arXiv subjects

Jian Kang

Publications and source records attributed to Jian Kang.

At least 163 records · Page 9Linked to original sources

arXiv2019

So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification

Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark dataset named "So2Sat LCZ42," which consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. As rarely done in other labeled remote sensing dataset, we conducted rigorous quality assessment by domain experts. The dataset achieved an overall confidence of 85%. We believe this LCZ dataset is a first step towards an unbiased globallydistributed dataset for urban growth monitoring using machine learning methods, because LCZ provide a rather objective measure other than many other semantic land use and land cover classifications. It provides measures of the morphology, compactness, and height of urban areas, which are less dependent on human and culture. This dataset can be accessed from http://doi.org/10.14459/2018mp1483140.

cs.CV↗

arXiv2019

Intertwined spin-orbital coupled orders in the iron-based superconductors

The underdoped phase diagram of the iron-based superconductors exemplifies the complexity common to many correlated materials. Indeed, multiple ordered states that break different symmetries but display comparable transition temperatures are present. Here, we argue that such a complexity can be understood within a simple unifying framework. This framework, built to respect the symmetries of the non-symmorphic space group of the FeAs/Se layer, consists of primary magnetically-ordered states and their vestigial phases that intertwine spin and orbital degrees of freedom. All vestigial phases have Ising-like and zero wave-vector order parameters, described in terms of composite spin order and exotic orbital-order patterns such as spin-orbital loop-currents, staggered atomic spin-orbit coupling, and emergent Rashba- and Dresselhaus-type spin-orbit interactions. Moreover, they host unusual phenomena, such as the electro-nematic effect, by which electric fields acts as transverse fields to the nematic order parameter, and the ferro-Néel effect, by which a uniform magnetic field induces Néel order. We discuss the experimental implications of our findings to iron-based superconductors and possible extensions to other correlated compounds with similar space groups.

cond-mat.supr-con↗

arXiv2018

AURORA: Auditing PageRank on Large Graphs

Ranking on large-scale graphs plays a fundamental role in many high-impact application domains, ranging from information retrieval, recommender systems, sports team management, biology to neuroscience and many more. PageRank, together with many of its random walk based variants, has become one of the most well-known and widely used algorithms, due to its mathematical elegance and the superior performance across a variety of application domains. Important as it might be, state-of-the-art lacks an intuitive way to explain the ranking results by PageRank (or its variants), e.g., why it thinks the returned top-k webpages are most important ones in the entire graph; why it gives a higher rank to actor John than actor Smith in terms of their relevance w.r.t. a particular movie? In order to answer these questions, this paper proposes a paradigm shift for PageRank, from identifying which nodes are most important to understanding why the ranking algorithm gives a particular ranking result. We formally define the PageRank auditing problem, whose central idea is to identify a set of key graph elements (e.g., edges, nodes, subgraphs) with the highest influence on the ranking results. We formulate it as an optimization problem and propose a family of effective and scalable algorithms (AURORA) to solve it. Our algorithms measure the influence of graph elements and incrementally select influential elements w.r.t. their gradients over the ranking results. We perform extensive empirical evaluations on real-world datasets, which demonstrate that the proposed methods (AURORA) provide intuitive explanations with a linear scalability.

cs.SI↗

arXiv2018

Superconductivity at an antiferromagnetic quantum critical point: the role of energy fluctuations

Motivated by recent experiments reporting superconductivity only at very low temperature in a class of heavy fermion compounds, we study the impact of energy fluctuations with small momentum transfer on the pairing instability near an antiferromagnetic quantum critical point. While these fluctuations, formed by composite spin fluctuations, were proposed to explain the thermodynamic and transport properties near the quantum critical point of compounds such as YbRh$_{2}$Si$_{2}$ and CeCu$_{6-x}$Au$_{x}$ at $x\approx0.1$, here they are found to strongly suppress $T_{c}$ of the $d$-wave pairing of the hot quasiparticles promoted by the spin fluctuations. Interestingly, if energy fluctuations are strong enough, they can induce triplet pairing involving the quasiparticles of the cold regions of the Fermi surface. Overall, the opposing effects of energy and spin fluctuations lead to a suppression of $T_{c}$.

cond-mat.supr-con↗

arXiv2018

Strong coupling phases of partially filled twisted bilayer graphene narrow bands

We identify states favored by Coulomb interactions projected onto the Wannier basis of the four narrow bands of the "magic angle" twisted bilayer graphene. At the filling of two electrons/holes per moire unit cell, such interactions favor an insulating SU(4) ferromagnet. The kinetic terms select the ground state in which the two valleys with opposite spins are equally mixed, with vanishing magnetic moment per particle. We also find extended excited states, the gap to which decreases in magnetic field. An insulating stripe ferromagnetic phase is favored at one electron/hole per unit cell.

