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Kai Yang

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

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Applicationsastro-ph.SRmath.NAComputer VisionMachine Learningmath.APNumerical Analysiscond-mat.mes-hallArtificial Intelligenceastro-ph.GAeess.SPInformation Theorymath.ITastro-ph.EPComputationComputer Science and Game Theorycond-mat.mtrl-sciCryptography and SecurityDistributed, Parallel, and Cluster Computingeess.SYGeneral Literaturemath.CAmath.OCMultimediaNetworking and Internet Architecturephysics.atm-clusphysics.med-phphysics.soc-phSocial and Information NetworksSoftware EngineeringSoundSystems and Control

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Published work

44 published item(s)

preprint2026arXiv

Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding

This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object segmentation. By introducing previously unreleased challenging data and analyzing the cutting-edge, multimodal solutions submitted by participants, this report highlights the community's latest technical advancements and charts promising future directions for robust video scene comprehension.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Surveys of clumps, cores, and condensations in Cygnus-X:Searching for circumstellar disks

To investigate whether disk-mediated accretion is the primary mechanism in high-mass star formation, we have established a survey of a large sample of massive dense cores within a giant molecular cloud. We used high angular resolution ($\sim 1.8''$) observations with SMA to study the dust emission and molecular line emission of about 50 massive dense cores in Cygnus-X. At a typical distance of 1.4 kpc for Cygnus-X, these massive dense cores are resolved into $\sim 2000$ au condensations. We combined the CO outflow emission and gas kinematics traced by several high-density tracers to search for disk candidates. We extracted hundreds of dust condensations from the SMA 1.3 mm dust continuum emission. The CO data show bipolar or unipolar outflow signatures toward 49 dust condensations. Among them, only 27 sources are detected in dense gas tracers, which reveals the gas kinematics, and nine sources show evidence of rotating envelopes, suggesting the existence of embedded accretion disks. The position-velocity diagrams along the velocity gradient of all rotating condensations suggest that four condensations are possible to host Keplerian-like disks. A detailed investigation of the 27 sources detected in dense gas tracers suggests that the nine disk candidates are at earlier evolutionary stages compared to the remaining 18 sources. Non-detection of rotating disks in our sample may be due to several factors, including an unknown inclination angle of the rotation axis and an early evolutionary stage of the central source, and the latter could be important, considering that young and powerful outflows could confuse the observational evidence for rotation. The detection rate of disk candidates in our sample is 1/3, which confirms that disk accretion is a viable mechanism for high-mass star formation, although it may not be the only one.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Holistic Robust Motion Controller Framework for Autonomous Platooning

Safety is the foremost concern for autonomous platooning. The vehicle-to-vehicle (V2V) communication delay and the sudden appearance of obstacles will trigger the safety of the intended functionality (SOTIF) issues for autonomous platooning. This research proposes a holistic robust motion controller framework (MCF) for an intelligent and connected vehicle platoon system. The MCF utilizes a hierarchical structure to resolve the longitudinal string stability and the lateral control problem under the complex driving environment and time-varying communication delay. Firstly, the H-infinity feedback controller is developed to ensure the robustness of the platoon under time-varying communication delay in the upper-level coordination layer (UCL). The output from UCL will be delivered to the lower-level motion-planning layer (LML) as reference signals. Secondly, the model predictive control (MPC) algorithm is implemented in the LML to achieve multi-objective control, which comprehensively considers the reference signals, the artificial potential field, and multiple vehicle dynamics constraints. Furthermore, three critical scenarios are co-simulated for case studies, including platooning under time-varying communication delay, merging, and obstacle avoidance scenarios. The simulation results indicate that, compared with single-structure MPC, the proposed MCF can offer a better suppression on position error propagation, and get improvements on maximum position error in the three scenarios by $19.2\%$, $59.8\%$, and $15.3\%$, respectively. Last, the practicability and effectiveness of the proposed MCF are verified via hardware-in-the-loop experiment. The average conducting time of the proposed method on Speedgoat real-time target machine is 1.1 milliseconds, which meets the real-time requirements.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties

Nesterov's accelerated gradient (AG) is a popular technique to optimize objective functions comprising two components: a convex loss and a penalty function. While AG methods perform well for convex penalties, such as the LASSO, convergence issues may arise when it is applied to nonconvex penalties, such as SCAD. A recent proposal generalizes Nesterov's AG method to the nonconvex setting. The proposed algorithm requires specification of several hyperparameters for its practical application. Aside from some general conditions, there is no explicit rule for selecting the hyperparameters, and how different selection can affect convergence of the algorithm. In this article, we propose a hyperparameter setting based on the complexity upper bound to accelerate convergence, and consider the application of this nonconvex AG algorithm to high-dimensional linear and logistic sparse learning problems. We further establish the rate of convergence and present a simple and useful bound to characterize our proposed optimal damping sequence. Simulation studies show that convergence can be made, on average, considerably faster than that of the conventional proximal gradient algorithm. Our experiments also show that the proposed method generally outperforms the current state-of-the-art methods in terms of signal recovery.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Arbitrarily high-order conservative schemes for the generalized Korteweg-de Vries equation

