Searcharxiv⌕ Search

arXiv subjects

Xiaowei Wang

Publications and source records attributed to Xiaowei Wang.

At least 37 records · Page 2Linked to original sources
arXiv2023

Graph Contrastive Learning with Generative Adversarial Network

Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with the widespread label scarcity issue in real-world applications, Graph Contrastive Learning (GCL) is leveraged to train GNNs with limited or even no labels by maximizing the mutual information between nodes in its augmented views generated from the original graph. However, the distribution of graphs remains unconsidered in view generation, resulting in the ignorance of unseen edges in most existing literature, which is empirically shown to be able to improve GCL's performance in our experiments. To this end, we propose to incorporate graph generative adversarial networks (GANs) to learn the distribution of views for GCL, in order to i) automatically capture the characteristic of graphs for augmentations, and ii) jointly train the graph GAN model and the GCL model. Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning. GACN develops a view generator and a view discriminator to generate augmented views automatically in an adversarial style. Then, GACN leverages these views to train a GNN encoder with two carefully designed self-supervised learning losses, including the graph contrastive loss and the Bayesian personalized ranking Loss. Furthermore, we design an optimization framework to train all GACN modules jointly. Extensive experiments on seven real-world datasets show that GACN is able to generate high-quality augmented views for GCL and is superior to twelve state-of-the-art baseline methods. Noticeably, our proposed GACN surprisingly discovers that the generated views in data augmentation finally conform to the well-known preferential attachment rule in online networks.

cs.LG↗
arXiv2023

Moduli of boundary polarized Calabi-Yau pairs

We develop the moduli theory of boundary polarized CY pairs, which are slc Calabi-Yau pairs $(X,D)$ such that $D$ is ample. The motivation for studying this moduli problem is to construct a moduli space at the Calabi-Yau wall interpolating between certain K-moduli and KSBA moduli spaces. We prove that the moduli stack of boundary polarized CY pairs is S-complete, $Θ$-reductive, and satisfies the existence part of the valuative criterion for properness, which are steps towards constructing a proper moduli space. A key obstacle in this theory is that the irreducible components of the moduli stack are not in general of finite type. Despite this issue, in the case of pairs $(X,D)$ where $X$ is a degeneration of $\mathbb{P}^2$, we construct a projective moduli space on which the Hodge line bundle is ample. As a consequence, we complete the proof of a conjecture of Prokhorov and Shokurov in relative dimension two.

math.AG↗
arXiv2023

Instant Representation Learning for Recommendation over Large Dynamic Graphs

Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendation models start leveraging information from various behavior types exhibited by users. In real-world scenarios, the user behavioral graph is not only multiplex but also dynamic, i.e., the graph evolves rapidly over time, with various types of nodes and edges added or deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing methods neglect such streaming dynamics and thus need to be retrained once the graph has significantly evolved, making them unsuitable in the online learning environment. Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models. To this end, we propose SUPA, a novel graph neural network for dynamic multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture, SUPA develops a sample-update-propagate architecture to alleviate neighborhood disturbance. Specifically, for each new edge, SUPA samples an influenced subgraph, updates the representations of the two interactive nodes, and propagates the interaction information to the sampled subgraph. Furthermore, to train SUPA incrementally online, we propose InsLearn, an efficient workflow for single-pass training of large dynamic graphs. Extensive experimental results on six real-world datasets show that SUPA has a good generalization ability and is superior to sixteen state-of-the-art baseline methods. The source code is available at https://github.com/shatter15/SUPA.

cs.IR↗
arXiv2023

Multi-behavior Self-supervised Learning for Recommendation

Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly, sparse target signals and noisy auxiliary interactions remain an issue. Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task. Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. To increase the robustness to data sparsity under the target behavior and noisy interactions from auxiliary behaviors, we propose a novel self-supervised learning paradigm to conduct node self-discrimination at both inter-behavior and intra-behavior levels. In addition, we develop a customized optimization strategy through hybrid manipulation on gradients to adaptively balance the self-supervised learning task and the main supervised recommendation task. Extensive experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our model implementation at: https://github.com/Scofield666/MBSSL.git.

cs.IR↗
arXiv2022

Moment map, convex function and extremal point

The moment map $μ$ is a central concept in the study of Hamiltonian actions of compact Lie groups $K$ on symplectic manifolds. In this short note, we propose a theory of moment maps coupled with an $\mathrm{Ad}_K$-invariant convex function $f$ on $\mathfrak{k}^{\ast}$, the dual of Lie algebra of $K$, and study the properties of the critical point of $f\circμ$. Our motivation comes from Donaldson \cite{Donaldson2017} which is an example of infinite dimensional version of our setting. As an application, we interpret Kähler-Ricci solitons as a special case of the generalized extremal metric.

