检索结果-内蒙古大学图书馆

Low resistivity and high breakdown current density of 10-nm diameter van der waals tase3nanowires by chemical vapor deposition

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Empante, Thomas A. Martinez, Aimee Wurch, Michelle Zhu, Yanbing Geremew, Adane K. Yamaguchi, Koichi Isarraraz, Miguel Rumyantsev, Sergey Reed, Evan J. Balandin, Alexander A. Bartels, Ludwig Department of Chemistry and Material Science & Engineering Program University of California - Riverside RiversideCA92521 United States Department of Materials Science and Engineering Stanford University StanfordCA94304 United States Nano-Device Laboratory Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Center for Terahertz Research and Applications Institute of High Pressure Physics Polish Academy of Sciences Warsaw01-142 Poland

Micron-scale single-crystal nanowires of metallic TaSe3, a material that forms -Ta-Se3-TaSe3- stacks separated from one another by a tubular van der Waals (vdW) gap, have been synthesized using chemical vapor deposition (CVD) on a SiO2/Si substrate, in a process compatible with semiconductor industry requirements. Their electrical resistivity was found unaffected by downscaling from the bulk to as little as 7 nm in width and height, in striking contrast to the resistivity of copper for the same dimensions. While the bulk resistivity of TaSe3is substantially higher than that of bulk copper, at the nanometer scale the TaSe3wires become competitive to similar-sized copper ones. Moreover, we find that the vdW TaSe3nanowires sustain current densities in excess of 108A/cm2and feature an electromigration energy barrier twice that of copper. The results highlight the promise of quasi-onedimensional transition metal trichalcogenides for electronic interconnect applications and the potential of van der Waals materials for downscaled electronics. Copyright © 2019, The Authors. All rights reserved.

关键词： Van der Waals forces

Adaptive Neural Control of a Class of Pure-Feedback Nonlinear Systems with Full State Constrains

学校读者我要写书评

暂无评论

Adaptive Neural Control of a Class of Pure-Feedback Nonlinea...

第37届中国控制会议

作者： Min Wang Yongtao Zou School of Automation Science and Engineering Guangzhou Key Laboratory of Brain Computer Interaction and ApplicationsSouth China University of Technology

In this paper, a new adaptive control design technique is proposed for a class of pure-feedback nonlinear systems with full state constraints. With a nonlinear state transition function proposed, the considered pure-feedback nonlinear system is converted into a new pure-feedback system without state constraints. Then the mean value theorem is used to transform the non-affine system into the affine one. Subsequently, a useful lemma is developed, which effectively conquers the reconstruction question of the controller, so that the adaptive backstepping technique can be extended to the non-strict nonlinear system. As a result, a novel control scheme is proposed to ensure that the closed-loop system are semi-globally uniformly ultimately bounded and the full state constraints are not violated. Numerical simulation is presented to illustrate the effectiveness of the proposed approach.

关键词： State constraints adaptive control pure-feedback systems

SAAGs: Biased Stochastic variance reduction methods for large-scale learning

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Chauhan, Vinod Kumar Sharma, Anuj Dahiya, Kalpana Computer Science & Applications Panjab University Chandigarh India University Institute of Engineering and Technology Panjab University Chandigarh India

Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients. In this paper, we have proposed novel variants of SAAG-I and II (Stochastic Average Adjusted Gradient) Chauhan et el. (2017) [6], called SAAG-III and IV, respectively. Unlike SAAG-I, starting point is set to average of previous epoch in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to average and last iterate of previous epoch in SAAG-IV, respectively. To determine the step size, we have used Stochastic Backtracking-Armijo line Search (SBAS) which performs line search only on selected mini-batch of data points. Since backtracking line search is not suitable for large-scale problems and the constants used to find the step size, like Lipschitz constant, are not always available so SBAS could be very effective in such cases. We have extended SAAGs (I, II, III and IV) to solve non-smooth problems and designed two update rules for smooth and non-smooth problems. Moreover, our theoretical results have proved linear convergence of SAAG-IV for all the four combinations of smoothness and strong-convexity, in expectation. Finally, our experimental studies have proved the efficacy of proposed methods against the state-of-art techniques. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Brain Tumor Segmentation and Classification Using MRI Images via Fully Convolution Neural Networks

学校读者我要写书评

暂无评论

Brain Tumor Segmentation and Classification Using MRI Images...

