arXiv subjects

Bingbing Liu

Publications and source records attributed to Bingbing Liu.

At least 73 records · Page 4Linked to original sources

arXiv2022

Pressure-induced mixed states caused by spin-elastic interactions during first-order spin phase transition in spin crossover compounds

Recently, the possibility of exploiting the phenomenon of spin transition (ST) has been intensively investigated, therefore, it is particularly important to study the behavior of ST under various stimuli. Here, the shape and content of the intermediate phase of ST in Hoffmann-like compounds [Fe(Fpz)2M(CN)4](M = Pt, Pd) under external stimuli are studied. For this purpose, magnetic and Raman spectroscopy measurements were carried out. In pressure-induced spin transition (PIST), a mixture of high-spin and low-spin states appears, while in temperature-induced spin transition (TIST), a homogeneous state occurs. The first-order ST induced by pressure has a hysteresis, but is not abrupt. Whereas, the temperature-induced spin transition at ambient pressure is hysteretic and abrupt. To investigate this difference, we discuss using a thermodynamic model that considers elastic interactions, showing that the slope of the hysteresis loop is related to the appearance of internal pressure, which is related to the difference in sample compressibility under high spin and low spin states.

cond-mat.mtrl-sci↗

arXiv2022

Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters

In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance of an existing segmentation model when training (source domain) and testing (target domain) data originate from different LiDAR sensors. To overcome this shortcoming, we propose an unsupervised domain adaptation framework that leverages unlabeled target domain data for self-supervision, coupled with an unpaired mask transfer strategy to mitigate the impact of domain shifts. Furthermore, we introduce the gated adapter module with a small number of parameters into the network to account for target domain-specific information. Experiments adapting from both real-to-real and synthetic-to-real LiDAR semantic segmentation benchmarks demonstrate the significant improvement over prior arts.

cs.CV↗

arXiv2022

A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation

3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation information to further guide the detection process. In this paper, we propose a novel multi-task framework that jointly performs 3D object detection and panoptic segmentation. In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone. This enables the detection backbone to leverage multi-view information to address the shortcomings of each projection view. Furthermore, foreground semantic information is incorporated to ease the detection task by highlighting the locations of each object class in the feature maps. Finally, a new center density heatmap generated based on the instance-level information further guides the detection backbone by suggesting possible box center locations for objects. Our method works with any BEV-based 3D object detection method, and as shown by extensive experiments on the nuScenes dataset, it provides significant performance gains. Notably, the proposed method based on a single-stage CenterPoint 3D object detection network achieved state-of-the-art performance on nuScenes 3D Detection Benchmark with 67.3 NDS.

cs.CV↗

arXiv2021

Effects of the initial perturbations on the Rayleigh-Taylor-Kelvin-Helmholtz instability system

In the paper, the effects of initial perturbations on the Rayleigh-Taylor instability (RTI), Kelvin-Helmholtz instability (KHI), and the coupled Rayleigh-Taylor-Kelvin-Helmholtz instability (RTKHI) systems are investigated using a multiple-relaxation-time discrete Boltzmann model. Six different perturbation interfaces are designed to study the effects of the initial perturbations on the instability systems. Based on the mean heat flux strength $D_{3,1}$, the effects of initial interfaces on the coupled RTKHI are examined in detail. The research is focused on two aspects: (i) the main mechanism in the early stage of the RTKHI, (ii) the transition point from KHI-like to RTI-like for the case where the KHI dominates at earlier time and the RTI dominates at later time. It is found that the early main mechanism is related to the shape of the initial interface, which is represented by both the bilateral contact angle $θ_{1}$ and the middle contact angle $θ_{2}$. The influence of inverted parabolic and inverted ellipse perturbations ($θ_{1}<90$) on the transition point of the RTKHI system is greater than that of other interfaces.

physics.flu-dyn↗

arXiv2021

Novel Multiferroic Nanocomposite with High Pressure-Modulated Magnetoelectric Coupling