math.DG↗
arXiv2022

A novel adversarial learning strategy for medical image classification

Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and optimized through the minimization of a single loss function measured at the end of the networks. However, such a single loss design could potentially lead to optimization of one specific value of interest but fail to leverage informative features from intermediate layers that might benefit classification performance and reduce the risk of overfitting. Recently, auxiliary convolutional neural networks (AuxCNNs) have been employed on top of traditional classification networks to facilitate the training of intermediate layers to improve classification performance and robustness. In this study, we proposed an adversarial learning-based AuxCNN to support the training of deep neural networks for medical image classification. Two main innovations were adopted in our AuxCNN classification framework. First, the proposed AuxCNN architecture includes an image generator and an image discriminator for extracting more informative image features for medical image classification, motivated by the concept of generative adversarial network (GAN) and its impressive ability in approximating target data distribution. Second, a hybrid loss function is designed to guide the model training by incorporating different objectives of the classification network and AuxCNN to reduce overfitting. Comprehensive experimental studies demonstrated the superior classification performance of the proposed model. The effect of the network-related factors on classification performance was investigated.

eess.IV↗
arXiv2022

Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy

Structural re-parameterization (Rep) methods has achieved significant performance improvement on traditional convolutional network. Most current Rep methods rely on prior knowledge to select the reparameterization operations. However, the performance of architecture is limited by the type of operations and prior knowledge. To break this restriction, in this work, an improved re-parameterization search space is designed, which including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further improved by the search space. To effectively explore this search space, an automatic re-parameterization enhancement strategy is designed based on neural architecture search (NAS), which can search a excellent re-parameterization architecture. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k.

cs.AI↗
arXiv2022

1T-FeS$_2$$:$ a new type of two-dimensional metallic ferromagnet

Discovery of intrinsic two-dimensional (2D) magnetic materials is crucial for understanding the fundamentals of 2D magnetism and realizing next-generation magnetoelectronic and magneto-optical devices. Although significant efforts have been devoted to identifying 2D magnetism by exfoliating bulk magnetic layered materials, seldom studies are performed to synthesize ultra-thin magnetic materials directly for non-layered magnetic materials. Here, we report the successful synthesis of a new type of theoretically proposed 2D metallic ferromagnet 1T FeS2, through the molten-salt-assisted chemical vapor deposition (CVD) method. The long-range 2D ferromagnetic order is confirmed by the observation of a large anomalous Hall effect (AHE) and a hysteretic magnetoresistance. The experimentally detected out-of-plane ferromagnetic ordering is theoretically suported with Stoner criterion. Our findings open up new possibilities to search novel 2D ferromagnets in non-layered compounds and render opportunities for realizing realistic ultra-thin spintronic devices.

cond-mat.mtrl-sci↗
arXiv2022

Optimal monitoring location for risk tracking of geotechnical systems: theory and application to tunneling excavation risks

The maturity of structural health monitoring technology brings ever-increasing opportunities for geotechnical structures and underground infrastructure systems to track the risk of structural failure, such as settlement-induced building damage, based on the monitored data. Reliability updating techniques can offer solutions to estimate the probability of failing to meet a prescribed objective using various types of information that are inclusive of equality and inequality. However, the update in reliability can be highly sensitive to monitoring location. Therefore, there may exist optimal locations in a system for monitoring that yield the maximum value for reliability updating. This paper proposes a computational framework for optimal monitoring location based on an innovative metric called sensitivity of information (SOI) that quantifies the relative change in unconditional and conditional reliability indexes. A state-of-the-practice case of risks posed by tunneling-induced settlement to buildings is explored in-depth to demonstrate and evaluate the computational efficiency of the proposed framework.

stat.AP↗
arXiv2021

Delayed Rewards Calibration via Reward Empirical Sufficiency

Appropriate credit assignment for delay rewards is a fundamental challenge for reinforcement learning. To tackle this problem, we introduce a delay reward calibration paradigm inspired from a classification perspective. We hypothesize that well-represented state vectors share similarities with each other since they contain the same or equivalent essential information. To this end, we define an empirical sufficient distribution, where the state vectors within the distribution will lead agents to environmental reward signals in the consequent steps. Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards. We examine the correctness of sufficient state extraction by tracking the real-time extraction and building different reward functions in environments. The results demonstrate that the classifier could generate timely and accurate calibrated rewards. Moreover, the rewards are able to make the model training process more efficient. Finally, we identify and discuss that the sufficient states extracted by our model resonate with the observations of humans.

