Reviewerly/exHarmony huggingface dataset:
Quantum remote control utilizing multiple degrees of freedom

Authors:
raw_author_name: Meiyu Wang
raw_affiliation_string: College of Physics, Hebei Normal University, Shijiazhuang 050024, China; Hebei Key Laboratory of Photophysics Research and Application, Shijiazhuang 050024, China
raw_author_name: Hao Guo
raw_affiliation_string: College of Physics, Hebei Normal University, Shijiazhuang 050024, China; Hebei Key Laboratory of Photophysics Research and Application, Shijiazhuang 050024, China

Abstract: We present protocols for remote implementation of general operators between two nodes, which are considered: the first where an arbitrary single-qubit operation acts on photon state in the polarization degree of freedom, and the second with photon state in the temporal degree of freedom. By employing simultaneous entanglement in two independent degrees of freedom, both protocols are fulfilled near deterministically in two steps. In the first step, the effective cross-Kerr interaction and X-quadrature measurements of the coherent states are main tools for achieving the goal. Together with the available existing optical elements and common single-photon detectors, the construction circuit of the second step is simple and feasible with current technologies. Our protocols are beneficial to long-distance quantum communication and quantum metrology.
Improved error tolerance of programmable photonic integrated circuits for MNIST handwritten digit classification

Authors:
raw_author_name: Zhiyuan Wu
raw_affiliation_string: Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Yang Wang
raw_affiliation_string: Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China

Abstract: Programmable photonic integrated circuits (PPICs) can realize linear optical unitary transformation between arbitrary multi-port input and multi-port output for optical computing and signal processing. With the scale increase of PPICs, the accumulation of manufacturing errors will lead to overall performance deviations. A symmetrical Mach-Zehnder interferometer (MZI) circuit with a shorter optical path and lower propagation loss has been proposed recently to replace the traditional asymmetrical MZI circuit. However, no systematic research on the error compensation of symmetrical MZI circuits has been reported yet. In this paper, the error tolerance characteristics of symmetrical MZI circuits and asymmetrical MZI circuits are analyzed and compared in detail. An effective strategy to compensate the 3 dB beam splitter error of a symmetrical MZI circuit is proposed. A deep learning task of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset classification was carried out with two hidden layers, which are implemented by rectangular symmetrical MZI circuit structures. The recognition accuracy can be significantly improved with this error compensation method.
Unified Representation Learning for Multi-View Clustering by Between/Within View Deep Majorization

Authors:
raw_author_name: Yue Zhang
raw_affiliation_string: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
raw_author_name: Weitian Huang
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
raw_author_name: Chang-Dong Wang
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Hongmin Cai
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

Abstract: Multi-view data are characterized by a variety of features that are semantically coherent, with different views usually complementing each other. Multi-view clustering seeks to learn a unified representation that is comprehensive yet discriminative to partition the samples. However, existing research ignored the possible redundancy of latent feature in each view, then yielded a unified task-oriented feature based on the correlation of these multi-view latent features without explicit consideration of the influence of the unified feature. Such a way failed to accurately measure the association among views. To fully take the association into account, we propose a deep multi-view Unified Representation Learning network, named by deep-URL, using measurement between multi-view data to realize desirable unified representation. Specifically, deep-URL employs a multi-view encoder-decoder framework that maps multi-scale data into a same latent space to obtain view-specific latent representation. Within each view, the view-specific features are pruned to eliminate the redundant information in the latent space by minimizing the within-view mutual information. Between the multiple views, a unified informative representation is derived to accurately estimate the nonlinear associations between views via maximizing conditional mutual information of the view-specific features. The deep unified representation is further elaborated to achieve the full reconstruction of each view to ensure its reliability. The proposed network is trained by between/within view deep representation majorization. It achieves redundant representation removal within each view, unified features integration across views, and universal reconstruction via decoding. Extensive experiments on five real datasets with largely varied sample sizes demonstrate that the deep-URL achieved superior clustering performance by comparing with fourteen baseline methods.
Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

