arXiv subjects

Zheng Liu

Publications and source records attributed to Zheng Liu.

At least 361 records · Page 20Linked to original sources

arXiv2019

OD-GCN: Object Detection Boosted by Knowledge GCN

Classical CNN based object detection methods only extract the objects' image features, but do not consider the high-level relationship among objects in context. In this article, the graph convolutional networks (GCN) is integrated into the object detection framework to exploit the benefit of category relationship among objects, which is able to provide extra confidence for any pre-trained object detection model in our framework. In experiments, we test several popular base detection models on COCO dataset. The results show promising improvement on mAP by 1-5pp. In addition, visualized analysis reveals the benchmark improvement is quite reasonable in human's opinion.

cs.CV↗

arXiv2019

A Novel User Representation Paradigm for Making Personalized Candidate Retrieval

Candidate retrieval is a fundamental issue in recommendation system. Given user's recommendation request, relevant candidates need to be retrieved in realtime for subsequent ranking operations. Considering that the retrieval operation is conducted over considerable items, it has to be both precise and scalable so that high-quality candidates can be acquired within tolerable latency. Unfortunately, conventional methods would trade off precision for high running efficiency, which leads to inferior retrieval quality. In contrast, those deep learning-based approaches can be highly accurate in identifying relevant items; yet, they are unsuitable for candidate retrieval due to their inherent limitation on scalability. In this work, a novel framework is proposed to address the above challenges. The underlying intuition is to rely on a well-trained ranking model for the supervision of an efficient retrieval model, such that it will unify the scalability and precision as a whole. We have implemented our conceptual framework and made comprehensive evaluation for it, where promising results are achieved against representative baselines. Our work is undergoing a anonymous review, and it will soon be released after the notification. If you're also interested in this problem, please feel free to contact us.

cs.IR↗

arXiv2019

Machine learning driven synthesis of few-layered WTe2

Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for the further study. Traditional exploration of the optimal synthesis conditions of novel materials is based on the trial-and-error approach, which is time consuming, costly and laborious. Recently, machine learning (ML) has demonstrated promising capability in guiding material synthesis through effectively learning from the past data and then making recommendations. Here, we report the implementation of supervised ML for the chemical vapor deposition (CVD) synthesis of high-quality 1D few-layered WTe2 nanoribbons (NRs). The synthesis parameters of the WTe2 NRs are optimized by the trained ML model. On top of that, the growth mechanism of as-synthesized 1T' few-layered WTe2 NRs is further proposed, which may inspire the growth strategies for other 1D nanostructures. Our findings suggest that ML is a powerful and efficient approach to aid the synthesis of 1D nanostructures, opening up new opportunities for intelligent material development.

cond-mat.mtrl-sci↗

arXiv2019

Vehicle Re-identification with Viewpoint-aware Metric Learning

This paper considers vehicle re-identification (re-ID) problem. The extreme viewpoint variation (up to 180 degrees) poses great challenges for existing approaches. Inspired by the behavior in human's recognition process, we propose a novel viewpoint-aware metric learning approach. It learns two metrics for similar viewpoints and different viewpoints in two feature spaces, respectively, giving rise to viewpoint-aware network (VANet). During training, two types of constraints are applied jointly. During inference, viewpoint is firstly estimated and the corresponding metric is used. Experimental results confirm that VANet significantly improves re-ID accuracy, especially when the pair is observed from different viewpoints. Our method establishes the new state-of-the-art on two benchmarks.

cs.CV↗

arXiv2019

In-plane ordering of O vacancies in a high-Tc cuprate superconductor with compressed Cu-O octahedrons: a first-principles cluster expansion study

A recently discovered high-Tc cuprate superconductor Ba2CuO$_{4-δ}$ exhibits exceptional Jahn-Teller distortion, wherein the CuO6 octahedrons are compressed along the c axis. As a consequence, the O vacancies prefer to reside in the CuO2 plane, but the exact structure is not known. By combining first-principles total energy calculation with the automated structure inversion method, the effective cluster interactions of O vacancies are mapped out. Around $δ$=0.8, where the 73K superconductivity was observed experimentally, we predict that the ordered O vacancies slice the CuO2 plane into not only 1D chains and but also two-leg ladders. A Monte Carlo simulation is performed based on the effective cluster interaction model, showing that such an ordering pattern is stable up to ~900 K. Our results put forth a concrete structural basis to discuss the underlying superconducting mechanism.

