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"Liu, Yu"
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Atomistic simulation of quantum transport in nanoelectronic devices
\"Computational nanoelectronics is an emerging multi-disciplinary field covering condensed matter physics, applied mathematics, computer science, and electronic engineering. In recent decades, a few state-of-the-art software packages have been developed to carry out first-principle atomistic device simulations. Nevertheless those packages are either black boxes (commercial codes) or accessible only to very limited users (private research codes). The purpose of this book is to open one of the commercial black boxes, and to demonstrate the complete procedure from theoretical derivation, to numerical implementation, all the way to device simulation. Meanwhile the affiliated source code constitutes an open platform for new researchers. This is the first book of its kind. We hope the book will make a modest contribution to the field of computational nanoelectronics\"-- Provided by publisher.
Book
Constraining Palatini–Horndeski theory with gravitational waves after GW170817
In this paper, we investigate the possible parameter space of Palatini–Horndeski theory with gravitational waves in a spatially flat Universe. We develop a general method for obtaining the speed of gravitational waves in the Palatini formalism in the cosmological background and we find that if the theory satisfies the following condition: in any spatially flat cosmological background, the tensor gravitational wave speed is the speed of light
c
, then only
S
=
∫
d
4
x
-
g
[
K
(
ϕ
,
X
)
-
G
3
(
ϕ
,
X
)
□
~
ϕ
+
G
4
(
ϕ
)
R
~
]
is left as the possible action in Palatini–Horndeski theory. We also find that when
G
5
(
ϕ
,
X
)
≠
0
, the tensor part of the connection will propagate and there are two different tensor gravitational wave speeds.
Journal Article
Reciprocity, evolution, and decision games in network and data science
\"Learn how to analyze and manage evolutionary and sequential user behaviors in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision-making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behavior and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing\"-- Provided by publisher.
Book
Visual perception based deep learning transformers for classifying paintings and photographs through feature extraction
The application of computer vision techniques that have been used in various domains and artwork is not an exception. The latest models of deep learning are applied for processing and classification of digital images to analyze textures, color compositions, and lighting patterns across artistic and real-world imagery. In this research work, we aim to classify the given images to identify whether they are human artwork of paintings or captured photos. We apply state-of-the-art Vision Transformer (ViT) architecture to classify artistic images, achieving a classification accuracy of 95% outperforming existing relevant in the relevant literature. To validate and benchmark the approach, comprehensive experiments are carried out on the standard dataset using DenseNet, Convolutional Neural Networks (CNN), and Visual Geometry Group (VGG19) models. The results highlight the superior performance of ViT in capturing complex visual features, such as texture variation and compositional details. Grad-CAM further enhances the interpretability of the model by highlighting the specific visual regions influencing the model’s decisions, confirming that ViT able to capture meaningful artistic attributes such as brushstroke patterns, smoothness, and illumination gradients. This combination of high performance and transparency analysis of proposed model ensures a reliable and explainable solution for automated artwork classification.
Journal Article
Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph
Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.
Journal Article
Classification of gravitational waves in higher-dimensional space-time and possibility of observation
The direct detection of gravitational waves opens the possibility to test general relativity and its alternatives in the strong field regime. Here we focus on the test of the existence of extra dimensions. The classification of gravitational waves in metric gravity theories according to their polarizations in higher-dimensional space-time and the possible observation of these polarizations in three-dimensional subspace are discussed in this work. We also show that the difference in the response of gravitational waves in detectors with and without extra dimensions can serve as evidence for the existence of extra dimensions.
Journal Article
Cataracts
An estimated 95 million people worldwide are affected by cataract. Cataract still remains the leading cause of blindness in middle-income and low-income countries. With the advancement of surgical technology and techniques, cataract surgery has evolved to small-incisional surgery with rapid visual recovery, good visual outcomes, and minimal complications in most patients. With the development of advanced technology in intraocular lenses, the combined treatment of cataract and astigmatism or presbyopia, or both, is possible. Paediatric cataracts have a different pathogenesis, surgical concerns, and postoperative clinical course from those of age-related cataracts, and the visual outcome is multifactorial and dependent on postoperative visual rehabilitation. New developments in cataract surgery will continue to improve the visual, anatomical, and patient-reported outcomes. Future work should focus on promoting the accessibility and quality of cataract surgery in developing countries.
Journal Article
Feature Extraction and Image Recognition with Convolutional Neural Networks
The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.
Journal Article