arXiv subjects

Qi Wang

Publications and source records attributed to Qi Wang.

At least 487 records · Page 27Linked to original sources

arXiv2022

Correlation driven near-flat band Stoner excitations in a Kagome magnet

Among condensed matter systems, Mott insulators exhibit diverse properties that emerge from electronic correlations. In itinerant metals, correlations are usually weak, but can also be enhanced via geometrical confinement of electrons, that manifest as `flat' dispersionless electronic bands. In the fast developing field of topological materials, which includes Dirac and Weyl semimetals, flat bands are one of the important components that can result in unusual magnetic and transport behaviour. To date, characterisation of flat bands and their magnetism is scarce, hindering the design of novel materials. Here, we investigate the ferromagnetic Kagomé semimetal Co$_3$Sn$_2$S$_2$ using resonant inelastic X-ray scattering. Remarkably, nearly non-dispersive Stoner spin excitation peaks are observed, sharply contrasting with the featureless Stoner continuum expected in conventional ferromagnetic metals. Our band structure and dynamic spin susceptibility calculations, and thermal evolution of the excitations, confirm the nearly non-dispersive Stoner excitations as unique signatures of correlations and spin-polarized electronic flat bands in Co$_3$Sn$_2$S$_2$. These observations serve as a cornerstone for further exploration of band-induced symmetry-breaking orders in topological materials.

cond-mat.str-el↗

arXiv2022

Adjoint-based Particle Forcing Reconstruction and Uncertainty Quantification

The forcing of particles in turbulent environments influences dynamical properties pertinent to many fundamental applications involving particle-flow interactions. Current study explores the determination of forcing for one-way coupled passive particles, under the assumption that the ambient velocity fields are known. When measurements regarding particle locations are available but sparse, direct evaluation of the forcing is intractable. Nevertheless, the forcing for finite-size particles can be determined using adjoint-based data assimilation. This inverse problem is formulated with the framework of optimization, where the cost function is defined as the difference between the measured and predicted particle locations. The gradient of the cost function, with respect to the forcing can be calculated from the adjoint dynamics. When measurements are subject to Gaussian noise, samples within the probability distribution of the forcing can be drawn using Hamiltonian Monte Carlo. The algorithm is tested in the Arnold-Beltrami-Childress flow as well as the homogeneous isotropic turbulence. Results demonstrate that the forcing can only be determined accurately for particle Reynolds number between 1 and 5, where the majority of Reynolds number history along the particle trajectory falls in.

physics.flu-dyn↗

arXiv2022

Pressure-induced superconductivity in PdTeI with quasi-one-dimensional PdTe chains

The quasi-one-dimensional material PdTeI exhibits unusual electronic transport properties at ambient pressure. Here, we systematically investigate both the structural and electronic responses of PdTeI to external pressure, through a combination of electrical transport, synchrotron x-ray diffraction (XRD), and Raman spectroscopy measurements. The charge density wave (CDW) order in PdTeI is fragile and the transition temperature TCDW decreases rapidly with the application of external pressure. The resistivity hump is indiscernible when the pressure is increased to 1 GPa. Upon further compression, zero resistance is established above 20 GPa, suggesting the occurrence of superconductivity. Combined XRD and Raman data evidence that the emergence of superconductivity is accompanied by a pressure-induced amorphization of PdTeI.

cond-mat.supr-con↗

arXiv2022

Pressured-induced superconductivity extending across the topological phase transition in thallium-based topological materials TlBi(S1-xSex)2

The coexistence of superconductivity and topology holds the potential to realize exotic quantum states of matter. Here we report that superconductivity induced by high pressure in three thallium-based materials, covering the phase transition from a normal insulator (TlBiS2) to a topological insulator (TlBiSe2) through a Dirac semimetal (TlBiSeS). By increasing the pressure up to 60 GPa, we observe superconductivity phase diagrams with maximal Tc values at 6.0-8.1 K. Our density-functional theory calculations reveal topological surface states in superconductivity phases for all three compounds. Our study paves the path to explore topological superconductivity and topological phase transitions.

cond-mat.supr-con↗

arXiv2022

Pressure-Tuning Superconductivity in Noncentrosymmetric Topological Materials ZrRuAs

