arXiv subjects

Xiaoyu Zhang

Publications and source records attributed to Xiaoyu Zhang.

At least 199 records · Page 11Linked to original sources

arXiv2022

On Stability of Two Kinds of Delayed Chemical Reaction Networks

For the networks that are linear conjugate to complex balanced systems, the delayed version may include two classes of networks: one class is still linear conjugate to the delayed complex balanced network, the other is not. In this paper, we prove the existence of the first class of networks, and emphasize the local asymptotic stability relative to a certain defined invariant set. For the second class of systems, we define a special subclass and derive the local asymptotic stability for the subclass. Two examples are provided to illustrate our results.

math.DS↗

arXiv2022

Entropy-driven order in an array of nanomagnets

Long-range ordering is typically associated with a decrease in entropy. Yet, it can also be driven by increasing entropy in certain special cases. We demonstrate that artificial spin ice arrays of single-domain nanomagnets can be designed to produce entropy-driven order. We focus on the tetris artificial spin ice structure, a highly frustrated array geometry with a zero-point Pauli entropy, which is formed by selectively creating regular vacancies on the canonical square ice lattice. We probe thermally active tetris artificial spin ice both experimentally and through simulations, measuring the magnetic moments of the individual nanomagnets. We find two-dimensional magnetic ordering in one subset of these moments, which we demonstrate to be induced by disorder (i.e., increased entropy) in another subset of the moments. In contrast with other entropy-driven systems, the discrete degrees of freedom in tetris artificial spin ice are binary and are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This example, in which the system's interactions and ground state entropy are well-defined, expands the experimental landscape for the study of entropy-driven ordering.

cond-mat.mes-hall↗

arXiv2022

Perception-and-Regulation Network for Salient Object Detection

Effective fusion of different types of features is the key to salient object detection. The majority of existing network structure design is based on the subjective experience of scholars and the process of feature fusion does not consider the relationship between the fused features and highest-level features. In this paper, we focus on the feature relationship and propose a novel global attention unit, which we term the "perception- and-regulation" (PR) block, that adaptively regulates the feature fusion process by explicitly modeling interdependencies between features. The perception part uses the structure of fully-connected layers in classification networks to learn the size and shape of objects. The regulation part selectively strengthens and weakens the features to be fused. An imitating eye observation module (IEO) is further employed for improving the global perception ability of the network. The imitation of foveal vision and peripheral vision enables IEO to scrutinize highly detailed objects and to organize the broad spatial scene to better segment objects. Sufficient experiments conducted on SOD datasets demonstrate that the proposed method performs favorably against 22 state-of-the-art methods.

cs.CV↗

arXiv2022

Squeezed metallic droplet with tunable Kubo gap and charge injection in transition metal dichalcogenides

Shrinking the size of a bulk metal into nanoscale leads to the discreteness of electronic energy levels, the so-called Kubo gap. Renormalization of the electronic properties with a tunable and size-dependent Kubo gap renders fascinating photon emission and electron tunneling. In contrast with usual three-dimensional (3D) metal clusters, here we demonstrate that Kubo gap can be achieved with a two-dimensional (2D) metallic transition metal dichalcogenide (i.e., 1T'-phase MoTe2) nanocluster embedded in a semiconducting polymorph (i.e., 1H-phase MoTe2). Such a 1T'-1H MoTe2 nanodomain resembles a 3D metallic droplet squeezed in a 2D space which shows a strong polarization catastrophe while simultaneously maintains its bond integrity which is absent in traditional delta-gapped 3D clusters. The weak screening of the host 2D MoTe2 leads to photon emission of such pseudo-metallic systems and a ballistic injection of carriers in the 1T'-1H-1T' homojunctions which may find applications in sensors and 2D reconfigurable devices.

cond-mat.mtrl-sci↗

arXiv2022

VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks

The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications. The ability to understand and compare various CNN models available is thus essential. The conventional approach with visualizing each model's quantitative features, such as classification accuracy and computational complexity, is not sufficient for a deeper understanding and comparison of the behaviors of different models. Moreover, most of the existing tools for assessing CNN behaviors only support comparison between two models and lack the flexibility of customizing the analysis tasks according to user needs. This paper presents a visual analytics system, VAC-CNN (Visual Analytics for Comparing CNNs), that supports the in-depth inspection of a single CNN model as well as comparative studies of two or more models. The ability to compare a larger number of (e.g., tens of) models especially distinguishes our system from previous ones. With a carefully designed model visualization and explaining support, VAC-CNN facilitates a highly interactive workflow that promptly presents both quantitative and qualitative information at each analysis stage. We demonstrate VAC-CNN's effectiveness for assisting novice ML practitioners in evaluating and comparing multiple CNN models through two use cases and one preliminary evaluation study using the image classification tasks on the ImageNet dataset.

