arXiv subjects

Yi Gu

Publications and source records attributed to Yi Gu.

At least 91 records · Page 5Linked to original sources

arXiv2021

Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.

eess.SP↗

arXiv2021

Surface fibrations with large equivariant automorphism group

For a relatively minimal surface fibration $f: X\to C$, the equivariant automorphism group of $f$ is, roughly speaking, the group of automorphisms of $X$ preserving the fibration structure. We present a classification of such fibrations of fibre genus $g\ge 1$ with smooth generic fibre over an arbitrary algebraically closed field $\mathbf{k}$ whose equivariant automorphism group is infinite.

math.AG↗

arXiv2021

Association: Remind Your GAN not to Forget

Neural networks are susceptible to catastrophic forgetting. They fail to preserve previously acquired knowledge when adapting to new tasks. Inspired by human associative memory system, we propose a brain-like approach that imitates the associative learning process to achieve continual learning. We design a heuristics mechanism to potentiatively stimulate the model, which guides the model to recall the historical episodes based on the current circumstance and obtained association experience. Besides, a distillation measure is added to depressively alter the efficacy of synaptic transmission, which dampens the feature reconstruction learning for new task. The framework is mediated by potentiation and depression stimulation that play opposing roles in directing synaptic and behavioral plasticity. It requires no access to the original data and is more similar to human cognitive process. Experiments demonstrate the effectiveness of our method in alleviating catastrophic forgetting on image-to-image translation tasks.

cs.CV↗

arXiv2020

Degree Counting Theorems for 2x2 non-symmetric singular Liouville Systems

Let $(M,g)$ be a compact Riemann surface with no boundary and $u=(u_1,u_2)$ be a solution of the following singular Liouville system: $$Δ_g u_i+\sum_{j=1}^2 a_{ij}ρ_j(\frac{h_je^{u_j}}{\int_M h_je^{u_j}dV_g}-1)=\sum_{l=1}^{N}4πγ_l(δ_{p_l}-1), $$ where $h_1,h_2$ are positive smooth functions, $p_1,\cdots,p_N$ are distinct points on $M$, $δ_{p_l}$ are Dirac masses, $ρ=(ρ_1,ρ_2)(ρ_i\geq 0)$ and $(γ_1,\cdots,γ_N)(γ_l > -1)$ are constant vectors. In the previous work, we derive a degree counting formula for the singular Liouville system when $A$ satisfies standard assumptions. In this article, we establish a more general degree counting formula for 2$\times$2 singular Liouville system when the coefficient matrix $A$ is non-symmetric and non-invertible. Finally, the existence of solution can be proved by the degree counting formula which depends only on the topology of the domain and the location of $ρ$.

math.AP↗

arXiv2020

Generative and Discriminative Learning for Distorted Image Restoration

Liquify is a common technique for image editing, which can be used for image distortion. Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task. To edit images in an efficient way, distorted images are expected to be restored automatically. This paper aims at the distorted image restoration, which is characterized by seeking the appropriate warping and completion of a distorted image. Existing methods focus on the hardware assistance or the geometric principle to solve the specific regular deformation caused by natural phenomena, but they cannot handle the irregularity and uncertainty of artificial distortion in this task. To address this issue, we propose a novel generative and discriminative learning method based on deep neural networks, which can learn various reconstruction mappings and represent complex and high-dimensional data. This method decomposes the task into a rectification stage and a refinement stage. The first stage generative network predicts the mapping from the distorted images to the rectified ones. The second stage generative network then further optimizes the perceptual quality. Since there is no available dataset or benchmark to explore this task, we create a Distorted Face Dataset (DFD) by forward distortion mapping based on CelebA dataset. Extensive experimental evaluation on the proposed benchmark and the application demonstrates that our method is an effective way for distorted image restoration.

eess.IV↗

arXiv2020

VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics

We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. Such uncertainty information is useful, for example, for an adaptive testing scenario, where additional tests can be administered if the model is not quite certain about a students' skill level estimation. Traditional Bayesian inference methods that produce such uncertainty information are computationally expensive and do not scale to large data sets. VarFA utilizes variational inference which makes it possible to efficiently perform Bayesian inference even on very large data sets. We use the sparse factor analysis model as a case study and demonstrate the efficacy of VarFA on both synthetic and real data sets. VarFA is also very general and can be applied to a wide array of factor analysis models.

