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Researcher profile

Chen Yang

Chen Yang contributes to research discovery and scholarly infrastructure.

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physics.opticsComputer VisionInformation RetrievalMachine LearningSoftware Engineeringquant-phArtificial IntelligenceComputation and Languagecond-mat.mes-hallcond-mat.mtrl-sciBiological PhysicsDatabaseseess.SPeess.SYGraphicsHuman-Computer Interactionmath.APMethodologyNetworking and Internet ArchitectureNeurons and Cognitionphysics.app-phphysics.atom-phphysics.chem-phphysics.med-phRoboticsSocial and Information NetworksSystems and Control

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Published work

34 published item(s)

preprint2026arXiv

A Pilot Kinematic Study on the Forehand Reverse Flick: Feasibility of a Novel Short Return Technique in Table Tennis

Background Following changes in table tennis ball materials, offensive returns have become more important for initiating sustained topspin offense. However, using the backhand flick (BF) to return forehand short balls often increases the difficulty of recovery and continuity, revealing a technical gap. This study preliminarily verified a novel forehand short return technique, the forehand reverse flick (FRF), and analyzed its similarities and differences with the BF. Methods Four elite athletes completed seven consecutive days of FRF specific training. Infrared motion capture and ultra-high-speed cameras were used to collect data on racket kinematics, movement duration, and ball performance. Results The success rate of the FRF increased steadily, reaching 86%. Racket trajectories of the two techniques were highly similar along the X (r = 1) and Y (r = 0.99) axes but differed along the Z (r = -0.04) axis. Racket and ball velocities were comparable between techniques, whereas the FRF showed lower resultant acceleration (approximately 265.57 m/s) and required about 0.03 s more for movement duration. Ball velocity was comparable between techniques, for the ball spin, the FRF generated lower spin (approximately 76.61 r/s) about 64% of the BF value (approximately 120.13 r/s). The highest participant mean spin rate reached 93 r/s, about 77% of the BF mean. Conclusion Overall, the FRF was found to have favorable learnability and training value, with potential for further optimization and competitive application.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

A Platform for Interactive AI Character Experiences

From movie characters to modern science fiction - bringing characters into interactive, story-driven conversations has captured imaginations across generations. Achieving this vision is highly challenging and requires much more than just language modeling. It involves numerous complex AI challenges, such as conversational AI, maintaining character integrity, managing personality and emotions, handling knowledge and memory, synthesizing voice, generating animations, enabling real-world interactions, and integration with physical environments. Recent advancements in the development of foundation models, prompt engineering, and fine-tuning for downstream tasks have enabled researchers to address these individual challenges. However, combining these technologies for interactive characters remains an open problem. We present a system and platform for conveniently designing believable digital characters, enabling a conversational and story-driven experience while providing solutions to all of the technical challenges. As a proof-of-concept, we introduce Digital Einstein, which allows users to engage in conversations with a digital representation of Albert Einstein about his life, research, and persona. While Digital Einstein exemplifies our methods for a specific character, our system is flexible and generalizes to any story-driven or conversational character. By unifying these diverse AI components into a single, easy-to-adapt platform, our work paves the way for immersive character experiences, turning the dream of lifelike, story-based interactions into a reality.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Efficient and Robust Video Defense Framework against 3D-field Personalized Talking Face

State-of-the-art 3D-field video-referenced Talking Face Generation (TFG) methods synthesize high-fidelity personalized talking-face videos in real time by modeling 3D geometry and appearance from reference portrait video. This capability raises significant privacy concerns regarding malicious misuse of personal portraits. However, no efficient defense framework exists to protect such videos against 3D-field TFG methods. While image-based defenses could apply per-frame 2D perturbations, they incur prohibitive computational costs, severe video quality degradation, failing to disrupt 3D information for video protection. To address this, we propose a novel and efficient video defense framework against 3D-field TFG methods, which protects portrait video by perturbing the 3D information acquisition process while maintain high-fidelity video quality. Specifically, our method introduces: (1) a similarity-guided parameter sharing mechanism for computational efficiency, and (2) a multi-scale dual-domain attention module to jointly optimize spatial-frequency perturbations. Extensive experiments demonstrate that our proposed framework exhibits strong defense capability and achieves a 47x acceleration over the fastest baseline while maintaining high fidelity. Moreover, it remains robust against scaling operations and state-of-the-art purification attacks, and the effectiveness of our design choices is further validated through ablation studies. Our project is available at https://github.com/Richen7418/VDF.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Koopman Identification of Nonlinear Systems via Reservoir Liftings

