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Researcher profile

Jing Sun

Jing Sun contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SYSystems and Controlmath.NAMachine LearningNumerical Analysismath.OCComputer VisionCryptography and Securityastro-ph.COastro-ph.GAastro-ph.IMBiological PhysicsComputationComputational Engineering, Finance, and Sciencephysics.chem-phphysics.comp-phphysics.geo-phSoftware Engineering

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Published work

25 published item(s)

preprint2026arXiv

Battery State of Health Estimation and Incremental Capacity Analysis under Dynamic Charging Profile Using Neural Networks

Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are two effective approaches for battery degradation monitoring. One limiting factor for their real-world application is that they require constant-current (CC) charging profiles. This research removes this limitation and proposes an approach that extends ICA/DVA-based degradation monitoring from CC charging to dynamic charging profiles. A novel concept of virtual incremental capacity (VIC) and virtual differential voltage (VDV) is proposed. Then, two related convolutional neural networks (CNNs), called U-Net and Conv-Net, are proposed to construct VIC/VDV curves and estimate the state of health (SOH) from dynamic charging profiles across any state-of-charge (SOC) range that satisfies some constraints. Finally, two CNNs called Mobile U-Net and Mobile-Net are proposed as replacements for the U-Net and Conv-Net, respectively, to reduce the computational footprint and memory requirements, while keeping similar performance. Using an extensive experimental dataset of battery modules, the proposed CNNs are demonstrated to provide accurate VIC/VDV curves and enable ICA/DVA-based battery degradation monitoring under various fast-charging protocols and different SOC ranges.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Noise Reduction for Pufferfish Privacy: A Practical Noise Calibration Method

This paper introduces a relaxed noise calibration method to enhance data utility while attaining pufferfish privacy. This work builds on the existing $1$-Wasserstein (Kantorovich) mechanism by alleviating the existing overly strict condition that leads to excessive noise, and proposes a practical mechanism design algorithm as a general solution. We prove that a strict noise reduction by our approach always exists compared to $1$-Wasserstein mechanism for all privacy budgets $ε$ and prior beliefs, and the noise reduction (also represents improvement on data utility) gains increase significantly for low privacy budget situations--which are commonly seen in real-world deployments. We also analyze the variation and optimality of the noise reduction with different prior distributions. Moreover, all the properties of the noise reduction still exist in the worst-case $1$-Wasserstein mechanism we introduced, when the additive noise is largest. We further show that the worst-case $1$-Wasserstein mechanism is equivalent to the $\ell_1$-sensitivity method. Experimental results on three real-world datasets demonstrate $47\%$ to $87\%$ improvement in data utility.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distributed acoustic sensing (DAS) arrays, and large-scale 2D and 3D surveys. This expansion makes complex noise suppression increasingly challenging, especially when signal fidelity must be preserved. Conventional supervised deep learning methods are often task-specific, require large paired datasets, and can suffer from domain shift under new acquisition conditions. Foundation models offer a promising alternative, but pre-training seismic foundation models from scratch requires massive domain-specific data and substantial computation. We propose an efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks through Parameter-Efficient Fine-Tuning. The architecture uses a pre-trained VFM, a DINOv3 encoder, adapted with Low-Rank Adaptation (LoRA) to enable effective feature adaptation with few additional parameters. To improve robustness under unseen field conditions without ground truth, we introduce a kurtosis-guided unsupervised test-time adaptation module that updates only LoRA parameters during inference. This module self-calibrates the model to site-specific noise by identifying information-rich regions via kurtosis and performing self-training without labeled data. Experiments on public exploration seismic images and DAS vertical seismic profiling data from the Utah FORGE site show that the framework matches or outperforms domain-specific models. Tests on unseen cross-site data from a land survey in China and the Groß Schönebeck geothermal site in Germany further demonstrate strong generalization and effective signal-noise separation. These results highlight the potential of adapting pre-trained VFMs to data-intensive problems in exploration seismology.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Energy-optimal Three-dimensional Path-following Control of Autonomous Underwater Vehicles under Ocean Currents