Authors:
raw_author_name: P. Rajmic
raw_affiliation_string: Department of Telecommunications, Brno University of Technology, Brno, Czech Republic
raw_author_name: F. Hlawatsch
raw_affiliation_string: Institute of Telecommunications, TU Wien, Vienna, Austria

Abstract: The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.
Enhanced D-CNN architecture and centroid-based algorithm for real-time vehicle tracking and accident detection from surveillance videos

Authors:
raw_author_name: Ann Rija Paul
raw_affiliation_string: CSE Department, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
raw_author_name: E. Grace Mary Kanaga
raw_affiliation_string: CSE Department, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

Abstract: In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.
GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

Authors:
raw_author_name: Yunhao Shi
raw_affiliation_string: Air Force Engineering University, Xi&#x2019;an, China
raw_author_name: Hua Xu
raw_affiliation_string: Air Force Engineering University, Xi&#x2019;an, China
raw_author_name: Zisen Qi
raw_affiliation_string: Air Force Engineering University, Xi&#x2019;an, China
raw_author_name: Dan Wang
raw_affiliation_string: Air Force Engineering University, Xi&#x2019;an, China

Abstract: With the development of deep learning (DL), several fields have ushered in leapfrog development, such as image classification and natural language processing. Combing the powerful feature extraction tool, DL-based Automatic Modulation Classification (AMC) emerges. However, most DL-based AMC methods require massive labeled samples for training, which is difficult for non-cooperative scenarios. In this paper, we propose a novel self-supervised AMC method called GAF-MAE. Gramian Angular Field (GAF) is applied for domain transformation from time series to images first, then a Masked Autoencoder (MAE) is built for self-supervised reconstruction tasks through unlabeled samples. After self-supervised pretrain, a small number of labeled samples are used for downstream classification finetuning. We conduct a comprehensive evaluation of a public dataset RadioML.2016.10a. The simulation results show that GAF-MAE can achieve a relatively high average accuracy of 54.85% even if the label proportion is 5% and 61.38% when the label proportion reaches 100%, which outperforms other well-known models.
Towards constrained optimization of cloud applications: A hybrid approach

Authors:
raw_author_name: Spyridon Chouliaras
raw_affiliation_string: Department of Computer Science and Information Systems, Birkbeck, University of London, United Kingdom
raw_author_name: Stelios Sotiriadis
raw_affiliation_string: Department of Computer Science and Information Systems, Birkbeck, University of London, United Kingdom

Abstract: A database system is one of the most common cloud applications where elasticity allows to dynamically allocate resources in response to changing workload demands. In such systems, users usually configure resources based on empirical decisions, for example by boosting computational resources with the expectation of improving database throughput. The latter also suggests that users manually scale resources and tune the database configurations offline to meet the application demands. However, this approach could rapidly increase infrastructure expenditures while this is also time consuming. In this paper, we propose SONA, a framework to support constrained performance optimization of cloud applications using a hybrid approach of artificial neural networks and genetic algorithms. The proposed framework monitors the source system to identify the optimal configurations that maximize application performance based on genuine workload executions. As a result, the experimental analysis presents a novel dataset collected from TPC-C runs on MySQL server. The optimization process is being constrained to satisfy user and application requirements including the cloud infrastructure expenditures, the cost-performance ratio, the baseline performance and the average percentage of idle CPU resources. Furthermore, SONA uses a cloned containerized environment that replicates the main application to avoid system overhead during the optimization process. Our results demonstrate the effectiveness of SONA framework to optimize application performance of OLTP applications deployed on cloud.
Controllable Style Transfer via Test-time Training of Implicit Neural Representation

Authors:
raw_author_name: Sunwoo Kim
raw_affiliation_string: Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
raw_author_name: Youngjo Min
raw_affiliation_string: Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
raw_author_name: Seungryong Kim
raw_affiliation_string: Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea

Abstract: Existing CNN-based style transfer frameworks have suffered from inaccurate control of pixel-wise stylization. As the CNN operation is designed based on kernel-wise operation, such a design unavoidably makes pixels affect each other. To mitigate this problem, we propose a controllable style transfer framework that leverages Implicit Neural Representation to encode each pixel respectively and optimize each style and content pair via test-time training. Unlike previous CNN-based style transfer frameworks, this formulation naturally enables accurate pixel-wise stylization control. In addition, to give explicit controllability on the degree of stylization, we define two vectors that represent the content and style respectively, enabling control by interpolating these vectors. We further demonstrate that, after being test-time trained once, our framework can show a various range of applications by precisely controlling the stylized images pixel-wise and freely adjusting image resolution and degree of stylization without further optimization or training.
Information geometry based extreme low-bit neural network for point cloud

Authors:
raw_author_name: Yanxin Ma
raw_affiliation_string: College of Meteorology and Oceanography, National University of Defence Technology, Changsha, China
raw_author_name: Ke Xu
raw_affiliation_string: College of Electronic Science, National University of Defence Technology, Changsha, China
raw_author_name: Jianwei Wan
raw_affiliation_string: College of Electronic Science, National University of Defence Technology, Changsha, China

Abstract: Deep learning has significantly advanced three-dimensional computer vision applied to point clouds. Nevertheless, the substantial consumption of time, storage, and energy substantially limits its deployment on edge devices with constrained resources. Extremely low bit quantization has received wide attention due to its extremely high compression ratio, but the problem of a significant drop in accuracy cannot be ignored. To alleviate the obvious accuracy degradation for extreme low-bit quantization, this paper proposes a novel compression framework for binary and ternary neural networks applied to point clouds, which introduces information geometry to model quantization to compensate the severe feature manifold distortion. It applies differential geometry on manifolds to study the implicit information of the point cloud feature data. Based on the theoretical analysis from the novel perspective of information geometry, two optimization modules are proposed to alleviate severe geometry distortions on differential manifolds. The first module, scaling recovery, provides layer-wise scaling parameters to reduce geometric distortion caused by quantization. The second module, Pooling Recovery, is specially designed to alleviate more severe pooling geometry distortions in point clouds. These two modules benefit both binary and ternary neural networks with ignored overheads. For ternary quantization, optimizations on convolution weights and gradients are additionally introduced. The proposed self-adaptive gradient estimation provides a more accurate approximation to the non-differential ternary staircase function. Convolution weight optimization is implemented on an information-geometry optimized model to achieve even higher accuracy and less memory consumption. Experimental results validate that the proposed models significantly outperform state-of-the-art methods and demonstrate better scalability. Overall, this compression framework has the potential to facilitate the deployment of deep learning models on edge devices with limited resources, opening up new opportunities for applications of three-dimensional computer vision.
Blind quality-based pairwise ranking of contrast changed color images using deep networks

Authors:
raw_author_name: Aladine Chetouani
raw_affiliation_string: Laboratoire PRISME, Université d'Orléans, France
raw_author_name: Muhammad Ali Qureshi
raw_affiliation_string: The Islamia University of Bahawalpur, 63100, Pakistan
raw_author_name: Mohamed Deriche
raw_affiliation_string: Artificial Intelligence Research Centre, College of Engineering, Ajman University, UAE
raw_author_name: Azeddine Beghdadi
raw_affiliation_string: L2TI, Institut Galilée, Université Sorbonne Paris Nord, France

Abstract: Next-generation multimedia networks are expected to provide systems and applications with top Quality of Experience (QoE) to users. To this end, robust quality evaluation metrics are critical. Unfortunately, most current research focuses only on modeling and evaluating mainly distortions across the pipeline of multimedia networks. While distortions are important, it is also as important to consider the effects of enhancement and other manipulations of multimedia content, especially images and videos. In contrast to most existing works dedicated to evaluating image/video quality in its traditional context, very few research efforts have been devoted to Image Quality Enhancement Assessment (IQEA) and more specifically, Contrast Enhancement Evaluation (CEE). Our contribution fills this gap by proposing a pairwise ranking scheme for estimating and evaluating the perceptual quality of image contrast change (contrast enhancement and/or contrast-distorted images) process. We propose a novel Deep Learning-based Blind Quality pairwise Ranking scheme for Contrast-Changed (Deep-BQRCC) images. This method provides an automatic pairwise ranking of a set of contrast-changed images. The proposed framework is based on using a pair of Convolutional Neural Networks (CNN) together with a saliency-based attention model and a color-difference visual map. Extensive experiments were conducted to validate the effectiveness of the proposed workflow through an ablation analysis. Different combinations of CNN models and pooling strategies were analyzed. The proposed Deep-BQRCC approach was evaluated over three dedicated publicly available datasets. The experimental results showed an increase in performance within a range of 3–10% compared to state-of-the-art IQEA measures.
Fast anchor graph preserving projections

