arXiv subjects

Hao Zhang

Publications and source records attributed to Hao Zhang.

At least 1,045 records · Page 58Linked to original sources

arXiv2021

Fast particle acceleration in three-dimensional relativistic reconnection

Magnetic reconnection is invoked as one of the primary mechanisms to produce energetic particles. We employ large-scale three-dimensional (3D) particle-in-cell simulations of reconnection in magnetically-dominated ($σ=10$) pair plasmas to study the energization physics of high-energy particles. We identify a novel acceleration mechanism that only operates in 3D. For weak guide fields, 3D plasmoids / flux ropes extend along the $z$ direction of the electric current for a length comparable to their cross-sectional radius. Unlike in 2D simulations, where particles are buried in plasmoids, in 3D we find that a fraction of particles with $γ\gtrsim 3σ$ can escape from plasmoids by moving along $z$, and so they can experience the large-scale fields in the upstream region. These "free" particles preferentially move in $z$ along Speiser-like orbits sampling both sides of the layer, and are accelerated linearly in time -- their Lorentz factor scales as $γ\propto t$, in contrast to $γ\propto \sqrt{t}$ in 2D. The energy gain rate approaches $\sim eE_{\rm rec}c$, where $E_{\rm rec}\simeq 0.1 B_0$ is the reconnection electric field and $B_0$ the upstream magnetic field. The spectrum of free particles is hard, $dN_{\rm free}/dγ\propto γ^{-1.5}$, contains $\sim 20\%$ of the dissipated magnetic energy independently of domain size, and extends up to a cutoff energy scaling linearly with box size. Our results demonstrate that relativistic reconnection in GRB and AGN jets may be a promising mechanism for generating ultra-high-energy cosmic rays.

astro-ph.HE↗

arXiv2021

Generation of quasi-monoenergetic proton beams via quantum radiative compression

Dense high-energy monoenergetic proton beams are vital for wide applications, thus modern laser-plasma-based ion acceleration methods are aiming to obtain high-energy proton beams with energy spread as low as possible. In this work, we put forward a quantum radiative compression method to post-compress a highly accelerated proton beam and convert it to a dense quasi-monoenergetic one. We find that when the relativistic plasma produced by radiation pressure acceleration collides head-on with an ultraintense laser beam, large-amplitude plasma oscillations are excited due to quantum radiation-reaction and the ponderomotive force, which induce compression of the phase space of protons located in its acceleration phase with negative gradient. Our three-dimensional spin-resolved QED particle-in-cell simulations show that hollow-structure proton beams with a peak energy $\sim$ GeV, relative energy spread of few percents and number $N_p\sim10^{10}$ (or $N_p\sim 10^9$ with a $1\%$ energy spread) can be produced in near future laser facilities, which may fulfill the requirements of important applications, such as, for radiography of ultra-thick dense materials, or as injectors of hadron colliders.

physics.plasm-ph↗

arXiv2021

Prompt Acceleration of a Short-Lifetime Low-Energy Muon Beam

An energetic muon beam is an attractive key to unlock new physics beyond the Standard Model: the lepton flavor violation or the anomalous magnetic moment, and also is a competitive candidate for the expected neutrino factory. Lots of the muon scientific applications are limited by low flux cosmic-ray muons, low energy muon sources or extremely expensive muon accelerators. An prompt acceleration of the low-energy muon beam is found in the beam-driven plasma wakefield up to $\mathrm{TV/m}$. The muon beam is accelerated from $275\mathrm{MeV}$ to more than $10\mathrm{GeV}$ within $22.5\mathrm{ps}$. Choosing the injection time of the muon beam in a proper range, the longitudinal spatial distribution and the energy distribution of the accelerated muon beam are compressed. The efficiency of the energy transfer from the driven electron beam to the muon beam can reach $20\%$. The prompt acceleration scheme is a promising avenue to bring the expected neutrino factory and the muon collider into reality and to catch new physics beyond the Standard Model.

physics.acc-ph↗

arXiv2021

Ballistic Majorana nanowire devices

Majorana modes are zero-energy excitations of a topological superconductor that exhibit non-Abelian statistics. Following proposals for their detection in a semiconductor nanowire coupled to an s-wave superconductor, several tunneling experiments reported characteristic Majorana signatures. Reducing disorder has been a prime challenge for these experiments because disorder can mimic the zero-energy signatures of Majoranas, and renders the topological properties inaccessible. Here, we show characteristic Majorana signatures in InSb nanowire devices exhibiting clear ballistic transport properties. Application of a magnetic field and spatial control of carrier density using local gates generates a zero bias peak that is rigid over a large region in the parameter space of chemical potential, Zeeman energy, and tunnel barrier potential. The reduction of disorder allows us to resolve separate regions in the parameter space with and without a zero bias peak, indicating topologically distinct phases. These observations are consistent with the Majorana theory in a ballistic system, and exclude for the first time the known alternative explanations that invoke disorder or a nonuniform chemical potential.