Recently, the hexagonal phase of ternary transition metal pnictides TT'X (T = Zr, Hf; T'= Ru; X = P, As), which are well-known noncentrosymmetric superconductors, were predicted to host nontrivial bulk topology. In this work, we systematically investigate the electronic responses of ZrRuAs to external pressure. At ambient pressure, ZrRuAs show superconductivity with Tc ~ 7.74 K, while a large upper critical field ~ 13.03 T is obtained for ZrRuAs, which is comparable to the weak-coupling Pauli limit. The resistivity of ZrRuAs exhibits a non-monotonic evolution with increasing pressure. The superconducting transition temperature Tc increases with applied pressure and reaches a maximum value of 7.93 K at 2.1 GPa, followed by a decrease. The nontrivial topology is robust and persists up to the high-pressure regime. Considering both robust superconductivity and intriguing topology in this material, our results could contribute to studies of the interplay between topological electronic states and superconductivity.

cond-mat.supr-con↗

arXiv2022

Thermodynamically consistent dynamic boundary conditions of phase field models

We present a general, constructive method to derive thermodynamically consistent models and consistent dynamic boundary conditions hierarchically following the generalized Onsager principle. The method consists of two steps in tandem: the dynamical equation is determined by the generalized Onsager principle in the bulk firstly, and then the surface chemical potential and the thermodynamically consistent boundary conditions are formulated subsequently by applying the generalized Onsager principle at the boundary. The application strategy of the generalized Onsager principle in two-step yields thermodynamically consistent models together with the consistent boundary conditions that warrant a non-negative entropy production rate (or equivalently non-positive energy dissipation rate in isothermal cases) in the bulk as well as at the boundary. We illustrate the method using phase field models of binary materials elaborate on two sets of thermodynamically consistent dynamic boundary conditions. These two types of boundary conditions differ in how the across boundary mass flux participates in boundary surface dynamics. We then show that many existing thermodynamically consistent, binary phase field models together with their dynamic or static boundary conditions are derivable from this method. As an illustration, we show numerically how dynamic boundary conditions affect crystal growth in the bulk using a binary phase field model.

cond-mat.stat-mech↗

arXiv2022

Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

eess.IV↗

arXiv2022

Nonreciprocal Charge Transport in Topological Kagome Superconductor CsV$_{3}$Sb$_{5}$

Nonreciprocal charge transport phenomena are widely studied in two-dimensional superconductors, which demonstrate unidirectional-anisotropy magnetoresistances as a result of symmetry breaking. Here, we report a strong nonreciprocal transport phenomenon in superconducting CsV$_{3}$Sb$_{5}$ thin flakes. The second harmonic voltages, mainly originating from the rectification effect of vortex motion, are unambiguously developed with in-plane and out-of-plane magnetic fields, and their magnitudes are comparable to those in noncentrosymmetric superconductors. The second harmonic magnetoresistances split into several peaks and some of them reverse their signs by ramping the magnetic field or the current within the superconducting transition. The nonreciprocity suggests a strong asymmetry in CsV$_{3}$Sb$_{5}$. The centrosymmetric structure and symmetric electronic phases in CsV$_{3}$Sb$_{5}$ can hardly induce the distinct nonreciprocal transport phenomenon, which could be correlated to a symmetry breaking from an unconventional superconducting order parameter symmetry.

cond-mat.supr-con↗

arXiv2022

Learning Independent Instance Maps for Crowd Localization

Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse prediction, and segmentation/detection-based methods cannot handle extremely dense scenes and large-range scale-variations crowds. To this end, we propose an end-to-end and straightforward framework for crowd localization, named Independent Instance Map segmentation (IIM). Different from density maps and boxes regression, each instance in IIM is non-overlapped. By segmenting crowds into independent connected components, the positions and the crowd counts (the centers and the number of components, respectively) are obtained. Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps. BM brings two advantages into localization models: 1) adaptively learn a threshold map for different images to detect each instance more accurately; 2) directly train the model using loss on binary predictions and labels. Extensive experiments verify the proposed method is effective and outperforms the-state-of-the-art methods on the five popular crowd datasets. Significantly, IIM improves F1-measure by 10.4% on the NWPU-Crowd Localization task. The source code and pre-trained models will be released at https://github.com/taohan10200/IIM.

cs.CV↗

arXiv2022

Generation of Spin-Wave Pulses by Inverse Design

The development of fast magnonic information processing nanodevices requires operating with short spin-wave pulses, but, the shorter the pulses, the more affected they are by information loss due to broadening and dispersion. The capability of engineering spin-wave pulses and controlling their propagation could solve this problem. Here, we provide a method to generate linear spin-wave pulses with a desired spatial-temporal profile in magnonic waveguides based on inverse design. As relevant examples, we theoretically predict that both rectangular and self-compressing spin-wave pulses can be generated in state-of-the-art waveguides with fidelities >96% using narrow stripline antennas. The method requires minimal computational overhead and is universal, i.e., it applies to arbitrary targeted pulse shapes, type of waves (exchange or dipolar), waveguide materials, and waveguide geometries. It can also be extended to more complex magnonic structures. Our results could lead to the utilization of large-scale magnonic circuits for classical and quantum information processing.