In this work, we have designed obtained a novel multiferroic nanocomposite using the high-pressure torsion (HPT) method. The crystal structure, phase composition, morphology, ferromagnetic (FM) and ferroelectric (FE) properties of the initial powders and "multiferroic-ferromagnetic" nanocomposites have been studied comprehensively. The initial powders and their composites show the perovskite and spinel crystalline phases for the FE and FM fractions, respectively. After HPT, the particle sizes of the initial powders are decreased significantly. It is shown that the novel nanocomposite consists of exchange-interacting FE and FM phases and demonstrates improved magnetic and electrical properties in low fields at room temperature. A colossal increase in residual polarization with an increase in external high-pressure is found in new composite. The obtained results make it possible to consider the novel nanocomposite as a new functional material for its use both in electronic devices for monitoring ultra-high-pressure and in integrated circuits of high-speed computing nanosystems with low switching energy. The HPT method is a promising method for obtaining new heterophase multiferroic nanosystems.

cond-mat.mtrl-sci↗

arXiv2021

How to Build a Curb Dataset with LiDAR Data for Autonomous Driving

Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand...

cs.CV↗

arXiv2021

Superconductivity in Layered van der Waals Hydrogenated Germanene at High Pressure

Structural and superconducting transitions of layered van der Waals (vdW) hydrogenated germanene (GeH) were observed under high-pressure compression and decompression processes. GeH possesses a superconducting transition at critical temperature (Tc) of 5.41 K at 8.39 GPa. A crystalline to amorphous transition occurs at 16.80 GPa while superconductivity remains. An abnormally increased Tc up to 6.1 K has been observed in the decompression process while the GeH remained amorphous. Thorough in-situ high-pressure synchrotron X-ray diffraction and in-situ high-pressure Raman spectroscopy with the density functional theory simulations suggest that the superconductivity of GeH should be attributed to the increased density of states at the Fermi level as well as the enhanced electron-phonon coupling effect under high pressure. The decompression-driven superconductivity enhancement arises from pressure-induced phonon softening related to an in-plane Ge-Ge phonon mode. As an amorphous metal hydride superconductor, GeH provides a platform to study amorphous hydride superconductivity in layered vdW materials.

cond-mat.supr-con↗

arXiv2021

Bidirectional Attention Network for Monocular Depth Estimation

In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks. The structure of this mechanism derives from a strong conceptual foundation of neural machine translation, and presents a light-weight mechanism for adaptive control of computation similar to the dynamic nature of recurrent neural networks. We introduce bidirectional attention modules that utilize the feed-forward feature maps and incorporate the global context to filter out ambiguity. Extensive experiments reveal the high degree of capability of this bidirectional attention model over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets -- KITTI and DIODE. We show that our proposed approach either outperforms or performs at least on a par with the state-of-the-art monocular depth estimation methods with less memory and computational complexity.

cs.CV↗

arXiv2021

(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network

Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one the essential components of environmental perception that provides semantic information of the scene. Recently, several methods have been introduced for 3D LiDAR semantic segmentation. While, they can lead to improved performance, they are either afflicted by high computational complexity, therefore are inefficient, or lack fine details of smaller instances. To alleviate this problem, we propose AF2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic segmentation. We present a novel multi-branch attentive feature fusion module in the encoder and a unique adaptive feature selection module with feature map re-weighting in the decoder. Our AF2-S3Net fuses the voxel based learning and point-based learning into a single framework to effectively process the large 3D scene. Our experimental results show that the proposed method outperforms the state-of-the-art approaches on the large-scale SemanticKITTI benchmark, ranking 1st on the competitive public leaderboard competition upon publication.

cs.CV↗

arXiv2020

TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module

Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving. In this paper, we introduce TORNADO-Net - a neural network for 3D LiDAR point cloud semantic segmentation. We incorporate a multi-view (bird-eye and range) projection feature extraction with an encoder-decoder ResNet architecture with a novel diamond context block. Current projection-based methods do not take into account that neighboring points usually belong to the same class. To better utilize this local neighbourhood information and reduce noisy predictions, we introduce a combination of Total Variation, Lovasz-Softmax, and Weighted Cross-Entropy losses. We also take advantage of the fact that the LiDAR data encompasses 360 degrees field of view and uses circular padding. We demonstrate state-of-the-art results on the SemanticKITTI dataset and also provide thorough quantitative evaluations and ablation results.

