arXiv subjects

Yanbo Zhang

Publications and source records attributed to Yanbo Zhang.

52 records · Page 3Linked to original sources

arXiv2024

Size Ramsey numbers of small graphs versus fans or paths

For two graphs $G_1$ and $G_2$, the size Ramsey number $\hat{r}(G_1,G_2)$ is the smallest positive integer $m$ for which there exists a graph $G$ of size $m$ such that for any red-blue edge-coloring of the graph $G$, $G$ contains either a red subgraph isomorphic to $G_1$, or a blue subgraph isomorphic to $G_2$. Let $P_n$ be a path with $n$ vertices, $nK_2$ a matching with $n$ edges, and $F_n$ a graph with $n$ triangles sharing exactly one vertex. If $G_1$ is a small fixed graph and $G_2$ denotes any graph from a graph class, one can sometimes completely determine $\hat{r}(G_1,G_2)$. Faudree and Sheehan confirmed all size Ramsey numbers of $P_3$ versus complete graphs in 1983. The next year Erdős and Faudree confirmed that of $2K_2$ versus complete graphs and complete bipartite graphs. We obtain three more Ramsey results of this type. For $n\ge 3$, we prove that $\hat{r}(P_3,F_n)=4n+4$ if $n$ is odd, and $\hat{r}(P_3,F_n)=4n+5$ if $n$ is even. This result refutes a conjecture proposed by Baskoro et al. We also show that $\hat{r}(2K_2,F_2)=12$ and $\hat{r}(2K_2,F_n)=5n+3$ for $n\ge 3$. In addition, we prove that $\hat{r}(2K_2,nP_m)=\min\{nm+1, (n+1)(m-1)\}$. This result verifies a conjecture posed by Vito and Silaban.

math.CO↗

arXiv2024

Matching-star size Ramsey numbers under connectivity constraint

Recently, Caro, Patkós, and Tuza (2022) introduced the concept of connected Turán number. We study a similar parameter in Ramsey theory. Given two graphs $G_1$ and $G_2$, the size Ramsey number $\hat{r}(G_1,G_2)$ refers to the smallest number of edges in a graph $G$ such that for any red-blue edge-coloring of $G$, either a red subgraph $G_1$ or a blue subgraph $G_2$ is present in $G$. If we further restrict the host graph $G$ to be connected, we obtain the connected size Ramsey number, denoted as $\hat{r}_c(G_1,G_2)$. Erdős and Faudree (1984) proved that $\hat r(nK_2,K_{1,m})=mn$ for all positive integers $m,n$. In this paper, we concentrate on the connected analog of this result. Rahadjeng, Baskoro, and Assiyatun (2016) provided the exact values of $\hat r_c(nK_2,K_{1,m})$ for $n=2,3$. We establish a more general result: for all positive integers $m$ and $n$ with $m\ge (n^2+2pn+n-3)/2$, we have $\hat r_c(nK_{1,p},K_{1,m})=n(m+p)-1$. As a corollary, $\hat r_c(nK_2,K_{1,m})=nm+n-1$ for $m\ge (n^2+3n-3)/2$. We also propose a conjecture for the interested reader.

math.CO↗

arXiv2023

Ramsey goodness of fans

Given two graphs $G_1$ and $G_2$, the Ramsey number $r(G_1,G_2)$ refers to the smallest positive integer $N$ such that any graph $G$ with $N$ vertices contains $G_1$ as a subgraph, or the complement of $G$ contains $G_2$ as a subgraph. A connected graph $H$ is said to be $p$-good if $r(K_p,H)=(p-1)(|H|-1)+1$. A generalized fan, denoted as $K_1+nH$, is formed by the disjoint union of $n$ copies of $H$ along with an additional vertex that is connected to each vertex of $nH$. Recently Chung and Lin proved that $K_1+nH$ is $p$-good for $n\ge cp\ell/|H|$, where $c\approx 52.456$ and $\ell=r(K_{p},H)$. They also posed the question of improving the lower bound of $n$ further so that $K_1+nH$ remains $p$-good. In this paper, we present three different methods to improve the range of $n$. First, we apply the Andrásfai-Erdős-Sós theorem to reduce $c$ from $52.456$ to $3$. Second, we utilize the approach established by Chen and Zhang to achieve a further reduction of $c$ to $2$. Lastly, we employ a new method to bring $c$ down to $1$. In addition, when $K_1+nH$ forms a fan graph $F_n$, we can further obtain a slightly more refined bound of $n$.

math.CO↗

arXiv2023

Proof of a Conjecture on Online Ramsey Numbers of Paths

For two graphs $G_1$ and $G_2$, the online Ramsey number $\tilde{r}(G_1,G_2)$ is the smallest number of edges that Builder draws on an infinite empty graph to guarantee that there is either a red copy of $G_1$ or a blue copy of $G_2$, under the condition that Builder draws one edge in each round and Painter immediately colors it red or blue. For online Ramsey numbers of paths, Cyman, Dzido, Lapinskas, and Lo conjectured that $\tilde{r}(P_4, P_{\ell+1}) = \lceil(7\ell+2)/5\rceil$ for all $\ell \ge 3$ [Electron. J. Combin. 22 (2015) #P1.15]. We verify the conjecture in this paper.