cond-mat.mes-hall↗

arXiv2021

CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly

We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies. Our network takes an input 3D shape that can be provided as a point cloud or voxel grids, and reconstructs it by a compact assembly of quadric surface primitives via constructive solid geometry (CSG) operations. The network is self-supervised with a reconstruction loss, leading to faithful 3D reconstructions with sharp edges and plausible CSG trees, without any ground-truth shape assemblies. While the parametric nature of CAD models does make them more predictable locally, at the shape level, there is a great deal of structural and topological variations, which present a significant generalizability challenge to state-of-the-art neural models for 3D shapes. Our network addresses this challenge by adaptive training with respect to each test shape, with which we fine-tune the network that was pre-trained on a model collection. We evaluate our learning framework on both ShapeNet and ABC, the largest and most diverse CAD dataset to date, in terms of reconstruction quality, shape edges, compactness, and interpretability, to demonstrate superiority over current alternatives suitable for neural CAD reconstruction.

cs.CV↗

arXiv2021

LayoutGMN: Neural Graph Matching for Structural Layout Similarity

We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements.

cs.CV↗

arXiv2021

Dissipative Transport and Phonon Scattering Suppression via Valley Engineering in Single-Layer Antimonene and Arsenene Field-Effect Transistors

Two-dimensional (2D) semiconductors are promising channel materials for next-generation field-effect transistors (FETs) thanks to their unique mechanical properties and enhanced electrostatic control. However, the performance of these devices can be strongly limited by the scattering processes between carriers and phonons, usually occurring at high rates in 2D materials. Here, we use quantum transport simulations calibrated on first-principle computations to report on dissipative transport in antimonene and arsenene $n$-type FETs at the scaling limit. We show that the widely-used approximations of either ballistic transport or simple acoustic deformation potential scattering result in large overestimation of the ON current, due to neglecting the dominant intervalley and optical phonon scattering processes. We additionally investigate valley engineering strategy [Nano Lett. \textbf{19}, 3723 (2019)] to improve the device performance by removing the valley degeneracy and suppressing most of the intervalley scattering channels via an uniaxial strain along the zigzag direction. The method is applicable to other similar 2D semiconductors characterized by multivalley transport.

cond-mat.mes-hall↗

arXiv2021

DECOR-GAN: 3D Shape Detailization by Conditional Refinement

We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties of high-resolution and detailed 3D geometry from a small set of exemplars by treating the problem as that of geometric detail transfer. Given a low-resolution coarse voxel shape, our network refines it, via voxel upsampling, into a higher-resolution shape enriched with geometric details. The output shape preserves the overall structure (or content) of the input, while its detail generation is conditioned on an input "style code" corresponding to a detailed exemplar. Our 3D detailization via conditional refinement is realized by a generative adversarial network, coined DECOR-GAN. The network utilizes a 3D CNN generator for upsampling coarse voxels and a 3D PatchGAN discriminator to enforce local patches of the generated model to be similar to those in the training detailed shapes. During testing, a style code is fed into the generator to condition the refinement. We demonstrate that our method can refine a coarse shape into a variety of detailed shapes with different styles. The generated results are evaluated in terms of content preservation, plausibility, and diversity. Comprehensive ablation studies are conducted to validate our network designs. Code is available at https://github.com/czq142857/DECOR-GAN.

cs.CV↗

arXiv2021

RISC-NN: Use RISC, NOT CISC as Neural Network Hardware Infrastructure

Neural Networks (NN) have been proven to be powerful tools to analyze Big Data. However, traditional CPUs cannot achieve the desired performance and/or energy efficiency for NN applications. Therefore, numerous NN accelerators have been used or designed to meet these goals. These accelerators all fall into three categories: GPGPUs, ASIC NN Accelerators and CISC NN Accelerators. Though CISC NN Accelerators can achieve considerable smaller memory footprint than GPGPU thus improve energy efficiency; they still fail to provide same level of data reuse optimization achieved by ASIC NN Accelerators because of the inherited poor pragrammability of their CISC architecture. We argue that, for NN Accelerators, RISC is a better design choice than CISC, as is the case with general purpose processors. We propose RISC-NN, a novel many-core RISC-based NN accelerator that achieves high expressiveness and high parallelism and features strong programmability and low control-hardware costs. We show that, RISC-NN can implement all the necessary instructions of state-of-the-art CISC NN Accelerators; in the meantime, RISC-NN manages to achieve advanced optimization such as multiple-level data reuse and support for Sparse NN applications which previously only existed in ASIC NN Accelerators. Experiment results show that, RISC-NN achieves on average 11.88X performance efficiency compared with state-of-the-art Nvidia TITAN Xp GPGPU for various NN applications. RISC-NN also achieves on average 1.29X, 8.37X and 21.71X performance efficiency over CISC-based TPU in CNN, MLP and LSTM applications, respectively. Finally, RISC-NN can achieve additional 26.05% performance improvement and 33.13% energy reduction after applying pruning for Sparse NN applications.

