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Zhiwei Li

Publications and source records attributed to Zhiwei Li.

At least 91 records · Page 5Linked to original sources
arXiv2022

Atomic-scale imaging of emergent order at a magnetic-field-induced Lifshitz transition

The phenomenology and radical changes seen in materials properties traversing a quantum phase transition has captivated condensed matter research over past decades. Strong electronic correlations lead to novel electronic ground states, including magnetic order, nematicity and unconventional superconductivity. Providing a microscopic model for these requires detailed knowledge of the electronic structure in the vicinity of the Fermi energy, promising a complete understanding of the physics of the quantum critical point. Here, we demonstrate such a measurement at the surface of Sr$_3$Ru$_2$O$_7$. Our results show that, even in zero field, the electronic structure is strongly $C_2$ symmetric and that a magnetic-field drives both a Lifshitz transition and induces a charge-stripe order. We track the changes of the electronic structure as a function of field via quasi-particle interference imaging at ultralow temperatures. Our results provide a complete microscopic picture of the field-induced changes of the electronic structure across the Lifshitz transition.

cond-mat.str-el↗
arXiv2022

CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion

3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However, camera-based methods suffer in the case of occlusions and it can be challenging to accurately track the irregular motion of objects for LiDAR-based methods. Some fusion methods work well but do not consider the untrustworthy issue of appearance features under occlusion. At the same time, the false detection problem also significantly affects tracking. As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection. For occlusion problems, we are the first to propose an occlusion head to select the best object appearance features multiple times effectively, reducing the influence of occlusions. To decrease the impact of false detection in tracking, we design a motion cost matrix based on confidence scores which improve the positioning and object prediction accuracy in 3D space. As existing multi-object tracking methods only consider a single category, we also propose to build a multi-category loss to implement multi-object tracking in multi-category scenes. A series of validation experiments are conducted on the KITTI and nuScenes tracking benchmarks. Our proposed method achieves state-of-the-art performance and the lowest identity switches (IDS) value (23 for Car and 137 for Pedestrian) among all multi-modal MOT methods on the KITTI test dataset. And our proposed method achieves state-of-the-art performance among all algorithms on the nuScenes test dataset with 75.3% AMOTA.

cs.CV↗
arXiv2022

Nuclear phase retrieval spectroscopy using resonant x-ray scattering

Light-matter interaction is exploited in spectroscopic techniques to access information about molecular, atomic or nuclear constituents of the sample of interest. While scattered light carries both amplitude and phase information of the electromagnetic field, most of the time the latter is lost in intensity measurements. However, often the phase information is paramount to reconstruct the desired information of the target, as it is well known from coherent x-ray imaging. Here we introduce a new phase retrieval algorithm which allows us to reconstruct the field phase information from two-dimensional time- and energy-resolved spectra. We apply this method to the particular case of x-ray scattering off Mössbauer nuclei at a synchrotron radiation source. Knowledge of the phase allows also for an excellent reconstruction of the energy spectra from experimental data, which could not be achieved with this resolution otherwise. Our approach provides an efficient novel data analysis tool which will benefit x-ray quantum optics and Mössbauer spectroscopy with synchrotron radiation alike.

physics.ins-det↗
arXiv2022

A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility

Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for material property prediction. As a representative example, polymer compatibility is chosen to demonstrate the effectiveness of our method. Specifically, we mine data from related literature to build a specific database and give a prediction based on the basic molecular structures of blending polymers and, as auxiliary, the blending composition. Our model obtains at least 75% accuracy on the dataset consisting of thousands of entries. We demonstrate that the relationship between structure and properties can be learned and simulated by machine learning method.

cs.LG↗
arXiv2021

Coexistence of ferromagnetism, antiferromagnetism, and superconductivity in magnetically anisotropic (Eu,La)FeAs2

