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Haiyan Wang

Haiyan Wang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mes-hallcond-mat.mtrl-scimath.APmath.CAPopulations and EvolutionComputer VisionMachine Learningphysics.soc-phquant-phArtificial Intelligenceeess.SPphysics.app-phmath.DSSocial and Information NetworksApplicationsComputation and LanguageInformation Theorymath.ITmath.OCMethodologyMultiagent Systemsq-fin.PRQuantitative MethodsTissues and Organs

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Published work

42 published item(s)

preprint2026arXiv

ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models

Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretraining, making machine unlearning (MU) crucial. Existing methods typically evaluate unlearning effectiveness based on output deviations, while overlooking the generation quality after unlearning. This can easily lead to hallucinated or rigid responses, thereby affecting the usability and safety of the unlearned model. To address this issue, we propose ASRU, a controllable multimodal unlearning framework that incorporates generation quality as a core evaluation objective. ASRU first induces initial refusal behavior through activation redirection, and then optimizes fine-grained refusal boundaries using a customized reward function, thereby achieving a better trade-off between target knowledge unlearning and model utility. Experiments on Qwen3-VL show that ASRU significantly improves unlearning effectiveness (+24.6%) on average and generation quality (5.8x) on average while effectively preserving model utility, using only a small amount of retained supervision data.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models

Multimodal Large Language Models (MLLMs) may memorize sensitive cross-modal information during pretraining. However, existing MLLM unlearning benchmarks rely on synthetic knowledge injection or complete subject-level deletion, which fail to capture realistic, personalized deletion requests that require fine-grained factual control. In this paper, we introduce PPU-Bench, a real-world and fine-tuning-free benchmark for personalized partial unlearning in MLLMs. PPU-Bench contains 24K multimodal and unimodal samples derived from pre-existing knowledge of 500 public figures under three progressively challenging settings: Complete, Selective, and Personalized unlearning. The benchmark evaluates whether methods can remove target knowledge while preserving non-target facts, model utility, and cross-modal consistency. Extensive experiments show that Complete Unlearning often suppresses visual identity rather than factual knowledge, while Selective and Personalized Unlearning expose significant forget--retain trade-offs and challenges in intra-subject factual boundaries. Robustness analysis under cross-image and prompt-based attacks reveals distinct vulnerabilities across different unlearning settings. Motivated by these findings, we propose Boundary-Aware Optimization (BAO), which explicitly models intra-subject forget-retain boundaries. Experimental results on two representative methods demonstrate that BAO can effectively enforce intra-subject factual boundaries.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Message Feedback Interference Cancellation Aided UAMP Iterative Detector for OTFS Systems

The designing of efficient signal detectors is important and yet challenge for orthogonal time frequency space (OTFS) systems in high-mobility scenarios. In this letter, we develop an efficient message feedback interference cancellation aided unitary approximate message passing (denoted as UAMPMFIC) iterative detector, where the latest feedback messages from variable nodes are utilized for more reliable interference cancellation and performance improvement. A fast recursive scheme is leveraged in the proposed UAMP-MFIC detector to prevent complexity increasing. To further alleviate the error-propagation and improve the receiver performance, we also develop the bidirectional symbol detection structures, where Turbo UAMP-MFIC detector and iterative weight UAMP-MFIC detector are proposed to efficiently fuse the estimation results of forward and backward UAMP-MFIC detectors. The simulation results are finally provided to demonstrate performance improvement of our proposed detectors over existing detectors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Adaptive Algorithm for Quantum Amplitude Estimation

Quantum amplitude estimation is a key sub-routine of a number of quantum algorithms with various applications. We propose an adaptive algorithm for interval estimation of amplitudes. The quantum part of the algorithm is based only on Grover's algorithm. The key ingredient is the introduction of an adjustment factor, which adjusts the amplitude of good states such that the amplitude after the adjustment, and the original amplitude, can be estimated without ambiguity in the subsequent step. We show with numerical studies that the proposed algorithm uses a similar number of quantum queries to achieve the same level of precision $ε$ compared to state-of-the-art algorithms, but the classical part, i.e., the non-quantum part, has substantially lower computational complexity. We rigorously prove that the number of oracle queries achieves $O(1/ε)$, i.e., a quadratic speedup over classical Monte Carlo sampling, and the computational complexity of the classical part achieves $O(\log(1/ε))$, both up to a double-logarithmic factor.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Experimental Quantum End-to-End Learning on a Superconducting Processor