This paper proposes a new class of arbitrarily high-order conservative numerical schemes for the generalized Korteweg-de Vries (KdV) equation. This approach is based on the scalar auxiliary variable (SAV) method. The equation is reformulated into an equivalent system by introducing a scalar auxiliary variable, and the energy is reformulated into a sum of two quadratic terms. Therefore, the quadratic preserving Runge-Kutta method will preserve all the three invariants (momentum, mass and the reformulated energy) in the discrete time flow (assuming the spatial variable is continuous). With the Fourier pseudo-spectral spatial discretization, the scheme conserves the first and third invariant quantities (momentum and energy) exactly in the space-time full discrete sense. The discrete mass possesses the precision of the spectral accuracy. Our numerical experiment shows the great efficiency of this scheme in simulating the breathers for the mKdV equation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Arbitrary Handwriting Image Style Transfer

This paper proposed a method to imitate handwriting style by style transfer. We proposed an neural network model based on conditional generative adversarial networks (cGAN) for handwriting style transfer. This paper improved the loss function on the basis of the GAN. Compared with other handwriting imitation methods, the handwriting style transfer's effect and efficiency have been significantly improved. The experiments showed that the shape of the generated Chinese characters is clear and the analysis of experimental data showed the Generative adversarial networks showed excellent performance in handwriting style transfer. The generated text image is closer to the real handwriting and achieved a better performance in term of handwriting imitation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging

Federated learning, conducive to solving data privacy and security problems, has attracted increasing attention recently. However, the existing federated boosting model sequentially builds a decision tree model with the weak base learner, resulting in redundant boosting steps and high interactive communication costs. In contrast, the federated bagging model saves time by building multi-decision trees in parallel, but it suffers from performance loss. With the aim of obtaining an outstanding performance with less time cost, we propose a novel model in a vertically federated setting termed as Federated Gradient Boosting Forest (FedGBF). FedGBF simultaneously integrates the boosting and bagging's preponderance by building the decision trees in parallel as a base learner for boosting. Subsequent to FedGBF, the problem of hyperparameters tuning is rising. Then we propose the Dynamic FedGBF, which dynamically changes each forest's parameters and thus reduces the complexity. Finally, the experiments based on the benchmark datasets demonstrate the superiority of our method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Few-shot Object Counting with Similarity-Aware Feature Enhancement

This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image. The major challenge lies in that the target objects can be densely packed in the query image, making it hard to recognize every single one. To tackle the obstacle, we propose a novel learning block, equipped with a similarity comparison module and a feature enhancement module. Concretely, given a support image and a query image, we first derive a score map by comparing their projected features at every spatial position. The score maps regarding all support images are collected together and normalized across both the exemplar dimension and the spatial dimensions, producing a reliable similarity map. We then enhance the query feature with the support features by employing the developed point-wise similarities as the weighting coefficients. Such a design encourages the model to inspect the query image by focusing more on the regions akin to the support images, leading to much clearer boundaries between different objects. Extensive experiments on various benchmarks and training setups suggest that we surpass the state-of-the-art methods by a sufficiently large margin. For instance, on a recent large-scale FSC-147 dataset, we surpass the state-of-the-art method by improving the mean absolute error from 22.08 to 14.32 (35%$\uparrow$). Code has been released in https://github.com/zhiyuanyou/SAFECount.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Interaction with an obstacle in the 2d focusing nonlinear Schrödinger equation

We present a numerical study of solutions to the $2d$ cubic and quintic focusing nonlinear Schrödinger equation in the exterior of a smooth, compact and strictly convex obstacle (a disk) with Dirichlet boundary condition. We first investigate the effect of the obstacle on the behavior of solutions traveling toward the obstacle at different angles and with different velocities directions. We introduce a new concept of weak and strong interactions of the solutions with the obstacle. Next, we study the existence of blow-up solutions depending on the type of the interaction and show how the presence of the obstacle changes the overall behavior of solutions (e.g., from blow-up to global existence), especially in the strong interaction case, as well as how it affects the shape of solutions compared to their initial data, (e.g., splitting into transmitted and reflected parts). We also investigate the influence of the size of the obstacle on the eventual existence of blow-up solutions in the strong interaction case in terms of the transmitted and the reflected parts of the mass. Moreover, we show that the sharp threshold for global existence vs. finite time blow-up solutions in the mass critical case in the presence of the obstacle is the same as the one given by Weinstein for {\rm{NLS}} in the whole Euclidean space $\R^d$. Finally, we construct new Wall-type initial data that blows up in finite time after a strong interaction with an obstacle and having a very distinct dynamics compared with all other blow-up scenarios and dynamics for the {\rm{NLS}} in the whole Euclidean space $\R^d$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Observed Rate Variation in Superflaring G-type Stars

