arXiv subjects

Zhen Yang

Publications and source records attributed to Zhen Yang.

At least 199 records · Page 11Linked to original sources

arXiv2022

Improving Stack Overflow question title generation with copying enhanced CodeBERT model and bi-modal information

Context: Stack Overflow is very helpful for software developers who are seeking answers to programming problems. Previous studies have shown that a growing number of questions are of low quality and thus obtain less attention from potential answerers. Gao et al. proposed an LSTM-based model (i.e., BiLSTM-CC) to automatically generate question titles from the code snippets to improve the question quality. However, only using the code snippets in the question body cannot provide sufficient information for title generation, and LSTMs cannot capture the long-range dependencies between tokens. Objective: This paper proposes CCBERT, a deep learning based novel model to enhance the performance of question title generation by making full use of the bi-modal information of the entire question body. Method: CCBERT follows the encoder-decoder paradigm and uses CodeBERT to encode the question body into hidden representations, a stacked Transformer decoder to generate predicted tokens, and an additional copy attention layer to refine the output distribution. Both the encoder and decoder perform the multi-head self-attention operation to better capture the long-range dependencies. This paper builds a dataset containing around 200,000 high-quality questions filtered from the data officially published by Stack Overflow to verify the effectiveness of the CCBERT model. Results: CCBERT outperforms all the baseline models on the dataset. Experiments on both code-only and low-resource datasets show the superiority of CCBERT with less performance degradation. The human evaluation also shows the excellent performance of CCBERT concerning both readability and correlation criteria.

cs.CL↗

arXiv2022

Asymmetric Ground States in La$_{0.67}$Sr$_{0.33}$MnO$_3$/BaTiO$_3$ heterostructures Induced by Flexoelectric Bending

Misfit strain delivered from single-crystal substrates typically modifies the ground states of transition metal oxides, generating increasing interests in designing modern transducers and sensors. Here, we demonstrate that magnetotransport properties of La$_{0.67}$Sr$_{0.33}$MnO$_3$ (LSMO) films were continuously tuned by uniaxial strain produced by a home-designed bending jig. The electrical conductivity and Curie temperature of LSMO films are enhanced by bending stresses. The resistivity of a u-shape bended LSMO decays three times faster than that of a n-shape bended LSMO as a response to the same magnitude of strain. The asymmetric magnetic states in uniaxially strained LSMO are attributed to the dual actions of Jahn-Teller distortion and strain gradient mediated flexoelectric fields in an adjacent ferroelectric layer. These findings of multi-field regulation in a single material provide a feasible means for developing flexible electronic and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

ONCE-3DLanes: Building Monocular 3D Lane Detection

We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performance of following planning and control tasks in autonomous driving due to the case of uneven road. Predicting the 3D lane layout is thus necessary and enables effective and safe driving. However, existing 3D lane detection datasets are either unpublished or synthesized from a simulated environment, severely hampering the development of this field. In this paper, we take steps towards addressing these issues. By exploiting the explicit relationship between point clouds and image pixels, a dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations from 2D lane annotations in 211K road scenes. In addition, we present an extrinsic-free, anchor-free method, called SALAD, regressing the 3D coordinates of lanes in image view without converting the feature map into the bird's-eye view (BEV). To facilitate future research on 3D lane detection, we benchmark the dataset and provide a novel evaluation metric, performing extensive experiments of both existing approaches and our proposed method. The aim of our work is to revive the interest of 3D lane detection in a real-world scenario. We believe our work can lead to the expected and unexpected innovations in both academia and industry.

cs.CV↗

arXiv2022

Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation

Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaning-preserving restriction. However, a potential pitfall for this approach is that we cannot decide whether the generated examples are adversarial to the target NMT model or the auxiliary backward one, as the reconstruction error through the RTT can be related to either. To remedy this problem, we propose a new criterion for NMT adversarial examples based on the Doubly Round-Trip Translation (DRTT). Specifically, apart from the source-target-source RTT, we also consider the target-source-target one, which is utilized to pick out the authentic adversarial examples for the target NMT model. Additionally, to enhance the robustness of the NMT model, we introduce the masked language models to construct bilingual adversarial pairs based on DRTT, which are used to train the NMT model directly. Extensive experiments on both the clean and noisy test sets (including the artificial and natural noise) show that our approach substantially improves the robustness of NMT models.

