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Applied Physics Letters 2021年第2期118卷

作者： Lutong Sheng Yawen Liu Jilei Chen Hanchen Wang Jianyu Zhang Mingfeng Chen Ji Ma Chuanpu Liu Sa Tu Ce-Wen Nan Haiming Yu 1Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China 2Department of Physics and Astronomy University of California Riverside California 92521 USA 3State Key Lab of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 China

In the original article,1 the line spectrum in Fig. 2(b) is missing. The correct Fig. 2(b), given here, shows how we get the frequency difference from this spec

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Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

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Learning Structured Twin-Incoherent Twin-Projective Latent D...

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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Determination factors of internet advertising affecting internet users’ behavior using canonical correlation analysis

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AIP Conference Proceedings 2024年第1期2895卷

作者： Farah Nabilah Zabidi Nurain Ibrahim Nur Izzah Atiqah Nurul Athirah Norzaidi Salsabilla Kamaludin Adina Najwa Kamarudin School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Regional Department Suu Balm Malaysia Livemed Sdn Bhd Aurora Place Plaza No 1 Persiaran Bukit Jalil 57000 Kuala Lumpur Malaysia Federal Government Administrative Centre Block C6 Complex C Lebuh Perdana Selatan Presint 1 62514 Putrajaya Wilayah Persekutuan Putrajaya Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

One of the most important and fundamental parts of today’s business world is advertising. Advertising is necessary for both business and consumers. We can now observe that the advertising industry has experienced substantial changes and is now employed globally. An advertising company has found this platform to be quite useful for increasing revenue by influencing the online users’ behaviour, which may change in either a positive or bad way. Therefore, the primary topic of this study paper is on how internet advertising affects consumers’ online behaviour in UiTM Shah Alam. Through surveys, we gathered information about two sets of variables: Internet Users’ Behaviour (Purchase Intention, Effective Medium, Frequency of Browsing, and Demographic Variable), and sets of variables Internet Advertising (Webpage Content, Informativeness and Interactivity). To determine the aspects of internet advertising influencing internet users’ behaviour, Canonical Correlation Analysis was applied to the data that was gathered. Internet advertising and internet users’ behaviour are significantly correlated, according to the results analysis. As a result, Internet advertising plays a big role in influencing consumers’ behaviour because of its close connection to their purchasing decisions. Based on findings, it can be concluded that internet advertising plays significant roles in examining internet users’ behaviour, which may be advantageous for many industries globally, particularly in current era of digitalization. With information on consumer behaviour both now and in the future, these findings can assist marketers and advertisers in developing more effective online advertising strategies that better engage young adults’ attention and influence their behaviour, particularly with regard to purchase intention, leading to a misalignment. It will then be able to create more successful online advertising.

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Lattice Blind Signatures with Forward Security

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arXiv 2020年

作者： Le, Huy Quoc Duong, Dung Hoang Susilo, Willy Tran, Ha Thanh Nguyen Trinh, Viet Cuong Pieprzyk, Josef Plantard, Thomas Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia Department of Mathematical and Physical Sciences Concordia University of Edmonton 7128 Ada Blvd NW EdmontonABT5B 4E4 Canada Faculty of Information and Communication Technology Hong Duc University 565 Quang Trung Thanh Hoa Viet Nam CSIRO Data61 SydneyNSW Australia Institute of Computer Science Polish Academy of Sciences Warsaw Poland

Blind signatures play an important role in both electronic cash and electronic voting systems. Blind signatures should be secure against various attacks (such as signature forgeries). The work puts a special attention to secret key exposure attacks, which totally break digital signatures. Signatures that resist secret key exposure attacks are called forward secure in the sense that disclosure of a current secret key does not compromise past secret keys. This means that forward-secure signatures must include a mechanism for secret-key evolution over time periods. This paper gives a construction of the first blind signature that is forward secure. The construction is based on the SIS assumption in the lattice setting. The core techniques applied are the binary tree data structure for the time periods and the trapdoor delegation for the key-evolution mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

Ab initio evaluation of complexation energies for cyclodextrin drug inclusion complexes

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arXiv 2020年

作者： Oqmhula, Kenji Hongo, Kenta Maezono, Ryo Ichibha, Tom School of Materials Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan

We examined the reliability of exchange-correlation functionals for molecular encapsulations combined by van der Waals forces, comparing their predictions with those of diffusion Monte Carlo method. We established that functionals with D3 dispersion force correction and including sufficient proportion of exact-exchange in long-ranged interaction can comparatively reliably estimate the binding strength. Our finding agrees with a previous ab initio study on argon dimer. However we found that even such functionals may not be able to distinguish the energy differences among different conformations. Copyright © 2020, The Authors. All rights reserved.

