorithms.Comment: Accepted in ICCV'17. Code is available at https://sites.google.com/site/yihsuantsai/research/iccv17-segflo

Optomechanical approach to controlling the temperature and chemical potential of light

Massless particles, including photons, are not governed by particle conservation law during their typical interaction with matter even at low energies, and thus have no chemical potential. However, in driven systems, near equilibrium dynamics can lead to equilibration of photons with a finite number, describable using an effective chemical potential [M. Hafezi et al., Phys. Rev. B 92, 174305 (2015)]. Here we build upon this general concept with an implementation appropriate for a photon-based quantum simulator. We consider how laser cooling of a well-isolated mechanical mode can provide an effective low-frequency bath for the quantum simulator system. We show that the use of auxiliary photon modes, coupled by the mechanical system, enables control of both the chemical potential and temperature of the resulting photonic quantum simulator's grand canonical ensemble.Comment: 10 pages, 4 figure

Spin dynamics of possible density wave states in the pseudogap phase of the high temperature superconductors

In a recent inelastic neutron scattering experiment in the pseudogap state of the high temperature superconductor $\mathrm{YBa_{2}Cu_{3}O_{6.6}}$ an unusual `vertical' dispersion of the spin excitations with a large in-plane anisotropy was observed. In this paper we discuss in detail the spin susceptibility of the singlet $d$-density wave, the triplet $d$-density wave, as well as the more common spin density wave orders with hopping anisotropies. From numerical calculations within the framework of random phase approximation, we find nearly vertical dispersion relations for spin excitations with anisotropic incommensurability at low energy $\omega \le 90 meV$, which are reminiscent of the experiments. At very high energy $\omega \ge 165 meV$, we also find energy-dependent incommensurability. Although there are some important difference between the three cases, unpolarized neutron measurements cannot discriminate between these alternate possibilities; the vertical dispersion, however, is a distinct feature of all three density wave states in contrast to the superconducting state, which shows an hour-glass shape dispersion.Comment: 8 pages, 9 figure

PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network

Music creation is typically composed of two parts: composing the musical score, and then performing the score with instruments to make sounds. While recent work has made much progress in automatic music generation in the symbolic domain, few attempts have been made to build an AI model that can render realistic music audio from musical scores. Directly synthesizing audio with sound sample libraries often leads to mechanical and deadpan results, since musical scores do not contain performance-level information, such as subtle changes in timing and dynamics. Moreover, while the task may sound like a text-to-speech synthesis problem, there are fundamental differences since music audio has rich polyphonic sounds. To build such an AI performer, we propose in this paper a deep convolutional model that learns in an end-to-end manner the score-to-audio mapping between a symbolic representation of music called the piano rolls and an audio representation of music called the spectrograms. The model consists of two subnets: the ContourNet, which uses a U-Net structure to learn the correspondence between piano rolls and spectrograms and to give an initial result; and the TextureNet, which further uses a multi-band residual network to refine the result by adding the spectral texture of overtones and timbre. We train the model to generate music clips of the violin, cello, and flute, with a dataset of moderate size. We also present the result of a user study that shows our model achieves higher mean opinion score (MOS) in naturalness and emotional expressivity than a WaveNet-based model and two commercial sound libraries. We open our source code at https://github.com/bwang514/PerformanceNetComment: 8 pages, 6 figures, AAAI 2019 camera-ready versio

A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

THE INCIDENCE AND WAGE EFFECTS OF OVEREDUCATION: THE CASE OF TAIWAN

This paper, based on data from Survey of Family Income and Expenditure of Taiwan, shows that the recent trends of job match in Taiwan labor market have been marked by increasing proportion of overeducated workers due to the higher education expansion policy, while the incidence of undereducation continues to decline. Furthermore, workers¡¯ economic position is not completely determined by their educational levels. Working experience also plays an important role in workers¡¯ job placement and their wages. Workers with relatively less working experience are more likely to be overeducated, while workers with relatively more working experience are more likely to be undereducated. Overeducated (Undereducated) workers would earn more (less) than their co-workers with adequate education but less (more) than the workers having the same educational level with adequate education for jobs. However, the rewards (penalties) to adequate education and overeducation (undereducation) decline as more experience accumulated. Evidence also shows effect of bumping down from overeducation on the wages and employment of lower educated workers.Overeducation, Wage, Bumping Down, Labor Market, Taiwan