283 research outputs found
Topological superfluid phases of an atomic Fermi gas with in- and out-of-plane Zeeman fields and equal Rashba-Dresselhaus spin-orbit coupling
We analyze the effects of in- and out-of-plane Zeeman fields on the BCS-BEC
evolution of a Fermi gas with equal Rashba-Dresselhaus (ERD) spin-orbit
coupling (SOC). We show that the ground state of the system involves novel
gapless superfluid phases that can be distinguished with respect to the
topology of the momentum-space regions with zero excitation energy. For the
BCS-like uniform superfluid phases with zero center-of-mass momentum, the zeros
may correspond to one or two doubly-degenerate spheres, two or four spheres,
two or four concave spheroids, or one or two doubly-degenerate circles,
depending on the combination of Zeeman fields and SOC. Such changes in the
topology signal a quantum phase transition between distinct superfluid phases,
and leave their signatures on some thermodynamic quantities. We also analyze
the possibility of Fulde-Ferrell-Larkin-Ovchinnikov (FFLO)-like nonuniform
superfluid phases with finite center-of-mass momentum and obtain an even richer
phase diagram.Comment: 9 pages with 5 figures; FFLO analysis include
Stability of spin-orbit coupled Fermi gases with population imbalance
We use the self-consistent mean-field theory to analyze the effects of
Rashba-type spin-orbit coupling (SOC) on the ground-state phase diagram of
population-imbalanced Fermi gases throughout the BCS-BEC evolution. We find
that the SOC and population imbalance are counteracting, and that this
competition tends to stabilize the uniform superfluid phase against the phase
separation. However, we also show that the SOC stabilizes (destabilizes) the
uniform superfluid phase against the normal phase for low (high) population
imbalances. In addition, we find topological quantum phase transitions
associated with the appearance of momentum space regions with zero
quasiparticle energies, and study their signatures in the momentum
distribution.Comment: 4+ pages with 3 figures; to appear in PR
Quantum phases of atomic Fermi gases with anisotropic spin-orbit coupling
We consider a general anisotropic spin-orbit coupling (SOC) and analyze the
phase diagrams of both balanced and imbalanced Fermi gases for the entire
BCS--Bose-Einstein condensate (BEC) evolution. In the first part, we use the
self-consistent mean-field theory at zero temperature, and show that the
topological structure of the ground-state phase diagrams is quite robust
against the effects of anisotropy. In the second part, we go beyond the
mean-field description, and investigate the effects of Gaussian fluctuations
near the critical temperature. This allows us to derive the time-dependent
Ginzburg-Landau theory, from which we extract the effective mass of the Cooper
pairs and their critical condensation temperature in the molecular BEC limit.Comment: 10 pages with 7 figures; to appear in PR
Compressibility of a two-dimensional electron gas in a parallel magnetic field
Cataloged from PDF version of article.The thermodynamic compressibility of a two-dimensional electron system in the presence of an in-plane magnetic field is calculated. We use accurate correlation energy results from quantum Monte Carlo simulations to construct the ground state energy and obtain the critical magnetic field Be required to fully spin polarize the system. Inverse compressibility as a function of density shows a kink-like behavior in the presence of an applied magnetic field, which can be identified as B-c. Our calculations suggest an alternative approach to transport measurements of determining full spin polarization. (c) 2007 Elsevier Ltd. All rights reserved
Measuring the Cosmic Ray Muon-Induced Fast Neutron Spectrum by (n,p) Isotope Production Reactions in Underground Detectors
While cosmic ray muons themselves are relatively easy to veto in underground
detectors, their interactions with nuclei create more insidious backgrounds
via: (i) the decays of long-lived isotopes produced by muon-induced spallation
reactions inside the detector, (ii) spallation reactions initiated by fast
muon-induced neutrons entering from outside the detector, and (iii) nuclear
recoils initiated by fast muon-induced neutrons entering from outside the
detector. These backgrounds, which are difficult to veto or shield against, are
very important for solar, reactor, dark matter, and other underground
experiments, especially as increased sensitivity is pursued. We used fluka to
calculate the production rates and spectra of all prominent secondaries
produced by cosmic ray muons, in particular focusing on secondary neutrons, due
to their importance. Since the neutron spectrum is steeply falling, the total
neutron production rate is sensitive just to the relatively soft neutrons, and
not to the fast-neutron component. We show that the neutron spectrum in the
range between 10 and 100 MeV can instead be probed by the (n, p)-induced
isotope production rates 12C(n, p)12B and 16O(n, p)16N in oil- and water-based
detectors. The result for 12B is in good agreement with the recent KamLAND
measurement. Besides testing the calculation of muon secondaries, these results
are also of practical importance, since 12B (T1/2 = 20.2 ms, Q = 13.4 MeV) and
16N (T1/2 = 7.13 s, Q = 10.4 MeV) are among the dominant spallation backgrounds
in these detectors
A Regression Model to Investigate the Performance of Black-Scholes using Macroeconomic Predictors
As it is well known an option is defined as the right to buy sell a certain asset, thus, one can look at the purchase of an option as a bet on the financial instrument under consideration. Now while the evaluation of options is a completely different mathematical topic than the prediction of future stock prices, there is some relationship between the two. It is worthy to note that henceforth we will only consider options that have a given fixed expiration time T, i.e., we restrict the discussion to the so called European options. Now, for a simple illustration of the relationship between true stock prices and options let us consider the following situation: if at the beginning of January the S&P index is valued at 1,400 then the fair price of the option to buy this in January would be 122 or less then he or she gains while if the holder purchases the option for 123 is neutral for both parties. As one can see from this simple illustration predicting the fair price of an option is directly related to predicting the value of the stock price in a future time T
A Multitier Deep Learning Model for Arrhythmia Detection
Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework
Chemistry in Second Life
This review will focus on the current level on chemistry research, education, and visualization possible within the multi-user virtual environment of Second Life. We discuss how Second Life has been used as a platform for the interactive and collaborative visualization of data from molecules and proteins to spectra and experimental data. We then review how these visualizations can be scripted for immersive educational activities and real-life collaborative research. We also discuss the benefits of the social networking affordances of Second Life for both chemists and chemistry students
A Multitier Deep Learning Model for Arrhythmia Detection
An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected
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