arXiv subjects

Hao Zeng

Publications and source records attributed to Hao Zeng.

At least 73 records · Page 4Linked to original sources

arXiv2022

Bone tumor suppression in rabbits by hyperthermia below the clinical safety limit using aligned magnetic bone cement

Demonstrating highly efficient alternating current (AC) magnetic field heating of nanoparticles in physiological environments under clinically safe field parameters has remained a great challenge, hindering clinical applications of magnetic hyperthermia. In this work, we report exceptionally high loss power of magnetic bone cement under clinical safety limit of AC field parameters, incorporating DC field-aligned soft magnetic Zn0.3Fe2.7O4 nanoparticles with low concentration. Under an AC field of 4 kA/m at 430 kHz, the aligned bone cement with 0.2 wt% nanoparticles achieved a temperature increase of 30 C in 180 s. This amounts to a specific loss power value of 327 W/gmetal and an intrinsic loss power of 47 nHm^2/kg, which is enhanced by 50-fold compared to randomly oriented samples. The high-performance magnetic bone cement allows for the demonstration of effective hyperthermia suppression of tumor growth in the bone marrow cavity of New Zealand White Rabbits subjecting to rapid cooling due to blood circulation, and significant enhancement of survival rate.

physics.med-ph↗

arXiv2021

SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds

Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and the projection methods in previous works fail to establish the relationships between the local point sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly contains voxel-graph module and sparse-to-dense regression module to achieve comparable 3D detection tasks from raw LIDAR data. Specifically, SVGA-Net constructs the local complete graph within each divided 3D spherical voxel and global KNN graph through all voxels. The local and global graphs serve as the attention mechanism to enhance the extracted features. In addition, the novel sparse-to-dense regression module enhances the 3D box estimation accuracy through feature maps aggregation at different levels. Experiments on KITTI detection benchmark demonstrate the efficiency of extending the graph representation to 3D object detection and the proposed SVGA-Net can achieve decent detection accuracy.

cs.CV↗

arXiv2021

Signature of Electron-magnon Umklapp Scattering in L10 FePt probed by Thermoelectric Measurements

We report unconventional thermoelectric power (Seebeck coefficient, S) in L10 structured FePt films. The temperature dependence of S can be well fitted by a phenomenological expression consisting of electron diffusion and magnon-drag contributions. Interestingly, the magnon drag coefficient carries an opposite sign to that of electron diffusion, revealing a dominant contribution from the elusive electron-magnon Umklapp scattering. DFT calculations identify several bands crossing the Brillouin zone boundaries, facilitating the Umklapp process. The large spin-orbit coupling in FePt results in strong mixing of majority and minority spins among some of those bands, greatly enhancing the electron-magnon scattering.

cond-mat.mtrl-sci↗

arXiv2021

Visualizing Van der Waals Epitaxial Growth of Two-Dimensional Heterostructures

Understanding the growth mechanisms of two-dimensional (2D) van der Waals (vdW) heterostructures is of great importance in exploring their functionalities and device applications. A custom-built system integrating physical vapor deposition and optical microscopy/Raman spectroscopy was employed to study the dynamic growth processes of 2D vdW heterostructures in situ. This allows us to identify a new growth mode with a distinctly different growth rate and morphology from those of the conventional linear growth mode. We propose a model that explains the difference in morphologies and quantifies the growth rates of the two modes by taking the role of surface diffusion into account. We have systematically investigated a range of material combinations including CdI2/WS2, CdI2/MoS2, CdI2/WSe2, PbI2/WS2, PbI2/MoS2, PbI2/WSe2 and Bi2Se3/WS2. These findings may be generalized to the synthesis of many other 2D heterostructures with controlled morphologies and physical properties, benefiting future device applications.

cond-mat.mtrl-sci↗

arXiv2021

Magnetotransport in Fe3O4 nanoparticle arrays dominated by non-collinear surface spins

Magnetotransport in arrays of monodisperse magnetite nanoparticles has been studied as a function of annealing temperatures. Charge transport mechanisms change from thermally assisted interparticle tunneling to hopping between Fe-sites within the particle as the interparticle spacing is decreased. Despite this difference, magnetoresistance (MR) as a function of field shows a ubiquitous behavior dominated by non-collinear surface spins. All MR as a function of field can be fitted accurately by a Langevin-like function.

