检索结果-内蒙古大学图书馆

Virtual Reality & intelligent Hardware 2021年第3期3卷 I0001-I0002页

作者： Xilin CHEN Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China

Hands play an important role in our daily *** use our hands for manipulation in working,emphasis in speaking,communication in non-verbal environment,*** gesture not only uses for simple commands in traffic control,but also extends as a kind of language-sign *** the areas of VR/AR and HCI,understanding hand and its action can greatly improve user *** covers a broad topics related to hands,including hand detection,tracking,hand pose estimation,gesture recognition,and sign language *** papers are collected in this *** cover different topics related to hand and gesture.

关键词： verbal language. hands

来源：评论

学校读者我要写书评

暂无评论

Zr_(2)SeB and Hf_(2)SeB:Two new MAB phase compounds with the Cr_(2)AlC-type MAX phase(211 phase)crystal structures

引用

Journal of advanced Ceramics 2022年第11期11卷 1764-1776页

作者： Qiqiang ZHANG Yanchun ZHOU Xingyuan SAN Wenbo LI Yiwang BAO Qingguo FENG Salvatore GRASSO Chunfeng HU Key Laboratory of Advanced Technologies of Materials Ministry of EducationSchool of Materials Science and EngineeringSouthwest Jiaotong UniversityChengdu 610031China Science and Technology on Advanced Functional Composite Laboratory Aerospace Research Institute of Materials&Processing TechnologyBeijing 100076China Hebei Key Lab of Optic–Electronic Information and Materials College of Physics Science and TechnologyHebei UniversityBaoding 071002China State Key Laboratory of Green Building Materials China Building Materials AcademyBeijing 100024China

The ternary or quaternary layered compounds called MAB phases are frequently mentioned recently together with the well-known MAX ***,MAB phases are generally referred to layered transition metal borides,while MAX phases are layered transition metal carbides and nitrides with different types of crystal structure although they share the common nano-laminated structure *** order to prove that MAB phases can share the same type of crystal structure with MAX phases and extend the composition window of MAX phases from carbides and nitrides to borides,two new MAB phase compounds Zr_(2)SeB and Hf_(2)SeB with the Cr_(2)AlC-type MAX phase(211 phase)crystal structure were discovered by a combination of first-principles calculations and experimental verification in this ***-principles calculations predicted the stability and lattice parameters of the two new MAB phase compounds Zr_(2)SeB and Hf_(2)*** they were successfully synthesized by using a thermal explosion method in a spark plasma sintering(SPS)*** crystal structures of Zr_(2)SeB and Hf_(2)SeB were determined by a combination of the X-ray diffraction(XRD),scanning electron microscopy(SEM),and high-resolution transmission electron microscopy(HRTEM).The lattice parameters of Zr_(2)SeB and Hf_(2)SeB are a=3.64398Å,c=12.63223Åand a=3.52280Å,c=12.47804Å,*** the atomic positions are M at 4f(1/3,2/3,0.60288[Zr]or 0.59889[Hf]),Se at 2c(1/3,2/3,1/4),and B at 2a(0,0,0).And the atomic stacking sequences follow those of the Cr_(2)AlC-type MAX *** work opens up the composition window for the MAB phases and MAX phases and will trigger the interests of material scientists and physicists to explore new compounds and properties in this new family of materials.

关键词： Zr_(2)SeB and Hf_(2)SeB MAB phase MAX phase first-principles calculations thermal explosion synthesis

来源：评论

学校读者我要写书评

暂无评论

On alpha-mean of Kullback-Leibler divergences for subspace selection

On alpha-mean of Kullback-Leibler divergences for subspace s...

引用

2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Chen, Si-Bao Wang, Hai-Xian Zhang, Xing-Yi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China Key Lab of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9781612841816

Fisher's linear discriminant analysis (FLDA) is one of the most well-known linear subspace selection methods. However, FLDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. Recent results show that maximizing the geometric mean or harmonic mean of Kullback-Leibler (KL) divergences of class pairs can significantly reduce this problem. In this paper, to further reduce the class separation problem, the alpha-mean of divergences as a framework is proposed for subspace selection, with arithmetic mean, geometric mean and harmonic mean as special cases. We named it the maximization of the alpha-mean of all pairs of KL divergences (MAMD) criterion. A quasi-Newton method is applied to solve the optimization problem. Experiments on synthetic data and two datasets in UCI machine learning repository show the validity of MAMD. © 2011 IEEE.

关键词： Newton-Raphson method

来源：评论

学校读者我要写书评

暂无评论

LENSELET IMAGE COMPRESSION SCHEME BASED ON SUBAPERTURE IMAGES STREAMING

LENSELET IMAGE COMPRESSION SCHEME BASED ON SUBAPERTURE IMAGE...

引用

IEEE International Conference on Image processing

作者： Feng Dai Jun Zhang Yike Ma Yongdong Zhang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (纸本)9781479983407

Plenoptic cameras capture the light field in a scene with a single shot and produce lenselet images. From a lenselet image, light field can be reconstructed, with which we can render images with different viewpoints and focal length. Because of large volume data, high efficient image compression scheme for storage and transmission is urgent. Containing 4D light field information, lenselet images have much more redundant information than traditional 2D images. In this paper, we propose a subaperture images streaming scheme to compress lenselet images, in which rotation scan mapping is adopted to further improve compression efficiency. The experiment results show our approach can efficient compress the redundancy in lenselet images and outperform traditional image compression method.

关键词： Plenoptic camera Lenselet image Light field Image compression Subaperture images streaming Light fields Image compression Data compression images image compression method single-shot two dimentional images FOCAL LENGTH redundancy

来源：评论

学校读者我要写书评

暂无评论

Parametric local multimodal hashing for cross-view similarity search

Parametric local multimodal hashing for cross-view similarit...

引用

23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Zhai, Deming Chang, Hong Zhen, Yi Liu, Xianming Chen, Xilin Gao, Wen School of Computer Science and Technology Harbin Institute of Technology Harbin China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781577356332

Recent years have witnessed the growing popularity of hashing for efficient large-scale similarity search. It has been shown that the hashing quality could be boosted by hash function learning (HFL). In this paper, we study HFL in the context of multimodal data for cross-view similarity search. We present a novel multimodal HFL method, called Parametric Local Multimodal Hashing (PLMH), which learns a set of hash functions to locally adapt to the data structure of each modality. To balance locality and computational efficiency, the hashing projection matrix of each instance is parameterized, with guaranteed approximation error bound, as a linear combination of basis hashing projections of a small set of anchor points. A local optimal conjugate gradient algorithm is designed to learn the hash functions for each bit, and the overall hash codes are learned in a sequential manner to progressively minimize the bias. Experimental evaluations on cross-media retrieval tasks demonstrate that PLMH performs competitively against the state-of-the-art methods.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

Evaluating Structure of Complex Networks by Navigation Entropy

Evaluating Structure of Complex Networks by Navigation Entro...

引用

第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Xiaoping Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Complex networks are extensively studied in various areas such as social networks,biological networks,Internet and networks have many characters such as small-diameter,higher cluster and power-law degree ***-world is evolved for efficient information transformation and ***,navigation is an important functional character of *** researches mostly focus on understanding the navigability of small-world networks by analyzing the diameter and the routing *** this paper,we use the navigability to model the basic structural complexity of a *** is,given a network topology,we need a model to evaluate how complex the topology *** network complexity models have been proposed but none of them consider the navigability factor of the network *** believe that using the navigability factor to evaluate the network structural complexity is a feasible and reasonable *** use the adjacent matrix to build a navigation transition matrix and evaluate the randomness of random walks on the transition matrix by defining navigation entropy on *** use the iteration of the random walk matrix to evaluate the navigability of a network and the complexity of *** is,the higher the navigation entropy,the higher the randomness of a *** lower the navigation entropy,the higher the structure of a *** apply the navigation entropy model on a set of structural and random network topologies to show how the model can show the different complexity of networks.

关键词： navigation entropy structure complexity complex network

来源：评论

学校读者我要写书评

暂无评论

A Semantic Overlay on Blockchain for Supporting Traceability in Cross-border Logistics

A Semantic Overlay on Blockchain for Supporting Traceability...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Xiaoping Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

Traceability is important for maintaining quality of logistics transactions and improving logistics efficiency. Implementing traceability for cross-border logistics is facing challenges when it lacks global centralized logistics information management services. Blockchain techniques enable a decentralized cross-border logistics management by publishing logistics transaction on blockchain transactions to support privacy protection and traceability. Logistics transactions involves complex interactions among participants. How to publish those interactions on a blockchain platform is a challenge. We propose a semantic overlay model that publishes a logistics transaction as a semantic link on a blockchain platform for supporting traceability. A semantic link schema model is designed and published on the blockchain platform to validate semantics links for ensuring the correctness of logistics transaction execution. The proposed semantic overlay on blockchain supports further extension of semantic computing in decentralized applications.

关键词： Privacy Overlay networks Publishing Law Computational modeling Semantics Smart contracts

来源：评论

学校读者我要写书评

暂无评论

Competition on robust deep learning

引用

National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

来源：评论

学校读者我要写书评

暂无评论

T-GraphDTA: A Drug-Target Binding Affinity Prediction Framework Based on Protein Pre-training Model and Hybrid Graph Neural Network 1st

T-GraphDTA: A Drug-Target Binding Affinity Prediction Framew...

引用

1st International Conference on Applied Intelligence, ICAI 2023

作者： Wu, Yijia Lin, Yanmei Peng, Yuzhong Zhang, Ru Cai, Li Key Lab of Scientific Computing and Intelligent Information Processing Nanning Normal University Guangxi Nanning530000 China Guangxi Academy of Sciences Guangxi Nanning530007 China School of Software Yunnan University Yunnan Kunming650000 China

ISBN: (纸本)9789819709021

Drug-target affinity (DTA) prediction is an important task in computer-aided drug design and drug repositioning, which can speed up drug development and reduce resource consumption. researchers have explored some deep learning-based methods to improve DTA prediction in recent years, demonstrating the great potential of deep learning in DTA prediction. They have developed several molecular representation learning methods for drug compounds in deep learning-based DTA prediction methods. However, most of the existing deep learning-based DTA prediction models use one-hot encoding-based methods for protein representation learning, or use recursive neural network-based methods for learning feature representations from raw protein sequences. These may affect the ability of the DTA prediction model to learn the potential features of the protein, thus weakening the predictive power of the model. To tackle this problem, we developed a novel protein pre-training method (PTR) for protein representation learning, then proposed a DTA prediction framework, called Transformer-Graph drug-target affinity prediction (T-GraphDTA), based on PTR and hybrid graph neural network. The hybrid graph neural network is mainly responsible for molecular presentation learning of drugs. Extensive experiments were conducted on four benchmark datasets of drug-target binding affinity, comparing T-GraphDTA against state-of-the-art models. The experimental results show that T-GraphDTA achieves significantly better performance than state-of-the-art models on all four benchmark datasets. It indicates that T-GraphDTA is expected to be an excellent practical tool for predicting the affinity of drug-target pairs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

SASA-Net: A spatial-aware self-attention mechanism for building protein 3D structure directly from inter-residue distances

SASA-Net: A spatial-aware self-attention mechanism for build...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Gong, Tiansu Ju, Fusong Sun, Shiwei Bu, Dongbo Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Zhongke Big Data Academy Henan Zhengzhou450046 China

ISBN: (纸本)9781665401265

Protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning *** of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising;however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential ***, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue's pose according to all other residues' features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at https://*** gongtiansu/SASA-Net/ © 2021 IEEE.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：