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Accurate multi-view 3D object detection is essential for applications such as autonomous driving. Researchers have consistently aimed to leverage LiDAR's precise spatial information to enhance camera-based detectors through methods like depth supervision and bird-eye-view (BEV) feature distillation. However, existing approaches often face challenges due to the inherent differences between LiDAR and camera data representations. In this paper, we introduce the TiGDistill-BEV, a novel approach that effectively bridges this gap by leveraging the strengths of both sensors. Our method distills knowledge from diverse modalities(e.g., LiDAR) as the teacher model to a camera-based student detector, utilizing the Target Inner-Geometry learning scheme to enhance camera-based BEV detectors through both depth and BEV features by leveraging diverse modalities. Specially, we propose two key modules: an inner-depth supervision module to learn the low-level relative depth relations within objects which equips detectors with a deeper understanding of object-level spatial structures, and an inner-feature BEV distillation module to transfer high-level semantics of different key points within foreground targets. To further alleviate the domain gap, we incorporate both inter-channel and inter-keypoint distillation to model feature similarity. Extensive experiments on the nuScenes benchmark demonstrate that TiGDistill-BEV significantly boosts camera-based only detectors achieving a state-of-the-art with 62.8% NDS and surpassing previous methods by a significant margin. The codes is available at: https://github.com/Public-BOTs/TiGDistill-BEV.git., Comment: 13 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:2212.13979

Anisotropic magnetoresistance (AMR) is a well-known magnetoelectric coupling phenomenon, commonly exhibiting two-fold symmetry relative to the magnetic field. In this study, we reveal the existence of high-order AMRs in two-dimensional (2D) magnetic monolayers. Based on density functional theory (DFT) calculations of Fe3GeTe2 and CrTe2 monolayers, we find that different energy bands contribute uniquely to AMR behavior. The high-order AMR is attributed to strong spin mixing at band crossing points, which induces significant Berry curvature. This curvature also contributes to the AMR for electrons with dominant spin-up or spin-down polarization characteristics. However, for electrons exhibiting strong spin mixing, the Berry curvature effect becomes nontrivial, resulting in high-order AMR. Our findings provide an effective approach to identifying and optimizing materials with high-order AMR, which is critical for designing high-performance spintronic devices., Comment: 6 figures

Two-dimensional (2D) van der Waals (vdW) magnets present a promising platform for spintronic applications due to their unique structural and electronic properties. The ability to electrostatically control their interlayer magnetic coupling between ferromagnetic and antiferromagnetic phases is particularly advantageous for the development of energy-efficient spintronic components. While effective in bilayer CrI3, achieving this control in other 2D magnets remains a challenge. In this work, we demonstrate that bilayer Cr2Ge2Te6 can achieve efficient electrostatic control through interlayer hopping modulation. We show that an external electric field can effectively manipulate the FM-AFM phase transition when interlayer hopping is enhanced by pressure or sliding. We further develop a four-site interlayer hopping model, revealing that the phase transition is driven by a combined effect of on-site energy splitting and interlayer electronic hopping. These findings pave the way for designing novel, electrically tunable spintronic devices, offering substantial potential for energy-efficient information processing and storage., Comment: 4 figures

Recent experiments have demonstrated efficient spin transfer across layers in the van der Waals heterostructure composed of WTe2 and Fe3GeTe2, signaling a potential breakthrough in developing all-van der Waals spin-orbit torque devices. However, the reasons behind the unusually high interlayer spin transparency observed, despite the weak van der Waals interactions between layers, remain unclear. In this study, we employ density functional theory and the non-equilibrium Green's function method to explore this phenomenon. We find that the efficient cross-layer spin transfer arises from direct hybridization of p-orbitals between tellurium atoms at the interface. This interlayer orbital hybridization lowers the electronic potential barrier and significantly modifies the spin-polarized electronic structure of Fe3GeTe2. Consequently, an effective channel for spin-polarized transport is established between WTe2 and Fe3GeTe2, leading to high interlayer spin transparency. Combining this enhanced spin transparency with the large spin Hall angle of WTe2 explains the high spin-orbit torque efficiency observed experimentally. Furthermore, we predict that applying a gate voltage can further increase this efficiency. Our findings offer a pathway for designing high-performance, all-van der Waals spin-orbit torque devices., Comment: 5 figures

