Systematic study of proton-neutron pairing correlations in the nuclear shell model

A shell-model study of proton-neutron pairing in $2p1f$ shell nuclei using a parametrized hamiltonian that includes deformation and spin-orbit effects as well as isoscalar and isovector pairing is reported. By working in a shell-model framework we are able to assess the role of the various modes of proton-neutron pairing in the presence of nuclear deformation without violating symmetries. Results are presented for $^{44}$Ti, $^{45}$Ti, $^{46}$Ti, $^{46}$V and $^{48}$Cr to assess how proton-neutron pair correlations emerge under different scenarios. We also study how the presence of a one-body spin-obit interaction affects the contribution of the various pairing modes.Comment: 12 pages, 16 figure

Evaluating the Usability of Automatically Generated Captions for People who are Deaf or Hard of Hearing

The accuracy of Automated Speech Recognition (ASR) technology has improved, but it is still imperfect in many settings. Researchers who evaluate ASR performance often focus on improving the Word Error Rate (WER) metric, but WER has been found to have little correlation with human-subject performance on many applications. We propose a new captioning-focused evaluation metric that better predicts the impact of ASR recognition errors on the usability of automatically generated captions for people who are Deaf or Hard of Hearing (DHH). Through a user study with 30 DHH users, we compared our new metric with the traditional WER metric on a caption usability evaluation task. In a side-by-side comparison of pairs of ASR text output (with identical WER), the texts preferred by our new metric were preferred by DHH participants. Further, our metric had significantly higher correlation with DHH participants' subjective scores on the usability of a caption, as compared to the correlation between WER metric and participant subjective scores. This new metric could be used to select ASR systems for captioning applications, and it may be a better metric for ASR researchers to consider when optimizing ASR systems.Comment: 10 pages, 8 figures, published in ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '17

Entrainment transition in populations of random frequency oscillators

The entrainment transition of coupled random frequency oscillators is revisited. The Kuramoto model (global coupling) is shown to exhibit unusual sample-dependent finite size effects leading to a correlation size exponent $\bar\nu=5/2$. Simulations of locally coupled oscillators in $d$-dimensions reveal two types of frequency entrainment: mean-field behavior at $d>4$, and aggregation of compact synchronized domains in three and four dimensions. In the latter case, scaling arguments yield a correlation length exponent $\nu=2/(d-2)$, in good agreement with numerical results.Comment: published versio

Glassy Dynamics in a Frustrated Spin System: Role of Defects

In an effort to understand the glass transition, the kinetics of a spin model with frustration but no quenched randomness has been analyzed. The phenomenology of the spin model is remarkably similiar to that of structural glasses. Analysis of the model suggests that defects play a major role in dictating the dynamics as the glass transition is approached.Comment: 9 pages, 5 figures, accepted in J. Phys.: Condensed Matter, proceedings of the Trieste workshop on "Unifying Concepts in Glass Physics

Nuclear pairing reduction due to rotation and blocking

Nuclear pairing gaps of normally deformed and superdeformed nuclei are investigated using the particle-number conserving (PNC) formalism for the cranked shell model, in which the blocking effects are treated exactly. Both rotational frequency $\omega$-dependence and seniority (number of unpaired particles) $\nu$-dependence of the pairing gap $\tilde{\Delta}$ are investigated. For the ground-state bands of even-even nuclei, PNC calculations show that in general $\tilde{\Delta}$ decreases with increasing $\omega$, but the $\omega$-dependence is much weaker than that calculated by the number-projected Hartree-Fock-Bogolyubov approach. For the multiquasiparticle bands (seniority $\nu> 2$), the pairing gaps keep almost $\omega$-independent. As a function of the seniority $\nu$, the bandhead pairing gaps $\tilde{\Delta}(\nu,\omega=0)$ decrease slowly with increasing $\nu$. Even for the highest seniority $\nu$ bands identified so far, $\tilde{\Delta}(\nu,\omega=0)$ remains greater than 70% of $\tilde{\Delta}(\nu=0,\omega=0)$.Comment: 15 pages, 5 figure

To synchronize or not to synchronize, that is the question: finite-size scaling and fluctuation effects in the Kuramoto model