stat.ML↗

arXiv2019

Slope inequalities and a Miyaoka-Yau type inequality

For a minimal smooth projective surface $S$ of general type over a field of characteristic $p>0$, we prove that $K^2_S\le 32χ(\cal{O}_S).$ Moreover, if $18χ(\cal{O}_S) 0$, which answers completely a question of Shepherd-Barron.

math.AG↗

arXiv2019

Surfaces on the Severi line in positive characteristics

Let $X$ be a minimal surface of general type over an algebraically closed field $\mathbf{k}$ of $\mathrm{char}.(\mathbf{k})=p\ge 0$. If the Albanese morphism $a_X:X\to \mathrm{Alb}_X$ is generically finite onto its image, we formulate a constant $c(X,L)\ge 0$ for a very ample line bundle $L$ on $\mathrm{Alb}_X$ such that $c(X,L)=0$ if and only if $\dim \mathrm{Alb}_X=2$ and $a_X: X\to \mathrm{Alb}_X$ is a double cover. A refined Severi inequality $$K^2_X\ge (4+{\rm min}\{\,c(X,L),\,\frac{1}{3}\,\})χ(\mathcal{O}_X)$$ is proved. Then we prove that $K^2_X=4χ(\mathcal{O}_X)$ if and only if the canonical model of $X$ is a flat double cover of an Abelian surface.

math.AG↗

arXiv2019

Deep levels analysis in wavelength extended InGaAsBi photodetector

InP based dilute Bismide InGaAsBi material is emerging as a promising candidate for extending short wavelength infrared detection. One critical factor to limit the performance of these InGaAsBi photodiodes is dark current caused by defects within the material. In this work, low frequency noise spectroscopy (LFNS) and temperature varied photoluminescence was used to characterize the defect levels in the devices. Three deep levels located at Ec -0.33 eV, Ev +0.14 eV, and Ec -0.51 eV were identified from the LFNS spectra, which are consistent with emission peak energy found by photoluminescence spectra of InGaAsBi.

physics.app-ph↗

arXiv2019

Degree counting theorems for singular Liouville systems

Let $(M,g)$ be a compact Riemann surface with no boundary and $u=(u_1,...,u_n)$ be a solution of the following singular Liouville system: \begin{equation*} Δ_g u_i+\sum_{j=1}^na_{ij}ρ_j(\frac{h_je^{u_j}}{\int_M h_j e^{u_j}dV_g}-\frac{1}{vol_g(M)})=\sum_{t=1}^N4πγ_t( δ_{p_t}-\frac{1}{vol_g(M)}), \end{equation*} where $i=1,...,n$, $h_1,...,h_n$ are positive smooth functions, $p_1,...,p_N$ are distinct points on $M$, $δ_{p_t}$ are Dirac masses, $ρ=(ρ_1,...,ρ_n)$ ($ρ_i\ge 0)$ and $(γ_{1},...,γ_{N})$ ($γ_{t}>-1$ ) are constant vectors. If the coefficient matrix $A=(a_{ij})_{n\times n}$ satisfies standard assumptions we identify a family of critical hyper-surfaces $Γ_k$ for $ρ=(ρ_1,..,ρ_n)$ so that a priori estimate of $u$ holds if $ρ$ is not on any of the $Γ_k$s. Thanks to the a priori estimate, a topological degree for $u$ is well defined for $ρ$ staying between every two consecutive $Γ_k$s. In this article we establish this degree counting formula which depends only on the Euler Characteristic of $M$ and the location of $ρ$. Finally if the Liouville system is defined on a bounded domain in $\mathbb R^2$ with Dirichlet boundary condition, a similar degree counting formula that depends only on the topology of the domain and the location of $ρ$ is also determined.

math.AP↗

arXiv2019

Multiple agile Earth observation satellites, oversubscribed targets scheduling using complex networks theory

The Earth observation satellites (EOSs) scheduling is of great importance to achieve efficient observation missions. The agile EOSs (AEOS) with stronger attitude maneuvering capacity can greatly improve observation efficiency while increasing scheduling complexity. The multiple AEOSs, oversubscribed targets scheduling problem with multiple observations are addressed, and the potential observation missions are modeled as nodes in the complex networks. To solve the problem, an improved feedback structured heuristic is designed by defining the node and target importance factors. On the basis of a real world Chinese AEOS constellation, simulation experiments are conducted to validate the heuristic efficiency in comparison with a constructive algorithm and a structured genetic algorithm.