Self-supervised learning has made substantial strides in image processing, while visual pre-training for autonomous driving is still in its infancy. Existing methods often focus on learning geometric scene information while neglecting texture or treating both aspects separately, hindering comprehensive scene understanding. In this context, we are excited to introduce GaussianPretrain, a novel pre-training paradigm that achieves a holistic understanding of the scene by uniformly integrating geometric and texture representations. Conceptualizing 3D Gaussian anchors as volumetric LiDAR points, our method learns a deepened understanding of scenes to enhance pre-training performance with detailed spatial structure and texture, achieving that 40.6% faster than NeRF-based method UniPAD with 70% GPU memory only. We demonstrate the effectiveness of GaussianPretrain across multiple 3D perception tasks, showing significant performance improvements, such as a 7.05% increase in NDS for 3D object detection, boosts mAP by 1.9% in HD map construction and 0.8% improvement on Occupancy prediction. These significant gains highlight GaussianPretrain's theoretical innovation and strong practical potential, promoting visual pre-training development for autonomous driving. Source code will be available at https://github.com/Public-BOTs/GaussianPretrain, Comment: 10 pages, 5 figures

Thermoelectric materials traditionally incorporate heavy metals to achieve low lattice thermal conductivity. However, elements such as Te, Bi, and Pb are costly and pose environmental hazards. In this study, we introduce a novel design strategy for thermoelectric materials, focusing on room-temperature, light-element, and high-ZT materials such as coronene-cyclobutadienoid graphene nanoribbons (cor4GNRs). This material demonstrates a ZT value exceeding 2.1, attributed to its exceptionally low phonon thermal conductivity resulting from its unique edge structure. Importantly, its electrical conductance and Seebeck coefficient remain relatively high and nearly unaffected by the edge structure. This distinct behavior in phonon and electronic transport properties leads to a remarkably high ZT value. Additionally, we discover that applying strain can significantly reduce phonon thermal conductivity, potentially increasing the ZT value to over 3.0. Our findings provide innovative insights for the design and application of advanced thermoelectric materials., Comment: 6 Figures

Anisotropic magnetoresistance (AMR) has a crucial feature for developing highly sensitive sensors and innovative memory devices. While extensively studied in bulk materials, AMR effects in these materials are typically weak. Recent advancements indicate that two-dimensional (2D) van der Waals magnetic materials possess unique magnetic properties, potentially including significant AMR characteristics. In this study, we utilize density functional theory and the Boltzmann transport equation to investigate AMR in magnetic monolayers CrPX3 (X = S, Se, Te). Our findings reveal a substantially large AMR in these 2D magnetic compounds. This enhancement is attributed to magnetization (M)-dependent spin-orbit coupling (SOC), arising from the broken symmetry between in-plane and out-of-plane orientations. This results in significant M-dependent band splitting and subsequent variations in electron velocity. Additionally, we find that the M-dependent SOC is significantly enhanced by increasing the atomic number of the chalcogen X in CrPX3, achieving an exceptional 150% AMR in CrPTe3. Furthermore, our study demonstrates that AMR can be effectively modulated by applying biaxial strain, resulting in a twofold increase with a 4% strain. These findings propose a novel approach to enhancing 2D-based AMR spintronic devices, making a substantial contribution to the field., Comment: 7 figures

The study of heat transport in two-dimensional (2D) materials reveals novel behaviors due to quantum confinement effects, where in-plane and out-of-plane phonons play crucial roles. In 2D materials like graphene, it is widely recognized that the out-of-plane vibrational mode is the primary contributor to thermal conductivity owing to the mirror symmetry. Based on this perspective, the introduction of interlayer coupling, which breaks this symmetry, is expected to induce a significant reduction in thermal conductivity within 2D materials. Nevertheless, recent studies have presented unexpected findings, indicating that interlayer coupling can actually increase thermal conductivity of 2D materials. This controversial result suggests a nontrivial underlying mechanism governing the effects of interlayer coupling on thermal conductivity in 2D materials, necessitating further exploration. In our work, we investigate the modulation of thermal conductivity through interlayer coupling in a sandwich structure composed of hexagonal boron nitride (h-BN) and bilayer graphene (BG), specifically a h- BN/BG/h-BN system. Through molecular dynamics simulations, we find that the thermal conductivity from out-of-plane phonons can be significantly reduced, while that from in-plane phonons can be significantly increased, as the interlayer coupling strength increases. This results in a nontrivial, coupling-strength-dependent overall thermal conductivity. The phonon spectrum analysis conducted using our modified package reveals that the upshift and flattening of the out-of-plane (ZA and ZO) phonon modes are mainly responsible for these variations, and the extent of the upshift and flattening is proportional to the strength of interlayer coupling. This work offers new insights into manipulating the thermal conductivity of 2D materials., Comment: 4 figures