Machine learning can be substantially powered by a quantum computer owing to its huge Hilbert space and inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed in an end-to-end fashion, i.e., the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Such gate-free models are hardware friendly and can fully exploit limited quantum resources. Here, we report the first experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model can achieve 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National Institute of Standards and Technology) database. The experimental results exhibit the great potential of quantum end-to-end learning for resolving complex real-world tasks when more qubits are available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents

Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents. In many industrial applications, the number of available agents can change at any given day and even when the number of agents is known ahead of time, it is common for an agent to break during the operation and become unavailable for a period of time. In this paper, we propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents. We demonstrate the application of our algorithm using a fleet management simulator developed by Hitachi to generate realistic scenarios in a production site.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation

In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP2) transformer and a novel Cross-Perspective Projection (CP2) layer. The stereo-view SP2 transformer is used to implicitly infer correspondences between views, and can handle noisy poses. The pose-aware CP2 layer is designed to render features from the adjacent view to the anchor (reference) view, in order to perform view fusion and estimate the visible layout. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art layout estimators, especially for large and complex room spaces.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Scaled indium oxide transistors fabricated using atomic layer deposition

In order to continue to improve integrated circuit performance and functionality, scaled transistors with short channel lengths and low thickness are needed. But the further scaling of silicon-based devices and the development of alternative semiconductor channel materials that are compatible with current fabrication processes is challenging. Here we report atomic-layer-deposited indium oxide transistors with channel lengths down to 8 nm, channel thicknesses down to 0.5 nm and equivalent dielectric oxide thickness down to 0.84 nm. Due to the scaled device dimensions and low contact resistance, the devices exhibit high on-state currents of 3.1 A/mm at a drain voltage of 0.5 V and a transconductance of 1.5 S/mm at a drain voltage 1 V. Our devices are a promising alternative channel material for scaled transistors with back-end-of-line processing compatibility.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Sequential Point Clouds: A Survey

Point cloud has drawn more and more research attention as well as real-world applications. However, many of these applications (e.g. autonomous driving and robotic manipulation) are actually based on sequential point clouds (i.e. four dimensions) because the information of the static point cloud data could provide is still limited. Recently, researchers put more and more effort into sequential point clouds. This paper presents an extensive review of the deep learning-based methods for sequential point cloud research including dynamic flow estimation, object detection \& tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Finally, this paper is concluded by discussing the challenges in the current sequential point cloud research and pointing out insightful potential future research directions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Ferroelectric Semiconductor Field-Effect Transistor

Ferroelectric field-effect transistors employ a ferroelectric material as a gate insulator, the polarization state of which can be detected using the channel conductance of the device. As a result, the devices are of potential to use in non-volatile memory technology, but suffer from short retention times, which limits their wider application. Here we report a ferroelectric semiconductor field-effect transistor in which a two-dimensional ferroelectric semiconductor, indium selenide (α-In2Se3), is used as the channel material in the device. α-In2Se3 was chosen due to its appropriate bandgap, room temperature ferroelectricity, ability to maintain ferroelectricity down to a few atomic layers, and potential for large-area growth. A passivation method based on the atomic-layer deposition of aluminum oxide (Al2O3) was developed to protect and enhance the performance of the transistors. With 15-nm-thick hafnium oxide (HfO2) as a scaled gate dielectric, the resulting devices offer high performance with a large memory window, a high on/off ratio of over 108, a maximum on-current of 862 μA μm-1, and a low supply voltage.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Assess the impacts of human mobility change on COVID-19 dynamics in Arizona, U.S.: a modeling study incorporating Google Community Mobility Reports

