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Jian Kang

Publications and source records attributed to Jian Kang.

At least 181 records · Page 10Linked to original sources

Spin anisotropy due to spin-orbit coupling in optimally hole-doped Ba$_{0.67}$K$_{0.33}$Fe$_{2}$As$_{2}$

We use polarized inelastic neutron scattering to study the temperature and energy dependence of spin space anisotropies in the optimally hole-doped iron pnictide Ba$_{0.67}$K$_{0.33}$Fe$_{2}$As$_{2}$ ($T_{\rm c}=38$ K). In the superconducting state, while the high-energy part of the magnetic spectrum is nearly isotropic, the low-energy part displays a pronouced anisotropy, manifested by a $c$-axis polarized resonance. We also observe that the spin anisotropy in superconducting Ba$_{0.67}$K$_{0.33}$Fe$_{2}$As$_{2}$ extends to higher energies compared to electron-doped BaFe$_{2-x}TM_{x}$As$_{2}$ ($TM=$Co, Ni) and isovalent-doped BaFe$_{2}$As$_{1.4}$P$_{0.6}$, suggesting a connection between $T_{\rm c}$ and the energy scale of the spin anisotropy. In the normal state, the low-energy spin anisotropy for optimally hole- and electron-doped iron pnictides onset at temperatures similar to the temperatures at which the elastoresistance deviate from Curie-Weiss behavior, pointing to a possible connection between the two phenomena. Our results highlight the relevance of the spin-orbit coupling to the superconductivity of the iron pnictides.

cond-mat.supr-con↗

Classification with Ultrahigh-Dimensional Features

Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work. This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-feature correlations, the proposed method enables detection of marginally weak and sparse signals and recovery of the true informative feature set, and achieves asymptotic optimal misclassification rates. We also show that the proposed procedure provides more powerful discovery boundaries compared to those in \citet{CaiSun:2014} and \citet{JJin:2009}. The performance of the proposed procedure is evaluated using simulation studies and demonstrated via classification of patients with different post-transplantation renal functional types.

stat.ML↗

Superconductivity in FeSe thin films driven by the interplay between nematic fluctuations and spin-orbit coupling

The origin of the high-temperature superconducting state observed in FeSe thin films, whose phase diagram displays no sign of magnetic order, remains a hotly debated topic. Here we investigate whether fluctuations arising due to the proximity to a nematic phase, which is observed in the phase diagram of this material, can promote superconductivity. We find that nematic fluctuations alone promote a highly degenerate pairing state, in which both $s$-wave and $d$-wave symmetries are equally favored, and $T_{c}$ is consequently suppressed. However, the presence of a sizable spin-orbit coupling or inversion symmetry-breaking at the film interface lifts this harmful degeneracy and selects the $s$-wave state, in agreement with recent experimental proposals. The resulting gap function displays a weak anisotropy, which agrees with experiments in monolayer FeSe and intercalated Li$_{1-x}$(OH)$_{x}$FeSe.

cond-mat.supr-con↗

Robustness of quantum critical pairing against disorder

The remarkable robustness of high-temperature superconductors against disorder remains a controversial obstacle towards the elucidation of their pairing state. Indeed, experiments report a weak suppression rate of the transition temperature $T_{c}$ with disorder, significantly smaller than the universal value predicted by extensions of the conventional theory of dirty superconductors. However, in many high-$T_{c}$ compounds, superconductivity appears near a putative magnetic quantum critical point, suggesting that quantum fluctuations, which suppress coherent electronic spectral weight, may also promote unconventional pairing. Here we investigate theoretically the impact of disorder on such a quantum critical pairing state, considering the coupling of impurities both to the low-energy electronic states and to the pairing interaction itself. We find a significant reduction in the suppression rate of $T_{c}$ with disorder near the magnetic quantum critical point, shedding new light on the nature of unconventional superconductivity in correlated materials.

cond-mat.supr-con↗

Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process

The focus of this work is on spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models, soft-thresholded Gaussian processes and develop the efficient posterior computation algorithms. Theoretically, soft-thresholded Gaussian processes provide large prior support for the spatially varying coefficients that enjoy piecewise smoothness, sparsity and continuity, characterizing the important features of imaging data. Also, under some mild regularity conditions, the soft-thresholded Gaussian process leads to the posterior consistency for both parameter estimation and variable selection for scalar-on-image regression, even when the number of true predictors is larger than the sample size. The proposed method is illustrated via simulations, compared numerically with existing alternatives and applied to Electroencephalography (EEG) study of alcoholism.