Flare occurrence on the Sun is highly variable, exhibiting both short term variation due to the emergence and evolution of active regions, and long-term variation from the solar cycle. On solar-like stars, much larger stellar flares (superflares) have been observed, and it is of interest to determine whether observed rates of superflare occurrence exhibit similar variability to solar flares. We analyse 274 G-type stars using data from the Transiting Exoplanet Survey Satellite (TESS) and identify seven stars which exhibit statistically significant changes in the rate of superflare occurrence by fitting a piecewise constant-rate model with the Bayesian Blocks algorithm (Scargle et al 2012; arXiv:1207.5578). We investigate the properties of these stars and their flaring rates, and discuss the possible reasons for the low number of stars with detectable rate variation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SIND: A Drone Dataset at Signalized Intersection in China

Intersection is one of the most challenging scenarios for autonomous driving tasks. Due to the complexity and stochasticity, essential applications (e.g., behavior modeling, motion prediction, safety validation, etc.) at intersections rely heavily on data-driven techniques. Thus, there is an intense demand for trajectory datasets of traffic participants (TPs) in intersections. Currently, most intersections in urban areas are equipped with traffic lights. However, there is not yet a large-scale, high-quality, publicly available trajectory dataset for signalized intersections. Therefore, in this paper, a typical two-phase signalized intersection is selected in Tianjin, China. Besides, a pipeline is designed to construct a Signalized INtersection Dataset (SIND), which contains 7 hours of recording including over 13,000 TPs with 7 types. Then, the behaviors of traffic light violations in SIND are recorded. Furthermore, the SIND is also compared with other similar works. The features of the SIND can be summarized as follows: 1) SIND provides more comprehensive information, including traffic light states, motion parameters, High Definition (HD) map, etc. 2) The category of TPs is diverse and characteristic, where the proportion of vulnerable road users (VRUs) is up to 62.6% 3) Multiple traffic light violations of non-motor vehicles are shown. We believe that SIND would be an effective supplement to existing datasets and can promote related research on autonomous driving.The dataset is available online via: https://github.com/SOTIF-AVLab/SinD

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Task-aware Similarity Learning for Event-triggered Time Series

Time series analysis has achieved great success in diverse applications such as network security, environmental monitoring, and medical informatics. Learning similarities among different time series is a crucial problem since it serves as the foundation for downstream analysis such as clustering and anomaly detection. It often remains unclear what kind of distance metric is suitable for similarity learning due to the complex temporal dynamics of the time series generated from event-triggered sensing, which is common in diverse applications, including automated driving, interactive healthcare, and smart home automation. The overarching goal of this paper is to develop an unsupervised learning framework that is capable of learning task-aware similarities among unlabeled event-triggered time series. From the machine learning vantage point, the proposed framework harnesses the power of both hierarchical multi-scale sequence autoencoders and Gaussian Mixture Model (GMM) to effectively learn the low-dimensional representations from the time series. Finally, the obtained similarity measure can be easily visualized for explaining. The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series. Through extensive qualitative and quantitative experiments, it is revealed that the proposed method outperforms state-of-the-art methods considerably.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Well-posedness and dynamics of solutions to the generalized KdV with low power nonlinearity

We consider two types of the generalized Korteweg - de Vries equation, where the nonlinearity is given with or without absolute values, and, in particular, including the low powers of nonlinearity, an example of which is the Schamel equation. We first prove the local well-posedness of both equations in a weighted subspace of $H^1$ that includes functions with polynomial decay, extending the result of Linares et al [39] to fractional weights. We then investigate solutions numerically, confirming the well-posedness and extending it to a wider class of functions that includes exponential decay. We include a comparison of solutions to both types of equations, in particular, we investigate soliton resolution for the positive and negative data with different decay rates. Finally, we study the interaction of various solitary waves in both models, showing the formation of solitons, dispersive radiation and even breathers, all of which are easier to track in nonlinearities with lower power.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A 4-6 GHz Radio Recombination Line Survey in the Milky Way

We performed a radio recombination line (RRL) survey to construct a high-mass star-forming region (HMSFR) sample in the Milky Way based on the all-sky Wide-Field Infrared Survey Explorer ($\textit{All-WISE}$) point source catalog. The survey was observed with the Shanghai 65m Tianma radio telescope (TMRT) covering 10 hydrogen RRL transitions ranging from H98$α$ to H113$α$ (corresponding to the rest frequencies of 4.5$-$6.9 GHz) simultaneously. Out of 3348 selected targets, we identified an HMSFR sample consisting of 517 sources traced by RRLs, a large fraction of this sample (486) locate near the Galactic plane ($|$$\textit{b}$$|$ $<$ 2 deg). In addition to the hydrogen RRLs, we also detected helium and carbon RRLs towards 49 and 23 sources respectively. We cross-match the RRL detections with the 6.7 methanol maser sources built up in previous works for the same target sample, as a result, 103 HMSFR sources were found to harbor both emissions. In this paper, we present the HMSFR catalog accompanied by the measured RRL line properties and a correlation with our methanol maser sample, which is believed to tracer massive stars at earlier stages. The construction of an HMSFR sample consisting of sources in various evolutionary stages indicated by different tracers is fundamental for future studies of high-mass star formation in such regions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Asymptotic stability of solitary waves of the 3D quadratic Zakharov-Kuznetsov equation

