7,157 research outputs found
First-principles method of propagation of tightly bound excitons: exciton band structure of LiF and verification with inelastic x-ray scattering
We propose a simple first-principles method to describe propagation of
tightly bound excitons. By viewing the exciton as a composite object (an
effective Frenkel exciton in Wannier orbitals), we define an exciton kinetic
kernel to encapsulate the exciton propagation and decay for all binding energy.
Applied to prototypical LiF, our approach produces three exciton bands, which
we verified quantitatively via inelastic x-ray scattering. The proposed
real-space picture is computationally inexpensive and thus enables study of the
full exciton dynamics, even in the presence of surfaces and impurity
scattering. It also provides intuitive understanding to facilitate practical
exciton engineering in semiconductors, strongly correlated oxides, and their
nanostructures.Comment: 5 pages, 4 figures. Accepted by PR
Crosstalk Impacts on Homogeneous Weakly-Coupled Multicore Fiber Based IM/DD System
We numerically discussed crosstalk impacts on homogeneous weakly-coupled
multicore fiber based intensity modulation/direct-detection (IM/DD) systems
taking into account mean crosstalk power fluctuation, walk-off between cores,
laser frequency offset, and laser linewidth.Comment: 3 pages, 11 figures
AI-Generated Content (AIGC): A Survey
To address the challenges of digital intelligence in the digital economy,
artificial intelligence-generated content (AIGC) has emerged. AIGC uses
artificial intelligence to assist or replace manual content generation by
generating content based on user-inputted keywords or requirements. The
development of large model algorithms has significantly strengthened the
capabilities of AIGC, which makes AIGC products a promising generative tool and
adds convenience to our lives. As an upstream technology, AIGC has unlimited
potential to support different downstream applications. It is important to
analyze AIGC's current capabilities and shortcomings to understand how it can
be best utilized in future applications. Therefore, this paper provides an
extensive overview of AIGC, covering its definition, essential conditions,
cutting-edge capabilities, and advanced features. Moreover, it discusses the
benefits of large-scale pre-trained models and the industrial chain of AIGC.
Furthermore, the article explores the distinctions between auxiliary generation
and automatic generation within AIGC, providing examples of text generation.
The paper also examines the potential integration of AIGC with the Metaverse.
Lastly, the article highlights existing issues and suggests some future
directions for application.Comment: Preprint. 14 figures, 4 table
Bis[ÎĽ-1,2-diphenyl-N,N′-bisÂ(di-2-pyridylÂmethylÂeneamino)ethane-1,2-diimine]disilver(I) bisÂ(hexaÂfluoridoÂphosphate) acetonitrile disolvate
In the centrosymmetric dinuclear title compound, [Ag2(C36H26N8)2](PF6)2·2C2H3N, the Ag+ ion is bound to four N atoms from two 1,2-diphenyl-N,N′-bisÂ(di-2-pyridylÂmethylÂeneamino)ethane-1,2-diimine ligands in a distorted tetraÂhedral geometry. The ligand adopts a twist conformation, coordinating two metal centers by three pyridyl N atoms and one imine N atom and spanning two Ag+ ions, resulting in the formation of a helical dimeric structure
Federated Learning Attacks and Defenses: A Survey
In terms of artificial intelligence, there are several security and privacy
deficiencies in the traditional centralized training methods of machine
learning models by a server. To address this limitation, federated learning
(FL) has been proposed and is known for breaking down ``data silos" and
protecting the privacy of users. However, FL has not yet gained popularity in
the industry, mainly due to its security, privacy, and high cost of
communication. For the purpose of advancing the research in this field,
building a robust FL system, and realizing the wide application of FL, this
paper sorts out the possible attacks and corresponding defenses of the current
FL system systematically. Firstly, this paper briefly introduces the basic
workflow of FL and related knowledge of attacks and defenses. It reviews a
great deal of research about privacy theft and malicious attacks that have been
studied in recent years. Most importantly, in view of the current three
classification criteria, namely the three stages of machine learning, the three
different roles in federated learning, and the CIA (Confidentiality, Integrity,
and Availability) guidelines on privacy protection, we divide attack approaches
into two categories according to the training stage and the prediction stage in
machine learning. Furthermore, we also identify the CIA property violated for
each attack method and potential attack role. Various defense mechanisms are
then analyzed separately from the level of privacy and security. Finally, we
summarize the possible challenges in the application of FL from the aspect of
attacks and defenses and discuss the future development direction of FL
systems. In this way, the designed FL system has the ability to resist
different attacks and is more secure and stable.Comment: IEEE BigData. 10 pages, 2 figures, 2 table
Web3: The Next Internet Revolution
Since the first appearance of the World Wide Web, people more rely on the Web
for their cyber social activities. The second phase of World Wide Web, named
Web 2.0, has been extensively attracting worldwide people that participate in
building and enjoying the virtual world. Nowadays, the next internet
revolution: Web3 is going to open new opportunities for traditional social
models. The decentralization property of Web3 is capable of breaking the
monopoly of the internet companies. Moreover, Web3 will lead a paradigm shift
from the Web as a publishing medium to a medium of interaction and
participation. This change will deeply transform the relations among users and
platforms, forces and relations of production, and the global economy.
Therefore, it is necessary that we technically, practically, and more broadly
take an overview of Web3. In this paper, we present a comprehensive survey of
Web3, with a focus on current technologies, challenges, opportunities, and
outlook. This article first introduces several major technologies of Web3.
Then, we illustrate the type of Web3 applications in detail. Blockchain and
smart contracts ensure that decentralized organizations will be less trusted
and more truthful than that centralized organizations. Decentralized finance
will be global, and open with financial inclusiveness for unbanked people. This
paper also discusses the relationship between the Metaverse and Web3, as well
as the differences and similarities between Web 3.0 and Web3. Inspired by the
Maslow's hierarchy of needs theory, we further conduct a novel hierarchy of
needs theory within Web3. Finally, several worthwhile future research
directions of Web3 are discussed.Comment: Preprint. 5 figures, 2 table
Metaverse in Education: Vision, Opportunities, and Challenges
Traditional education has been updated with the development of information
technology in human history. Within big data and cyber-physical systems, the
Metaverse has generated strong interest in various applications (e.g.,
entertainment, business, and cultural travel) over the last decade. As a novel
social work idea, the Metaverse consists of many kinds of technologies, e.g.,
big data, interaction, artificial intelligence, game design, Internet
computing, Internet of Things, and blockchain. It is foreseeable that the usage
of Metaverse will contribute to educational development. However, the
architectures of the Metaverse in education are not yet mature enough. There
are many questions we should address for the Metaverse in education. To this
end, this paper aims to provide a systematic literature review of Metaverse in
education. This paper is a comprehensive survey of the Metaverse in education,
with a focus on current technologies, challenges, opportunities, and future
directions. First, we present a brief overview of the Metaverse in education,
as well as the motivation behind its integration. Then, we survey some
important characteristics for the Metaverse in education, including the
personal teaching environment and the personal learning environment. Next, we
envisage what variations of this combination will bring to education in the
future and discuss their strengths and weaknesses. We also review the
state-of-the-art case studies (including technical companies and educational
institutions) for Metaverse in education. Finally, we point out several
challenges and issues in this promising area.Comment: IEEE BigData 2022. 10 pages, 5 figures, 3 table
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