cond-mat.str-el↗

arXiv2018

Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

stat.ME↗

arXiv2018

Superconductivity in FeSe: the role of nematic order

Bulk FeSe is a special iron-based material in which superconductivity emerges inside a well-developed nematic phase. We present a microscopic model for this nematic superconducting state, which takes into account the mixing between $s-$wave and $d-$wave pairing channels and the changes in the orbital spectral weight promoted by the sign-changing nematic order parameter. We show that nematicity gives rise to a $\cos2θ$ variation of the pairing gap on the hole pocket that agrees with ARPES and STM data for experimentally-extracted Fermi surface parameters. We further argue that, in BCS theory, $d_{xz}$ and $d_{yz}$ orbitals give nearly equal contributions to the pairing glue, i.e. nematic order alone accounts for the gap anisotropy, but has little effect on $T_{c}$. This result questions the validity of the concept of orbital-selective pairing. Self-energy corrections, however, make $d_{xz}$ orbital more incoherent and reduce its contribution to pairing.

cond-mat.supr-con↗

arXiv2018

Symmetry, maximally localized Wannier states, and low energy model for the twisted bilayer graphene narrow bands

We build symmetry adapted maximally localized Wannier states, and construct the low energy tight binding model for the four narrow bands of the twisted bilayer graphene. We do so when the twist angle is commensurate, near the `magic' value, and the narrow bands are separated from the rest of the bands by energy gaps. On each layer and sublattice, every Wannier state has three peaks near the triangular Moire lattice sites. However, each Wannier state is localized and centered around a site of the honeycomb lattice that is dual to the triangular Moire lattice. Space group and the time reversal symmetries are realized locally. The corresponding tight binding model provides a starting point for studying the correlated many-body phases.

cond-mat.str-el↗

arXiv2018

Covariance-Insured Screening

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss these weak signals. By incorporating the inter-feature dependence, we propose a covariance-insured screening methodology to identify predictors that are jointly informative but only marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and real data studies for selecting potential genetic factors related to the onset of cancer.

stat.ML↗

arXiv2018

Time-reversal symmetry-breaking nematic superconductivity in FeSe

FeSe is a unique member of the family of iron-based superconductors, not only because of the high values of $T_{c}$ in FeSe monolayer, but also because in bulk FeSe superconductivity emerges inside a nematic phase without competing with long-range magnetic order. Near $T_{c}$, superconducting order necessarily has $s+d$ symmetry, because nematic order couples linearly the $s$-wave and $d$-wave harmonics of the superconducting order parameter. Here we argue that the near-degeneracy between $s$-wave and $d$-wave pairing instabilities in FeSe, combined with the sign-change of the nematic order parameter between hole and electron pockets, allows the superconducting order to break time-reversal symmetry at a temperature $T^{*}<T_{c}$. The transition from an $s+d$ state to an $s+\mathrm{e}^{iα}d$ state should give rise to a peak in the specific heat and to the emergence of a soft collective mode that can be potentially detected by Raman spectroscopy.

cond-mat.supr-con↗

arXiv2018

Orbital loop currents in iron-based superconductors

We show that the antiferromagnetic state commonly observed in the phase diagrams of the iron-based superconductors necessarily triggers loop currents characterized by charge transfer between different Fe $ 3d $ orbitals. This effect is rooted on the glide-plane symmetry of these materials and on the existence of an atomic spin-orbit coupling that couples states at the $X$ and $Y$ points of the 1-Fe Brillouin zone. In the particular case in which the magnetic moments are aligned parallel to the magnetic ordering vector direction which is the moment configuration most commonly found in the iron-based superconductors these loop currents involve the $d_{xy}$ orbital and either the $d_{yz}$ orbital (if the moments point along the $y$ axis) or the $d_{xz}$ orbitals (if the moments point along the $x$ axis). We show that the two main manifestations of the orbital loop currents are the emergence of magnetic moments in the pnictide/chalcogen site and an orbital-selective band splitting in the magnetically ordered state, both of which could be detected experimentally. Our results highlight the unique intertwining between orbital and spin degrees of freedom in the iron-based superconductors, and reveal the emergence of an unusual correlated phase that may impact the normal state and superconducting properties of these materials.

cond-mat.str-el↗

arXiv2018

SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting

High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification of the confounding variable, without directly modeling the confounding effects. However, there is lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work, an R package, SurvBoost, is developed to implement the gradient boosting algorithm for fitting the SPH model with high-dimensional covariate variables and other confounders. Extensive simulation studies demonstrate that in many scenarios SurvBoost can achieve a better selection accuracy and reduce computational time substantially compared to the existing R package that implements boosting algorithms without stratification. The proposed R package is also illustrated by an analysis of the gene expression data with survival outcome in The Cancer Genome Atlas (TCGA) study. In addition, a detailed hands-on tutorial for SurvBoost is provided.