cond-mat.mes-hall↗

arXiv2021

What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning

As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and structure design are handcrafted and fixed for all domains before model learning, resulting in the learning inflexibility and computational intensiveness. With this motivation, we propose to learn a data-driven adapter plugging strategy with Neural Architecture Search (NAS), which automatically determines where to plug for those adapter modules. Furthermore, we propose a NAS-adapter module for adapter structure design in a NAS-driven learning scheme, which automatically discovers effective adapter module structures for different domains. Experimental results demonstrate the effectiveness of our MDL model against existing approaches under the conditions of comparable performance.

cs.LG↗

arXiv2021

Chalcogenide perovskite BaZrS3 thin-film electronic and optoelectronic devices by low temperature processing

Owing to its superior visible light absorption and high chemical stability, chalcogenide perovskite barium zirconium sulfide has attracted significant attention in the past few years as a potential alternative to hybrid halide perovskites for optoelectronics. However, the high processing temperatures of BaZrS3 thin films at above 1000 C severely limits their potential for device applications. Herein, we report the synthesis of BaZrS3 thin films at temperatures as low as 500 C, by changing the chemical reaction pathway. The single phase BaZrS3 thin film was confirmed by X-ray diffraction and Raman spectroscopies. Atomic force microscopy and scanning electron microscopy show that crystalline size and surface roughness were consistently reduced with decreasing annealing temperature. The lower temperatures further eliminate sulfur vacancies and carbon contaminations associated with high temperature processing. The ability to synthesize chalcogenide perovskite thin films at lower temperatures removes a major hurdle for their device fabrication. The photodetectors demonstrate fast response and an on/off ratio of 80. The fabricated field effect transistors show an ambipolar behavior with electron and hole mobilities of 16.8 cm2/Vs and 2.6 cm2/Vs, respectively.

cond-mat.mtrl-sci↗

arXiv2021

PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization

Temporal action localization is an important and challenging task that aims to locate temporal regions in real-world untrimmed videos where actions occur and recognize their classes. It is widely acknowledged that video context is a critical cue for video understanding, and exploiting the context has become an important strategy to boost localization performance. However, previous state-of-the-art methods focus more on exploring semantic context which captures the feature similarity among frames or proposals, and neglect positional context which is vital for temporal localization. In this paper, we propose a temporal-position-sensitive context modeling approach to incorporate both positional and semantic information for more precise action localization. Specifically, we first augment feature representations with directed temporal positional encoding, and then conduct attention-based information propagation, in both frame-level and proposal-level. Consequently, the generated feature representations are significantly empowered with the discriminative capability of encoding the position-aware context information, and thus benefit boundary detection and proposal evaluation. We achieve state-of-the-art performance on both two challenging datasets, THUMOS-14 and ActivityNet-1.3, demonstrating the effectiveness and generalization ability of our method.

cs.CV↗

arXiv2021

Covalent 2D Cr$_2$Te$_3$ ferromagnet

To broaden the scope of van der Waals 2D magnets, we report the synthesis and magnetism of covalent 2D magnetic Cr$_2$Te$_3$ with a thickness down to one-unit-cell. The 2D Cr$_2$Te$_3$ crystals exhibit robust ferromagnetism with a Curie temperature of 180 K, a large perpendicular anisotropy of 7*105 J m-3, and a high coercivity of ~ 4.6 kG at 20 K. First-principles calculations further show a transition from canted to collinear ferromagnetism, a transition from perpendicular to in-plane anisotropy, and emergent half-metallic behavior in atomically-thin Cr$_2$Te$_3$, suggesting its potential application for injecting carriers with high spin polarization into spintronic devices.

cond-mat.mes-hall↗

arXiv2021

Convergence Rate Analysis of Proximal Iteratively Reweighted $\ell_1$ Methods for $\ell_p$ Regularization Problems