stat.ME↗

High Dimensional Tests for Functional Networks of Brain Anatomic Regions

There has been increasing interests in learning resting-state brain functional connectivity of autism disorders using functional magnetic resonance imaging (fMRI) data. The data in a standard brain template consist of over 200,000 voxel specific time series for each single subject. Such an ultra-high dimensionality of data makes the voxel-level functional connectivity analysis (involving four billion voxel pairs) lack of power and extremely inefficient. In this work, we introduce a new framework to identify functional brain network at brain anatomic region-level for each individual. We propose two pairwise tests to detect region dependence, and one multiple testing procedure to identify global structures of the network. The limiting null distributions of the test statistics are derived. It is also shown that the tests are rate optimal when the alternative networks are sparse. The numerical studies show the proposed tests are valid and powerful. We apply our method to a resting-state fMRI study on autism and identify patient-unique and control-unique hub regions. These findings are consistent with autism clinical symptoms.

stat.ME↗

Conditional Screening for Ultra-high Dimensional Covariates with Survival Outcomes

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorly known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a Diffuse large B-cell lymphoma (DLBCL) dataset.

stat.ME↗

Spin-Driven Nematic Instability of the Multi-Orbital Hubbard Model: Application to Iron-Based Superconductors

Nematic order resulting from the partial melting of density-waves has been proposed as the mechanism to explain nematicity in iron-based superconductors. An outstanding question, however, is whether the microscopic electronic model for these systems -- the multi-orbital Hubbard model -- displays such an ordered state as its leading instability. In contrast to usual electronic instabilities, such as magnetic and charge order, this fluctuation-driven phenomenon cannot be captured by the standard RPA method. Here, by including fluctuations beyond RPA in the multi-orbital Hubbard model, we derive its nematic susceptibility and contrast it with its ferro-orbital order susceptibility, showing that its leading instability is the spin-driven nematic phase. Our results also demonstrate the primary role played by the $d_{xy}$ orbital in driving the nematic transition, and reveal that high-energy magnetic fluctuations are essential to stabilize nematic order in the absence of magnetic order.

cond-mat.supr-con↗

Spin reorientation driven by the interplay between spin-orbit coupling and Hund's rule coupling in iron pnictides

In most magnetically-ordered iron pnictides, the magnetic moments lie in the FeAs planes, parallel to the modulation direction of the spin stripes. However, recent experiments in hole-doped iron pnictides have observed a reorientation of the magnetic moments from in-plane to out-of-plane. Interestingly, this reorientation is accompanied by a change in the magnetic ground state from a stripe antiferromagnet to a tetragonal non-uniform magnetic configuration. Motivated by these recent observations, here we investigate the origin of the spin anisotropy in iron pnictides using an itinerant microscopic electronic model that respects all the symmetry properties of a single FeAs plane. We find that the interplay between the spin-orbit coupling and the Hund's rule coupling can account for the observed spin anisotropies, including the spin reorientation in hole-doped pnictides, without the need to invoke orbital or nematic order. Our calculations also reveal an asymmetry between the magnetic ground states of electron- and hole-doped compounds, with only the latter displaying tetragonal magnetic states.

cond-mat.supr-con↗

Phenomenological theory of the superconducting state inside the hidden-order phase of URu$_{2}$Si$_{2}$

Recent experiments have unveiled important properties of the ground state of the elusive heavy fermion $\mathrm{URu_{2}Si_{2}}$. While tetragonal symmetry-breaking was reported below the hidden-order (HO) transition at $T_{HO}\approx17.5$ K, time-reversal symmetry-breaking was observed below the superconducting transition temperature $T_{c}<T_{HO}$. Although the latter results have been used to argue in favor of a chiral $d+id$ superconducting state, such an order parameter is incompatible with broken tetragonal symmetry. Here, we employ a phenomenological model to investigate the properties of a chiral superconducting state that develops inside the hidden-order phase. In this case, there are actually two superconducting transition temperatures: while $T_{c}$ marks a normal-state to superconducting transition, $T_{c}^{*}<T_{c}$ signals a superconducting-to-superconducting transition in which time-reversal symmetry is broken. In the phase $T_{c}^{*}<T<T_{c}$, the low-energy density of states $ρ\left(ω\right)$ is enhanced due to the crossing of two nodal lines, giving rise to an unusual $ω\log(ω)$ dependence of $ρ\left(ω\right)$, which is manifested in several thermodynamic properties. We also investigate the emergence of a soft amplitude gap mode near $T_{c}^{*}$. In contrast to the usual amplitude mode near a regular normal-state to superconducting transition, this mode becomes soft near a superconducting-to-superconducting transition, which in principle allows for its detection by Raman spectroscopy. Finally, we investigate the impact of twin domains on the anisotropic properties of the superconducting state, and propose experiments in mechanically strained samples to explore the interplay between hidden order and superconductivity in $\mathrm{URu_{2}Si_{2}}$.