cond-mat.supr-con↗

arXiv2019

Generalised uncertainty relations from superpositions of geometries

Phenomenological approaches to quantum gravity implement a minimum resolvable length-scale but do not link it to an underlying formalism describing geometric superpositions. Here, we introduce an intuitive approach in which points in the classical spatial background are delocalised, or `smeared', giving rise to an entangled superposition of geometries. The model uses additional degrees of freedom to parameterise the superposed classical backgrounds. Our formalism contains both minimum length and minimum momentum resolutions and we naturally identify the former with the Planck length. In addition, we argue that the minimum momentum is determined by the de Sitter scale, and may be identified with the effects of dark energy in the form of a cosmological constant. Within the new formalism, we obtain both the generalised uncertainty principle (GUP) and extended uncertainty principle (EUP), which may be combined to give an uncertainty relation that is symmetric in position and momentum. Crucially, our approach does not imply a significant modification of the position-momentum commutator, which remains proportional to the identity matrix. It therefore yields generalised uncertainty relations without violating the equivalence principle, in contradistinction to existing models based on nonlinear dispersion relations. Implications for cosmology and the black hole uncertainty principle correspondence are briefly discussed, and prospects for future work on the smeared-space model are outlined.

quant-ph↗

arXiv2019

Robustifying deep networks for image segmentation

Purpose: The purpose of this study is to investigate the robustness of a commonly-used convolutional neural network for image segmentation with respect to visually-subtle adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. Materials and Methods: In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas. A three-dimensional UNet model was implemented to segment four different MR series (T1-weighted, post-contrast T1-weighted, T2- weighted, and T2-weighted FLAIR) into four pixelwise labels (Gd-enhancing tumor, peritumoral edema, necrotic and non-enhancing tumor, and background). We developed attack strategies based on the Fast Gradient Sign Method (FGSM), iterative FGSM (i-FGSM), and targeted iterative FGSM (ti-FGSM) to produce effective attacks. Additionally, we explored the effectiveness of distillation and adversarial training via data augmentation to counteract adversarial attacks. Robustness was measured by comparing the Dice coefficient for each attack method using Wilcoxon signed-rank tests. Results: Attacks based on FGSM, i-FGSM, and ti-FGSM were effective in significantly reducing the quality of image segmentation with reductions in Dice coefficient by up to 65%. For attack defenses, distillation performed significantly better than adversarial training approaches. However, all defense approaches performed worse compared to unperturbed test images. Conclusion: Segmentation networks can be adversely affected by targeted attacks that introduce visually minor (and potentially undetectable) modifications to existing images. With an increasing interest in applying deep learning techniques to medical imaging data, it is important to quantify the ramifications of adversarial inputs (either intentional or unintentional).

eess.IV↗

arXiv2019

Does the metallic 1T phase WS2 really exist?

The electronic and optical properties of transition metal dichalcogenides (TMDCs) in distinctive phases, such as 1H, 1T, and 1T' phases, are of fundamental importance for variety of applications. The 1H phase has been understood as a direct bandgap semiconductor. On the other hand, the electronic properties of the 1T and 1T' phases remain controversy in the theoretical and experimental perspectives. In this study, we explore the optical properties of monolayer WS2 in 1H, 1T, and 1T' phases using Raman and photoluminescence, corroborated with the atomic structure identified by scanning transmission electron microscopy. Despite of earlier theoretical investigations which all predict the metallic 1T phase, we experimentally discovered that the 1T-phase WS2 is a direct band gap semiconductor and optically indistinguishable from the 1H phase. In a sharp contrast, the 1T'-phase WS2 shows a gapless nature in its bandstructure with the quenched exciton transition as expected. Our experimental findings may give an interesting twist on the existing literatures reporting the metallic nature of 1T phase because the 1T phase has a strong tendency to co-exist with the metallic 1T' phase.

cond-mat.mtrl-sci↗

arXiv2019

Phase evolution and superconductivity enhancement in Se-substituted MoTe$_2$ thin films

