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Researcher profile

Hua Wang

Hua Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-scicond-mat.mes-halleess.SPInformation Theorymath.COmath.ITMachine LearningComputer Visioncond-mat.othereess.SYphysics.opticsSystems and ControlArtificial IntelligenceComputation and Languagecond-mat.supr-coneess.IVmath.CAmath.FAmath.NAmath.OCmath.STNetworking and Internet ArchitectureNumerical AnalysisQuantitative MethodsRoboticsStatistics Theory

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Published work

30 published item(s)

preprint2026arXiv

What Will Happen Next: Large Models-Driven Deduction for Emergency Instances

Traditional simulation methods reproduce occurred emergency instances through presetting to assist people in risk assessment and emergency decision-making. However, due to the lack of randomness and diversity, existing simulation systems struggle to fully explore the potential risk as emergency instances are scarce. In contrast, Large Models (LMs) can dynamically adjust generation strategies to introduce controllable randomness, while also possessing extensive prior knowledge and cross-domain knowledge transfer capabilities. Inspired by it, we propose the LMs-driven World Line Divergence System (WLDS), which enables diversified visualization and deduction of emergency instances in different domains. WLDS leverages LMs to deduce emergency instances in various development directions, and introduces the factual calibration and logical calibration mechanism to ensure factual accuracy and logical rigor during the deduction process. The interactive module can independently select deduction directions to avoid potential hallucinations that are difficult for the system to identify. Furthermore, by introducing the visualization module, WLDS forms simulation and deduction that combine text and images, which enhances interpretability. Extensive experiments conducted on the proposed Emergency Instances Deduction (EID) benchmark dataset demonstrate that WLDS achieves high-precision and high-fidelity simulation and deduction of emergency instances in multiple specific domains. Relevant experiments further demonstrate that WLDS can generate more emergency instances deduction data for users and provide support for better decision-making in similar emergency instances in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network

This paper presents a deep learning assisted synthesis approach for direct end-to-end generation of RF/mm-wave passive matching network with 3D EM structures. Different from prior approaches that synthesize EM structures from target circuit component values and target topologies, our proposed approach achieves the direct synthesis of the passive network given the network topology from desired performance values as input. We showcase the proposed synthesis Neural Network (NN) model on an on-chip 1:1 transformer-based impedance matching network. By leveraging parameter sharing, the synthesis NN model successfully extracts relevant features from the input impedance and load capacitors, and predict the transformer 3D EM geometry in a 45nm SOI process that will match the standard 50$Ω$ load to the target input impedance while absorbing the two loading capacitors. As a proof-of-concept, several example transformer geometries were synthesized, and verified in Ansys HFSS to provide the desired input impedance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Generalized Wilson Loop Method for Nonlinear Light-Matter Interaction

Nonlinear light-matter interaction, as the core of ultrafast optics, bulk photovoltaics, nonlinear optical sensing and imaging, and efficient generation of entangled photons, has been traditionally studied by first-principles theoretical methods with the sum-over-states approach. However, this indirect method often suffers from the divergence at band degeneracy and optical zeros as well as convergence issues and high computation costs when summing over the states. Here, using shift vector and shift current conductivity tensor as an example, we present a gauge-invariant generalized approach for efficient and direct calculations of nonlinear optical responses by representing interband Berry curvature, quantum metric, and shift vector in a generalized Wilson loop. This generalized Wilson loop method avoids the above cumbersome challenges and allows for easy implementation and efficient calculations. More importantly, the Wilson loop representation provides a succinct geometric interpretation of nonlinear optical processes and responses based on quantum geometric tensors and quantum geometric potentials and can be readily applied to studying other excited-state responses.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Homogeneous fractional integral operators on Lebesgue and Morrey spaces, Hardy--Littlewood--Sobolev and Olsen-type inequalities