Materials with exceptional magnetism and superconductivity usually conceive emergent physical phenomena. Here, we investigate the physical properties of the (Eu,La)FeAs2 system with double magnetic sublattices. The parent EuFeAs2 shows anisotropy-associated magnetic behaviors, such as Eu-related moment canting and exchange bias. Through La doping, the magnetic anisotropy is enhanced with ferromagnetism of Eu2+ realized in the overdoped region, and a special exchange bias of the superposed ferromagnetic/superconducting loop revealed in Eu0.8La0.2FeAs2. Meanwhile, the Fe-related antiferromagnetism shows unusual robustness against La doping. Theoretical calculation and 57Fe Mössbauer spectroscopy investigation reveal a doping-tunable dual itinerant/localized nature of the Fe-related antiferromagnetism. Coexistence of the Eu-related ferromagnetism, Fe-related robust antiferromagnetism, and superconductivity is further revealed in Eu0.8La0.2FeAs2, providing a platform for further exploration of potential applications and emergent physics. Finally, an electronic phase diagram is established for (Eu,La)FeAs2 with the whole superconducting dome adjacent to the Fe-related antiferromagnetic phase, which is of benefit for seeking underlying clues to high-temperature superconductivity.

cond-mat.supr-con↗
arXiv2021

IPS300+: a Challenging Multimodal Dataset for Intersection Perception System

Due to the high complexity and occlusion, insufficient perception in the crowded urban intersection can be a serious safety risk for both human drivers and autonomous algorithms, whereas CVIS (Cooperative Vehicle Infrastructure System) is a proposed solution for full-participants perception in this scenario. However, the research on roadside multimodal perception is still in its infancy, and there is no open-source dataset for such scenario. Accordingly, this paper fills the gap. Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multimodal dataset for the intersection perception task. The center of the experimental intersection covers an area of 3000m2, and the extended distance reaches 300m, which is typical for CVIS. The first batch of open-source data includes 14198 frames, and each frame has an average of 319.84 labels, which is 9.6 times larger than the most crowded dataset (H3D dataset in 2019) by now. In order to facilitate further study, this dataset tries to keep the label documents consistent with the KITTI dataset, and a standardized benchmark is created for algorithm evaluation. Our dataset is available at: http://www.openmpd.com/column/other_datasets.

cs.CV↗
arXiv2021

A novel multimodal fusion network based on a joint coding model for lane line segmentation

There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practical utility using Light Detection and Ranging (LiDAR) camera fusion networks. In particular, we develop, for the first time, a multimodal fusion network as a joint coding model, where each single node, layer, and pipeline is represented as a channel. The forward propagation is thus equal to the information transmission in the channels. Then, we can qualitatively and quantitatively analyze the effect of different fusion approaches. We argue the optimal fusion architecture is related to the essential capacity and its allocation based on the source and channel. To test this multimodal fusion hypothesis, we progressively determine a series of multimodal models based on the proposed fusion methods and evaluate them on the KITTI and the A2D2 datasets. Our optimal fusion network achieves 85%+ lane line accuracy and 98.7%+ overall. The performance gap among the models will inform continuing future research into development of optimal fusion algorithms for the deep multimodal learning community.

cs.CV↗
arXiv2021

The Brundan-Kleshchev isomorphism revisited

We give a short and unified proof of the Brundan-Kleshchev isomorphism between blocks of cyclotomic Hecke algebras and cyclotomic KhovanovLauda-Rouquier algebras of type A.

math.RT↗
arXiv2021

High Precision Medicine Bottles Vision Online Inspection System and Classification Based on Multi-Features and Ensemble Learning via Independence Test

To address the problem of online automatic inspection of drug liquid bottles in production line, an implantable visual inspection system is designed and the ensemble learning algorithm for detection is proposed based on multi-features fusion. A tunnel structure is designed for visual inspection system, which allows bottles inspection to be automated without changing original

cs.CV↗
arXiv2020

A Cascade Electron Source Based on Series Horizontal Tunneling Junctions

On-chip electron sources have wide potential applications in miniature vacuum electronic devices and emission efficiency is one of their performance benchmarks. A cascade electron source based on series metal-insulator-metal horizontal tunneling junctions is proposed, where free electrons are additively extracted from each tunneling junction. A cascade electron source with n horizontal tunneling junctions shows a theoretical emission efficiency of approximately η(n)=1-(1-η_0 )^n, with η_0 being the efficiency of a single tunneling junction. Experimentally, a cascade electron source with three Si-SiOx-Si tunneling junctions is demonstrated, achieving an emission efficiency as high as 47.6%. This work provides a new way of realizing highly efficient on-chip electron sources.