The entrainment transition of coupled random frequency oscillators presents a long-standing problem in nonlinear physics. The onset of entrainment in populations of large but finite size exhibits strong sensitivity to fluctuations in the oscillator density at the synchronizing frequency. This is the source for the unusual values assumed by the correlation size exponent $\nu'$. Locally coupled oscillators on a $d$-dimensional lattice exhibit two types of frequency entrainment: symmetry-breaking at $d > 4$, and aggregation of compact synchronized domains in three and four dimensions. Various critical properties of the transition are well captured by finite-size scaling relations with simple yet unconventional exponent values.Comment: 9 pages, 1 figure, to appear in a special issue of JSTAT dedicated to Statphys2

Low-temperature transport through a quantum dot between two superconductor leads

We consider a quantum dot coupled to two BCS superconductors with same gap energies $\Delta$. The transport properties are investigated by means of infinite-$U$ noncrossing approximation. In equilibrium density of states, Kondo effect shows up as two sharp peaks around the gap bounds. Application of a finite voltage bias leads these peaks to split, leaving suppressed peaks near the edges of energy gap of each lead. The clearest signatures of the Kondo effect in transport are three peaks in the nonlinear differential conductance: one around zero bias, another two at biases $\pm 2\Delta$. This result is consistent with recent experiment. We also predict that with decreasing temperature, the differential conductances at biases $\pm 2\Delta$ anomalously increase, while the linear conductance descends.Comment: replaced with revised versio

Counting statistics of tunneling through a single molecule: effect of distortion and displacement of vibrational potential surface

We analyze the effects of a distortion of the nuclear potential of a molecular quantum dot (QD), as well as a shift of its equilibrium position, on nonequilibrium-vibration-assisted tunneling through the QD with a single level ($\epsilon_d$) coupled to the vibrational mode. For this purpose, we derive an explicit analytical expression for the Franck-Condon (FC) factor for a displaced-distorted oscillator surface of the molecule and establish rate equations in the joint electron-phonon representation to examine the current-voltage characteristics and zero-frequency shot noise, and skewness as well. Our numerical analyses shows that the distortion has two important effects. The first one is that it breaks the symmetry between the excitation spectra of the charge states, leading to asymmetric tunneling properties with respect to $\epsilon_d>0$ and $\epsilon_d<0$. Secondly, distortion (frequency change of the oscillator) significantly changes the voltage-activated cascaded transition mechanism, and consequently gives rise to a different nonequilibrium vibrational distribution from that of the case without distortion. Taken in conjunction with strongly modified FC factors due to distortion, this results in some new transport features: the appearance of strong NDC even for a single-level QD with symmetric tunnel couplings; a giant Fano factor even for a molecule with an extremely weak electron-phonon interaction; and enhanced skewness that can have a large negative value under certain conditions.Comment: 29 pages, 11 figures, published versio

Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images

The current deep convolutional neural networks for very-high-resolution (VHR) remote-sensing image land-cover classification often suffer from two challenges. First, the feature maps extracted by network encoders based on vanilla convolution usually contain a lot of redundant information, which easily causes misclassification of land cover. Moreover, these encoders usually require a large number of parameters and high computational costs. Second, as remote-sensing images are complex and contain many objects with large-scale variances, it is difficult to use the popular feature fusion modules to improve the representation ability of networks. To address the above issues, we propose a dynamic convolution self-attention network (DCSA-Net) for VHR remote-sensing image land-cover classification. The proposed network has two advantages. On one hand, we designed a lightweight dynamic convolution module (LDCM) by using dynamic convolution and a self-attention mechanism. This module can extract more useful image features than vanilla convolution, avoiding the negative effect of useless feature maps on land-cover classification. On the other hand, we designed a context information aggregation module (CIAM) with a ladder structure to enlarge the receptive field. This module can aggregate multi-scale contexture information from feature maps with different resolutions using a dense connection. Experiment results show that the proposed DCSA-Net is superior to state-of-the-art networks due to higher accuracy of land-cover classification, fewer parameters, and lower computational cost. The source code is made public available.National Natural Science Foundation of China (Program No. 61871259, 62271296, 61861024), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2021JC-47), in part by Key Research and Development Program of Shaanxi (Program No. 2022GY-436, 2021ZDLGY08-07), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2022JQ-634, 2022JQ-018), and in part by Shaanxi Joint Laboratory of Artificial Intelligence (No. 2020SS-03)