A Cockcroft-Walton PMT base with signal processing circuit

Design a surface mount 14-PIN Cockcroft-Walton photomultiplier tube base for a muon detector, which provides both high voltage power supply and signal processing. The whole system, including the detector, adopts a +5V DC power input, and features as tiny size, low power-consumption and good portability, extremely well meeting the requirements of the power supply with a battery on a mobile workstation. Detailed descriptions and test results of a prototype are presented.Comment: 5 pages, 11 figures, Journa

The relation between Hawking radiation via tunnelling and the laws of black hole thermodynamics

In Parikh and Wilczek's original works, the laws of black hole thermodynamics are not referred and it seems that there is no relation between Hawking radiation via tunnelling and the laws of black hole thermodynamics in their works. However, taking examples for the R-N black hole and the Kerr black hole, we find that they are correlated and even consistent if the tunnelling process is a reversible process.Comment: 6 pages, no figur

Exhaustive Ghost Solutions to Einstein-Weyl Equations for Two Dimensional Spacetimes

Exhaustive ghost solutions to Einstein-Weyl equations for two dimensional spacetimes are obtained, where the ghost neutrinos propagate in the background spacetime, but do not influence the background spacetime due to the vanishing stress-energy-momentum tensor for the ghost neutrinos. Especially, those non-trivial ghost solutions provide a counterexample to the traditional claim that the Einstein-Hilbert action has no meaningful two dimensional analogue.Comment: 10 pages, errors in Section 1 and typos in Appendix correcte

Misner-Sharp Mass in NN-dimensional f(R)f(R) Gravity

We study the Misner-Sharp mass for the $f(R)$ gravity in an $n$-dimensional (n$\geq$3) spacetime which permits three-type $(n-2)$-dimensional maximally symmetric subspace. We obtain the Misner-Sharp mass via two approaches. One is the inverse unified first law method, and the other is the conserved charge method by using a generalized Kodama vector. In the first approach, we assume the unified first still holds in the $n$-dimensional $f(R)$ gravity, which requires a quasi-local mass form (We define it as the generalized Misner-Sharp mass). In the second approach, the conserved charge corresponding to the generalized local Kodama vector is the generalized Misner-Sharp mass. The two approaches are equivalent, which are bridged by a constraint. This constraint determines the existence of a well-defined Misner-Sharp mass. As an important special case, we present the explicit form for the static space, and we calculate the Misner-Sharp mass for Clifton-Barrow solution as an example.Comment: 8 page

A Generalized Model for Light Transport in Scintillators

Transported light in the medium usually shows as an exponential decay tendency. In the DAMPE strip scintillators, however, the phenomenon of light attenuation as the hit position approaches the end of the scintillator can not be described by the simple exponential decay model. The spread angle of PMT relative to hit position is distance-dependent, so the larger the angle, the larger the proportion $\eta$ of emitted light to becomes the effective input light. We consider the contribution of the spread angle, and propose a generalized model: $f(x)=I_0\eta_{a}(x)e^{-x/\lambda}+I_0\eta_{b}(x)e^{-(2L-x)/\lambda }$. The model well describes the light attenuation in the scintillator, reducing the maximum deviation of the sample from the fit function from 29\% to below 2\%. Moreover, our model contains most of the traditional models, so the experimental data that traditional models can fit and our models fit well.Comment: 10pages, 5figure

CFSNet: Toward a Controllable Feature Space for Image Restoration

Deep learning methods have witnessed the great progress in image restoration with specific metrics (e.g., PSNR, SSIM). However, the perceptual quality of the restored image is relatively subjective, and it is necessary for users to control the reconstruction result according to personal preferences or image characteristics, which cannot be done using existing deterministic networks. This motivates us to exquisitely design a unified interactive framework for general image restoration tasks. Under this framework, users can control continuous transition of different objectives, e.g., the perception-distortion trade-off of image super-resolution, the trade-off between noise reduction and detail preservation. We achieve this goal by controlling the latent features of the designed network. To be specific, our proposed framework, named Controllable Feature Space Network (CFSNet), is entangled by two branches based on different objectives. Our framework can adaptively learn the coupling coefficients of different layers and channels, which provides finer control of the restored image quality. Experiments on several typical image restoration tasks fully validate the effective benefits of the proposed method. Code is available at https://github.com/qibao77/CFSNet.Comment: Accepted by ICCV 201

Molecular Regulation of Histamine Synthesis

Histamine is a critical mediator of IgE/ cell-mediated anaphylaxis, a neurotransmitter and a regulator of gastric acid secretion. Histamine is a monoamine synthesized from the amino acid histidine through a reaction catalyzed by the enzyme histidine decarboxylase (HDC), which removes carboxyl group from histidine. Despite the importance of histamine, transcriptional regulation of HDC gene expression in mammals is still poorly understood. In this Review, we focus on discussing advances in the understanding of molecular regulation of mammalian histamine synthesis.Comment: 1.added references for introduction section; 2.added references and typos added for histamine-producing cells in mammals and stimuli that trigger histamine release; 3.typos added for section of histidine decarboxylase and histamine synthesis in mammals; 4.added references and typos added for section of hdc gene expression and histamine synthesis in basophils and mast cells. 5. added 2 figure

An Attempt towards Interpretable Audio-Visual Video Captioning

Automatically generating a natural language sentence to describe the content of an input video is a very challenging problem. It is an essential multimodal task in which auditory and visual contents are equally important. Although audio information has been exploited to improve video captioning in previous works, it is usually regarded as an additional feature fed into a black box fusion machine. How are the words in the generated sentences associated with the auditory and visual modalities? The problem is still not investigated. In this paper, we make the first attempt to design an interpretable audio-visual video captioning network to discover the association between words in sentences and audio-visual sequences. To achieve this, we propose a multimodal convolutional neural network-based audio-visual video captioning framework and introduce a modality-aware module for exploring modality selection during sentence generation. Besides, we collect new audio captioning and visual captioning datasets for further exploring the interactions between auditory and visual modalities for high-level video understanding. Extensive experiments demonstrate that the modality-aware module makes our model interpretable on modality selection during sentence generation. Even with the added interpretability, our video captioning network can still achieve comparable performance with recent state-of-the-art methods.Comment: 11 pages, 4 figure

Residual Dense Network for Image Restoration

Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in IR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via densely connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory mechanism. To adaptively learn more effective features from preceding and current local features and stabilize the training of wider network, we proposed local feature fusion in RDB. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. We demonstrate the effectiveness of RDN with several representative IR applications, single image super-resolution, Gaussian image denoising, image compression artifact reduction, and image deblurring. Experiments on benchmark and real-world datasets show that our RDN achieves favorable performance against state-of-the-art methods for each IR task quantitatively and visually.Comment: To appear in TPAMI. arXiv admin note: substantial text overlap with arXiv:1802.0879

Residual Dense Network for Image Super-Resolution

A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Extensive experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods.Comment: To appear in CVPR 2018 as spotligh