Recently, video object segmentation (VOS) networks typically use memory-based methods: for each query frame, the mask is predicted by space-time matching to memory frames. Despite these methods having superior performance, they suffer from two issues: 1) Challenging data can destroy the space-time coherence between adjacent video frames. 2) Pixel-level matching will lead to undesired mismatching caused by the noises or distractors. To address the aforementioned issues, we first propose to generate an auxiliary frame between adjacent frames, serving as an implicit short-temporal reference for the query one. Next, we learn a prototype for each video object and prototype-level matching can be implemented between the query and memory. The experiment demonstrated that our network outperforms the state-of-the-art method on the DAVIS 2017, achieving a J&F score of 86.4%, and attains a competitive result 85.0% on YouTube VOS 2018. In addition, our network exhibits a high inference speed of 32+ FPS., Comment: Accepted by ICME 2024. 6 pages, 10 figures

Recently, two monolayer magnetic materials, i.e., FePS3 and NiPS3, have been successfully fabricated. Despite that they have the same atomic structure, the two monolayers exhibit distinct magnetic properties. FePS3 holds an out-of-plane zigzag antiferromagnetic (AFM-ZZ) structure, while NiPS3 exhibits an in-plane AFM-ZZ structure. However, there is no theoretical model which can properly describe its magnetic ground state due to the lack of a full understanding of its magnetic interactions. Here, by combining the first-principles calculations and the newly developed machine learning method, we construct an exact spin Hamiltonian of the two magnetic materials. Different from the previous studies which failed to fully consider the spin-orbit coupling effect, we find that the AFM-ZZ ground state in FePS3 is stabilized by competing ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor exchange interactions, and combining single-ion anisotropy. Whereas, the often ignored nearest-neighbor biquadratic exchange is responsible for the in-plane AFM-ZZ ground state in NiPS3. We additionally calculate spin-wave spectrum of AFM-ZZ structure in the two monolayers based on the exact spin Hamiltonian, which can be directly verified by the experimental investigation. Our work provides a theoretical framework for the origin of AFM-ZZ ground state in two-dimensional materials., Comment: 7 pages, 4 figures

First principles-based predictions of lattice thermal conductivity (TC) from perturbation theory have achieved significant success. In general, it only includes three-phonon (3ph) scattering. However, recent studies have revealed that four-phonon (4ph) scattering, treated under single-mode relaxation time approximation (RTA), has a comparable impact to 3ph scattering at medium and high temperatures in various materials, particularly in two-dimensional (2D) materials. Nonetheless, the influence of 4ph scattering on TC at low temperatures has not been explored so far, owing to the assumption that 4ph processes are generally insignificant at low temperatures. By combining the first-principles calculations, machine learning techniques, Boltzmann transport equation (BTE), and molecular dynamics (MD) simulations, we find that there are unusually strong 4ph processes in the low-frequency range of 2D materials such as h-XN (X=B, Al, Ga), which have remarkable influence on the low-temperature TC. We also find that the strong 4ph processes are originated from the flexural out-of-plane acoustic (ZA) phonon mode of 2D materials. We further discover the remarkable normal (N) processes of 4ph scattering in 2D materials, which make the conventionally adopted perturbation methods for TC calculation far from sufficient at low temperatures. Finally, we find that the intensity of 4ph scattering and thus TC can be effectively manipulated by changing the dispersion of ZA phonon mode, which can be easily achieved through strain engineering. Our study provides new insights into low-temperature phonon transport and its manipulation in 2D materials., Comment: 5 figures

