2,906 research outputs found
Measurement of Parity Violation in the Early Universe using Gravitational-wave Detectors
A stochastic gravitational-wave background (SGWB) is expected to arise from
the superposition of many independent and unresolved gravitational-wave
signals, of either cosmological or astrophysical origin. Some cosmological
models (characterized, for instance, by a pseudo-scalar inflaton, or by some
modification of gravity) break parity, leading to a polarized SGWB. We present
a new technique to measure this parity violation, which we then apply to the
recent results from LIGO to produce the first upper limit on parity violation
in the SGWB, assuming a generic power-law SGWB spectrum across the LIGO
sensitive frequency region. We also estimate sensitivity to parity violation of
the future generations of gravitational-wave detectors, both for a power-law
spectrum and for a model of axion inflation. This technique offers a new way of
differentiating between the cosmological and astrophysical sources of the
isotropic SGWB, as astrophysical sources are not expected to produce a
polarized SGWB.Comment: 5 pages, 2 figures, 1 tabl
Do Small Group Health Insurance Regulations Influence Small Business Size?
The cost of health insurance has been the primary concern of small business owners for several decades. State small group health insurance reforms, implemented in the 1990s, aimed to control the variability of health insurance premiums and to improve access to health insurance. Small group reforms only affected firms within a specific size range, and the definition of the upper size threshold for small firms varied by state and over time. As a result, small group reforms may have affected the size of small firms around the legislative threshold and may also have affected the propensity of small firms to offer health insurance. Previous research has examined the second issue, finding little to no effect of health insurance reforms on the propensity of small firms to offer health insurance. In this paper, we examine the relationship between small group reform and firm size. We use data from a nationally representative repeated cross-section survey of employers and data on state small group health insurance reform. Contrary to the intent of the reform, we find evidence that small firms just below the regulatory threshold that were offering health insurance grew in order to bypass reforms.Health insurance, small business
Health Savings Accounts for Small Businesses and Entrepreneurs: Shopping, Take-Up and Implementation Challenges
A combination of high deductible health plans (HDHPs) and health savings accounts (HSAs) holds promise for expanding health insurance for small firms. We provide information on HSA take-up and shopping behavior from a 2008 survey of female small business owners, revealing that the HSA marketplace can be confusing for small firms. HSAs may have expanded access to health insurance for the smallest firms (under three employees), but not for small firms more generally. A sizable number of firms offering HSA-eligible insurance did not offer attached HSAs. Firms offering HSAs were satisfied with their experiences, but faced challenges in implementing them.Health Savings Accounts, Health Insurance Costs, Small Business
Health Savings Accounts for Small Businesses and Entrepreneurs: Shopping, Take-Up and Implementation Challenges
A combination of high deductible health plans (HDHPs) and health savings accounts (HSAs) holds promise for expanding health insurance for small firms. We provide information on HSA take-up and shopping behavior from a 2008 survey of female small business owners, revealing that the HSA marketplace can be confusing for small firms. HSAs may have expanded access to health insurance for the smallest firms (under three employees), but not for small firms more generally. A sizable number of firms offering HSA-eligible insurance did not offer attached HSAs. Firms offering HSAs were satisfied with their experiences, but faced challenges in implementing them.Health Savings Accounts, Health Insurance Costs, Small Business
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Simulation of underground gravity gradients from stochastic seismic fields
We present results obtained from a finite-element simulation of seismic
displacement fields and of gravity gradients generated by those fields. The
displacement field is constructed by a plane wave model with a 3D isotropic
stochastic field and a 2D fundamental Rayleigh field. The plane wave model
provides an accurate representation of stationary fields from distant sources.
Underground gravity gradients are calculated as acceleration of a free test
mass inside a cavity. The results are discussed in the context of
gravity-gradient noise subtraction in third generation gravitational-wave
detectors. Error analysis with respect to the density of the simulated grid
leads to a derivation of an improved seismometer placement inside a 3D array
which would be used in practice to monitor the seismic field.Comment: 24 pages, 12 figure
Gravitational-Wave Stochastic Background from Kinks and Cusps on Cosmic Strings
We compute the contribution of kinks on cosmic string loops to stochastic
background of gravitational waves (SBGW).We find that kinks contribute at the
same order as cusps to the SBGW.We discuss the accessibility of the total
background due to kinks as well as cusps to current and planned gravitational
wave detectors, as well as to the big bang nucleosynthesis (BBN), the cosmic
microwave background (CMB), and pulsar timing constraints. As in the case of
cusps, we find that current data from interferometric gravitational wave
detectors, such as LIGO, are sensitive to areas of parameter space of cosmic
string models complementary to those accessible to pulsar, BBN, and CMB bounds.Comment: 24 pages, 3 figure
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