86 research outputs found
ANALYSIS OF THE INFLUENCE OF IDEOLOGICAL AND POLITICAL TEACHING ON COLLEGE STUDENTS’ DEPRESSION IN HIGHER VOCATIONAL COLLEGES
ANALYSIS OF THE INFLUENCE OF IDEOLOGICAL AND POLITICAL TEACHING ON COLLEGE STUDENTS’ DEPRESSION IN HIGHER VOCATIONAL COLLEGES
Diffuse large B-cell lymphoma: sub-classification by massive parallel quantitative RT-PCR.
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity with remarkably variable clinical outcome. Gene expression profiling (GEP) classifies DLBCL into activated B-cell like (ABC), germinal center B-cell like (GCB), and Type-III subtypes, with ABC-DLBCL characterized by a poor prognosis and constitutive NF-κB activation. A major challenge for the application of this cell of origin (COO) classification in routine clinical practice is to establish a robust clinical assay amenable to routine formalin-fixed paraffin-embedded (FFPE) diagnostic biopsies. In this study, we investigated the possibility of COO-classification using FFPE tissue RNA samples by massive parallel quantitative reverse transcription PCR (qRT-PCR). We established a protocol for parallel qRT-PCR using FFPE RNA samples with the Fluidigm BioMark HD system, and quantified the expression of the COO classifier genes and the NF-κB targeted-genes that characterize ABC-DLBCL in 143 cases of DLBCL. We also trained and validated a series of basic machine-learning classifiers and their derived meta classifiers, and identified SimpleLogistic as the top classifier that gave excellent performance across various GEP data sets derived from fresh-frozen or FFPE tissues by different microarray platforms. Finally, we applied SimpleLogistic to our data set generated by qRT-PCR, and the ABC and GCB-DLBCL assigned showed the respective characteristics in their clinical outcome and NF-κB target gene expression. The methodology established in this study provides a robust approach for DLBCL sub-classification using routine FFPE diagnostic biopsies in a routine clinical setting.The research in Du lab was supported by research grants (LLR10006 & LLR13006) from Leukaemia & Lymphoma Research, U.K. XX was supported by a visiting fellowship from the China Scholarship Council, Ministry of Education, P.R. China.This is the accepted manuscript. The final version is available from NPG at http://www.nature.com/labinvest/journal/v95/n1/full/labinvest2014136a.html
Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning
Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pro-MobilenetV3 network). Three time-frequency analysis methods are used to obtain time-frequency distribution. Among them, short time Fourier transform (STFT) combined with Pro-MobilenetV3 network takes the shortest time and has the highest accuracy. Furthermore, transfer learning is introduced into the model, and the optimal training parameters are selected training the network. Using the dataset from Southeast University, the TL-Pro-MobilenetV3 model is compared with four classical fault diagnosis models. The experimental results show the accuracy of the method proposed can reach 100% and the training time is the shortest in two working conditions, proving the proposed model has a good performance in generalization ability, recognition accuracy and training time
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
Semantic communications can reduce the resource consumption by transmitting
task-related semantic information extracted from source messages. However, when
the source messages are utilized for various tasks, e.g., wireless sensing data
for localization and activities detection, semantic communication technique is
difficult to be implemented because of the increased processing complexity. In
this paper, we propose the inverse semantic communications as a new paradigm.
Instead of extracting semantic information from messages, we aim to encode the
task-related source messages into a hyper-source message for data transmission
or storage. Following this paradigm, we design an inverse semantic-aware
wireless sensing framework with three algorithms for data sampling,
reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised
decoding, respectively. Specifically, on the one hand, we propose a novel RIS
hardware design for encoding several signal spectrums into one MetaSpectrum. To
select the task-related signal spectrums for achieving efficient encoding, a
semantic hash sampling method is introduced. On the other hand, we propose a
self-supervised learning method for decoding the MetaSpectrums to obtain the
original signal spectrums. Using the sensing data collected from real-world, we
show that our framework can reduce the data volume by 95% compared to that
before encoding, without affecting the accomplishment of sensing tasks.
Moreover, compared with the typically used uniform sampling scheme, the
proposed semantic hash sampling scheme can achieve 67% lower mean squared error
in recovering the sensing parameters. In addition, experiment results
demonstrate that the amplitude response matrix of the RIS enables the
encryption of the sensing data
Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study
As the next-generation paradigm for content creation, AI-Generated Content
(AIGC), i.e., generating content automatically by Generative AI (GAI) based on
user prompts, has gained great attention and success recently. With the
ever-increasing power of GAI, especially the emergence of Pretrained Foundation
Models (PFMs) that contain billions of parameters and prompt engineering
methods (i.e., finding the best prompts for the given task), the application
range of AIGC is rapidly expanding, covering various forms of information for
human, systems, and networks, such as network designs, channel coding, and
optimization solutions. In this article, we present the concept of mobile-edge
AI-Generated Everything (AIGX). Specifically, we first review the building
blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX
applications. Then, we present a unified mobile-edge AIGX framework, which
employs edge devices to provide PFM-empowered AIGX services and optimizes such
services via prompt engineering. More importantly, we demonstrate that
suboptimal prompts lead to poor generation quality, which adversely affects
user satisfaction, edge network performance, and resource utilization.
