arXiv subjects

Zheng Liu

Publications and source records attributed to Zheng Liu.

At least 343 records · Page 19Linked to original sources

arXiv2021

BALM: Bundle Adjustment for Lidar Mapping

A local Bundle Adjustment (BA) on a sliding window of keyframes has been widely used in visual SLAM and proved to be very effective in lowering the drift. But in lidar SLAM, BA method is hardly used because the sparse feature points (e.g., edge and plane) make the exact point matching impossible. In this paper, we formulate the lidar BA as minimizing the distance from a feature point to its matched edge or plane. Unlike the visual SLAM (and prior plane adjustment method in lidar SLAM) where the feature has to be co-determined along with the pose, we show that the feature can be analytically solved and removed from the BA, the resultant BA is only dependent on the scan poses. This greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used. To speedup the optimization, we derive the analytical derivatives of the cost function, up to second order, in closed form. Moreover, we propose a novel adaptive voxelization method to search feature correspondence efficiently. The proposed formulations are incorporated into a LOAM back-end for map refinement. Results show that, although as a back-end, the local BA can be solved very efficiently, even in real-time at 10Hz when optimizing 20 scans of point-cloud. The local BA also considerably lowers the LOAM drift. Our implementation of the BA optimization and LOAM are open-sourced to benefit the community.

cs.RO↗

arXiv2021

All at Once: Temporally Adaptive Multi-Frame Interpolation with Advanced Motion Modeling

Recent advances in high refresh rate displays as well as the increased interest in high rate of slow motion and frame up-conversion fuel the demand for efficient and cost-effective multi-frame video interpolation solutions. To that regard, inserting multiple frames between consecutive video frames are of paramount importance for the consumer electronics industry. State-of-the-art methods are iterative solutions interpolating one frame at the time. They introduce temporal inconsistencies and clearly noticeable visual artifacts. Departing from the state-of-the-art, this work introduces a true multi-frame interpolator. It utilizes a pyramidal style network in the temporal domain to complete the multi-frame interpolation task in one-shot. A novel flow estimation procedure using a relaxed loss function, and an advanced, cubic-based, motion model is also used to further boost interpolation accuracy when complex motion segments are encountered. Results on the Adobe240 dataset show that the proposed method generates visually pleasing, temporally consistent frames, outperforms the current best off-the-shelf method by 1.57db in PSNR with 8 times smaller model and 7.7 times faster. The proposed method can be easily extended to interpolate a large number of new frames while remaining efficient because of the one-shot mechanism.

cs.CV↗

arXiv2021

Dynamic Graph Collaborative Filtering

Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Based on this assumption, many previous works focus on interaction sequences and learn evolutionary embeddings of users and items. However, we argue that sequence-based models are not able to capture collaborative information among users and items directly. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. We propose three update mechanisms: zero-order 'inheritance', first-order 'propagation', and second-order 'aggregation', to represent the impact on a user or item when a new interaction occurs. Based on them, we update related user and item embeddings simultaneously when interactions occur in turn, and then use the latest embeddings to make recommendations. Extensive experiments conducted on three public datasets show that DGCF significantly outperforms the state-of-the-art dynamic recommendation methods up to 30. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.

cs.IR↗

arXiv2020

Dimensionality-dependent type-II Weyl semimetal state in Mo$_{0.25}$W$_{0.75}$Te$_{2}$

Weyl nodes and Fermi arcs in type-II Weyl semimetals (WSMs) have led to lots of exotic transport phenomena. Recently, Mo$_{0.25}$W$_{0.75}$Te$_{2}$ has been established as a type-II WSM with Weyl points located near Fermi level, which offers an opportunity to study its intriguing band structure by electrical transport measurements. Here, by selecting a special sample with the thickness gradient across two- (2D) and three-dimensional (3D) regime, we show strong evidences that Mo$_{0.25}$W$_{0.75}$Te$_{2}$ is a type-II Weyl semimetal by observing the following two dimensionality-dependent transport features: 1) A chiral-anomaly-induced anisotropic magneto-conductivity enhancement, proportional to the square of in-plane magnetic field (B$_{in}$$^{2}$); 2) An additional quantum oscillation with thickness-dependent phase shift. Our theoretical calculations show that the observed quantum oscillation originates from a Weyl-orbit-like scenario due to the unique band structure of Mo$_{0.25}$W$_{0.75}$Te$_{2}$. The in situ dimensionality-tuned transport experiment offers a new strategy to search for type-II WSMs.

cond-mat.mes-hall↗

arXiv2020

Van der Waals Heterostructure Pt$_{2}$HgSe$_{3}$/CrI$_3$ for Topological Valleytronics