This paper presents a three-dimensional (3D) energy-optimal path-following control design for autonomous underwater vehicles subject to ocean currents. The proposed approach has a two-stage control architecture consisting of the setpoint computation and the setpoint tracking. In the first stage, the surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing the required vehicle propulsion energy under currents, and the line-of-sight (LOS) guidance law is used to generate the yaw angle setpoint that ensures path following. In the second stage, two model predictive controllers are designed to control the vehicle motion in the horizontal and vertical planes by tracking the optimal setpoints. The proposed controller is compared with a conventional LOS-based control that maintains zero heave velocity relative to the current (i.e., relative heave velocity) and derives pitch angle setpoint using LOS guidance to reach the desired depth. Through simulations, we show that the proposed approach can achieve more than 13% energy saving on a lawnmower-type and an inspection mission under different ocean current conditions. The simulation results demonstrate that allowing motions with non-zero relative heave velocity improves energy efficiency in 3D path-following applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A stochastic reduced-order model for statistical microstructure descriptors evolution

Integrated Computational Materials Engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), are modeled by the Fokker-Planck equation. We discuss how to calibrate the drift and diffusion terms of the Fokker-Planck equation from the theoretical and computational perspectives. The calibrated Fokker-Planck equation can be used as a stochastic reduced-order model (ROM) to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Control Co-design of a Hydrokinetic Turbine with Open-loop Optimal Control

This paper introduces a control co-design (CCD) framework to simultaneously explore the physical parameters and control spaces for a hydro-kinetic turbine (HKT) rotor optimization. The optimization formulation incorporates a coupled dynamic-hydrodynamic model to maximize the rotor power efficiency for various time-variant flow profiles. The open-loop optimal control is applied for maximum power tracking, and the blade element momentum theory (BEMT) is used to model the hydrodynamics. Case studies with different control constraints are investigated for CCD. Sensitivity analyses were conducted with respect to different flow profiles and initial geometries. Comparisons are made between CCD and the sequential process, with physical design followed by a control design process under the same conditions. The results demonstrate the benefits of CCD and reveal that, with control constraints, CCD leads to increased energy production compared to the design obtained from the sequential design process.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Liuer Mihou: A Practical Framework for Generating and Evaluating Grey-box Adversarial Attacks against NIDS

Due to its high expressiveness and speed, Deep Learning (DL) has become an increasingly popular choice as the detection algorithm for Network-based Intrusion Detection Systems (NIDSes). Unfortunately, DL algorithms are vulnerable to adversarial examples that inject imperceptible modifications to the input and cause the DL algorithm to misclassify the input. Existing adversarial attacks in the NIDS domain often manipulate the traffic features directly, which hold no practical significance because traffic features cannot be replayed in a real network. It remains a research challenge to generate practical and evasive adversarial attacks. This paper presents the Liuer Mihou attack that generates practical and replayable adversarial network packets that can bypass anomaly-based NIDS deployed in the Internet of Things (IoT) networks. The core idea behind Liuer Mihou is to exploit adversarial transferability and generate adversarial packets on a surrogate NIDS constrained by predefined mutation operations to ensure practicality. We objectively analyse the evasiveness of Liuer Mihou against four ML-based algorithms (LOF, OCSVM, RRCF, and SOM) and the state-of-the-art NIDS, Kitsune. From the results of our experiment, we gain valuable insights into necessary conditions on the adversarial transferability of anomaly detection algorithms. Going beyond a theoretical setting, we replay the adversarial attack in a real IoT testbed to examine the practicality of Liuer Mihou. Furthermore, we demonstrate that existing feature-level adversarial defence cannot defend against Liuer Mihou and constructively criticise the limitations of feature-level adversarial defences.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Numerical Approximation for Stochastic Nonlinear Fractional Diffusion Equation Driven by Rough Noise