Learning tractable linear representations of nonlinear dynamical systems via Koopman operator theory is often hindered by dictionary selection, temporal memory encoding, and numerical ill-conditioning. Inspired by Reservoir Computing (RC) paradigm, this paper introduces the RC-Koopman framework, which interprets reservoir as a stateful, finite-dimensional Koopman dictionary whose temporal depth is explicitly controlled by its spectral radius. We show that the Echo State Property (ESP) guarantees well-posedness and favorable numerical conditioning of the lifted Koopman approximation. A correlation-based spectral radius selection algorithm aligns reservoir memory with dominant system timescales. Analysis reveals how the finite memory of the reservoir determines which Koopman eigenfunctions remain observable from the lifted features. Evaluation on synthetic benchmarks demonstrates that RC-Koopman achieves a favorable balance between reconstruction accuracy of the underlying nonlinear dynamics and dynamical stability, compared to Extended Dynamic Mode Decomposition (EDMD) and Hankel-based lifting approaches. Code available at: https://github.com/NEAR-the-future/RC-Koopman.git

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes

Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability and physical interaction. This restricts their use in immersive content creation and embodied simulation, where generated worlds must be actively modified and manipulated. To tackle this challenge, we present WorldAct, a framework that converts static generated 3D worlds into editable and interaction-ready scenes. WorldAct uses a multimodal agent to guide scene decomposition, identify actionable objects, reconstruct geometrically aligned object-level meshes for interaction, and restore the residual background via 3D inpainting. The resulting scenes support object-level editing, collision-aware manipulation, and embodied task execution while preserving global scene coherence. Experiments show that WorldAct enables richer interaction scenarios than the original generated scenes, suggesting a practical path toward editable and interactive 3D world models.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Concentration behavior of normalized ground states for mass critical Kirchhoff equations in bounded domains

In present paper, we study the limit behavior of normalized ground states for the following mass critical Kirchhoff equation $$ \left\{\begin{array}{ll} -(a+b\int_Ω|\nabla u|^2\mathrm{d}x)Δu+V(x)u=μu+β^*|u|^{\frac{8}{3}}u &\mbox{in}\ Ω, \\[0.1cm] u=0&\mbox{on}\ {\partialΩ}, \\[0.1cm] \int_Ω|u|^2\mathrm{d}x=1, \\[0.1cm] \end{array} \right. $$ where $a\geq0$, $b>0$, the function $V(x)$ is a trapping potential in a bounded domain $Ω\subset\mathbb R^3$, $β^*:=\frac{b}{2}|Q|_2^{\frac{8}{3}}$ and $Q$ is the unique positive radially symmetric solution of equation $-2Δu+\frac{1}{3}u-|u|^{\frac{8}{3}}u=0.$ We consider the existence of constraint minimizers for the associated energy functional involving the parameter $a$. The minimizer corresponds to the normalized ground state of above problem, and it exists if and only if $a>0$. Moreover, when $V(x)$ attains its flattest global minimum at an inner point or only at the boundary of $Ω$, we analyze the fine limit profiles of the minimizers as $a\searrow 0$, including mass concentration at an inner point or near the boundary of $Ω$. In particular, we further establish the local uniqueness of the minimizer if it is concentrated at a unique inner point.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Multifunctional fiber-based optoacoustic emitter for non-genetic bidirectional neural communication

