Reviewerly/exHarmony huggingface dataset:
Role of radial index in self-reconstruction of structured beams

Authors:
raw_author_name: Yankun Wang
raw_affiliation_string: School of Physics, Xidian University, Xi’an 710071, Shaanxi, China
raw_author_name: Lixin Guo
raw_affiliation_string: School of Physics, Xidian University, Xi’an 710071, Shaanxi, China

Abstract: We demonstrate that the self-reconstruction performance of structured light is positively correlated with its radial degrees of freedom; specifically, structured beams with larger radial indices have better self-reconstruction abilities and higher tolerance to obstacles. These results were determined by quantifying the self-reconstruction degree of the Laguerre-Gauss beams. After the beam is partially blocked by an obstacle, we present convincing evidence theoretically and experimentally that an increase in the radial index of the Laguerre-Gauss beam is accompanied by a better self-reconstruction ability. We provide a physical explanation for this result in terms of non-diffraction, traveling-wave solution, and transverse momentum. Finally, we theoretically demonstrate that the contribution of the radial index to the self-reconstruction ability of the Hermite-Gauss and Ince-Gauss modes, the results are consistent. We believe that our results are applicable to all beams with complex radial structure, which provides another perspective on the self-reconstruction of structured beams: the contribution of the radial structure index.
Rational local unitary invariants of symmetrically mixed states of two qubits

Authors:
raw_author_name: Vladimir Y. Chernyak
raw_affiliation_string: Department of Chemistry, Wayne State University, 5101 Cass Ave, Detroit, MI, 48202 USA

Abstract: We compute the field of rational local unitary invariants for locally maximally mixed states and symmetrically mixed states of two qubits. In both cases, we prove that the field of rational invariants is purely transcendental. We also construct explicit geometric quotients and prove that they are always rational. All the results are obtained by working over the field of real numbers, employing methods from classical and geometric invariant theory over arbitrary fields of characteristic zero.
Scalable and Parallel Optimization of the Number Theoretic Transform Based on FPGA

Authors:
raw_author_name: Bin Li
raw_affiliation_string: Henan Key Laboratory of Network Cryptography Technology, Zhengzhou, China; School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
raw_author_name: Yunfei Yan
raw_affiliation_string: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
raw_author_name: Yuanxin Wei
raw_affiliation_string: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China

Abstract: In lattice-based postquantum cryptography (PQC), polynomial multiplication is complex and time-consuming, which affects the overall computational efficiency. In addition, the parameters of different lattice-based algorithms require different number theoretic transform (NTT) structures, which limits the versatility of hardware design. To this end, this article proposes scalable and parallel optimization of the NTT based on a field-programmable gate array (FPGA). By analyzing the algorithm flow of the NTT, inverse NTT (INTT), and pointwise multiplication (PWM), an FPGA loosely coupled structure is designed, which can be used to place butterfly units of multiple pipelines in parallel and supports various modulo operations of a polynomial. In addition, to improve computing efficiency and scalability, key algorithm modules such as multipliers and modular reduction are deeply optimized. Moreover, the storage optimization of multiple RAM channels is carried out, and the alternate access control of data and the multiplexing of RAM resources reduce resource consumption and improve data access efficiency. For the SHA-3 algorithm, the scalable Keccak algorithm is implemented in a serial–parallel hybrid manner and supports multiple hash modes. Finally, taking the Dilithium algorithm as an example, through the parallelization of SHA-3 and NTT, the calculation cycle of key generation, signature, and verification is shortened. The experimental results and analysis show that the scheme in this article shortens the NTT calculation period while ensuring a high frequency, and the calculation time is significantly better than that of other schemes. Furthermore, it can support the optimized parallelization of multiple moduli and give full play to the computing advantages of an FPGA.
Three-dimensional reconstruction of light field based on cubic B-spline curve fitting

Authors:
raw_author_name: Wei Feng
raw_affiliation_string: Hubei Key Laboratory of Modern Manufacturing Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
raw_author_name: Jiahao Fan
raw_affiliation_string: Hubei Key Laboratory of Modern Manufacturing Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
raw_author_name: Qianqian Liu
raw_affiliation_string: Hubei Key Laboratory of Modern Manufacturing Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
raw_author_name: Henghui Wang
raw_affiliation_string: Hubei Key Laboratory of Modern Manufacturing Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
raw_author_name: Xuanze Wang
raw_affiliation_string: Hubei Key Laboratory of Modern Manufacturing Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China