In this work, we are interested in building the fully discrete scheme for stochastic fractional diffusion equation driven by fractional Brownian sheet which is temporally and spatially fractional with Hurst parameters $H_{1}, H_{2} \in(0,\frac{1}{2}]$. We first provide the regularity of the solution. Then we employ the Wong-Zakai approximation to regularize the rough noise and discuss the convergence of the approximation. Next, the finite element and backward Euler convolution quadrature methods are used to discretize spatial and temporal operators for the obtained regularized equation, and the detailed error analyses are developed. Finally, some numerical examples are presented to confirm the theory.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On the Suppression and Enhancement of Thermal Chemical Rates in a Cavity

The observed modification of thermal chemical rates in Fabry-Perot cavities remains a poorly understood effect theoretically. Recent breakthroughs explain some of the observations through the Grote-Hynes theory, where the cavity introduces friction with the reaction coordinate, thus reducing the transmission coefficient and the rate. The regime of rate enhancement, the observed sharp resonances at varying cavity frequencies, and the survival of these effects in the collective regime remain mostly unexplained. In this paper, we consider the \emph{cis}-\emph{trans} isomerization of HONO atomistically using an \emph{ab-initio} potential energy surface. We evaluate the transmission coefficient using the reactive flux method and identify the conditions for rate acceleration. In the underdamped, low-friction regime of the reaction coordinate, the cavity coupling enhances the rate with increasing coupling strength until reaching the Kramers turnover point. Sharp resonances in this regime are related to cavity-enabled energy redistribution channels.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Optimal convergence for the regularized solution of the model describing the competition between super- and sub- diffusions driven by fractional Brownian sheet noise

Super- and sub- diffusions are two typical types of anomalous diffusions in the natural world. In this work, we discuss the numerical scheme for the model describing the competition between super- and sub- diffusions driven by fractional Brownian sheet noise. Based on the obtained regulization result of the solution by using the properties of Mittag-Leffler function and the regularized noise by Wong-Zakai approximation, we make full use of the regularity of the solution operators to achieve optimal convergence of the regularized solution. The spectral Galerkin method and the Mittag-Leffler Euler integrator are respectively used to deal with the space and time operators. In particular, by contour integral, the fast evaluation of the Mittag-Leffler Euler integrator is realized. We provide complete error analyses, which are verified by the numerical experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

Domain adaptation has received a lot of attention in recent years, and many algorithms have been proposed with impressive progress. However, it is still not fully explored concerning the joint probability distribution (P(X, Y)) distance for this problem, since its empirical estimation derived from the maximum mean discrepancy (joint maximum mean discrepancy, JMMD) will involve complex tensor-product operator that is hard to manipulate. To solve this issue, this paper theoretically derives a unified form of JMMD that is easy to optimize, and proves that the marginal, class conditional and weighted class conditional probability distribution distances are our special cases with different label kernels, among which the weighted class conditional one not only can realize feature alignment across domains in the category level, but also deal with imbalance dataset using the class prior probabilities. From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one. Therefore, we leverage Hilbert Schmidt independence criterion and propose a novel MMD matrix to promote the dependence, and devise a novel label kernel that is robust to label distribution shift. Finally, we conduct extensive experiments on several cross-domain datasets to demonstrate the validity and effectiveness of the revealed theoretical results.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

High-fidelity complex engineering simulations are highly predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. In this paper, an asynchronous constrained batch-parallel Bayesian optimization method is proposed to efficiently solve the computationally-expensive simulation-based optimization problems on the HPC platform, with a budgeted computational resource, where the maximum number of simulations is a constant. The advantages of this method are three-fold. First, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated massively parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime. This efficiency feature is further improved so that when each of the inputs is finished, another input is queried without waiting for the whole batch to complete. Second, the method can handle both known and unknown constraints. Third, the proposed method considers several acquisition functions at the same time and sample based on an evolving probability mass distribution function using a modified GP-Hedge scheme, where parameters are corresponding to the performance of each acquisition function. The proposed framework is termed aphBO-2GP-3B, which corresponds to asynchronous parallel hedge Bayesian optimization with two Gaussian processes and three batches. The aphBO-2GP-3B framework is demonstrated using two high-fidelity expensive industrial applications, where the first one is based on finite element analysis (FEA) and the second one is based on computational fluid dynamics (CFD) simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Experimental Validation of Eco-Driving and Eco-Heating Strategies for Connected and Automated HEVs