cond-mat.supr-con↗

Direct evidence for the emergence of a pressure induced nodal superconducting gap in the iron-based superconductor Ba_0.65Rb_0.35Fe_2As_2

Identifying the superconducting (SC) gap structure of the iron-based high-temperature superconductors (Fe-HTS's) remains a key issue for the understanding of superconductivity in these materials. In contrast to other unconventional superconductors, in the Fe-HTS's both $d$-wave and extended s-wave pairing symmetries are close in energy, with the latter believed to be generally favored over the former. Probing the proximity between these very different SC states and identifying experimental parameters that can tune them, are of central interest. Here we report high-pressure muon spin rotation experiments on the temperature-dependent magnetic penetration depth (lambda) in the optimally doped Fe-HTS Ba_0.65Rb_0.35Fe_2As_2. At ambient pressure this material is known to be a nodeless s-wave superconductor. Upon pressure a strong decrease of (lambda) is observed, while the SC transition temperature remains nearly constant. More importantly, the low-temperature behavior of (1/lambda^{2}) changes from exponential saturation at zero pressure to a power-law with increasing pressure, providing unambiguous evidence that hydrostatic pressure promotes nodal SC gaps. Comparison to microscopic models favors a d-wave over a nodal s^{+-}-wave pairing as the origin of the nodes. Our results provide a new route of understanding the complex topology of the SC gap in Fe-HTS's.

cond-mat.supr-con↗

Thresholded Multiscale Gaussian Processes with Application to Bayesian Feature Selection for Massive Neuroimaging Data

Motivated by the needs of selecting important features for massive neuroimaging data, we propose a spatially varying coefficient model (SVCMs) with sparsity and piecewise smoothness imposed on the coefficient functions. A new class of nonparametric priors is developed based on thresholded multiresolution Gaussian processes (TMGP). We show that the TMGP has a large support on a space of sparse and piecewise smooth functions, leading to posterior consistency in coefficient function estimation and feature selection. Also, we develop a method for prior specifications of thresholding parameters in TMGPs and discuss their theoretical properties. Efficient posterior computation algorithms are developed by adopting a kernel convolution approach, where a modified square exponential kernel is chosen taking the advantage that the analytical form of the eigen decomposition is available. Based on simulation studies, we demonstrate that our methods can achieve better performance in estimating the spatially varying coefficient. Also, the proposed model has been applied to an analysis of resting state functional magnetic resonance imaging (Rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) study, it provides biologically meaningful results.

stat.ME↗

Magnetic order without tetragonal-symmetry-breaking in iron arsenides: microscopic mechanism and spin-wave spectrum

Most iron-based superconductors undergo a transition to a magnetically ordered state characterized by staggered stripes of parallel spins. With ordering vectors $(π,0)$ or $(0,π)$, this magnetic state breaks the high-temperature tetragonal symmetry of the system, which is manifested by a splitting of the lattice Bragg peaks. Remarkably, recent experiments in hole-doped iron arsenides reported an ordered state that displays magnetic Bragg peaks at $(π,0)$ and $(0,π)$ but remains tetragonal. Despite being inconsistent with a magnetic stripe configuration, this unusual magnetic phase can be described in terms of a double-$\mathbf{Q}$ magnetic structure consisting of an equal-weight superposition of the ordering vectors $(π,0)$ and $(0,π)$. Here we show that a non-collinear double-$\mathbf{Q}$ magnetic configuration, dubbed \emph{orthomagnetic}, arises naturally within an itinerant three-band microscopic model for the iron pnictides. In particular, we find that strong deviations from perfect nesting and residual interactions between the electron pockets favor the orthomagnetic over the stripe magnetic state. Using an effective low-energy model, we also calculate the spin-wave spectrum of the orthomagnetic state. In contrast to the stripe state, there are three Goldstone modes, manifested in all diagonal and one off-diagonal component of the spin-spin correlation function. The total magnetic structure factor displays two anisotropic spin-wave branches emerging from both $(π,0)$ and $(0,π)$ momenta, in contrast to the case of domains of stripe order, where only one spin-wave branch emerges from each momentum. We propose that these unique features of the orthomagnetic state can be used to unambiguously distinguish it from the stripe state via neutron scattering experiments, and discuss the implications of its existence to the nature of the magnetism of the iron arsenides.

cond-mat.supr-con↗

Interplay between tetragonal magnetic order, stripe magnetism, and superconductivity in iron-based materials