In this paper, we focus on the local convergence rate analysis of the proximal iteratively reweighted $\ell_1$ algorithms for solving $\ell_p$ regularization problems, which are widely applied for inducing sparse solutions. We show that if the Kurdyka-Lojasiewicz (KL) property is satisfied, the algorithm converges to a unique first-order stationary point; furthermore, the algorithm has local linear convergence or local sublinear convergence. The theoretical results we derived are much stronger than the existing results for iteratively reweighted $\ell_1$ algorithms.

math.OC↗

arXiv2021

An Extrapolated Iteratively Reweighted l1 Method with Complexity Analysis

The iteratively reweighted l1 algorithm is a widely used method for solving various regularization problems, which generally minimize a differentiable loss function combined with a nonconvex regularizer to induce sparsity in the solution. However, the convergence and the complexity of iteratively reweighted l1 algorithms is generally difficult to analyze, especially for non-Lipschitz differentiable regularizers such as nonconvex lp norm regularization. In this paper, we propose, analyze and test a reweighted l1 algorithm combined with the extrapolation technique under the assumption of Kurdyka-Lojasiewicz (KL) property on the objective. Unlike existing iteratively reweighted l1 algorithms with extrapolation, our method does not require the Lipschitz differentiability on the regularizers nor the smoothing parameters in the weights bounded away from zero. We show the proposed algorithm converges uniquely to a stationary point of the regularization problem and has local linear complexity--a much stronger result than existing ones. Our numerical experiments show the efficiency of our proposed method.

math.OC↗

arXiv2020

Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks

Hyperspectral image classification (HIC) is an important but challenging task, and a problem that limits the algorithmic development in this field is that the ground truths of hyperspectral images (HSIs) are extremely hard to obtain. Recently a handful of HIC methods are developed based on the graph convolution networks (GCNs), which effectively relieves the scarcity of labeled data for deep learning based HIC methods. To further lift the classification performance, in this work we propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node correlations and also effectively reduce graph size. In particular, we first cluster the pixels with similar spectral features into a superpixel and build the graph based on the superpixels of the input HSI. Then instead of performing convolution over this superpixel graph, we further partition it into several sub-graphs by pruning the edges with weak weights, so as to strengthen the correlations of nodes with high similarity. This second round of clustering also further reduces the graph size, thus reducing the computation burden of graph convolution. Experimental results on three widely used benchmark datasets well prove the effectiveness of our proposed framework.

cs.CV↗

arXiv2020

Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection

3D object detection has become an emerging task in autonomous driving scenarios. Previous works process 3D point clouds using either projection-based or voxel-based models. However, both approaches contain some drawbacks. The voxel-based methods lack semantic information, while the projection-based methods suffer from numerous spatial information loss when projected to different views. In this paper, we propose the Stereo RGB and Deeper LIDAR (SRDL) framework which can utilize semantic and spatial information simultaneously such that the performance of network for 3D object detection can be improved naturally. Specifically, the network generates candidate boxes from stereo pairs and combines different region-wise features using a deep fusion scheme. The stereo strategy offers more information for prediction compared with prior works. Then, several local and global feature extractors are stacked in the segmentation module to capture richer deep semantic geometric features from point clouds. After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method. The decent experimental results on the challenging KITTI detection benchmark demonstrate the effectiveness of utilizing both stereo images and point clouds for 3D object detection.

cs.CV↗

arXiv2020

Controlling valley splitting and Polarization of dark- and bi-excitons in monolayer WS$_2$ by a tilted magnetic field