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source. To test and validate these solutions, a power-split hybrid electric vehicle (HEV) has been instrumented for cabin thermal management, allowing to regulate heating, ventilation, and air conditioning (HVAC) system inputs (cabin temperature setpoint and blower flow rate) in real-time. Experiments were conducted to demonstrate the energy-saving benefits of eco-driving and eco-heating strategies over real-world city driving cycles at different cold ambient temperatures. The data confirmed average fuel savings of 14.5% and 4.7% achieved by eco-driving and eco-heating, respectively, offering a combined energy saving of more than 19% when comparing to the baseline vehicle driven by a human driver with a constant-heating strategy.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Finite difference method for inhomogeneous fractional Dirichlet problem

We make the split of the integral fractional Laplacian as $(-Δ)^s u=(-Δ)(-Δ)^{s-1}u$, where $s\in(0,\frac{1}{2})\cup(\frac{1}{2},1)$. Based on this splitting, we respectively discretize the one- and two-dimensional integral fractional Laplacian with the inhomogeneous Dirichlet boundary condition and give the corresponding truncation errors with the help of the interpolation estimate. Moreover, the suitable corrections are proposed to guarantee the convergence in solving the inhomogeneous fractional Dirichlet problem and an $\mathcal{O}(h^{1+α-2s})$ convergence rate is obtained when the solution $u\in C^{1,α}(\barΩ^δ_{n})$, where $n$ is the dimension of the space, $α\in(\max(0,2s-1),1]$, $δ$ is a fixed positive constant, and $h$ denotes mesh size. Finally, the performed numerical experiments confirm the theoretical results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the V2V/V2I-based short-range prediction, is difficult and error-prone. The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short- and long-term vehicle speed preview. To this end, a model predictive controller (MPC) with a multi-range speed preview for integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC is formulated to manage the power-split between the engine and the battery while enforcing the power and thermal (engine coolant and catalytic converter temperatures) constraints. The MPC exploits prediction and optimization over a shorter receding horizon and longer shrinking horizon. Over the longer shrinking horizon, the vehicle speed estimation is based on the data collected from the connected vehicles traveling on the same route as the ego-vehicle. Simulation results of applying the MPC over real-world urban driving cycles in Ann Arbor, MI are presented to demonstrate the effectiveness and fuel-saving potentials of the proposed iPTM strategy under the uncertainty associated with long-term predictions of the CV's speed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Importance Filtered Cross-Domain Adaptation

In Domain Adaptation (DA), the category-relevant losses usually occupy a dominant position, while they are usually built with hard or soft labels in existing models. We observed that hard labels are overconfident due to hard samples existed, and soft labels are ambiguous as too many small noisy probabilities involved, and both of them are easily to cause negative transfer. Besides, the category-irrelevant losses in Closed-Set DA (CSDA) paradigm fail to work in Open-Set DA (OSDA), and they also have to be in a category-relevant form, since target data samples are split into shared and private classes. To this end, we propose a newly-unified DA framework (i.e., Importance Filtered Cross-Domain Adaptation, IFCDA). Firstly, an importance filtered mechanism is devised to generate filtered soft labels to mitigate negative transfer desirably. Specifically, the soft labels are divided into confident and ambiguous ones. Then, only the maximum probability in each confident label is retained, and a threshold value is set to truncate each ambiguous label so that only prominent probabilities are reserved. Moreover, a general graph-based label propagation is contrived to attain soft labels in both CSDA and OSDA, where an extra component is embedded into label vector, so that it could detect target novel classes. Finally, the category-relevant losses in both scenarios are reformulated using filtered soft labels, while the category-irrelevant MMD loss in CSDA is reformulated as a form like class-wise MMD using newly-designed importance filtered soft labels. Notably, CSDA paradigm is a special case when all extra components are set to 0, thus the proposed approach is geared to both CSDA and OSDA. Comprehensive experiments on benchmark cross-domain object recognition datasets verify that the proposed approach outperforms several state-of-the-art methods in both scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Individual Cell Fault Detection for Parallel-Connected Battery Cells Based on the Statistical Model and Analysis