Let $T_{Ω,α}$ be the homogeneous fractional integral operator defined as \begin{equation*} T_{Ω,α}f(x):=\int_{\mathbb R^n}\frac{Ω(x-y)}{|x-y|^{n-α}}f(y)\,dy, \end{equation*} and the related fractional maximal operator $M_{Ω,α}$ is given by \begin{equation*} M_{Ω,α}f(x):=\sup_{r>0}\frac{1}{|B(x,r)|^{1-α/n}}\int_{|x-y|<r}|Ω(x-y)f(y)|\,dy. \end{equation*} In this article, we will use the idea of Hedberg to reprove that the operators $T_{Ω,α}$ and $M_{Ω,α}$ are bounded from $L^p(\mathbb R^n)$ to $L^q(\mathbb R^n)$ provided that $Ω\in L^s(\mathbf{S}^{n-1})$, $s&#39;<p<n/α$ and $1/q=1/p-α/n$, which was obtained by Muckenhoupt and Wheeden. We also reprove that under the assumptions that $Ω\in L^s(\mathbf{S}^{n-1})$, $s&#39;\leq p<n/α$ and $1/q=1/p-α/n$, the operators $T_{Ω,α}$ and $M_{Ω,α}$ are bounded from $L^p(\mathbb R^n)$ to $L^{q,\infty}(\mathbb R^n)$, which was obtained by Chanillo, Watson and Wheeden. We will use the idea of Adams to show that $T_{Ω,α}$ and $M_{Ω,α}$ are bounded from $L^{p,κ}(\mathbb R^n)$ to $L^{q,κ}(\mathbb R^n)$ whenever $s&#39;<p<n/α$ and $1/q=1/p-α/{n(1-κ)}$, and bounded from $L^{p,κ}(\mathbb R^n)$ to $WL^{q,κ}(\mathbb R^n)$ whenever $s&#39;\leq p<n/α$ and $1/q=1/p-α/{n(1-κ)}$. Some new estimates in the limiting cases are also established. The results obtained are substantial improvements and extensions of some known results. Moreover, we will apply these results to several well-known inequalities such as Hardy--Littlewood--Sobolev and Olsen-type inequalities.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Modelling and Experimental Validation for Battery Lifetime Estimation in NB-IoT and LTE-M

Internet of Things (IoT) is one of the main features in 5G. Low-power wide-area networking (LPWAN) has attracted enormous research interests to enable large scale deployment of IoT, with the design objectives of low cost, wide coverage area, as well as low power consumption. In particular, long battery lifetime is essential since many of the IoT devices will be deployed in hard-to-access locations. Prediction of the battery lifetime depends on the accurate modelling of energy consumption. This paper presents a comprehensive power consumption model for battery lifetime estimation, which is based on User Equipment(UE) states and procedures, for two cellular IoT technologies: Narrowband Internet of Things (NB-IoT) and Long Term Evolution for Machines (LTE-M). A measurement testbed has been setup and the proposed model has been tested and validated via extensive measurements under various traffic patterns and network scenarios, achieving the modelling inaccuracy within5%. The measurement results show that the battery lifetime of an IoT device can reach up to 10 years as required by 3GPP, with proper configuration of the traffic profile, the coverage scenario, as well as the network configuration parameters.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nonlinear Nonreciprocal Photocurrents under Phonon Dressing

Nonlinear optical (NLO) effects have attracted great interest recently. However, by far the computational studies on NLO use the independent particle approximation and ignore many-body effects. Here we develop a generic Green&#39;s function framework to calculate the NLO response functions, which can incorporate various many-body interactions. We focus on the electron-phonon coupling and reveal that phonon dressing can make significant impacts on nonlinear photocurrent, such as the bulk photovoltaic (BPV) and bulk spin photovoltaic (BSPV) currents. BPV/BSPV should be zero for centrosymmetric crystals, but when phonons are driven out-of-equilibrium, by for example, a temperature gradient $\nabla T$, the optical selections rules are altered and phonon-pumped BPV/BSPV currents can be non-zero in nominally centrosymmetric crystal. Moreover, we elucidate that such NLO responses under non-equilibrium phonon dressing can be nonreciprocal, as the direction of the current does not necessarily get reversed when the direction of the temperature gradient is reversed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SLAM-TKA: Real-time Intra-operative Measurement of Tibial Resection Plane in Conventional Total Knee Arthroplasty

