Reviewerly/exHarmony huggingface dataset:
High-precision phase shift method for heavy-load reference mirrors based on nano-precision grating sensor monitoring

Authors:
raw_author_name: Liang Tang
raw_affiliation_string: MIIT Key Laboratory of Complex-field Intelligent Exploration, Beijing Institute of Technology, School of Optics and Photonics, Beijing 100081, China
raw_author_name: Lirong Qiu
raw_affiliation_string: MIIT Key Laboratory of Complex-field Intelligent Exploration, Beijing Institute of Technology, School of Optics and Photonics, Beijing 100081, China
raw_author_name: Shuai Yang
raw_affiliation_string: MIIT Key Laboratory of Complex-field Intelligent Exploration, Beijing Institute of Technology, School of Optics and Photonics, Beijing 100081, China

Abstract: In large-aperture interferometry, achieving high-precision phase shifting for heavy-load reference mirrors is challenging. This study introduces a method based on nano-precision grating sensor monitoring. The technique utilizes spatial three-point synchronous driving by piezoelectric ceramics, combined with flexible hinges, to enable nanoscale phase shifting of heavy-load reference mirrors. Additionally, it integrates in-situ PID closed-loop drive monitoring at these three points, using nano-precision grating sensors and piezoelectric ceramics. To ensure nanoscale resolved spatial translation, the mirrors are supported by air-bearings that counteract gravity, coupled with a phase shift error calibration model. The final realization of high-precision and high-stability mechanical phase shifting in large-aperture interferometry circumvents the principle defects of existing large-aperture wavelength-tuned phase shifting. The experiments show that the established mechanical phase shifting system with heavy-load reference mirror has a resolution of 1.5 nm, a frequency response of 116 Hz, and a synchronization accuracy of 2° for the three phase shifting quantities, and the method provides a new method and means of phase shifting for the construction of large-aperture phase shifting interferometer.
Temporal Action Segmentation: An Analysis of Modern Techniques

Authors:
raw_author_name: Guodong Ding
raw_affiliation_string: School of Computing, National University of Singapore, Singapore
raw_author_name: Fadime Sener
raw_affiliation_string: research scientist, Meta Reality Labs
raw_author_name: Angela Yao
raw_affiliation_string: School of Computing, National University of Singapore, Singapore

Abstract: Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. This survey analyzes and summarizes the most significant contributions and trends. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , frame representation and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps. In addition, we have curated a list of TAS resources, which is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nus-cvml/awesome-temporal-action-segmentation</uri> .
Taxonomy and definition of audio augmented reality (AAR): A grounded theory study

Authors:
raw_author_name: Abhraneil Dam
raw_affiliation_string: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, United States
raw_author_name: Arsh Siddiqui
raw_affiliation_string: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, United States
raw_author_name: Myounghoon Jeon
raw_affiliation_string: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, United States

Abstract: AR applications have mostly considered visual augmentations while excluding other modalities. Recent developments in audio augmented reality (AAR) applications have been based on the definitions of visual AR or mixed reality (MR), and thus, AAR technology development has lacked systematic efforts. We investigated the concept of augmented reality through audio to provide a systematic understanding and generate a taxonomy and a definition for AAR. A conference workshop (N = 28), focus groups (N = 18), and expert interviews (N = 6) generated qualitative data regarding the concept of AAR. Grounded Theory (GT) was used to analyze the data and produce a new taxonomy and a definition. The AAR taxonomy consists of three categories – Environment Connected, Goal Directed, and Context Adapted, with three subcategories respectively. The need for a separate taxonomy for AAR is highlighted to aid in the development of AAR applications in a systematic manner. The taxonomy is expected to be used as a heuristic tool that can guide developers to build AAR applications and can be used in evaluating user experience with AAR applications.
Condition Monitoring of Underground Power Cables Via Power-Line Modems and Anomaly Detection

Authors:
raw_author_name: Mojtaba Yeganejou
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
raw_author_name: Scott Dick
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
raw_author_name: Michael G Lipsett
raw_affiliation_string: Department of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada

Abstract: Underground power cables are a widely-deployed class of assets in electric power distribution systems. They are robust in adverse weather conditions and are an aesthetically pleasing alternative to overhead power lines. Unfortunately, the physical observability of underground cables is poor. Current methods for detecting and localizing defects known to progress to failure are cumbersome and expensive, both financially and operationally. In-service cable failures can lead to lengthy power outages and financial losses and can endanger utility staff and the general public. There is thus a need for non-invasive condition monitoring techniques that will allow for early detection of defects likely to progress to a fault, so cost-effective repair or replacement interventions can be planned. In this study, insulation overheating is specifically considered. Thermal damage of XLPE insulation is a prominent damage mechanism for extruded dielectric cable and is thus well studied and understood. A defect detection solution combining powerline modems with semi-supervised anomaly detection for underground cables is proposed. The performance of five well-regarded machine-learning-based anomaly detection algorithms is analyzed through simulation of a real-world 15kV distribution network. The best-performing algorithm studied had a false alarm rate of under 0.1% with no missed alarms across a range of damage scenarios.
High-accuracy vanishing-constraints-based calibration of fringe projection vision sensor

Authors:
raw_author_name: Junhua Sun
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
raw_author_name: Yanjun Zhang
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
raw_author_name: Fuqiang Zhou
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China

Abstract: In fringe projection profilometry (FPP), calibration of the vision sensor is crucial to ensure the accuracy of three-dimensional (3D) shape measurement. However, due to correspondence errors of planar calibration target (PCT) 3D points and its image points, and low mapping accuracy between projector pixels and camera pixels, traditional calibration methods based on the PCT sometimes cannot provide reliable and accurate results. Aiming at these problems, a high-accuracy calibration method based on vanishing constraints for fringe projection vision sensor (FPVS) is proposed in this paper. The intrinsic parameters are calibrated using the constraints of vanishing points, the rotation matrix is calibrated using the constraints of vanishing lines, and the optimal calibration parameters are estimated by the objective function established based on the 2D and 3D constraints of feature points. Experimental results demonstrated the performance of our calibration method.
NOVAction23: Addressing the data diversity gap by uniquely generated synthetic sequences for real-world human action recognition

Authors:
raw_author_name: Ufuk Celikcan
raw_affiliation_string: Hacettepe University, Department of Computer Engineering, Ankara, Turkey

Abstract: Recognition of human actions using machine learning requires extensive datasets to develop robust models. Nevertheless, obtaining real-world data presents challenges due to the costly and time-consuming process involved. Additionally, existing datasets mostly contain indoor videos due to the challenges of capturing pose data outdoors. Synthetic data have been used to overcome these difficulties, yet the currently available synthetic datasets for human action recognition lack photorealism and diversity in their features. Addressing these shortcomings, we develop the NOVAction engine to generate highly diversified and photorealistic synthetic human action sequences. We use NOVAction to create the NOVAction23 dataset comprising 25,415 human action sequences with corresponding poses and labels (available at https://github.com/celikcan-cglab/NOVAction23). In NOVAction23, the performed motions and viewpoints are varied on the fly through procedural generation, to ensure that, for a given action class, each generated sequence features a distinct motion performed by one of the 1,105 synthetic humans captured from a unique viewpoint. Moreover, each synthetic human is unique in terms of body shape (height and weight), skin tone, gender, hair, facial hair, clothing, shoes and accessories. To further increase data diversity, the motion sequences are rendered under various weather conditions and at different times of day, across three outdoor and two indoor settings. We evaluate NOVAction23 by training three state-of-the-art recognizers on it, in addition to the NTU 120 dataset, and corroborating using real-world videos from YouTube. Our results confirm that the NOVAction23 dataset can improve the performance of state-of-the-art human action recognition.
Entropy-Oriented Domain Adaptation for Intelligent Diagnosis of Rotating Machinery

Authors:
raw_author_name: Jing Lin
raw_affiliation_string: School of Reliability and Systems Engineering, Beihang University, Beijing, China
raw_author_name: Hui Zhang
raw_affiliation_string: School of Transportation Science and Engineering, Beihang University, Beijing, China

Abstract: To cater to fault diagnosis of rotating machinery under complex working conditions, unsupervised domain adaptation technology has been widely explored and applied. Existing methods mainly reduce domain bias in two ways, including metric learning and discriminator-based adversarial learning. Different from these technologies, in this work, we only resort to entropy optimization strategies and develop a novel entropy-oriented domain adaptation (EODA) model for intelligent diagnosis of rotating machinery. Specifically, a convolutional network with a cosine-distance classifier is introduced to construct the model framework, which can reduce intraclass variation and make the output more confident. In addition, negentropy-guided prediction diversity optimization and minimax entropy game-guided prototype-feature alignment are co-designed to realize domain adaptation. Extensive experiments based on two different mechanical systems are used to validate our method. Comprehensive results and discussions demonstrate that our EODA can achieve compelling performance.
Convergence rates for shallow neural networks learned by gradient descent

Authors:
raw_author_name: Michael Kohler
raw_affiliation_string: Department of Mathematics, Technical University of Darmstadt, Darmstadt, Germany
raw_author_name: Sophie Langer
raw_affiliation_string: Department of Applied Mathematics, University of Twente, Enschede, The Netherlands
raw_author_name: Harro Walk
raw_affiliation_string: Department of Mathematics, University of Stuttgart, Stuttgart, Germany

Abstract: In this paper we analyze the L2 error of neural network regression estimates with one hidden layer. Under the assumption that the Fourier transform of the regression function decays suitably fast, we show that an estimate, where all initial weights are chosen according to proper uniform distributions and where the weights are learned by gradient descent, achieves a rate of convergence of 1∕n (up to a logarithmic factor). Our statistical analysis implies that the key aspect behind this result is the proper choice of the initial inner weights and the adjustment of the outer weights via gradient descent. This indicates that we can also simply use linear least squares to choose the outer weights. We prove a corresponding theoretical result and compare our new linear least squares neural network estimate with standard neural network estimates via simulated data. Our simulations show that our theoretical considerations lead to an estimate with an improved performance in many cases.
Risk-Averse Trajectory Optimization via Sample Average Approximation

Authors:
raw_author_name: Riccardo Bonalli
raw_affiliation_string: Laboratory of Signals and Systems, University of Paris-Saclay, CNRS, CentraleSup&#x00E9;lec, Gif-sur-Yvette, France
raw_author_name: Marco Pavone
raw_affiliation_string: Department of Aeronautics and Astronautics, Stanford University

Abstract: Trajectory optimization under uncertainty underpins a wide range of applications in robotics.However, existing methods are limited in terms of reasoning about sources of epistemic and aleatoric uncertainty, space and time correlations, nonlinear dynamics, and non-convex constraints.In this work, we first introduce a continuous-time planning formulation with an average-value-at-risk constraint over the entire planning horizon.Then, we propose a sample-based approximation that unlocks an efficient and general-purpose algorithm for risk-averse trajectory optimization.We prove that the method is asymptotically optimal and derive finite-sample error bounds.Simulations demonstrate the high speed and reliability of the approach on problems with stochasticity in nonlinear dynamics, obstacle fields, interactions, and terrain parameters.
Structural asymmetric convolution for wireframe parsing

Authors:
raw_author_name: Jinfu Yang
raw_affiliation_string: Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
raw_author_name: Fuji Fu
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
raw_author_name: Jiaqi Ma
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

Abstract: Simultaneously extracting junctions and their corresponding line segments from images presents a promising approach to structural environment cognition. However, conventional methods employ square convolution for line feature extraction, resulting in the exclusion of long-range dependencies and the generation of suboptimal wireframe predictions. In this paper, we introduce an efficient and concise parsing method named Structural Asymmetric Convolution-based Wireframe Parser (SACWP). Taking advantage of the inherent similarities between structural asymmetric convolution and the predominant distribution of line segments in man-made environments, we propose a Structural Asymmetric Convolution module (SAC) that captures long-range contextual features while efficiently filtering out irrelevant information from neighboring pixels. Additionally, we introduce a feature aggregation module based on dilated convolution (DCFA) to seamlessly integrate contextual information from multiple receptive fields. We thoroughly evaluate our approach on the Wireframe and YorkUrban datasets, achieving preferable results of 69.3% and 29.7% msAP respectively. On the other hand, the promising results adequately demonstrate the effectiveness of SACWP to Wireframe Parsing task.
A novel privacy-preserving graph convolutional network via secure matrix multiplication