stat.CO↗

arXiv2018

Building Instance Classification Using Street View Images

Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US. Keywords: CNN, Building instance classification, Street view images, OpenStreetMap

cs.CV↗

arXiv2018

Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition

The most unique advantage of multipass SAR interferometry (InSAR) is the retrieval of long term geophysical parameters, e.g. linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed in [1], as an alternative to the typical single-pixel methods, e.g. Persistent Scatterer Interferometry (PSI), or pixel-cluster-based methods, e.g. SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a followon, this paper investigates the inherent low rank property of such phase tensors, and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition (RoMIO). We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g. PSI, by a factor of ten to thirty in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits in turn can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.

eess.IV↗

arXiv2017

Network induced large correlation matrix estimation

The correlation matrix of massive biomedical data (e.g. gene expression or neuroimaging data) often exhibits a complex and organized, yet latent graph topological structure. We propose a two step procedure that first detects the latent graph topology with parsimony from the sample correlation matrix and then regularizes the correlation matrix by leveraging the detected graph topological information. We show that the graph topological information guided thresholding can reduce false positive and false negative rates simultaneously because it allows edges to borrow strengths from each other precisely. Several examples illustrate that the parsimoniously detected latent graph topological structures may reveal underlying biological networks and guide correlation matrix estimation.

stat.AP↗

arXiv2017

Competing magnetic orders in the superconducting state of Nd-doped CeRhIn$_{5}$ under pressure

Applied pressure drives the heavy-fermion antiferromagnet CeRhIn$_{5}$ towards a quantum critical point that becomes hidden by a dome of unconventional superconductivity. Magnetic fields suppress this superconducting dome, unveiling the quantum phase transition of local character. Here, we show that $5\%$ magnetic substitution at the Ce site in CeRhIn$_{5}$, either by Nd or Gd, induces a zero-field magnetic instability inside the superconducting state. This magnetic state not only should have a different ordering vector than the high-field local-moment magnetic state, but it also competes with the latter, suggesting that a spin-density-wave phase is stabilized in zero field by Nd and Gd impurities - similarly to the case of Ce$_{0.95}$Nd$_{0.05}$CoIn$_{5}$. Supported by model calculations, we attribute this spin-density wave instability to a magnetic-impurity driven condensation of the spin excitons that form inside the unconventional superconducting state.

cond-mat.str-el↗

arXiv2017

Temperature and Angular Dependence of the Upper Critical Field in K$_{2}$Cr$_{3}$As$_{3}$

We report measurements of the upper critical field $H_{\mathrm{c2}}$ as functions of temperature $T$, polar angle $θ$ (of the field direction with respect to the crystallographic $c$ axis), and azimuthal angle $ϕ$ (of the field direction relative to the $a$ axis within the $ab$ plane) for the Cr-based superconductor K$_{2}$Cr$_{3}$As$_{3}$ with a quasi-one-dimensional and non-centrosymmetric crystal structure. We confirm that the anisotropy in $H_{\mathrm{c2}}(T)$ becomes inverse with decreasing temperature. At low temperatures, $H_{\mathrm{c2}}(θ)$ data are featured by two maxima at $θ$ = 0 ($\mathbf{H}\parallel c$) and $π/2$ ($\mathbf{H}\bot c$), which can be quantitatively understood only if uniaxial effective-mass anisotropy and absence of Pauli paramagnetic effect for $\mathbf{H}\bot c$ are taken simultaneously into consideration. The in-plane $H_{\mathrm{c2}}(ϕ)$ profile shows a unique threefold modulation especially at low temperatures. Overall, the characteristic of the $H_{\mathrm{c2}}(θ, ϕ, T)$ data mostly resemble those of the heavy-fermion superconductor UPt$_3$, and we argue in favor of a dominant spin-triplet superconductivity with odd parity in K$_{2}$Cr$_{3}$As$_{3}$.

cond-mat.supr-con↗

arXiv2017

A Network Object Method to Uncover Hidden Disorder-Related Brain Connectome

Neuropsychiatric disorders impact functional connectivity of the brain at the network level. The identification and statistical testing of disorder-related networks remains challenging. We propose novel methods to streamline the detection and testing of the hidden, disorder-related connectivity patterns as network-objects. We define an abnormal connectome subnetwork as a network-object that includes three classes: nodes of brain areas, edges representing brain connectomic features, and an organized graph topology formed by these nodes and edges. Comparing to the conventional statistical methods, the proposed approach simultaneously reduces false positive and negative discovery rates by letting edges borrow strengths precisely with the guidance of graph topological information, which effectively improves the reproducibility of findings across brain connectome studies. The network-object analyses may provide insights into how brain connectome is systematically impaired by brain illnesses.

stat.AP↗