We identify a valley-polarized Chern insulator in the van der Waals heterostructure, Pt$_{2}$HgSe$_{3}$/CrI$_3$, for potential applications with interplay between electric, magnetic, optical, and mechanical effects. The interlayer proximity magnetic coupling nearly closes the band gap of Pt$_{2}$HgSe$_{3}$ and the strong intra-layer spin-orbit coupling further lifts the valley degeneracy by over 100 meV leading to positive and negative band gaps at opposite valleys. In the valley with negative gap, the interfacial Rashba spin-orbit coupling opens a topological band gap of 17.8 meV, which is enlarged to 30.8 meV by adding an $h$-BN layer. We find large orbital magnetization in Pt$_{2}$HgSe$_{3}$ layer that is much larger than spin, which can induce measurable optical Kerr effect. The valley polarization and Chern number are coupled to the magnetic order of the nearest neighboring CrI$_3$ layer, which is switchable by electric, magnetic, and mechanical means in experiments. The presence of $h$-BN protects the topological phase allowing the construction of superlattices with valley, spin, and layer degrees of freedoms.

cond-mat.mtrl-sci↗

arXiv2020

Shuffle and Learn: Minimizing Mutual Information for Unsupervised Hashing

Unsupervised binary representation allows fast data retrieval without any annotations, enabling practical application like fast person re-identification and multimedia retrieval. It is argued that conflicts in binary space are one of the major barriers to high-performance unsupervised hashing as current methods failed to capture the precise code conflicts in the full domain. A novel relaxation method called Shuffle and Learn is proposed to tackle code conflicts in the unsupervised hash. Approximated derivatives for joint probability and the gradients for the binary layer are introduced to bridge the update from the hash to the input. Proof on $ε$-Convergence of joint probability with approximated derivatives is provided to guarantee the preciseness on update applied on the mutual information. The proposed algorithm is carried out with iterative global updates to minimize mutual information, diverging the code before regular unsupervised optimization. Experiments suggest that the proposed method can relax the code optimization from local optimum and help to generate binary representations that are more discriminative and informative without any annotations. Performance benchmarks on image retrieval with the unsupervised binary code are conducted on three open datasets, and the model achieves state-of-the-art accuracy on image retrieval task for all those datasets. Datasets and reproducible code are provided.

cs.CV↗

arXiv2020

Sampling-Decomposable Generative Adversarial Recommender

Recommendation techniques are important approaches for alleviating information overload. Being often trained on implicit user feedback, many recommenders suffer from the sparsity challenge due to the lack of explicitly negative samples. The GAN-style recommenders (i.e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective. However, producing samples from the generator is very time-consuming, and our empirical study shows that the discriminator performs poor in top-k item recommendation. To this end, a theoretical analysis is made for the GAN-style algorithms, showing that the generator of limit capacity is diverged from the optimal generator. This may interpret the limitation of discriminator's performance. Based on these findings, we propose a Sampling-Decomposable Generative Adversarial Recommender (SD-GAR). In the framework, the divergence between some generator and the optimum is compensated by self-normalized importance sampling; the efficiency of sample generation is improved with a sampling-decomposable generator, such that each sample can be generated in O(1) with the Vose-Alias method. Interestingly, due to decomposability of sampling, the generator can be optimized with the closed-form solutions in an alternating manner, being different from policy gradient in the GAN-style algorithms. We extensively evaluate the proposed algorithm with five real-world recommendation datasets. The results show that SD-GAR outperforms IRGAN by 12.4% and the SOTA recommender by 10% on average. Moreover, discriminator training can be 20x faster on the dataset with more than 120K items.

cs.IR↗

arXiv2020

Prediction of intrinsic topological superconductivity in Mn-doped GeTe monolayer from first-principles

Recent discovery of topological superconductors (TSCs) has sparked enormous interest. Realization of TSC requires a delicate tuning of multiple microscopic parameters, which remains a great challenge. Here, we develop a first-principles approach to quantify realistic conditions of TSC by solving self-consistently Bogoliubov-de Gennes equation based on Wannier function construction of band structure, in presence of Rashba spin-orbit coupling, Zeeman splitting and electron-phonon coupling. We further demonstrate the power of this new method by predicting the Mn-doped GeTe (Ge$_{1-x}$Mn$_x$Te) monolayer - a well-known dilute magnetic semiconductor showing superconductivity under hole doping - to be a Class D TSC with Chern number of -1 and chiral Majorana edge modes. By constructing a first-principles phase diagram in the parameter space of temperature and Mn concentration, we propose the TSC phase can be induced at a lower-limit transition temperature of ~40 mK and the Mn concentration of $x$~0.015%. Our approach can be generally applied to TSCs with a phonon-mediated pairing, providing useful guidance for future experiments.

