3,277 research outputs found
A fluorometric method for determining chlorophylls a, b, and c
A method for the determination of chlorophylls a, b, and c by fluorometric techniques is presented. The effects of accessory carotenoids and chlorophyll derivatives on the accuracy of this method are analyzed…
Spectroscopy of the a^3\Sigma_u^+ state and the coupling to the X^1\Sigma_g^+ state of K_2
We report on high resolution Fourier-transform spectroscopy of fluorescence
to the a^3\Sigma_u^+ state excited by two-photon or two-step excitation from
the X^1\Sigma_g^+ state to the 2^3\Pi_g state in the molecule K_2. These
spectroscopic data are combined with recent results of Feshbach resonances and
two-color photoassociation spectra for deriving the potential curves of
X^1\Sigma_g^+ and a^3\Sigma_u^+ up to the asymptote. The precise relative
position of the triplet levels with respect of the singlet levels was achieved
by including the excitation energies from the X^1\Sigma_g^+ state to the
2^3\Pi_g state and down to the a^3\Sigma_u^+ state in the simultaneous fit of
both potentials. The derived precise potential curves allow for reliable
modeling of cold collisions of pairs of potassium atoms in their ^2S ground
state
Causal Dependence Plots
Explaining artificial intelligence or machine learning models is increasingly
important. To use such data-driven systems wisely we must understand how they
interact with the world, including how they depend causally on data inputs. In
this work we develop Causal Dependence Plots (CDPs) to visualize how one
variable--an outcome--depends on changes in another variable--a
predictor--. Crucially, CDPs differ from standard methods based on holding
other predictors constant or assuming they are independent. CDPs make use of an
auxiliary causal model because causal conclusions require causal assumptions.
With simulations and real data experiments, we show CDPs can be combined in a
modular way with methods for causal learning or sensitivity analysis. Since
people often think causally about input-output dependence, CDPs can be powerful
tools in the xAI or interpretable machine learning toolkit and contribute to
applications like scientific machine learning and algorithmic fairness
Impact Remediation: Optimal Interventions to Reduce Inequality
A significant body of research in the data sciences considers unfair
discrimination against social categories such as race or gender that could
occur or be amplified as a result of algorithmic decisions. Simultaneously,
real-world disparities continue to exist, even before algorithmic decisions are
made. In this work, we draw on insights from the social sciences and humanistic
studies brought into the realm of causal modeling and constrained optimization,
and develop a novel algorithmic framework for tackling pre-existing real-world
disparities. The purpose of our framework, which we call the "impact
remediation framework," is to measure real-world disparities and discover the
optimal intervention policies that could help improve equity or access to
opportunity for those who are underserved with respect to an outcome of
interest. We develop a disaggregated approach to tackling pre-existing
disparities that relaxes the typical set of assumptions required for the use of
social categories in structural causal models. Our approach flexibly
incorporates counterfactuals and is compatible with various ontological
assumptions about the nature of social categories. We demonstrate impact
remediation with a real-world case study and compare our disaggregated approach
to an existing state-of-the-art approach, comparing its structure and resulting
policy recommendations. In contrast to most work on optimal policy learning, we
explore disparity reduction itself as an objective, explicitly focusing the
power of algorithms on reducing inequality
Narrow Line Cooling: Finite Photon Recoil Dynamics
We present an extensive study of the unique thermal and mechanical dynamics
for narrow-line cooling on the 1S0 - 3P1 88Sr transition. For negative
detuning, trap dynamics reveal a transition from the semiclassical regime to
the photon-recoil-dominated quantum regime, yielding an absolute minima in the
equilibrium temperature below the single-photon recoil limit. For positive
detuning, the cloud divides into discrete momentum packets whose alignment
mimics lattice points on a face-centered-cubic crystal. This novel behavior
arises from velocity selection and "positive feedback" acceleration due to a
finite number of photon recoils. Cooling is achieved with blue-detuned light
around a velocity where gravity balances the radiative force.Comment: 4 pages, 3 figures, Phys. Rev. Lett., in pres
Measurement of Linear Stark Interference in 199Hg
We present measurements of Stark interference in the 6
6 transition in Hg, a process whereby a static electric field
mixes magnetic dipole and electric quadrupole couplings into an electric
dipole transition, leading to -linear energy shifts similar to those
produced by a permanent atomic electric dipole moment (EDM). The measured
interference amplitude, = = (5.8 1.5) (kV/cm), agrees with relativistic, many-body predictions and
confirms that earlier central-field estimates are a factor of 10 too large.
More importantly, this study validates the capability of the Hg EDM
search apparatus to resolve non-trivial, controlled, and sub-nHz Larmor
frequency shifts with EDM-like characteristics.Comment: 4 pages, 4 figures, 1 table; revised in response to reviewer comment
Improved limit on the permanent electric dipole moment of 199Hg
We report the results of a new experimental search for a permanent electric
dipole moment of 199Hg utilizing a stack of four vapor cells. We find d(199Hg)
= (0.49 \pm 1.29_stat \pm 0.76_syst) x 10^{-29} e cm, and interpret this as a
new upper bound, |d(199Hg)| < 3.1 x 10^{-29} e cm (95% C.L.). This result
improves our previous 199Hg limit by a factor of 7, and can be used to set new
constraints on CP violation in physics beyond the standard model.Comment: 4 pages, 4 figures. additional reference, minor edits in response to
reviewer comment
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