The strong spin$-$orbit coupling (SOC) and numerous crystal phases in few$-$layer transition metal dichalcogenides (TMDCs) MX$_2$ (M$=$W, Mo, and X$=$Te, Se, S) has led to a variety of novel physics, such as Ising superconductivity and quantum spin Hall effect realized in monolayer 2H$-$ and Td$-$MX$_2$, respectively. Consecutive tailoring of the MX$_2$ structure from 2H to Td phase may realize the long$-$sought topological superconductivity in one material system by incorporating superconductivity and quantum spin Hall effect together. In this work, by combing Raman spectrum, X-ray photoelectron spectrum (XPS), scanning transmission electron microscopy imaging (STEM) as well as electrical transport measurements, we demonstrate that a consecutively structural phase transitions from Td to 1T$'$ to 2H polytype can be realized as the Se-substitution concentration increases. More importantly, the Se$-$substitution has been found to notably enhance the superconductivity of the MoTe$_2$ thin film, which is interpreted as the introduction of the two$-$band superconductivity. The chemical constituent induced phase transition offers a new strategy to study the s$_{+-}$ superconductivity and the possible topological superconductivity as well as to develop phase$-$sensitive devices based on MX$_2$ materials.

cond-mat.mes-hall↗

arXiv2019

Feedback Network for Image Super-Resolution

Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. However, the feedback mechanism, which commonly exists in human visual system, has not been fully exploited in existing deep learning based image SR methods. In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. Specifically, we use hidden states in an RNN with constraints to achieve such feedback manner. A feedback block is designed to handle the feedback connections and to generate powerful high-level representations. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution image step by step. In addition, we introduce a curriculum learning strategy to make the network well suitable for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. Extensive experimental results demonstrate the superiority of the proposed SRFBN in comparison with the state-of-the-art methods. Code is avaliable at https://github.com/Paper99/SRFBN_CVPR19.

cs.CV↗

arXiv2019

A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion

In computer vision and image processing tasks, image fusion has evolved into an attractive research field. However, recent existing image fusion methods are mostly built on pixel-level operations, which may produce unacceptable artifacts and are time-consuming. In this paper, a symmetric encoder-decoder with a residual block (SEDR) for infrared and visible image fusion is proposed. For the training stage, the SEDR network is trained with a new dataset to obtain a fixed feature extractor. For the fusion stage, first, the trained model is utilized to extract the intermediate features and compensation features of two source images. Then, extracted intermediate features are used to generate two attention maps, which are multiplied to the input features for refinement. In addition, the compensation features generated by the first two convolutional layers are merged and passed to the corresponding deconvolutional layers. At last, the refined features are fused for decoding to reconstruct the final fused image. Experimental results demonstrate that the proposed fusion method (named as SEDRFuse) outperforms the state-of-the-art fusion methods in terms of both subjective and objective evaluations.

cs.CV↗

arXiv2019

Machine learning-guided synthesis of advanced inorganic materials

Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods leads to high uncertainty, numerous trials and exorbitant cost. Recently, machine learning (ML) has demonstrated tremendous potential for material research. Here, we report the application of ML to optimize and accelerate material synthesis process in two representative multi-variable systems. A classification ML model on chemical vapor deposition-grown MoS2 is established, capable of optimizing the synthesis conditions to achieve higher success rate. While a regression model is constructed on the hydrothermal-synthesized carbon quantum dots, to enhance the process-related properties such as the photoluminescence quantum yield. Progressive adaptive model is further developed, aiming to involve ML at the beginning stage of new material synthesis. Optimization of the experimental outcome with minimized number of trials can be achieved with the effective feedback loops. This work serves as proof of concept revealing the feasibility and remarkable capability of ML to facilitate the synthesis of inorganic materials, and opens up a new window for accelerating material development.

cond-mat.mtrl-sci↗

arXiv2019

Topological superconducting phase in high-Tc superconductor MgB2 with Dirac-nodal-line fermions