physics.app-ph↗
arXiv2020

Interlocking nodal chains and their examples in carbon networks

Nodal chain is a typical topological phase in nodal line semimetals. Here, we propose a new topological phase -- interlocking nodal chains, in which two sets of nodal chains are interlocked each other. It includes one- (1D), two- (2D) and three-dimensional (3D) versions, which can be produced by a three-band model. The 2D and 3D interlocking nodal chains will evolve into some other phases, such as double concentric isolated (or intersecting) nodal rings, coexisting nodal chain and isolated (or intersecting) nodal rings. These phases exhibit diverse surface states and Landau levels, which implies that there are rich electronic and magnetic properties associating with them. Moreover, the 2D interlocking nodal chains and related phase transitions can be realized in a series of carbon networks under strain. Without strain, topological phase in the carbon structures is double concentric isolated nodal rings. A larger tensile strain leads to the phase transiting to an interlocking nodal chain, while the middle phase is a coexisting phase of a nodal chain and isolated nodal rings. In addition, stability and synthesis of the carbon networks are discussed.

cond-mat.mtrl-sci↗
arXiv2020

Cosmic muon flux measurement and tunnel overburden structure imaging

We present a cosmic ray muon tomographic experiment for measuring the muon flux and imaging the tunnel overburden structures in Changshu, China. The device used in this study is a tracking detector based on the plastic scintillator with SiPM technology, which can be conveniently operated in field works. The compact system with sensitive area of $6400 cm^2$ can measure the angular distribution of cosmic muons. It's able to image the overburden density length from the surface of overburden to the detector along the muon tracks. The open sky muon flux measurement outside the tunnel has a good agreement with the modified Gassier Formula model. The distributions of muon flux at three positions inside the tunnel are very similar to that of open sky. Assuming the average density of overburden compact sandstone is $2.65 g/cm^3$, the overburden thickness can be obtained from the density length derived from the difference of muon flux inside and outside the tunnel. Moreover, for known penetrated lengths (i.e., topography of overburden), the density anomalies of the overburden can also been obtained. This study suggests a potential application for imaging and detecting subsurface structures in civil engineering, tunnels or caverns with the cosmic ray muon telescope.

physics.ins-det↗
arXiv2020

JULOC: A Local 3-D Refined Crust Model for the Geoneutrino Measurement at JUNO

Geothermal energy is the key to drive the plate tectonics and interior thermodynamics of the Earth. The surface heat flux, as measured in boreholes, provide limited insights into the relative contributions of primordial versus radiogenic sources of the heat budget of the mantle. Geoneutrinos, electron antineutrinos that produced from the radioactive decay of the heat producing elements, are unique probes that bring direct information about the amount and distribution of heat producing elements in the crust and mantle. Cosmochemical, geochemical, and geodynamic compositional models of the Bulk Silicate Earth (BSE) individually predicts different mantle neutrino fluxes, and therefore can be distinguished by the direct measurement of geoneutrinos. The 20 kton detector of the Jiangmen Underground Neutrino Observatory (JUNO), currently under construction in the Guangdong Province (China), is expected to provide an exciting opportunity to obtain a high statistics measurement, which will produce sufficient data to address several key questions of geological importance. To test different compositional models of the mantle, an accurate estimation of the crust geoneutrino flux based on a three-dimensional (3-D) crust model in advance is important. This paper presents a 3-D crust model over a surface area of 10-degrees-times-10-degrees grid surrounding the JUNO detector and a depth down to the Moho discontinuity, based on the geological, geophysical and geochemistry properties. The 3-D model provides a distinction of the volumes of the different geological layers together with the corresponding Th and U abundances. We also present our predicted local contribution to the total geoneutrino flux and the corresponding radiogenic heat.