math.CO↗

arXiv2023

Proof of a Conjecture on Online Ramsey Numbers of Stars versus Paths

Given two graphs $G$ and $H$, the online Ramsey number $\tilde{r}(G,H)$ is defined to be the minimum number of rounds that Builder can always guarantee a win in the following $(G, H)$-online Ramsey game between Builder and Painter. Starting from an infinite set of isolated vertices, in each round Builder draws an edge between some two vertices, and Painter immediately colors it red or blue. Builder's goal is to force either a red copy of $G$ or a blue copy of $H$ in as few rounds as possible, while Painter's goal is to delay it for as many rounds as possible. Let $K_{1,3}$ denote a star with three edges and $P_{\ell}$ a path with $\ell$ vertices. Latip and Tan conjectured that $\tilde{r}(K_{1,3}, P_{\ell})=(3/2+o(1))\ell$ [Bull. Malays. Math. Sci. Soc. 44 (2021) 3511--3521]. We show that $\tilde{r}(K_{1,3}, P_{\ell})=\lfloor 3\ell/2 \rfloor$ for $\ell\ge 2$, which verifies the conjecture in a stronger form.

math.CO↗

arXiv2022

A Relational Macrostate Theory Guides Artificial Intelligence to Learn Macro and Design Micro

The high-dimesionality, non-linearity and emergent properties of complex systems pose a challenge to identifying general laws in the same manner that has been so successful in simpler physical systems. In Anderson's seminal work on why "more is different" he pointed to how emergent, macroscale patterns break symmetries of the underlying microscale laws. Yet, less recognized is that these large-scale, emergent patterns must also retain some symmetries of the microscale rules. Here we introduce a new, relational macrostate theory (RMT) that defines macrostates in terms of symmetries between two mutually predictive observations, and develop a machine learning architecture, MacroNet, that identifies macrostates. Using this framework, we show how macrostates can be identifed across systems ranging in complexity from the simplicity of the simple harmonic oscillator to the much more complex spatial patterning characteristic of Turing instabilities. Furthermore, we show how our framework can be used for the inverse design of microstates consistent with a given macroscopic property -- in Turing patterns this allows us to design underlying rule with a given specification of spatial patterning, and to identify which rule parameters most control these patterns. By demonstrating a general theory for how macroscopic properties emerge from conservation of symmetries in the mapping between observations, we provide a machine learning framework that allows a unified approach to identifying macrostates in systems from the simple to complex, and allows the design of new examples consistent with a given macroscopic property.

cs.AI↗

arXiv2022

Connected size Ramsey numbers of matchings versus a small path or cycle

Given two graphs $G_1, G_2$, the connected size Ramsey number ${\hat{r}}_c(G_1,G_2)$ is defined to be the minimum number of edges of a connected graph $G$, such that for any red-blue edge colouring of $G$, there is either a red copy of $G_1$ or a blue copy of $G_2$. Concentrating on ${\hat{r}}_c(nK_2,G_2)$ where $nK_2$ is a matching, we generalise and improve two previous results as follows. Vito, Nabila, Safitri, and Silaban obtained the exact values of ${\hat{r}}_c(nK_2,P_3)$ for $n=2,3,4$. We determine its exact values for all positive integers $n$. Rahadjeng, Baskoro, and Assiyatun proved that ${\hat{r}}_c(nK_2,C_4)\le 5n-1$ for $n\ge 4$. We improve the upper bound from $5n-1$ to $\lfloor (9n-1)/2 \rfloor$. In addition, we show a result which has the same flavour and has exact values: ${\hat{r}}_c(nK_2,C_3)=4n-1$ for all positive integers $n$.

math.CO↗

arXiv2020

The Ramsey number of a long even cycle versus a star

We find the exact value of the Ramsey number $R(C_{2\ell},K_{1,n})$, when $\ell$ and $n=O(\ell^{10/9})$ are large. Our result is closely related to the behaviour of Turán number $ex(N, C_{2\ell})$ for an even cycle whose length grows quickly with $N$.

math.CO↗

arXiv2019

DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

The potential huge advantage of spectral computed tomography (CT) is its capability to provide accuracy material identification and quantitative tissue information. This can benefit clinical applications, such as brain angiography, early tumor recognition, etc. To achieve more accurate material components with higher material image quality, we develop a dictionary learning based image-domain material decomposition (DLIMD) for spectral CT in this paper. First, we reconstruct spectral CT image from projections and calculate material coefficients matrix by selecting uniform regions of basis materials from image reconstruction results. Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique. Third, the trained dictionary is employed to explore the similarities from decomposed material images by constructing the DLIMD model. Fourth, more constraints (i.e., volume conservation and the bounds of each pixel within material maps) are further integrated into the model to improve the accuracy of material decomposition. Finally, both physical phantom and preclinical experiments are employed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.

