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arXiv 2021年

作者： Lin, Jian Zhang, Zhengfeng Zhang, Junping Li, Xiaopeng State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing Department of Physics Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Qi Zhi Institute AI Tower Xuhui District Shanghai200232 China

Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography. With programmable quantum devices, adiabatic quantum computing has been proposed as a plausible approach to solve prime factorization, having promising advantage over classical computing. Here, we find there are certain hard instances that are consistently intractable for both classical simulated annealing and un-configured adiabatic quantum computing (AQC). Aiming at an automated architecture for optimal configuration of quantum adiabatic factorization, we apply a deep reinforcement learning (RL) method to configure the AQC algorithm. By setting the success probability of the worst-case problem instances as the reward to RL, we show the AQC performance on the hard instances is dramatically improved by RL configuration. The success probability also becomes more evenly distributed over different problem instances, meaning the configured AQC is more stable as compared to the un-configured case. Through a technique of transfer learning, we find prominent evidence that the framework of AQC configuration is scalable—the configured AQC as trained on five qubits remains working efficiently on nine qubits with a minimal amount of additional training cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Factorization

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OpenAUC: Towards AUC-Oriented Open-Set Recognition

arXiv

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arXiv 2022年

作者： Wang, Zitai Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China BDKM University of Chinese Academy of Sciences China Peng Cheng Laboratory China

Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes (open-set). To fix this issue, Open-Set Recognition (OSR), whose goal is to make correct predictions on both close-set samples and open-set samples, has attracted rising attention. In this direction, the vast majority of literature focuses on the pattern of open-set samples. However, how to evaluate model performance in this challenging task is still unsolved. In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden’s index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction. (2) Novelty detection AUC, which measures the ranking performance between close-set and open-set samples, ignores the close-set performance. To fix these issues, we propose a novel metric named OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise formulation that evaluates open-set performance and close-set performance in a coupling manner. Further analysis shows that OpenAUC is free from the aforementioned inconsistency properties. Finally, an end-to-end learning method is proposed to minimize the OpenAUC risk, and the experimental results on popular benchmark datasets speak to its effectiveness. Project Page: https://***/wang22ti/OpenAUC. © 2022, CC BY.

关键词： Forecasting

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Universal deep network for steganalysis of color image based on channel representation

arXiv

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arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

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Relapse risk prediction for children with henoch-schonlein purpura based on GA-SVM

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International Journal Bioautomation 2020年第2期24卷 117-130页

作者： Liu, Yijun Wang, Beihong Li, Renpu He, Sheng Xi, Haixu Luo, Ye Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology 1801 Zhongwu Road Changzhou213001 China Department of Pediatrics Changzhou No. 2 People's Hospital 29 Xinglong Road Changzhou213003 China

The relapse risk prediction for children with Henoch-Schonlein purpura can help pediatricians make an accurate prognosis and offer personalized and appropriate follow-up nursing and relapse control to patients. In this study, we propose a Genetic algorithm-Support vector machine (GA-SVM) learning method combining the support vector machine with the genetic algorithm for parameter optimization to capture the nonlinear mapping from a panel of biomarkers to the relapse risk of HSP children. The GA-SVM prediction model is created by using the dataset of 40 samples in clinical treatment and observation of patients. The inputs of the model consist of 19 biomarkers including gender, age, immunoglobulin M, immunoglobulin G, immunoglobulin A, prothrombin time, etc. The outputs consist of 1 and-1, where 1 indicates high relapse risk and-1 indicates low relapse risk. For comparison, the GS-SVM prediction model based on parameter optimization of grid search is also created. The experimental results show that the GA-SVM prediction model has a high prediction accuracy of 90% and is strong in generalization ability. The GA-SVM model for predicting the relapse risk of HSP children is a promising decision support tool of clinical prognosis, which provides pediatricians with valuable assistance to offer rehabilitation treatment to patients. © 2020 by the authors.

关键词： Forecasting

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An axial-ratio beam-width enhancement of antenna based on electromagnetic bandgap (EBG)

An axial-ratio beam-width enhancement of antenna based on el...

引用

IEEE information Technology, Networking, Electronic and Automation Control Conference

作者： Shijie Huang Liang Zhang Chao Wang Changqing Liu Shenbing Wu Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

In order to meet the requirements of the navigation system for antennas with wide 3-dB AR beam-width, this paper proposes an axial-ratio beam-width enhancement of antenna based on electromagnetic bandgap (EBG). The effect of surface wave on the circular polarization of the antenna is suppressed by loading two layers of mushroom-like EBG unit cells around the patch. The loading of the EBG increases the 3-dB AR beam-width of the antenna by 78% in the xoz plane and the by 84% in the yoz plane, the simulation and test results demonstrate the effectiveness of this method.

关键词：

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A circularly polarized low-profile Metasurface antenna based on CPW feed

A circularly polarized low-profile Metasurface antenna based...