Authors:
raw_author_name: Jikui Wang
raw_affiliation_string: School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Lanzhou 730000, Gansu, China; State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou, China
raw_author_name: Yiwen Wu
raw_affiliation_string: School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Lanzhou 730000, Gansu, China
raw_author_name: Bing Li
raw_affiliation_string: School of Management, Xiamen University, Xiamen 361005, China
raw_author_name: Zhenguo Yang
raw_affiliation_string: School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Lanzhou 730000, Gansu, China
raw_author_name: Feiping Nie
raw_affiliation_string: School of Artificial Intelligence, Optics and ElectroNics(iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China

Abstract: The existing graph-based dimensionality reduction algorithms need to learn an adjacency matrix or construct it in advance, therefore the time complexity of the graph-based dimensionality reduction algorithms is not less than O(n2d), where n denotes the number of samples, d denotes the number of dimensions. Moreover, the existing dimensionality reduction algorithms do not consider the cluster information in the original space, resulting in the weakening or even loss of valuable information after dimensionality reduction. To address the above problems, we propose Fast Anchor Graph Preserving Projections (FAGPP), which learns the projection matrix, the anchors and the membership matrix at the same time. Especially, FAGPP has a built-in Principal Component Analysis (PCA) item, which makes our model not only deal with the cluster information of data, but also deal with the global information of data. The time complexity of FAGPP is O(nmd), where m denotes the number of the anchors and m is much less than n. We propose a novel iterative algorithm to solve the proposed model and the convergence of the algorithm is proved theoretically. The experimental results on a large number of high-dimensional benchmark image data sets demonstrate the efficiency of FAGPP. The data sets and the source code are available from https://github.com/511lab/FAGPP.
Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey

Authors:
raw_author_name: Hongying Liu
raw_affiliation_string: Medical College, Tianjin University, Tianjin, 300072, China
raw_author_name: Zekun Li
raw_affiliation_string: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, China
raw_author_name: Fanhua Shang
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
raw_author_name: Yuanyuan Liu
raw_affiliation_string: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, China
raw_author_name: Liang Wan
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China; Medical College, Tianjin University, Tianjin, 300072, China
raw_author_name: Wei Feng
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
raw_author_name: Radu Timofte
raw_affiliation_string: Computer Vision Lab, CAIDAS & IFI, University of Wurzburg, Wurzburg, Germany; ETH Zurich, Zurich, Switzerland

Abstract: Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve the resolution of images or videos in computer vision. In recent years, significant progress has been made in image and video super-resolution techniques based on deep learning. Nevertheless, most of the methods only consider SR with a few integer scale factors, which limits the application of the SR techniques to real-world problems. Recently, the methods to achieve arbitrary-scale super-resolution via a single model have attracted much attention. However, there is no work to thoroughly analyze the arbitrary-scale methods based on deep learning. In this work, we present a comprehensive and systematic review of 45 existing deep learning-based methods for arbitrary-scale image and video SR. We first classify the existing SR methods according to the resolved scale factors. Furthermore, we propose an in-depth taxonomy for state-of-the-art methods based on the core problem of how to achieve arbitrary-scale super-resolution, i.e., how to perform arbitrary-scale upsampling. Moreover, the performance of existing arbitrary-scale SR methods is compared, and their advantages and limitations are analyzed. We also provide some guidance for the selection of these methods in different real-world applications. Finally, we briefly discuss the future directions of arbitrary-scale super-resolution, which shows some inspirations for the progress of subsequent works on arbitrary-scale image and video super-resolution tasks. The repository of this work is available at https://github.com/Weepingchestnut/Arbitrary-Scale-SR.