Abstract: Fringe projection profilometry has been widely used in three-dimensional (3D) reconstruction. In this paper, we replace the camera with a self-made light field camera to achieve structured light field 3D reconstruction. Our self-made light field camera boasts higher resolution and lower cost with the ability to adjust spatial and angular resolution as needed. A novel 3D reconstruction method based on cubic B-spline curve fitting is proposed to directly fit the relationship between phase and 3D coordinates, and the look up table method is utilized to accelerate the entire calculation process. The experimental results demonstrate that the proposed method can accomplish 3D reconstruction of the structured light field under various viewing angles. Specifically, it has respectively reduced the computation time for phase and modulation intensity by 64.03% and 41.43%, and the reconstruction time is decreased by 32.99% without compromising the reconstruction accuracy.
Sequential Learning Network With Residual Blocks: Incorporating Temporal Convolutional Information Into Recurrent Neural Networks

Authors:
raw_author_name: Dongjing Shan
raw_affiliation_string: Army Engineering University, Chongqing, China
raw_author_name: Kun Yao
raw_affiliation_string: Laboratory of Intelligent Information Processing, Army Engineering University, Nanjing, China
raw_author_name: Xiongwei Zhang
raw_affiliation_string: Laboratory of Intelligent Information Processing, Army Engineering University, Nanjing, China

Abstract: Temporal convolutional networks (TCNs) have shown remarkable performance in sequence modeling and surpassed recurrent neural networks (RNNs) in a number of tasks. However, performing exceptionally on extremely long sequences remains an obstacle due to the restrained receptive field of temporal convolutions and a lack of forgetting mechanism. Although RNNs can carry state transmission down the full sequence length and latch information by forgetting mechanism, the issues of information saturation and gradient vanishing or exploding still persist, which usually occur in back-propagation due to the effect of multiplicative accumulation. To benefit from both temporal convolutions and RNNs, we propose a neural architecture that merge temporal convolutional data into recurrent networks. The temporal convolutions are employed intermittently and fused into the hidden states of RNNs with the assistance of attention for providing long-term information. With this architecture, it is not needed for convolutional networks to cover the total length of the sequence, thus gradient and saturation issues in RNNs are ameliorated since convolutions are integrated into its cells and the state is updated with convolutional information. Extensive experiments illustrate the superiority of our network against other competitive counterparts, such as TCNs and the different variants of RNNs.
Differentially Private Federated Learning With Stragglers’ Delays in Cross-Silo Settings: An Online Mirror Descent Approach

Authors:
raw_author_name: Olusola Odeyomi
raw_affiliation_string: Department of Computer Science, North Carolina Agricultural and Technical State University, Greensboro, NC, USA
raw_author_name: Earl Tankard
raw_affiliation_string: Howard University, Washington, DC, USA
raw_author_name: Danda Rawat
raw_affiliation_string: Howard University, Washington, DC, USA

Abstract: Federated learning is a privacy-preserving machine learning paradigm to protect the data of clients against privacy breaches. A lot of work on federated learning considers the cross-device setting where the number of clients is large and the data sample size of each client is low. However, this work focuses on cross-silo settings, where clients are few and have large sample sizes. We consider a fully decentralized setting where clients communicate with their immediate time-varying neighbors without the need for a central aggregator prone to congestion and a single point of failure. Our goal is to address stragglers’ delays in cross-silo settings. Existing algorithms designed to overcome stragglers’ delays work with fixed data distributions. They cannot work in real-time settings, such as wireless communication, characterized by time-varying data distributions. Therefore, this paper proposes two online learning algorithms that work with time-varying data and address stragglers’ delays while guaranteeing differential privacy, strong convergence, and communication efficiency. Using the mirror descent technique, the first proposed algorithm addresses the case where the loss gradient is easily computed while the second proposed algorithm addresses the case where the loss gradient is difficult to compute. Simulation results show the performance of the proposed algorithms.
Totally-ordered Sequential Rules for Utility Maximization

Authors:
raw_author_name: Chunkai Zhang
raw_affiliation_string: Harbin Institute of Technology (Shenzhen), China
raw_author_name: Wensheng Gan
raw_affiliation_string: Jinan University, China
raw_author_name: Philip S. Yu
raw_affiliation_string: University of Illinois Chicago, USA

Abstract: High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this article, we formulate the problem of high-utility totally-ordered sequential rule mining and propose a novel algorithm, called TotalSR, which aims to identify all high-utility totally-ordered sequential rules (HTSRs). TotalSR introduces a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR also designs a new utility upper bound: RSPEU , which is tighter than the existing upper bounds. TotalSR can drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. To effectively compute the information, TotalSR proposes an auxiliary antecedent record table that can efficiently calculate the antecedent’s support and a utility prefix sum list that can compute the upper bound in O (1) time for a sequence. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is more efficient than the existing algorithms.
Reliable Event Generation With Invertible Conditional Normalizing Flow

Authors:
raw_author_name: Daxin Gu
raw_affiliation_string: State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China
raw_author_name: Jia Li
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, China; State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China
raw_author_name: Lin Zhu
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, China
raw_author_name: Jimmy S. Ren
raw_affiliation_string: Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China; SenseTime Research and Tetras.AI, Beijing, China

