arXiv subjects

Junjie Zhang

Publications and source records attributed to Junjie Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2021

Strong Superexchange in a $d^{9-δ}$ Nickelate Revealed by Resonant Inelastic X-Ray Scattering

The discovery of superconductivity in a $d^{9-δ}$ nickelate has inspired disparate theoretical perspectives regarding the essential physics of this class of materials. A key issue is the magnitude of the magnetic superexchange, which relates to whether cuprate-like high-temperature nickelate superconductivity could be realized. We address this question using Ni L-edge and O K-edge spectroscopy of the reduced trilayer nickelate $d^{9-1/3}$ La4Ni3O8 and associated theoretical modeling. A magnon energy scale of ~80 meV resulting from a nearest-neighbor magnetic exchange of $J = 69(4)4$ meV is observed, proving that $d^{9-δ}$ nickelates can host a large superexchange. This value, along with that of the Ni-O hybridization estimated from our O K-edge data, implies that trilayer nickelates represent an intermediate case between the infinite-layer nickelates and the cuprates, and suggests that they represent a promising route towards higher-temperature nickelate superconductivity.

cond-mat.supr-con↗

arXiv2020

Deep Bayesian Local Crystallography

The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural distortions, and physical functionalities. Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information. However, the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics, notably translational and point group symmetries and symmetry lowering phenomena. Here, we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework. We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting, namely rotationally invariant variational autoencoders.

physics.comp-ph↗

arXiv2020

DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets

Inverse Distance Weighted models (IDW) have been widely used for predicting and modeling multidimensional space in multimodal industrial processes. However, the more complex the structure of multidimensional space, the lower the performance of IDW models, and real industrial datasets tend to have more complex spatial structure. To solve this problem, a new framework for spatial prediction and modeling based on deep reinforcement learning network is proposed. In the proposed framework, the internal relationship between state and action is enhanced by reusing the state values in the Q network, and the convergence rate and stability of the deep reinforcement learning network are improved. The improved deep reinforcement learning network is then used to search for and learn the hyperparameters of each sample point in the inverse distance weighted model. These hyperparameters can reflect the spatial structure of the current industrial dataset to some extent. Then a spatial distribution of hyperparameters is constructed based on the learned hyperparameters. Each interpolation point obtains corresponding hyperparameters from the hyperparametric spatial distribution and brings them into the classical IDW models for prediction, thus achieving differential spatial prediction and modeling. The simulation results show that the proposed framework is suitable for real industrial datasets with complex spatial structure characteristics and is more accurate than current IDW models in spatial prediction.

cs.LG↗

arXiv2020

Spin dynamics and a nearly continuous magnetic phase transition in an entropy-stabilized oxide antiferromagnet

The magnetic order and the spin dynamics in the antiferromagnetic entropy-stabilized oxide (Mg$_{0.2}$Co$_{0.2}$Ni$_{0.2}$Cu$_{0.2}$Zn$_{0.2}$)O (MgO-ESO) have been studied using muon spin relaxation ($μ$SR) and inelastic neutron scattering. We find that antiferromagnetic order develops gradually in the sample volume as it is cooled below 140 K, becoming fully ordered around 100 K. The spin dynamics show a critical slowing down in the vicinity of the transition, and the magnetic order parameter grows continuously in the ordered state. These results indicate that the antiferromagnetic transition is continuous but proceeds with a Gaussian distribution of ordering temperatures. The magnetic contribution to the specific heat determined from inelastic neutron scattering likewise shows a broad feature centered around 120 K. High-resolution inelastic neutron scattering further reveals an initially gapped spectrum at low temperature which sees an increase in a quasielastic contribution upon heating until the ordering temperature.

cond-mat.mtrl-sci↗

arXiv2020

High oxygen pressure floating zone growth and crystal structure of the layered nickelates R$_4$Ni$_3$O$_{10}$ (R=La, Pr)