We consider the quadratic Zakharov-Kuznetsov equation $$ \partial_t u + \partial_x Δu + \partial_x u^2 =0 $$ on $\mathbb{R}^3$. A solitary wave solution is given by $Q(x-t,y,z)$, where $Q$ is the ground state solution to $-Q + ΔQ + Q^2 =0$. We prove the asymptotic stability of these solitary wave solutions. Specifically, we show that initial data close to $Q$ in the energy space, evolves to a solution that, as $t\to\infty$, converges to a rescaling and shift of $Q(x-t,y,z)$ in $L^2$ in a rightward shifting region $x> δt -\tan θ\sqrt{y^2+z^2} $ for $0 \leq θ\leq \fracπ{3}-δ$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CO observations toward HI-rich Ultra Diffuse Galaxies

We present CO observations toward a sample of six HI-rich Ultra-diffuse galaxies (UDGs) as well as one UDG (VLSB-A) in the Virgo Cluster with the IRAM 30-m telescope. CO 1-0 is marginally detected at 4sigma level in AGC122966, as the first detection of CO emission in UDGs. We estimate upper limits of molecular mass in other galaxies from the non-detection of CO lines. These upper limits and the marginal CO detection in AGC122966 indicate low mass ratios between molecular and atomic gas masses. With the star formation efficiency derived from the molecular gas, we suggest that the inefficiency of star formation in such HI-rich UDGs is likely caused by the low efficiency in converting molecules from atomic gas, instead of low efficiency in forming stars from molecular gas.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Communication-Efficient Edge AI Inference Over Wireless Networks

Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e.g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future. As such, the intelligent communication networks will be designed to leverage advanced wireless techniques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we shall present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distributed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model inference. The communication efficiency of edge inference systems is further improved by building up a smart radio propagation environment via intelligent reflecting surface.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference

Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge. This paper presents an energy-efficient edge processing framework to execute deep learning inference tasks at the edge computing nodes whose wireless connections to mobile devices are prone to channel uncertainties. Aimed at minimizing the sum of computation and transmission power consumption with probabilistic quality-of-service (QoS) constraints, we formulate a joint inference tasking and downlink beamforming problem that is characterized by a group sparse objective function. We provide a statistical learning based robust optimization approach to approximate the highly intractable probabilistic-QoS constraints by nonconvex quadratic constraints, which are further reformulated as matrix inequalities with a rank-one constraint via matrix lifting. We design a reweighted power minimization approach by iteratively reweighted $\ell_1$ minimization with difference-of-convex-functions (DC) regularization and updating weights, where the reweighted approach is adopted for enhancing group sparsity whereas the DC regularization is designed for inducing rank-one solutions. Numerical results demonstrate that the proposed approach outperforms other state-of-the-art approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Experimental and numerical studies on kV scattered x-ray imaging for real-time image guidance in radiation therapy

Motion management is a critical component of image guidance radiotherapy for lung cancer. We previously proposed a scheme using kV scattered x-ray photons for marker-less real-time image guidance in lung cancer radiotherapy. This study reports our recently progress using the photon counting detection technique to demonstrate potential feasibility of this method and using Monte Carlo (MC) simulations and ray-tracing calculations to characterize the performance. In our scheme, a thin slice of x-ray beam was directed to the target and we measured the outgoing scattered photons using a photon counting detector with a parallel-hole collimator to establish the correspondence between detector pixels and scatter positions. Image corrections of geometry, beam attenuation and scattering angle were performed to convert the raw image to the actual image of Compton attenuation coefficient. We set up a MC simulation system using an in-house developed GPU-based MC package modeling the image formation process. We also performed ray-tracing calculations to investigate the impacts of imaging system geometry on resulting image resolution. The experiment demonstrated feasibility of using a photon counting detector to measure scattered x-ray photons and generate the proposed scattered x-ray image. After correction, x-ray scattering image intensity and Compton scattering attenuation coefficient were linearly related, with R2=0.91. Contrast to Noise Ratios of different objects were improved and the values in experimental results and MC simulation results agreed with each other. Ray-tracing calculations revealed the dependence of image resolution on imaging geometry. The image resolution increases with reduced source to object distance and increased collimator height. The study demonstrated potential feasibility of using scattered x-ray imaging as a real-time image guidance method in radiation therapy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 730K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. To evaluate a tracker on different attributes, we define 4 scenario attributes and 12 challenge attributes in the evaluation dataset. By releasing LSOTB-TIR, we encourage the community to develop deep learning based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance. Furthermore, we re-train several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Revealing the Atomic Site-Dependent g Factor within a Single Magnetic Molecule via the Extended Kondo Effect

The site-dependent g factor of a single magnetic molecule, with intramolecular resolution, is demonstrated for the first time by low-temperature, high-magnetic-field scanning tunneling microscopy of dehydrogenated Mn-phthalocyanine molecules on Au(111). This is achieved by exploring the magneticfield dependence of the extended Kondo effect at different atomic sites of the molecule. Importantly, an inhomogeneous distribution of the g factor inside a single molecule is revealed. Our results open up a new route to access local spin properties within a single molecule.