cond-mat.mes-hall↗

arXiv2022

Skipping the boundary layer: high-speed droplet-based immunoassay using Rayleigh acoustic streaming

Acoustic mixing of droplets is a promising way to implement biosensors that combine high speed and minimal reagent consumption. To date, this type of droplet mixing is driven by a volume force resulting from the absorption of high-frequency acoustic waves in the bulk of the fluid. Here, we show that the speed of these sensors is limited by the slow advection of analyte to the sensor surface due to the formation of a hydrodynamic boundary layer. We eliminate this hydrodynamic boundary layer by using much lower ultrasonic frequencies to excite the droplet, which drives a Rayleigh streaming that behaves essentially like a slip velocity. Three-dimensional simulations show that this provides a threefold speedup compared to Eckart streaming. Experimentally, we shorten a SARS-CoV-2 antibody immunoassay from 20 min to 40 s.

physics.flu-dyn↗

arXiv2022

Efficient Probabilistic Computing with Stochastic Perovskite Nickelates

Probabilistic computing has emerged as a viable approach to solve hard optimization problems. Devices with inherent stochasticity can greatly simplify their implementation in electronic hardware. Here, we demonstrate intrinsic stochastic resistance switching controlled via electric fields in perovskite nickelates doped with hydrogen. The ability of hydrogen ions to reside in various metastable configurations in the lattice leads to a distribution of transport gaps. With experimentally characterized p-bits, a shared-synapse p-bit architecture demonstrates highly-parallelized and energy-efficient solutions to optimization problems such as integer factorization and Boolean-satisfiability. The results introduce perovskite nickelates as scalable potential candidates for probabilistic computing and showcase the potential of light-element dopants in next-generation correlated semiconductors.

cond-mat.mtrl-sci↗

arXiv2022

Machine Learning Interatomic Potential for Anisotropic Thermal Transport in Bulk Hexagonal Boron Nitride

The highly anisotropic thermal conductivity in layered materials is crucial for a broad range of applications such as thermal management of electronic devices, thermal insulation, and thermoelectrics. Understanding of anisotropic thermal transport in layered materials largely depends on atomistic simulations based on density functional theory (DFT) or empirical potentials, which however suffer either low computational efficiency or accuracy. Recently, machine learning interatomic potentials (MLIPs) are emerging as a powerful tool to bridge the gap. Despite the recent progress in developing MLIPs, little attention has been paid to constructing a potential that can accurately predict the thermal properties of layered materials, which is more challenging compared with the case of isotropic materials because of the highly anisotropic bonding and weak van der Waals interactions in layered materials. Here, we introduce a MLIP within the Gaussian approximation potential (GAP) framework for bulk hexagonal boron nitride (h-BN) with a typical layered structure. The GAP can well predict the highly anisotropic phonon transport properties and thermal conductivity of bulk h-BN with DFT-level accuracy at orders of magnitude reduced cost. Our work demonstrates the ability of GAP to reproduce the subtle features of anisotropic potential energy surfaces of bulk h-BN and potentially other layered materials. Atomistic simulations based on MLIPs are expected to be able to greatly promote the understanding of phonon transport and the prediction of thermophysical properties in layered materials.

cond-mat.mtrl-sci↗

arXiv2022

MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting

RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RGB-based methods in scenes with low-illumination or similar backgrounds. Most existing methods propose well-designed structures for cross-modal fusion in RGB-T crowd counting. However, these methods have difficulty in encoding cross-modal contextual semantic information in RGB-T image pairs. Considering the aforementioned problem, we propose a two-stream RGB-T crowd counting network called Multi-Attention Fusion Network (MAFNet), which aims to fully capture long-range contextual information from the RGB and thermal modalities based on the attention mechanism. Specifically, in the encoder part, a Multi-Attention Fusion (MAF) module is embedded into different stages of the two modality-specific branches for cross-modal fusion at the global level. In addition, a Multi-modal Multi-scale Aggregation (MMA) regression head is introduced to make full use of the multi-scale and contextual information across modalities to generate high-quality crowd density maps. Extensive experiments on two popular datasets show that the proposed MAFNet is effective for RGB-T crowd counting and achieves the state-of-the-art performance.

