It Takes (Only) Two: Adversarial Generator-Encoder Networks

We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the prior distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparable quality to some recently-proposed more complex architectures

Generative Models for Fast Calorimeter Simulation.LHCb case

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 order of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a big enough amount of simulated data needed by the next HL LHC experiments using limited computing resources.Comment: Proceedings of the presentation at CHEP 2018 Conferenc

DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding

Corrosion of diffusion zinc coatings in sodium chloride solutions

Diffusion galvanizing is widely used in the pipe industry for coating the threaded surface of pipe couplings, protecting water and gas pipelines, and other metal products. Diffusion coatings have a number of advantages over other types of zinc coatings. In this work, electrochemical and gravimetric methods are used to study the corrosion behavior of diffusion zinc coatings in sodium chloride solutions. The corrosion rate depends non-linearly on the thickness of the coating. At the initial stages, the corrosion rate of coatings depends on the structure of the phases on the surface, and with an increase in the holding time, the corrosion rate depends to a greater extent on the properties of the products formed during the corrosion process. Films of corrosion products of diffusion zinc coatings consist of zinc oxide/hydroxide and basic zinc salts, while the composition of the film changes with increasing coating thickness

Style Separation and Synthesis via Generative Adversarial Networks

Style synthesis attracts great interests recently, while few works focus on its dual problem "style separation". In this paper, we propose the Style Separation and Synthesis Generative Adversarial Network (S3-GAN) to simultaneously implement style separation and style synthesis on object photographs of specific categories. Based on the assumption that the object photographs lie on a manifold, and the contents and styles are independent, we employ S3-GAN to build mappings between the manifold and a latent vector space for separating and synthesizing the contents and styles. The S3-GAN consists of an encoder network, a generator network, and an adversarial network. The encoder network performs style separation by mapping an object photograph to a latent vector. Two halves of the latent vector represent the content and style, respectively. The generator network performs style synthesis by taking a concatenated vector as input. The concatenated vector contains the style half vector of the style target image and the content half vector of the content target image. Once obtaining the images from the generator network, an adversarial network is imposed to generate more photo-realistic images. Experiments on CelebA and UT Zappos 50K datasets demonstrate that the S3-GAN has the capacity of style separation and synthesis simultaneously, and could capture various styles in a single model

Design, Performance, and Calibration of CMS Hadron Endcap Calorimeters

Detailed measurements have been made with the CMS hadron calorimeter endcaps (HE) in response to beams of muons, electrons, and pions. Readout of HE with custom electronics and hybrid photodiodes (HPDs) shows no change of performance compared to readout with commercial electronics and photomultipliers. When combined with lead-tungstenate crystals, an energy resolution of 8\% is achieved with 300 GeV/c pions. A laser calibration system is used to set the timing and monitor operation of the complete electronics chain. Data taken with radioactive sources in comparison with test beam pions provides an absolute initial calibration of HE to approximately 4\% to 5\%