Simultaneous pi/2 rotation of two spin species of different gyromagnetic ratios

We examine the characteristics of the pi/2 pulse for simultaneously rotating two spin species of different gyromagnetic ratios with the same sign. For a pi/2 pulse using a rotating magnetic field, we derive the equation relating the frequency and strength of the pulse to the gyromagnetic ratios of the two particles and the strength of the constant holding field. For a pi/2 pulse using a linear oscillatory magnetic field, we obtain the solutions numerically, and compare them with the solutions for the rotating pi/2 pulse. Application of this analysis to the specific case of rotating neutrons and 3He atoms simultaneously with a pi/2 pulse, proposed for a neutron electric dipole moment experiment, is also presented

Warm asymmetric quark matter and proto-quark stars within the confined-isospin-density-dependent mass model

We extend the confined-isospin-density-dependent mass (CIDDM) model to include temperature dependence of the equivalent mass for quarks. Within the CIDDM model, we study the equation of state (EOS) for $\beta$-equilibrium quark matter, quark symmetry energy, quark symmetry free energy, and the properties of quark stars at finite temperature. We find that including the temperature dependence of the equivalent mass can significantly influence the properties of the strange quark matter (SQM) as well as the quark symmetry energy, the quark symmetry free energy, and the maximum mass of quark stars at finite temperature. The mass-radius relations for different stages of the proto-quark stars (PQSs) along the star evolution are analyzed. Our results indicate that the heating (cooling) process for PQSs will increase (decrease) the maximum mass within CIDDM model by including temperature dependence of the equivalent mass for quarks.Comment: 9 pages, 5 figures. Presentation improved and discussions added. Accepted version to appear in PR

Isovector properties of quark matter and quark stars in an isospin-dependent confining model

The confining quark matter (CQM) model, in which the confinement and asymptotic freedom are modeled via the Richardson potential for quark-quark vector interaction and the chiral symmetry restoration at high density is described by the density dependent quark mass, is extended to include isospin dependence of the quark mass. Within this extended isospin-dependent confining quark matter (ICQM) model, we study the properties of strange quark matter and quark stars. We find that including isospin dependence of the quark mass can significantly influence the quark matter symmetry energy, the stability of strange quark matter and the mass-radius relation of quark stars. In particular, we demonstrate although the recently discovered large mass pulsars PSR J1614.2230 and PSR J0348+0432 with masses around two times solar mass ($2M_{\odot}$) cannot be quark stars within the original CQM model, they can be well described by quark stars in the ICQM model if the isospin dependence of quark mass is strong enough so that the quark matter symmetry energy is about four times that of a free quark gas. We also discuss the effects of the density dependence of quark mass on the properties of quark stars. Our results indicate that the heavy quark stars with mass around $2M_{\odot}$ (if exist) can put strong constraints on isospin and density dependence of the quark mass as well as the quark matter symmetry energy.Comment: 10 pages, 6 figures, 2 tables. Presentation improved, 2 tables and discussions added. Accepted version to appear in PR

Quark matter symmetry energy and quark stars

We extend the confined-density-dependent-mass (CDDM) model to include isospin dependence of the equivalent quark mass. Within the confined-isospin-density-dependent-mass (CIDDM) model, we study the quark matter symmetry energy, the stability of strange quark matter, and the properties of quark stars. We find that including isospin dependence of the equivalent quark mass can significantly influence the quark matter symmetry energy as well as the properties of strange quark matter and quark stars. While the recently discovered large mass pulsars PSR J1614-2230 and PSR J0348+0432 with masses around $2M_{\odot}$ cannot be quark stars within the CDDM model, they can be well described by quark stars in the CIDDM model. In particular, our results indicate that the two-flavor $u$-$d$ quark matter symmetry energy should be at least about twice that of a free quark gas or normal quark matter within conventional Nambu-Jona-Lasinio model in order to describe the PSR J1614-2230 and PSR J0348+0432 as quark stars.Comment: 13 pages, 8 figures, 1 table. Results with varied quark mass scaling parameter z and discussions added. Accepted version to appear in Ap

Implicit Regularization via Hadamard Product Over-Parametrization in High-Dimensional Linear Regression

We consider Hadamard product parametrization as a change-of-variable (over-parametrization) technique for solving least square problems in the context of linear regression. Despite the non-convexity and exponentially many saddle points induced by the change-of-variable, we show that under certain conditions, this over-parametrization leads to implicit regularization: if we directly apply gradient descent to the residual sum of squares with sufficiently small initial values, then under proper early stopping rule, the iterates converge to a nearly sparse rate-optimal solution with relatively better accuracy than explicit regularized approaches. In particular, the resulting estimator does not suffer from extra bias due to explicit penalties, and can achieve the parametric root-$n$ rate (independent of the dimension) under proper conditions on the signal-to-noise ratio. We perform simulations to compare our methods with high dimensional linear regression with explicit regularizations. Our results illustrate advantages of using implicit regularization via gradient descent after over-parametrization in sparse vector estimation