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preprint2020arXiv

Reversible Single Spin Control of Individual Magnetic Molecule by Hydrogen Atom Adsorption

The reversible control of a single spin of an atom or a molecule is of great interest in Kondo physics and a potential application in spin based electronics.Here we demonstrate that the Kondo resonance of manganese phthalocyanine molecules on an Au(111) substrate have been reversibly switched off and on via a robust route through attachment and detachment of single hydrogen atom to the magnetic core of the molecule. As further revealed by density functional theory calculations, even though the total number of electrons of the Mn ion remains almost the same in the process, gaining one single hydrogen atom leads to redistribution of charges within 3d orbitals with a reduction of the molecular spin state from S = 3/2 to S = 1 that directly contributes to the Kondo resonance disappearance. This process is reversed by a local voltage pulse or thermal annealing to desorb the hydrogen atom.

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preprint2020arXiv

Robust block preconditioners for poroelasticity

In this paper we study the linear systems arising from discretized poroelasticity problems. We formulate one block preconditioner for the two-filed Biot model and several preconditioners for the classical three-filed Biot model under the unified relationship framework between well-posedness and preconditioners. By the unified theory, we show all the considered preconditioners are uniformly optimal with respect to material and discretization parameters. Numerical tests demonstrate the robustness of these preconditioners.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Stable blow-up dynamics in the $L^2$-critical and $L^2$-supercritical generalized Hartree equation

We study stable blow-up dynamics in the generalized Hartree equation with radial symmetry, a Schrödinger-type equation with a nonlocal, convolution-type nonlinearity: $iu_t+Δu +\left(|x|^{-(d-2)} \ast |u|^{p} \right) |u|^{p-2}u = 0, x \in \mathbb{R}^d$. First, we consider the $L^2$-critical case in dimensions d=3, 4, 5, 6, 7 and obtain that a generic blow-up has a self-similar structure and exhibits not only the square root blowup rate but also the log-log correction (via asymptotic analysis and functional fitting). In this setting we also study blow-up profiles and show that generic blow-up solutions converge to the rescaled $Q$, a ground state solution of the elliptic equation $-ΔQ+Q- \left(|x|^{-(d-2)} \ast |Q|^p \right) |Q|^{p-2} Q =0$. We also consider the $L^2$-supercritical case in dimensions d=3,4. We derive the profile equation for the self-similar blow-up and establish the existence and local uniqueness of its solutions. As in the NLS $L^2$-supercritical regime, the profile equation exhibits branches of non-oscillating, polynomially decaying (multi-bump) solutions. A numerical scheme of putting constraints into solving the corresponding ODE is applied during the process of finding the multi-bump solutions. Direct numerical simulation of solutions to the generalized Hartree equation by the dynamic rescaling method indicates that the $Q_{1,0}$ is the profile for the stable blow-up. In this supercritical case, we obtain the blow-up rate without any correction. This blow-up happens at the focusing level $10^{-5}$, and thus, numerically observable (unlike the $L^2$-critical case). In summary, we find that the results are similar to the behavior of stable blowup dynamics in the corresponding NLS settings. Consequently, one may expect that the form of the nonlinearity in the Schrödinger-type equations is not essential in stable blow-up.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Tunable giant magnetoresistance in a single-molecule junction

Controlling electronic transport through a single-molecule junction is crucial for molecular electronics or spintronics. In magnetic molecular devices, the spin degree-of-freedom can be used to this end since the magnetic properties of the magnetic ion centers fundamentally impact the transport through the molecules. Here we demonstrate that the electron pathway in a single-molecule device can be selected between two molecular orbitals by varying a magnetic field, giving rise to a tunable anisotropic magnetoresistance up to 93%. The unique tunability of the electron pathways is due to the magnetic reorientation of the transition metal center, resulting in a re-hybridization of molecular orbitals. We obtain the tunneling electron pathways by Kondo effect, which manifests either as a peak or a dip line shape. The energy changes of these spin-reorientations are remarkably low and less than one millielectronvolt. The large tunable anisotropic magnetoresistance could be used to control electronic transport in molecular spintronics.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Bidirectional outflows as evidence of magnetic reconnection leading to a solar microflare

Magnetic reconnection is a rapid energy release process that is believed to be responsible for flares on the Sun and stars. Nevertheless, such flare-related reconnection is mostly detected to occur in the corona, while there have been few studies concerning the reconnection in the chromosphere or photosphere. Here we present both spectroscopic and imaging observations of magnetic reconnection in the chromosphere leading to a microflare. During the flare peak time, chromospheric line profiles show significant blueshifted/redshifted components on the two sides of the flaring site, corresponding to upflows and downflows with velocities of $\pm$(70--80) km s$^{-1}$, comparable with the local Alfvén speed as expected by the reconnection in the chromosphere. The three-dimensional nonlinear force-free field configuration further discloses twisted field lines (a flux rope) at a low altitude, cospatial with the dark threads in He I 10830 Å images. The instability of the flux rope may initiate the flare-related reconnection. These observations provide clear evidence of magnetic reconnection in the chromosphere and show the similar mechanisms of a microflare to those of major flares.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Dynamic Spectrum Leasing with Two Sellers