Accordingly, we conduct a case study, showcasing how to train an effective
prompt optimizer using ChatGPT and investigating how much improvement is
possible with prompt engineering in terms of user experience, quality of
generation, and network performance.Comment: 9 pages, 6 figur
Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
As generative artificial intelligence (GAI) models continue to evolve, their
generative capabilities are increasingly enhanced and being used extensively in
content generation. Beyond this, GAI also excels in data modeling and analysis,
benefitting wireless communication systems. In this article, we investigate
applications of GAI in the physical layer and analyze its support for
integrated sensing and communications (ISAC) systems. Specifically, we first
provide an overview of GAI and ISAC, touching on GAI's potential support across
multiple layers of ISAC. We then concentrate on the physical layer,
investigating GAI's applications from various perspectives thoroughly, such as
channel estimation, and demonstrate the value of these GAI-enhanced physical
layer technologies for ISAC systems. In the case study, the proposed diffusion
model-based method effectively estimates the signal direction of arrival under
the near-field condition based on the uniform linear array, when antenna
spacing surpassing half the wavelength. With a mean square error of 1.03
degrees, it confirms GAI's support for the physical layer in near-field sensing
and communications
A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0
Web 3.0 enables user-generated contents and user-selected authorities. With
decentralized wireless edge computing architectures, Web 3.0 allows users to
read, write, and own contents. A core technology that enables Web 3.0 goals is
blockchain, which provides security services by recording content in a
decentralized and transparent manner. However, the explosion of on-chain
recorded contents and the fast-growing number of users cause increasingly
unaffordable computing and storage resource consumption. A promising paradigm
is to analyze the semantic information of contents that can convey precisely
the desired meanings without consuming many resources. In this article, we
propose a unified blockchain-semantic ecosystems framework for wireless edge
intelligence-enabled Web 3.0. Our framework consists of six key components to
exchange semantic demands. We then introduce an Oracle-based proof of semantic
mechanism to implement on-chain and off-chain interactions of Web 3.0
ecosystems on semantic verification algorithms while maintaining service
security. An adaptive Deep Reinforcement Learning-based sharding mechanism on
Oracle is designed to improve interaction efficiency, which can facilitate Web
3.0 ecosystems to deal with varied semantic demands. Finally, a case study is
presented to show that the proposed framework can dynamically adjust Oracle
settings according to varied semantic demands.Comment: 8 pages, 5 figures, 1 tabl
A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse
As the Metaverse continues to grow, the need for efficient communication and
intelligent content generation becomes increasingly important. Semantic
communication focuses on conveying meaning and understanding from user inputs,
while AI-Generated Content utilizes artificial intelligence to create digital
content and experiences. Integrated Semantic Communication and AI-Generated
Content (ISGC) has attracted a lot of attentions recently, which transfers
semantic information from user inputs, generates digital content, and renders
graphics for Metaverse. In this paper, we introduce a unified framework that
captures ISGC two primary benefits, including integration gain for optimized
resource allocation and coordination gain for goal-oriented high-quality
content generation to improve immersion from both communication and content
perspectives. We also classify existing ISGC solutions, analyze the major
components of ISGC, and present several use cases. We then construct a case
study based on the diffusion model to identify an optimal resource allocation
strategy for performing semantic extraction, content generation, and graphic
rendering in the Metaverse. Finally, we discuss several open research issues,
encouraging further exploring the potential of ISGC and its related
applications in the Metaverse.Comment: 8 pages, 6 figure
Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study
Recognizing the tremendous improvements that the integration of generative AI
can bring to intelligent transportation systems, this article explores the
integration of generative AI technologies in vehicular networks, focusing on
their potential applications and challenges. Generative AI, with its
capabilities of generating realistic data and facilitating advanced
decision-making processes, enhances various applications when combined with
vehicular networks, such as navigation optimization, traffic prediction, data
generation, and evaluation. Despite these promising applications, the
integration of generative AI with vehicular networks faces several challenges,
such as real-time data processing and decision-making, adapting to dynamic and
unpredictable environments, as well as privacy and security concerns. To
address these challenges, we propose a multi-modality semantic-aware framework
to enhance the service quality of generative AI. By leveraging multi-modal and
semantic communication technologies, the framework enables the use of text and
image data for creating multi-modal content, providing more reliable guidance
to receiving vehicles and ultimately improving system usability and efficiency.
To further improve the reliability and efficiency of information transmission
and reconstruction within the framework, taking generative AI-enabled
vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning
(DRL)-based approach is proposed for resource allocation. Finally, we discuss
potential research directions and anticipated advancements in the field of
generative AI-enabled vehicular networks.Comment: 8 pages, 4 figure
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