cs.LG↗
arXiv2021

Controlled synthesis of MoxW1-xTe2 atomic layers with emergent quantum states

Recently, new states of matter like superconducting or topological quantum states were found in transition metal dichalcogenides (TMDs) and manifested themselves in a series of exotic physical behaviors. Such phenomena have been demonstrated to exist in a series of transition metal tellurides including MoTe2, WTe2 and alloyed MoxW1-xTe2. However, the behaviors in the alloy system have been rarely addressed due to their difficulty in obtaining atomic layers with controlled composition, albeit the alloy offers a great platform to tune the quantum states. Here, we report a facile CVD method to synthesize the MoxW1-xTe2 with controllable thickness and chemical composition ratios. The atomic structure of monolayer MoxW1-xTe2 alloy was experimentally confirmed by scanning transmission electron microscopy (STEM). Importantly, two different transport behaviors including superconducting and Weyl semimetal (WSM) states were observed in Mo-rich Mo0.8W0.2Te2 and W-rich Mo0.2W0.8Te2 samples respectively. Our results show that the electrical properties of MoxW1-xTe2 can be tuned by controlling the chemical composition, demonstrating our controllable CVD growth method is an efficient strategy to manipulate the physical properties of TMDCs. Meanwhile, it provides a perspective on further comprehension and shed light on the design of device with topological multicomponent TMDCs materials.

cond-mat.mtrl-sci↗
arXiv2021

Inductive Granger Causal Modeling for Multivariate Time Series

Granger causal modeling is an emerging topic that can uncover Granger causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from different individuals with sharing commonalities. However, there are ongoing concerns regarding Granger causality's applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal structure reconstruction. Existing methods usually train a distinct model for each individual, suffering from inefficiency and over-fitting issues. To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals. In particular, we train one global model for individuals with different Granger causal structures through a novel attention mechanism, called prototypical Granger causal attention. The model can detect common causal structures for different individuals and infer Granger causal structures for newly arrived individuals. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of InGRA.

cs.LG↗
arXiv2021

Some estimates of the generalized beukers integral with techniques of partial fraction decomposition

In this paper we establish the generalized Beukers integral $I_{m}(a_{1},...,a_{n})$ with some methods of partial fraction decomposition. Thus one obtains an explicit expression of the generalized Beukers integral. Further, we estimate the rational denominator of $I$ and. In the second section of this paper, we provide some estimates of the upper and lower bound of the value $J_{3}$, which involves the generalized Beukers integral and is related to $ζ(5)$.

math.NT↗
arXiv2021

A New Series Representation Involving Root Of Unity For The Values Of Riemann Zeta Function At Integer Arguments

In this paper we provide a new series representation for the values of Riemann zeta function at integer arguments, namely: $ ζ(m)=\sum_{n=1}^{\infty}\frac{m(-1)^{n-1}Γ(1-ω_{m}n)...Γ(1-ω_{m}^{m-1}n)}{n!n^m}$, where $n$ is an integer that lager than $1$ and $ω$ is the $m$-th root of unity. This series converges quite fast. It's derived by some technique of infinite partial fraction decomposition. With this technique we also establish other useful formulas related to gamma function.

math.NT↗
arXiv2021

On Some Integral Representation Of $ζ(n)$ Involving Nielsen's Generalized Polylogarithms And The Related Partition Problem

In this paper, we study a family of single variable integral representations for some products of $ζ(2n+1)$, where $ζ(z)$ is Riemann zeta function and $n$ is positive integer. Such representation involves the integral $Lz(a,b):=\frac{1}{(a-1)!b!}\int_{0}^{1}\log^a (t)\log^b (1-t)dt/t$ with positive integers $a,b$, which is related to Nielsen's generalized polylogarithms. By analyzing the related partition problem, we discuss the structure of such integral representation, especially the condition of expressing products of $ζ(2n+1)$ by finite $\mathbb{Q}(π)$-linear combination of $Lz(a,b)$.

math.NT↗
arXiv2021

Algebraicity of the Metric Tangent Cones and Equivariant K-stability

We prove two new results on the K-polystability of Q-Fano varieties based on purely algebro-geometric arguments. The first one says that any K-semistable log Fano cone has a special degeneration to a uniquely determined K-polystable log Fano cone. As a corollary, we combine it with the differential-geometric results to complete the proof of Donaldson-Sun's Conjecture which says that the metric tangent cone of any close point appearing on a Gromov-Hausdorff limit of Kahler-Einstein Fano manifolds only depends on the algebraic structure of the singularity. The second result says that for any log Fano variety with a torus action, the K-polystability is equivalent to the equivariant K-polystability, that is, to check K-polystability, it is sufficient to check special test configurations which are equivariant under the torus action.

math.AG↗
arXiv2020

An Analytic Proof of the Stable Reduction Theorem

The stable reduction theorem says that a family of curves of genus $g\geq 2$ over a punctured curve can be uniquely completed (after possible base change) by inserting certain stable curves at the punctures. We give a new proof of this result for curves defined over $\mathbb{C}$ using the Kähler-Einstein metrics on the fibers to obtain the limiting stable curves at the punctures.

math.DG↗