Fault diagnosis is extremely important to the safe operation of Lithium-ion batteries. To avoid severe safety issues (e.g., thermal runaway), initial faults should be timely detected and resolved. In this paper, we consider parallel-connected battery cells with only one voltage and one current sensor. The lack of independent current sensors makes it difficult to detect individual cell degradation. To this end, based on the high-frequency response of the battery, a simplified fault detection-oriented model is derived and validated by a physics-informed battery model. The resistance of the battery string, which is significantly influenced by the faulty cell, is estimated and used as the health indicator. The statistical resistance distribution of battery strings is first analyzed considering the distribution of fresh and aged cells. A fault diagnosis algorithm is proposed and the thresholds (i.e., 2 standard deviation interval) are obtained through statistical analysis. Monte Carlo simulation results show that the proposed fault diagnosis algorithm can balance false alarms and missed detections well. In addition, it is verified that the proposed algorithm is robust to the uniform parameter changes of individual battery cells.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Integrated Power and Thermal Management of Connected HEVs via Multi-Horizon MPC

In this paper, a multi-horizon model predictive controller (MH-MPC) is developed for integrated power and thermal management (iPTM) of a power-split hybrid electric vehicle (HEV). The proposed MH-MPC leverages an accurate short-horizon vehicle speed preview and an approximate forecast over a longer shrinking horizon till the end of the driving cycle. This multiple-horizon scheme is developed to cope with fast and slow dynamics associated with power and thermal responses. The main objective of the proposed MH-MPC is to minimize fuel consumption and enforce the power and thermal constraints on the battery state-of-charge and engine coolant temperature, while meeting the driving (traction) and cabin air conditioning (heating) demands. The proposed MH-MPC allows for exploiting the engine coolant as thermal energy storage, providing more flexibility for the HEV energy flow optimization. The simulation results show that the proposed MH-MPC provides near-optimal results in reference to the Dynamic Programming (DP) solution with an affordable computational cost. Moreover, compared with a more conventional MPC strategy, the MH-MPC can leverage the speed previews with different resolutions effectively to achieve the desired performance with satisfactory robustness.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Measuring the Quality of B Abstract Machines with ISO/IEC 25010

The B method has facilitated the development of software by specifying the design of software as abstract machines and formally verifying the correctness of the abstract machines. The quality of B abstract machines can significantly impact the quality of final software products. In this paper, we propose a set of criteria for measuring the quality of B abstract machines based on ISO/IEC 25010, which is one of the latest international standards for evaluating software quality in software engineering. These criteria evaluate abstract machines using a number of general-purpose and domain-independent equations and model checking techniques, so that the quality of abstract machines can be quantified as vectors. The proposed criteria are implemented as a B model quality evaluator, and they are explained and justified using a number of examples.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Sparsely-Labeled Source Assisted Domain Adaptation

Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available in the source domain. However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild. This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples. Specifically, due to the label scarcity problem, the projected clustering is conducted on both the source and target domains, so that the discriminative structures of data could be leveraged elegantly. Then the label propagation is adopted to propagate the labels from those limited labeled source samples to the whole unlabeled data progressively, so that the cluster labels are revealed correctly. Finally, we jointly align the marginal and conditional distributions to mitigate the cross-domain mismatch problem, and optimize those three procedures iteratively. However, it is nontrivial to incorporate those three procedures into a unified optimization framework seamlessly since some variables to be optimized are implicitly involved in their formulas, thus they could not promote to each other. Remarkably, we prove that the projected clustering and conditional distribution alignment could be reformulated as different expressions, thus the implicit variables are revealed in different optimization steps. As such, the variables related to those three quantities could be optimized in a unified optimization framework and facilitate to each other, to improve the recognition performance obviously.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Strong convergence order for the scheme of fractional diffusion equation driven by fractional Gaussion noise

