The City: Art and the Urban Environment

The City: Art and the Urban Environment is the fifth annual exhibition curated by students enrolled in the Art History Methods class. This exhibition draws on the students’ newly developed expertise in art-historical methodologies and provides an opportunity for sustained research and an engaged curatorial experience. Working with a selection of paintings, prints, and photographs, students Angelique Acevedo ’19, Sidney Caccioppoli ’21, Abigail Coakley ’20, Chris Condon ’18, Alyssa DiMaria ’19, Carolyn Hauk ’21, Lucas Kiesel ’20, Noa Leibson ’20, Erin O’Brien ’19, Elise Quick ’21, Sara Rinehart ’19, and Emily Roush ’21 carefully consider depictions of the urban environment in relation to significant social, economic, artistic, and aesthetic developments. [excerpt]https://cupola.gettysburg.edu/artcatalogs/1029/thumbnail.jp

Dos and don’ts in response priming research

Response priming is a well-understood but sparsely employed paradigm in cognitive science. The method is powerful and well-suited for exploring early visuomotor processing in a wide range of tasks and research fields. Moreover, response priming can be dissociated from visual awareness, possibly because it is based on the first sweep of feedforward processing of primes and targets. This makes it a theoretically interesting device for separating conscious and unconscious vision. We discuss the major opportunities of the paradigm and give specific recommendations (e.g., tracing the time-course of priming in parametric experiments). Also, we point out typical confounds, design flaws, and data processing artifacts

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for narrow resonances in dilepton mass spectra in proton-proton collisions at root s=13 TeV and combination with 8 TeV data

Peer reviewe

Search for new physics with dijet angular distributions in proton-proton collisions at root S = 13 TeV

Peer reviewe

Search for light bosons in decays of the 125 GeV Higgs boson in proton-proton collisions at root s=8 TeV

Peer reviewe

Search for supersymmetry in events with photons and missing transverse energy in pp collisions at 13 TeV

Peer reviewe

Mechanical stability of the CMS strip tracker measured with a laser alignment system

Peer reviewe

Measurement of the top quark mass using single top quark events in proton-proton collisions at root s=8 TeV

Peer reviewe