A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Here we report a miniaturized multifunctional fiber based optoacoustic emitter (mFOE) that first integrates simultaneous non-genetic optoacoustic stimulation for "write" and electrophysiology recording of neural circuits for "read". The non-genetic feature addresses the challenges of the viral transfection required by optogenetics in primates and human. The orthogonality between optoacoustic waves and electrical field provides a solution to avoid the interference between electrical stimulation and recording. We first validated the non-genetic stimulation function of the mFOE in rat cultured neurons using calcium imaging. In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities was confirmed in mouse hippocampus in both acute and chronical applications up to 1 month. Minimal brain tissue damage has been confirmed after these applications. The capability of non-genetic neural stimulation and recording enabled by mFOE opens up new possibilities for the investigation of neural circuits and brings new insights into the study of ultrasound neurostimulation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Unified Description for Polarization-Transfer Mechanisms in Magnetic Resonance in Static Solids: Cross Polarization and DNP

Polarization transfers are crucial building blocks in magnetic resonance experiments, i.e., they can be used to polarize insensitive nuclei and correlate nuclear spins in multidimensional NMR spectroscopy. The polarization can be transferred either across different nuclear spin species or from electron spins to the relatively low-polarized nuclear spins. The former route occurring in solid-state NMR (ssNMR) can be performed via cross polarization (CP), while the latter route is known as dynamic nuclear polarization (DNP). Despite having different operating conditions, we opinionate that both mechanisms are theoretically similar processes in ideal conditions, i.e., the electron is merely another spin-1/2 particle with a much higher gyromagnetic ratio. Here, we show that the CP and DNP processes can be described using a unified theory based on average Hamiltonian theory (AHT) combined with fictitious operators. The intuitive and unified approach has allowed new insights on the cross effect (CE) DNP mechanism, leading to better design of DNP polarizing agents and extending the applications beyond just hyperpolarization. We explore the possibility of exploiting theoretically predicted DNP transients for electron-nucleus distance measurements--like routine dipolar-recoupling experiments in solid-state NMR.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsity in two ways: learning probabilistic embedding, and incorporating trainable and regularized priors which utilize the rich side information of cold start users and advertisements (Ads). The two techniques are naturally integrated into a variational inference framework, forming an end-to-end training process. Abundant empirical tests on benchmark datasets well demonstrate the advantages of our proposed VELF. Besides, extended experiments confirmed that our parameterized and regularized priors provide more generalization capability than traditional fixed priors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Decisions in Continuous Integration and Delivery: An Exploratory Study

In recent years, Continuous Integration (CI) and Continuous Delivery (CD) has been heatedly discussed and widely used in part or all of the software development life cycle as the practices and pipeline to deliver software products in an efficient way. There are many tools, such as Travis CI, that offer various features to support the CI/CD pipeline, but there is a lack of understanding about what decisions are frequently made in CI/CD. In this work, we explored one popular open-source project on GitHub, Budibase, to provide insights on the types of decisions made in CI/CD from a practitioners' perspective. We first explored the GitHub Trending page, conducted a pilot repository extraction, and identified the Budibase repository as the case for our study. We then crawled all the closed issues from the repository and got 1,168 closed issues. Irrelevant issues were filtered out based on certain criteria, and 370 candidate issues that contain decisions were obtained for data extraction. We analyzed the issues using a hybrid approach combining pre-defined types and the Constant Comparison method to get the categories of decisions. The results show that the major type of decisions in the Budibase closed issues is Functional Requirement Decision (67.6%), followed by Architecture Decision (11.1%). Our findings encourage developers to put more effort on the issues and making decisions related to CI/CD, and provide researchers with a reference of decision classification made in CI/CD.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Detecting systematic anomalies affecting systems when inputs are stationary time series

We develop an anomaly-detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly-free inputs (i.e., those before contamination) to originate from a wide class of random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally non-stationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly-free inputs following an ARMA time series model under various contamination scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Federating and querying heterogeneous and distributed Web APIs and triple stores