cs.CV↗

arXiv2020

Adaptive Hierarchical Down-Sampling for Point Cloud Classification

While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network has not been rigorously studied. Existing methods down-sample the points regardless of their importance for the output. As a result, some important points in the point cloud may be removed, while less valuable points may be passed to the next layers. In contrast, adaptive down-sampling methods sample the points by taking into account the importance of each point, which varies based on the application, task and training data. In this paper, we propose a permutation-invariant learning-based adaptive down-sampling layer, called Critical Points Layer (CPL), which reduces the number of points in an unordered point cloud while retaining the important points. Unlike most graph-based point cloud down-sampling methods that use $k$-NN search algorithm to find the neighbouring points, CPL is a global down-sampling method, rendering it computationally very efficient. The proposed layer can be used along with any graph-based point cloud convolution layer to form a convolutional neural network, dubbed CP-Net in this paper. We introduce a CP-Net for $3$D object classification that achieves the best accuracy for the ModelNet$40$ dataset among point cloud-based methods, which validates the effectiveness of the CPL.

cs.CV↗

arXiv2019

Superconducting Praseodymium Superhydrides

Superhydrides have complex hydrogenic sublattices and are important prototypes for studying metallic hydrogen and high-temperature superconductors. Encouraged by the results on LaH10, in consideration of the differences between La and Pr, Pr-H system is especially worth studying because of the magnetism and valence-band f-electrons in element Pr. Here we successfully synthesized praseodymium superhydrides (PrH9) in laser-heated diamond anvil cells. Synchrotron X-ray diffraction (XRD) analysis demonstrated the presence of previously predicted F43m-PrH9 and unexpected P63/mmc-PrH9 phases. Moreover, Fm3m-PrH3, P4/nmm-PrH(3-δ) and Fm3m-PrH(1+x) were found below 52 GPa. F43m-PrH9 and P63/mmc-PrH9 were stable above 100 GPa in experiment. Experimental studies of electrical resistance in the PrH9 sample showed the emergence of superconducting transition (Tc) below 9K and a dependent Tc on applied magnetic field. Theoretical calculations indicate that magnetic order and electron-phonon interaction coexist in a very close range of pressures in the PrH9 sample which may contribute to its low superconducting temperature Tc. Our results highlight the intimate connections among hydrogenic sublattices, density of states, magnetism and superconductivity in Pr-based superhydrides.

cond-mat.mtrl-sci↗

arXiv2019

RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving

In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving. Without much computational overhead, we propose a supervised and an unsupervised sparsification scheme of PseudoLiDAR prior to 3D detection. Both the strategies assist the standard 3D detector gain better performance over the raw PseudoLiDAR baseline using only ~5% of its points on the KITTI object detection benchmark, thus making our monocular framework and LiDAR-based counterparts computationally equivalent (Figure 1). Moreover, our architecture agnostic refinements provide state-of-the-art results on KITTI3D test set for "Car" and "Pedestrian" categories with 54% relative improvement for "Pedestrian". Finally, exploratory analysis is performed on the discrepancy between monocular and LiDAR-based 3D detection frameworks to guide future endeavours.

cs.CV↗

arXiv2019

BoxNet: A Deep Learning Method for 2D Bounding Box Estimation from Bird's-Eye View Point Cloud

We present a learning-based method to estimate the object bounding box from its 2D bird's-eye view (BEV) LiDAR points. Our method, entitled BoxNet, exploits a simple deep neural network that can efficiently handle unordered points. The method takes as input the 2D coordinates of all the points and the output is a vector consisting of both the box pose (position and orientation in LiDAR coordinate system) and its size (width and length). In order to deal with the angle discontinuity problem, we propose to estimate the double-angle sinusoidal values rather than the angle itself. We also predict the center relative to the point cloud mean to boost the performance of estimating the location of the box. The proposed method does not rely on the ordering of points as in many existing approaches, and can accurately predict the actual size of the bounding box based on the prior information that is obtained from the training data. BoxNet is validated using the KITTI 3D object dataset, with significant improvement compared with the state-of-the-art non-learning based methods

