arXiv subjects
Publications and source records attributed to Xiaodong Jiang.
After the recent success of acceleration molecular H$_2^+$ beam (from EBIS) in Booster to 1.0 GeV/u kinetic energy, we propose to carry out tests to accelerate H$_2^+$ beam in AGS (up to 12 GeV/u) and to RHIC (up to 100 GeV/u). In this Note, we address the main effects that might hamper H$_2^+$ beam acceleration, outline the impacts and benefits of high energy H$_2^+$ beam to BNL.
Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot readily be used in large organizations where thousands of users monitor millions of use cases and metrics with varied time series characteristics and anomaly patterns. The selection of algorithm and parameters needs to be precise for each use case: manual tuning does not scale, and automated tuning requires ground truth, which is rarely available. In this paper, we explore MOSPAT, an end-to-end automated machine learning based approach for model and parameter selection, combined with a generative model to produce labeled data. Our scalable end-to-end system allows individual users in large organizations to tailor time-series monitoring to their specific use case and data characteristics, without expert knowledge of anomaly detection algorithms or laborious manual labeling. Our extensive experiments on real and synthetic data demonstrate that this method consistently outperforms using any single algorithm.
Hyper-parameters of time series models play an important role in time series analysis. Slight differences in hyper-parameters might lead to very different forecast results for a given model, and therefore, selecting good hyper-parameter values is indispensable. Most of the existing generic hyper-parameter tuning methods, such as Grid Search, Random Search, Bayesian Optimal Search, are based on one key component - search, and thus they are computationally expensive and cannot be applied to fast and scalable time-series hyper-parameter tuning (HPT). We propose a self-supervised learning framework for HPT (SSL-HPT), which uses time series features as inputs and produces optimal hyper-parameters. SSL-HPT algorithm is 6-20x faster at getting hyper-parameters compared to other search based algorithms while producing comparable accurate forecasting results in various applications.
Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of machine learning tasks. Graph embedding is a way to transform and encode the data structure in high dimensional and non-Euclidean feature space to a low dimensional and structural space, which is easily exploited by other machine learning algorithms. We have witnessed a huge surge of such embedding methods, from statistical approaches to recent deep learning methods such as the graph convolutional networks (GCN). Deep learning approaches usually outperform the traditional methods in most graph learning benchmarks by building an end-to-end learning framework to optimize the loss function directly. However, most of the existing GCN methods can only perform convolution operations with node features, while ignoring the handy information in edge features, such as relations in knowledge graphs. To address this problem, we present CensNet, Convolution with Edge-Node Switching graph neural network, for learning tasks in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach achieves or matches the state-of-the-art performance in four graph learning tasks, including semi-supervised node classification, multi-task graph classification, graph regression, and link prediction.
By engineering atomic geometries composed of nearly 1000 atomic segments embedded in micro-resonators we observe Bragg resonances induced by the atomic lattice at the telecommunication wavelength. The geometrical arrangement of erbium atoms into a lattice inside a silicon nitride microring resonator reduces the scattering loss at a wavelength commensurate with the lattice. We confirm dependency of light emission to the atomic positions and lattice spacing and also observe Fano interference between resonant modes in the system.
Part 2 of "Project X: Accelerator Reference Design, Physics Opportunities, Broader Impacts". In this Part, we outline the particle-physics program that can be achieved with Project X, a staged superconducting linac for intensity-frontier particle physics. Topics include neutrino physics, kaon physics, muon physics, electric dipole moments, neutron-antineutron oscillations, new light particles, hadron structure, hadron spectroscopy, and lattice-QCD calculations. Part 1 is available as arXiv:1306.5022 [physics.acc-ph] and Part 3 is available as arXiv:1306.5024 [physics.acc-ph].
A polarized $ep/eA$ collider (Electron--Ion Collider, or EIC), with polarized proton and light-ion beams and unpolarized heavy-ion beams with a variable center--of--mass energy $\sqrt{s} \sim 20$ to $\sim100$~GeV (upgradable to $\sim 150$ GeV) and a luminosity up to $\sim 10^{34} \, \textrm{cm}^{-2} \textrm{s}^{-1}$, would be uniquely suited to address several outstanding questions of Quantum Chromodynamics, and thereby lead to new qualitative and quantitative information on the microscopic structure of hadrons and nuclei. During this meeting at Jefferson Lab we addressed recent theoretical and experimental developments in the spin and the three--dimensional structure of the nucleon (sea quark and gluon spatial distributions, orbital motion, polarization, and their correlations). This mini--review contains a short update on progress in these areas since the EIC White paper~\cite{Accardi:2012qut}.
In this paper we will prove a uniformity result for the Iitaka fibration $f:X \rightarrow Y$, provided that the generic fiber has a good minimal model and the variation of $f$ is zero or that $κ(X)=\rm{dim}(X)-1$.
It has been proposed that the Siverse transverse single spin asymmetry in Drell-Yan production in transversely polarized p+p collisions would have an opposite sign compared to what has been observed in the polarized Semi-Inclusive Deep Inelastic Scattering (SIDIS) experiments. Experimental confirmation or disproval of this prediction would provide a new fundamental test of QCD and shed new light on our theoretical understanding of the transverse spin physics phenomena. We discuss the prospects and physics sensitivities of polarized fixed target Drell-Yan experiments that could utilize the existing proton and other hadron beams at Fermilab, and polarized proton beams at RHIC with a polarized solid proton and/or neutron target option. We show that if realized, the new experiments would provide critical measurements of not only the sign change (or not) of Sivers functions, but also the information of quark and antiquark's Sivers distributions over a wide kinematic range.
A Jefferson Lab experiment proposal was discussed in this talk. The experiment is designed to measure the beam-target double-spin asymmetries $A_{1n}^h$ in semi-inclusive deep-inelastic $\vec n({\vec e}, e^\prime π^+)X$ and $\vec n({\vec e}, e^\prime π^-)X$ reactions on a longitudinally polarized $^3$He target. In addition to $A_{1n}^h$, the flavor non-singlet combination $A_{1n}^{π^+ - π^-}$, in which the gluons do not contribute, will be determined with high precision to extract $Δd_v(x)$ independent of the knowledge of the fragmentation functions. The data will also impose strong constraints on quark and gluon polarizations through a global NLO QCD fit.
Experiment E04-113 at Jefferson Lab Hall C plans to measure the beam-target double-spin asymmetries in semi-inclusive deep-inelastic $\vec p(e, e^\prime h)X$ and $\vec d(e, e^\prime h)X$ reactions ($h=π^+, π^-, K^+$ or$K^-$) with a 6 GeV polarized electron beam and longitudinally polarized NH$_3$ and LiD targets. The high statistic data will allow a spin-flavor decomposition in the region of $x=0.12 \sim 0.41$ at $Q^2=1.21\sim 3.14$ GeV$^2$. Especially, leading-order and next-to-leading order spin-flavor decomposition of $Δu_v$, $Δd_v$ and $Δ\bar{u} - Δ\bar{d}$ will be extracted based on the measurement of the combined asymmetries $A_{1N}^{π^+ - π^-}$. The possible flavor asymmetry of the polarized sea will be addressed in this experiment.