cs.CV↗

arXiv2022

Physics-informed Deep Super-resolution for Spatiotemporal Data

High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an increasing interest in leveraging deep learning to augment scientific data based on the coarse-grained simulations, which is of cheap computational expense and retains satisfactory solution accuracy. However, the major existing work focuses on data-driven approaches which rely on rich training datasets and lack sufficient physical constraints. To this end, we propose a novel and efficient spatiotemporal super-resolution framework via physics-informed learning, inspired by the independence between temporal and spatial derivatives in partial differential equations (PDEs). The general principle is to leverage the temporal interpolation for flow estimation, and then introduce convolutional-recurrent neural networks for learning temporal refinement. Furthermore, we employ the stacked residual blocks with wide activation and sub-pixel layers with pixelshuffle for spatial reconstruction, where feature extraction is conducted in a low-resolution latent space. Moreover, we consider hard imposition of boundary conditions in the network to improve reconstruction accuracy. Results demonstrate the superior effectiveness and efficiency of the proposed method compared with baseline algorithms through extensive numerical experiments.

cs.LG↗

arXiv2022

Demonstration of room-temperature continuous-wave operation of InGaAs/AlGaAs quantum well lasers directly grown on on-axis silicon (001)

Room-temperature continuous-wave operation of InGaAs/AlGaAs quantum well lasers directly grown on on-axis silicon (001) has been demonstrated. A 420 nm thick GaAs epilayer completely free of antiphase domains was initially grown on the silicon substrate in a metal-organic chemical vapor deposition system and the other epilayers including four sets of five-period strained-layer superlattices and the laser-structural layers were successively grown in a molecular beam epitaxy system. The lasers were prepared as broad-stripe Fabry-Perot ones with a stripe width of 21.5 um and a cavity length of 1 mm. Typically, the threshold current and the corresponding threshold current density are 186.4 mA and 867 A/cm2, respectively. The lasing wavelength is around 980 nm and the slope efficiency is 0.097 W/A with a single-facet output power of 22.5 mW at an injection current of 400 mA. This advancement makes the silicon-based monolithic optoelectronic integration relevant to quantum well lasers more promising with an enhanced feasibility.

physics.optics↗

arXiv2022

Real-Variable Characterizations and Their Applications of Matrix-Weighted Triebel--Lizorkin Spaces

Let $α\in\mathbb R$, $q\in(0,\infty]$, $p\in(0,\infty)$, and $W$ be an $A_p(\mathbb{R}^n,\mathbb{C}^m)$-matrix weight. In this article, the authors characterize the matrix-weighted Triebel-Lizorkin space $\dot{F}_{p}^{α,q}(W)$ via the Peetre maximal function, the Lusin area function, and the Littlewood-Paley $g_λ^{*}$-function. As applications, the authors establish the boundedness of Fourier multipliers on matrix-weighted Triebel-Lizorkin spaces under the generalized Hörmander condition. The main novelty of these results exists in that their proofs need to fully use both the doubling property of matrix weights and the reducing operator associated to matrix weights, which are essentially different from those proofs of the corresponding cases of classical Triebel-Lizorkin spaces that strongly depend on the Fefferman-Stein vector-valued maximal inequality on Lebesgue spaces.

math.FA↗

arXiv2022

SHDM-NET: Heat Map Detail Guidance with Image Matting for Industrial Weld Semantic Segmentation Network

In actual industrial production, the assessment of the steel plate welding effect is an important task, and the segmentation of the weld section is the basis of the assessment. This paper proposes an industrial weld segmentation network based on a deep learning semantic segmentation algorithm fused with heatmap detail guidance and Image Matting to solve the automatic segmentation problem of weld regions. In the existing semantic segmentation networks, the boundary information can be preserved by fusing the features of both high-level and low-level layers. However, this method can lead to insufficient expression of the spatial information in the low-level layer, resulting in inaccurate segmentation boundary positioning. We propose a detailed guidance module based on heatmaps to fully express the segmented region boundary information in the low-level network to address this problem. Specifically, the expression of boundary information can be enhanced by adding a detailed branch to predict segmented boundary and then matching it with the boundary heat map generated by mask labels to calculate the mean square error loss. In addition, although deep learning has achieved great success in the field of semantic segmentation, the precision of the segmentation boundary region is not high due to the loss of detailed information caused by the classical segmentation network in the process of encoding and decoding process. This paper introduces a matting algorithm to calibrate the boundary of the segmentation region of the semantic segmentation network to solve this problem. Through many experiments on industrial weld data sets, the effectiveness of our method is demonstrated, and the MIOU reaches 97.93%. It is worth noting that this performance is comparable to human manual segmentation ( MIOU 97.96%).

cs.CV↗