cs.CL↗

arXiv2022

Superconductivity in TlBi$_2$ with a large Kadowaki-Woods ratio

In this article, the superconducting and normal state properties of TlBi$_2$ with the AlB$_2$-type structure were studied by the resistivity, magnetization and specific heat measurements. It was found that bulk superconductivity with $T_{C}$ = 6.2 K emerges in TlBi$_2$, which is a phonon-mediated $s$-wave superconductor with a strong electron-phonon coupling ($λ$$_{ep}$ = 1.38) and a large superconducting gap ($Δ_{0}$/$k_{B}T_{C}$ = 2.25). We found that the $ρ$($T$) exhibits an unusual $T$-linear dependence above 50 K, and can be well described by the Fermi-liquid theory below 20 K. Interestingly, its Kadowaki-Woods ratio $A/γ^{2}$ [9.2$\times$10$^{-5}$ $μΩ$ cm(mol K$^{2}$/mJ)$^{2}$] is unexpectedly one order of magnitude larger than that obtained in many heavy Fermi compounds, although the electron correlation is not so strong.

cond-mat.supr-con↗

arXiv2022

Universal Graph Filter Design based on Butterworth, Chebyshev and Elliptic Functions

Graph filters are crucial tools in processing the spectrum of graph signals. In this paper, we propose to design universal IIR graph filters with low computational complexity by using three kinds of functions, which are Butterworth, Chebyshev, and Elliptic functions, respectively. Specifically, inspired by the classical analog filter design method, we first derive the zeros and poles of graph frequency responses. With these zeros and poles, we construct the conjugate graph filters to design the Butterworth high pass graph filter, Chebyshev high pass graph filter, and Elliptic high pass graph filter, respectively. On this basis, we further propose to construct a desired graph filter of low pass, band pass, and band stop by mapping the parameters of the desired graph filter to those of the equivalent high pass graph filter. Furthermore, we propose to set the graph filter order given the maximum passband attenuation and the minimum stopband attenuation. Our numerical results show that the proposed graph filter design methods realize the desired frequency response more accurately than the autoregressive moving average (ARMA) graph filter design method, the linear least-squares fitting (LLS) based graph filter design method, and the Chebyshev FIR polynomial graph filter design method.

eess.SP↗

arXiv2022

Laneformer: Object-aware Row-Column Transformers for Lane Detection

We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving. The dominant paradigms rely on purely CNN-based architectures which often fail in incorporating relations of long-range lane points and global contexts induced by surrounding objects (e.g., pedestrians, vehicles). Inspired by recent advances of the transformer encoder-decoder architecture in various vision tasks, we move forwards to design a new end-to-end Laneformer architecture that revolutionizes the conventional transformers into better capturing the shape and semantic characteristics of lanes, with minimal overhead in latency. First, coupling with deformable pixel-wise self-attention in the encoder, Laneformer presents two new row and column self-attention operations to efficiently mine point context along with the lane shapes. Second, motivated by the appearing objects would affect the decision of predicting lane segments, Laneformer further includes the detected object instances as extra inputs of multi-head attention blocks in the encoder and decoder to facilitate the lane point detection by sensing semantic contexts. Specifically, the bounding box locations of objects are added into Key module to provide interaction with each pixel and query while the ROI-aligned features are inserted into Value module. Extensive experiments demonstrate our Laneformer achieves state-of-the-art performances on CULane benchmark, in terms of 77.1% F1 score. We hope our simple and effective Laneformer will serve as a strong baseline for future research in self-attention models for lane detection.

cs.CV↗

arXiv2022

In-depth Analysis of Durations of Discretionary Lane Changes on Freeway Under Varying Traffic Conditions