Explicit calculation on two-loop correction to the chiral magnetic effect with NJL model

Chiral Magnetic Effect(CME) is usually believed not receiving higher order corrections due to the non-renormalization of AVV triangle diagram in the framework of quantum field theory. However, the CME-relevant triangle, which is obtained by expanding the current-current correlation requires zero momentum on the axial vertex, is not equivalent to the general AVV triangle when taking the zero-momentum limit owing to the infrared problem on the axial vertex. Therefore, it is still significant to check if there exists perturbative higher order corrections to the current-current correlation. In this paper, we explicitly calculate the two-loop corrections of CME within NJL model with Chern-Simons term which ensures a consistent $\mu_5$. The result shows the two-loop corrections to the CME conductivity are zero, which confirms the non-renomalization of CME conductivity.Comment: 7 pages, 3 figure

Quark stars under strong magnetic fields

Within the confined isospin- and density-dependent mass model, we study the properties of strange quark matter (SQM) and quark stars (QSs) under strong magnetic fields. The equation of state of SQM under a constant magnetic field is obtained self-consistently and the pressure perpendicular to the magnetic field is shown to be larger than that parallel to the magnetic field, implying that the properties of magnetized QSs generally depend on both the strength and the orientation of the magnetic fields distributed inside the stars. Using a density-dependent magnetic field profile which is introduced to mimic the magnetic field strength distribution in a star, we study the properties of static spherical QSs by assuming two extreme cases for the magnetic field orientation in the stars, i.e., the radial orientation in which the local magnetic fields are along the radial direction and the transverse orientation in which the local magnetic fields are randomly oriented but perpendicular to the radial direction. Our results indicate that including the magnetic fields with radial (transverse) orientation can significantly decrease (increase) the maximum mass of QSs, demonstrating the importance of the magnetic field orientation inside the magnetized compact stars.Comment: 9 pages, 4 figures. Discussions added. Accepted version to appear in PR

Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection

Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units. In this paper, we propose dense RNNs for scene labeling by exploring various long-range semantic dependencies among image units. Different from existing RNN based approaches, our dense RNNs are able to capture richer contextual dependencies for each image unit by enabling immediate connections between each pair of image units, which significantly enhances their discriminative power. Besides, to select relevant dependencies and meanwhile to restrain irrelevant ones for each unit from dense connections, we introduce an attention model into dense RNNs. The attention model allows automatically assigning more importance to helpful dependencies while less weight to unconcerned dependencies. Integrating with convolutional neural networks (CNNs), we develop an end-to-end scene labeling system. Extensive experiments on three large-scale benchmarks demonstrate that the proposed approach can improve the baselines by large margins and outperform other state-of-the-art algorithms.Comment: 10 pages. arXiv admin note: substantial text overlap with arXiv:1801.0683

Clustered Object Detection in Aerial Images

Detecting objects in aerial images is challenging for at least two reasons: (1) target objects like pedestrians are very small in pixels, making them hardly distinguished from surrounding background; and (2) targets are in general sparsely and non-uniformly distributed, making the detection very inefficient. In this paper, we address both issues inspired by observing that these targets are often clustered. In particular, we propose a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework. The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet). Given an input image, CPNet produces object cluster regions and ScaleNet estimates object scales for these regions. Then, each scale-normalized cluster region is fed into DetecNet for object detection. ClusDet has several advantages over previous solutions: (1) it greatly reduces the number of chips for final object detection and hence achieves high running time efficiency, (2) the cluster-based scale estimation is more accurate than previously used single-object based ones, hence effectively improves the detection for small objects, and (3) the final DetecNet is dedicated for clustered regions and implicitly models the prior context information so as to boost detection accuracy. The proposed method is tested on three popular aerial image datasets including VisDrone, UAVDT and DOTA. In all experiments, ClusDet achieves promising performance in comparison with state-of-the-art detectors. Code will be available in \url{https://github.com/fyangneil}

Motif-based Rule Discovery for Predicting Real-valued Time Series

Time series prediction is of great significance in many applications and has attracted extensive attention from the data mining community. Existing work suggests that for many problems, the shape in the current time series may correlate an upcoming shape in the same or another series. Therefore, it is a promising strategy to associate two recurring patterns as a rule's antecedent and consequent: the occurrence of the antecedent can foretell the occurrence of the consequent, and the learned shape of consequent will give accurate predictions. Earlier work employs symbolization methods, but the symbolized representation maintains too little information of the original series to mine valid rules. The state-of-the-art work, though directly manipulating the series, fails to segment the series precisely for seeking antecedents/consequents, resulting in inaccurate rules in common scenarios. In this paper, we propose a novel motif-based rule discovery method, which utilizes motif discovery to accurately extract frequently occurring consecutive subsequences, i.e. motifs, as antecedents/consequents. It then investigates the underlying relationships between motifs by matching motifs as rule candidates and ranking them based on the similarities. Experimental results on real open datasets show that the proposed approach outperforms the baseline method by 23.9%. Furthermore, it extends the applicability from single time series to multiple ones