Fractional Gaussian noise models the time series with long-range dependence; when the Hurst index $H>1/2$, it has positive correlation reflecting a persistent autocorrelation structure. This paper studies the numerical method for solving stochastic fractional diffusion equation driven by fractional Gaussian noise. Using the operator theoretical approach, we present the regularity estimate of the mild solution and the fully discrete scheme with finite element approximation in space and backward Euler convolution quadrature in time. The $\mathcal{O}(τ^{H-ρα})$ convergence rate in time and $\mathcal{O}(h^{\min(2,2-2ρ,\frac{H}α)})$ in space are obtained, showing the relationship between the regularity of noise and convergence rates, where $ρ$ is a parameter to measure the regularity of noise and $α\in(0,1)$. Finally, numerical experiments are performed to support the theoretical results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Sixteenth Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra

This paper documents the sixteenth data release (DR16) from the Sloan Digital Sky Surveys; the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the southern hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey (TDSS) and new data from the SPectroscopic IDentification of ERosita Survey (SPIDERS) programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library "MaStar"). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Effects of restrained degradation on gene expression and regulation

The effects of carrying capacity of environment $K$ for degradation (the $K$ effect for short) on the constitutive gene expression and a simple genetic regulation system, are investigated by employing a stochastic Langevin equation combined with the corresponding Fokker-Planck equation for the two stochastic systems subjected to internal and external noises. This $K$ effect characterizes the limited degradation ability of the environment for RNA or proteins, such as insufficient catabolic enzymes. The $K$ effect could significantly change the distribution of mRNA copy-number in constitutive gene expression, and interestingly, it leads to the Fano factor slightly larger than 1 if only the internal noise exists. Therefore, that the recent experimental measurements suggests the Fano factor deviates from 1 slightly (Science {\bf 346} (2014) 1533), probably originates from the $K$ effect. The $K$ effects on the steady and transient properties of genetic regulation system, have been investigated in detail. It could enhance the mean first passage time significantly especially when the noises are weak and reduce the signal-to-noise ratio in stochastic resonance substantially.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Error estimates for backward fractional Feynman-Kac equation with non-smooth initial data

In this paper, we are concerned with the numerical solution for the backward fractional Feynman-Kac equation with non-smooth initial data. Here we first provide the regularity estimate of the solution. And then we use the backward Euler and second-order backward difference convolution quadratures to approximate the Riemann-Liouville fractional substantial derivative and get the first- and second-order convergence in time. The finite element method is used to discretize the Laplace operator with the optimal convergence rates. Compared with the previous works for the backward fractional Feynman-Kac equation, the main advantage of the current discretization is that we don't need the assumption on the regularity of the solution in temporal and spatial directions. Moreover, the error estimates of the time semi-discrete schemes and the fully discrete schemes are also provided. Finally, we perform the numerical experiments to verify the effectiveness of the presented algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Numerical scheme for the Fokker-Planck equations describing anomalous diffusions with two internal states

Recently, the fractional Fokker-Planck equations (FFPEs) with multiple internal states are built for the particles undergoing anomalous diffusion with different waiting time distributions for different internal states, which describe the distribution of positions of the particles [Xu and Deng, Math. Model. Nat. Phenom., $\mathbf{13}$, 10 (2018)]. In this paper, we first develop the Sobolev regularity of the FFPEs with two internal states, including the homogeneous problem with smooth and nonsmooth initial values and the inhomogeneous problem with vanishing initial value, and then we design the numerical scheme for the system of fractional partial differential equations based on the finite element method for the space derivatives and convolution quadrature for the time fractional derivatives. The optimal error estimates of the scheme under the above three different conditions are provided for both space semidiscrete and fully discrete schemes. Finally, one- and two-dimensional numerical experiments are performed to confirm our theoretical analysis and the predicted convergence order.

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