Binding potentials for vapour nanobubbles on surfaces using density functional theory

We calculate density profiles of a simple model fluid in contact with a planar surface using density functional theory (DFT), in particular for the case where there is a vapour layer intruding between the wall and the bulk liquid. We apply the method of Hughes et al. [J. Chem. Phys. 142, 074702 (2015)] to calculate the density profiles for varying (specified) amounts of the vapour adsorbed at the wall. This is equivalent to varying the thickness $h$ of the vapour at the surface. From the resulting sequence of density profiles we calculate the thermodynamic grand potential as $h$ is varied and thereby determine the binding potential as a function of $h$. The binding potential obtained via this coarse-graining approach allows us to determine the disjoining pressure in the film and also to predict the shape of vapour nano-bubbles on the surface. Our microscopic DFT based approach captures information from length scales much smaller than some commonly used models in continuum mechanics.Comment: 15 pages, 15 figure

Phase slip in a superfluid Fermi gas near a Feshbach resonance

In this paper, we study the properties of a phase slip in a superfluid Fermi gas near a Feshbach resonance. The phase slip can be generated by the phase imprinting method. Below the superfluid transition temperature, it appears as a dip in the density profile, and becomes more pronounced when the temperature is lowered. Therefore the phase slip can provide a direct evidence of the superfluid state. The condensation energy of the superfluid state can be extracted from the density profile of the phase slip, due to the unitary properties of the Fermi gas near the resonance. The width of the phase slip is proportional to the square root of the difference between the transition temperature and the temperature. The signature of the phase slip in the density profile becomes more robust across the BCS-BEC crossover.Comment: 5 pages, 2 figures, the density profile of a phase slip under experimental conditions was calculate

Phase diagram of a Bose gas near a wide Feshbach resonance

In this paper, we study the phase diagram of a homogeneous Bose gas with a repulsive interaction near a wide Feshbach resonance at zero temperature. The Bose-Einstein-condensation (BEC) state of atoms is a metastable state. When the scattering length $a$ exceeds a critical value depending on the atom density $n$, $na^3>0.035$, the molecular excitation energy is imaginary and the atomic BEC state is dynamically unstable against molecule formation. The BEC state of diatomic molecules has lower energy, where the atomic excitation is gapped and the molecular excitation is gapless. However when the scattering length is above another critical value, $na^3>0.0164$, the molecular BEC state becomes a unstable coherent mixture of atoms and molecules. In both BEC states, the binding energy of diatomic molecules is reduced due to the many-body effect.Comment: 5 pages, 4 figure

Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)

NMR Investigation of the Low Temperature Dynamics of solid 4He doped with 3He impurities

The lattice dynamics of solid 4He has been explored using pulsed NMR methods to study the motion of 3He impurities in the temperature range where experiments have revealed anomalies attributed to superflow or unexpected viscoelastic properties of the solid 4He lattice. We report the results of measurements of the nuclear spin-lattice and spin-spin relaxation times that measure the fluctuation spectrum at high and low frequencies, respectively, of the 3He motion that results from quantum tunneling in the 4He matrix. The measurements were made for 3He concentrations 16<x_3<2000 ppm. For 3He concentrations x_3 = 16 ppm and 24 ppm, large changes are observed for both the spin-lattice relaxation time T_1 and the spin-spin relaxation time T_2 at temperatures close to those for which the anomalies are observed in measurements of torsional oscillator responses and the shear modulus. These changes in the NMR relaxation rates were not observed for higher 3He concentrations.Comment: 23 pages, 10 figure

Scaling Theory of Polyelectrolyte Adsorption on Repulsive Charged Surface

We studied polyelectrolyte adsorption on a repulsive charged surface by scaling analysis. At low ionic strength and low surface charge density in which a single polyelectrolyte is able to be adsorbed onto the surface, different regimes in the phase diagram are identified. The possibility of multi-layer structure formed by polyelectrolytes of like charge is also investigated.Comment: 4 pages, 2 figure

Environment-Mediated Quantum State Transfer

We propose a scheme for quantum state transfer(QST) between two qubits which is based on their individual interaction with a common boson environment. The corresponding single mode spin-boson Hamiltonian is solved by mapping it onto a wave propagation problem in a semi-infinite ladder and the fidelity is obtained. High fidelity occurs when the qubits are equally coupled to the boson while the fidelity becomes smaller for nonsymmetric couplings. The complete phase diagram for such an arbitrary QST mediated by bosons is discussed.Comment: 6 pages and 5 figure

Quark and Gluon Condensates in Isospin Matter

Applying the Hellmann-Feynman theorem to a charged pion gas, the quark and gluon condensates at low isospin density are determined by precise pion properties. At intermediate density around $f_\pi^2m_\pi$, from both the estimation for the dilute pion gas and the calculation with Nambu--Jona-Lasinio model, the quark condensate is strongly and monotonously suppressed, while the gluon condensate is enhanced and can be larger than its vacuum value. This unusual behavior of the gluon condensate is universal for Bose condensed matter of mesons. Our results can be tested by lattice calculations at finite isospin density.Comment: 4 pages, 2 figures. Published version in PR

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis