Reviewerly/exHarmony huggingface dataset:
Timeframe of eastern Paleo-Tethys closure: Constraint on the Songpan-Ganzi Complex by big data-based multiproxy provenance analysis

Authors:
raw_author_name: Huan Kang
raw_affiliation_string: State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China
raw_author_name: Dapeng Li
raw_affiliation_string: School of Earth Science and Resources, China University of Geosciences, Beijing 100083, China
raw_author_name: Xiaosheng Hu
raw_affiliation_string: Command Center of the Integrated Natural Resources Survey Center, China Geological Survey, Beijing 100055, China

Abstract: The Songpan-Ganzi Complex (SGC) is one of the largest turbidite complexes on Earth. The source-to-sink processes of the SGC are closely linked to the closure of the Paleo-Jinsha Ocean in the eastern Paleo-Tethys realm, so the spatiotemporal transitions in SGC provenance provide the potential to constrain the closure time. A comprehensive multiproxy provenance investigation, integrating detrital zircon UPb geochronology, whole-rock SmNd isotope data, and paleocurrent analysis, shed light on the origin of the Triassic SGC turbidites. In this study, it was determined that a significant proportion (exceeding 70%) of the detrital material comprising the SGC turbidites was sourced from the northwestern margin of the Yangtze Craton and the Qinling Orogen. A comprehensive and quantitative provenance study revealed that the Songpan-Ganzi turbidites primarily originated from collisional orogenic belts at convergent continental plate margins and secondarily from magmatic arcs and other tectonic units surrounding the basin. In this study, it was demonstrated that the SGC contains important information on the convergence of Eurasia and the tectonic evolution of surrounding terranes. (1) The South Qinling Orogen experienced significant rapid exhumation during the Ladinian to early Carnian. (2) The closure of the Paleo-Jinsha Ocean most likely occurred during the transition period between the upper and lower members of the Zhuwo Formation (ca. 231 Ma, middle Carnian). (3) During the Late Triassic, the crust of the Yidun Terrane underwent significant vertical accretion, resulting in a topography exceeding that of the Jomda-Weixi arc by the late Norian.
SCTracker: Multi-Object Tracking With Shape and Confidence Constraints

Authors:
raw_author_name: Huan Mao
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
raw_author_name: Yulin Chen
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
raw_author_name: Pingping Chen
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China

Abstract: Detection-based tracking is one of the main methods of multi-object tracking. It can achieve good tracking performance when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this article proposes a novel multi-object tracker (SCTracker) by exploiting shape constraint and confidence. In the data association stage, an intersection of union (IoU) distance with shape constraints is developed to calculate the cost matrix between tracks and detections, which can reduce the track of the wrong target with the similar position but inconsistent shape. Moreover, the detection confidence is calculated in the update stage of the Kalman filter to improve the track performance with the inaccurate detection result. Experimental results on the MOT 17 dataset show that the proposed SCTracker can improve the tracking performance of multi-object tracking when compared with the state-of-the-art methods.
Development of an OTDR-Based Hand Glove Optical Sensor for Sign Language Prediction

Authors:
raw_author_name: Amitesh Kumar
raw_affiliation_string: Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, Jharkhand, India
raw_author_name: Vikas Kumar
raw_affiliation_string: Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, Jharkhand, India
raw_author_name: Pradeep Tomar
raw_affiliation_string: Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, Jharkhand, India

Abstract: This article demonstrates a wearable optical fiber-based sensing glove for predicting hand gestures for sign language. The sensors were created using macrobends on single mode fiber (SMF) for each finger to simultaneously monitor the change in bending radius due to the change in position of each finger by creating different hand gestures. The glove sensing capability is based on the change in macrobending loss due to a change in bending radius. The system includes an optical time-domain reflectometer (OTDR), optical fiber, and hand glove. The functionality and performance of the glove were thoroughly evaluated on different participants for repeatability. We retrieved hand gesture information using data from each finger macrobend loss through the sensor’s response on OTDR—the developed glove acquired real-time raw data on alphabet and numerical hand postures. A set of unique features were extracted from the acquired raw data. The selected features were used to classify hand gestures using the ensemble classifier. The performance of this classifier was optimized using the training dataset (70%), and the performance of this trained algorithm was evaluated to recognize hand gestures using a testing dataset (30%). Compared to some existing methods, the proposed method enhances sign language recognition accuracy and improves tracking of hand gesture movement. This article presents statistical results with 93.57% classification accuracy on test data representing good dynamic gesture recognition scenarios.
MAD-MEL: Combining Entity and Metric Learning for Anomaly Detection in Multivariate Time Series

Authors:
raw_author_name: Jimson Mathew
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India

Abstract: Detection of anomalies in multivariate time series (MVTS) data has been crucial for various application areas. Though recent approaches have addressed such issues, still the problem is challenging not only due to identifying anomalous behavior but also to reducing false alarms. The problem becomes more exciting and challenging when the model takes care of errors due to measurement, environmental effects, and temporal dependency among the observations. Toward this, we propose a framework MAD-MEL, an MVTS Anomaly Detection Method using metric and entity learning (EL) approaches. The metric learning-based model identifies the behavior of individual series as well as the relationship between different attributes, whereas the entity-based model learns the behavior of MVTS as a single entity. An empirical mode decomposition-based feature learning (FL) component is incorporated to learn more generalized behavior of data and to handle the problem of minor disturbances. Also, the proposed method combines a linear component to control the spatial scaling effect in the train and test set. The idea is to learn the normal behavior of MVTS by combining all the components as a prediction-based model and to use prediction error for anomaly detection. We adopt transfer learning to reduce the time complexity while learning the individual series in case of a large number of attributes or multiple instances of MVTS. Our proposed multivariate time series anomaly detection using metric and entity learning (MAD-MEL) achieves the F1-score of 94% on public dataset, which is an improvement of 3% from SOTA.
Intra-Inter Region Adaptive Graph Convolutional Networks for skeleton-based action recognition

Authors:
raw_author_name: Wenting Xu
raw_affiliation_string: School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
raw_author_name: Chuanxu Wang
raw_affiliation_string: School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
raw_author_name: Zhe Zhang
raw_affiliation_string: School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
raw_author_name: Guocheng Lin
raw_affiliation_string: School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
raw_author_name: Yue Sun
raw_affiliation_string: School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

Abstract: To effectively capture the spatio-temporal dependencies of the skeletal data, graph convolution has been widely applied. However, it tends to emphasize the dependence relationship between adjacent joints and does not consider long-distance dependence relationships among joints. Another problem is single-structure temporal convolution, which is difficult to extract global temporal features. To address the above issues, we propose Intra-Inter Region Adaptive Graph Convolutional Networks (IIR-AGCN), which models the long-distance relationships of skeleton sequences at temporal and spatial scales. Our contributions are three-fold: First, to enhance global topological learning capabilities of graph convolution, we propose a regional-coupled attention module, which divides joint features into multiple sub-regions, and then constructs coupled relationships between intra-inter regions by self-attention mechanism, which realizes global joint information interaction. Second, to address the issue of difficulty in extracting global temporal features, we propose a pyramid multi-scale temporal module that extracts deep multi-scale temporal features and implements adaptive cross-scale feature fusion. Third, IIR-AGCN adopts a two-stream architecture, evaluating performances on two large-scale human skeleton datasets, NTU-RGB+D 60 and NTU-RGB+D 120, respectively.
Stabilized activation scale estimation for precise Post-Training Quantization

Authors:
raw_author_name: Xinggang Wang
raw_affiliation_string: Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology,, Wuhan, Hubei, China
raw_author_name: Jiawei Liu
raw_affiliation_string: Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology,, Wuhan, Hubei, China
raw_author_name: Zhihang Yuan
raw_affiliation_string: Houmo AI, Nanjing, Jiangsu, China
raw_author_name: Dawei Yang
raw_affiliation_string: Houmo AI, Nanjing, Jiangsu, China
raw_author_name: Wenyu Liu
raw_affiliation_string: Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology,, Wuhan, Hubei, China

Abstract: In recent years, there have been more and more studies on Post-Training Quantization (PTQ). Many outstanding works have emerged, which has greatly promoted the availability of PTQ methods. However, in terms of low-bit quantization, there is still a wide gap between PTQ and the current state-of-the-art Quantization-Aware Training (QAT) methods. In this work, we find that the current way of obtaining the activation scale is not completely reasonable in PTQ, which leads to the fact that the weight cannot be well adapted to the biased quantized activation during inference. Based on experiments and analysis, we propose a method called StablePTQ. It obtains a stable activation scale and mixes rich input into the block reconstruction to achieve an improving quantization accuracy. StablePTQ achieves remarkable improvements in several different bit quantization especially W2A4, and can be applied to other quantization algorithms as a plug-and-play approach. The source code of StablePTQ is avaiable at .
Sampling-based epoch differentiation calibrated graph convolution network for point-of-interest recommendation

Authors:
raw_author_name: Fan Mo
raw_affiliation_string: Dept. of CSCE, Waseda University, Tokyo, Japan
raw_author_name: Xin Fan
raw_affiliation_string: Dept. of CSCE, Waseda University, Tokyo, Japan
raw_author_name: Hayato Yamana
raw_affiliation_string: Faculty of Science and Engineering, Waseda University, Tokyo, Japan

Abstract: In location-based social networks, calibrating a point-of-interest (POI) recommendation system is as important as its accuracy for improving user satisfaction. POI recommendation calibration is primarily classified as categorical or geographical calibration. Categorical calibration ensures that the recommended items are distributed proportionally among the past interest categories of the target user. When a target user checks 80 Chinese, 10 Japanese, and 10 French restaurants, a recommendation list with a ratio of 8:1:1 for Chinese, Japanese, and French restaurants can be reasonably expected. In addition to categorical calibration, geographical calibration has been proposed to increase user interest in the recommended results. Users have a high probability of revisiting locations in their subareas. Therefore, the POIs recommended in multiple subareas of interest are more suitable than those from one small and frequently visited subarea. However, improving the calibration and accuracy are conflicting tasks. To achieve high calibration while maintaining accuracy, previous studies proposed reranking-based techniques to rerank the candidate list and return POIs with high calibration. However, optimizing the calibration by reranking is independent of the basic-candidate-item generation model, resulting in a suboptimal system. To tackle the problem, we propose a novel sampling-based differentiation technique to merge the task of improving calibration into the GCN model training process and directly generate the final recommendation list. The model is flexible and can be applied to different domains, where a domain can be a subarea or category. In a three-layer GCN, the layer one represents the historical check-ins of the user, whereas layer three includes the candidate POIs from which the target user aggregates information. We trained the model to make the distribution of the POI domains at layer three approximated the distribution at layer one. Experimental results on Philadelphia and Tucson datasets confirmed that the proposed method outperforms all state-of-the-art GCN+ geo-reranking and GCN+ MCF baselines, improving Recall@ 5 from 0.0394 to 0.0412 (4.57%) and Jensen–Shannon measure (JS)@ 5 from 0.5931 to 0.6734 (13.54%) on the Philadelphia dataset and improving Recall@ 5 from 0.0495 to 0.0517 (4.40%) and JS@ 5 from 0.5869 to 0.6598 (12.42%) on the Tucson dataset for categorical calibration. The model was also tested in the geographical domain and a similar trend was observed.
Deep style transfer to deal with the domain shift problem on spheroid segmentation

Authors:
raw_author_name: Manuel García-Domínguez
raw_affiliation_string: Department of Mathematics and Computer Science, University of La Rioja, Spain
raw_author_name: César Domínguez
raw_affiliation_string: Department of Mathematics and Computer Science, University of La Rioja, Spain
raw_author_name: Jónathan Heras
raw_affiliation_string: Department of Mathematics and Computer Science, University of La Rioja, Spain
raw_author_name: Eloy Mata
raw_affiliation_string: Department of Mathematics and Computer Science, University of La Rioja, Spain
raw_author_name: Vico Pascual
raw_affiliation_string: Department of Mathematics and Computer Science, University of La Rioja, Spain

Abstract: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumor behaviour, and for testing different preclinical and clinical treatments. Image analysis of spheroids is crucial to understand the formation and behaviour of such tumors, but when performed manually is a time-consuming task. Deep convolutional models can help in the analysis of spheroid images since they provide accurate results for the segmentation of tumors, a task that is necessary to further study features of spheroids. However, the images from tumor spheroids greatly vary depending on the experimental conditions, and also on the equipment (microscopes) and conditions (focus and magnification) employed to capture the images. This issue, known as domain shift, hinders the applicability of deep learning models for spheroid segmentation. In this work, we address this problem by studying style transfer methods based on both convolutional neural networks and generative adversarial networks to transfer the style from the images used for training segmentation models to the images from a new domain. This approach successfully handled the domain shift problem in the context of spheroid segmentation for 6 different deep learning models. Finally, we developed a high-level API that facilitates the of our approach to other contexts where the domain-shift problem might occur.
Consistency–exclusivity guided unsupervised multi-view feature selection

Authors:
raw_author_name: Shixuan Zhou
raw_affiliation_string: School of Computer and Control Engineering, Yantai University, Yantai 264005, China
raw_author_name: Peng Song
raw_affiliation_string: School of Computer and Control Engineering, Yantai University, Yantai 264005, China

Abstract: Unsupervised multi-view feature selection (UMFS) is an effective dimension reduction for multi-view data. It aims to obtain the important feature subset from multi-view data, which can significantly minimize the impacts of noises, outliers and redundancy. Although previous UMFS methods achieve remarkable achievements, they fail to fully take into account the consistency or the exclusivity hidden in multi-view data. To address this, this article presents a consistency–exclusivity guided unsupervised multi-view feature selection (CE-UMFS) method. Specifically, we design a multi-view matrix factorization model to simultaneously explore the consistency and exclusivity in multi-view data. Meanwhile, we employ the Hilbert–Schmidt independence criterion (HSIC) to preserve the exclusivity of different views. Furthermore, we impose a nuclear norm on the consistent representation matrix to explore the consistency across views. At last, promising experimental results demonstrate the superiority of the proposed method compared with some state-of-the-art methods.
Theoretical analysis of co-existing periodic orbits in sparse binary neural networks

Authors:
raw_author_name: Toshimichi Saito
raw_affiliation_string: Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Hosei University, Koganei-shi, Tokyo, 184-8584, Japan
raw_author_name: Hiroki Nonaka
raw_affiliation_string: Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Hosei University, Koganei-shi, Tokyo, 184-8584, Japan
raw_author_name: Taiji Okano
raw_affiliation_string: Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Hosei University, Koganei-shi, Tokyo, 184-8584, Japan

Abstract: A sparse binary neural network is a discrete-time recurrent neural network characterized by local binary connections and signum-type neuron models. The dynamics is described by an autonomous difference equation of binary state variables. Depending on the parameters, the network generates various periodic orbits of binary vectors. Multiple periodic orbits can co-exist and the network exhibits one of them depending on initial condition. Real/potential applications of the periodic orbits include control signals of switching circuits and basic signals for time-series approximation. The network is well suited for theoretical analysis and simple hardware implementation. This paper considers shift-type periodic orbits in the networks. As the main theoretical results, we clarify the number, period, and stability of the periodic orbits for key parameters. As a first step to the applications in the future, we present an FPGA-based hardware prototype and confirm typical periodic orbits experimentally.
GlocalFuse-Depth: Fusing transformers and CNNs for all-day self-supervised monocular depth estimation

Authors:
raw_author_name: Zezheng Zhang
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
raw_author_name: Kenneth K.Y. Wong
raw_affiliation_string: Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong, China; Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China

Abstract: In recent years, self-supervised monocular depth estimation has drawn much attention since it frees of depth annotations and achieves remarkable results on standard benchmarks. However, most of existing methods only focus on either daytime or nighttime images, their performance degrades on the other domain because of the large gap between daytime and nighttime images. To address this problem, we propose a two-branch network named GlocalFuse-Depth for self-supervised depth estimation of all-day images in this paper. The daytime and nighttime images in input image pair are fed into the two branches: CNN branch and Transformer branch, respectively, where both local details and global dependency can be effectively captured. Besides, a novel fusion module is proposed to fuse multi-dimensional features from the two branches. Extensive experiments demonstrate that GlocalFuse-Depth achieves state-of-the-art results for all-day images of the Oxford RobotCar dataset, which proves the superiority of our method.