cs.LG↗

arXiv2021

OmiTrans: generative adversarial networks based omics-to-omics translation framework

With the rapid development of high-throughput experimental technologies, different types of omics (e.g., genomics, epigenomics, transcriptomics, proteomics, and metabolomics) data can be produced from clinical samples. The correlations between different omics types attracts a lot of research interest, whereas the stduy on genome-wide omcis data translation (i.e, generation and prediction of one type of omics data from another type of omics data) is almost blank. Generative adversarial networks and the variants are one of the most state-of-the-art deep learning technologies, which have shown great success in image-to-image translation, text-to-image translation, etc. Here we proposed OmiTrans, a deep learning framework adopted the idea of generative adversarial networks to achieve omics-to-omics translation with promising results. OmiTrans was able to faithfully reconstruct gene expression profiles from DNA methylation data with high accuracy and great model generalisation, as demonstrated in the experiments.

q-bio.GN↗

arXiv2021

Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection

The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the information validity of the two modes, and balance their influence. Our framework is divided into three phases: perception phase, recoding mixing phase and feature integration phase. First, A perception encoder is adopted to extract multi-level single-modal features, which lays the foundation for multi-modal semantic comparative analysis. Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder. The recoding mixer is responsible for recoding and mixing the balanced multi-modal information. Finally, the hybrid branch decoder completes the multi-level feature integration under the guidance of an optional edge guidance stream (OEGS). Experiments and analysis on eight popular benchmarks verify that our framework performs favorably against 9 state-of-art methods.

cs.CV↗

arXiv2021

Consensus-Based Decentralized Energy Trading for Distributed Energy Resources

In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy. Residential households and commercial buildings with DERs have become prosumers in the local grids, since they can sell surplus power to others. Researches have been initiated to integrate and utilize DERs through better control and communication strategies. With the advances in the Internet of Things (IoT) technology, unprecedented coordination among DERs can be achieved to facilitate energy trading and transactive energy management. However, preventing leakage of users' information during the optimization process keeps challenging researchers, which drives them to develop privacy-preserving energy management systems. In this paper, we develop a fully decentralized transactive energy management using the consensus-based algorithm. To be specific, we design a virtual pool for prosumers to trade energy and exchange information with IoT technologies' support. The consensus-based algorithm enables prosumers to obtain the optimal energy schedule independently in a coordinated manner without revealing any personal data. We use real-world data to perform simulations and validate our developed algorithm. The results show that our consensus-based decentralized transactive energy management strategy is feasible and can significantly reduce the overall system cost.

eess.SY↗

arXiv2021

String Phase in an Artificial Spin Ice

One-dimensional strings of local excitations are a fascinating feature of the physical behavior of strongly correlated topological quantum matter. Here we study strings of local excitations in a classical system of interacting nanomagnets, the Santa Fe Ice geometry of artificial spin ice. We measured the moment configuration of the nanomagnets, both after annealing near the ferromagnetic Curie point and in a thermally dynamic state. While the Santa Fe Ice lattice structure is complex, we demonstrate that its disordered magnetic state is naturally described within a framework of emergent strings. We show experimentally that the string length follows a simple Boltzmann distribution with an energy scale that is associated with the system's magnetic interactions and is consistent with theoretical predictions. The results demonstrate that string description and associated topological characteristics are not unique to quantum models but can also provide a simplifying description of complex classical systems with non-trivial frustration.

cond-mat.mes-hall↗

arXiv2021

SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints

Object SLAM introduces the concept of objects into Simultaneous Localization and Mapping (SLAM) and helps understand indoor scenes for mobile robots and object-level interactive applications. The state-of-art object SLAM systems face challenges such as partial observations, occlusions, unobservable problems, limiting the mapping accuracy and robustness. This paper proposes a novel monocular Semantic Object SLAM (SO-SLAM) system that addresses the introduction of object spatial constraints. We explore three representative spatial constraints, including scale proportional constraint, symmetrical texture constraint and plane supporting constraint. Based on these semantic constraints, we propose two new methods - a more robust object initialization method and an orientation fine optimization method. We have verified the performance of the algorithm on the public datasets and an author-recorded mobile robot dataset and achieved a significant improvement on mapping effects. We will release the code here: https://github.com/XunshanMan/SoSLAM.