This paper studies dynamic spectrum leasing in a cognitive radio network. There are two spectrum sellers, who are two primary networks, each with an amount of licensed spectrum bandwidth. When a seller has some unused spectrum, it would like to lease the unused spectrum to secondary users. A coordinator helps to perform the spectrum leasing stage-by-stage. As the two sellers may have different leasing period, there are three epochs, in which seller 1 has spectrum to lease in Epochs II and III, while seller 2 has spectrum to lease in Epochs I and II. Each seller needs to decide how much spectrum it should lease to secondary users in each stage of its leasing period, with a target at revenue maximization. It is shown that, when the two sellers both have spectrum to lease (i.e., in Epoch II), the spectrum leasing can be formulated as a non-cooperative game. Nash equilibria of the game are found in closed form. Solutions of the two users in the three epochs are derived.

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preprint2016arXiv

Low-Rank Matrix Completion for Mobile Edge Caching in Fog-RAN via Riemannian Optimization

The upcoming big data era is likely to demand tremendous computation and storage resources for communications. By pushing computation and storage to network edges, fog radio access networks (Fog-RAN) can effectively increase network throughput and reduce transmission latency. Furthermore, we can exploit the benefits of cache enabled architecture in Fog-RAN to deliver contents with low latency. Radio access units (RAUs) need content delivery from fog servers through wireline links whereas multiple mobile devices acquire contents from RAUs wirelessly. This work proposes a unified low-rank matrix completion (LRMC) approach to solving the content delivery problem in both wireline and wireless parts of Fog-RAN. To attain a low caching latency, we present a high precision approach with Riemannian trust-region method to solve the challenging LRMC problem by exploiting the quotient manifold geometry of fixed-rank matrices. Numerical results show that the new approach has a faster convergence rate, is able to achieve optimal results, and outperforms other state-of-art algorithms.

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preprint2016arXiv

Quantifying the Topology and Evolution of a Magnetic Flux Rope Associated with Multi-flare Activities

Magnetic flux rope (MFR) plays an important role in solar activities. A quantitative assessment of the topology of an MFR and its evolution is crucial for a better understanding of the relationship between the MFR and the associated activities. In this paper, we investigate the magnetic field of active region 12017 from 2014 March 28 to 29, where 12 flares were triggered by the intermittent eruptions of a filament (either successful or confined). Using the vector magnetic field data from the Helioseismic and Magnetic Imager on board the \textit{Solar Dynamics Observatory}, we calculate the magnetic energy and helicity injection in the active region, and extrapolate the 3D magnetic field with a nonlinear force-free field model. From the extrapolations, we find an MFR that is cospatial with the filament. We further determine the configuration of this MFR by a closed quasi-separatrix layer (QSL) around it. Then, we calculate the twist number and the magnetic helicity for the field lines composing the MFR. The results show that the closed QSL structure surrounding the MFR gets smaller as a consequence of the flare occurrence. We also find that the flares in our sample are mainly triggered by kink instability. Moreover, the twist number varies more sensitively than other parameters to the occurrence of flares.

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preprint2016arXiv

Slipping Magnetic Reconnection of Flux Rope Structures as a Precursor to an Eruptive X-class Solar Flare

We present the quasi-periodic slipping motion of flux rope structures prior to the onset of an eruptive X-class flare on 2015 March 11, obtained by the \emph{Interface Region Imaging Spectrograph} (\emph{IRIS}) and the \emph{Solar Dynamics Observatory} (\emph{SDO}). The slipping motion occurred at the north part of the flux rope and seemed to successively peel off the flux rope. The speed of the slippage was 30$-$40 km s$^{-1}$, with an average period of 130$\pm$30 s. The Si {\sc iv} 1402.77 Å line showed a redshift of 10$-$30 km s$^{-1}$ and a line width of 50$-$120 km s$^{-1}$ at the west legs of slipping structures, indicative of reconnection downflow. The slipping motion lasted about 40 min and the flux rope started to rise up slowly at the late stage of the slippage. Then an X2.1 flare was initiated and the flux rope was impulsively accelerated. One of the flare ribbons swept across a negative-polarity sunspot and the penumbral segments of the sunspot decayed rapidly after the flare. We studied the magnetic topology at the flaring region and the results showed the existence of a twisted flux rope, together with quasi-separatrix layers (QSLs) structures binding the flux rope. Our observations imply that quasi-periodic slipping magnetic reconnection occurs along the flux-rope-related QSLs in the preflare stage, which drives the later eruption of the flux rope and the associated flare.

