arXiv subjects

Jie Yao

Publications and source records attributed to Jie Yao.

At least 37 records · Page 2Linked to original sources

arXiv2023

Geometry Enhanced Optimal Control Technique for Acrobatic Flip Motion of Quadcopter

A nonlinear optimal control strategy, named the geometry enhanced finite time $\boldsymbol{θ-}$D technique, is proposed to manipulate the acrobatic flip flight of variable pitch (VP) quadcopter unmanned aerial vehicles (abbreviated as VP copter). A unique superiority of the VP copter, which can provide the thrust in both positive and negative vertical directions by varying the pitch angles of blades, facilitates the acrobatic flip motion. The finite time $\boldsymbol{θ-}$D technique can offer a closed-form near-optimal state feedback control law with online computational efficiency as compared with the finite time state-dependent Riccati equation (SDRE) technique. Meanwhile, by virtue of the geometric technique, the singularity issue of the rotation matrix in the acrobatic flip maneuver can be avoided. The simulation experiments verify the proposed control strategy is effective and efficient.

eess.SY↗

arXiv2023

Universal spin wavepacket transport in van der Waals antiferromagnets

Antiferromagnets (AFMs) are promising platforms for the transmission of quantum information via magnons (the quanta of spin waves), offering advantages over ferromagnets with regard to dissipation, speed of response, and immunity to external fields. Recently, it was shown that in the insulating van der Waals (vdW) semiconductor, CrSBr, strong spin-exciton coupling enables readout of magnon density and propagation using photons of visible light. This exciting observation came with a puzzle: photogenerated magnons were observed to propagate 10$^3$ times faster than the velocity inferred from neutron scattering, leading to a conjecture that spin wavepackets are carried along by coupling to much faster elastic modes. Here we show, through a combination of theory and experiment, that the propagation mechanism is, instead, coupling within the magnetic degrees of freedom through long range dipole-dipole coupling. This mechanism is an inevitable consequence of Maxwell's equations, and as such, will dominate the propagation of spin at long wavelengths in the entire class of vdW magnets currently under intense investigation. Moreover, identifying the mechanism of spin propagation provides a set of optimization rules, as well as caveats, that are essential for any future applications of these promising systems.

cond-mat.str-el↗

arXiv2023

MathAttack: Attacking Large Language Models Towards Math Solving Ability

With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the security of LLMs in math solving ability. Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems. Compared to traditional text adversarial attack, it is essential to preserve the mathematical logic of original MWPs during the attacking. To this end, we propose logical entity recognition to identify logical entries which are then frozen. Subsequently, the remaining text are attacked by adopting a word-level attacker. Furthermore, we propose a new dataset RobustMath to evaluate the robustness of LLMs in math solving ability. Extensive experiments on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth show that MathAttack could effectively attack the math solving ability of LLMs. In the experiments, we observe that (1) Our adversarial samples from higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy (e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot prompts); (2) Complex MWPs (such as more solving steps, longer text, more numbers) are more vulnerable to attack; (3) We can improve the robustness of LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our practice and observation can serve as an important attempt towards enhancing the robustness of LLMs in math solving ability. We will release our code and dataset.

cs.CL↗

arXiv2023

Solving Math Word Problem with Problem Type Classification

Math word problems (MWPs) require analyzing text descriptions and generating mathematical equations to derive solutions. Existing works focus on solving MWPs with two types of solvers: tree-based solver and large language model (LLM) solver. However, these approaches always solve MWPs by a single solver, which will bring the following problems: (1) Single type of solver is hard to solve all types of MWPs well. (2) A single solver will result in poor performance due to over-fitting. To address these challenges, this paper utilizes multiple ensemble approaches to improve MWP-solving ability. Firstly, We propose a problem type classifier that combines the strengths of the tree-based solver and the LLM solver. This ensemble approach leverages their respective advantages and broadens the range of MWPs that can be solved. Furthermore, we also apply ensemble techniques to both tree-based solver and LLM solver to improve their performance. For the tree-based solver, we propose an ensemble learning framework based on ten-fold cross-validation and voting mechanism. In the LLM solver, we adopt self-consistency (SC) method to improve answer selection. Experimental results demonstrate the effectiveness of these ensemble approaches in enhancing MWP-solving ability. The comprehensive evaluation showcases improved performance, validating the advantages of our proposed approach. Our code is available at this url: https://github.com/zhouzihao501/NLPCC2023-Shared-Task3-ChineseMWP.