cs.RO↗

arXiv2021

Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning

Researchers are increasingly focusing on intelligent games as a hot research area.The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training.At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict.Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations. Then determine the threat posed by each opponent's chess pieces.Using the red side reinforcement learning reward function, the AC framework is trained on the reward function, and an algorithm combining multi-attribute decision-making with reinforcement learning is obtained. A simulation experiment confirms that the algorithm of multi-attribute decision-making combined with reinforcement learning presented in this paper is significantly more intelligent than the pure reinforcement learning algorithm.By resolving the shortcomings of the agent's neural network, coupled with sparse rewards in large-map combat games, this robust algorithm effectively reduces the difficulties of convergence. It is also the first time in this field that an algorithm design for intelligent wargaming combines multi-attribute decision making with reinforcement learning.Attempt interdisciplinary cross-innovation in the academic field, like designing intelligent wargames and improving reinforcement learning algorithms.

cs.AI↗

arXiv2021

Stereo Plane SLAM Based on Intersecting Lines

Plane feature is a kind of stable landmark to reduce drift error in SLAM system. It is easy and fast to extract planes from dense point cloud, which is commonly acquired from RGB-D camera or lidar. But for stereo camera, it is hard to compute dense point cloud accurately and efficiently. In this paper, we propose a novel method to compute plane parameters using intersecting lines which are extracted from the stereo image. The plane features commonly exist on the surface of man-made objects and structure, which have regular shape and straight edge lines. In 3D space, two intersecting lines can determine such a plane. Thus we extract line segments from both stereo left and right image. By stereo matching, we compute the endpoints and line directions in 3D space, and then the planes from two intersecting lines. We discard those inaccurate plane features in the frame tracking. Adding such plane features in stereo SLAM system reduces the drift error and refines the performance. We test our proposed system on public datasets and demonstrate its robust and accurate estimation results, compared with state-of-the-art SLAM systems. To benefit the research of plane-based SLAM, we release our codes at https://github.com/fishmarch/Stereo-Plane-SLAM.

cs.CV↗

arXiv2021

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.

q-bio.GN↗

arXiv2021

OmiEmbed: a unified multi-task deep learning framework for multi-omics data

High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed support multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy comparing to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various application of high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making.

q-bio.GN↗

arXiv2021

Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.

cs.CR↗

arXiv2021

Hida theory for Shimura varieties of Hodge type

In this article, we generalize the work of H.Hida and V.Pilloni to construct $p$-adic families of $μ$-ordinary modular forms on Shimura varieties of Hodge type $Sh(G,X)$ associated to a Shimura datum $(G,X)$ where $G$ is a connected reductive group over $\mathbb{Q}$ and is unramified at $p$, such that the adjoint quotient $G^\mathrm{ad}$ has no simple factors isomorphic to $\mathrm{PGL}_2$.

math.NT↗

arXiv2021

DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of efforts made on static graphs, among which Graph Convolutional Network (GCN) has emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In this work, we propose an efficient dynamic graph embedding approach, Dynamic Graph Convolutional Network (DyGCN), which is an extension of GCN-based methods. We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings. The most affected nodes are first updated, and then their changes are propagated to the further nodes and leads to their update. Extensive experiments conducted on various dynamic graphs demonstrate that our model can update the node embeddings in a time-saving and performance-preserving way.

cs.LG↗

arXiv2021

ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology

Current text visualization techniques typically provide overviews of document content and structure using intrinsic properties such as term frequencies, co-occurrences, and sentence structures. Such visualizations lack conceptual overviews incorporating domain-relevant knowledge, needed when examining documents such as research articles or technical reports. To address this shortcoming, we present ConceptScope, a technique that utilizes a domain ontology to represent the conceptual relationships in a document in the form of a Bubble Treemap visualization. Multiple coordinated views of document structure and concept hierarchy with text overviews further aid document analysis. ConceptScope facilitates exploration and comparison of single and multiple documents respectively. We demonstrate ConceptScope by visualizing research articles and transcripts of technical presentations in computer science. In a comparative study with DocuBurst, a popular document visualization tool, ConceptScope was found to be more informative in exploring and comparing domain-specific documents, but less so when it came to documents that spanned multiple disciplines.

cs.HC↗