Motivated by recent experiments in Ba$_{1-x}$K$_{x}$Fe$_{2}$As$_{2}$ {[}A. E. Böhmer \textit{et al}, to be published{]}, we analyze the type of spin-density wave (SDW) order in doped iron-pnictides and the discontinuities of the superconducting transition temperature $T_{c}$ in the coexistence phase with SDW magnetism. By tracking the magnetic transition line $T_{N}(x)$ towards optimal doping within an itinerant fermionic model, we find a sequence of transitions from the stripe-orthorhombic ($C_{2}$) SDW order to the tetragonal ($C_{4}$) order and then back to the $C_{2}$ order. We argue that the superconducting $T_{c}$ has two discontinuities -- it jumps to a smaller value upon entering the coexistence region with the $C_{4}$ magnetic phase, and then jumps to a larger value inside the SDW state when it crosses the boundary between the $C_{4}$ and $C_{2}$ SDW orders. The full agreement with the experimental phase diagram provides a strong indication that the itinerant approach is adequate to describe the physics of weakly/moderately doped iron-pnictides.

cond-mat.str-el↗

Transport Theory of Metallic B20 Helimagnets

B20 compounds are a class of cubic helimagnets harboring nontrivial spin textures such as spin helices and skyrmions. It has been well understood that the Dzyaloshinskii-Moriya (DM) interaction is the origin of these textures, and the physics behind the DM interaction is the spin-orbital coupling (SOC). However the SOC shows its effect not only on the spins, but also on the electrons. In this paper, we will discuss effects of the SOC on the electron and spin transports in B20 compounds. An effective Hamiltonian is presented from symmetry analysis, and the spin-orbital coupling therein shows anomalous behaviors in anisotropic magnetoresistance (AMR) and helical resistance. New effects such as inverse spin-galvanic effect is proposed, and the origin of the DM interaction is discussed.

cond-mat.mes-hall↗

A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis

Neuroimaging meta-analysis is an important tool for finding consistent effects over studies that each usually have 20 or fewer subjects. Interest in meta-analysis in brain mapping is also driven by a recent focus on so-called "reverse inference": where as traditional "forward inference" identifies the regions of the brain involved in a task, a reverse inference identifies the cognitive processes that a task engages. Such reverse inferences, however, require a set of meta-analysis, one for each possible cognitive domain. However, existing methods for neuroimaging meta-analysis have significant limitations. Commonly used methods for neuroimaging meta-analysis are not model based, do not provide interpretable parameter estimates, and only produce null hypothesis inferences; further, they are generally designed for a single group of studies and cannot produce reverse inferences. In this work we address these limitations by adopting a nonparametric Bayesian approach for meta-analysis data from multiple classes or types of studies. In particular, foci from each type of study are modeled as a cluster process driven by a random intensity function that is modeled as a kernel convolution of a gamma random field. The type-specific gamma random fields are linked and modeled as a realization of a common gamma random field, shared by all types, that induces correlation between study types and mimics the behavior of a univariate mixed effects model. We illustrate our model on simulation studies and a meta-analysis of five emotions from 219 studies and check model fit by a posterior predictive assessment. In addition, we implement reverse inference by using the model to predict study type from a newly presented study. We evaluate this predictive performance via leave-one-out cross-validation that is efficiently implemented using importance sampling techniques.

stat.AP↗

Manipulation of gap nodes by uniaxial strain in iron-based superconductors

In the iron pnictides and chalcogenides, multiple orbitals participate in the superconducting state, enabling different gap structures to be realized in distinct materials. Here we argue that the spectral weights of these orbitals can in principle be controlled by a tetragonal symmetry-breaking uniaxial strain, due to the enhanced nematic susceptibility of many iron-based superconductors. By investigating multi-orbital microscopic models in the presence of orbital order, we show that not only $T_{c}$ can be enhanced, but pairs of accidental gap nodes can be annihilated and created in the Fermi surface by an increasing external strain. We explain our results as a mixture of nearly-degenerate superconducting states promoted by strain, and show that the annihilation and creation of nodes can be detected experimentally via anisotropic penetration depth measurements. Our results provide a promising framework to externally control the superconducting properties of iron-based materials.

cond-mat.supr-con↗

Magnetoresistance due to Broken C4 Symmetry in Cubic B20 Chiral Magnets

The B20 chiral magnets with broken inversion symmetry and C4 rotation symmetry have attracted much attention. The broken inversion symmetry leads to the Dzyaloshinskii-Moriya that gives rise to the helical and Skyrmion states. We report the unusual magnetoresistance (MR) of B20 chiral magnet Fe0.85Co0.15Si that directly reveals the broken C4 rotation symmetry. We present a microscopic theory, a minimal theory with two spin-orbit terms, that satisfies all the symmetry requirements and accounts for the transport experiments.

cond-mat.mtrl-sci↗