The evolution of a citation network topology: The development of the journal Scientometrics

By mapping the electronic database containing all papers in Scientometrics for a 26-year period (1978-2004), we uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The citation network of the journal displays the characteristic features of a “small-world” network of local dense clusters of highly specialized literature. These clusters, however, are efficiently connected into a large single component by a small number of “hub” papers that allow short-distance connection among increasingly large numbers of papers. The patterns of evolution of the network toward this “small-world” are also explored

Numerical modeling of pile penetration in silica sands considering the effect of grain breakage

International audienceCurrent numerical platforms rarely consider the effect of grain breakage in the design of sandy soil foundations. This paper presents an enhanced platform for large deformation analyses which considers the effect of grain breakage during pile penetration in silica sand. For this purpose, a model based on critical state theory has been developed within the framework of multisurface plasticity to account in the same constitutive platform the effect of stress dilatancy and particle fragmentation. Furthermore, to implement the underlying constitutive equations into a finite element code, a stress integration scheme has been adopted by extending a cutting plane algorithm to the model with multiple yielding mechanisms. A laboratory model test and a series of centrifuge tests of pile penetration are simulated to verify the performance of the selected constitutive approach in terms of pile resistance and grain breakage distribution, with the parameters of sand calibrated through a set of drained triaxial compression tests from low to very high confining pressure. Some extra features of the enhanced platform are also discussed, such as: i) the effect of sand crushability on pile resistance and ii) the nonlinear relation of pile resistance to sand density. The proposed findings demonstrate the capability of this numerical platform to proper design of pile foundation in sandy soils and highlight the interplay between stress dilatancy and grain breakage mechanisms during pile penetration processes

Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks

In a smart manufacturing system involving workers, recognition of the worker\u27s activity can be used for quantification and evaluation of the worker\u27s performance, as well as to provide onsite instructions with augmented reality. In this paper, we propose a method for activity recognition using Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals obtained from a Myo armband. The raw 10-channel IMU signals are stacked to form a signal image. This image is transformed into an activity image by applying Discrete Fourier Transformation (DFT) and then fed into a Convolutional Neural Network (CNN) for feature extraction, resulting in a high-level feature vector. Another feature vector representing the level of muscle activation is evaluated with the raw 8-channel sEMG signals. Then these two vectors are concatenated and used for work activity classification. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab tool/part, hammer nail, use power-screwdriver, rest arm, turn screwdriver, and use wrench. The developed CNN model is evaluated on this dataset and achieves 98% and 87% recognition accuracy in the half-half and leave-one-out experiments, respectively

Photorealistic Audio-driven Video Portraits

Video portraits are common in a variety of applications, such as videoconferencing, news broadcasting, and virtual education and training. We present a novel method to synthesize photorealistic video portraits for an input portrait video, automatically driven by a person’s voice. The main challenge in this task is the hallucination of plausible, photorealistic facial expressions from input speech audio. To address this challenge, we employ a parametric 3D face model represented by geometry, facial expression, illumination, etc., and learn a mapping from audio features to model parameters. The input source audio is first represented as a high-dimensional feature, which is used to predict facial expression parameters of the 3D face model. We then replace the expression parameters computed from the original target video with the predicted one, and rerender the reenacted face. Finally, we generate a photorealistic video portrait from the reenacted synthetic face sequence via a neural face renderer. One appealing feature of our approach is the generalization capability for various input speech audio, including synthetic speech audio from text-to-speech software. Extensive experimental results show that our approach outperforms previous general-purpose audio-driven video portrait methods. This includes a user study demonstrating that our results are rated as more realistic than previous methods

PanoVPR: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows across the Panoramic View

Visual place recognition has gained significant attention in recent years as a crucial technology in autonomous driving and robotics. Currently, the two main approaches are the perspective view retrieval (P2P) paradigm and the equirectangular image retrieval (E2E) paradigm. However, it is practical and natural to assume that users only have consumer-grade pinhole cameras to obtain query perspective images and retrieve them in panoramic database images from map providers. To address this, we propose \textit{PanoVPR}, a perspective-to-equirectangular (P2E) visual place recognition framework that employs sliding windows to eliminate feature truncation caused by hard cropping. Specifically, PanoVPR slides windows over the entire equirectangular image and computes feature descriptors for each window, which are then compared to determine place similarity. Notably, our unified framework enables direct transfer of the backbone from P2P methods without any modification, supporting not only CNNs but also Transformers. To facilitate training and evaluation, we derive the Pitts250k-P2E dataset from the Pitts250k and establish YQ360, latter is the first P2E visual place recognition dataset collected by a mobile robot platform aiming to simulate real-world task scenarios better. Extensive experiments demonstrate that PanoVPR achieves state-of-the-art performance and obtains 3.8% and 8.0% performance gain on Pitts250k-P2E and YQ360 compared to the previous best method, respectively. Code and datasets will be publicly available at https://github.com/zafirshi/PanoVPR.Comment: Accepted to ITSC 2023. Code and datasets will be made available at https://github.com/zafirshi/PanoVP