Abstract: Event streams provide a novel paradigm to describe visual scenes by capturing intensity variations above specific thresholds along with various types of noise. Existing event generation methods usually rely on one-way mappings using hand-crafted parameters and noise rates, which may not adequately suit diverse scenarios and event cameras. To address this limitation, we propose a novel approach to learn a bidirectional mapping between the feature space of event streams and their inherent parameters, enabling the generation of reliable event streams with enhanced generalization capabilities. We first randomly generate a vast number of parameters and synthesize massive event streams using an event simulator. Subsequently, an event-based normalizing flow network is proposed to learn the invertible mapping between the representation of a synthetic event stream and its parameters. The invertible mapping is implemented by incorporating an intensity-guided conditional affine simulation mechanism, facilitating better alignment between event features and parameter spaces. Additionally, we impose constraints on event sparsity, edge distribution, and noise distribution through novel event losses, further emphasizing event priors in the bidirectional mapping. Our framework surpasses state-of-the-art methods in video reconstruction, optical flow estimation, and parameter estimation tasks on synthetic and real-world datasets, exhibiting excellent generalization across diverse scenes and cameras.
Back to Reality: Learning Data-Efficient 3D Object Detector With Shape Guidance

Authors:
raw_author_name: Xiuwei Xu
raw_affiliation_string: Beijing National Research Center for Information Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Ziwei Wang
raw_affiliation_string: Beijing National Research Center for Information Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Jie Zhou
raw_affiliation_string: Beijing National Research Center for Information Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Jiwen Lu
raw_affiliation_string: Beijing National Research Center for Information Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China

Abstract: In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers and categories). In order to remedy the information loss from box annotations to centers, our method makes use of synthetic 3D shapes to convert the position-level annotations into virtual scenes with box-level annotations, and in turn utilizes the fully-annotated virtual scenes to complement the real labels. Specifically, we first present a shape-guided label-enhancement method, which assembles 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we transfer the information contained in the virtual scenes back to real ones by applying a virtual-to-real domain adaptation method, which refines the annotated object centers and additionally supervises the training of detector with the virtual scenes. Since the shape-guided label enhancement method generates virtual scenes by human-heuristic physical constraints, the layout of the fixed virtual scenes may be unreasonable with varied object combinations. To address this, we further present differentiable label enhancement to optimize the virtual scenes including object scales, orientations and locations in a data-driven manner. Moreover, we further propose a label-assisted self-training strategy to fully exploit the capability of detector. By reusing the position-level annotations and virtual scenes, we fuse the information from both domains and generate box-level pseudo labels on the real scenes, which enables us to directly train a detector in fully-supervised manner. Extensive experiments on the widely used ScanNet and Matterport3D datasets show that our approach surpasses current weakly-supervised and semi-supervised methods by a large margin, and achieves comparable detection performance with some popular fully-supervised methods with less than 5% of the labeling labor.
Gray code based gradient-free optimization algorithm for parameterized quantum circuit

Authors:
raw_author_name: Anqi Zhang
raw_affiliation_string: Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, 210003, CHINA
raw_author_name: Chunhui Wu
raw_affiliation_string: Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, 210003, CHINA
raw_author_name: Shengmei Zhao
raw_affiliation_string: Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, 210003, CHINA

Abstract: A Gray code based gradient-free optimization (GCO) algorithm is proposed to update the parameters of parameterized quantum circuits (PQCs) in this work. Each parameter of PQCs is encoded as a binary string, named as a gene, and a genetic-based method is adopted to select the offsprings. The individuals in the offspring are decoded in Gray code way to keep Hamming distance, and then are evaluated to obtain the best one with the lowest cost value in each iteration. The algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is reached. The GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets, and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization algorithm. The simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification tasks. Importantly, the GCO algorithm has a robust performance in the noise environment.
Differentiable Image Data Augmentation and Its Applications: A Survey

Authors:
raw_author_name: Jian Shi
raw_affiliation_string: Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
raw_author_name: Hakim Ghazzai
raw_affiliation_string: Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
raw_author_name: Yehia Massoud
raw_affiliation_string: Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

Abstract: Data augmentation is an effective method to improve model robustness and generalization. Conventional data augmentation pipelines are commonly used as preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the searching of augmentation policy strategies. This survey provides a comprehensive and structured overview of the advances in DDA. Specifically, we focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations, then categorize existing DDA works accordingly, and investigate the utilization of DDA in selected of practical applications, specifically neural augmentation networks and differentiable augmentation search. Finally, we discuss current challenges of DDA and future research directions.
Graph embedding approach to analyze sentiments on cryptocurrency

Authors:
raw_author_name: Ihab Moudhich
raw_affiliation_string: University Abdelmalek Essaadi
raw_author_name: Abdelhadi Fennan
raw_affiliation_string: University Abdelmalek Essaadi

Abstract: This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.