关键词： Van der Waals forces

Quantum speedup for twin support vector machine

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arXiv 2019年

作者： Ye, Zekun Li, Lvzhou Situ, Haozhen Institute of Computer Science Theory School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Center for Quantum Computing Pengcheng Laboratory Shenzhen518055 China The Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

In this work, we investigate how to speed up machine learning tasks based on quantum computation. We show an important classifier in machine learning, the twin support vector machine, can be exponentially speeded up on quantum computers. Specifically, for a training set with m samples which are represented by n-dimensional vectors, the proposed quantum algorithm can learn two non-parallel hyperplanes in O(log mn) time, and then classify a new sample in O(log n) time by comparing the distances from the simple to the two hyperplanes. Note that the classical algorithm requires polynomial time both in the training and classification procedures. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

Better approximations of high dimensional smooth functions by deep neural networks with rectified power units

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arXiv 2019年

作者： Li, Bo Tang, Shanshan Yu, Haijun NCMIS & LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China China Justice Big Data Institute Beijing100043 China

MSC Codes 65D15, 65M12, 65M15Deep neural networks with rectified linear units (ReLU) are getting more and more popular due to their universal representation power and successful applications. Some theoretical progress regarding the approximation power of deep ReLU network for functions in Sobolev space and Korobov space have recently been made by [D. Yarotsky, Neural Network, 94:103-114, 2017] and [H. Montanelli and Q. Du, SIAM J Math. data Sci., 1:78-92, 2019], etc. In this paper, we show that deep networks with rectified power units (RePU) can give better approximations for smooth functions than deep ReLU networks. Our analysis bases on classical polynomial approximation theory and some efficient algorithms proposed in this paper to convert polynomials into deep RePU networks of optimal size with no approximation error. Comparing to the results on ReLU networks, the sizes of RePU networks required to approximate functions in Sobolev space and Korobov space with an error tolerance Ε, by our constructive proofs, are in general O(log1Ε ) times smaller than the sizes of corresponding ReLU networks constructed in most of the existing literature. Comparing to the classical results of Mhaskar [Mhaskar, Adv. Comput. Math. 1:61-80, 1993], our constructions use less number of activation functions and numerically more stable, they can be served as good initials of deep RePU networks and further trained to break the limit of linear approximation theory. The functions represented by RePU networks are smooth functions, so they naturally fit in the places where derivatives are involved in the loss function. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Design of Gesture Recognition System Based on Multi-Channel Myoelectricity Correlation

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Design of Gesture Recognition System Based on Multi-Channel ...

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Di Wu Huiyong Li Xuefeng Liu Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Gesture recognition systems based on myoelectric signal have raised more and more attention from researchers. Traditional gesture recognition methods are susceptible to multiple types of noise and require a large number of features, which increase overhead and decrease recognition efficiency. Fully utilizing the characteristics of the signal to recognize gestures is a big challenge. This paper proposes an improved empirical mode decomposition method (XB-EMD) based on autocorrelation function to denoise myoelectric signal. In addition, a novel deep neural network (CRNet) which combines the CNN and RNN layers together is trained for classifying the gestures based on denoised myoelectric signal. Experimental results show that the proposed gesture recognition system can improve recognition effectiveness at 97.4\% with 10 typical gestures, and 72.55\% with 16 complicated gestures.

关键词： Correlation Gesture recognition Feature extraction Empirical mode decomposition Recurrent neural networks Sensors

Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF2X (X=Br, I) and LaFI2

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Angewandte Chemie 2023年第30期135卷

作者： Dr. Daichi Kato Dr. Peng Song Dr. Hiroki Ubukata Haruki Taguro Dr. Cédric Tassel Dr. Kohei Miyazaki Prof. Takeshi Abe Dr. Kousuke Nakano Prof. Kenta Hongo Prof. Ryo Maezono Prof. Hiroshi Kageyama Department of Energy and Hydrocarbon Chemistry Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan PRESTO Japan Science and Technology Agency (JST) Kawaguchi Saitama 332-0012 Japan Contribution: Data curation (lead) Formal analysis (lead) Funding acquisition (lead) Investigation (lead) Methodology (lead) Project administration (lead) Resources (supporting) Supervision (lead) Validation (equal) Visualization (equal) Writing - original draft (lead) Writing - review & editing (lead) School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Contribution: Data curation (supporting) Formal analysis (supporting) Funding acquisition (supporting) Methodology (supporting) Software (supporting) Validation (supporting) Writing - original draft (supporting) Contribution: Formal analysis (supporting) Writing - original draft (supporting) Contribution: Data curation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Writing - original draft (supporting) Contribution: Funding acquisition (supporting) Resources (supporting) Writing - original draft (supporting) Center for Basic Research on Materials (CBRM) National Institute for Materials Science (NIMS) 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan Contribution: Writing - original draft (supporting) Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Contribution: Conceptualization (lead) Funding acquisition (lead) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - original draft (supporting)

Mixed-anion compounds have attracted growing attentions, but their synthesis is challenging, making a rational search desirable. Here, we explored LaF 3 -LaX 3 (X=Cl, Br, I) system using ab initio structure searches based on evolutionary algorithms, and predicted LaF 2 X and LaFX 2 (X=Br, I), which are respectively isostructural with LaHBr 2 and YH 2 I, consisting of layered La-F blocks with single and double ordered honeycomb lattices, separated by van der Waals gaps. We successfully synthesized these compounds: LaF 2 Br and LaFI 2 crystallize in the predicted structure, while LaF 2 I is similar to the predicted one but with different layer stacking. LaF 2 I exhibits fluoride ion conductivity comparable to that of non-doped LaF 3 , and has the potential to show better ionic conductivity upon appropriate doping, given the theoretically lower diffusion energy barrier and the presence of soft iodine anions. This study shows the structure prediction using evolutionary algorithms will accelerate the discovery of mixed-anion compounds in future, in particular those with an ordered anion arrangement.

关键词： Evolutionary Algorithm Fluoride Ion Conductor Mixed Anion

Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery

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Adaptive Structure-Constrained Robust Latent Low-Rank Coding...

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Lei Wang Sheng Li Yang Wang Zheng Zhang Zhengjun Zha Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia Athens GA Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

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