Today's international corporations such as BASF, a leading company in the crop protection industry, produce and consume more and more data that are often fragmented and accessible through Web APIs. In addition, part of the proprietary and public data of BASF's interest are stored in triple stores and accessible with the SPARQL query language. Homogenizing the data access modes and the underlying semantics of the data without modifying or replicating the original data sources become important requirements to achieve data integration and interoperability. In this work, we propose a federated data integration architecture within an industrial setup, that relies on an ontology-based data access method. Our performance evaluation in terms of query response time showed that most queries can be answered in under 1 second.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Measuring the tuning curve of spontaneous parameter down-conversion using a comet-tail-like pattern

The comet-tail-like interference patterns are observed using photons from the spontaneous parametric down-conversion (SPDC) process. The patterns are caused by the angular-spectrum-dependent interference and the diffraction of a blazed grating. We present the theoretical explanation and simulation results for these patterns, which are in good agreement with the experimental results. The most significant feature of the patterns is the bright parabolic contour profile, from which, one can deduce the parameter of the parabolic tuning curve of the SPDC process. This method could be helpful in designing experiments based on SPDC.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Modeling Two-Way Selection Preference for Person-Job Fit

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dual-perspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi tasks RetinaNet for mitosis detection

The account of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues. At the same time, although advanced deep learning method have achieved great success in cell detection, the performance is often unsatisfactory when tested data from another domain (i.e. the different tumor types and different scanners). Therefore, it is necessary to develop algorithms for detecting mitotic cells with robustness in domain shifts scenarios. Our work further proposes a foreground detection and tumor classification task based on the baseline(Retinanet), and utilizes data augmentation to improve the domain generalization performance of our model. We achieve the state-of-the-art performance (F1 score: 0.5809) on the challenging premilary test dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Parallel measurements of vibrational modes in a few-layer graphene nanomechanical resonator using software-defined radio dongles

Software-defined radio dongles are small and inexpensive receivers well known to amateur radio enthusiasts. When connected to an antenna, they enable monitoring of a wide range of the radio spectrum by conditioning the input signal and transferring a downconverted version of it to a personal computer for software processing. Here, we employ a composite of two such dongles, interfaced with codes written in MATLAB and GNU Radio, as a measuring instrument to study the flexural vibrations of a few-layer graphene nanomechanical resonator. Instead of an antenna, we connect the dongles to the split output of a photodetector used to detect vibrations optically. We first perform a quantitative analysis of the dynamics of the first vibrational mode. We then measure the response of the first two vibrational modes in parallel. To illustrate our technique, we detect changes in the vibrational amplitude of both modes induced by periodic strain modulation with a delay of $\approx1$ ms between measurements. Last, we show that our software-based instrument can be employed to demodulate human voice encoded in the vibrations of our resonator. For parallel measurements of several frequency channels, and provided that the input signal is not too weak, our composite system may offer an alternative to the use of multiple lock-in amplifiers or multiple spectrum analyzers, with the distinct advantage of being cost-effective per frequency channel.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. First of all, from a data perspective, we consider three important topics related to data issues (i.e., sparsity, bias and distribution shift), and develop five packages accordingly: meta-learning, data augmentation, debiasing, fairness and cross-domain recommendation. Furthermore, from a model perspective, we develop two benchmarking packages for Transformer-based and graph neural network (GNN)-based models, respectively. All the packages (consisting of 65 new models) are developed based on a popular recommendation framework RecBole, ensuring that both the implementation and interface are unified. For each package, we provide complete implementations from data loading, experimental setup, evaluation and algorithm implementation. This library provides a valuable resource to facilitate the up-to-date research in recommender systems. The project is released at the link: https://github.com/RUCAIBox/RecBole2.0.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Theoretical Study of the Enhancement of Light Saturation Phenomena of Krypton at Critical Ionization Photon Energies