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preprint2015arXiv

Modeling and Simulation for Fluid-Rotating Structure Interaction

In this paper, we study a dynamic fluid-structure interaction (FSI) model for an elastic structure that is immersed and spinning in the fluid. We develop a linear constitutive model to describe the motion of a rotational elastic structure which is suitable for the application of arbitrary Lagrangian-Eulerian (ALE) method in FSI simulation. Additionally, a novel ALE mapping method is designed to generate the moving fluid mesh while the deformable structure spins in a non-axisymmetric fluid channel. The structure velocity is adopted as the principle unknown to form a monolithic saddle-point system together with fluid velocity and pressure. We discretize the nonlinear saddle-point system with mixed finite element method and Newton's linearization, and prove that the derived saddle-point problem is well-posed. The developed methodology is applied to a self-defined elastic structure and a realistic hydro-turbine under a prescribed angular velocity. Both illustrate the satisfactory numerical results of an elastic structure that is deforming and rotating while interacting with the fluid. The numerical validation is also conducted to demonstrate the modeling consistency.

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preprint2015arXiv

On the 2012 October 23 circular ribbon flare: emission features and magnetic topology

Circular ribbon flares are usually related to spine-fan type magnetic topology containing null-points. In this paper, we investigate an X-class circular ribbon flare on 2012 October 23, using the multi-wavelength data from the \textit{Solar Dynamics Observatory}, \textit{Hinode}, and the \textit{Ramaty High Energy Solar Spectroscopic Imager}. In \ion{Ca}{2} H emission, the flare showed three ribbons with two highly elongated ones inside and outside a quasi-circular one, respectively. A hot channel was displayed in the extreme ultraviolet (EUV) emissions that infers the existence of a magnetic flux rope. Two hard X-ray (HXR) sources in the 12--25 keV energy band were located at the footpoints of this hot channel. Using a nonlinear force-free magnetic field extrapolation, we identify three topological structures: (1) a 3D null-point, (2) a flux rope below the fan of the null-point, and (3) a large-scale quasi-separatrix layers (QSL) induced by the quadrupolar-like magnetic field of the active region. We find that the null-point is embedded within the large-scale QSL. In our case, all three identified topological structures must be considered to explain all the emission features associated with the observed flare. Besides, the HXR sources are regarded as the consequence of the reconnection within or near the border of the flux rope.

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preprint2014arXiv

A fast clustering algorithm for mining social network data

Many groups with diverse convictions are interacting online. Interactions in online communities help people to engage each other and enhance understanding across groups. Online communities include multiple sub-communities whose members are similar due to social ties, characteristics, or ideas on a topic. In this research, we are interested in understanding the changes in the relative size and activity of these sub-communities, their merging or splitting patterns, and the changes in the perspectives of the members of these sub-communities due to endogenous dynamics inside the community.

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preprint2014arXiv

A multivariate hierarchical Bayesian framework for healthcare predictions with application to medical home study in the Department of Veteran Affairs

Recently the patient centered medical home (PCMH) model has become a popular approach to deliver better care to patients. Current research shows that the most important key for succession of this method is to make balance between healthcare supply and demand. Without such balance in clinical supply and demand, issues such as excessive under and over utilization of physicians, long waiting time for receiving the appropriate treatment, and non continuity of care will eliminate many advantages of the medical home strategy. To reach this end we need to have information about both supply and demand in healthcare system. Healthcare supply can be calculated easily based on head counts and available hours which is offered by professionals for a specific time period while healthcare demand is not easy to calculate, and it is affected by some healthcare, diagnostic and demographic attributes. In this paper, by extending the hierarchical generalized linear model to include multivariate responses, we develop a clinical workload prediction model for care portfolio demands in a Bayesian framework. Our analyses of a recent data from Veteran Health Administration indicate that our prediction model works for clinical data with high performance.

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preprint2014arXiv

A predictive analytics approach to reducing avoidable hospital readmission

Hospital readmission has become a critical metric of quality and cost of healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20% of patients who are readmitted within 30 days of discharge. Although several interventions such as transition care management and discharge reengineering have been practiced in recent years, the effectiveness and sustainability depends on how well they can identify and target patients at high risk of rehospitalization. Based on the literature, most current risk prediction models fail to reach an acceptable accuracy level; none of them considers patient's history of readmission and impacts of patient attribute changes over time; and they often do not discriminate between planned and unnecessary readmissions. Tackling such drawbacks, we develop a new readmission metric based on administrative data that can identify potentially avoidable readmissions from all other types of readmission. We further propose a tree based classification method to estimate the predicted probability of readmission that can directly incorporate patient's history of readmission and risk factors changes over time. The proposed methods are validated with 2011-12 Veterans Health Administration data from inpatients hospitalized for heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease in the State of Michigan. Results shows improved discrimination power compared to the literature (c-statistics>80%) and good calibration.

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preprint2014arXiv

An analytics approach to designing patient centered medical homes

Recently the patient-centered medical home (PCMH) model has become a popular team-based approach focused on delivering more streamlined care to patients. In current practices of medical homes, a clinical-based prediction frame is recommended because it can help match the portfolio capacity of PCMH teams with the actual load generated by a set of patients. Without such balances in clinical supply and demand, issues such as excessive under and over utilization of physicians, long waiting time for receiving the appropriate treatment, and non-continuity of care will eliminate many advantages of the medical home strategy. In this paper, by extending the hierarchical generalized linear model to include multivariate responses, we develop a clinical workload prediction model for care portfolio demands in a Bayesian framework. The model allows for heterogeneous variances and unstructured covariance matrices for nested random effects that arise through complex hierarchical care systems. We show that using a multivariate approach substantially enhances the precision of workload predictions at both primary and non-primary care levels. We also demonstrate that care demands depend not only on patient demographics but also on other utilization factors, such as length of stay. Our analyses of a recent data from Veteran Health Administration further indicate that risk adjustment for patient health conditions can considerably improve the prediction power of the model.

