检索结果-内蒙古大学图书馆

TechRxiv 2021年

作者： Conchouso, David Al-Ma’abadi, Amani Behzad, Hayedeh Alarawi, Mohammed Hosokawa, Masahito Nishikawa, Yohei Takeyama, Haruko Mineta, Katsuhiko Gojobori, Takashi Departamento de Ingeniería Industrial y Mecánica Escuela de Ingeniería Universidad de las Américas Puebla Puebla72810 Mexico Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Research Organization for Nano & Life Innovation Waseda University Tokyo162–0041 Japan Department of Life Science and Medical Bioscience Waseda University Tokyo162–8480 Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Waseda University Tokyo169-8555 Japan Computational Bio Big-Data Open Innovation Laboratory AIST-Waseda University Tokyo169–0072 Japan Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

Droplet microfluidics techniques have shown promising results to study single-cells at high throughput. However, their adoption in laboratories studying "-omics" sciences is still irrelevant because of the field’s complex and multidisciplinary nature. To facilitate their use, here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput. First, a device encapsulating single-cells in droplets at a rate of ~ 250 Hz is described considering droplet size and cell growth. Then, we expand on previously reported fluorescent activated droplet sorting (FADS) systems to integrate the use of 4 independent fluorescence-exciting lasers (e.g., 405, 488, 561, 637 nm) in a single platform to make it compatible with different fluorescence-emitting biosensors. For this sorter, both hardware and software are provided and optimized for effortlessly sorting droplets at 60 Hz. Then, a passive droplet merger was also integrated into our method to enable adding new reagents to already made droplets at a rate of 200 Hz. Finally, we provide an optimized recipe for manufacturing these chips using silicon dry-etching tools. Because of the overall integration and the technical details presented here, our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput (> 50,000 cells/day) capabilities to mining and bioprospecting metagenomic data. © 2021, CC BY-NC-SA.

关键词： Cell proliferation

来源：评论

学校读者我要写书评

暂无评论

Advances in machine and deep learning for modeling and real-time detection of multi-messenger sources

arXiv

引用

arXiv 2021年

作者： Huerta, Eliu A. Zhao, Zhizhen Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mathematics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Statistics National Center for Supercomputing Applications Center for Artificial Intelligence Innovation University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and *** Codes 83-02, 83-04, 83-08, 85-08, 85-10, 68T07, 68T20, Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

来源：评论

学校读者我要写书评

暂无评论

PC-RGNN: Point cloud completion and graph neural network for 3D object detection

arXiv

引用

arXiv 2020年

作者： Zhang, Yanan Huang, Di Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire views with original structures preserved. On the other hand, a graph neural network module is designed, which comprehensively captures relations among points through a local-global attention mechanism as well as multi-scale graph based context aggregation, substantially strengthening encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Observability quantification for linear stochastic system and its application in state estimation

Observability quantification for linear stochastic system an...

引用

Chinese Automation Congress (CAC)

作者： Tinghui Xin Yuan Liang Xiwang Dong Sibo Hu Qingdong Li Zhang Ren China Innovation Fund Management Co. Ltd. Beijing P. R. China School of Automation Science and Electrical Engineering Beihang University Beijing P. R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper presents a method for quantitatively evaluating the observability of linear stochastic systems and pre-evaluating the accuracy of state estimations. In this method, according to the relation among the current state vector, measurement sequence and noise sequence, the observability degree for the state variables of linear stochastic systems is defined. And it is further proved that, the proposed observability degree can be used to examine the existence of unbiased estimated states and measure the estimation error lower bound. In addition, the necessary and sufficient condition for the state variable to be observable is also given according to the proposed observability degree. Finally, a numerical simulation example on target tracking problem is presented to validate the efficiency of the proposed observability degree.

关键词： Observability Estimation Estimation error Stochastic systems Noise measurement Time-varying systems Target tracking

来源：评论

学校读者我要写书评

暂无评论

Finite Time Decentralized Cubature Information Filtering with Directed Graph

Finite Time Decentralized Cubature Information Filtering wit...

引用

Chinese Automation Congress (CAC)

作者： Yuan Liang Tinghui Xin Sibo Hu Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Beihang University Beijing P. R. China China Innovation Fund Management Co. Ltd. Beijing P. R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper focuses on the distributed state estimation problem with finite communication time and directed graph for the nonlinear discrete system. A finite time decentralized cubature information filtering algorithm is proposed, of which the goal is to estimate the interested states in a fully distributed manner. The proposed filtering algorithm is applicable for the directed graph and only demands finite communication time to obtain satisfactory performance. Besides, the proposed filtering algorithm possesses guaranteed stability in the mean square sense. Finally, the effectiveness of the proposed algorithm is validated through the numerical simulation.