Single crystals of the metallic Ruddlesden-Popper trilayer nickelates R$_4$Ni$_3$O$_{10}$ (R=La, Pr) were successfully grown using an optical-image floating zone furnace under oxygen pressure (pO$_2$) of 20 bar for La$_4$Ni$_3$O$_{10}$ and 140 bar for Pr$_4$Ni$_3$O$_{10}$. A combination of synchrotron and laboratory x-ray single crystal diffraction, high-resolution synchrotron x-ray powder diffraction and measurements of physical properties revealed that R$_4$Ni$_3$O$_{10}$ (R=La, Pr) crystallizes in the monoclinic $P$2$_1$/$a$ (Z=2) space group at room temperature, and that a metastable orthorhombic phase ($Bmab$) can be trapped by post-growth rapid cooling. Both La$_4$Ni$_3$O$_{10}$ and Pr$_4$Ni$_3$O$_{10}$ crystals undergo a metal-to-metal transition (MMT) below room temperature. In the case of Pr$_4$Ni$_3$O$_{10}$, the MMT is found at ~157.6 K. For La$_4$Ni$_3$O$_{10}$, the MMT depends on the lattice symmetry: 147.5 K for $Bmab$ vs. 138.6 K for $P$2$_1$/$a$. Lattice anomalies were found at the MMT that, when considered together with the pronounced dependence of the transition temperature on subtle structural differences between $Bmab$ and $P$2$_1$/$a$ phases, demonstrates a not insignificant coupling between electronic and lattice degrees of freedom in these trilayer nickelates.

cond-mat.mtrl-sci↗

arXiv2020

Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification

Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real-world applications. Recently, Few-Shot learning (FS), as an attempt to address the shortage of training samples, has made significant progress in generic classification tasks. Nonetheless, it is still challenging for current FS models to distinguish the subtle differences between fine-grained categories given limited training data. To filling the classification gap, in this paper, we address the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting. A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric. Moreover, a feature alignment layer is designed to match the support image features with query ones before the comparison. We name the proposed model Low-Rank Pairwise Alignment Bilinear Network (LRPABN), which is trained in an end-to-end fashion. Comprehensive experimental results on four widely used fine-grained classification datasets demonstrate that our LRPABN model achieves the superior performances compared to state-of-the-art methods.

cs.CV↗

arXiv2020

CFR-RL: Traffic Engineering with Reinforcement Learning in SDN

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using Software-Defined Networking (SDN) to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is enormous. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this paper, we propose CFR-RL (Critical Flow Rerouting-Reinforcement Learning), a Reinforcement Learning-based scheme that learns a policy to select critical flows for each given traffic matrix automatically. CFR-RL then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL achieves near-optimal performance by rerouting only 10%-21.3% of total traffic.

cs.NI↗

arXiv2020

To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions

Real-world visual data often exhibits a long-tailed distribution, where some ''head'' classes have a large number of samples, yet only a few samples are available for ''tail'' classes. Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training. On the other hand, oversampling tail classes makes the network prone to over-fitting, since head class samples are often consequently under-represented. To resolve this dilemma, in this paper, we propose a simple-yet-effective auxiliary learning approach. The key idea is to split a network into a classifier part and a feature extractor part, and then employ different training strategies for each part. Specifically, to promote the awareness of tail-classes, a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor. For the feature extractor, we also introduce an auxiliary training task, which is to train a classifier under the regular random sampling scheme. In this way, the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue. Apart from this basic auxiliary task, we further explore the benefit of using self-supervised learning as the auxiliary task. Without using any bells and whistles, our model achieves superior performance over the state-of-the-art solutions.

cs.CV↗

arXiv2019

High pO2 Floating Zone Crystal Growth of the Perovskite Nickelate PrNiO3

Single crystals of PrNiO3 were grown under an oxygen pressure of 295 bar using a unique high-pressure optical-image floating zone furnace. The crystals, with volume in excess of 1 mm3, were characterized structurally using single crystal and powder X-ray diffraction. Resistivity, specific heat, and magnetic susceptibility were measured, all of which evidenced an abrupt, first order metal-insulator transition (MIT) at ~130 K, in agreement with previous literature reports on polycrystalline specimens. Temperature-dependent single crystal diffraction was performed to investigate changes through the MIT. Our study demonstrates the opportunity space for high fugacity, reactive environments for single crystal growth specifically of perovskite nickelates but more generally to correlated electron oxides.