cond-mat.supr-con↗

arXiv2020

Many-body effect in optical properties of monolayer molybdenum diselenide

Excitons in monolayer transition metal dichalcogenide (TMD) provide a paradigm of composite Boson in 2D system. This letter reports a photoluminescence and reflectance study of excitons in monolayer molybdenum diselenide (MoSe2) with electrostatic gating. We observe the repulsive and attractive Fermi polaron modes of the band edge exciton, its excited state and the spin-off excitons. Our data validate the polaronic behavior of excitonic states in the system quantitatively where the simple three-particle trion model is insufficient to explain.

cond-mat.mes-hall↗

arXiv2020

Quantum phase interference in a fullerene-based molecular qutrit

High spin magnetic molecules are promising candidates for quantum information processing because they intrinsically have multiple sublevels for information storage and computational operations. However, due to their susceptibility to the environment and limitation from the selection rule, the arbitrary control of the quantum state of a multilevel system on a molecular and electron spin basis has not been realized. Here we exploit the photoexcited triplet of C70 as a molecular electron spin qutrit. After the system was initialized by photoexcitation, we prepared it into representative three-level superposition states characteristic of the qutrit, measured their density matrices, and showed the interference of the quantum phases in the superposition. The interference pattern is further interpreted as a map of evolution through time under different conditions.

quant-ph↗

arXiv2020

Electric field manipulation enhanced by strong spin-orbit coupling: promoting rare-earth ions as qubits

Quantum information processing based on magnetic ions are considered potential candidates for applications because they can be modified and scaled up by a variety of chemical methods. For these systems to achieve individual spin addressability and high energy efficiency, we exploited the electric field as a tool to manipulate their quantum behaviours, functioning via spin-orbit coupling. A Ce:YAG single crystal was employed due to that rare-earth ions have strong spin-orbit coupling and with considerations regarding the dynamics and the symmetry requirements. The Stark effect of the Ce3+ ion was observed and measured. When demonstrated as a quantum phase gate, the electric field manipulation exhibited high efficiency which allowed up to 57 π/2 operations before decoherence with optimized field directions. It was also utilized to carry out quantum bang-bang control, as a method of dynamic decoupling, and the refined Deutsch-Jozsa algorithm. Our experiments highlighted rare-earth ions as potentially applicable qubits since they offer enhanced spin-electric coupling which enables high-efficiency quantum manipulation.

cond-mat.mtrl-sci↗

arXiv2020

Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning

High performance lidars are essential in autonomous robots such as self-driving cars, automated ground vehicles and intelligent machines. Traditional mechanical scanning lidars offer superior performance in autonomous vehicles, but the potential mass application is limited by the inherent manufacturing difficulty. We propose a robotic lidar sensor based on incommensurable scanning that allows straightforward mass production and adoption in autonomous robots. Some unique features are additionally permitted by this incommensurable scanning. Similar to the fovea in human retina, this lidar features a peaked central angular density, enabling in applications that prefers eye-like attention. The incommensurable scanning method of this lidar could also provide a much higher resolution than conventional lidars which is beneficial in robotic applications such as sensor calibration. Examples making use of these advantageous features are demonstrated.

cs.RO↗

arXiv2020

Efficient Growth and Characterization of One Dimensional Transition Metal Tellurides Inside Carbon Nanotubes

Atomically thin one dimensional (1D) van der Waals wires of transition metal monochalocogenides (TMMs) have been anticipated as promising building blocks for integrated nanoelectronics. While reliable production of TMM nanowires has eluded scientists over the past few decades, we finally demonstrated a bottom up fabrication of MoTe nanowires inside carbon nanotubes (CNTs). Still, the current synthesis method is based on vacuum annealing of reactive MoTe2, and limits access to a variety of TMMs. Here we report an expanded framework for high yield synthesis of the 1D tellurides including WTe, an unprecedented family of TMMs. Experimental and theoretical analyses revealed that the choice of suitable metal oxides as a precursor provides useful yield for their characterization. These TMM nanowires exhibit a significant optical absorption in the visible light region. More important, electronic properties of CNTs can be tuned by encapsulating different TMM nanowires.