Total knee arthroplasty (TKA) is a common orthopaedic surgery to replace a damaged knee joint with artificial implants. The inaccuracy of achieving the planned implant position can result in the risk of implant component aseptic loosening, wear out, and even a joint revision, and those failures most of the time occur on the tibial side in the conventional jig-based TKA (CON-TKA). This study aims to precisely evaluate the accuracy of the proximal tibial resection plane intra-operatively in real-time such that the evaluation processing changes very little on the CON-TKA operative procedure. Two X-ray radiographs captured during the proximal tibial resection phase together with a pre-operative patient-specific tibia 3D mesh model segmented from computed tomography (CT) scans and a trocar pin 3D mesh model are used in the proposed simultaneous localisation and mapping (SLAM) system to estimate the proximal tibial resection plane. Validations using both simulation and in-vivo datasets are performed to demonstrate the robustness and the potential clinical value of the proposed algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Price of Competition: Effect Size Heterogeneity Matters in High Dimensions

In high-dimensional sparse regression, would increasing the signal-to-noise ratio while fixing the sparsity level always lead to better model selection? For high-dimensional sparse regression problems, surprisingly, in this paper we answer this question in the negative in the regime of linear sparsity for the Lasso method, relying on a new concept we term effect size heterogeneity. Roughly speaking, a regression coefficient vector has high effect size heterogeneity if its nonzero entries have significantly different magnitudes. From the viewpoint of this new measure, we prove that the false and true positive rates achieve the optimal trade-off uniformly along the Lasso path when this measure is maximal in a certain sense, and the worst trade-off is achieved when it is minimal in the sense that all nonzero effect sizes are roughly equal. Moreover, we demonstrate that the first false selection occurs much earlier when effect size heterogeneity is minimal than when it is maximal. The underlying cause of these two phenomena is, metaphorically speaking, the ``competition&#39;&#39; among variables with effect sizes of the same magnitude in entering the model. Taken together, our findings suggest that effect size heterogeneity shall serve as an important complementary measure to the sparsity of regression coefficients in the analysis of high-dimensional regression problems. Our proofs use techniques from approximate message passing theory as well as a novel technique for estimating the rank of the first false variable.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ultrafast switching dynamics of the ferroelectric order in stacking-engineered ferroelectrics

The recently discovered ferroelectricity of van der Waals bilayers offers an unconventional route to improve the performance of devices. Key parameters such as switching field and speed depend on the static and dynamic properties of domain walls (DWs). Here we theoretically explore the properties of textures in stacking-engineered ferroelectrics from first principles. Employing a machine-learning potential model, we present results of large-scale atomistic simulations of stacking DWs and Moiré structure of boron nitride bilayers. We predict that the competition between the switching barrier of stable ferroelectric states and the in-plane lattice distortion leads to a DW width of the order of ten nanometers. DWs motion reduces the critical ferroelectric switching field of a monodomain by two orders of magnitude, while high domain-wall velocities allow domain switching on a picosecond-timescale. The superior performance compared to conventional ferroelectrics (or ferromagnets) may enable ultrafast and power-saving non-volatile memories. By twisting the bilayer into a stacking Moiré structure, the ferroelectric transforms into a super-paraelectric since DWs move under ultralow electric fields.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Abnormal Surface Nonlinear Optical Responses in Topological Materials

Nonlinear optical (NLO) responses of topological materials are under active research in recent years. Yet by far, most studies focused on the bulk properties, whereas the surface effects and the difference between surface and bulk responses have not been systematically studied. Here we develop a generic Green&#39;s function framework to investigate the surface NLO properties of topological materials. The Green&#39;s function framework can naturally incorporate many-body effects and can be easily extended to high-order NLO responses. Using Td-WTe2 as an example, we reveal that the surface can behave disparately from the bulk under light illumination. Remarkably, the shift and circular currents on the surface can flow in opposite directions to those in the bulk interior. Moreover, the light-induced spin current on the surface can be orders of magnitude stronger than its bulk counterpart. We also study the responses under inhomogeneous field and higher-order NLO effect, which are all distinct on the surface. These anomalous surface NLO responses suggest that light can be a valuable tool for probing the surface states of topological materials. On the other hand, the surface effects shall be prudently considered when investigating the optical properties of topological materials, especially if the material is of nanoscale and/or the light penetration depth is small.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Colossal switchable photocurrents in topological Janus transition metal dichalcogenides