cond-mat.mtrl-sci↗

arXiv2019

Local Metallic and Structural Properties of the Strongly Correlated Metal LaNiO$_{3}$ using $^{8}$Li $β$-NMR

We report $β$-detected NMR of ion-implanted $^{8}$Li in a single crystal and thin film of the strongly correlated metal LaNiO$_{3}$. In both samples, spin-lattice relaxation measurements reveal two distinct local metallic environments, as is evident from $T$-linear Korringa $1/T_{1}$ below 200 K with slopes comparable to other metals. A small, approximately temperature independent Knight shift of $\sim 74$ ppm is observed, yielding a normalized Korringa product characteristic of substantial antiferromagnetic correlations, but, we find no evidence for a magnetic transition from 4 to 310 K. Two distinct, equally abundant $^{8}$Li sites is inconsistent with the widely accepted rhombohedral structure of LaNiO$_{3}$, but cannot be simply explained by either of the common alternative orthorhombic or monoclinic distortions.

cond-mat.str-el↗

arXiv2019

Spin stripe order in a square planar trilayer nickelate

Trilayer nickelates, which exhibit a high degree of orbital polarization combined with an electron count (d8.67) corresponding to overdoped cuprates, have been identified as a promising candidate platform for achieving high-Tc superconductivity. One such material, La4Ni3O8, undergoes a semiconductor-insulator transition at ~105 K, which was recently shown to arise from the formation of charge stripes. However, an outstanding issue has been the origin of an anomaly in the magnetic susceptibility at the transition and whether it signifies formation of spin stripes akin to single layer nickelates. Here we report single crystal neutron diffraction measurements (both polarized and unpolarized) that establish that the ground state is indeed magnetic. The ordering is modeled as antiferromagnetic spin stripes that are commensurate with the charge stripes, the magnetic ordering occurring in individual trilayers that are essentially uncorrelated along the crystallographic c-axis. Comparison of the charge and spin stripe order parameters reveals that, in contrast to single-layer nickelates such as La2-xSrxNiO4 as well as related quasi-2D oxides including manganites, cobaltates, and cuprates, these orders uniquely appear simultaneously, thus demonstrating a stronger coupling between spin and charge than in these related low-dimensional correlated oxides.

cond-mat.str-el↗

arXiv2019

Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning

The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, we propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG) recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning. The proposed method, named Pairwise Alignment Bilinear Network (PABN), is an end-to-end deep neural network. Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric. In order to match base image features with query image features, we design feature alignment losses before the proposed pairwise bilinear pooling. Experiment results on four fine-grained classification datasets and one generic few-shot dataset demonstrate that the proposed model outperforms both the state-ofthe-art few-shot fine-grained and general few-shot methods.

cs.CV↗

arXiv2019

Long range antiferromagnetic order in a rocksalt high entropy oxide

We report for the first time the magnetic structure of the high entropy oxide $(Mg_{0.2}Co_{0.2}Ni_{0.2}Cu_{0.2}Zn_{0.2})O$ using neutron powder diffraction. This material exhibits a sluggish magnetic transition but possesses a long-range ordered antiferromagnetic ground state, as revealed by DC and AC magnetic susceptibility, elastic and inelastic neutron scattering measurements. The magnetic propagation wavevector is k=(1/2, 1/2, 1/2) based on the cubic structure Fm-3m, and the magnetic structure consists of ferromagnetic sheets in the (111) planes with spins antiparallel between two neighboring planes. Inelastic neutron scattering reveals strong magnetic excitations at 100 K that survive up to room temperature. This work demonstrates that entropy-stabilized oxides represent a unique platform to study long range magnetic order with extreme chemical disorder.

cond-mat.mtrl-sci↗

arXiv2018

A Pulmonary Nodule Detection Model Based on Progressive Resolution and Hierarchical Saliency