cs.AR↗

arXiv2021

Magnetic properties of the Shastry-Sutherland lattice material BaNd$_2$ZnO$_5$

We investigate the physical properties of the Shastry-Sutherland lattice material BaNd$_2$ZnO$_5$. Neutron diffraction, magnetic susceptibility, and specific heat measurements reveal antiferromagnetic order below 1.65 K. The magnetic order is found to be a 2-$\boldsymbol{Q}$ magnetic structure with the magnetic moments lying in the Shastry-Sutherland lattice planes comprising the tetragonal crystal structure of BaNd$_2$ZnO$_5$. The ordered moment for this structure is 1.9(1) $μ_B$ per Nd ion. Inelastic neutron scattering measurements reveal that the crystal field ground state doublet is well separated from the first excited state at 8 meV. The crystal field Hamiltonian is determined through simultaneous refinement of models with both the LS coupling and intermediate coupling approximations to the inelastic neutron scattering and magnetic susceptibility data. The ground state doublet indicates that the magnetic moments lie primarily in the basal plane with magnitude consistent with the size of the determined ordered moment.

cond-mat.str-el↗

arXiv2021

Interpreting and Boosting Dropout from a Game-Theoretic View

This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions. We prove that dropout can suppress the strength of interactions between input variables of deep neural networks (DNNs). The theoretic proof is also verified by various experiments. Furthermore, we find that such interactions were strongly related to the over-fitting problem in deep learning. Thus, the utility of dropout can be regarded as decreasing interactions to alleviate the significance of over-fitting. Based on this understanding, we propose an interaction loss to further improve the utility of dropout. Experimental results have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.

cs.LG↗

arXiv2021

In-plane selective area InSb-Al nanowire quantum networks

Strong spin-orbit semiconductor nanowires coupled to a superconductor are predicted to host Majorana zero modes. Exchange (braiding) operations of Majorana modes form the logical gates of a topological quantum computer and require a network of nanowires. Here, we develop an in-plane selective-area growth technique for InSb-Al semiconductor-superconductor nanowire networks with excellent quantum transport properties. Defect-free transport channels in InSb nanowire networks are realized on insulating, but heavily mismatched InP substrates by 1) full relaxation of the lattice mismatch at the nanowire/substrate interface on a (111)B substrate orientation, 2) nucleation of a complete network from a single nucleation site, which is accomplished by optimizing the surface diffusion length of the adatoms. Essential quantum transport phenomena for topological quantum computing are demonstrated in these structures including phase-coherent transport up to 10 $μ$m and a hard superconducting gap accompanied by 2$e$-periodic Coulomb oscillations with an Al-based Cooper pair island integrated in the nanowire network.

cond-mat.mes-hall↗

arXiv2021

Gate Tunable Supercurrent in Josephson Junctions Based on Bi2Te3 Topological Insulator Thin Films

We report transport measurements on Josephson junctions consisting of Bi2Te3 topological insulator (TI) thin films contacted by superconducting Nb electrodes. For a device with junction length L = 134 nm, the critical supercurrent Ic can be modulated by an electrical gate which tunes the carrier type and density of the TI film. Ic can reach a minimum when the TI is near the charge neutrality regime with the Fermi energy lying close to the Dirac point of the surface state. In the p-type regime the Josephson current can be well described by a short ballistic junction model. In the n-type regime the junction is ballistic at 0.7 K < T < 3.8 K while for T < 0.7 K the diffusive bulk modes emerge and contribute a larger Ic than the ballistic model. We attribute the lack of diffusive bulk modes in the p-type regime to the formation of p-n junctions. Our work provides new clues for search of Majorana zero mode in TI-based superconducting devices.

cond-mat.supr-con↗

arXiv2021

Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization

Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles. To enable collaborative localization, several model-based state estimation and learning-based localization methods have been developed. Given their encouraging performance, model-based state estimation often lacks the ability to model the complex relationships among multiple objects, while learning-based methods are typically not able to fuse the observations from an arbitrary number of views and cannot well model uncertainty. In this paper, we introduce a novel spatiotemporal graph filter approach that integrates graph learning and model-based estimation to perform multi-view sensor fusion for collaborative object localization. Our approach models complex object relationships using a new spatiotemporal graph representation and fuses multi-view observations in a Bayesian fashion to improve location estimation under uncertainty. We evaluate our approach in the applications of connected autonomous driving and multiple pedestrian localization. Experimental results show that our approach outperforms previous techniques and achieves the state-of-the-art performance on collaboration localization.