Nonlinear optical properties, such as bulk photovoltaic effects, possess great potential in energy harvesting, photodetection, rectification, etc. To enable efficient light-current conversion, materials with strong photo-responsivity are highly desirable. In this work, we predict that monolayer Janus transition metal dichalcogenides (JTMDs) in the 1T&#39; phase possess colossal nonlinear photoconductivity owing to their topological band mixing, strong inversion symmetry breaking, and small electronic bandgap. 1T&#39; JTMDs have inverted bandgaps on the order of 10 meV and are exceptionally responsive to light in the terahertz (THz) range. By first-principles calculations, we reveal that 1T&#39; JTMDs possess shift current (SC) conductivity as large as $2300 ~\rm nm \cdot μA / V^2$, equivalent to a photo-responsivity of $2800 ~\rm mA/W$. The circular current (CC) conductivity of 1T&#39; JTMDs is as large as $10^4~ \rm nm \cdot μA / V^2$. These remarkable photo-responsivities indicate that the 1T&#39; JTMDs can serve as efficient photodetectors in the THz range. We also find that external stimuli such as the in-plane strain and out-of-plane electric field can induce topological phase transitions in 1T&#39; JTMDs and that the SC can abruptly flip their directions. The abrupt change of the nonlinear photocurrent can be used to characterize the topological transition and has potential applications in 2D optomechanics and nonlinear optoelectronics.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Complex Dirac-like Electronic Structure in Atomic Site Ordered Rh3In3.4Ge3.6

We report the synthesis via an indium flux method of a novel single-crystalline compound Rh3In3.4Ge3.6 that belongs to the cubic Ir3Ge7 structure type. In Rh3In3.4Ge3.6, the In and Ge atoms choose to preferentially occupy, respectively, the 12d and 16f sites of the Im-3m space group, thus creating a colored version of the Ir3Ge7 structure. Like the other compounds of the Ir3Ge7 family, Rh3In3.4Ge3.6 shows potential as a thermoelectric displaying a relatively large power factor, PF ~ 2 mW/cmK2, at a temperature T ~ 225 K albeit showing a modest figure of merit, ZT = 8 x 10-4, due to the lack of a finite band gap. These figures might improve through a use of chemical substitution strategies to achieve band gap opening. Remarkably, electronic band structure calculations reveal that this compound displays a complex Dirac-like electronic structure relatively close to the Fermi level. The electronic structure is composed of several Dirac type-I and type-II nodes, and even Dirac type-III nodes that result from the touching between a flat band and a linearly dispersing band. This rich Dirac-like electronic dispersion offers the possibility to observe Dirac type-III nodes and study their role in the physical properties of Rh3In3.4Ge3.6 and related Ir3Ge7-type materials.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the unique {\emph{triple delay-beam-Doppler squint effects}} of aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designed {\emph{Rotman lens}}-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Berry curvature memory through electrically driven stacking transitions

In two-dimensional layered quantum materials, the stacking order of the layers determines both the crystalline symmetry and electronic properties such as the Berry curvature, topology and electron correlation. Electrical stimuli can influence quasiparticle interactions and the free-energy landscape, making it possible to dynamically modify the stacking order and reveal hidden structures that host different quantum properties. Here we demonstrate electrically driven stacking transitions that can be applied to design nonvolatile memory based on Berry curvature in few-layer WTe$_2$. The interplay of out-of-plane electric fields and electrostatic doping controls in-plane interlayer sliding and creates multiple polar and centrosymmetric stacking orders. In situ nonlinear Hall transport reveals such stacking rearrangements result in a layer-parity-selective Berry curvature memory in momentum space, where the sign reversal of the Berry curvature and its dipole only occurs in odd-layer crystals. Our findings open an avenue towards exploring coupling between topology, electron correlations, and ferroelectricity in hidden stacking orders and demonstrate a new low-energy-cost, electrically controlled topological memory in the atomically thin limit.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Electrically-Tunable High Curie Temperature Two-Dimensional Ferromagnetism in Van der Waals Layered Crystals

