13,863 research outputs found
Short proofs of the Quantum Substate Theorem
The Quantum Substate Theorem due to Jain, Radhakrishnan, and Sen (2002) gives
us a powerful operational interpretation of relative entropy, in fact, of the
observational divergence of two quantum states, a quantity that is related to
their relative entropy. Informally, the theorem states that if the
observational divergence between two quantum states rho, sigma is small, then
there is a quantum state rho' close to rho in trace distance, such that rho'
when scaled down by a small factor becomes a substate of sigma. We present new
proofs of this theorem. The resulting statement is optimal up to a constant
factor in its dependence on observational divergence. In addition, the proofs
are both conceptually simpler and significantly shorter than the earlier proof.Comment: 11 pages. Rewritten; included new references; presented the results
in terms of smooth relative min-entropy; stronger results; included converse
and proof using SDP dualit
Precision measurement of cosmic magnification from 21 cm emitting galaxies
We show how precision lensing measurements can be obtained through the
lensing magnification effect in high redshift 21cm emission from galaxies.
Normally, cosmic magnification measurements have been seriously complicated by
galaxy clustering. With precise redshifts obtained from 21cm emission line
wavelength, one can correlate galaxies at different source planes, or exclude
close pairs to eliminate such contaminations.
We provide forecasts for future surveys, specifically the SKA and CLAR. SKA
can achieve percent precision on the dark matter power spectrum and the galaxy
dark matter cross correlation power spectrum, while CLAR can measure an
accurate cross correlation power spectrum. The neutral hydrogen fraction was
most likely significantly higher at high redshifts, which improves the number
of observed galaxies significantly, such that also CLAR can measure the dark
matter lensing power spectrum. SKA can also allow precise measurement of
lensing bispectrum.Comment: 11 pages, 8 figures. Accepted to MNRAS. We deleted two figures and
shortened the paper to meet MNRAS's requirement. All main results remain
unchange
Likelihood Analysis of Cosmic Shear on Simulated and VIRMOS-DESCART Data
We present a maximum likelihood analysis of cosmological parameters from
measurements of the aperture mass up to 35 arcmin, using simulated and real
cosmic shear data. A four-dimensional parameter space is explored which
examines the mean density \Omega_M, the mass power spectrum normalization
\sigma_8, the shape parameter \Gamma and the redshift of the sources z_s.
Constraints on \Omega_M and \sigma_8 (resp. \Gamma and z_s) are then given by
marginalizing over \Gamma and z_s (resp. \Omega_M and \sigma_8). For a flat
LCDM cosmologies, using a photometric redshift prior for the sources and \Gamma
\in [0.1,0.4], we find \sigma_8=(0.57\pm0.04) \Omega_M^{(0.24\mp 0.18)
\Omega_M-0.49} at the 68% confidence level (the error budget includes
statistical noise, full cosmic variance and residual systematic). The estimate
of \Gamma, marginalized over \Omega_M \in [0.1,0.4], \sigma_8 \in [0.7,1.3] and
z_s constrained by photometric redshifts, gives \Gamma=0.25\pm 0.13 at 68%
confidence. Adopting h=0.7, a flat universe, \Gamma=0.2 and \Omega_m=0.3 we
find \sigma_8=0.98 \pm0.06 . Combined with CMB, our results suggest a non-zero
cosmological constant and provide tight constraints on \Omega_M and \sigma_8.
We finaly compare our results to the cluster abundance ones, and discuss the
possible discrepancy with the latest determinations of the cluster method. In
particular we point out the actual limitations of the mass power spectrum
prediction in the non-linear regime, and the importance for its improvement.Comment: 11 pages, submitted to A&
Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric
Biometric techniques are often used as an extra security factor in
authenticating human users. Numerous biometrics have been proposed and
evaluated, each with its own set of benefits and pitfalls. Static biometrics
(such as fingerprints) are geared for discrete operation, to identify users,
which typically involves some user burden. Meanwhile, behavioral biometrics
(such as keystroke dynamics) are well suited for continuous, and sometimes more
unobtrusive, operation. One important application domain for biometrics is
deauthentication, a means of quickly detecting absence of a previously
authenticated user and immediately terminating that user's active secure
sessions. Deauthentication is crucial for mitigating so called Lunchtime
Attacks, whereby an insider adversary takes over (before any inactivity timeout
kicks in) authenticated state of a careless user who walks away from her
computer. Motivated primarily by the need for an unobtrusive and continuous
biometric to support effective deauthentication, we introduce PoPa, a new
hybrid biometric based on a human user's seated posture pattern. PoPa captures
a unique combination of physiological and behavioral traits. We describe a low
cost fully functioning prototype that involves an office chair instrumented
with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa
can be used in a typical workplace to provide continuous authentication (and
deauthentication) of users. We experimentally assess viability of PoPa in terms
of uniqueness by collecting and evaluating posture patterns of a cohort of
users. Results show that PoPa exhibits very low false positive, and even lower
false negative, rates. In particular, users can be identified with, on average,
91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several
prominent biometric based deauthentication techniques
A Spatial Decision Support System for Livestock Production Planning and Environmental Management
Spatial decision support systems are used to plan production systems and direct the implementation management strategies that are compatible with environmental protection goals. They enable resource managers to select appropriate production technologies that minimize environmental damage, and evaluate alternative management practices. This article describes a spatial decision support system (SDSS) developed to facilitate planning and management of environmentally sound livestock production. The SDSS integrates a geographic information system, spatial and biophysical modeling, and a knowledge-based system into an interactive tool to select suitable watershed land areas for siting livestock production, to select fields for manure application, and to determine the potential impacts of livestock production practices on groundwater quality
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