Topological superconductors are an intriguing and elusive quantum phase, characterized by topologically protected gapless surface/edge states residing in a bulk superconducting gap, which hosts Majorana fermions. Unfortunately, all currently known topological superconductors have a very low transition temperature, limiting experimental measurements of Majorana fermions. Here we discover the existence of a topological Dirac-nodal-line state in a well-known conventional high-temperature superconductor, MgB2. First-principles calculations show that the Dirac-nodal-line structure exhibits a unique one-dimensional dispersive Dirac-nodal line, protected by both spatial-inversion and time-reversal symmetry, which connects the electron and hole Dirac states. Most importantly, we show that the topological superconducting phase can be realized with a conventional s-wave superconducting gap, evidenced by the topological edge mode of the MgB2 thin films showing chiral edge states. Our discovery may enable the experimental measurement of Majorana fermions at high temperature.

cond-mat.supr-con↗

arXiv2019

Atomic scale electronic structure of the ferromagnetic semiconductor Cr2Ge2Te6

Cr2Ge2Te6 is an intrinsic ferromagnetic semiconductor with van der Waals type layered structure, thus represents a promising material for novel electronic and spintronic devices. Here we combine scanning tunneling microscopy and first-principles calculations to investigate the electronic structure of Cr2Ge2Te6. Tunneling spectroscopy reveals a surprising large energy level shift and change of energy gap size across the ferromagnetic to paramagnetic phase transition, as well as a peculiar double-peak electronic state on the Cr-site defect. These features can be quantitatively explained by density functional theory calculations, which uncover a close relationship between the electronic structure and magnetic order. These findings shed important new lights on the microscopic electronic structure and origin of magnetic order in Cr2Ge2Te6.

cond-mat.mtrl-sci↗

arXiv2019

Electron-Nuclear Hyperfine Coupling in Quantum Kagome Antiferromagnets from First-Principles Calculation and a Reflection of the Defect Effect

The discovery of ideal spin-1/2 kagome antiferromagnets Herbertsmithite and Zn-doped Barlowite represents a breakthrough in the quest for quantum spin liquids (QSLs), and nuclear magnetic resonance (NMR) spectroscopy plays a prominent role in revealing the quantum paramagnetism in these compounds. However, interpretation of NMR data that is often masked by defects can be controversial. Here, we show that the most significant interaction strength for NMR, i.e. the hyperfine coupling (HFC) strength, can be reasonably reproduced by first-principles calculations for these proposed QSLs. Applying this method to a supercell containing Cu-Zn defects enables us to map out the variation and distribution of HFC at different nuclear sites. This predictive power is expected to bridge the missing link in the analysis of the low-temperature NMR data.

cond-mat.str-el↗

arXiv2019

The doubling Archimedean zeta integrals for p-adic interpolation

We compute the Archimedean doubling zeta integrals which appear in the interpolation formulas for the p-adic L-functions of Siegel modular forms, and verify that they agree with the modified Archimedean Euler factors for p-adic interpolation conjectured by Coates--Perrin-Riou.

math.NT↗

arXiv2019

Towards end-to-end pulsed eddy current classification and regression with CNN

Pulsed eddy current (PEC) is an effective electromagnetic non-destructive inspection (NDI) technique for metal materials, which has already been widely adopted in detecting cracking and corrosion in some multi-layer structures. Automatically inspecting the defects in these structures would be conducive to further analysis and treatment of them. In this paper, we propose an effective end-to-end model using convolutional neural networks (CNN) to learn effective features from PEC data. Specifically, we construct a multi-task generic model, based on 1D CNN, to predict both the class and depth of flaws simultaneously. Extensive experiments demonstrate our model is capable of handling both classification and regression tasks on PEC data. Our proposed model obtains higher accuracy and lower error compared to other standard methods.

cs.CV↗

arXiv2019

Experimental Progress on Layered Topological Semimetals

We review recent experimental progresses on layered topological materials, mainly focusing on transitional metal dichalcogenides with various lattice types including 1T, Td and 1T' structural phases. Their electronic quantum states are interestingly rich, and many appear to be topological nontrivial, such as Dirac/Weyl semimetallic phase in multilayers and quantum spin hall insulator phase in monolayers. The content covers recent major advances from material synthesis, basic characterizations, angle-resolved photoemission spectroscopy measurements, transport and optical responses. Following those, we outlook the exciting future possibilities enabled by the marriage of topological physics and two dimensional van der Waals layered heterostructures.

cond-mat.mes-hall↗