cond-mat.mtrl-sci↗

arXiv2020

Electronic nematicity in FeSe: a first-principles perspective

Electronic nematicity is an important order in most iron-based superconductors, and FeSe represents a unique example, in which nematicity disentangles from spin ordering. It is commonly perceived that this property arises from strong electronic correlation, which can not be properly captured by density functional theory (DFT). Here, we show that by properly considering the paramagnetic condition and carefully searching the energy landscape with symmetry-preconditioned wavefunctions, two nematic solutions stand out at either the DFT+$U$ or hybrid functional level, both of which are lower in energy than the symmetric solution. The ground-state band structure and Fermi surface can be well compared with the recent experimental results. Symmetry analysis assigns these two new solutions to the $B_{1g}$ and $E_u$ irreducible representations of the D$_{4h}$ point group. While the $B_{1g}$ Ising nematicity has been widely discussed in the context of vestigial stripe antiferromagnetic order, the two-component $E_u$ vector nematicity is beyond previous theoretical discussion. Distinct from the $B_{1g}$ order, the $E_u$ order features mixing of the Fe $d$-orbitals and inversion symmetry breaking, which lead to striking experimental consequences, e.g. missing of an electron pocket.

cond-mat.str-el↗

arXiv2020

Renormalization of the Mott gap by lattice entropy: The case of 1T-TaS2

In many transition-metal oxides and dichalcogenides, the electronic and lattice degrees of freedom are strongly coupled, giving rise to remarkable phenomena, such as metal-insulator transition (MIT) and charge-density wave (CDW) order. We study this interplay by tracing the instant electronic structure under ab initio molecular dynamics. Applying this method to a 1T-TaS2 layer, we show that the CDW-triggered Mott gap undergoes a continuous reduction as the lattice temperature raises, despite a nearly constant CDW amplitude. Before the CDW order undergoes a sharp first-order transition around the room temperature, the dynamical CDW fluctuation already shrinks the Mott gap size by half. The gap size reduction is one order of magnitude larger than the lattice temperature variation. Our calculation not only provides an important clue to understand the thermodynamics behavior in 1T-TaS2, but also demonstrates a general approach to quantify the lattice entropy effect in MIT.

cond-mat.mes-hall↗

arXiv2020

Giant renormalization of correlation strength in 1T-TaS2 by lattice vibration

The lattice thermodynamics of a 1T-TaS2 layer, e.g. the spontaneous formation of a sqrt13*sqrt13 commensurate charge density wave (CCDW) and vibrations around the equilibrium position, is calculated by ab initio molecular dynamics. Based on that, we examine how the ground-state electronic structure is renormalized by lattice temperature. We show that the band gap within the density functional theory plus onsite-U correction shrinks by half when the temperature raises from 0 K to 200 K. The gap size reduction is one order of magnitude larger than the temperature variation in energy. This giant temperature dependence is closely related to the CCDW-triggered Mottness in 1T-TaS2, and is expected to result in unconventional thermodynamic properties.

cond-mat.str-el↗

arXiv2019

Phase-controllable growth of ultrathin 2D magnetic FeTe crystals

Two-dimensional (2D) magnets with intrinsic ferromagnetic/antiferromagnetic (FM/AFM) ordering are highly desirable for future spintronics devices. However, the synthesis of 2D magnetic crystals, especially the direct growth on SiO2/Si substrate, is just in its infancy. Here, we report a chemical vapor deposition (CVD)-based rational growth approach for the synthesis of ultrathin FeTe crystals with controlled structural and magnetic phases. By precisely optimizing the growth temperature (Tgrowth), FeTe nanoplates with either layered tetragonal or non-layered hexagonal phase can be controlled with high-quality. The two controllable phases lead to square and triangular morphologies with a thickness down to 3.6 and 2.8 nm, respectively. More importantly, transport measurements reveal that tetragonal FeTe is antiferromagnetic with a Neel temperature (TN) about 71.8 K, while hexagonal FeTe is ferromagnetic with a Curie temperature (TC) around 220 K. Theoretical calculations indicate that the ferromagnetic order in hexagonal FeTe is originated from a concomitant lattice distortion and the spin-lattice coupling. This study represents a major step forward in the CVD growth of 2D magnetic materials on SiO2/Si substrates and highlights on their potential applications in the future spintronic devices.

cond-mat.mtrl-sci↗

arXiv2019

Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. This paper proposes an end-to-end generative method for image dehazing. It is based on designing a fully convolutional neural network to recognize haze structures in input images and restore clear, haze-free images. The proposed method is agnostic in the sense that it does not explore the atmosphere scattering model. Somewhat surprisingly, it achieves superior performance relative to all existing state-of-the-art methods for image dehazing even on SOTS outdoor images, which are synthesized using the atmosphere scattering model. Project detail and code can be found here: https://github.com/Seanforfun/GMAN_Net_Haze_Removal

cs.CV↗