In June 2020, Arizona, U.S., emerged as one of the world's worst coronavirus disease 2019(COVID-19) spots after the stay-at-home order was lifted in the middle of May. However, with the decisions to reimpose restrictions, the number of COVID-19 cases has been declining, and Arizona is considered to be a good model in slowing the epidemic. In this paper, we aimed to examine the COVID-19 situation in Arizona and assess the impact of human mobility change. We constructed the mobility integrated metapopulation susceptible-infectious-removed model and fitted to publicly available datasets on COVID-19 cases and mobility changes in Arizona. Our simulations showed that by reducing human mobility, the peak time was delayed, and the final size of the epidemic was decreased in all three regions. Our analysis suggests that rapid and effective decision making is crucial to control human mobility and, therefore, COVID-19 epidemics. Until a vaccine is available, reimplementations of mobility restrictions in response to the increase of new COVID-19 cases might need to be considered in Arizona and beyond.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Demonstration of Controlled-Phase Gates between Two Error-Correctable Photonic Qubits

To realize fault-tolerant quantum computing, it is necessary to store quantum information in logical qubits with error correction functions, realized by distributing a logical state among multiple physical qubits or by encoding it in the Hilbert space of a high-dimensional system. Quantum gate operations between these error-correctable logical qubits, which are essential for implementation of any practical quantum computational task, have not been experimentally demonstrated yet. Here we demonstrate a geometric method for realizing controlled-phase gates between two logical qubits encoded in photonic fields stored in cavities. The gates are realized by dispersively coupling an ancillary superconducting qubit to these cavities and driving it to make a cyclic evolution depending on the joint photonic state of the cavities, which produces a conditional geometric phase. We first realize phase gates for photonic qubits with the logical basis states encoded in two quasiorthogonal coherent states, which have important implications for continuous-variable-based quantum computation. Then we use this geometric method to implement a controlled-phase gate between two binomially encoded logical qubits, which have an error-correctable function.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Diffusion LMS with Communication Delays: Stability and Performance Analysis

We study the problem of distributed estimation over adaptive networks where communication delays exist between nodes. In particular, we investigate the diffusion Least-Mean- Square (LMS) strategy where delayed intermediate estimates (due to the communication channels) are employed during the combination step. One important question is: Do the delays affect the stability condition and performance? To answer this question, we conduct a detailed performance analysis in the mean and in the mean-square-error sense of the diffusion LMS with delayed estimates. Stability conditions, transient and steady-state mean-square-deviation (MSD) expressions are provided. One of the main findings is that diffusion LMS with delays can still converge under the same step-sizes condition of the diffusion LMS without delays. Finally, simulation results illustrate the theoretical findings.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Experimental implementation of universal nonadiabatic geometric quantum gates in a superconducting circuit

Using geometric phases to realize noise-resilient quantum computing is an important method to enhance the control fidelity. In this work, we experimentally realize a universal nonadiabatic geometric quantum gate set in a superconducting qubit chain. We characterize the realized single- and two-qubit geometric gates with both quantum process tomography and randomized benchmarking methods. The measured average fidelities for the single-qubit rotation gates and two-qubit controlled-Z gate are 0.9977(1) and 0.977(9), respectively. Besides, we also experimentally demonstrate the noise-resilient feature of the realized single-qubit geometric gates by comparing their performance with the conventional dynamical gates with different types of errors in the control field. Thus, our experiment proves a way to achieve high-fidelity geometric quantum gates for robust quantum computation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect the subsurface objects (i.e. rebars, utility pipes) and reveal the underground scene. One of the biggest challenges in GPR based inspection is the subsurface targets reconstruction. In order to address this issue, this paper presents a 3D GPR migration and dielectric prediction system to detect and reconstruct underground targets. This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i.e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets. Our proposed DepthNet processes the GPR data by removing the noise in B-scan image as well as predicting depth of subsurface objects. For DepthNet model training and testing, we collect the real GPR data in the concrete test pit at Geophysical Survey System Inc. (GSSI) and create the synthetic GPR data by using gprMax3.0 simulator. The dataset we create includes 350 labeled GPR images. The DepthNet achieves an average accuracy of 92.64% for B-scan feature detection and an 0.112 average error for underground target depth prediction. In addition, the experimental results verify that our proposed method improve the migration accuracy and performance in generating 3D GPR image compared with the traditional migration methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Health Indicator Forecasting for Improving Remaining Useful Life Estimation

Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are gaining popularity. One of the most important categories of data-driven approaches relies on a predefined or learned health indicator to characterize the equipment condition up to the present time and make inference on how it is likely to evolve in the future. In these approaches, health indicator forecasting that constructs the health indicator curve over the lifespan using partially observed measurements (i.e., health indicator values within an initial period) plays a key role. Existing health indicator forecasting algorithms, such as the functional Empirical Bayesian approach, the regression-based formulation, a naive scenario matching based on the nearest neighbor, have certain limitations. In this paper, we propose a new `generative + scenario matching' algorithm for health indicator forecasting. The key idea behind the proposed approach is to first non-parametrically fit the underlying health indicator curve with a continuous Gaussian Process using a sample of run-to-failure health indicator curves. The proposed approach then generates a rich set of random curves from the learned distribution, attempting to obtain all possible variations of the target health condition evolution process over the system's lifespan. The health indicator extrapolation for a piece of functioning equipment is inferred as the generated curve that has the highest matching level within the observed period. Our experimental results show the superiority of our algorithm over the other state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Integrating Tensor Similarity to Enhance Clustering Performance

The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix. However, the pairwise similarity is notoriously known for its vulnerability of noise contamination or the imbalance in samples or features, and thus hinders accurate clustering. To tackle this issue, we propose to use information among samples to boost the clustering performance. We proved that a simplified similarity for pairs, denoted by a fourth order tensor, equals to the Kronecker product of pairwise similarity matrices under decomposable assumption, or provide complementary information for which the pairwise similarity missed under indecomposable assumption. Then a high order similarity matrix is obtained from the tensor similarity via eigenvalue decomposition. The high order similarity capturing spatial information serves as a robust complement for the pairwise similarity. It is further integrated with the popular pairwise similarity, named by IPS2, to boost the clustering performance. Extensive experiments demonstrated that the proposed IPS2 significantly outperformed previous similarity-based methods on real-world datasets and it was capable of handling the clustering task over under-sampled and noisy datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Spatio-Temporal Functional Neural Networks

Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The spatio-temporal regression problem is of paramount importance from both the methodology development and real-world application perspectives. Given the observed spatially encoded time series covariates and real-valued response data samples, the goal of spatio-temporal regression is to leverage the temporal and spatial dependencies to build a mapping from covariates to response with minimized prediction error. Prior arts, including the convolutional Long Short-Term Memory (CovLSTM) and variations of the functional linear models, cannot learn the spatio-temporal information in a simple and efficient format for proper model building. In this work, we propose two novel extensions of the Functional Neural Network (FNN), a temporal regression model whose effectiveness and superior performance over alternative sequential models have been proven by many researchers. The effectiveness of the proposed spatio-temporal FNNs in handling varying spatial correlations is demonstrated in comprehensive simulation studies. The proposed models are then deployed to solve a practical and challenging precipitation prediction problem in the meteorology field.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Using A Partial Differential Equation with Google Mobility Data to Predict COVID-19 in Arizona

The outbreak of COVID-19 disrupts the life of many people in the world. The state of Arizona in the U.S. emerges as one of the country's newest COVID-19 hot spots. Accurate forecasting for COVID-19 cases will help governments to implement necessary measures and convince more people to take personal precautions to combat the virus. It is difficult to accurately predict the COVID-19 cases due to many human factors involved. This paper aims to provide a forecasting model for COVID-19 cases with the help of human activity data from the Google Community Mobility Reports. To achieve this goal, a specific partial differential equation (PDE) is developed and validated with the COVID-19 data from the New York Times at the county level in the state of Arizona in the U.S. The proposed model describes the combined effects of transboundary spread among county clusters in Arizona and human actives on the transmission of COVID-19. The results show that the prediction accuracy of this model is well acceptable (above 94\%). Furthermore, we study the effectiveness of personal precautions such as wearing face masks and practicing social distancing on COVID-19 cases at the local level. The localized analytical results can be used to help to slow the spread of COVID-19 in Arizona. To the best of our knowledge, this work is the first attempt to apply PDE models on COVID-19 prediction with the Google Community Mobility Reports.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Using Networks and Partial Differential Equations to Predict Bitcoin Price

Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting bitcoin price. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends Index is incorporated to the PDE model to reflect the effect of bitcoin market sentiment. The experiment shows that the average accuracy of daily bitcoin price prediction is 0.82 for 362 consecutive days in 2017. The results demonstrate the PDE model is capable of predicting bitcoin price. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes

The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects. To alleviate this issue, we propose a novel deep graph convolutional network-based framework for large-scale semantic scene segmentation in point clouds with sole 2D supervision. Different with numerous preceding multi-view supervised approaches focusing on single object point clouds, we argue that 2D supervision is capable of providing sufficient guidance information for training 3D semantic segmentation models of natural scene point clouds while not explicitly capturing their inherent structures, even with only single view per training sample. Specifically, a Graph-based Pyramid Feature Network (GPFN) is designed to implicitly infer both global and local features of point sets and an Observability Network (OBSNet) is introduced to further solve object occlusion problem caused by complicated spatial relations of objects in 3D scenes. During the projection process, perspective rendering and semantic fusion modules are proposed to provide refined 2D supervision signals for training along with a 2D-3D joint optimization strategy. Extensive experimental results demonstrate the effectiveness of our 2D supervised framework, which achieves comparable results with the state-of-the-art approaches trained with full 3D labels, for semantic point cloud segmentation on the popular SUNCG synthetic dataset and S3DIS real-world dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Why In2O3 Can Make 0.7 nm Atomic Layer Thin Transistors?

In this work, we demonstrate enhancement-mode field-effect transistors by atomic-layer-deposited (ALD) amorphous In2O3 channel with thickness down to 0.7 nm. Thickness is found to be critical on the materials and electron transport of In2O3. Controllable thickness of In2O3 at atomic scale enables the design of sufficient 2D carrier density in the In2O3 channel integrated with the conventional dielectric. The threshold voltage and channel carrier density are found to be considerably tuned by channel thickness. Such phenomenon is understood by the trap neutral level (TNL) model where the Fermi-level tends to align deeply inside the conduction band of In2O3 and can be modulated to the bandgap in atomic layer thin In2O3 due to quantum confinement effect, which is confirmed by density function theory (DFT) calculation. The demonstration of enhancement-mode amorphous In2O3 transistors suggests In2O3 is a competitive channel material for back-end-of-line (BEOL) compatible transistors and monolithic 3D integration applications.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

A comparing study on optoelectronic properties of phototransistors based on MEH-PPV and PbS QD hybrids with bulk- and layer-heterojunction

As the responsivity (R) of a thin film photo detector is proportional to the product of the photo-induced carrier density (n) and mobility (u) (Z. Sun, Z. Liu, J. Li, G.-a. Tai, S. Lau and F. Yan, Adv. Mater., 24, 5878, 2012), which of the types is conducive to photo detection, choosing between layer-heterojunction (LH) and bulk-heterojunction (BH) field effect phototransistors (FEpTs), is still unknown. A comparison study is performed based on an MEH-PPV and PbS QDs hybrid.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Modeling Boyciana-fish-human Interaction with Partial Differential Algebraic Equations

With human social behaviors influence, some boyciana-fish reaction-diffusion system coupled with elliptic human distribution equation is considered. Firstly, under homogeneous Neumann boundary conditions and ratio-dependent functional response the system can be described as a nonlinear partial differential algebraic equations (PDAEs) and the corresponding linearized system is discussed with singular system theorem. In what follows we discuss the elliptic subsystem and show that the three kinds of nonnegative are corresponded to three different human interference conditions: human free, overdevelopment and regular human activity. Next we examine the system persistence properties: absorbtion region and the stability of positive steady states of three systems. And the diffusion-driven unstable property is also discussed. Moreover, we propose some energy estimation discussion to reveal the dynamic property among the boyciana-fish-human interaction systems.Finally, using the realistic data collected in the past fourteen years, by PDAEs model parameter optimization, we carry out some predicted results about wetland boyciana population. The applicability of the proposed approaches are confirmed analytically and are evaluated in numerical simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Which is conducive to high responsivity in a hybrid graphene-quantum dot transistor: Bulk- or layer- heterojunction