This paper aims to investigate the characteristics of durations of discretionary lane changes (LCs) on freeways based on an enriched dataset. A comprehensive analysis of LC durations was conducted based on vehicle types, LC directions and navigation speeds. It was found that the heavy vehicle takes longer time to complete LC maneuver. The LC direction significantly influences the durations of passenger cars but has no significant influence on heavy vehicles. The navigation speed was found to have important influence on LC durations. However, it has different impacts according to vehicle types and LC directions. Further analysis of LC durations at different stages showed that drivers of passenger cars might use different strategies to perform LCs when they change lanes to different directions. However, drivers of heavy vehicles in both directions used less time to occupy the target lanes. Results of this study can be beneficial to understand the mechanism of LC process and the influence of LC on traffic flow.

eess.SY↗

arXiv2022

Weighted Mean and Median graph Filters with Attenuation Factor for Sensor Network

This paper proposes a weighted attenuation k-hop graph, which depicts the spatial neighbor nodes with their hops from the central node. Based on this k-kop graph, we further propose a node selecting graph, which selects temporal neighbor nodes of multiple instances of the central node. With this node selecting graph, we propose a graph mean filter. In addition, we also apply the proposed node selecting graph to the median filter. Finally, the experimental results show that the proposed mean filter performs better than the original median filter in the signal denoising polluted by white noise and the median filter using node selecting graph also has better performance than the original median filter.

eess.SP↗

arXiv2022

Optimal Fractional Fourier Filtering in Time-vertex Graphs signal processing

Graph signal processing (GSP) is an effective tool in dealing with data residing in irregular domains. In GSP, the optimal graph filter is one of the essential techniques, owing to its ability to recover the original signal from the distorted and noisy version. However, most current research focuses on static graph signals and ordinary space/time or frequency domains. The time-varying graph signals have a strong ability to capture the features of real-world data, and fractional domains can provide a more suitable space to separate the signal and noise. In this paper, the optimal time-vertex graph filter and its Wiener-Hopf equation are developed, using the product graph framework. Furthermore, the optimal time-vertex graph filter in fractional domains is also developed, using the graph fractional Laplacian operator and graph fractional Fourier transform. Numerical simulations on real-world datasets will demonstrate the superiority of the optimal time-vertex graph filter in fractional domains over the optimal time-vertex graph filter in ordinary domains and the optimal static graph filter in fractional domains.

eess.SP↗

arXiv2021

Ferromagnetic Enhancement in LaMnO3 Films with Release and Flexure

A variety of novel phenomena and functionalities emerge from lowering the dimensionality of materials and enriching the degrees of freedom in modulation. In this work, it is found that the saturation magnetization of LaMnO3 (LMO) films is largely enhanced by 56% after releasing from a brand-new phase of tetragonal strontium aluminate buffer layer, and is significantly increased by 92% with bending films to a curvature of 1 mm-1 using a water-assisted direct-transferring method. Meanwhile, the Curie temperature of LMO films has been improved by 13 K. High-resolution spherical aberration-corrected scanning transmission electron microscopy and first-principles calculations unambiguously demonstrate that the enhanced ferromagnetism is attributed to the strengthened Mn-O-Mn super-exchange interactions from the augmented characteristics of the unconventional P21/n structure caused by the out-of-plane lattice shrinking after strain releasing and increased flexure degree of freestanding LMO films. This work paves a way to achieve large-scale and crack-and-wrinkle-free freestanding films of oxides with largely improved functionalities.

cond-mat.mtrl-sci↗

arXiv2021

Enhanced valley polarization in WS$_2$/LaMnO$_3$ heterostructure

Monolayer transition metal dichalcogenides have attracted great attentions for potential applications in valleytronics. However, the valley polarization degree is usually not high because of the intervalley scattering. Here, we demonstrate a largely enhanced valley polarization up to 80\% in monolayer WS$_2$ under non-resonant excitation at 4.2 K using WS$_2$/LaMnO$_3$ thin film heterostructure, which is much higher than that for monolayer WS$_2$ on SiO$_2$/Si substrate with a valley polarization of 15\%. Furthermore, the greatly enhanced valley polarization can be maintained to a high temperature of about 160 K with a valley polarization of 53\%. The temperature dependence of valley polarization is strongly correlated with the thermomagnetic curve of LaMnO$_3$, indicating an exciton-magnon coupling between WS$_2$ and LaMnO$_3$. A simple model is introduced to illustrate the underlying mechanisms. The coupling of WS$_2$ and LaMnO$_3$ is further confirmed with an observation of two interlayer excitons with opposite valley polarizations in the heterostructure, resulting from the spin-orbit coupling induced splitting of the conduction bands in monolayer transition metal dichalcogenides. Our results provide a pathway to control the valleytronic properties of transition metal dichalcogenides by means of ferromagnetic van der Waals engineering, paving a way to practical valleytronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game