cs.CL↗

arXiv2023

PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts

Key Information Extraction (KIE) is a challenging multimodal task that aims to extract structured value semantic entities from visually rich documents. Although significant progress has been made, there are still two major challenges that need to be addressed. Firstly, the layout of existing datasets is relatively fixed and limited in the number of semantic entity categories, creating a significant gap between these datasets and the complex real-world scenarios. Secondly, existing methods follow a two-stage pipeline strategy, which may lead to the error propagation problem. Additionally, they are difficult to apply in situations where unseen semantic entity categories emerge. To address the first challenge, we propose a new large-scale human-annotated dataset named Complex Layout form for key information EXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entity categories. To solve the second challenge, we introduce Parallel Pointer-based Network (PPN), an end-to-end model that can be applied in zero-shot and few-shot scenarios. PPN leverages the implicit clues between semantic entities to assist extracting, and its parallel extraction mechanism allows it to extract multiple results simultaneously and efficiently. Experiments on the CLEX dataset demonstrate that PPN outperforms existing state-of-the-art methods while also offering a much faster inference speed.

cs.AI↗

arXiv2023

Learning by Analogy: Diverse Questions Generation in Math Word Problem

Solving math word problem (MWP) with AI techniques has recently made great progress with the success of deep neural networks (DNN), but it is far from being solved. We argue that the ability of learning by analogy is essential for an MWP solver to better understand same problems which may typically be formulated in diverse ways. However most existing works exploit the shortcut learning to train MWP solvers simply based on samples with a single question. In lack of diverse questions, these methods merely learn shallow heuristics. In this paper, we make a first attempt to solve MWPs by generating diverse yet consistent questions/equations. Given a typical MWP including the scenario description, question, and equation (i.e., answer), we first generate multiple consistent equations via a group of heuristic rules. We then feed them to a question generator together with the scenario to obtain the corresponding diverse questions, forming a new MWP with a variety of questions and equations. Finally we engage a data filter to remove those unreasonable MWPs, keeping the high-quality augmented ones. To evaluate the ability of learning by analogy for an MWP solver, we generate a new MWP dataset (called DiverseMath23K) with diverse questions by extending the current benchmark Math23K. Extensive experimental results demonstrate that our proposed method can generate high-quality diverse questions with corresponding equations, further leading to performance improvement on Diverse-Math23K. The code and dataset is available at: https://github.com/zhouzihao501/DiverseMWP

cs.CL↗

arXiv2022

Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning

In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional network that has drawn the attention from considerable researchers in recent years. Most of existing methods only consider the weighted sum of adjacency matrices, yet a joint neural network of both feature and graph fusion is still under-explored. To cope with these issues, this paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF), consisting of two stages: feature fusion network and learnable graph convolutional network. The former aims to learn an underlying feature representation from heterogeneous views, while the latter explores a more discriminative graph fusion via learnable weights and a parametric activation function dubbed Differentiable Shrinkage Activation (DSA) function. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification.

cs.CV↗

arXiv2022

Controlled Ordering of Room-Temperature Magnetic Skyrmions in a Polar Van der Waals Magnet

Control and understanding of ensembles of skyrmions is important for realization of future technologies. In particular, the order-disorder transition associated with the 2D lattice of magnetic skyrmions can have significant implications for transport and other dynamic functionalities. To date, skyrmion ensembles have been primarily studied in bulk crystals, or as isolated skyrmions in thin film devices. Here, we investigate the condensation of the skyrmion phase at room temperature and zero field in a polar, Van der Waals magnet. We demonstrate that we can engineer an ordered skyrmion crystal through structural confinement on the $μ$m scale, showing control over this order-disorder transition on scales relevant for device applications.

cond-mat.mtrl-sci↗

arXiv2022

Direct numerical simulation of turbulent pipe flow up to $Re_τ=5200$

Well-resolved direct numerical simulations (DNSs) have been performed of the flow in a smooth circular pipe of radius $R$ and axial length $10πR$ at friction Reynolds numbers up to $Re_τ=5200$. Various turbulence statistics are documented and compared with other DNS and experimental data in pipes as well as channels.Small but distinct differences between various datasets are identified. The friction factor $λ$ overshoots by $2\%$ and undershoots by $0.6\%$ of the Prandtl friction law at low and high $Re$ ranges, respectively. In addition, $λ$ in our results is slightly higher than that in Pirozzoli et al. (J. Fluid. Mech., 926, A28, 2021), but matches well with the experiments in Furuichi et al. (Phys. Fluids, 27, 095108, 2015). The log-law indicator function, which is nearly indistinguishable between the pipe and channel flows up to $y^+=250$, has not yet developed a plateau further away from the wall in the pipes even for the $Re_τ=5200$ cases. The wall shear stress fluctuations and the inner peak of the axial velocity intensity -- which grow monotonically with $Re_τ$ -- are lower in the pipe than in the channel, but the difference decreases with increasing $Re_τ$. While the wall values are slightly lower in channel than pipe flows at the same $Re_τ$, the inner peaks of the pressure fluctuations show negligible differences between them. The Reynolds number scaling of all these quantities agrees with both the logarithmic and defect power laws if the coefficients are properly chosen. The one-dimensional spectrum of the axial velocity fluctuation exhibits a $k^{-1}$ dependence at an intermediate distance from the wall -- as also seen in the channel flow. In summary, this high-fidelity data enable us to provide better insights into the flow physics in the pipes and the similarity/difference among different types of wall turbulence.