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preprint2014arXiv

Estimating Multiple Step Shifts in a Gaussian Process Mean with an Application to Phase I Control Chart Analysis

In preliminary analysis of control charts, one may encounter multiple shifts and/or outliers especially with a large number of observations. The following paper addresses this problem. A statistical model for detecting and estimating multiple change points in a finite batch of retrospective (phase I)data is proposed based on likelihood ratio test. We consider a univariate normal distribution with multiple step shifts occurred in predefined locations of process mean. A numerical example is performed to illustrate the efficiency of our method. Finally, performance comparisons, based on accuracy measures and precision measures, are explored through simulation studies.

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preprint2014arXiv

Modeling clustered non-stationary Poisson processes for stochastic simulation inputs

A validated simulation model primarily requires performing an appropriate input analysis mainly by determining the behavior of real-world processes using probability distributions. In many practical cases, probability distributions of the random inputs vary over time in such a way that the functional forms of the distributions and/or their parameters depend on time. This paper answers the question whether a sequence of observations from a process follow the same statistical distribution, and if not, where the exact change points are, so that observations within two consecutive change points follow the same distribution. We propose two different methods based on likelihood ratio test and cluster analysis to detect multiple change points when observations follow non-stationary Poisson process with diverse occurrence rates over time. Results from a comprehensive Monte Carlo study indicate satisfactory performance for the proposed methods. A well-known example is also considered to show the application of our findings in real world cases.

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preprint2014arXiv

On modeling nonhomogeneous Poisson process for stochastic simulation input analysis

A validated simulation model primarily requires performing an appropriate input analysis mainly by determining the behavior of real-world processes using probability distributions. In many practical cases, probability distributions of the random inputs vary over time in such a way that the functional forms of the distributions and/or their parameters depend on time. This paper answers the question whether a sequence of observations from a process follows the same statistical distribution, and if not, where the exact change points are. We propose a Likelihood Ratio Test (LRT) based method to detect multiple change points when observations follow non-stationary Poisson process with diverse occurrence rates over time. Results from a comprehensive Monte Carlo study indicate satisfactory performance for the proposed method.

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preprint2014arXiv

Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs

Reducing potentially preventable readmissions has been identified as an important issue for decreasing Medicare costs and improving quality of care provided by hospitals. Based on previous research by medical professionals, preventable readmissions are caused by such factors as flawed patient discharging process, inadequate follow-ups after discharging, and noncompliance of patients on discharging and follow up instructions. It is also found that the risk of preventable readmission also may relate to some patient's characteristics, such as age, health condition, diagnosis, and even treatment specialty. In this study, using both general demographic information and individual past history of readmission records, we develop a risk prediction model based on hierarchical nonlinear mixed effect framework to extract significant prognostic factors associated with patient risk of 30-day readmission. The effectiveness of our proposed approach is validated based on a real dataset from four VA facilities in the State of Michigan. Simultaneously explaining both patient and population based variations of readmission process, such an accurate model can be used to recognize patients with high likelihood of discharging non-compliances, and then targeted post-care actions can be designed to reduce further rehospitalization.

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preprint2013arXiv

Spatial Imaging of Landé g Factor through Extended Kondo Effect in a Single Magnetic Molecule

A methodology of atomically resolved Landé g factor mapping of a single molecule is reported. Mn(II)-phthalocyanine (MnPc) molecules on Au(111) surface can be dehydrogenated via atomic manipulation with manifestation of tailored extended Kondo effect, which can allow atomically resolved imaging of the Landé g factor inside a molecule for the first time. By employing dehydrogenated MnPc molecules with removal of six H atoms (-6H-MnPc) as an example, Landé g factor of atomic constituents of the molecule can be obtained, therefore offering a unique g factor mapping of single molecule. Our results open up a new avenue to access local spin texture of a single molecule.

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preprint2012arXiv

MIDI-LAB, a Powerful Visual Basic Program for Creating MIDI Music

Creating MIDI music can be a practical challenge. In the past, working with it was difficult and frustrating to all but the most accomplished and determined. Now, however, we are offering a powerful Visual Basic program called MIDI-LAB, that is easy to learn, and instantly rewarding to even the newest users. MIDI-LAB has been developed to give users the ability to quickly create music with a limitless variety of tunes, tempos, speeds, volumes, instruments, rhythms and major scales. This program has a simple, intuitive, and user-friendly interface, which provides a straightforward way to enter musical data with Numbered Musical Notation (NMN) and immediately create MIDI music. The key feature of this program is the digitalization of music input. It vastly simplifies creating, editing, and saving MIDI music. MIDI-LAB can be used virtually anywhere to write music for entertainment, teaching, computer games, and mobile phone ringtones.

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