cs.CV↗

arXiv2016

Direct Meissner Effect Observation of Superconductivity in Compressed H2S

Recently, an extremely high superconducting temperature (Tc) of ~200 K has been reported in the sulfur hydride system above 100 GPa. This result is supported by theoretical predictions and verified experimentally. The crystal structure of the superconducting phase was also identified experimentally, confirming the theoretically predicted structure as well as a decomposition mechanism from H2S to H3S+S. Even though nuclear resonant scattering has been successfully used to provide magnetic evidence for a superconducting state, a direct measurement of the important Meissner effect is still lacking. Here we report in situ alternating-current magnetic susceptibility measurements on compressed H2S under high pressures. It is shown that superconductivity suddenly appears at 117 GPa and that Tc reaches 183 K at 149 GPa before decreasing monotonically with a further increase in pressure. This evolution agrees with both theoretical calculations and earlier experimental measurements. The idea of conventional high temperature superconductivity in hydrogen-dominant compounds has thus been realized in the sulfur hydride system under hydrostatic pressure, opening further exciting perspectives for possibly realizing room temperature superconductivity in hydrogen-based compounds.

cond-mat.supr-con↗

arXiv2016

Crossover from Metal to Insulator in Dense Lithium-Rich Compound $\textrm{CLi}_{4}$

Crystal structures of $\textrm{CLi}_4$ compounds are explored through \emph{ab} \emph{initio} evolutionary methodology. Phase transition from metal to semimetal and semiconductor, and eventually to insulator with increasing pressure are revealed under pressure. Pressure-induced evolution of anti-metallization has been described quantitatively by Fermi Surface Filling Ratio and electron energy band gap using \emph{ab} \emph{initio} \emph{GW} calculations. Anti-metallization is attributed to the hybrid valence electrons and their repulsion by core electrons into the lattice interstices. Very weak electron-phonon coupling interactions are found in the metallic phases, resulting in very low superconducting temperature.

cond-mat.mtrl-sci↗

arXiv2015

High-pressure structures and superconductivity of bismuth hydrides

We have systematically searched for the ground state structures of bismuth hydrides based on evolutionary algorithm method and particle swarm optimization algorithm method. Given only rich-hydrogen region, except for BiH$_{3}$, other hydrides (BiH, BiH$_{2}$, BiH$_{4}$, BiH$_{5}$, BiH$_{6}$) have been predicted to be stable with pressurization. With the increase of hydrogen content, hydrogen exists in bismuth hydrides with the different forms and presents the characteristics of ionicity. Under high pressure, the remarkable structural feature is the emergence of H$_{2}$ units in BiH$_{2}$, BiH$_{4}$ and BiH$_{6}$, and BiH$_{6}$ adopts a startling layered structure intercalated by H$_{2}$ and the linear H$_{3}$ units. Further calculations show these energetically stable hydrides are good metal and their metallic pressures are lower than that of pure solid hydrogen because of the doping impurities. The $T_{c}$ in the range of 20-119 K has been calculated by the Allen-Dynes modified McMillan equation, which indicates all these stable hydrides are potential high-temperature superconductors. Remarkably, it is the H-Bi-H and Bi atoms vibrations rather than the high-frequency H$_{2}$ or H$_{3}$ units that dominate the superconductivity. In addition, hydrogen content has a great influence on the superconducting transition temperature.

cond-mat.supr-con↗

arXiv2015

Pressure-induced reversible phase transition and amorphization of CH$_3$NH$_3$PbI$_3$

Recent advances of highly efficient solar cells based on organic-inorganic halide perovskites have triggered intense research efforts to establish the fundamental properties of these materials. In this work, we utilized diamond anvil cell to investigate the pressure-induced structural and electronic transformations in methylammonium lead iodide (CH$_3$NH$_3$PbI$_3$) up to 7 GPa at room temperature. The synchrotron X-ray diffraction experiment show that the sample transformed from tetragonal to orthorhombic phase at 0.3 GPa and amorphized above 4 GPa. Further high pressure IR spectroscopy experiments illustrated the high pressure behavior of organic (CH$_3$NH$_3$)$^+$ cations. We also analyzed the pressure dependence of the band gap energy based on the optical absorption and photoluminescence (PL) results. Moreover, all the observed changes were fully reversible when the pressure was completely released. Our in situ high pressure studies provide essential information for the intrinsic properties and stability of organic-inorganic halide perovskites, which significantly affect the performance of perovskite solar cells.

cond-mat.mtrl-sci↗