By calculating the correlation between the total photoionization cross-section of the ground state of the Kr atom and photon energy, three particular photon energies close to the near inner orbital energy of 1.75 keV, 1.90 keV, and 14.30 keV are determined in this work. The dynamical simulation under 17.50 keV photon energy in the experimental conditions is achieved by implementing the Monte Carlo method and optimizing the photon flux modeling parameters. As a result, our calculated data are more consistent with the experimental phenomena. The light saturation phenomenon of Kr at 1.75 keV, 1.90 keV, 14.30 keV, and 17.50 keV energies is further calculated and researched using the optimized photon flux model theory. We statistically compare the main ionization paths under those four specific photon energies and calculate the population changes of various hollow atoms. The results demonstrate that the ratio of hollow atoms produced at the critical ionization photon energy is high. Furthermore, the change of position is smooth, showing the significant difference between the generation mode of ions with low photon energy and those with high photon energy, which has important reference significance for studying hollow atoms with medium and high charge states.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Using 5G in Smart Cities: A Systematic Mapping Study

5G is the fifth generation wireless network, with a set of characteristics, e.g., high bandwidth and data rates. The scenarios of using 5G include enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and ultra-Reliable and Low-Latency Communications (uRLLC). 5G is expected to support a wide variety of applications. We conducted a systematic mapping study that covers the literature published between Jan 2012 and Dec 2019 regarding using 5G in smart cities. The scenarios, architecture, technologies, challenges, and lessons learned of using 5G in smart cities are summarized and further analyzed based on 32 selected studies, and the results are that: (1) The studies are distributed over 27 publication venues. 17 studies report results based on academic studies and 13 studies use demonstration or toy examples. Only 2 studies report using 5G in smart cities based on industrial studies. 16 studies include assumptions of 5G network design or smart city scenarios. (2) The most discussed smart city scenario is transportation, followed by public safety, healthcare, city tourism, entertainment, and education. (3) 28 studies propose and/or discuss the architecture of 5G-enabled smart cities, containing smart city architecture (treating 5G as a component), 5G network architecture in smart cities, and business architecture of using 5G in smart cities. (4) The most mentioned 5G-related technologies are radio access technologies, network slicing, and edge computing. (5) Challenges are mainly about complex context, challenging requirements, and network development of using 5G in smart cities. (6) Most of the lessons learned identified are benefits regarding 5G itself or the proposed 5G-related methods in smart cities. This work provides a reflection of the past eight years of the state of the art on using 5G in smart cities, which can benefit both researchers and practitioners.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging

The bug triaging process, an essential process of assigning bug reports to the most appropriate developers, is related closely to the quality and costs of software development. As manual bug assignment is a labor-intensive task, especially for large-scale software projects, many machine-learning-based approaches have been proposed to automatically triage bug reports. Although developer collaboration networks (DCNs) are dynamic and evolving in the real-world, most automated bug triaging approaches focus on static tossing graphs at a single time slice. Also, none of the previous studies consider periodic interactions among developers. To address the problems mentioned above, in this article, we propose a novel spatial-temporal dynamic graph neural network (ST-DGNN) framework, including a joint random walk (JRWalk) mechanism and a graph recurrent convolutional neural network (GRCNN) model. In particular, JRWalk aims to sample local topological structures in a graph with two sampling strategies by considering both node importance and edge importance. GRCNN has three components with the same structure, i.e., hourly-periodic, daily-periodic, and weekly-periodic components, to learn the spatial-temporal features of dynamic DCNs. We evaluated our approach's effectiveness by comparing it with several state-of-the-art graph representation learning methods in two domain-specific tasks that belong to node classification. In the two tasks, experiments on two real-world, large-scale developer collaboration networks collected from the Eclipse and Mozilla projects indicate that the proposed approach outperforms all the baseline methods.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Imaging vibrations of locally gated, electromechanical few layer graphene resonators with a moving vacuum enclosure

