26,315 research outputs found
Wilson ratio of Fermi gases in one dimension
We calculate the Wilson ratio of the one-dimensional Fermi gas with spin
imbalance. The Wilson ratio of attractively interacting fermions is solely
determined by the density stiffness and sound velocity of pairs and of excess
fermions for the two-component Tomonaga-Luttinger liquid (TLL) phase. The ratio
exhibits anomalous enhancement at the two critical points due to the sudden
change in the density of states. Despite a breakdown of the quasiparticle
description in one dimension, two important features of the Fermi liquid are
retained, namely the specific heat is linearly proportional to temperature
whereas the susceptibility is independent of temperature. In contrast to the
phenomenological TLL parameter, the Wilson ratio provides a powerful parameter
for testing universal quantum liquids of interacting fermions in one, two and
three dimensions.Comment: 5+2 pages, 4+1 figures, Eq. (4) is proved, figures were refine
Universal local pair correlations of Lieb-Liniger bosons at quantum criticality
The one-dimensional Lieb-Liniger Bose gas is a prototypical many-body system
featuring universal Tomonaga-Luttinger liquid (TLL) physics and free fermion
quantum criticality. We analytically calculate finite temperature local pair
correlations for the strong coupling Bose gas at quantum criticality using the
polylog function in the framework of the Yang-Yang thermodynamic equations. We
show that the local pair correlation has the universal value in the quantum critical regime, the TLL phase and the
quasi-classical region, where is the pressure per unit length rescaled by
the interaction energy with interaction
strength and linear density . This suggests the possibility to test
finite temperature local pair correlations for the TLL in the relativistic
dispersion regime and to probe quantum criticality with the local correlations
beyond the TLL phase. Furthermore, thermodynamic properties at high
temperatures are obtained by both high temperature and virial expansion of the
Yang-Yang thermodynamic equation.Comment: 8 pages, 6 figures, additional text and reference
Exactly solvable models and ultracold Fermi gases
Exactly solvable models of ultracold Fermi gases are reviewed via their
thermodynamic Bethe Ansatz solution. Analytical and numerical results are
obtained for the thermodynamics and ground state properties of two- and
three-component one-dimensional attractive fermions with population imbalance.
New results for the universal finite temperature corrections are given for the
two-component model. For the three-component model, numerical solution of the
dressed energy equations confirm that the analytical expressions for the
critical fields and the resulting phase diagrams at zero temperature are highly
accurate in the strong coupling regime. The results provide a precise
description of the quantum phases and universal thermodynamics which are
applicable to experiments with cold fermionic atoms confined to one-dimensional
tubes.Comment: based on an invited talk at Statphys24, Cairns (Australia) 2010. 16
pages, 6 figure
The correlations between the twin kHz QPO frequencies of LMXBs
We analyzed the recently published kHz QPO data in the neutron star low-mass
X-ray binaries (LMXBs), in order to investigate the different correlations of
the twin peak kilohertz quasi-eriodic oscillations (kHz QPOs) in bright Z
sources and in the less luminous Atoll sources. We find that a power-law
relation \no\sim\nt^{b} between the upper and the lower kHz QPOs with
different indices: 1.5 for the Atoll source 4U 1728-34 and
1.9 for the Z source Sco X-1. The implications of our results for
the theoretical models for kHz QPOs are discussed.Comment: 6 pages, accepted by MNRA
Quantum simulation of artificial Abelian gauge field using nitrogen-vacancy center ensembles coupled to superconducting resonators
We propose a potentially practical scheme to simulate artificial Abelian
gauge field for polaritons using a hybrid quantum system consisting of
nitrogen-vacancy center ensembles (NVEs) and superconducting transmission line
resonators (TLR). In our case, the collective excitations of NVEs play the role
of bosonic particles, and our multiport device tends to circulate polaritons in
a behavior like a charged particle in an external magnetic field. We discuss
the possibility of identifying signatures of the Hofstadter "butterfly" in the
optical spectra of the resonators, and analyze the ground state crossover for
different gauge fields. Our work opens new perspectives in quantum simulation
of condensed matter and many-body physics using hybrid spin-ensemble circuit
quantum electrodynamics system. The experimental feasibility and challenge are
justified using currently available technology.Comment: 6 papes+supplementary materia
A Novel Method for Landslide Displacement Prediction by Integrating Advanced Computational Intelligence Algorithms
Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability
Active class discovery and learning for networked data
With the recent explosion of social network applications, active learning has increasingly become an important paradigm for classifying networked data. While existing research has shown promising results by exploiting network properties to improve the active learning performance, they are all based on a static setting where the number and the type of classes underlying the networked data remain stable and unchanged. For most social network applications, the dynamic change of users and their evolving relationships, along with the emergence of new social events, often result in new classes that need to be immediately discovered and labeled for classification. This paper proposes a novel approach called ADLNET for active class discovery and learning with networked data. Our proposed method uses the Dirichlet process defined over class distributions to enable active discovery of new classes, and explicitly models label correlations in the utility function of active learning. Experimental results on two real-world networked data sets demonstrate that our proposed approach outperforms other state-of-the-art methods
User profile preserving social network embedding
This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks
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