72 research outputs found
AIGC In China: Current Developments And Future Outlook
The increasing attention given to AI Generated Content (AIGC) has brought a
profound impact on various aspects of daily life, industrial manufacturing, and
the academic sector. Recognizing the global trends and competitiveness in AIGC
development, this study aims to analyze China's current status in the field.
The investigation begins with an overview of the foundational technologies and
current applications of AIGC. Subsequently, the study delves into the market
status, policy landscape, and development trajectory of AIGC in China,
utilizing keyword searches to identify relevant scholarly papers. Furthermore,
the paper provides a comprehensive examination of AIGC products and their
corresponding ecosystem, emphasizing the ecological construction of AIGC.
Finally, this paper discusses the challenges and risks faced by the AIGC
industry while presenting a forward-looking perspective on the industry's
future based on competitive insights in AIGC
The current opportunities and challenges of Web 3.0
With recent advancements in AI and 5G technologies,as well as the nascent
concepts of blockchain and metaverse,a new revolution of the Internet,known as
Web 3.0,is emerging. Given its significant potential impact on the internet
landscape and various professional sectors,Web 3.0 has captured considerable
attention from both academic and industry circles. This article presents an
exploratory analysis of the opportunities and challenges associated with Web
3.0. Firstly, the study evaluates the technical differences between Web 1.0,
Web 2.0, and Web 3.0, while also delving into the unique technical architecture
of Web 3.0. Secondly, by reviewing current literature, the article highlights
the current state of development surrounding Web 3.0 from both economic and
technological perspective. Thirdly, the study identifies numerous research and
regulatory obstacles that presently confront Web 3.0 initiatives. Finally, the
article concludes by providing a forward-looking perspective on the potential
future growth and progress of Web 3.0 technology
High-dimensional Clustering onto Hamiltonian Cycle
Clustering aims to group unlabelled samples based on their similarities. It
has become a significant tool for the analysis of high-dimensional data.
However, most of the clustering methods merely generate pseudo labels and thus
are unable to simultaneously present the similarities between different
clusters and outliers. This paper proposes a new framework called
High-dimensional Clustering onto Hamiltonian Cycle (HCHC) to solve the above
problems. First, HCHC combines global structure with local structure in one
objective function for deep clustering, improving the labels as relative
probabilities, to mine the similarities between different clusters while
keeping the local structure in each cluster. Then, the anchors of different
clusters are sorted on the optimal Hamiltonian cycle generated by the cluster
similarities and mapped on the circumference of a circle. Finally, a sample
with a higher probability of a cluster will be mapped closer to the
corresponding anchor. In this way, our framework allows us to appreciate three
aspects visually and simultaneously - clusters (formed by samples with high
probabilities), cluster similarities (represented as circular distances), and
outliers (recognized as dots far away from all clusters). The experiments
illustrate the superiority of HCHC
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Adapters, a plug-in neural network module with some tunable parameters, have
emerged as a parameter-efficient transfer learning technique for adapting
pre-trained models to downstream tasks, especially for natural language
processing (NLP) and computer vision (CV) fields. Meanwhile, learning
recommendation models directly from raw item modality features -- e.g., texts
of NLP and images of CV -- can enable effective and transferable recommender
systems (called TransRec). In view of this, a natural question arises: can
adapter-based learning techniques achieve parameter-efficient TransRec with
good performance?
To this end, we perform empirical studies to address several key
sub-questions. First, we ask whether the adapter-based TransRec performs
comparably to TransRec based on standard full-parameter fine-tuning? does it
hold for recommendation with different item modalities, e.g., textual RS and
visual RS. If yes, we benchmark these existing adapters, which have been shown
to be effective in NLP and CV tasks, in the item recommendation settings.
Third, we carefully study several key factors for the adapter-based TransRec in
terms of where and how to insert these adapters? Finally, we look at the
effects of adapter-based TransRec by either scaling up its source training data
or scaling down its target training data. Our paper provides key insights and
practical guidance on unified & transferable recommendation -- a less studied
recommendation scenario. We promise to release all code & datasets for future
research
A Kind of New Surface Modeling Method Based on DEM Data
Surface elevation changes greatly in the river erosion area. Due to the limitation of the acquisition equipment and cost, the traditional seismic acquisition data has sparse physical points both horizontally and longitudinally, the density of surface measurement data is not enough to survey the surface structure in detail. With the development of science and technology, and the application of satellite technology, the DEM elevation data obtained from the geographic information system (GIS) are becoming more and more accurate. In this paper, a precise modeling is performed on the surface based on the geographic information from the river erosion area and combined with the results of the surface survey control points, a good effect is achieved.Key words: River erosion area; Geographic information; Similarity coefficient; Kriging interpolation; Surface modeling; High and low frequency static
EVNet: An Explainable Deep Network for Dimension Reduction
Dimension reduction (DR) is commonly utilized to capture the intrinsic
structure and transform high-dimensional data into low-dimensional space while
retaining meaningful properties of the original data. It is used in various
applications, such as image recognition, single-cell sequencing analysis, and
biomarker discovery. However, contemporary parametric-free and parametric DR
techniques suffer from several significant shortcomings, such as the inability
to preserve global and local features and the pool generalization performance.
On the other hand, regarding explainability, it is crucial to comprehend the
embedding process, especially the contribution of each part to the embedding
process, while understanding how each feature affects the embedding results
that identify critical components and help diagnose the embedding process. To
address these problems, we have developed a deep neural network method called
EVNet, which provides not only excellent performance in structural
maintainability but also explainability to the DR therein. EVNet starts with
data augmentation and a manifold-based loss function to improve embedding
performance. The explanation is based on saliency maps and aims to examine the
trained EVNet parameters and contributions of components during the embedding
process. The proposed techniques are integrated with a visual interface to help
the user to adjust EVNet to achieve better DR performance and explainability.
The interactive visual interface makes it easier to illustrate the data
features, compare different DR techniques, and investigate DR. An in-depth
experimental comparison shows that EVNet consistently outperforms the
state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
RadViz Deluxe: An Attribute-Aware Display for Multivariate Data
Modern data, such as occurring in chemical engineering, typically entail large collections of samples with numerous dimensional components (or attributes). Visualizing the samples in relation of these components can bring valuable insight. For example, one may be able to see how a certain chemical property is expressed in the samples taken. This could reveal if there are clusters and outliers that have specific distinguishing properties. Current multivariate visualization methods lack the ability to reveal these types of information at a sufficient degree of fidelity since they are not optimized to simultaneously present the relations of the samples as well as the relations of the samples to their attributes. We propose a display that is designed to reveal these multiple relations. Our scheme is based on the concept of RadViz, but enhances the layout with three stages of iterative refinement. These refinements reduce the layout error in terms of three essential relationships—sample to sample, attribute to attribute, and sample to attribute. We demonstrate the effectiveness of our method via various real-world domain examples in the domain of chemical process engineering. In addition, we also formally derive the equivalence of RadViz to a popular multivariate interpolation method called generalized barycentric coordinates
- …