Zobrazeno 1 - 10
of 1 562
pro vyhledávání: '"LI, Jialiang"'
Poly2Vec: Polymorphic Encoding of Geospatial Objects for Spatial Reasoning with Deep Neural Networks
Encoding geospatial data is crucial for enabling machine learning (ML) models to perform tasks that require spatial reasoning, such as identifying the topological relationships between two different geospatial objects. However, existing encoding meth
Externí odkaz:
http://arxiv.org/abs/2408.14806
Autor:
Li, Jialiang, Wang, Haoyue, Li, Sheng, Qian, Zhenxing, Zhang, Xinpeng, Vasilakos, Athanasios V.
Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fake images. To attribute the AI-generated images, existing schemes usua
Externí odkaz:
http://arxiv.org/abs/2407.14570
This paper proposes a generative model to detect change points in time series of graphs. The proposed framework consists of learnable prior distributions for low-dimensional graph representations and of a decoder that can generate graphs from the lat
Externí odkaz:
http://arxiv.org/abs/2404.04719
Fair graph learning plays a pivotal role in numerous practical applications. Recently, many fair graph learning methods have been proposed; however, their evaluation often relies on poorly constructed semi-synthetic datasets or substandard real-world
Externí odkaz:
http://arxiv.org/abs/2403.06017
Autor:
Li, Jialiang, Huang, Zitao, Yu, Chunlin, Wu, Jiajie, Zhao, Tongge, Zhu, Xiangwei, Sun, Shihai
Publikováno v:
Optics Express,Vol.32,No.4, 2024
Quantum random number generator (QRNG) utilizes the intrinsic randomness of quantum systems to generate completely unpredictable and genuine random numbers, finding wide applications across many fields. QRNGs relying on the phase noise of a laser hav
Externí odkaz:
http://arxiv.org/abs/2401.08325
Autor:
Alobaid, Khalid A., Abduallah, Yasser, Wang, Jason T. L., Wang, Haimin, Fan, Shen, Li, Jialiang, Cavus, Huseyin, Yurchyshyn, Vasyl
Publikováno v:
The Astrophysical Journal Letters, 958:L34, 2023
Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estima
Externí odkaz:
http://arxiv.org/abs/2312.01691
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 11, Pp 2823-2847 (2024)
Automatic text summarization (ATS) is a popular research direction in natural language processing, and its main implementation methods are divided into two categories: extractive and abstractive. Extractive summarization directly uses the text conten
Externí odkaz:
https://doaj.org/article/87b6914561a14512a13e94b5a6b9ed9b
Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training
Externí odkaz:
http://arxiv.org/abs/2307.04937
Publikováno v:
AAAI 2024
We propose a combinatorial optimisation model called Limited Query Graph Connectivity Test. We consider a graph whose edges have two possible states (On/Off). The edges' states are hidden initially. We could query an edge to reveal its state. Given a
Externí odkaz:
http://arxiv.org/abs/2302.13036