Heterojunction, a photoelectric conversion center, plays a critical role in photo detection. A compare study is performed on field effect phototransistors (FEpT) with two typical heterojunctions based on graphene and PbSe quantum dots (QDs) hybrid, including layer-heterojunction (LH) and bulk-heterojunction (BH). LH-FEpT exhibits a higher mobility (μn) 677 cm2V-1s-1, whereas μn of BH-FEpT is as low as 314 cm2V-1s-1, however the higher responsivity (106 A/W) is achieved by the former. Because R is proportional to the product of mobility and transfer rate, and high transfer rate of LH-FEpT overcompensates the shortage of low mobility. Although large area of heterojunction and high mobility benefit high density of photo-induced carriers, lack of transport mechanism becomes to the main constrain factor in BH-FEpT. Therefore, LH-FEpT is a better candidate of near infrared (NIR) photo detectors.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Traveling Wave Phenomena in a Kermack-McKendrick SIR model

We study the existence and nonexistence of traveling waves of general diffusive Kermack-McKendrick SIR models with standard incidence where the total population is not constant. The three classes, susceptible $S$, infected $I$ and removed $R$, are all involved in the traveling wave solutions. We show that the minimum speed for the existence of traveling waves for this three-dimensional non-monotonic system can be derived from its linearizaion at the initial disease-free equilibrium. The proof in this paper is based on Schauder fixed point theorem and Laplace transform and provides a promising method to deal with high dimensional epidemic models.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Traveling Wave Solutions of a Reaction-Diffusion Equation with State-Dependent Delay

This paper is concerned with the traveling wave solutions of a reaction-diffusion equation with state-dependent delay. When the birth function is monotone, the existence and nonexistence of monotone traveling wave solutions are established. When the birth function is not monotone, the minimal wave speed of nontrivial traveling wave solutions is obtained. The results are proved by the construction of upper and lower solutions and application of the fixed point theorem.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Modeling Information Diffusion in Online Social Networks with Partial Differential Equations

Online social networks such as Twitter and Facebook have gained tremendous popularity for information exchange. The availability of unprecedented amounts of digital data has accelerated research on information diffusion in online social networks. However, the mechanism of information spreading in online social networks remains elusive due to the complexity of social interactions and rapid change of online social networks. Much of prior work on information diffusion over online social networks has based on empirical and statistical approaches. The majority of dynamical models arising from information diffusion over online social networks involve ordinary differential equations which only depend on time. In a number of recent papers, the authors propose to use partial differential equations(PDEs) to characterize temporal and spatial patterns of information diffusion over online social networks. Built on intuitive cyber-distances such as friendship hops in online social networks, the reaction-diffusion equations take into account influences from various external out-of-network sources, such as the mainstream media, and provide a new analytic framework to study the interplay of structural and topical influences on information diffusion over online social networks. In this survey, we discuss a number of PDE-based models that are validated with real datasets collected from popular online social networks such as Digg and Twitter. Some new developments including the conservation law of information flow in online social networks and information propagation speeds based on traveling wave solutions are presented to solidify the foundation of the PDE models and highlight the new opportunities and challenges for mathematicians as well as computer scientists and researchers in online social networks.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Oxygen-Vacancy-Induced Antiferromagnetism to Ferromagnetism Transformation in Multiferroic Thin Films

Oxygen vacancies (VOs) effects on magnetic ordering in Eu0.5Ba0.5TiO3-δ (EBTO3-δ) thin films have been investigated using a combination of experimental measurements and first-principles density-functional calculations. Two kinds of EBTO3-δthin films with different oxygen deficiency have been fabricated. A nuclear resonance backscattering spectrometry technique has been used to quantitatively measure contents of the VOs. Eu0.5Ba0.5TiO3 ceramics have been known to exhibit ferroelectric (FE) and G-type antiferromagnetic (AFM) properties. While, a ferromagnetic (FM) behavior with a Curie temperature of 1.85 K has been found in the EBTO3-δ thin films. Spin-polarized Ti3+ ions, which originated from the VOs, has been proven to mediate a FM coupling between the local Eu 4f spins and were believed to be responsible for the great change of the magnetic ordering. Our work opens up a new avenue for developing FM-FE materials by manipulating the oxygen deficiency in AFM-FE multiferroics.

