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The Laser Interferometer Space Antenna (LISA) will play a vital role in constraining the origin and evolution of massive black holes throughout the Universe. In this study we use a waveform model (IMRPhenomXPHM) that includes both…
The Laser Interferometer Space Antenna (LISA), an ESA L-class mission, is designed to detect gravitational waves in the millihertz frequency band, with operations expected to begin in the next decade. LISA will enable studies of…
The advancement of The Laser Interferometer Gravitational-Wave Observatory (LIGO) has significantly enhanced the feasibility and reliability of gravitational wave detection. However, LIGO's high sensitivity makes it susceptible to transient…
The next decade is expected to see the launch of one or more space based gravitational wave detectors: the European lead Laser Interferometer Space Antenna (LISA); and one or more Chinese mission concepts, Taiji and TianQin. One of the…
Handling missing data remains a fundamental challenge in real-world tabular datasets, especially when data are heterogeneous with both numerical and categorical features. Existing imputation methods often fail to capture complex structural…
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as…
In this paper, we introduce a deep learning-based decoder designed for concatenated coding schemes over a deletion/substitution channel. Specifically, we focus on serially concatenated codes, where the outer code is either a convolutional…
The space-based gravitational wave detector LISA will observe mergers of massive black hole binary systems (MBHBs) to cosmological distances, as well as inspiralling stellar-origin (or stellar-mass) binaries (SBHBs) years before they enter…
We introduce DenoiseGapFiller (DGF), a deep-learning framework specifically designed to reconstruct gravitational-wave ringdown signals corrupted by data gaps and instrumental noise. DGF employs a dual-branch encoder-decoder architecture,…
In the last few years there has been an enormous effort in parameter estimation studies for different sources with the space based gravitational wave detector, LISA. While these studies have investigated sources of differing complexity, the…
Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic prediction. To address this, we propose a Spatio-Temporal Decoupled Autoencoder (STDAE), a…
Recently it was shown that the inclusion of higher signal harmonics in the inspiral signals of binary supermassive black holes (SMBH) leads to dramatic improvements in parameter estimation with the Laser Interferometer Space Antenna (LISA).…
The Laser Interferometer Space Antenna (LISA) is scheduled to launch in the mid 2030s, and is expected to observe gravitational-wave candidates from massive black-hole binary mergers, extreme mass-ratio inspirals, and more. Accurately…
We anticipate that the data acquired by the Laser Interferometer Space Antenna (LISA) will be dominated by the gravitational wave signals from several astrophysical populations. The analysis of these data is a new challenge and is the main…
One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity. Previous studies, which increase the information bottleneck during training, tend to lose…
The planned space-based gravitational wave detector, LISA, will provide a fundamentally new means of studying the orbital alignment of close white dwarf binaries. However, due to the inherent symmetry of their gravitational wave signals, a…
Infrared and visible image fusion, as a hot topic in image processing and image enhancement, aims to produce fused images retaining the detail texture information in visible images and the thermal radiation information in infrared images. A…
The Laser Interferometer Space Antenna (LISA) gravitational-wave (GW) observatory will be limited in its ability to detect mergers of binary black holes (BBHs) in the stellar-mass range. A future ground-based detector network, meanwhile,…
A variety of real-world systems can be modeled as bipartite networks. One of the most powerful and simple link prediction methods is Linear-Graph Autoencoder(LGAE) which has promising performance on challenging tasks such as link prediction…
We propose a deep learning approach based on an autoencoder for identifying and localizing fiber faults in passive optical networks. The experimental results show that the proposed method detects faults with 97% accuracy, pinpoints them…