Identifying intrinsic low-dimensional ferromagnets with high transition temperature and electrically tunable magnetism is crucial for the development of miniaturized spintronics and magnetoelectrics. Recently long-range 2D ferromagnetism was observed in van der Waals crystals CrI$_3$ and Cr$_2$Ge$_2$Te$_6$, however their Curie temperature is significantly lowered when reducing down to monolayer/few layers. Herein, using renormalized spin-wave theory and first-principles electronic structure theory, we present a theoretical study of electrically tunable 2D ferromagnetism in van der Waals layered CrSBr and CrSeBr semiconductors with high Curie temperature of ~150K and sizable band gap. High transition temperature is attributed to strong anion-mediated superexchange interaction and a sizable spin-wave excitation gap due to large exchange and single-ion anisotropy. Remarkably, hole and electron doping can switch magnetization easy axis from in-plane to out-of-plane direction. These unique characteristics establish monolayer CrSBr and CrSeBr as promising platform for realizing 2D spintronics and magnetoelectrics such as 2D spin field effect transistor.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Enhanced Superconductivity in Monolayer $T_d$-MoTe$_2$ with Tilted Ising Spin Texture

Crystalline two-dimensional (2D) superconductors with low carrier density are an exciting new class of materials in which superconductivity coexists with strong interactions, the effects of complex topology are not obscured by disorder, and electronic properties can be strongly tuned by electrostatic gating. Very recently, two such materials, &#39;magic-angle&#39; twisted bilayer graphene and monolayer $T_d$-WTe$_2$, have been reported to show superconductivity at temperatures near 1 K. Here we report superconductivity in semimetallic monolayer $T_d$-MoTe$_2$. The critical temperature $T_\textrm{c}$ reaches 8 K, a sixty-fold enhancement as compared to the bulk. This anomalous increase in $T_\textrm{c}$ is only observed in monolayers, and may be indicative of electronically mediated pairing. Reflecting the low carrier density, the critical temperature, magnetic field, and current density are all tunable by an applied gate voltage, revealing a superconducting dome that extends across both hole and electron pockets. The temperature dependence of the in-plane upper critical field is distinct from that of $2H$ transition metal dichalcogenides (TMDs), consistent with a tilted spin texture as predicted by \textit{ab initio} theory.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Exploiting Review Neighbors for Contextualized Helpfulness Prediction

Helpfulness prediction techniques have been widely used to identify and recommend high-quality online reviews to customers. Currently, the vast majority of studies assume that a review&#39;s helpfulness is self-contained. In practice, however, customers hardly process reviews independently given the sequential nature. The perceived helpfulness of a review is likely to be affected by its sequential neighbors (i.e., context), which has been largely ignored. This paper proposes a new methodology to capture the missing interaction between reviews and their neighbors. The first end-to-end neural architecture is developed for neighbor-aware helpfulness prediction (NAP). For each review, NAP allows for three types of neighbor selection: its preceding, following, and surrounding neighbors. Four weighting schemes are designed to learn context clues from the selected neighbors. A review is then contextualized into the learned clues for neighbor-aware helpfulness prediction. NAP is evaluated on six domains of real-world online reviews against a series of state-of-the-art baselines. Extensive experiments confirm the effectiveness of NAP and the influence of sequential neighbors on a current reviews. Further hyperparameter analysis reveals three main findings. (1) On average, eight neighbors treated with uneven importance are engaged for context construction. (2) The benefit of neighbor-aware prediction mainly results from closer neighbors. (3) Equally considering up to five closest neighbors of a review can usually produce a weaker but tolerable prediction result.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Finite Element Methods For Interface Problems On Local Anisotropic Fitting Mixed Meshes

A simple and efficient interface-fitted mesh generation algorithm is developed in this paper. This algorithm can produce a local anisotropic fitting mixed mesh which consists of both triangles and quadrilaterals near the interface. A new finite element method is proposed for second order elliptic interface problems based on the resulting mesh. Optimal approximation capabilities on anisotropic elements are proved in both the $H^1$ and $L^2$ norms. The discrete system is usually ill-conditioned due to anisotropic and small elements near the interface. Thereupon, a multigrid method is presented to handle this issue. The convergence rate of the multigrid method is shown to be optimal with respect to both the coefficient jump ratio and mesh size. Numerical experiments are presented to demonstrate the theoretical results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Giant nonlinear photocurrent in $\mathcal{PT}$-symmetric magnetic topological quantum materials