eess.IV↗

arXiv2018

Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction

Spectral computed tomography (CT) reconstructs material-dependent attenuation images with the projections of multiple narrow energy windows, it is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection dataset always contains strong complicated noise and result in the projections has a lower signal-noise-ratio (SNR). Very recently, the spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectrum similarities for spectral CT. The method constructs such a group by clustering up a series of non-local spatial-spectrum cubes. The small size of spatial patch for such a group make SSCMF fails to encode the sparsity and low-rank properties. In addition, the hard-thresholding and collaboration filtering operation in the SSCMF are also rough to recover the image features and spatial edges. While for all steps are operated on 4-D group, we may not afford such huge computational and memory load in practical. To avoid the above limitation and further improve image quality, we first formulate a non-local cube-based tensor instead of the group to encode the sparsity and low-rank properties. Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman strategy is adopted to solve the NLCTF model. Both numerical simulations and realistic preclinical mouse studies are performed to validate and assess the NLCTF algorithm. The results show that the NLCTF method outperforms the other competitors.

cs.CV↗

arXiv2018

Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient L0-norm, which is named as L0TDL. The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the L0-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed L0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.

cs.CV↗

arXiv2018

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transform theory, and promises to improve the performance of the GAN. The perceptual loss compares the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN helps migrate the data noise distribution from strong to weak. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task, is capable of not only reducing the image noise level but also keeping the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

cs.CV↗

arXiv2018

Convolutional Neural Network Based Metal Artifact Reduction in X-ray Computed Tomography

In the presence of metal implants, metal artifacts are introduced to x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this work, we develop a convolutional neural network (CNN) based open MAR framework, which fuses the information from the original and corrected images to suppress artifacts. The proposed approach consists two phases. In the CNN training phase, we build a database consisting of metal-free, metal-inserted and pre-corrected CT images, and image patches are extracted and used for CNN training. In the MAR phase, the uncorrected and pre-corrected images are used as the input of the trained CNN to generate a CNN image with reduced artifacts. To further reduce the remaining artifacts, water equivalent tissues in a CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal-affected projections, followed by the FBP reconstruction. The effectiveness of the proposed method is validated on both simulated and real data. Experimental results demonstrate the superior MAR capability of the proposed method to its competitors in terms of artifact suppression and preservation of anatomical structures in the vicinity of metal implants.

physics.med-ph↗

arXiv2018

Definition and Identification of Information Storage and Processing Capabilities as Possible Markers for Turing-universality in Cellular Automata

To identify potential universal cellular automata, a method is developed to measure information processing capacity of elementary cellular automata. We consider two features of cellular automata: Ability to store information, and ability to process information. We define local collections of cells as particles of cellular automata and consider information contained by particles. By using this method, information channels and channels' intersections can be shown. By observing these two features, potential universal cellular automata are classified into a certain class, and all elementary cellular automata can be classified into four groups, which correspond to S. Wolfram's four classes: 1) Homogeneous; 2) Regular; 3) Chaotic and 4) Complex. This result shows that using abilities of store and processing information to characterize complex systems is effective and succinct. And it is found that these abilities are capable of quantifying the complexity of systems.

nlin.CG↗

arXiv2014

Single-scan scatter correction in CBCT by using projection correlation based view interpolation (PC-VI) and a stationary ring-shaped beam stop array (BSA)

In the scatter correction for x-ray Cone Beam (CB) CT, the single-scan scheme with moving Beam Stop Array (BSA) offers reliable scatter measurement with low dose, and by using Projection Correlation based View Interpolation (PC-VI), the primary fluence shaded by the moving BSA (during scatter measurement) could be recovered with high accuracy. However, the moving BSA may increase the mechanical burden in real applications. For better practicability, in this paper we proposed a PC-VI based single-scan scheme with a ring-shaped stationary BSA, which serves as a virtual moving BSA during CB scan, so the shaded primary fluence by this stationary BSA can be also well recovered by PC-VI. The principle in designing the whole system is deduced and evaluated. The proposed scheme greatly enhances the practicability of the single-scan scatter correction scheme.

physics.med-ph↗

arXiv2014

Metal artifact reduction based on the combined prior image

Metallic implants introduce severe artifacts in CT images, which degrades the image quality. It is an effective method to reduce metal artifacts by replacing the metal affected projection with the forward projection of a prior image. How to find a good prior image is the key of this class methods, and numerous algorithms have been proposed to address this issue recently. In this work, by using image mutual correlation, pixels in the original reconstructed image or linear interpolation corrected image, which are less affected by artifacts, are selected to build a combined image. Thereafter, a better prior image is generated from the combined image by using tissue classification. The results of three patients' CT images show that the proposed method can reduce metal artifacts remarkably.

physics.med-ph↗