Zobrazeno 1 - 10
of 13 448
pro vyhledávání: '"An, Siyang"'
Hydrodynamic and acoustic scales separate as the Mach number decreases, making the modelling of aeroacoustic phenomena singular in this flow regime. The benchmark of the flow developing around an oscillating and vibrating cylinder is one of the scarc
Externí odkaz:
http://arxiv.org/abs/2408.04250
Autor:
Li, Siyang, Anand, Gagandeep S., Riess, Adam G., Casertano, Stefano, Yuan, Wenlong, Breuval, Louise, Macri, Lucas M., Scolnic, Daniel, Beaton, Rachael, Anderson, Richard I.
The Hubble Tension, a >5 sigma discrepancy between direct and indirect measurements of the Hubble constant (H0), has persisted for a decade and motivated intense scrutiny of the paths used to infer H0. Comparing independently-derived distances for a
Externí odkaz:
http://arxiv.org/abs/2408.00065
Non-Bayesian social learning enables multiple agents to conduct networked signal and information processing through observing environmental signals and information aggregating. Traditional non-Bayesian social learning models only consider single sign
Externí odkaz:
http://arxiv.org/abs/2407.20770
Autor:
Hu, Guanyu, Wei, Jie, Song, Siyang, Kollias, Dimitrios, Yang, Xinyu, Sun, Zhonglin, Kaloidas, Odysseus
The objective of the Multiple Appropriate Facial Reaction Generation (MAFRG) task is to produce contextually appropriate and diverse listener facial behavioural responses based on the multimodal behavioural data of the conversational partner (i.e., t
Externí odkaz:
http://arxiv.org/abs/2407.15798
In this paper, we present a YOLO-based framework for layout hotspot detection, aiming to enhance the efficiency and performance of the design rule checking (DRC) process. Our approach leverages the YOLOv8 vision model to detect multiple hotspots with
Externí odkaz:
http://arxiv.org/abs/2407.14498
This paper aims to address the challenge of sparse and missing data in recommendation systems, a significant hurdle in the age of big data. Traditional imputation methods struggle to capture complex relationships within the data. We propose a novel a
Externí odkaz:
http://arxiv.org/abs/2407.10078
Autonomous driving holds great promise in addressing traffic safety concerns by leveraging artificial intelligence and sensor technology. Multi-Object Tracking plays a critical role in ensuring safer and more efficient navigation through complex traf
Externí odkaz:
http://arxiv.org/abs/2407.08049
Existing Graph Neural Networks (GNNs) are limited to process graphs each of whose vertices is represented by a vector or a single value, limited their representing capability to describe complex objects. In this paper, we propose the first GNN (calle
Externí odkaz:
http://arxiv.org/abs/2407.00696
Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. To address this ch
Externí odkaz:
http://arxiv.org/abs/2407.01621
Autor:
Granath, Andreas, Wang, Siyang
We develop a high order accurate numerical method for solving the elastic wave equation in second-order form. We hybridize the computationally efficient Cartesian grid formulation of finite differences with geometrically flexible discontinuous Galerk
Externí odkaz:
http://arxiv.org/abs/2406.18015