ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes

Recent studies on face attribute transfer have achieved great success. A lot of models are able to transfer face attributes with an input image. However, they suffer from three limitations: (1) incapability of generating image by exemplars; (2) being unable to transfer multiple face attributes simultaneously; (3) low quality of generated images, such as low-resolution or artifacts. To address these limitations, we propose a novel model which receives two images of opposite attributes as inputs. Our model can transfer exactly the same type of attributes from one image to another by exchanging certain part of their encodings. All the attributes are encoded in a disentangled manner in the latent space, which enables us to manipulate several attributes simultaneously. Besides, our model learns the residual images so as to facilitate training on higher resolution images. With the help of multi-scale discriminators for adversarial training, it can even generate high-quality images with finer details and less artifacts. We demonstrate the effectiveness of our model on overcoming the above three limitations by comparing with other methods on the CelebA face database. A pytorch implementation is available at https://github.com/Prinsphield/ELEGANT.Comment: Github: https://github.com/Prinsphield/ELEGAN

DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images

Disentangling factors of variation has become a very challenging problem on representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, poor quality of generated images from encodings, lack of identity information, etc. In this paper, we propose a supervised learning model called DNA-GAN which tries to disentangle different factors or attributes of images. The latent representations of images are DNA-like, in which each individual piece (of the encoding) represents an independent factor of the variation. By annihilating the recessive piece and swapping a certain piece of one latent representation with that of the other one, we obtain two different representations which could be decoded into two kinds of images with the existence of the corresponding attribute being changed. In order to obtain realistic images and also disentangled representations, we further introduce the discriminator for adversarial training. Experiments on Multi-PIE and CelebA datasets finally demonstrate that our proposed method is effective for factors disentangling and even overcome certain limitations of the existing methods.Comment: ICLR 2018 workshop, github: https://github.com/Prinsphield/DNA-GA

Development of front-end readout electronics for silicon strip detectors

A front-end readout electronics system has been developed for silicon strip detectors. The system uses an application specific integrated circuit (ASIC) ATHED to realize multi-channel E&T measurement. The slow control of ASIC chips is achieved by parallel port and the timing control signals of ASIC chips are provided by the CPLD. The data acquisition is implemented with a PXI-DAQ card. The system software has a user-friendly GUI which uses LabWindows/CVI in Windows XP operating system. Test results showed that the energy resolution is about 1.22 % for alphas at 5.48 MeV and the maximum channel crosstalk of system is 4.6%. The performance of the system is very reliable and suitable for nuclear physics experiments.Comment: This article has been submitted to Chinese Physics

Primordial non-Gaussianity in noncanonical warm inflation: three- and four-point correlations

Non-Gaussianity generated in inflation can be contributed by two parts. The first part, denoted by $f_{NL}^{\delta N}$, is the contribution from four-point correlation of inflaton field which can be calculated using $\delta N$ formalism, and the second part, denoted by $f_{NL}^{int}$, is the contribution from the three-point correlation function of the inflaton field. We consider the two contributions to the non-Gaussianity in noncanonical warm inflation throughout (noncanonical warm inflation is a new inflationary model which is proposed in \cite{Zhang2014}). We find the two contributions are complementary to each other. The four-point correlation contribution to the non-Gaussianity is overwhelmed by the three-point one in strong noncanonical limit, while the conclusion is opposite in the canonical case. We also discuss the influence of the field redefinition, thermal dissipative effect and noncanonical effect to the non-Gaussianity in noncanonical warm inflation.Comment: 7 pages. Accepted for publication in Physical Review

A new one-dimensional variable frequency photonic crystals

In this paper, we have firstly proposed a new one-dimensional variable frequency photonic crystals (VFPCs). We have calculated the transmissivity and the electronic field distribution of VFPCs and compare them with the conventional PCs, and obtained some new results, which should be help to design a new type optical devices, and the two-dimensional and three-dimensional VFPCs can be studied further.Comment: arXiv admin note: text overlap with arXiv:1301.6109 by other author

RDPD: Rich Data Helps Poor Data via Imitation

In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipments (e.g., intensive care units) often provides high-quality multi-modal data, which are acquired from multiple sensory devices and have rich-feature representations. On the other hand, an environment with poor observation equipment (e.g., at home) only provides low-quality, uni-modal data with poor-feature representations. To deploy a competitive model in a poor-data environment without requiring direct access to multi-modal data acquired from a rich-data environment, this paper develops and presents a knowledge distillation (KD) method (RDPD) to enhance a predictive model trained on poor data using knowledge distilled from a high-complexity model trained on rich, private data. We evaluated RDPD on three real-world datasets and shown that its distilled model consistently outperformed all baselines across all datasets, especially achieving the greatest performance improvement over a model trained only on low-quality data by 24.56% on PR-AUC and 12.21% on ROC-AUC, and over that of a state-of-the-art KD model by 5.91% on PR-AUC and 4.44% on ROC-AUC.Comment: Published in IJCAI 201

Meson spectrum in Regge phenomenology

Under the assumption that both light and heavy quarkonia populate approximately linear Regge trajectories with the requirements of additivity of intercepts and inverse slopes, the masses of different meson multiplets are estimated. The predictions derived from the quasi-linear Regge trajectories are in reasonable agreement with those given by many other references.Comment: 21 pages, to appear in Eur. Phys. J.

Understanding Image Quality and Trust in Peer-to-Peer Marketplaces

As any savvy online shopper knows, second-hand peer-to-peer marketplaces are filled with images of mixed quality. How does image quality impact marketplace outcomes, and can quality be automatically predicted? In this work, we conducted a large-scale study on the quality of user-generated images in peer-to-peer marketplaces. By gathering a dataset of common second-hand products (~75,000 images) and annotating a subset with human-labeled quality judgments, we were able to model and predict image quality with decent accuracy (~87%). We then conducted two studies focused on understanding the relationship between these image quality scores and two marketplace outcomes: sales and perceived trustworthiness. We show that image quality is associated with higher likelihood that an item will be sold, though other factors such as view count were better predictors of sales. Nonetheless, we show that high quality user-generated images selected by our models outperform stock imagery in eliciting perceptions of trust from users. Our findings can inform the design of future marketplaces and guide potential sellers to take better product images.Comment: WACV 201

Primordial non-Gaussianity in warm inflation using δN\delta N formalism

A $\delta N$ formalism is used to study the non-Gaussianity of the primordial curvature perturbation on an uniform density hypersurfaces generated by the warm inflation for the first time. After introducing the framework of the warm inflation and the $\delta N$ formalism, we obtain an analytic expression for the nonlinear parameter $f_{NL}$ that describes the non-Gaussianity in slow roll approximation, and find that the $\delta N$ formalism gives a very good result. We analyse the magnitude of $f_{NL}$ and compare our result with those of the standard inflation. Then we discuss two concrete examples: the quartic chaotic model and the hilltop model. The quartic potential model can again be in very good agreement with the Planck results in the warm inflationary scenario, and we give out the concrete results of how the nonlinear parameter depends on the dissipation strength of the warm inflation and the amounts of expansion. We find that the range of the nonlinear parameters in these two cases are both well inside of the allowed region of Planck.Comment: 9 pages, 5 figure

Variable frequency photonic crystals

In this paper, we have firstly proposed a new one-dimensional variable frequency photonic crystals (VFPCs), and calculated the transmissivity and the electronic field distribution of VFPCs with and without defect layer, and considered the effect of defect layer and variable frequency function on the transmissivity and the electronic field distribution. We have obtained some new characteristics for the VFPCs, which should be help to design a new type optical devices.Comment: arXiv admin note: substantial text overlap with arXiv:1502.0511