physics.flu-dyn↗

arXiv2022

Pervasive beyond room-temperature ferromagnetism in a doped van der Waals magnet: Ni doped Fe$_5$GeTe$_2$ with $T_{\text{C}}$ up to 478 K

The existence of long range magnetic order in low dimensional magnetic systems, such as the quasi-two-dimensional (2D) van der Waals (vdW) magnets, has attracted intensive studies of new physical phenomena. The vdW Fe$_N$GeTe$_2$ ($N$ = 3, 4, 5; FGT) family is exceptional owing to its vast tunability of magnetic properties. Particularly, a ferromagnetic ordering temperature ($T_{\text{C}}$) above room temperature at $N$ = 5 (F5GT) is observed. Here, our study shows that, by nickel (Ni) substitution of iron (Fe) in F5GT, a record high $T_{\text{C}}$ = 478(6) K is achieved. Importantly, pervasive, beyond-room-temperature ferromagnetism exists in almost the entire doping range of the phase diagram of Ni-F5GT. We argue that this striking observation in Ni-F5GT can be possibly due to several contributing factors, in which the structural alteration enhanced 3D magnetic couplings might be critical for enhancing the ferromagnetic order.

cond-mat.mtrl-sci↗

arXiv2021

Strong Scaling of OpenACC enabled Nek5000 on several GPU based HPC systems

We present new results on the strong parallel scaling for the OpenACC-accelerated implementation of the high-order spectral element fluid dynamics solver Nek5000. The test case considered consists of a direct numerical simulation of fully-developed turbulent flow in a straight pipe, at two different Reynolds numbers $Re_τ=360$ and $Re_τ=550$, based on friction velocity and pipe radius. The strong scaling is tested on several GPU-enabled HPC systems, including the Swiss Piz Daint system, TACC's Longhorn, Jülich's JUWELS Booster, and Berzelius in Sweden. The performance results show that speed-up between 3-5 can be achieved using the GPU accelerated version compared with the CPU version on these different systems. The run-time for 20 timesteps reduces from 43.5 to 13.2 seconds with increasing the number of GPUs from 64 to 512 for $Re_τ=550$ case on JUWELS Booster system. This illustrates the GPU accelerated version the potential for high throughput. At the same time, the strong scaling limit is significantly larger for GPUs, at about $2000-5000$ elements per rank; compared to about $50-100$ for a CPU-rank.

cs.DC↗

arXiv2021

A room temperature polar ferromagnetic metal

The advent of long-range magnetic order in non-centrosymmetric compounds has stimulated interest in the possibility of exotic spin transport phenomena and topologically protected spin textures for applications in next-generation spintronics. This work reports a novel wurtzite-structure polar magnetic metal, identified as AA'-stacked (Fe0.5Co0.5)5-xGeTe2, which exhibits a Neel-type skyrmion lattice as well as a Rashba-Edelstein effect at room temperature. Atomic resolution imaging of the structure reveals a structural transition as a function of Co-substitution, leading to the polar phase at 50% Co. This discovery reveals an unprecedented layered polar magnetic system for investigating intriguing spin topologies and ushers in a promising new framework for spintronics.

cond-mat.mtrl-sci↗

arXiv2021

Composite active drag control in turbulent channel flows

A composite drag control (CDC) combining the opposition (OC) and spanwise opposed wall-jet forcing (SOJF) methods is studied in a turbulent channel flow via direct numerical simulation of the incompressible Navier-Stokes equations. A maximum drag reduction of about 33% is obtained for CDC -- much higher than that produced by either individual method (namely, 19% for SOJF and 23% for OC). Due to the small power input required for both OC and SOJF methods, a significant net power saving (about 32%) is achieved via CDC. Flow analysis shows that CDC can take advantage of both OC and SOJF methods to better suppress drag producing, near-wall turbulent structures -- vortices and streaks. In particular, due to the presence of the large-scale coherent swirls generated by SOJF, it is more effective than OC in suppressing the random turbulence. Moreover, due to the OC's role in suppressing random small-scale turbulence, CDC requires weaker large-scale coherent swirls than those using SOJF only -- hence decreasing the drag contribution associated with large-scale swirls. In summary, our results suggest prospects of employing composite control strategy for effective skin friction drag reduction, particularly at very high Reynolds numbers.

