4,223 research outputs found
Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games
We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for
dynamic discrete choice games of incomplete information. We show that each
iteration in the k-EPL sequence is consistent and asymptotically efficient, so
the first-order asymptotic properties do not vary across iterations.
Furthermore, we show the sequence achieves higher-order equivalence to the
finite-sample maximum likelihood estimator with iteration and that the sequence
of estimators converges almost surely to the maximum likelihood estimator at a
nearly-superlinear rate when the data are generated by any regular Markov
perfect equilibrium, including equilibria that lead to inconsistency of other
sequential estimators. When utility is linear in parameters, k-EPL iterations
are computationally simple, only requiring that the researcher solve linear
systems of equations to generate pseudo-regressors which are used in a static
logit/probit regression. Monte Carlo simulations demonstrate the theoretical
results and show k-EPL's good performance in finite samples in both small- and
large-scale games, even when the game admits spurious equilibria in addition to
one that generated the data
Towards an Adaptive OS Noise Mitigation Technique for Microbenchmarking on Apple Ipad Devices
This study investigates levels of Operating System (OS) noise on Apple iPad mobile devices. OS noise causes variations in application performance that interfere with microbenchmark results. OS noise manifests in collected data through extreme outliers and variations in skewness. Using our collected data, we develop an iterative, semi-automated outlier removal process for Apple iPad OS noise profiles. The profiles generated by outlier removal represent the first step toward an adaptive noise mitigation technique, which presents opportunities for use in microbenchmarking across other mobile platforms
On the impact of heterogeneity and back-end scheduling in load balancing designs
Load balancing is a common approach for task
assignment in distributed architectures. In this paper, we show
that the degree of inefficiency in load balancing designs is highly
dependent on the scheduling discipline used at each of the backend
servers. Traditionally, the back-end scheduler can be modeled
as Processor Sharing (PS), in which case the degree of inefficiency
grows linearly with the number of servers. However, if the back-end
scheduler is changed to Shortest Remaining Processing Time
(SRPT), the degree of inefficiency can be independent of the
number of servers, instead depending only on the heterogeneity
of the speeds of the servers. Further, switching the back-end
scheduler to SRPT can provide significant improvements in
the overall mean response time of the system as long as the
heterogeneity of the server speeds is small
Analysis and mitigation of residual exchange coupling in linear spin qubit arrays
In recent advancements of quantum computing utilizing spin qubits, it has
been demonstrated that this platform possesses the potential for implementing
two-qubit gates with fidelities exceeding 99.5%. However, as with other qubit
platforms, it is not feasible to completely turn qubit couplings off. This
study aims to investigate the impact of coherent error matrices in gate set
tomography by employing a double quantum dot. We evaluate the infidelity caused
by residual exchange between spins and compare various mitigation approaches,
including the use of adjusted timing through simple drives, considering
different parameter settings in the presence of charge noise. Furthermore, we
extend our analysis to larger arrays of exchange-coupled spin qubits to provide
an estimation of the expected fidelity. In particular, we demonstrate the
influence of residual exchange on a single-qubit gate and the native
two-qubit SWAP gate in a linear chain. Our findings emphasize the significance
of accounting for residual exchange when scaling up spin qubit devices and
highlight the tradeoff between the effects of charge noise and residual
exchange in mitigation techniques.Comment: 15 pages, 8 figure
TurbuStat: Turbulence Statistics in Python
We present TurbuStat (v1.0): a Python package for computing turbulence
statistics in spectral-line data cubes. TurbuStat includes implementations of
fourteen methods for recovering turbulent properties from observational data.
Additional features of the software include: distance metrics for comparing two
data sets; a segmented linear model for fitting lines with a break-point; a
two-dimensional elliptical power-law model; multi-core fast-fourier-transform
support; a suite for producing simulated observations of fractional Brownian
Motion fields, including two-dimensional images and optically-thin HI data
cubes; and functions for creating realistic world coordinate system information
for synthetic observations. This paper summarizes the TurbuStat package and
provides representative examples using several different methods. TurbuStat is
an open-source package and we welcome community feedback and contributions.Comment: Accepted in AJ. 21 pages, 8 figure
A New Large Super-Fast Rotator: (335433) 2005 UW163
Asteroids of size larger than 150 m generally do not have rotation periods
smaller than 2.2 hours. This spin cutoff is believed to be due to the
gravitationally bound rubble-pile structures of the asteroids. Rotation with
periods exceeding this critical value will cause asteroid breakup. Up until
now, only one object, 2001 OE84, has been found to be an exception to this spin
cutoff. We report the discovery of a new super-fast rotator, (335433) 2005
UW163, spinning with a period of 1.290 hours and a lightcurve variation of
mag from the observations made at the P48 telescope and the P200
telescope of the Palomar Observatory. Its mag and
multi-band colors (i.e., mag, mag
and SDSS mag) show it is a V-type asteroid with a diameter of
km. This indicates (335433) 2005 UW163 is a super-fast rotator
beyond the regime of the small monolithic asteroid.Comment: 18 pages, 4 figures, 1 table Accepted by ApJ
The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Quasar Target Selection
As part of the Sloan Digital Sky Survey (SDSS) IV the extended Baryon Oscillation Spectroscopic Survey (eBOSS) will improve measurements of the cosmological distance scale by applying the Baryon Acoustic Oscillation (BAO) method to quasar samples. eBOSS will adopt two approaches to target quasars over 7500 deg^2. First, a "CORE" quasar sample will combine the optical selection in ugriz using a likelihood-based routine called XDQSOz, with a mid-IR-optical color cut. eBOSS CORE selection (to g 2.1 quasars. Second, a selection based on variability in multi-epoch imaging from the Palomar Transient Factory should recover an additional ~3–4 deg^(−2)z > 2.1 quasars to g 2.1 will be used to improve BAO measurements in the Lyα Forest. Beyond its key cosmological goals, eBOSS should be the next-generation quasar survey, comprising >500,000 new quasars and >500,000 uniformly selected spectroscopically confirmed 0.9 < z < 2.2 quasars. At the conclusion of eBOSS, the SDSS will have provided unique spectra for more than 800,000 quasars
Variability in antifungal and antiviral use in hospitalized children
We analyzed antifungal and antiviral prescribing among high-risk children across freestanding children’s hospitals. Antifungal and antiviral days of therapy varied across hospitals. Benchmarking antifungal and antiviral use and developing antimicrobial stewardship strategies to optimize use of these high cost agents is needed.Infect Control Hosp Epidemiol2017;38:743–746</jats:p
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