Results 1 to 10 of about 2,937,536 (254)

OTFDM: A Novel 2D Modulation Waveform Modeling Dot-product Doubly-selective Channel [PDF]

2023 IEEE PIMRC, Toronto, ON, Canada, 2023, pp. 1-, 2023
Recently, a two-dimension (2D) modulation waveform of orthogonal time-frequency-space (OTFS) has been a popular 6G candidate to replace existing orthogonal frequency division multiplexing (OFDM). The extensive OTFS researches help to make both the advantages and limitations of OTFS more and more clear.
arxiv   +1 more source

Electrical tuning of robust layered antiferromagnetism in MXene monolayer [PDF]

Appl.Phys.Lett.122,162403(2023), 2023
A-type antiferromagnetism, with an in-plane ferromagnetic order and the interlayer antiferromagnetic coupling, owns inborn advantages for electrical manipulations but is naturally rare in real materials except in those artificial antiferromagnetic heterostructures. Here, a robust layered antiferromagnetism with a high N\'eel temperature is predicted in
arxiv   +1 more source

Alterferroicity with seesaw-type magnetoelectricity [PDF]

Proceedings of the National Academy of Sciences 120, e2305197120 (2023), 2023
Primary ferroicities like ferroelectricity and ferromagnetism are essential physical properties of matter. Multiferroics, with coexisting multiple ferroic orders in a single phase, provide a convenient route to magnetoelectricity. Even so, the general trade-off between magnetism and polarity remains inevitable, which prevents practicable ...
arxiv   +1 more source

Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems [PDF]

, 2020
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) algorithms struggle in POMDP environments because the state of the system, essential in a RL framework, is not always visible. Additionally, hand-designed meta-RL architectures
arxiv   +1 more source

AppQ: Warm-starting App Recommendation Based on View Graphs [PDF]

arXiv, 2021
Current app ranking and recommendation systems are mainly based on user-generated information, e.g., number of downloads and ratings. However, new apps often have few (or even no) user feedback, suffering from the classic cold-start problem. How to quickly identify and then recommend new apps of high quality is a challenging issue.
arxiv  

Text Classification For Authorship Attribution Analysis [PDF]

Advanced Computing: An International Journal (ACIJ), Vol.4, No.5, September 2013, 2013
Authorship attribution mainly deals with undecided authorship of literary texts. Authorship attribution is useful in resolving issues like uncertain authorship, recognize authorship of unknown texts, spot plagiarism so on. Statistical methods can be used to set apart the approach of an author numerically.
arxiv   +1 more source

Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective [PDF]

arXiv, 2021
Deep neural networks (DNNs) often rely on easy-to-learn discriminatory features, or cues, that are not necessarily essential to the problem at hand. For example, ducks in an image may be recognized based on their typical background scenery, such as lakes or streams.
arxiv  

Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks [PDF]

arXiv, 2021
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i.e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making erroneous predictions. To tackle this, adversarial training is currently the most effective defense method, by augmenting the training set with adversarial samples generated
arxiv  

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation [PDF]

arXiv, 2022
Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs. In this paper, we argue that existing GNN-based EA methods inherit the inborn defects from their neural network lineage: weak scalability and poor interpretability.
arxiv  

Taming and Leveraging Interference in Mobile Radar Networks [PDF]

arXiv, 2020
Mobile radar networks, such as autonomous driving systems, are subject to the severe challenge of mutual interference. Despite the inborn interference-proof capability in frequency modulation continuous waveform (FMCW) radar, interference management is necessary for dense radar networks.
arxiv