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preprint2013arXiv

Twisted Bilayer Graphene Superlattices

Twisted bilayer graphene (tBLG) provides us with a large rotational freedom to explore new physics and novel device applications, but many of its basic properties remain unresolved. Here we report the synthesis and systematic Raman study of tBLG. Chemical vapor deposition was used to synthesize hexagon- shaped tBLG with a rotation angle that can be conveniently determined by relative edge misalignment. Superlattice structures are revealed by the observation of two distinctive Raman features: folded optical phonons and enhanced intensity of the 2D-band. Both signatures are strongly correlated with G-line resonance, rotation angle and laser excitation energy. The frequency of folded phonons decreases with the increase of the rotation angle due to increasing size of the reduced Brillouin zone (rBZ) and the zone folding of transverse optic (TO) phonons to the rBZ of superlattices. The anomalous enhancement of 2D-band intensity is ascribed to the constructive quantum interference between two Raman paths enabled by a near-degenerate Dirac cone. The fabrication and Raman identification of superlattices pave the way for further basic study and new applications of tBLG.

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preprint2012arXiv

Eigenvalues, bifurcation and one-sign solutions for the periodic $p$-Laplacian

In this paper, we establish a unilateral global bifurcation result for a class of quasilinear periodic boundary problems with a sign-changing weight. By the Ljusternik-Schnirelmann theory, we first study the spectrum of the periodic $p$-Laplacian with the sign-changing weight. In particular, we show that there exist two simple, isolated, principal eigenvalues $λ_0^+$ and $λ_0^-$. Furthermore, under some natural hypotheses on perturbation function, we show that $(λ_0^ν,0)$ is a bifurcation point of the above problems and there are two distinct unbounded sub-continua $\mathscr{C}_ν^{+}$ and $\mathscr{C}_ν^{-}$, consisting of the continuum $\mathscr{C}_ν$ emanating from $(λ_0^ν, 0)$, where $ν\in\{+,-\}$. As an application of the above result, we study the existence of one-sign solutions for a class of quasilinear periodic boundary problems with the sign-changing weight. Moreover, the uniqueness of one-sign solutions and the dependence of solutions on the parameter $λ$ are also studied.

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preprint2012arXiv

Front-like entire solutions for monostable reaction-diffusion systems

This paper is concerned with front-like entire solutions for monostable reactiondiffusion systems with cooperative and non-cooperative nonlinearities. In the cooperative case, the existence and asymptotic behavior of spatially independent solutions (SIS) are first proved. Combining a SIS and traveling fronts with different wave speeds and directions, the existence and various qualitative properties of entire solutions are then established using comparison principle. In the non-cooperative case, we introduce two auxiliary cooperative systems and establish some comparison arguments for the three systems. The existence of entire solutions is then proved via the traveling fronts and SIS of the auxiliary systems. Our results are applied to some biological and epidemiological models. To the best of our knowledge, it is the first work to study the entire solutions of non-cooperative reaction-diffusion systems.

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preprint2011arXiv

Dependence of transport on adatom location for armchair-edge graphene nanoribbons

We study the transport property for armchair-edge graphene nanoribbons (AGNRs) with an adatom coupling to a semi-infinite quantum wire. Using the nonequilibrium Green's function approach with tight-binding approximation, we demonstrate that the tunneling current through the system is sensitively dependent on both the AGNR width and adatom location. Interestingly, when the adatom locates onto a carbon atom in the 3$j$th chain from the edge of a metallic AGNR, the system shows a transmission gap accompanied by a threshold voltage in $I-V$ curve like a semiconducting AGNR. This effect may be useful in scanning tunneling microscopy experimental characterization on graphene samples.