physics.geo-ph↗
arXiv2019

Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and their estimated ones. However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained. In this paper, we propose to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities. In addition, variances of the unimodal distributions for each pixel are estimated to explicitly model matching uncertainty under different contexts. The proposed architecture achieves state-of-the-art performance on Scene Flow and two KITTI stereo benchmarks. In particular, our method ranked the $1^{st}$ place of KITTI 2012 evaluation and the $4^{th}$ place of KITTI 2015 evaluation (recorded on 2019.8.20). The codes of AcfNet are available at: https://github.com/DeepMotionAIResearch/DenseMatchingBenchmark.

cs.CV↗
arXiv2019

Robust diffusion parametric mapping of motion-corrupted data with a three-dimensional convolutional neural network

Head motion is inevitable in the acquisition of diffusion-weighted images, especially for certain motion-prone subjects and for data gathering of advanced diffusion models with prolonged scan times. Deficient accuracy of motion correction cause deterioration in the quality of diffusion model reconstruction, thus affecting the derived measures. This results in either loss of data, or introducing bias in outcomes from data of different motion levels, or both. Hence minimizing motion effects and reutilizing motion-contaminated data becomes vital to quantitative studies. We have previously developed a 3-dimensional hierarchical convolution neural network (3D H-CNN) for robust diffusion kurtosis mapping from under-sampled data. In this study, we propose to extend this method to motion-contaminated data for robust recovery of diffusion model-derived measures with a process of motion assessment and corrupted volume rejection. We validate the proposed pipeline in two in-vivo datasets. Results from the first dataset of individual subjects show that all the diffusion tensor and kurtosis tensor-derived measures from the new pipeline are minimally sensitive to motion effects, and are comparable to the motion-free reference with as few as eight volumes retained from the motion-contaminated data. Results from the second dataset of a group of children with attention deficit hyperactivity disorder demonstrate the ability of our approach in ameliorating spurious group differences due to head motion. This method shows great potential for exploiting some valuable but motion-corrupted DWI data which are likely to be discarded otherwise, and applying to data with different motion level thus improving their utilization and statistic power.

physics.med-ph↗
arXiv2019

Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors

Cloud detection is an important preprocessing step for the precise application of optical satellite imagery. In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors. In the network architecture of MSCFF, the symmetric encoder-decoder module, which provides both local and global context by densifying feature maps with trainable convolutional filter banks, is utilized to extract multi-scale and high-level spatial features. The feature maps of multiple scales are then up-sampled and concatenated, and a novel multi-scale feature fusion module is designed to fuse the features of different scales for the output. The two output feature maps of the network are cloud and cloud shadow maps, which are in turn fed to binary classifiers outside the model to obtain the final cloud and cloud shadow mask. The MSCFF method was validated on hundreds of globally distributed optical satellite images, with spatial resolutions ranging from 0.5 to 50 m, including Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and collected high-resolution images exported from Google Earth. The experimental results show that MSCFF achieves a higher accuracy than the traditional rule-based cloud detection methods and the state-of-the-art deep learning models, especially in bright surface covered areas. The effectiveness of MSCFF means that it has great promise for the practical application of cloud detection for multiple types of medium and high-resolution remote sensing images. Our established global high-resolution cloud detection validation dataset has been made available online.

cs.CV↗
arXiv2018

Study of the rare earth effects on the magnetic fluctuations in RbLn$_2$Fe$_4$As$_4$O$_2$ (Ln = Tb, Dy and Ho) by Mössbauer spectroscopy

In the current work, we report the investigation of RbLn$_2$Fe$_4$As$_4$O$_2$ (Ln = Tb, Dy and Ho) and RbFe$_2$As$_2$ by $^{57}$Fe Mössbauer spectroscopy. Singlet pattern has been observed for all the samples indicating the absence of static magnetic ordering down to 5.9\,K on the Fe sublattice. The observed intermediate value of the isomer shift confirms the effective self charge transfer effect for the studied superconductors. Debye temperatures of these samples have been determined by the temperature dependence of the isomer shift. Most importantly, we observe different spectral line width broadening behaviors for samples with different rare earth elements, and no line width broadenings for samples Ln=Tb and Ho. These results suggest that the observed magnetic fluctuations at the Fe site might be sensitive to the magnetic behaviors of the rare earth magnetic moments.

cond-mat.str-el↗