Detection of pulmonary nodules on chest CT is an essential step in the early diagnosis of lung cancer, which is critical for best patient care. Although a number of computer-aided nodule detection methods have been published in the literature, these methods still have two major drawbacks: missing out true nodules during the detection of nodule candidates and less-accurate identification of nodules from non-nodule. In this paper, we propose an automated pulmonary nodule detection algorithm that jointly combines progressive resolution and hierarchical saliency. Specifically, we design a 3D progressive resolution-based densely dilated FCN, namely the progressive resolution network (PRN), to detect nodule candidates inside the lung, and construct a densely dilated 3D CNN with hierarchical saliency, namely the hierarchical saliency network (HSN), to simultaneously identify genuine nodules from those candidates and estimate the diameters of nodules. We evaluated our algorithm on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a state-of-the-art detection score. Our results suggest that the proposed algorithm can effectively detect pulmonary nodules on chest CT and accurately estimate their diameters.

cs.CV↗

arXiv2017

Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards

Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge. Towards this end, we propose a Deep Reinforcement Learning framework based on three new intermediate rewards, namely goal-achieved, progressive and informativeness that encourage the generation of succinct questions, which in turn uncover valuable information towards the overall goal. By directly optimizing for questions that work quickly towards fulfilling the overall goal, we avoid the tendency of existing methods to generate long series of insane queries that add little value. We evaluate our model on the GuessWhat?! dataset and show that the resulting questions can help a standard Guesser identify a specific object in an image at a much higher success rate.

cs.CV↗

arXiv2017

Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement

The number of social images has exploded by the wide adoption of social networks, and people like to share their comments about them. These comments can be a description of the image, or some objects, attributes, scenes in it, which are normally used as the user-provided tags. However, it is well-known that user-provided tags are incomplete and imprecise to some extent. Directly using them can damage the performance of related applications, such as the image annotation and retrieval. In this paper, we propose to learn an image annotation model and refine the user-provided tags simultaneously in a weakly-supervised manner. The deep neural network is utilized as the image feature learning and backbone annotation model, while visual consistency, semantic dependency, and user-error sparsity are introduced as the constraints at the batch level to alleviate the tag noise. Therefore, our model is highly flexible and stable to handle large-scale image sets. Experimental results on two benchmark datasets indicate that our proposed model achieves the best performance compared to the state-of-the-art methods.

cs.CV↗

arXiv2017

JCTC: A Large Job posting Corpus for Text Classification

The absence of an appropriate text classification corpus makes the massive amount of online job information unusable for labor market analysis. This paper presents JCTC, a large job posting corpus for text classification. In JCTC construction framework, a formal specification issued by the Chinese central government is chosen as the classification standard. The unsupervised learning (WE-cos), supervised learning algorithm (SVM) and human judgements are all used in the construction process. JCTC has 102581 online job postings distributed in 465 categories. The method proposed here can not only ameliorate the high demands on people's skill and knowledge, but reduce the subjective influences as well. Besides, the method is not limited in Chinese. We benchmark five state-of-the-art deep learning approaches on JCTC providing baseline results for future studies. JCTC might be the first job posting corpus for text classification and the largest one in Chinese. With the help of JCTC, related organizations are able to monitor, analyze and predict the labor market in a comprehensive, accurate and timely manner.

cs.IR↗

arXiv2017

Mimicking cuprates: large orbital polarization in a metallic square-planar nickelate

High temperature cuprate superconductivity remains a defining problem in condensed matter physics. Among myriad approaches to addressing this problem has been the study of alternative transition metal oxides with similar structures and 3d electron count that are suggested as proxies for cuprate physics. None of these analogs has been superconducting, and few are even metallic. Here, we report that the low-valent, quasi-two-dimensional trilayer compound, Pr4Ni3O8 avoids a charge-stripe ordered phase previously reported for La4Ni3O8, leading to a metallic ground state. By combining x-ray absorption spectroscopy and density functional theory calculations, we further find that metallic Pr4Ni3O8 exhibits a low-spin configuration and significant orbital polarization of the unoccupied eg states with pronounced dx2-y2 character near the Fermi energy, both hallmarks of the cuprate superconductors. Belonging to a regime of 3d electron count found for hole-doped cuprates, Pr4Ni3O8 thus represents one of the closest analogies to cuprates yet reported and a singularly promising candidate for high-Tc superconductivity if appropriately doped.

cond-mat.str-el↗