In this study, we present first-principles calculations that introduce a novel nonvolatile spin field-effect transistor (Spin-FET) utilizing a van der Waals multiferroic heterostructure, specifically VSi2N4/Sc2CO2. We demonstrate that inverting the ferroelectric polarization in a Sc2CO2 monolayer can effectively modulate the electronic states of a VSi2N4 monolayer, enabling a transition from half-metal to half-semiconductor. This transition significantly alters the electronic transport properties. Furthermore, we construct a Spin-FET device based on this multiferroic heterostructure and observe that the VSi2N4/Sc2CO2-based Spin-FET exhibits exceptional all-electric-controlled performance. Notably, the inversion of the Sc2CO2 ferroelectric polarization yields a substantial on-off current ratio, approximately 650\%, under a minimal bias voltage of 0.02 V. Additionally, we identify a unique spatially-separated spin-polarized transport phenomenon, wherein pure spin-up electrons transport exclusively through VSi2N4, and spin-down electrons through Sc$_2$CO$_2$. Our findings suggest a promising pathway for developing low-energy-dissipation and nonvolatile FET devices., Comment: 5 figures

Recently, an extremely-air-stable one-dimensional 7-9-7-AGNR was successfully fabricated. To further reveal its potential application in sub-5-nm field-effect transistors (FETs), there is an urgent need to develop integrated circuits. Here, we report first-principles quantum-transport simulations on the performance limits of n- and p-type sub-5-nm one-dimensional 7-9-7-AGNR FET. We find that the on-state current (Ion) in 7-9-7-AGNR FET can be effectively manipulated by the length of the gate and underlap. Particularly, the optimized Ion in n-type (p-type) device can reach up to 2423 (4277) and 1988 (920) {\mu}A/{\mu}m for high-performance and low-power applications, respectively. The large Ion values are in the first class among the LD FETs, which can well satisfy the ITRS requirements. We also find that the 7-9-7-AGNR FET can have ultralow subthreshold swing below 60mV/dev, ultrashort delay time (<0.01 ps), and very small power-delay product (<0.01 fJ/{\mu}m). Our results show that the 7-9-7-AGNR based FETs have great potential applications in the high-speed and low-power consumption chips., Comment: 7 figures, 2 tables

The electron's charge and spin degrees of freedom are at the core of modern electronic devices. With the in-depth investigation of two-dimensional materials, another degree of freedom, valley, has also attracted tremendous research interest. The intrinsic spontaneous valley polarization in two-dimensional magnetic systems, ferrovalley material, provides convenience for detecting and modulating the valley. In this review, we first introduce the development of valleytronics. Then, the valley polarization forms by the p, d, and f-orbit that are discussed. Following, we discuss the investigation progress of modulating the valley polarization of two-dimensional ferrovalley materials by multiple physical fields, such as electric, stacking mode, strain, and interface. Finally, we look forward to the future developments of valleytronics., Comment: 13 pages, 12 figures Accepted Chinese Physics B (2023) [Invited review]

This paper proposes a Robust and Efficient Memory Network, referred to as REMN, for studying semi-supervised video object segmentation (VOS). Memory-based methods have recently achieved outstanding VOS performance by performing non-local pixel-wise matching between the query and memory. However, these methods have two limitations. 1) Non-local matching could cause distractor objects in the background to be incorrectly segmented. 2) Memory features with high temporal redundancy consume significant computing resources. For limitation 1, we introduce a local attention mechanism that tackles the background distraction by enhancing the features of foreground objects with the previous mask. For limitation 2, we first adaptively decide whether to update the memory features depending on the variation of foreground objects to reduce temporal redundancy. Second, we employ a dynamic memory bank, which uses a lightweight and differentiable soft modulation gate to decide how many memory features need to be removed in the temporal dimension. Experiments demonstrate that our REMN achieves state-of-the-art results on DAVIS 2017, with a $\mathcal{J\&F}$ score of 86.3% and on YouTube-VOS 2018, with a $\mathcal{G}$ over mean of 85.5%. Furthermore, our network shows a high inference speed of 25+ FPS and uses relatively few computing resources., Comment: Accepted by ICME 2023. 6 pages, 6 figures

