Deep inelastic scattering from light nuclei

We review recent developments in the study of deep inelastic scattering from light nuclei, focusing in particular on deuterium, helium, and lithium. Understanding the nuclear effects in these systems is essential for the extraction of information on the neutron structure function.Comment: 11 pages, 5 figures; talk given by W. Melnitchouk at the Workshop on Testing QCD through Spin Observables in Nuclear Targets, University of Virginia, April 200

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. We deploy PinSage at Pinterest and train it on 7.5 billion examples on a graph with 3 billion nodes representing pins and boards, and 18 billion edges. According to offline metrics, user studies and A/B tests, PinSage generates higher-quality recommendations than comparable deep learning and graph-based alternatives. To our knowledge, this is the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.Comment: KDD 201

A Deep Chandra Observation of the Distant Galaxy Cluster MS1137.5+6625

We present results from a deep Chandra observation of MS1137.5+66, a distant (z=0.783) and massive cluster of galaxies. Only a few similarly massive clusters are currently known at such high redshifts; accordingly, this observation provides much-needed information on the dynamical state of these rare systems. The cluster appears both regular and symmetric in the X-ray image. However, our analysis of the spectral and spatial X-ray data in conjunction with interferometric Sunyaev-Zel'dovich effect data and published deep optical imaging suggests the cluster has a fairly complex structure. The angular diameter distance we calculate from the Chandra and Sunyaev-Zel'dovich effect data assuming an isothermal, spherically symmetric cluster implies a low value for the Hubble constant for which we explore possible explanations.Comment: 16 pages, 6 figures, submitted to Ap

The Time Structure of Hadronic Showers in Imaging Calorimeters with Scintillator and RPC Readout

The intrinsic time structure of hadronic showers has been studied to evaluate its influence on the timing capability and on the required integration time of highly granular hadronic calorimeters in future collider experiments. The experiments have been carried with systems of 15 detector cells, using both scintillator tiles with SiPM readout and RPCs, read out with fast digitizers and deep buffers. These were installed behind the CALICE scintillator - Tungsten and RPC - Tungsten calorimeters as well as behind the CALICE semi-digital RPC - Steel calorimeter during test beam periods at the CERN SPS. We will discuss the technical aspects of these systems, and present results on the measurement of the time structure of hadronic showers in steel and tungsten calorimeters. These are compared to GEANT4 simulations, providing important information for the validation and the improvement of the physics models. In addition, a comparison of the observed time structure with scintillator and RPC active elements will be presented, which provides insight into the differences in sensitivity to certain aspects of hadronic showers depending on readout technology.Comment: 8 pages, 6 figures, proceedings for CHEF2013, Paris, France, April 201