Nonlinear photocurrent in time-reversal invariant noncentrosymmetric systems have attracted substantial interest. Here we propose two new types of second-order nonlinear direct photocurrent as the counterpart of normal shift photocurrent (NSC) and normal injection photocurrent (NIC), namely magnetic shift photocurrent (MSC) and magnetic injection photocurrent (MIC) in time-reversal symmetry and inversion symmetry broken system. We show that MSC is mainly governed by shift vector and interband Berry curvature, and MIC is dominated by absorption strength and asymmetry of the group velocity difference at time-reversed $\pm$$\textbf{k}$ points. MSC and MIC can be induced by circularly and linearly polarized light, respectively, in $\mathcal{PT}$-symmetric systems with $\mathcal{P}$ and $\mathcal{T}$ being individually broken. Taking $\mathcal{PT}$-symmetric magnetic topological quantum material bilayer antiferromagnetic (AFM) MnBi$_2$Te$_4$ as an example, we predict the presence of large MIC in the terahertz frequency regime which can be magnetically switched between two AFM states with time-reversed spin orderings. While NSC vanishes in $\mathcal{T}$-symmetric systems, external electric field breaks $\mathcal{PT}$ symmetry and enables large NSC response which can be electrically switched. MIC and NSC are perpendicular to each other upon linearly $x$/$y$-polarized light, and are highly tunable under electric field, resulting in giant nonlinear photocurrent response down to a few THz regime. It suggests bilayer AFM MnBi$_2$Te$_4$ as a tunable platform with rich THz and magneto-optoelectronic applications. The present work reveals that nonlinear photocurrent provides a powerful tool for deciphering magnetic structures and interactions, particularly fruitful for probing and understanding magnetic topological quantum materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Giant Photonic Response of Mexican-hat Topological Semiconductors for Mid-infrared to THz Applications

The mid-infrared (MIR), far-infrared (FIR) to terahertz (THz) frequencies are the least developed parts of the electromagnetic spectrum for applications. Traditional semiconductor technologies like laser diodes and photodetectors are successful in the visible light range, but are still confronted with great challenges when extended into the MIR/FIR/THz range. In this paper, we demonstrate that topological insulators (TIs), especially those with Mexican-hat band structure (MHBS), provide a route to overcome these challenges. The optical responses of MHBS TIs can be one to two orders of magnitude larger than that of normal semiconductors at the optical-transition edge. We explore the databases of topological materials and discover a number of MHBS TIs whose bandgaps lie between $0.05\sim 0.5~\rm eV$ and possess giant gains (absorption coefficients) on the order of $10^4 \sim 10^5~\rm cm^{-1}$ at the transition edge. These findings may significantly boost potential MIR/FIR/THz applications such as photon sources, detectors, ultrafast electro-optical devices, and quantum information technologies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Globally optimal dense and sparse spanning trees, and their applications

Finding spanning trees under various constraints is a classic problem with applications in many fields. Recently, a novel notion of &#34;dense&#34; (&#34;sparse&#34;) tree, and in particular spanning tree (DST and SST respectively), is introduced as the structure that have a large (small) number of subtrees, or small (large) sum of distances between vertices. We show that finding DST and SST reduces to solving the discrete optimization problems. New and efficient approaches to find such spanning trees is achieved by imposing certain conditions on the vertex degrees which are then used to define an objective function that is minimized over all spanning trees of the graph under consideration. Solving this minimization problem exactly may be prohibitively time consuming for large graphs. Hence, we propose to use genetic algorithm (GA) which is one of well known metaheuristics methods to solve DST and SST approximately. As far as we are aware this is the first time GA has been used in this context. We also demonstrate on a number of applications that GA approach is well suited for these types of problems both in computational efficiency and accuracy of the approximate solution. Furthermore, we improve the efficiency of the proposed method by using Kruskal&#39;s algorithm in combination with GA. The application of our methods to several practical large graphs and networks is presented. Computational results show that they perform faster than previously proposed heuristic methods and produce more accurate solutions. Furthermore, the new feature of the proposed approach is that it can be applied recursively to sub-trees or spanning trees with additional constraints in order to further investigate the graphical properties of the graph and/or network. The application of this methodology on the gene network of a cancer cell led to isolating key genes in a network that were not obvious from previous studies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO

Reconfigurable intelligent surface (RIS) is considered to be an energy-efficient approach to reshape the wireless environment for improved throughput. Its passive feature greatly reduces the energy consumption, which makes RIS a promising technique for enabling the future smart city. Existing beamforming designs for RIS mainly focus on optimizing the spectral efficiency for single carrier systems. To avoid the complicated bit allocation on different spatial domain subchannels in MIMO systems, in this paper, we propose a geometric mean decomposition-based beamforming for RIS-assisted millimeter wave (mmWave) hybrid MIMO systems so that multiple parallel data streams in the spatial domain can be considered to have the same channel gain. Specifically, by exploiting the common angular-domain sparsity of mmWave massive MIMO channels over different subcarriers, a simultaneous orthogonal match pursuit algorithm is utilized to obtain the optimal multiple beams from an oversampling 2D-DFT codebook. Moreover, by only leveraging the angle of arrival and angle of departure associated with the line of sight (LoS) channels, we further design the phase shifters for RIS by maximizing the array gain for LoS channel. Simulation results show that the proposed scheme can achieve better BER performance than conventional approaches. Our work is an initial attempt to discuss the broadband hybrid beamforming for RIS-assisted mmWave hybrid MIMO systems.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results

This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track 1) and a weakly-supervised track (track 2) for two benchmark datasets. In particular, track 1 offers a new Internet video benchmark, requiring algorithms to learn the map from more compressed videos to less compressed videos in a supervised training manner. In track 2, algorithms are required to learn the quality mapping from one device to another when their quality varies substantially and weakly-aligned video pairs are available. For track 1, in total 7 teams competed in the final test phase, demonstrating novel and effective solutions to the problem. For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Principal Component Analysis Based Broadband Hybrid Precoding for Millimeter-Wave Massive MIMO Systems

Hybrid analog-digital precoding is challenging for broadband millimeter-wave (mmWave) massive MIMO systems, since the analog precoder is frequency-flat but the mmWave channels are frequency-selective. In this paper, we propose a principal component analysis (PCA)-based broadband hybrid precoder/combiner design, where both the fully-connected array and partially-connected subarray (including the fixed and adaptive subarrays) are investigated. Specifically, we first design the hybrid precoder/combiner for fully-connected array and fixed subarray based on PCA, whereby a low-dimensional frequency-flat precoder/combiner is acquired based on the optimal high-dimensional frequency-selective precoder/combiner. Meanwhile, the near-optimality of our proposed PCA approach is theoretically proven. Moreover, for the adaptive subarray, a low-complexity shared agglomerative hierarchical clustering algorithm is proposed to group the antennas for the further improvement of spectral efficiency (SE) performance. Besides, we theoretically prove that the proposed antenna grouping algorithm is only determined by the slow time-varying channel parameters in the large antenna limit. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art schemes in SE, energy efficiency (EE), bit-error-rate performance, and the robustness to time-varying channels. Our work reveals that the EE advantage of adaptive subarray over fully-connected array is obvious for both active and passive antennas, but the EE advantage of fixed subarray only holds for passive antennas.

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preprint2020arXiv

Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits. As a proof of concept, we demonstrate the direct synthesis of a 1:1 transformer on a 45nm SOI process using our proposed neural network model. Using pre-existing transformer s-parameter files and their geometric design training samples, the model predicts target geometric designs.

