arXiv subjects

Chao Wang

Publications and source records attributed to Chao Wang.

At least 505 records · Page 28Linked to original sources

arXiv2022

Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning

Virtual network embedding (VNE) algorithm is always the key problem in network virtualization (NV) technology. At present, the research in this field still has the following problems. The traditional way to solve VNE problem is to use heuristic algorithm. However, this method relies on manual embedding rules, which does not accord with the actual situation of VNE. In addition, as the use of intelligent learning algorithm to solve the problem of VNE has become a trend, this method is gradually outdated. At the same time, there are some security problems in VNE. However, there is no intelligent algorithm to solve the security problem of VNE. For this reason, this paper proposes a security-aware VNE algorithm based on reinforcement learning (RL). In the training phase, we use a policy network as a learning agent and take the extracted attributes of the substrate nodes to form a feature matrix as input. The learning agent is trained in this environment to get the mapping probability of each substrate node. In the test phase, we map nodes according to the mapping probability and use the breadth-first strategy (BFS) to map links. For the security problem, we add security requirements level constraint for each virtual node and security level constraint for each substrate node. Virtual nodes can only be embedded on substrate nodes that are not lower than the level of security requirements. Experimental results show that the proposed algorithm is superior to other typical algorithms in terms of long-term average return, long-term revenue consumption ratio and virtual network request (VNR) acceptance rate.

cs.NI↗

arXiv2022

Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN's heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.

cs.NI↗

arXiv2022

Network Resource Allocation Strategy Based on Deep Reinforcement Learning

The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close to the substrate network environment is constructed to train the reinforcement learning agent. This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution. For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. In view of the problem that the existing VNE algorithm often ignores the underlying resource changes between virtual network requests, a DRL VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.

cs.DC↗

arXiv2022

IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode

The wireless network communication mode represented by the Internet of vehicles (IoV) has been widely used. However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. This paper focuses on the allocation of bandwidth resources in the virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE). The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators.

cs.NI↗

arXiv2022

Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning (ML) paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST and CIFAR-10 data sets to represent the data generated by IIoT. During the experiment, we employ the deep neural network (DNN) model to train the data, and experimental results show that the accuracy can reach more than 97\%, which corroborates the effectiveness of the proposed algorithm.

cs.LG↗

arXiv2022

LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions

The most scalable approaches to certifying neural network robustness depend on computing sound linear lower and upper bounds for the network's activation functions. Current approaches are limited in that the linear bounds must be handcrafted by an expert, and can be sub-optimal, especially when the network's architecture composes operations using, for example, multiplication such as in LSTMs and the recently popular Swish activation. The dependence on an expert prevents the application of robustness certification to developments in the state-of-the-art of activation functions, and furthermore the lack of tightness guarantees may give a false sense of insecurity about a particular model. To the best of our knowledge, we are the first to consider the problem of automatically computing tight linear bounds for arbitrary n-dimensional activation functions. We propose LinSyn, the first approach that achieves tight bounds for any arbitrary activation function, while only leveraging the mathematical definition of the activation function itself. Our approach leverages an efficient heuristic approach to synthesize bounds that are tight and usually sound, and then verifies the soundness (and adjusts the bounds if necessary) using the highly optimized branch-and-bound SMT solver, dReal. Even though our approach depends on an SMT solver, we show that the runtime is reasonable in practice, and, compared with state of the art, our approach often achieves 2-5X tighter final output bounds and more than quadruple certified robustness.

cs.LG↗

arXiv2022

Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition

We propose a novel multi-task pre-training method for Speech Emotion Recognition (SER). We pre-train SER model simultaneously on Automatic Speech Recognition (ASR) and sentiment classification tasks to make the acoustic ASR model more ``emotion aware''. We generate targets for the sentiment classification using text-to-sentiment model trained on publicly available data. Finally, we fine-tune the acoustic ASR on emotion annotated speech data. We evaluated the proposed approach on the MSP-Podcast dataset, where we achieved the best reported concordance correlation coefficient (CCC) of 0.41 for valence prediction.