physics.flu-dyn↗

arXiv2020

Growth and Properties of Dislocated Two-dimensional Layered Materials

Two-dimensional (2D) layered materials hosting dislocations have attracted considerable research attention in recent years. In particular, screw dislocations can result in a spiral topology and an interlayer twist in the layered materials, significantly impacting the stacking order and symmetry of the layers. Moreover, the dislocations with large strain and heavily distorted atomic registry can result in a local modification of the structures around the dislocation. The dislocations thus provide a useful route to engineering optical, electrical, thermal, mechanical and catalytic properties of the 2D layered materials, which show great potential to bring new functionalities. This article presents a comprehensive review of the experimental and theoretical progress on the growth and properties of the dislocated 2D layered materials. It also offers an outlook on the future works in this promising research field.

cond-mat.mtrl-sci↗

arXiv2020

Separation scaling for viscous vortex reconnection

Reconnection plays a significant role in the dynamics of plasmas, polymers and macromolecules, as well as in numerous laminar and turbulent flow phenomena in both classical and quantum fluids. Extensive studies in quantum vortex reconnection show that the minimum separation distance δ between interacting vortices follows a 1/2 scaling. Due to the complex nature of the dynamics (e.g., the formation of bridges and threads as well as successive reconnections and avalanche), such scaling has never been reported for (classical) viscous vortex reconnection. Using the direct numerical simulation of the Navier-Stokes equations, we study viscous reconnection of slender vortices, whose core size is much smaller than the radius of the vortex curvature. For separations that are large compared to the vortex core size, we discover that δ(t) between the two interacting viscous vortices surprisingly also follows the 1/2 power scaling for both pre- and post-reconnection events. The prefactors in this 1/2-power law are found to depend not only on the initial configuration but also on the vortex Reynolds number (or viscosity). Our finding in the viscous reconnection, complementing numerous works on quantum vortex reconnection, suggests that there is indeed a universal route for reconnection -- an essential result for understanding the various facets of the viscous vortex reconnection phenomena and their potential modeling, as well as possibly explaining turbulence cascade physics.

physics.flu-dyn↗

arXiv2020

Statistical mechanics of a nonequilibrium steady-state classical particle system driven by a constant external force

A classical particle system coupled with a thermostat driven by an external constant force reaches its steady state when the ensemble-averaged drift velocity does not vary with time. The statistical mechanics of such a system is derived merely based on the equal probability and ergodicity principles, free from any conclusions drawn on equilibrium statistical mechanics or local equilibrium hypothesis. The momentum space distribution is determined by a random walk argument, and the position space distribution is determined by employing the equal probability and ergodicity principles. The expressions for energy, entropy, free energy, and pressures are then deduced, and the relation among external force, drift velocity, and temperature is also established. Moreover, the relaxation towards its equilibrium is found to be an exponentially decaying process obeying the minimum entropy production theorem.

cond-mat.stat-mech↗

arXiv2019

Generalized Fourier transform method for nonlinear anomalous diffusion equation

The solution of a nonlinear diffusion equation is numerically investigated using the generalized Fourier transform method. This equation includes fractal dimensions and power-law dependence on the radial variable and on the diffusion function. The generalized Fourier transform approach is the extension of the Fourier transform method used for normal diffusion equation. The feasibility of the approach is validated by comparing the numerical result with the exact solution for point-source. The merit of numerical method is that it provide a way to calculate anomalous diffusion with an arbitrary initial condition.

physics.comp-ph↗

arXiv2019

Phase-change silicon as an ultrafast active photonic platform

Phase change material (PCM) features distinct optical or electronic properties between amorphous and crystalline states. Recently, it starts to play a key role in the emerging photonic applications like optoelectronic display, dynamic wavefront control, on-chip photonic memory and computation. However, current PCMs do not refract effectively at visible wavelengths and suffer from deformation and decomposition, limiting the repeatability and vast visible wavelength applications. Silicon as the fundamental material for electronics and photonics, has never been considered as phase change material, due to its ultrafast crystallization kinetics. Here we show the striking fact that nanoscale silicon domains can be reversibly crystallized and amorphized under nanosecond laser pulses. For a typical disk resonator, it also provides a 25% non-volatile modulation at nanosecond time scale. We further show proof-of-concept experiments that such attributes could enable ultra-high resolution dielectric color display and dynamic visible wavefront control.

physics.optics↗