Yang Gao

Bio: Yang Gao is an academic researcher from University of Surrey. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 168, co-authored 2047 publications receiving 146301 citations. Previous affiliations of Yang Gao include China Agricultural University & University of Kassel.

Measurement of the charge asymmetry in dileptonic decays of top quark pairs in pp collisions at √s = 7 TeV using the ATLAS detector

Abstract: A measurement of the top-antitop (t (t) over bar) charge asymmetry is presented using data corresponding to an integrated luminosity of 4.6 fb(-1) of LHC pp collisions at a centre-of-mass energy of 7 TeV collected by the ATLAS detector. Events with two charged leptons, at least two jets and large missing transverse momentum are selected. Two observables are studied: A(C)(ll) based on the identified charged leptons, and A(C)(t (t) over bar), based on the reconstructed t (t) over bar final state. The asymmetries are measured to be A(C)(ll) =0.024 +/- 0.015 (stat.) +/- 0.009 (syst.), A(C)(t (t) over bar) = 0.021 +/- 0.025 (stat.) +/- 0.017 (syst.). The measured values are in agreement with the Standard Model predictions.

Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation.

Abstract: Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable, providing objective scores. Yet, due to the high cost of the Pyramid method and the required expertise, researchers resorted to cheaper and less thorough manual evaluation methods, such as Responsiveness and pairwise comparison, attainable via crowdsourcing. We revisit the Pyramid approach, proposing a lightweight sampling-based version that is crowdsourcable. We analyze the performance of our method in comparison to original expert-based Pyramid evaluations, showing higher correlation relative to the common Responsiveness method. We release our crowdsourced Summary-Content-Units, along with all crowdsourcing scripts, for future evaluations.

Measurement of the production cross section of an isolated photon associated with jets in proton-proton collisions at √s=7TeV with the ATLAS detector

Abstract: A measurement of the cross section for the production of an isolated photon in association with jets in proton-proton collisions at a center-of-mass energy root s = 7 TeV is presented. Photons are ...

Search for New Phenomena in t(t)overbar Events with Large Missing Transverse Momentum in Proton-Proton Collisions at root s = 7 TeV with the ATLAS Detector

Abstract: A search for new phenomena in tt events with large missing transverse momentum in proton-proton collisions at a center-of-mass energy of 7 TeV is presented. The measurement is based on 1.04 fb-1 of data collected with the ATLAS detector at the LHC. Contributions to this final state may arise from a number of standard model extensions. The results are interpreted in terms of a model where new top-quark partners are pair produced and each decay to an on-shell top (or antitop) quark and a long-lived undetected neutral particle. The data are found to be consistent with standard model expectations. A limit at 95% confidence level is set excluding a cross section times branching ratio of 1.1 pb for a top-partner mass of 420 GeV and a neutral particle mass less than 10 GeV. In a model of exotic fourth generation quarks, top-partner masses are excluded up to 420 GeV and neutral particle masses up to 140 GeV.

Search for anomalous electroweak production of WW /WZ in association with a high-mass dijet system in pp collisions at s =8 TeV with the ATLAS detector

Abstract: A search is presented for anomalous quartic gauge boson couplings in vector-boson scattering. The data for the analysis correspond to $20.2$ fb$^{-1}$ of $\sqrt{s}=8$ TeV $pp$ collisions, and were collected in 2012 by the ATLAS experiment at the Large Hadron Collider. The search looks for the production of $WW$ or $WZ$ boson pairs accompanied by a high-mass dijet system, with one $W$ decaying leptonically, and a $W$ or $Z$ decaying hadronically. The hadronically decaying $W/Z$ is reconstructed as either two small-radius jets or one large-radius jet using jet substructure techniques. Constraints on the anomalous quartic gauge boson coupling parameters $\alpha_4$ and $\alpha_5$ are set by fitting the transverse mass of the diboson system, and the resulting 95% confidence intervals are $-0.024<\alpha_4<0.030$ and $-0.028<\alpha_5<0.033$.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).