We developed a comprehensive theoretical framework focusing on many-body effects of exciton species in monolayer WS$_2$, including bright and dark excitons, and intra- and inter-valley biexcitons, to investigate valley dynamics in monolayer WS$_2$ subjected to a tilted magnetic field B. In particular, the evolution of the exciton population densities and the many-body particle scatterings are considered to calculate the valley polarization (V P ) as a function of the magnetic field. We found valley splittings for the dark exciton and biexciton energy levels that are larger than those of bright excitons, of -0.23 meV/T. For example, -0.46 meV/T for dark excitons and -0.69 meV/T for bright-dark intra-valley biexcitons. Furthermore, inter-valley bright-dark excitons have an opposite valley energy splittings of +0.23 meV/T. For the samples pumped by linearly polarized light, V P exhibits distinct magnetic field dependence for different types of many-body particle states. Among them, the V P of the intra-valley bright-dark biexcitons increases with increasing magnetic field and reaches nearly 50% at B=65 T. The brightened dark exciton, on the other hand, exhibits vanishing VP, indicating long valley relaxation time. Remarkably, the inter-valley bright-dark biexciton shows unconventional behavior with an inverted VP. The opposite VP for intra- and inter-valley bright-dark biexcitons, coupled with their large valley splitting and long valley lifetime may facilitate their coherent manipulation for quantum computing.

cond-mat.mes-hall↗

arXiv2020

Ti-alloying of BaZrS3 chalcogenide perovskite for photovoltaics

BaZrS3, a prototypical chalcogenide perovskite, has been shown to possess a direct band gap, an exceptionally strong near band edge light absorption, and good carrier transport. Coupled with its great stability, non-toxicity with earth abundant elements, it is thus a promising candidate for thin film solar cells. However, its reported band gap in the range of 1.7-1.8 eV is larger than the optimal value required to reach the Shockley-Queisser limit of a single junction solar cell. Here we report the synthesis of Ba(Zr1-xTix)S3 perovskite compounds with a reduced band gap. It is found that Ti alloying is extremely effective in band gap reduction of BaZrS3: a mere 4 at% alloying decreases the band gap from 1.78 to 1.51 eV, resulting in a theoretical maximum power conversion efficiency of 32%. Higher Ti-alloying concentration is found to destabilize the distorted chalcogenide perovskite phase.

cond-mat.mtrl-sci↗

arXiv2020

Thermoelectric probe of defect state induced by ionic liquid gating in vanadium dioxide

Thermoelectric measurements detect the asymmetry between the density of states above and below the chemical potential in a material. It provides insights into small variations in the density of states near the chemical potential, complementing electron transport measurements. Here, combined resistance and thermoelectric power measurements are performed on vanadium dioxide (VO2), a prototypical correlated electron material, under ionic-liquid (IL) gating. With IL gating, charge transport below the metal-to-insulator-transition (MIT) temperature remains in the thermally activated regime, while the Seebeck coefficient exhibits an apparent transition from semiconducting to metallic behavior. The contrasting behavior indicates changes in electronic structure upon IL gating, due to the formation of oxygen defect states. The experimental results are corroborated by numerical simulations based on a model density of states incorporating a gating induced defect band. This study reveals thermoelectric measurements to be a convenient and sensitive probe for the role of defect states induced by IL gating in suppressing the MIT in VO2, which remains benign in charge transport measurements, and possibly for studying defect sates in other materials.

cond-mat.mtrl-sci↗

arXiv2020

Prediction of Ternary Fluorooxoborates with Coplanar Triangle Units [BOxF3-x]x- From First-Principles

Ten new ternary fluorooxoborate structures were obtained from first-principles prediction. Coplanar aligned triangle structure units [BO2F]2- and [BOF2]- like [BO3]3- in borates were found from the computational simulation. We identified new covalent coordination patterns of the F atom connected with the B atoms which are located in the bridging site, -B--F--B-. Besides, one molecular crystal with [B4O4F4] molecular unit was attached.

cond-mat.mtrl-sci↗

arXiv2019

Relating lp regularization and reweighted l1 regularization

We propose a general framework of iteratively reweighted l1 methods for solving lp regularization problems. We prove that after some iteration k, the iterates generated by the proposed methods have the same support and sign as the limit points, and are bounded away from 0, so that the algorithm behaves like solving a smooth problem in the reduced space. As a result, the global convergence can be easily obtained and an update strategy for the smoothing parameter is proposed which can automatically terminate the updates for zero components. We show that lp regularization problems are locally equivalent to a weighted l1 regularization problem and every optimal point corresponds to a Maximum A Posterior estimation for independently and non-identically distributed Laplace prior parameters. Numerical experiments exhibit the behaviors and the efficiency of our proposed methods.

math.OC↗