Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of few additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.Comment: Accepted by CVPR 2019 (oral); code is available: https://github.com/hejingwenhejingwen/AdaF

Exploring the deviation of cosmological constant by a generalized pressure dark energy model

We bring forward a generalized pressure dark energy (GPDE) model to explore the evolution of the universe. This model has covered three common pressure parameterization types and can be reconstructed as quintessence and phantom scalar fields, respectively. We adopt the cosmic chronometer (CC) datasets to constrain the parameters. The results show that the inferred late-universe parameters of the GPDE model are (within $1\sigma$): The present value of Hubble constant $H_{0}=(72.30^{+1.26}_{-1.37})$km s$^{-1}$ Mpc$^{-1}$; Matter density parameter $\Omega_{\text{m0}}=0.302^{+0.046}_{-0.047}$, and the universe bias towards quintessence. While when we combine CC data and the $H_0$ data from Planck, the constraint implies that our model matches the $\Lambda$CDM model nicely. Then we perform dynamic analysis on the GPDE model and find that there is an attractor or a saddle point in the system corresponding to the different values of parameters. Finally, we discuss the ultimate fate of the universe under the phantom scenario in the GPDE model. It is demonstrated that three cases of pseudo rip, little rip, and big rip are all possible.Comment: 11 pages, 5 figures, 5 table

A Combinatorial Perspective of the Protein Inference Problem

In a shotgun proteomics experiment, proteins are the most biologically meaningful output. The success of proteomics studies depends on the ability to accurately and efficiently identify proteins. Many methods have been proposed to facilitate the identification of proteins from the results of peptide identification. However, the relationship between protein identification and peptide identification has not been thoroughly explained before. In this paper, we are devoted to a combinatorial perspective of the protein inference problem. We employ combinatorial mathematics to calculate the conditional protein probabilities (Protein probability means the probability that a protein is correctly identified) under three assumptions, which lead to a lower bound, an upper bound and an empirical estimation of protein probabilities, respectively. The combinatorial perspective enables us to obtain a closed-form formulation for protein inference. Based on our model, we study the impact of unique peptides and degenerate peptides on protein probabilities. Here, degenerate peptides are peptides shared by at least two proteins. Meanwhile, we also study the relationship of our model with other methods such as ProteinProphet. A probability confidence interval can be calculated and used together with probability to filter the protein identification result. Our method achieves competitive results with ProteinProphet in a more efficient manner in the experiment based on two datasets of standard protein mixtures and two datasets of real samples. We name our program ProteinInfer. Its Java source code is available at http://bioinformatics.ust.hk/proteininfe

Search for C=+C=+ charmonium and bottomonium states in e+e−→γ+Xe^+e^-\to \gamma+ X at B factories

We study the production of $C=+$ charmonium states $X$ in $e^+e^-\to \gamma + X$ at B factories with $X=\eta_c(nS)$ (n=1,2,3), $\chi_{cJ}(mP)$ (m=1,2), and $^1D_2(1D)$. In the S and P wave case, contributions of tree-QED with one-loop QCD corrections are calculated within the framework of nonrelativistic QCD(NRQCD) and in the D-wave case only the tree-QED contribution are considered. We find that in most cases the QCD corrections are negative and moderate, in contrast to the case of double charmonium production $e^+e^-\to J/\psi + X$, where QCD corrections are positive and large in most cases. We also find that the production cross sections of some of these states in $e^+e^-\to \gamma + X$ are larger than that in $e^+e^-\to J/\psi + X$ by an order of magnitude even after the negative QCD corrections are included. So we argue that search for the X(3872), X(3940), Y(3940), and X(4160) in $e^+e^-\to \gamma + X$ at B factories may be helpful to clarify the nature of these states. For completeness, the production of bottomonium states in $e^+e^-$ annihilation is also discussed.Comment: 13pages, 4 figure

A Novel Transmission Scheme for the KK-user Broadcast Channel with Delayed CSIT

The state-dependent $K$-user memoryless Broadcast Channel~(BC) with state feedback is investigated. We propose a novel transmission scheme and derive its corresponding achievable rate region, which, compared to some general schemes that deal with feedback, has the advantage of being relatively simple and thus is easy to evaluate. In particular, it is shown that the capacity region of the symmetric erasure BC with an arbitrary input alphabet size is achievable with the proposed scheme. For the fading Gaussian BC, we derive a symmetric achievable rate as a function of the signal-to-noise ratio~(SNR) and a small set of parameters. Besides achieving the optimal degrees of freedom at high SNR, the proposed scheme is shown, through numerical results, to outperform existing schemes from the literature in the finite SNR regime.Comment: 30 pages, 3 figures, submitted to IEEE Transactions on Wireless Communications (revised version