Authors:
raw_author_name: Hai-Feng Zhang
raw_affiliation_string: The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Mathematical Science, Anhui University, Hefei 230601, China
raw_author_name: Feng Zhang
raw_affiliation_string: The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Mathematical Science, Anhui University, Hefei 230601, China
raw_author_name: Chuang Ma
raw_affiliation_string: School of Internet, Anhui University, Hefei 230601, China
raw_author_name: Pei-Can Zhu
raw_affiliation_string: School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NWPU), Xi'an 710072, Shaanxi, China

Abstract: Graph convolutional network (GCN) is one of the most representative methods in the realm of graph neural networks (GNNs). In the convolution process, GCN combines the structural information of the networks with the features of nodes. In practice, the structure information of the networks and the features of nodes may be controlled by different parties, which can render GCN ineffective when information sharing is constrained by privacy concerns or licensing issues. Therefore, it is of significant importance to design an effective and secure scheme for GCN that can collaboratively merge information from both parties while safeguarding their sensitive data. In this paper, we introduce S-GCN (i.e., Secure GCN Scheme), which employs secure matrix multiplication (SMM) to compute the product of two matrices in a privacy-preserving manner. The S-GCN scheme requires frequent utilization of SMM to merge information from both parties, resulting in high time and space complexity. To address this issue, we introduce SF-GCN (i.e., Secure and Fast GCN Scheme), which minimizes the use of SMM. Additionally, both S-GCN and SF-GCN may be susceptible to privacy breaches when dealing with dense networks. Hence, we further enhance security by introducing differential privacy through the Laplacian mechanism. Experimental results demonstrate that the proposed schemes do not significantly reduce accuracy in downstream tasks, and, more importantly, effectively protect the privacy information of both parties.
Comparison of current quantum devices for quantum computing of Heisenberg spin chain dynamics

Authors:
raw_author_name: Erik Lötstedt
raw_affiliation_string: Department of Chemistry, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
raw_author_name: Kaoru Yamanouchi
raw_affiliation_string: Institute for Attosecond Laser Facility, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Abstract: We simulate the time-dependent dynamics of a chain of three interacting spins described by the Heisenberg XXX Hamiltonian using two types of different quantum computers: Quantinuum H1-1, a trapped-ion quantum computer, and ibm_prague, a superconducting qubit type quantum computer. The time evolution of the wave function is accomplished by the Suzuki–Trotter approximation. We find that very accurate results are obtained using H1-1 after applying error mitigation by renormalization of the time-dependent spin-state populations as well as using ibm_prague after renormalization, dynamical decoupling, Pauli twirling, and measurement error mitigation of the populations.
Multiscale variational autoencoder regressor for production prediction and energy saving of industrial processes

Authors:
raw_author_name: Yongming Han
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
raw_author_name: Yue Wang
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
raw_author_name: Zhiwei Chen
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
raw_author_name: Yi Lu
raw_affiliation_string: Beijing Jiaboyunshu Technology Co., Ltd., Beijing 100089, China
raw_author_name: Xuan Hu
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
raw_author_name: Zhiqiang Geng
raw_affiliation_string: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China

Abstract: As modern industries increase in scale and integration, the industrial process data show more complex dynamic time variability. To solve the dynamic time-varying problem of industrial process data, a novel multiscale variational autoencoder (MSVAE) based Regressor (REG) (MSVAE-REG) is proposed for production prediction and energy saving. The encoder of the MSVAE is constructed by multiscale convolutional neural network (MSCNN) to extract the multiscale temporal dynamic features, including the overall and local trend features in the Gaussian latent space. Then, the decoder of the MSVAE recovers the input data to enhance the dynamic adaptability of multiscale features. Moreover, the REG utilizes the gated recurrent unit (GRU) to build a dynamic relationship between multiscale temporal dynamic features and the predicted output. Finally, the proposed model is verified on the propylene and ethylene production datasets. Compared with the back propagation (BP) network, the extreme learning machine (ELM), the radial basis function (RBF), the convolutional neural network (CNN) and the variational autoencoder (VAE), the MSVAE-REG achieves state-of-the-art results, with the prediction accuracy respectively reaching about 95% and 96%, providing a new judgment basis for optimizing the production process and saving energy.