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preprint2011arXiv

Diffusive Logistic Model Towards Predicting Information Diffusion in Online Social Networks

Online social networks have recently become an effective and innovative channel for spreading information and influence among hundreds of millions of end users. Many prior work have carried out empirical studies and proposed diffusion models to understand the information diffusion process in online social networks. However, most of these studies focus on the information diffusion in temporal dimension, that is, how the information propagates over time. Little attempt has been given on understanding information diffusion over both temporal and spatial dimensions. In this paper, we propose a Partial Differential Equation (PDE), specifically, a Diffusive Logistic (DL) equation to model the temporal and spatial characteristics of information diffusion in online social networks. To be more specific, we develop a PDE-based theoretical framework to measure and predict the density of influenced users at a given distance from the original information source after a time period. The density of influenced users over time and distance provides valuable insight on the actual information diffusion process. We present the temporal and spatial patterns in a real dataset collected from Digg social news site, and validate the proposed DL equation in terms of predicting the information diffusion process. Our experiment results show that the DL model is indeed able to characterize and predict the process of information propagation in online social networks. For example, for the most popular news with 24,099 votes in Digg, the average prediction accuracy of DL model over all distances during the first 6 hours is 92.08%. To the best of our knowledge, this paper is the first attempt to use PDE-based model to study the information diffusion process in both temporal and spatial dimensions in online social networks.

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preprint2011arXiv

Spreading speeds and traveling waves for non-cooperative integro-difference systems

The development of macroscopic descriptions for the joint dynamics and behavior of large heterogeneous ensembles subject to ecological forces like dispersal remains a central challenge for mathematicians and biological scientists alike. Over the past century, specific attention has been directed to the role played by dispersal in shaping plant communities, or on the dynamics of marine open-ocean and intertidal systems, or on biological invasions, or on the spread of disease, to name a few. Mathematicians and theoreticians, starting with the efforts of researchers that include Aronson, Fisher, Kolmogorov, Levin, Okubo, Skellam, Slobodkin, Weinberger and many others, set the foundation of a fertile area of research at the interface of ecology, mathematics, population biology and evolutionary biology. Integrodifference systems, the subject of this manuscript, arise naturally in the study of the spatial dispersal of organisms whose local population dynamics are prescribed by models with discrete generations. The brunt of the mathematical research has focused on the the study of existence traveling wave solutions and characterizations of the spreading speed particularly, in the context of cooperative systems. In this paper, we characterize the spreading speed for a large class of non cooperative systems, all formulated in terms of integrodifference equations, by the convergence of initial data to wave solutions. In this setting, the spreading speed is characterized as the slowest speed of a family of non-constant traveling wave solutions. Our results are applied to a specific non-cooperative competition system in detail.

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preprint2010arXiv

Positive periodic solutions of singular systems

The existence and multiplicity of positive periodic solutions for second order non-autonomous singular dynamical systems are established with superlinearity or sublinearity assumptions at infinity for an appropriately chosen parameter. Our results provide a unified treatment for the problem and significantly improve several results in the literature. The proof of our results is based on the Krasnoselskii fixed point theorem in a cone.

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preprint2010arXiv

Spreading speeds and traveling waves for a model of epidermal wound healing

In this paper, we shall establish the spreading speed and existence of traveling waves for a non-cooperative system arising from epidermal wound healing and characterize the spreading speed as the slowest speed of a family of non-constant traveling wave solutions. Our results on the spreading speed and traveling waves can also be applied to a large class of non-cooperative reaction-diffusion systems.

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preprint2010arXiv

Spreading speeds and traveling waves for non-cooperative reaction-diffusion systems

Much has been studied on the spreading speed and traveling wave solutions for cooperative reaction-diffusion systems. In this paper, we shall establish the spreading speed for a large class of non-cooperative reaction-diffusion systems and characterize the spreading speed as the slowest speed of a family of non-constant traveling wave solutions. As an application, our results are applied to a partially cooperative system describing interactions between ungulates and grass.

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