497 research outputs found
Quantum fluctuations in the BCS-BEC crossover of two-dimensional Fermi gases
We present a theoretical study of the ground state of the BCS-BEC crossover
in dilute two-dimensional Fermi gases. While the mean-field theory provides a
simple and analytical equation of state, the pressure is equal to that of a
noninteracting Fermi gas in the entire BCS-BEC crossover, which is not
consistent with the features of a weakly interacting Bose condensate in the BEC
limit and a weakly interacting Fermi liquid in the BCS limit. The inadequacy of
the 2D mean-field theory indicates that the quantum fluctuations are much more
pronounced than those in 3D. In this work, we show that the inclusion of the
Gaussian quantum fluctuations naturally recovers the above features in both the
BEC and the BCS limits. In the BEC limit, the missing logarithmic dependence on
the boson chemical potential is recovered by the quantum fluctuations. Near the
quantum phase transition from the vacuum to the BEC phase, we compare our
equation of state with the known grand canonical equation of state of 2D Bose
gases and determine the ratio of the composite boson scattering length to the fermion scattering length . We find , in good agreement with the exact four-body calculation. We
compare our equation of state in the BCS-BEC crossover with recent results from
the quantum Monte Carlo simulations and the experimental measurements and find
good agreements.Comment: Published versio
A Mini Immersed Finite Element Method for Two-Phase Stokes Problems on Cartesian Meshes
This paper presents a mini immersed finite element (IFE) method for solving
two- and three-dimensional two-phase Stokes problems on Cartesian meshes. The
IFE space is constructed from the conventional mini element with shape
functions modified on interface elements according to interface jump
conditions, while keeping the degrees of freedom unchanged. Both discontinuous
viscosity coefficients and surface forces are considered in the construction.
The interface is approximated via discrete level set functions and explicit
formulas of IFE basis functions and correction functions are derived, which
make the IFE method easy to implement. The optimal approximation capabilities
of the IFE space and the inf-sup stability and the optimal a priori error
estimate of the IFE method are derived rigorously with constants independent of
the mesh size and how the interface cuts the mesh. It is also proved that the
condition number has the usual bound independent of the interface. Numerical
experiments are provided to confirm the theoretical results
S-4-Chlorophenyl 9,10-dihydroacridine-9-carbothioate
In tricyclic fragment of the title molecule, C20H14ClNOS, the central 1,4-dihydropyridine ring adopts a boat conformation while the two benzene rings form a dihedral angle of 17.38 (5)°. In the crystal structure, weak intermolecular N—H⋯O hydrogen bonds link the molecules into chains propagating along the b axis
A Comparison of Formal Methods for Evaluating the Language of Preference in Engineering Design
In design, as with many fields, the bases of decisions are generally not formally modeled but only talked or written about. The research problem addressed in this paper revolves around the problem of modeling the direct evaluation of design alternatives and their attributes as they are realized in linguistic communication. The question is what types of linguistic data provide the most reliable linguistic displays of preference and utility. The paper compares two formal methods for assessing a design team’s preferences for alternatives based on the team’s discussion: APPRAISAL and Preferential Probabilities from Transcripts (PPT). Results suggest that the two methods are comparable in their assessment of preferences. This paper also examines the nature of consistency in the way design teams consider the attributes of a design. Findings suggest that assessment of an attribute can change substantially over time.National Science Foundation (U.S.) (Award CMMI- 0900255)Australian Research Council (Discovery Projects funding scheme (project number DP1095601)
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
Deep learning has achieved great success in learning features from massive
remote sensing images (RSIs). To better understand the connection between
feature learning paradigms (e.g., unsupervised feature learning (USFL),
supervised feature learning (SFL), and self-supervised feature learning
(SSFL)), this paper analyzes and compares them from the perspective of feature
learning signals, and gives a unified feature learning framework. Under this
unified framework, we analyze the advantages of SSFL over the other two
learning paradigms in RSIs understanding tasks and give a comprehensive review
of the existing SSFL work in RS, including the pre-training dataset,
self-supervised feature learning signals, and the evaluation methods. We
further analyze the effect of SSFL signals and pre-training data on the learned
features to provide insights for improving the RSI feature learning. Finally,
we briefly discuss some open problems and possible research directions.Comment: 24 pages, 11 figures, 3 table
Optimal coherent control of CARS: signal enhancement and background elimination
The ability to enhance resonant signals and eliminate the non-resonant
background is analyzed for Coherent Anti-Stokes Raman Scattering (CARS). The
analysis is done at a specific frequency as well as for broadband excitation
using femtosecond pulse-shaping techniques. An appropriate objective functional
is employed to balance resonant signal enhancement against non-resonant
background suppression. Optimal enhancement of the signal and minimization of
the background can be achieved by shaping the probe pulse alone while keeping
the pump and Stokes pulses in transform-limited-form (TLF). In some cases
analytical forms for the probe pulse can be found, and numerical simulations
are carried out for other circumstances. It is found that a good approximate
solution for the optimal pulse in the two-pulse CARS is a superposition of
linear and arctangent type phases for the pump. The well-known probe delay
method is shown to be a quasi-optimal scheme for background suppression. The
results should provide a basis to improve the performance of CARS spectroscopy
and microscopy.Comment: 11 pages,10 figures, JC
Association of Affected Neurocircuitry With Deficit of Response Inhibition and Delayed Gratification in Attention Deficit Hyperactivity Disorder: A Narrative Review
The neural networks that constitute corticostriatothalamocortical circuits between prefrontal cortex and subcortical structure provide a heuristic framework for bridging gaps between neurocircuitry and executive dysfunction in attention deficit hyperactivity disorder (ADHD). “Cool” and “Hot” executive functional theory and the models of dual pathway are supposed to be applied within the neuropsychology of ADHD. The theoretical model elaborated response inhibition and delayed gratification in ADHD. We aimed to review and summarize the literature about the circuits on ADHD and ADHD-related comorbidities, as well as the effects of neurocircuitry on the executive dysfunction in ADHD
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