Imaging the vibrations of nanomechanical resonators means measuring their flexural mode shapes from the dependence of their frequency response on in-plane position. Applied to two-dimensional resonators, this technique provides a wealth of information on the mechanical properties of atomically-thin membranes. We present a simple and robust system to image the vibrations of few layer graphene (FLG) resonators at room temperature and in vacuum with an in-plane displacement precision of $\approx0.20$ $μ$m. It consists of a sturdy vacuum enclosure mounted on a three-axis micropositioning stage and designed for free space optical measurements of vibrations. The system is equipped with ultra-flexible radio frequency waveguides to electrically actuate resonators. With it we characterize the lowest frequency mode of a FLG resonator by measuring its frequency response as a function of position on the membrane. The resonator is suspended over a nanofabricated local gate electrode acting both as a mirror and as a capacitor plate to actuate vibrations at radio frequencies. From these measurements, we estimate the ratio of thermal expansion coefficient to thermal conductivity of the membrane, and we measure the effective mass of the lowest frequency mode. We complement our study with a globally gated resonator and image its first three vibration modes. There, we find that folds in the membrane locally suppress vibrations.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Learning Better Sentence Representation with Syntax Information

Sentence semantic understanding is a key topic in the field of natural language processing. Recently, contextualized word representations derived from pre-trained language models such as ELMO and BERT have shown significant improvements for a wide range of semantic tasks, e.g. question answering, text classification and sentiment analysis. However, how to add external knowledge to further improve the semantic modeling capability of model is worth probing. In this paper, we propose a novel approach to combining syntax information with a pre-trained language model. In order to evaluate the effect of the pre-training model, first, we introduce RNN-based and Transformer-based pre-trained language models; secondly, to better integrate external knowledge, such as syntactic information integrate with the pre-training model, we propose a dependency syntax expansion (DSE) model. For evaluation, we have selected two subtasks: sentence completion task and biological relation extraction task. The experimental results show that our model achieves 91.2\% accuracy, outperforming the baseline model by 37.8\% on sentence completion task. And it also gets competitive performance by 75.1\% $F_{1}$ score on relation extraction task.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation. However, the generated pseudo labels from the model optimized on the source domain inevitably contain noise due to the domain gap. To tackle this issue, we advance a MetaCorrection framework, where a Domain-aware Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA semantic segmentation. In particular, we model the noise distribution of pseudo labels in target domain by introducing a noise transition matrix (NTM) and construct meta data set with domain-invariant source data to guide the estimation of NTM. Through the risk minimization on the meta data set, the optimized NTM thus can correct the noisy issues in pseudo labels and enhance the generalization ability of the model on the target data. Considering the capacity gap between shallow and deep features, we further employ the proposed DMLC strategy to provide matched and compatible supervision signals for different level features, thereby ensuring deep adaptation. Extensive experimental results highlight the effectiveness of our method against existing state-of-the-art methods on three benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Extra-cavity-enhanced difference-frequency generation at 1.63 μm

A 1632-nm laser has highly important applications in interfacing the wavelength of rubidium-based quantum memories (795 nm) and the telecom band (typically 1550 nm) by frequency conversion in three-wave mixing processes. A 1632-nm laser source based on pump-enhanced difference frequency generation is demonstrated. It has 300 mW of output power, in agreement with simulations, and a 55% quantum efficiency. An average power fluctuation of 0.51% over one hour was observed, and 200-kHz linewidth was measured using a delayed self-heterodyne method.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Increasing two-photon entangled dimensions by shaping input beam profiles

Photon pair entangled in high dimensional orbital angular momentum (OAM) degree of freedom (DOF) has been widely regarded as a possible source in improving the capacity of quantum information processing. The need for the generation of a high dimensional maximally entangled state in the OAM DOF is therefore much desired. In this work, we demonstrate a simple method to generate a broader and flatter OAM spectrum, i.e. a larger spiral bandwidth (SB), of entangled photon pairs generated through spontaneous parametric down-conversion by modifying the pump beam profile. By investigating both experimentally and theoretically, we have found that an exponential pump profile that is roughly the inverse of the mode profiles of the single-mode fibers used for OAM detection will provide a much larger SB when compared to a Gaussian shaped pump.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

