312 research outputs found
User Perspectives On Adoption Of A Hybrid Tagging System: A Case Of Topic Structure Of Zhihu Knowledge Community
Social tagging system has been prevalent thanks to its user-centric and flexible features. However, it suffers from its uncontrolled vocabulary and loose connection between tags. To overcome their drawbacks, a hybrid tagging system, which combines the ideas of the traditional taxonomy and social tagging, is adopting by some online knowledge communities. The top layers of the hybrid tagging system are determined by the website designer, while the bottom layers are constructed by users under certain restrictions. Due to the absence of sufficient research on user acceptance of this hybrid tagging system, cognitive factors affecting user adoption of the system is explored in this paper with topic structure of Zhihu, the famous Chinese knowledge community. An integrated model is proposed based on technology acceptance model and social cognitive theory. A survey will be conducted to empirically verify relationships between proposed constructs and actual usage. The research is expected to provide guidance for incremental improvement on a hybrid tagging system or development on new tagging systems
Image Deblurring by Exploring In-depth Properties of Transformer
Image deblurring continues to achieve impressive performance with the
development of generative models. Nonetheless, there still remains a
displeasing problem if one wants to improve perceptual quality and quantitative
scores of recovered image at the same time. In this study, drawing inspiration
from the research of transformer properties, we introduce the pretrained
transformers to address this problem. In particular, we leverage deep features
extracted from a pretrained vision transformer (ViT) to encourage recovered
images to be sharp without sacrificing the performance measured by the
quantitative metrics. The pretrained transformer can capture the global
topological relations (i.e., self-similarity) of image, and we observe that the
captured topological relations about the sharp image will change when blur
occurs. By comparing the transformer features between recovered image and
target one, the pretrained transformer provides high-resolution blur-sensitive
semantic information, which is critical in measuring the sharpness of the
deblurred image. On the basis of the advantages, we present two types of novel
perceptual losses to guide image deblurring. One regards the features as
vectors and computes the discrepancy between representations extracted from
recovered image and target one in Euclidean space. The other type considers the
features extracted from an image as a distribution and compares the
distribution discrepancy between recovered image and target one. We demonstrate
the effectiveness of transformer properties in improving the perceptual quality
while not sacrificing the quantitative scores (PSNR) over the most competitive
models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and
motion deblurring tasks
Pseudo-Phase Transitions of Ising and Baxter-Wu Models in Two-Dimensional Finite-Size Lattices
This article offers a detailed analysis of pseudo-phase transitions of Ising
and Baxter-Wu models in two-dimensional finite-size lattices. We carry out Wang
Landau sampling to obtain the density of states. Using microcanonical
inflection point analysis with microcanonical entropy, we obtain the order of
the psuedo-phase transitions in the models. The microcanonical analysis results
of the second-order transition for the Ising model and the first-order
transition for the Baxter-Wu model are consistent with the traditional
canonical results. In addition, the third-order transitions are found in both
models, implying the universality of higher-order phase transitions.Comment: 13 pages, 5 figure
NutriFD: Proving the medicinal value of food nutrition based on food-disease association and treatment networks
There is rising evidence of the health benefit associated with specific
dietary interventions. Current food-disease databases focus on associations and
treatment relationships but haven't provided a reasonable assessment of the
strength of the relationship, and lack of attention on food nutrition. There is
an unmet need for a large database that can guide dietary therapy. We fill the
gap with NutriFD, a scoring network based on associations and therapeutic
relationships between foods and diseases. NutriFD integrates 9 databases
including foods, nutrients, diseases, genes, miRNAs, compounds, disease
ontology and their relationships. To our best knowledge, this database is the
only one that can score the associations and therapeutic relationships of
everyday foods and diseases by weighting inference scores of food compounds to
diseases. In addition, NutriFD demonstrates the predictive nature of nutrients
on the therapeutic relationships between foods and diseases through machine
learning models, laying the foundation for a mechanistic understanding of food
therapy
DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling
While widely recognized as one of the most substantial weather forecasting
methodologies, Numerical Weather Prediction (NWP) usually suffers from
relatively coarse resolution and inevitable bias due to tempo-spatial
discretization, physical parametrization process, and computation limitation.
With the roaring growth of deep learning-based techniques, we propose the
Dual-Stage Adaptive Framework (DSAF), a novel framework to address regional NWP
downscaling and bias correction tasks. DSAF uniquely incorporates adaptive
elements in its design to ensure a flexible response to evolving weather
conditions. Specifically, NWP downscaling and correction are well-decoupled in
the framework and can be applied independently, which strategically guides the
optimization trajectory of the model. Utilizing a multi-task learning mechanism
and an uncertainty-weighted loss function, DSAF facilitates balanced training
across various weather factors. Additionally, our specifically designed
attention-centric learnable module effectively integrates geographic
information, proficiently managing complex interrelationships. Experimental
validation on the ECMWF operational forecast (HRES) and reanalysis (ERA5)
archive demonstrates DSAF's superior performance over existing state-of-the-art
models and shows substantial improvements when existing models are augmented
using our proposed modules. Code is publicly available at
https://github.com/pengwei07/DSAF
Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage
With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios
A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model
This paper presents a variation of the fuzzy local information c-means clustering (FLICM) algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF) together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images
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