astro-ph.IM↗

arXiv2019

Cameraless High-throughput 3D Imaging Flow Cytometry

Increasing demand for understanding the vast heterogeneity of cellular phenotypes has driven the development of imaging flow cytometry (IFC), that combines features of flow cytometry with fluorescence and bright field microscopy. IFC combines the throughput and statistical advantage of flow cytometry with the ability to discretely measure events based on a real or computational image, as well as conventional flow cytometry metrics. A limitation of existing IFC systems is that, regardless of detection methodology, only two-dimensional (2D) cell images are obtained. Without tomographic three-dimensional (3D) resolution the projection problem remains: collapsing 3D information onto a 2D image, limiting the reliability of spot counting or co-localization crucial to cell phenotyping. Here we present a solution to the projection problem: three-dimensional imaging flow cytometry (3D-IFC), a high-throughput 3D cell imager based on optical sectioning microscopy. We combine orthogonal light-sheet scanning illumination with our previous spatiotemporal transformation detection to produce 3D cell image reconstruction from a cameraless single-pixel photodetector readout. We further demonstrate this capability by co-capturing 3D fluorescence and label-free side-scattering images of single cells in flow at a velocity of 0.2 m s-1, corresponding to a throughput of approximately 500 cells per second with 60,000 voxels (resized subsequently to 106 voxels) for each cell image at a resolution of less than 1 micron in X, Y, and Z dimensions. Improved high-throughput imaging tools are needed to phenotype-genotype recognized heterogeneity in the fields of immunology, oncology, cell- and gene- therapy, and drug discovery.

q-bio.QM↗

arXiv2018

Positioning of Multiple Photon Interaction Events in Cross Strip Cadmium Zinc Telluride Detectors for PET

This report summarizes work done during PhD rotation that proposes an algorithm that uses anode- cathode trigger time difference and cathode to anode pulse height ratio to resolve photon event positioning degeneracy in a novel Cadmium Zinc Telluride (CZT) photon detector for positron emission tomography (PET). The detectors used comprise 40 mm x 40 mm x 5 mm monolithic CZT crystals that employ a cross-strip electrode pattern with interspersed steering electrodes to obtain high spatial and energy resolution. The specific positioning degeneracy studied results when a photon event interacts in two detector pixels, triggering two anode and two cathode strips, hence presenting four pixels as potential interaction locations. An experiment was performed where a 1 mm diameter collimated pencil beam of annihilation photons was used to target a detector pixel of known location. The data acquired provided an empirical basis to train and test the algorithm. The study shows that, depending on the restrictions applied on the data set, the proposed algorithm accurately identifies the true photon interaction locations 59.7% to 66.7% of the time, which is 19.4% to 33.4% better than an unbiased Bernoulli trial. Further improvements of the algorithm will enhance the effective system photon sensitivity by extracting useful information from events which would otherwise be discarded.

physics.med-ph↗

arXiv2018

Renewable Energy Integration in Distribution System -- Synchrophasor Sensor based Big Data Analysis, Visualization, and System Operation

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster computing framework based on Apache Spark is considering to discover the hidden knowledge of the bag-data. And the data is transmitted with a Open System Interconnection (OSI) model to the data visualization platform in a high-speed communication architecture. Google Earth and global geographic information system (GIS) are used to design the visualization platform and realize the result with the test bench. The alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. The proposed network reconfiguration is solved in parallel manner with the limited switches and the strong computing capability. Further more, a multi-timescale operation approach is modeled with a three-phase distributed system, which consists the hourly scheduling at substation level and the minutes power flow operation at feeder level. The system cost with renewable generation is minimized at the substation level. The given error distribution model of renewable generation is simulated with a chance constraint, and the derived deterministic form is modeled with Gaussian mixture model (GMM) with genetic algorithm-based expectationmaximization (GAEM). The system cost is further reduced with the OPF in real-time (RT) scheduling. The semidefinite programming (SDP) is used to relax the nonconvexity of the three-phase unbalanced distribution system into a convex problem, which make sure to achieve the global optimal result. In the parallel manner, the ADMM is realizing getting the results in a short time.

eess.SY↗

arXiv2017

Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.

eess.SY↗

arXiv2017

Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.

eess.SY↗

arXiv2017

A novel image tag completion method based on convolutional neural network

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.

cs.CV↗

arXiv2016

Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.

q-bio.NC↗