PSCS: A Path-based Neural Model for Semantic Code Search

To obtain code snippets for reuse, programmers prefer to search for related documents, e.g., blogs or Q&A, instead of code itself. The major reason is due to the semantic diversity and mismatch between queries and code snippets. Deep learning models have been proposed to address this challenge. Compared with approaches using information retrieval techniques, deep learning models do not suffer from the information loss caused by refining user intention into keywords. However, the performance of previous works is not satisfactory because they ignore the importance of code structure. When the semantics of code (e.g., identifier names, APIs) are ambiguous, code structure may be the only feature for the model to utilize. In that case, previous works relearn the structural information from lexical tokens of code, which is extremely difficult for a model without any domain knowledge. In this work, we propose PSCS, a path-based neural model for semantic code search. Our model encodes both the semantics and structures of code represented by AST paths. We train and evaluate our model over 330k-19k query-function pairs, respectively. The evaluation results demonstrate that PSCS achieves a SuccessRate of 47.6% and a Mean Reciprocal Rank (MRR) of 30.4% when considering the top-10 results with a match. The proposed approach significantly outperforms both DeepCS, the first approach that applies deep learning to code search task, and CARLCS, a state-of-the-art approach that introduces a co-attentive representation learning model on the basis of DeepCS. The importance of code structure is demonstrated with an ablation study on code features, which enlightens model design for further studies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Req2Lib: A Semantic Neural Model for Software Library Recommendation

Third-party libraries are crucial to the development of software projects. To get suitable libraries, developers need to search through millions of libraries by filtering, evaluating, and comparing. The vast number of libraries places a barrier for programmers to locate appropriate ones. To help developers, researchers have proposed automated approaches to recommend libraries based on library usage pattern. However, these prior studies can not sufficiently match user requirements and suffer from cold-start problem. In this work, we would like to make recommendations based on requirement descriptions to avoid these problems. To this end, we propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Besides, we apply a domain-specific pre-trained word2vec model for word embedding, which is trained over textual corpus from Stack Overflow posts. In the experiment, we train and evaluate the model with data from 5,625 java projects. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.

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preprint2020arXiv

Simulation Comparisons of Vehicle-based and Phase-based Traffic Control for Autonomous Vehicles at Isolated Intersections

With the advent of autonomous driving technologies, traffic control at intersections is expected to experience revolutionary changes. Various novel intersection control methods have been proposed in the existing literature, and they can be roughly divided into two categories: vehicle-based traffic control and phase-based traffic control. Phase-based traffic control can be treated as updated versions of the current intersection signal control with the incorporation of the performance of autonomous vehicle functions. Meanwhile, vehicle-based traffic control utilizes some brand-new methods, mostly in real-time fashion, to organize traffic at intersections for safe and efficient vehicle passages. However, to date, no systematic comparison between these two control categories has been performed to suggest their advantages and disadvantages. This paper conducts a series of numerical simulations under various traffic scenarios to perform a fair comparison of their performances. Specifically, we allow trajectory adjustments of incoming vehicles under phasebased traffic control, while for its vehicle-based counterpart, we implement two strategies, i.e., the first-come-first-serve strategy and the conflict-point based rolling-horizon optimization strategy. Overall, the simulation results show that vehicle-based traffic control generally incurs a negligible delay when traffic demand is low but lead to an excessive queuing time as the traffic volume becomes high. However, performance of vehicle-based traffic control may benefit from reduction in conflicting vehicle pairs. We also discovered that when autonomous driving technologies are not mature, the advantages of phase-based traffic control are much more distinct.

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preprint2020arXiv

Valley pseudospin in monolayer MoSi2N4 and MoSi2As4

For a long time, two-dimensional (2D) hexagonal MoS2 was proposed as a promising material for valleytronic system. However, the limited size of growth and low carrier motilities in MoS2 restrict its further application. Very recently, a new kind of hexagonal 2D MXene, MoSi2N4, was successfully synthesized with large size, excellent ambient stability, and considerable hole mobility. In this paper, based on the first-principles calculations, we predict that the valley-contrast properties can be realized in monolayer MoSi2N4 and its derivative MoSi2As4. Beyond the traditional two-level valleys, the valleys in monolayer MoSi2As4 are multiple-folded, implying a new valley dimension. Such multiple-folded valleys can be described by a three-band low-power Hamiltonian. This study presents the theoretical advance and the potential applications of monolayer MoSi2N4 and MoSi2As4 in valleytronic devices, especially multiple information processing.