Recently, the in-plane thermal transport in van der Waals (vdW) materials such as graphene, hexagonal boron nitride (h-BN), and transition metal dichalcogenides (TMDs) has been widely studied. Whereas, the cross-plane one is far from sufficient. Based on the non-equilibrium molecular dynamics simulations and Boltzmann transport equation, here we reveal the stacking and thickness effects on the cross-plane thermal conductivity (K) of h-BN. We find that K can be significantly modulated by both the stacking structure and thickness (d) of h-BN, which is unexpected from the viewpoint of its smooth in-plane structure and weak interlayer interaction. In the small thickness region (d<6 nm), K of h-BN at room temperature significantly increases with thickness, following a power law of K ~ d^b with b=0.84, 0.66, and 0.92 for AA', AB, and AB' stacking structures, respectively. Moreover, K of AB' structure reaches up to 60% larger than that of AA' and AB structures with d=5.3 nm, showing the remarkable stacking effect. We also find that the stacking effect on K changes dramatically with d increasing, where AA' stacking has the largest K with d > 200 nm. We finally clarify that such exotic stacking and thickness dependence of K is owing to the competing effect of excited number of phonons and phonon relaxation time, both of which directly affect the thermal conductivity. Our findings may provide new insights into the cross-plane thermal management in vdW materials., Comment: 5 figures

Recently, the twist-angle effect on 2D van der Walls (vdW) materials, such as bilayer graphene, has attracted great attention. Many novel electronic, magnetic and even optical properties induced by such effect have been discovered. However, the twist-angle effect on phononic property is not so remarkable. By investigating the thermal conductivity of twist bilayer graphene (TBG), here we reveal that the trivial twist-angle effect on phononic property observed in previous studies is owing to the non-localization nature of phonons. This characteristic makes phonons hardly trapped by the weak interlayer potentials induced by the twist-angle dependent Moir\'e pattern. We propose that the twist-angle effect can be effectively enhanced by increasing the interface coupling. In use of a sandwich structure composed of h-BN and TBG, we demonstrate that the thermal conductivity of TBG can be either significantly increased or dramatically decreased, under the synergistic modulation of interlayer coupling strength and twist angle. Particularly, the twist-angle effect can lead to a nontrivial reduction of thermal conductivity up to 78% when a strong interlayer coupling is applied. The reduction is several times larger than that observed in the freestanding TBG where the reduction is attributed to the twist-angle dependent phonon scatterings induced by the edge phonons. The underlying mechanism for the giant twist-angle dependence of thermal conductivity is further revealed on the basis of phonon transport theory. Our findings provide a platform for achieving efficient twist-angle modulation on phonon transport property of vdW materials., Comment: 5 figures

One of recent surprising discoveries is the unusual anisotropic magnetoresistance (UAMR) that depends on two magnetization components perpendicular to the current differently, in contrast to the conventional anisotropic magnetoresistance (AMR) that predicts no change in resistance when the magnetization varies in the plane perpendicular to the current. Using density functional theory and Boltzmann transport equation calculations for bcc Fe, hcp Co, and bcc FeCo alloys, we show that UAMR can be accounted by the magnetization-dependent spin-orbit interactions (SOI): Magnetization-dependent SOI modifies electron energy bands that, in turn, changes resistance. A phenomenological model reveals the intrinsic connection between SOI and order-parameters. Such a mechanism is confirmed by the strong biaxial stain effect on UAMR. Our findings provide an efficient way of searching and optimizing materials with large UAMR, important in the design of high-performance spintronic devices., Comment: 4 figures

Although the two-dimensional electron gas (2DEG) in (001) Zn$_{1-x}$Mg$_x$O/ZnO heterostructures has been discovered for about twenty years, the origin of the 2DEG is still inconclusive. In the present letter, the formation mechanisms of 2DEG near the interfaces of (001) Zn$_{1-x}$Mg$_x$O/ZnO heterostructures were investigated via the first-principles calculations method. It is found that the polarity discontinuity near the interface can neither lead to the formation of 2DEG in devices with thick Zn$_{1-x}$Mg$_{x}$O layers nor in devices with thin Zn$_{1-x}$Mg$_{x}$O layers. For the heterostructure with thick Zn$_{1-x}$Mg$_{x}$O layers, the oxygen vacancies near the interface introduce a defect band in the band gap, and the top of the defect band overlaps with the bottom of the conduction band, leading to the formation of the 2DEG near the interface of the device. For the heterostructure with thin Zn$_{1-x}$Mg$_{x}$O layers, the absorption of hydrogen atoms, oxygen atoms, or OH groups on the surface of Zn$_{1-x}$Mg$_{x}$O film plays a key role for the formation of 2DEG in the device. Our results manifest the sources of 2DEGs in Zn$_{1-x}$Mg$_x$O/ZnO heterostructures on the electronic structure level., Comment: 6 pages, 6 figures

LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark.