With the development of digital library technology, library books made of paper can be digital released and read, and Endangered Cultural Heritages can be preserved. Traditional library＇s contents and functions can b...With the development of digital library technology, library books made of paper can be digital released and read, and Endangered Cultural Heritages can be preserved. Traditional library＇s contents and functions can be greatly enhanced by digital technologies. For these new library objects, the primary key problem is precisely reconstructing their 3D models. When constructing complete 3D models, multiple color texture maps are often necessary. A commonly encountered problem uncounted during fusing of textures from multiple color images is color distortion. Each texture of a single 3D model may be obtained under possibly different lighting conditions and color response of the camera. To remove any visible seam and improve color consistency between the textures while avoiding color distortion, we propose a new efficient algorithm to relight all the texture images globally, spread residual light difference, and recolor each image by homogeneous transformation. A relative illumination model was adopted to obtain the relighting function. We choose lαβ color space with minimal correlation between channels for many natural scenes, for calculating the relighting result. Looking into two overlapped images A and B, we can pairwise relight B into A＇s luminosity condition in two steps. We first scale B＇s l channel by the lA/lB ratio of the overlapped region. We can assume A and B are in a same color plane now. Then a homogeneous transformation is applied to B＇s a and fl channels which moves B into A＇s hue and saturation condition. For multiple overlapped color textures, a patch based weighted global relighting method was proposed to minimize the total color difference. The pairwise relighting method was used between each two overlapped images, and the difference in every overlapped region after relighting was weighted and summed up to construct an energy value. We used Nelder-Mead method to find a minimal energy value and the relighting parameters for every image. After global relighting, textures become almost coherent. We simply blended the overlapped region along the texture border to remove small visual seams and get a final result. We illustrate our method by calibrating textures of a painted sculpture acquired with laser scanner. Experimental results were realistic and reliable and showed how this method can fuse multiple textures without color distortion.展开更多
Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,anywhere.More and more next-generation wireless network sma...Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,anywhere.More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life.As the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge.However,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and interoperability.To this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and Metaverse.Specifically,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse.Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions.Furthermore,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data.Finally,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.展开更多
The host load prediction problem in cloud computing has also been received much attention. To solve this problem, we have to use the historical load data to predict the future load level. Accurate prediction methods a...The host load prediction problem in cloud computing has also been received much attention. To solve this problem, we have to use the historical load data to predict the future load level. Accurate prediction methods are useful for host load balance and virtual machine migration. Although cloud is likely to grids at some extent, the length of tasks are much shorter and host loads change more frequently with higher noise. The above characteristics introduce challenges for host load prediction. In this paper, based on the proposed exponentially segmented pattern and the corresponding transformation, prediction problem is transformed into the traditional classification problem, This classification problem can be solved based on the traditional methods, and features are given for training the classification model. For achieving accurate prediction, a new feature periodical coefficient is introduced and some existed classification methods are implemented. Experiments on the real world dataset invalidate the efficiency of the new proposed feature, which is in the most effective combinations of features, it increases successful rate （SR） 1.33%-2.82% and decreases the mean square error （MSE） 1.37%-2.91%. And the results also show that support vector machine （SVM） method can achieve nearly the same performance as the Bayes methods and their performance is about 50% higher in successful rate and 17% better in the mean square error compared to the existed methods.展开更多
The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe.However,effectively analyzing this vast amount of data poses a significant challenge.I...The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe.However,effectively analyzing this vast amount of data poses a significant challenge.In response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate workloads.To overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and more.Considering the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively.The proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging Surveys.In particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 requested.In addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.展开更多
基金Project supported by the National Basic Research Program (973) ofChina (No. 2002CB312106)
文摘With the development of digital library technology, library books made of paper can be digital released and read, and Endangered Cultural Heritages can be preserved. Traditional library＇s contents and functions can be greatly enhanced by digital technologies. For these new library objects, the primary key problem is precisely reconstructing their 3D models. When constructing complete 3D models, multiple color texture maps are often necessary. A commonly encountered problem uncounted during fusing of textures from multiple color images is color distortion. Each texture of a single 3D model may be obtained under possibly different lighting conditions and color response of the camera. To remove any visible seam and improve color consistency between the textures while avoiding color distortion, we propose a new efficient algorithm to relight all the texture images globally, spread residual light difference, and recolor each image by homogeneous transformation. A relative illumination model was adopted to obtain the relighting function. We choose lαβ color space with minimal correlation between channels for many natural scenes, for calculating the relighting result. Looking into two overlapped images A and B, we can pairwise relight B into A＇s luminosity condition in two steps. We first scale B＇s l channel by the lA/lB ratio of the overlapped region. We can assume A and B are in a same color plane now. Then a homogeneous transformation is applied to B＇s a and fl channels which moves B into A＇s hue and saturation condition. For multiple overlapped color textures, a patch based weighted global relighting method was proposed to minimize the total color difference. The pairwise relighting method was used between each two overlapped images, and the difference in every overlapped region after relighting was weighted and summed up to construct an energy value. We used Nelder-Mead method to find a minimal energy value and the relighting parameters for every image. After global relighting, textures become almost coherent. We simply blended the overlapped region along the texture border to remove small visual seams and get a final result. We illustrate our method by calibrating textures of a painted sculpture acquired with laser scanner. Experimental results were realistic and reliable and showed how this method can fuse multiple textures without color distortion.
基金Provincial Natural Science Foundation of China(LH2020F044)2019-“Chunhui”Plan Cooperative Scientific Research Project of the Ministry of Education of China(HLJ2019015)+2 种基金Fundamental Research Funds for Heilongjiang University,China(2020-KYYWF-1014)NSFC(62102099)National Key R&D Program of China(2018YFE0205503)。
文摘Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,anywhere.More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life.As the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge.However,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and interoperability.To this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and Metaverse.Specifically,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse.Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions.Furthermore,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data.Finally,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.
基金supported by the National Key project of Scientific and Technical Supporting Programs of China (2013BAH10F01, 2013BAH07F02, 2014BAH26F02)The Research Fund for the Doctoral Program of Higher Education (20110005120007)+1 种基金Beijing Higher Education Young Elite Teacher Project (YETP0445)The Co-construction Program with Beijing Municipal Commission of Education
文摘The host load prediction problem in cloud computing has also been received much attention. To solve this problem, we have to use the historical load data to predict the future load level. Accurate prediction methods are useful for host load balance and virtual machine migration. Although cloud is likely to grids at some extent, the length of tasks are much shorter and host loads change more frequently with higher noise. The above characteristics introduce challenges for host load prediction. In this paper, based on the proposed exponentially segmented pattern and the corresponding transformation, prediction problem is transformed into the traditional classification problem, This classification problem can be solved based on the traditional methods, and features are given for training the classification model. For achieving accurate prediction, a new feature periodical coefficient is introduced and some existed classification methods are implemented. Experiments on the real world dataset invalidate the efficiency of the new proposed feature, which is in the most effective combinations of features, it increases successful rate （SR） 1.33%-2.82% and decreases the mean square error （MSE） 1.37%-2.91%. And the results also show that support vector machine （SVM） method can achieve nearly the same performance as the Bayes methods and their performance is about 50% higher in successful rate and 17% better in the mean square error compared to the existed methods.
基金the support from the National Natural Science Foundation of China(Grant Nos.12173027,12303105,12173062)the National Key R&D Program of China(Grant Nos.2023YFF0725300,2022YFF0503402)+5 种基金the Science Research Grants from the Square Kilometre Array(SKA)(2020SKA0110100)the Science Research Grants from the China Manned Space Project(Grant Nos.CMS-CSST-2021-A01,CMS-CSST-2021-A07,CMS-CSST-2021-B05)the CAS Project for Young Scientists in Basic ResearchChina(Grant No.YSBR-062)supported by the Young Data Scientist Project of the National Astronomical Data Centerthe Program of Science and Education Integration at the School of Astronomy and Space Science,University of Chinese Academy of Sciences,China。
文摘The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe.However,effectively analyzing this vast amount of data poses a significant challenge.In response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate workloads.To overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and more.Considering the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively.The proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging Surveys.In particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 requested.In addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.