cs.CL↗

arXiv2022

MOORe: Model-based Offline-to-Online Reinforcement Learning

With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online. A smooth transfer of the policy matters in safe real-world deployment. Besides, fast adaptation of the policy plays a vital role in practical online performance improvement. To tackle these challenges, we propose a simple yet efficient algorithm, Model-based Offline-to-Online Reinforcement learning (MOORe), which employs a prioritized sampling scheme that can dynamically adjust the offline and online data for smooth and efficient online adaptation of the policy. We provide a theoretical foundation for our algorithms design. Experiment results on the D4RL benchmark show that our algorithm smoothly transfers from offline to online stages while enabling sample-efficient online adaption, and also significantly outperforms existing methods.

cs.LG↗

arXiv2022

Packaging-enhanced optical fiber-chip interconnect with enlarged grating coupler and multimode fiber

Optical I/O plays a crucial role in the lifespan of lab-on-a-chip systems, from preliminary testing to operation in the target environment. However, due to the precise alignments required, efficient and reliable fiber-to-chip connections remain challenging, yielding inconsistent test results and unstable packaged performance. To overcome this issue, for use in single mode on-chip systems, we propose the incorporation of area-enlarged grating couplers working in conjunction with multimode fibers. This combination enables simpler, faster, and more reliable connections than the traditional small area grating coupler with single-mode fiber. In this work, we experimentally demonstrate a 3dB in-plane (X, Y) spatial tolerance of (10.2 μm, 17.3 μm) for the large area configuration, being at least (2.49, 3.33) times that of the small area one, and agreeing well with theoretical calculations. The simple concept is readily applicable to a range of photonic systems where cheaper more robust optical I/O is desired.

physics.app-ph↗

arXiv2022

Opinion Dynamics in Financial Markets via Random Networks

We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize the individuals in a financial market by their trading strategy, namely noise traders and fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the fundamentalist agents' decisions. We introduce a noise parameter $q$ to represent a level of anxiety and perceived uncertainty regarding the market behavior, enabling the possibility for an adrift financial action. We place the individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interaction. At a given time, they assume one of two possible opinion states $\pm 1$ regarding buying or selling an asset. The model exhibits such fundamental qualitative and quantitative real-world market features as the distribution of logarithmic returns with fat-tails, clustered volatility, and long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing the gradual shift from a leptokurtic to a mesokurtic regime, depending on the fraction of fundamentalist agents. We also compare our results with the distribution of logarithmic returns of several real-world financial indices.

q-fin.ST↗

arXiv2022

Evolutionary Multitasking AUC Optimization

Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimization is still an open issue due to its pairwise learning style. Maximizing AUC in the large-scale dataset can be considered as a non-convex and expensive problem. Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task. This paper develops an evolutionary multitasking framework (termed EMTAUC) to make full use of information among the constructed cheap and expensive tasks to obtain higher performance. In EMTAUC, one mission is to optimize AUC from the sampled dataset, and the other is to maximize AUC from the original dataset. Moreover, due to the cheap task containing limited knowledge, a strategy for dynamically adjusting the data structure of inexpensive tasks is proposed to introduce more knowledge into the multitasking AUC optimization environment. The performance of the proposed method is evaluated on a series of binary classification datasets. The experimental results demonstrate that EMTAUC is highly competitive to single task methods and online methods. Supplementary materials and source code implementation of EMTAUC can be accessed at https://github.com/xiaofangxd/EMTAUC.

cs.LG↗

arXiv2021

DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training

Understanding how the predictions of deep learning models are formed during the training process is crucial to improve model performance and fix model defects, especially when we need to investigate nontrivial training strategies such as active learning, and track the root cause of unexpected training results such as performance degeneration. In this work, we propose a time-travelling visual solution DeepVisualInsight (DVI), aiming to manifest the spatio-temporal causality while training a deep learning image classifier. The spatio-temporal causality demonstrates how the gradient-descent algorithm and various training data sampling techniques can influence and reshape the layout of learnt input representation and the classification boundaries in consecutive epochs. Such causality allows us to observe and analyze the whole learning process in the visible low dimensional space. Technically, we propose four spatial and temporal properties and design our visualization solution to satisfy them. These properties preserve the most important information when inverse-)projecting input samples between the visible low-dimensional and the invisible high-dimensional space, for causal analyses. Our extensive experiments show that, comparing to baseline approaches, we achieve the best visualization performance regarding the spatial/temporal properties and visualization efficiency. Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes.

cs.LG↗

arXiv2021

Local well-posedness of the capillary-gravity water waves with acute contact angles

We consider the two-dimensional capillary-gravity water waves problem where the free surface $Γ_t$ intersects the bottom $Γ_b$ at two contact points. In our previous works \cite{MW2, MW3}, the local well-posedness for this problem has been proved with the contact angles less than $π/16$. In this paper, we study the case where the contact angles belong to $(0, π/2)$. It involves much worse singularities generated from corresponding elliptic systems, which have a strong influence on the regularities for the free surface and the velocity field. Combining the theory of singularity decompositions for elliptic problems with the structure of the water waves system, we obtain a priori energy estimates. Based on these estimates, we also prove the local well-posedness of the solutions in a geometric formulation.

math.AP↗

arXiv2021

Discrete-variable quantum key distribution with homodyne detection

Most quantum key distribution (QKD) protocols can be classified as either a discrete-variable (DV) protocol or continuous-variable (CV) protocol, based on how classical information is being encoded. We propose a protocol that combines the best of both worlds -- the simplicity of quantum state preparation in DV-QKD together with the cost-effective and high-bandwidth of homodyne detectors used in CV-QKD. Our proposed protocol has two highly practical features: (1) it does not require the honest parties to share the same reference phase (as required in CV-QKD) and (2) the selection of decoding basis can be performed after measurement. We also prove the security of the proposed protocol in the asymptotic limit under the assumption of collective attacks. Our simulation suggests that the protocol is suitable for secure and high-speed practical key distribution over metropolitan distances.

quant-ph↗

arXiv2021

Community detection and portfolio optimization

Community detection methods can be used to explore the structure of complex systems. The well-known modular configurations in complex financial systems indicate the existence of community structures. Here we analyze the community properties of correlation-based networks in worldwide stock markets and use community information to construct portfolios. Portfolios constructed using community detection methods perform well. Our results can be used as new portfolio optimization and risk management tools.

q-fin.PM↗

arXiv2021

Investigation of the possible $D\bar{D}^$/$B\bar{B}^$ and $DD^$/$\bar{B}\bar{B}^$ molecule states

In this work, we systematically study the $D\bar{D}^*$/$B\bar{B}^*$ and $DD^*$/$\bar{B}\bar{B}^*$ systems with the Bethe-Salpeter equation in the ladder and instantaneous approximations for the kernel. By solving the Bethe-Salpeter equation numerically with the kernel containing the direct and crossed one particle-exchange diagrams and introducing three different form factors (monopole, dipole, and exponential form factors) at the vertices, we find only the isoscalar $D\bar{D}^*$/$B\bar{B}^*$ and $DD^*$/$\bar{B}\bar{B}^*$ systems can exist as bound states. This indicate that the $X(3872)$ and $T^+_{cc}$ could be accommodated as $I^G(J^{PC})=0^+(1^{++})$ $D\bar{D}^*$ and $(I)J^P =(0)1^+$ $DD^*$ molecular states while the molecular state explanations for $Z_b(10610)$ and $Z_c(3900)$ are excluded.

hep-ph↗

arXiv2021

The factorization-assisted topological-amplitude approach and its applications

Heavy meson decays provide an important platform for studies of both QCD and electroweak dynamics, which may contain some portals to understanding of nonperturbative QCD and physics beyond the Standard Model. The factorization-assisted topological-amplitude approach was proposed to study two-body non-leptonic $D$ meson decays, where a promising QCD inspired approach from first principles is still missing. It was also applied to $B$ meson decays whose subleading power contributions are difficult to calculate. By factorizing topological amplitudes into short distance Wilson coefficients and long distance hadronic matrix elements either to be calculated or to be parameterized, it provides an effective framework to extract information of nonperturbative dynamics involved. With important flavor SU(3) breaking effects taken into account, the data of the decay branching ratios (and also CP asymmetries in $B$ decays) can be fitted well. The extracted amplitudes were further applied to make predictions for other observables, such as CP asymmetries in $D$ decays, mixing parameters in the $D^0-\bar{D}^0$ system, and so on. By this review, we will describe the formulation of the factorization-assisted topological-amplitude approach and summarize its applications in $D$ and $B$ meson decays and highlight some of its achievements.

hep-ph↗

arXiv2021

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications towards understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This paper presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, definition extension, and application. We also create a software package for a variety of widely-used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting real-world applications.

cs.CV↗