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preprint2019arXiv

A high-dimensional quantum frequency converter

In high dimensional quantum communication networks, quantum frequency convertor (QFC) is indispensable as an interface in the frequency domain. For example, many QFCs have been built to link atomic memories and fiber channels. However, almost all of QFCs work in a two-dimensional space. It is still a pivotal challenge to construct a high-quality QFC for some complex quantum states, e.g., a high dimensional single-photon state that refers to a qudit. Here, we firstly propose a high-dimensional QFC for an orbital angular momentum qudit via sum frequency conversion with a flat top beam pump. As a proof-of-principle demonstration, we realize quantum frequency conversions for a qudit from infrared to visible range. Based on the qudit quantum state tomography, the fidelities of converted state are 98.29(95.02)\%, 97.42(91.74)\%, and 86.75(67.04)\% for a qudit without (with) dark counts in 2,3, and 5 dimensions, respectively. The demonstration is very promising for constructing a high capacity quantum communication network.

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preprint2019arXiv

Correlating the Electronic Structures of Metallic/Semiconductor MoTe2 Interface to its Atomic Structures

Contact interface properties are important in determining the performances of devices based on atomically thin two-dimensional (2D) materials, especially those with short channels. Understanding the contact interface is therefore quite important to design better devices. Herein, we use scanning transmission electron microscopy, electron energy loss spectroscopy, and first-principles calculations to reveal the electronic structures within the metallic (1T')-semiconducting (2H) MoTe2 coplanar phase boundary across a wide spectral range and correlate its properties and atomic structure. We find that the 2H-MoTe2 excitonic peaks cross the phase boundary into the 1T' phase within a range of approximately 150 nm. The 1T'-MoTe2 crystal field can penetrate the boundary and extend into the 2H phase by approximately two unit cells. The plasmonic oscillations exhibit strong angle dependence, i.e., a red-shift (approximately 0.3 eV-1.2 eV) occurs within 4 nm at 1T'/2H-MoTe2 boundaries with large tilt angles, but there is no shift at zero-tilted boundaries. These atomic-scale measurements reveal the structure-property relationships of 1T'/2H-MoTe2 boundary, providing useful information for phase boundary engineering and device development based on 2D materials.

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preprint2019arXiv

Frequency up-conversion of an infrared image via a flat-top pump beam

The infrared imaging detection is an important and promising technology having wide applications. In this work, we report on the frequency up-conversion detection of an image based on sum frequency generation in a nonlinear crystal with a flat-top beam acting as the pump instead of a Gaussian beam, the up-converted image at 525 nm falls in the sensitive band of visible detectors and human eyes. Both theoretical simulations and the experimental results clearly demonstrate that using a flat-top beam as a pump can improve the fidelity of an image after the up-conversion compared with a Gaussian pump beam. Our scheme will be very promising for infrared image detection based on frequency up-conversion.

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preprint2019arXiv

Plasmon-enhanced Stimulated Raman Scattering Microscopy with Single-molecule Detection Sensitivity

Stimulated Raman scattering (SRS) microscopy allows for high-speed label-free chemical imaging of biomedical systems. The imaging sensitivity of SRS microscopy is limited to ~10 mM for endogenous biomolecules. Electronic pre-resonant SRS allows detection of sub-micromolar chromophores. However, label-free SRS detection of single biomolecules having extremely small Raman cross-sections (~10-30 cm2 sr-1) remains unreachable. Here, we demonstrate plasmon-enhanced stimulated Raman scattering (PESRS) microscopy with single-molecule detection sensitivity. Incorporating pico-Joule laser excitation, background subtraction, and a denoising algorithm, we obtained robust single-pixel SRS spectra exhibiting the statistics of single-molecule events. Single-molecule detection was verified by using two isotopologues of adenine. We further demonstrated the capability of applying PESRS for biological applications and utilized PESRS to map adenine released from bacteria due to starvation stress. PESRS microscopy holds the promise for ultrasensitive detection of molecular events in chemical and biomedical systems.

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