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IEEE International Wireless Symposium (IWS)

作者： Changqing Liu Liang Zhang Shijie Huang Chao Wang Xingchuan Yu Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei China

ISBN: (数字)9781728167039

ISBN: (纸本)9781728167046

A low-profile circularly polarized Metasurfaces antenna based on compact coplanar waveguide (CPW) feed was proposed. The Metasurfaces consists of 16 periodic square-cut metal patches. An hourglass coupled slot was adopted to achieve broadband impedance matching. The antenna has a simple structure and a low cross section, and it can be implemented by a layer of a printed circuit substrate. A circular polarized wave is generated by changing the size of the cut angle of the Metasurfaces, and at the same time, the impedance bandwidth can be widened. To verify the simulation results, a 1λ 0 ×1λ 0 ×0.05λ 0 (at 10 GHz) model antenna was fabricated and measured. The simulation and measurement results show good consistency. The test results show that the -10dB return loss bandwidth reaches 45.3 %, and the circular polarization axis ratio(AR) is less than 3dB with a bandwidth of 18.5%.

关键词： Antenna measurements Polarization Bandwidth Metasurfaces Radar antennas Antenna feeds Coplanar waveguides

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Low Profile Dual-Band Antenna on an EBG Substrate

Low Profile Dual-Band Antenna on an EBG Substrate

引用

IEEE International Wireless Symposium (IWS)

作者： Shijie Huang Liang Zhang Chao Wang Changqing Liu Yifan Jiang Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei China

ISBN: (数字)9781728167039

ISBN: (纸本)9781728167046

This paper proposes a dual-band antenna based on the electromagnetic band gap structure suitable for WLAN communication systems. The proposed antenna is designed to work at 2.45/5.8 GHz WLAN band. By loading the EBG structure directly below a dual-frequency monopole antenna, the in-phase superposition of electromagnetic waves at the center frequency of the antenna is achieved. The antenna exhibits high gains of near 8.0 and 12.0 dB at the low and high working frequencies while maintaining the low profile characteristics.

关键词： Wireless LAN Directive antennas Reflector antennas Metamaterials Loaded antennas Gain Periodic structures

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12-Port Hybrid MIMO Antenna Array for 5G Smartphone Applications

12-Port Hybrid MIMO Antenna Array for 5G Smartphone Applicat...

引用

IEEE International Wireless Symposium (IWS)

作者： Peng Liu Yufa Sun Bingbing Chen Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Anhui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal University Hefei China

ISBN: (数字)9781728167039

ISBN: (纸本)9781728167046

A hybrid twelve-antenna array operating in the 3.3-3.6 GHz and 4.8-5.0 GHz band for multiple input multiple output (MIMO) applications in 5G smartphones is proposed. The proposed hybrid antenna array elements are symmetrically placed along the long edges of the smartphone, and they are composed of two inverted-F antennas (IFA) with different structural parameters. Simulated antenna isolation is better than -14.5 dB without any decoupling structures. Moreover, simulated radiation patterns, antenna efficiencies, and envelop correlation coefficients are also given in the paper.

关键词： Wireless communication 5G mobile communication Structural engineering Smart phones MIMO communication Antenna arrays Antenna radiation patterns

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Bayesian dual neural networks for recommendation

引用

Frontiers of Computer Science 2019年第6期13卷 1255-1265页

作者： Jia HE Fuzhen ZHUANG Yanchi LIU Qing HE Fen LIN Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Rutgers University Newark 07102USA Search Product Center WeChat Search Application DepartmentTencentBeijing 100080China

Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity *** address these problems,on the one hand,some CF methods are proposed to incorporate auxiliary information such as user/item profiles;on the other hand,deep neural networks,which have powerful ability in learning effective representations,have achieved great success in recommender ***,these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the ***,they maybe lack of calibrated probabilistic predictions and make overly confident *** this end,we propose a new Bayesian dual neural network framework,named BDNet,to incorporate auxiliary information for ***,we design two neural networks,one is to learn a common low dimensional space for users and items from the rating matrix,and another one is to project the attributes of users and items into another shared latent *** that,the outputs of these two neural networks are combined to produce the final ***,we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic *** experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.

关键词： collaborative filtering Bayesian neural network hybrid recommendation algorithm

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Learning to Undersampling for Class Imbalanced Credit Risk Forecasting

Learning to Undersampling for Class Imbalanced Credit Risk F...

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IEEE International Conference on Data Mining (ICDM)

作者： Jianfeng Chi Guanxiong Zeng Qiwei Zhong Ting Liang Jinghua Feng Xiang Ao Jiayu Tang Alibaba Group CN Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing CN University of Chinese Academy of Sciences Beijing CN

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Credit risk forecasting generally aims to evaluate the default probability of users in financial service. It is usually regarded as a binary classification problem, which suffers from the severe class-imbalance problem due to the extremely limited throngs and the concept drift problem brought by the delayed verification. In this paper, we investigate these problems in credit risk forecasting and propose a semi-supervised meta-learning based approach called TRUST (TRainable Undersampling with Self Training) to resolve. First, it decides whether to sample the data through meta-learning based reinforcement learning. Secondly, it learns the distribution of the data that have not yet shown financial performance via self-training and updates the model trained in the first step. Finally, the updated model is evaluated on the validation dataset, the result of which will be fed back through the evaluator. These three steps will be iterated until the model converges. With the real-world industrial dataset containing 1.75 million users, we investigate the effectiveness of our method. Experimental results exhibit that the proposed method is able to improve AP over 5.94% on credit risk forecasting task compared with the recent methods.

关键词： Training Reinforcement learning Data models Data mining Forecasting Task analysis Financial services

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