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preprint2020arXiv

Subtrees and independent subsets in unicyclic graphs and unicyclic graphs with fixed segment sequence

In the study of topological indices two negative correlations are well known: that between the number of subtrees and the Wiener index (sum of distances), and that between the Merrifield-Simmons index (number of independent vertex subsets) and the Hosoya index (number of independent edge subsets). That is, among a certain class of graphs, the extremal graphs that maximize one index usually minimize the other, and vice versa. In this paper, we first study the numbers of subtrees in unicyclic graphs and unicyclic graphs with a given girth, further confirming its opposite behavior to the Wiener index by comparing with known results. We then consider the unicyclic graphs with a given segment sequence and characterize the extremal structure with the maximum number of subtrees. Furthermore, we show that these graphs are not extremal with respect to the Wiener index. We also identify the extremal structures that maximize the number of independent vertex subsets among unicyclic graphs with a given segment sequence, and show that they are not extremal with respect to the number of independent edge subsets. These results may be the first examples where the above negative correlation failed in the extremal structures between these two pairs of indices.

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preprint2020arXiv

The expected subtree number index in random polyphenylene and spiro chains

Subtree number index $\emph{STN}(G)$ of a graph $G$ is the number of nonempty subtrees of $G$. It is a structural and counting based topological index that has received more and more attention in recent years. In this paper we first obtain exact formulas for the expected values of subtree number index of random polyphenylene and spiro chains, which are molecular graphs of a class of unbranched multispiro molecules and polycyclic aromatic hydrocarbons. Moreover, we establish a relation between the expected values of the subtree number indices of a random polyphenylene and its corresponding hexagonal squeeze. We also present the average values for subtree number indices with respect to the set of all polyphenylene and spiro chains with $n$ hexagons.

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preprint2020arXiv

The Steiner Wiener index of trees with a given segment sequence

The Steiner distance of vertices in a set $S$ is the minimum size of a connected subgraph that contain these vertices. The sum of the Steiner distances over all sets $S$ of cardinality $k$ is called the Steiner $k$-Wiener index and studied as the natural generalization of the famous Wiener index in chemical graph theory. In this paper we study the extremal structures, among trees with a given segment sequence, that maximize or minimize the Steiner $k$-Wiener index. The same extremal problems are also considered for trees with a given number of segments.

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preprint2019arXiv

An Efficient Pre-processing Method to Eliminate Adversarial Effects

Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated by imposing subtle perturbations to inputs that lead a model to predict incorrect outputs. Currently, a large number of researches on defending adversarial examples pay little attention to the real-world applications, either with high computational complexity or poor defensive effects. Motivated by this observation, we develop an efficient preprocessing method to defend adversarial images. Specifically, before an adversarial example is fed into the model, we perform two image transformations: WebP compression, which is utilized to remove the small adversarial noises. Flip operation, which flips the image once along one side of the image to destroy the specific structure of adversarial perturbations. Finally, a de-perturbed sample is obtained and can be correctly classified by DNNs. Experimental results on ImageNet show that our method outperforms the state-of-the-art defense methods. It can effectively defend adversarial attacks while ensure only very small accuracy drop on normal images.

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preprint2019arXiv

Ferroelectric nonlinear anomalous Hall effect in few-layer WTe$_2$

Under broken time reversal symmetry such as in the presence of external magnetic field or internal magnetization, a transverse voltage can be established in materials perpendicular to both longitudinal current and applied magnetic field, known as classical Hall effect. However, this symmetry constraint can be relaxed in the nonlinear regime, thereby enabling nonlinear anomalous Hall current in time-reversal invariant materials - an underexplored realm with exciting new opportunities beyond classical linear Hall effect. Here, using group theory and first-principles theory, we demonstrate a remarkable ferroelectric nonlinear anomalous Hall effect in time-reversal invariant few-layer WTe$_2$ where nonlinear anomalous Hall current switches in odd-layer WTe$_2$ while remaining invariant in even-layer WTe$_2$ upon ferroelectric transition. This even-odd oscillation of ferroelectric nonlinear anomalous Hall effect was found to originate from the absence and presence of Berry curvature dipole reversal and shift dipole reversal due to distinct ferroelectric transformation in even and odd-layer WTe$_2$. Our work not only treats Berry curvature dipole and shift dipole on an equal footing to account for intraband and interband contributions to nonlinear anomalous Hall effect, but also establishes Berry curvature dipole and shift dipole as new order parameters for noncentrosymmetric materials. The present findings, therefore, suggest that ferroelectric metals and Weyl semimetals may offer unprecedented opportunities for the development of nonlinear quantum electronics.

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