cs.CV↗

arXiv2021

Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception

Collaborative multi-robot perception provides multiple views of an environment, offering varying perspectives to collaboratively understand the environment even when individual robots have poor points of view or when occlusions are caused by obstacles. These multiple observations must be intelligently fused for accurate recognition, and relevant observations need to be selected in order to allow unnecessary robots to continue on to observe other targets. This research problem has not been well studied in the literature yet. In this paper, we propose a novel approach to collaborative multi-robot perception that simultaneously integrates view selection, feature selection, and object recognition into a unified regularized optimization formulation, which uses sparsity-inducing norms to identify the robots with the most representative views and the modalities with the most discriminative features. As our optimization formulation is hard to solve due to the introduced non-smooth norms, we implement a new iterative optimization algorithm, which is guaranteed to converge to the optimal solution. We evaluate our approach through a case-study in simulation and on a physical multi-robot system. Experimental results demonstrate that our approach enables effective collaborative perception through accurate object recognition and effective view and feature selection.

cs.RO↗

arXiv2021

Dynamic Heterogeneity, Cooperative Motion, and Johari-Goldstein $β$-Relaxation in a Metallic Glass-Forming Material Exhibiting a Fragile to Strong Transition

We investigate the Johari-Goldstein (JG) $β$-relaxation process in a model metallic glass-forming (GF) material (Al90Sm10), previously studied extensively by both frequency-dependent mechanical measurements and simulation studies devoted to equilibrium properties, by molecular dynamics simulations based on validated and optimized interatomic potentials with the primary aim of better understanding the nature of this universal relaxation process from a dynamic heterogeneity (DH) perspective. The present relatively low temperature and long-time simulations reveal a direct correspondence between the JG $β$-relaxation time $τ_{JG}$ and the lifetime of the mobile particle clusters $τ_M$, defined as in previous DH studies, a relationship dual to the corresponding previously observed relationship between the $α$-relaxation time $τ_α$ and the lifetime of immobile particle clusters $τ_{IM}$. Moreover, we find that the average diffusion coefficient D nearly coincides with D$_{Al}$, of the smaller atomic species (Al), and that the hopping time associated with D coincides with $τ_{JG}$ to within numerical uncertainty, both trends being in accord with experimental studies. This indicates that the JG $α$-relaxation is dominated by the smaller atomic species and the observation of a direct relation between this relaxation process and rate of molecular diffusion in GF materials at low temperatures where the JG $β$-relaxation becomes the prevalent mode of structural relaxation. As an unanticipated aspect of our study, we find that Al90Sm10 exhibits fragile-to-strong (FS) glass-formation, as found in many other metallic GF liquids, but this fact does not greatly alter the geometrical nature of DH in this material and the relation of DH to dynamical properties.

cond-mat.mtrl-sci↗

arXiv2021

Memory-Efficient Network for Large-scale Video Compressive Sensing

Video snapshot compressive imaging (SCI) captures a sequence of video frames in a single shot using a 2D detector. The underlying principle is that during one exposure time, different masks are imposed on the high-speed scene to form a compressed measurement. With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement. Unfortunately, though these methods can achieve decent results, the long running time of optimization algorithms or huge training memory occupation of deep networks still preclude them in practical applications. In this paper, we develop a memory-efficient network for large-scale video SCI based on multi-group reversible 3D convolutional neural networks. In addition to the basic model for the grayscale SCI system, we take one step further to combine demosaicing and SCI reconstruction to directly recover color video from Bayer measurements. Extensive results on both simulation and real data captured by SCI cameras demonstrate that our proposed model outperforms previous state-of-the-art with less memory and thus can be used in large-scale problems. The code is at https://github.com/BoChenGroup/RevSCI-net.

eess.IV↗

arXiv2021

Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach

Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC). We train a deep neural network (DNN) to predict the near-end speech from microphone signals with far-end signals used as additional information. We find that the deep learning approach avoids the non-uniqueness problem in traditional MCAEC algorithms. For the AEC setup with multiple microphones, rather than employing AEC for each microphone, a single DNN is trained to achieve echo removal for all microphones. Also, combining deep learning based AEC with deep learning based beamforming further improves the system performance. Experimental results show the effectiveness of both bidirectional long short-term memory (BLSTM) and convolutional recurrent network (CRN) based methods for MCAEC and MMAEC. Furthermore, deep learning based methods are capable of removing echo and noise simultaneously and work well in the presence of nonlinear distortions.

eess.AS↗