An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets. To solve the energy management and pricing problem of multi-community integrated energy systems (MCIESs) with multi-energy interaction, this study investigated a hierarchical stochastic optimal scheduling method for uncertain environments. To handle multiple uncertainties, a Wasserstein generative adversarial network with a gradient penalty was used to generate renewable scenarios, and the Kmeans++ clustering algorithm was employed to generate typical scenarios. A Stackelberg-based hierarchical stochastic schedule with an integrated demand response was constructed, where the MCIES operator acted as the leader pursuing the maximum net profit by setting energy prices, while the building users were followers who adjusted their energy consumption plans to minimize their total costs. Finally, a distributed iterative solution method based on a metaheuristic was designed. The effectiveness of the proposed method was verified using practical examples.

eess.SY↗

arXiv2021

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper- levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower- levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency.

eess.SY↗

arXiv2021

A hierarchical behavior prediction framework at signalized intersections

Road user behavior prediction is one of the most critical components in trajectory planning for autonomous driving, especially in urban scenarios involving traffic signals. In this paper, a hierarchical framework is proposed to predict vehicle behaviors at a signalized intersection, using the traffic signal information of the intersection. The framework is composed of two phases: a discrete intention prediction phase and a continuous trajectory prediction phase. In the discrete intention prediction phase, a Bayesian network is adopted to predict the vehicle's high-level intention, after that, maximum entropy inverse reinforcement learning is utilized to learn the human driving model offline; during the online trajectory prediction phase, a driver characteristic is designed and updated to capture the different driving preferences between human drivers. We applied the proposed framework to one of the most challenging scenarios in autonomous driving: the yellow light running scenario. Numerical experiment results are presented in the later part of the paper which show the viability of the method. The accuracy of the Bayesian network for discrete intention prediction is 91.1%, and the prediction results are getting more and more accurate as the yellow time elapses. The average Euclidean distance error in continuous trajectory prediction is only 0.85 m in the yellow light running scenario.

cs.RO↗

arXiv2021

Unrestricted Adversarial Attacks on ImageNet Competition

Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-world, the attacker does not need to comply with this restriction. In fact, more threats to the deep model come from unrestricted adversarial examples, that is, the attacker makes large and visible modifications on the image, which causes the model classifying mistakenly, but does not affect the normal observation in human perspective. Unrestricted adversarial attack is a popular and practical direction but has not been studied thoroughly. We organize this competition with the purpose of exploring more effective unrestricted adversarial attack algorithm, so as to accelerate the academical research on the model robustness under stronger unbounded attacks. The competition is held on the TianChi platform (\url{https://tianchi.aliyun.com/competition/entrance/531853/introduction}) as one of the series of AI Security Challengers Program.

cs.CV↗

arXiv2021

Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments

The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR)-enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity-gas-heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and micro-gas turbine (MT), as links of multi-energy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory (SOT) and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. Simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a trade-off between economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the Hybrid Intelligent Algorithm in terms of both optimization results and calculation efficiency.

eess.SY↗

arXiv2021

Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting

In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the first time to address the error accumulation issue suffered by existing multi-step forecasting methods, then the prediction values obtained by the proposed forecasting method are revised via the error distribution to improve the prediction accuracy; secondly, a scheduling model considering demand response is constructed to minimize the total microgrid operating costs, where the revised forecasting values are used as the dispatch basis, and a spinning reserve chance constraint is set according to the error distribution; finally, by transforming the original scheduling model into a readily solvable mixed integer linear programming via the sequence operation theory (SOT), the transformed model is solved by using CPLEX solver. The simulation results show that compared with the traditional scheduling model without forecasting, this approach manages to significantly reduce the system operating costs by improving the prediction accuracy.

eess.SP↗