关键词： Directed graphs Protocols State estimation Nonlinear systems Prediction algorithms Numerical models Information filtering

来源：评论

学校读者我要写书评

暂无评论

Synthetic High-Density Lipoprotein-Based Nanomedicine to Silence SOCS1 in Tumor Microenvironment and Trigger Antitumor Immunity against Glioma

引用

Angewandte Chemie 2023年第49期135卷

作者： Chunrong Yang Yujie Li Yuchen Yang Qiankun Ni Zeyu Zhang Yi Chai Prof. Jinghong Li Department of Chemistry Center for BioAnalytical Chemistry Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology Tsinghua University Beijing 100084 China Center for Bioanalytical Chemistry Hefei National Laboratory of Physical Science at Microscale University of Science and Technology of China Hefei 230026 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing 100191 China Department of Neurosurgery Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai 200025 China New Cornerstone Science Laboratory Shenzhen 518054 China Beijing Institute of Life Science and Technology Beijing 102206 China

Immunotherapies have shed light on the treatment of many cancers, but have not improved the outcomes of glioma (GBM). Here, we demonstrated that suppressor of cytokine signaling 1 (SOCS1) was associated with the GBM-associated immunosuppression and developed a multifunctional nanomedicine, which silenced SOCS1 in the tumor microenvironment (TME) of GBM and triggered strong antitumor immunity against GBM. Synthetic high-density lipoprotein (sHDL) was selected as the nanocarrier and a peptide was used to facilitate the blood-brain-barrier (BBB) penetration. The nanocarrier was loaded with a small interfering RNA (siRNA), a peptide, and an adjuvant to trigger antitumor immunity. The nanomedicine concentrated on the TME in vivo, further promoting dendritic cell maturation and T cell proliferation, triggering strong cytotoxic T lymphocyte responses, and inhibiting tumor growth. Our work provides an alternative strategy to simultaneously target and modulate the TME in GBM patients and points to an avenue for enhancing the efficacy of immunotherapeutics.

关键词： Glioma Nanomedicine SOCS1 Tumor Microenvironment

来源：评论

学校读者我要写书评

暂无评论

An information granulation-based fuzzy clustering method for time series segmentation

引用

Journal of Information and Intelligence 2025年

作者： Yashuang Mu Tian Liu Hongyue Guo Xianchao Zhu Lidong Wang Benhang Liu Linlin Guo School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 P.R.China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001P.R. China School of Maritime Economics and Management Dalian Maritime University Dalian 116026 P.R.China Collaborative Innovation Center for Transport Studies Dalian Maritime University Dalian 116026P.R. China School of Science Dalian Maritime University Dalian 116026 P.R. China

Time series segmentation aims to extract some meaningful subsequences from complex temporal information. A proper segmentation can effectively help users to analyze the structure of time series. In this study, we propose an information granulation-based fuzzy clustering method for the problem of time series segmentation. The suggested time series segmentation method follows the technological procedure of fuzzy c-means clustering method. First, the original time series is randomly divided into several segments. Then, an information granulation-based dynamic time warping approach is designed to update the series centers, where the principle of reasonable granularity is utilized to calculate the mean of the segments. Next, the time series segments are clustered by optimizing the objective function. Finally, the optimal segmentation points are generated by merging the contiguous segments in the same cluster. The experimental results show that the established segmentation method has more advantages than the existing segmentation methods.

关键词： Fuzzy c-means clustering Time series segmentation Information granulation Dynamic time warping

来源：评论

学校读者我要写书评

暂无评论

$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

引用

IEEE Transactions on Mobile Computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

来源：评论

学校读者我要写书评

暂无评论

Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross Correlation for Fast and Accurate Stereo Matching on Embedded GPU

Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross C...

引用

IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Qiong Chang Aolong Zha Weimin Wang Xin Liu Masaki Onishi Tsutomu Maruyama Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z 2 - ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.

关键词： Correlation Power demand Graphics processing units Hardware System-on-chip Acceleration Field programmable gate arrays

来源：评论

学校读者我要写书评

暂无评论

Magnetic skyrmions for unconventional computing

arXiv

引用

arXiv 2020年

作者： Li, Sai Kang, Wang Zhang, Xichao Nie, Tianxiao Zhou, Yan Wang, Kang L. Zhao, Weisheng School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 Hong Kong Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States

Improvements in computing performance have significantly slowed down over the past few years owing to the intrinsic limitations of computing hardware. However, the demand for data computing has increased exponentially. To solve this problem, tremendous attention has been focused on the continuous scaling of the Moore’s Law as well as the advanced non-von Neumann computing architecture. A rich variety of unconventional computing paradigms has been raised with the rapid development of nanoscale devices. Magnetic skyrmions, spin swirling quasiparticles, have been endowed with great expectations for unconventional computing due to their potential as the smallest information carriers by exploiting their physics and dynamics. In this paper, we provide an overview of the recent progress of skyrmion-based unconventional computing from a joint device-application perspective. This paper aims to build up a panoramic picture, analyze the remaining challenges, and most importantly to shed light on the outlook of skyrmion based unconventional computing for interdisciplinary researchers. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetism

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：