1,140 research outputs found
Comparing compact binary parameter distributions I: Methods
Being able to measure each merger's sky location, distance, component masses,
and conceivably spins, ground-based gravitational-wave detectors will provide a
extensive and detailed sample of coalescing compact binaries (CCBs) in the
local and, with third-generation detectors, distant universe. These
measurements will distinguish between competing progenitor formation models. In
this paper we develop practical tools to characterize the amount of
experimentally accessible information available, to distinguish between two a
priori progenitor models. Using a simple time-independent model, we demonstrate
the information content scales strongly with the number of observations. The
exact scaling depends on how significantly mass distributions change between
similar models. We develop phenomenological diagnostics to estimate how many
models can be distinguished, using first-generation and future instruments.
Finally, we emphasize that multi-observable distributions can be fully
exploited only with very precisely calibrated detectors, search pipelines,
parameter estimation, and Bayesian model inference
WIMP astronomy and particle physics with liquid-noble and cryogenic direct-detection experiments
Once weakly-interacting massive particles (WIMPs) are unambiguously detected
in direct-detection experiments, the challenge will be to determine what one
may infer from the data. Here, I examine the prospects for reconstructing the
local speed distribution of WIMPs in addition to WIMP particle-physics
properties (mass, cross sections) from next-generation cryogenic and
liquid-noble direct-detection experiments. I find that the common method of
fixing the form of the velocity distribution when estimating constraints on
WIMP mass and cross sections means losing out on the information on the speed
distribution contained in the data and may lead to biases in the inferred
values of the particle-physics parameters. I show that using a more general,
empirical form of the speed distribution can lead to good constraints on the
speed distribution. Moreover, one can use Bayesian model-selection criteria to
determine if a theoretically-inspired functional form for the speed
distribution (such as a Maxwell-Boltzmann distribution) fits better than an
empirical model. The shape of the degeneracy between WIMP mass and cross
sections and their offset from the true values of those parameters depends on
the hypothesis for the speed distribution, which has significant implications
for consistency checks between direct-detection and collider data. In addition,
I find that the uncertainties on theoretical parameters depends sensitively on
the upper end of the energy range used for WIMP searches. Better constraints on
the WIMP particle-physics parameters and speed distribution are obtained if the
WIMP search is extended to higher energy (~ 1 MeV).Comment: 25 pages, 27 figures, matches published versio
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample
sizes, along with significant intra-class variation and inter-class similarity.
While prior work has addressed intra-class variation using localization and
segmentation techniques, inter-class similarity may also affect feature
learning and reduce classification performance. In this work, we address this
problem using a novel optimization procedure for the end-to-end neural network
training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces
overfitting by intentionally {introducing confusion} in the activations. With
PC regularization, we obtain state-of-the-art performance on six of the most
widely-used FGVC datasets and demonstrate improved localization ability. {PC}
is easy to implement, does not need excessive hyperparameter tuning during
training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201
Bayesian Network Structure Learning with Permutation Tests
In literature there are several studies on the performance of Bayesian
network structure learning algorithms. The focus of these studies is almost
always the heuristics the learning algorithms are based on, i.e. the
maximisation algorithms (in score-based algorithms) or the techniques for
learning the dependencies of each variable (in constraint-based algorithms). In
this paper we investigate how the use of permutation tests instead of
parametric ones affects the performance of Bayesian network structure learning
from discrete data. Shrinkage tests are also covered to provide a broad
overview of the techniques developed in current literature.Comment: 13 pages, 4 figures. Presented at the Conference 'Statistics for
Complex Problems', Padova, June 15, 201
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
We present a framework for quantifying and mitigating algorithmic bias in
mechanisms designed for ranking individuals, typically used as part of
web-scale search and recommendation systems. We first propose complementary
measures to quantify bias with respect to protected attributes such as gender
and age. We then present algorithms for computing fairness-aware re-ranking of
results. For a given search or recommendation task, our algorithms seek to
achieve a desired distribution of top ranked results with respect to one or
more protected attributes. We show that such a framework can be tailored to
achieve fairness criteria such as equality of opportunity and demographic
parity depending on the choice of the desired distribution. We evaluate the
proposed algorithms via extensive simulations over different parameter choices,
and study the effect of fairness-aware ranking on both bias and utility
measures. We finally present the online A/B testing results from applying our
framework towards representative ranking in LinkedIn Talent Search, and discuss
the lessons learned in practice. Our approach resulted in tremendous
improvement in the fairness metrics (nearly three fold increase in the number
of search queries with representative results) without affecting the business
metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users
worldwide. Ours is the first large-scale deployed framework for ensuring
fairness in the hiring domain, with the potential positive impact for more than
630M LinkedIn members.Comment: This paper has been accepted for publication at ACM KDD 201
Local and Global Distinguishability in Quantum Interferometry
A statistical distinguishability based on relative entropy characterises the
fitness of quantum states for phase estimation. This criterion is employed in
the context of a Mach-Zehnder interferometer and used to interpolate between
two regimes, of local and global phase distinguishability. The scaling of
distinguishability in these regimes with photon number is explored for various
quantum states. It emerges that local distinguishability is dependent on a
discrepancy between quantum and classical rotational energy. Our analysis
demonstrates that the Heisenberg limit is the true upper limit for local phase
sensitivity. Only the `NOON' states share this bound, but other states exhibit
a better trade-off when comparing local and global phase regimes.Comment: 4 pages, in submission, minor revision
Complexity Measures from Interaction Structures
We evaluate new complexity measures on the symbolic dynamics of coupled tent
maps and cellular automata. These measures quantify complexity in terms of
-th order statistical dependencies that cannot be reduced to interactions
between units. We demonstrate that these measures are able to identify
complex dynamical regimes.Comment: 11 pages, figures improved, minor changes to the tex
Trade Statistics of the Zollverein, 1834-1871
The purpose of this paper is to highlight the fact that the lack of useable German trade statistics for the period preceding the German political unifica- tion is not a fatality. The documents published during the Zollverein period by the Central Bureau of the Zollverein, the Statistische Uebersichten über Waaren- Verkehr und Zoll-Ertrag im Deutschen Zoll-Vereine für das Jahr..., do not provide prices nor trade flows in value nor any indication of countries of ultimate origin and destination. To overcome these imperfections, a great number of estimates of Zollverein trade statistics have been published since 1842 but they are ques- tionable as well. Nevertheless, the good quality of Statistische Übersichten’s quantity data should make possible the reconstruction of consistent series of German trade, total, by product and in value, over the period 1834-1871
Dynamics of the Fisher Information Metric
We present a method to generate probability distributions that correspond to
metrics obeying partial differential equations generated by extremizing a
functional , where is the
Fisher metric. We postulate that this functional of the dynamical variable
is stationary with respect to small variations of these
variables. Our approach enables a dynamical approach to Fisher information
metric. It allows to impose symmetries on a statistical system in a systematic
way. This work is mainly motivated by the entropy approach to nonmonotonic
reasoning.Comment: 11 page
Probability density of determinants of random matrices
In this brief paper the probability density of a random real, complex and
quaternion determinant is rederived using singular values. The behaviour of
suitably rescaled random determinants is studied in the limit of infinite order
of the matrices
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