International Conference on Advances in Computing, Communication Control and Networking

作者： Sanjay Kumar J. N Singh Ashish Negi Amit Gaurav Department of Computer Science Engineering Uttarakhand Technical University Dehradun India Department of Computer Science & Applications G.B.Pant Engineering College Pauri Garhwal Uttarakhand India Department of Computer Science Golgotia University Grater Noida India Department of Computer Science Galgotia College of Engineering & technology Grater Noida India

ISBN: (纸本)9781538641200

Charismatic timbre Imaging be the favorite picture modality intended meant pro assess brains tumor plus segmentation be essential designed on behalf of analysis plus action preparation. Consequently vigorous routine segmentation method is requisite. Mechanism education proposal anywhere the representation be educated as of information be pretty victorious. Hierarchical segmentation approach first section the complete brain tumor follows through intra growth hankie classification. Currently fully convolutional networks approaches for segmentation are very efficient. Exact growth segmentation is necessary plus vital pace intended for mainframe aid brain tumor analysis plus surgical arrangement. They are motionless opposite a few challenge such because inferior segmentation correctness demanding priori information otherwise require the being meddling. Subsequent plan the sorting effect to double picture the placement dispensation is implementing through morphological strain toward acquire the concluding segmentation. During organize near appraise the future technique the research be practical in the direction of section the brain tumor used for the authentic tolerant dataset. The ending presentation show to the future brain tumor segmentation technique be additional precise plus competent.

关键词： Magnetic resonance imaging Fully convolutional networks Segmentation Classification Brain tumor

Faster learning by reduction of data access time

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Chauhan, Vinod Kumar Sharma, Anuj Dahiya, Kalpana Computer Science and Applications Panjab University Chandigarh India University Institute of Engineering and Technology Panjab University Chandigarh India

Nowadays, the major challenge in machine learning is the 'Big Data' challenge. The big data problems due to large number of data points or large number of features in each data point, or both, the training of models have become very slow. The training time has two major components: Time to access the data and time to process (learn from) the data. So far, the research has focused only on the second part, i.e., learning from the data. In this paper, we have proposed one possible solution to handle the big data problems in machine learning. The idea is to reduce the training time through reducing data access time by proposing systematic sampling and cyclic/sequential sampling to select mini-batches from the dataset. To prove the effectiveness of proposed sampling techniques, we have used empirical risk minimization, which is commonly used machine learning problem, for strongly convex and smooth case. The problem has been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each using two step determination techniques, namely, constant step size and backtracking line search method. Theoretical results prove similar convergence for systematic and cyclic sampling as the widely used random sampling technique, in expectation. Experimental results with bench marked datasets prove the efficacy of the proposed sampling techniques and show up to six times faster training. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

Minimum-link rectilinear covering tour is NP-hard in R4

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Chitturi, Bhadrachalam Pai, Jayakumar Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science University of Texas at Dallas RichardsonTX United States

Given a set P of n points in Rd, a tour is a closed simple path that covers all the given points, i.e. a Hamiltonian cycle. A link is a line segment connecting two points and a rectilinear link is parallel to one of the axes. The problems of defining a path and a tour with minimum number of links, also known as Minimum-Link Covering Path and Minimum-Link Covering Tour respectively are proven to be NP-hard in R2. The corresponding rectilinear versions are also NP-hard in R2. A set of points is said to be in general position for rectilinear versions of the problems if no two points share any coordinate. We call a set of points in Rd to be in relaxed general position if no three points share any coordinate and any two points can share at most one coordinate. That is, if the points are either in general position or in relaxed general position then an axis parallel line can contain at most one point. If points are in relaxed general position then these problems are NP-hard in R10. We prove that these two problems are in fact NP-hard in R4. If points in Rd, d > 1 are in general position then the time complexities of these problems, both basic and rectilinear versions, are *** codes 03D15 Copyright © 2018, The Authors. All rights reserved.

关键词： Vector spaces

Author Correction: Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

学校读者我要写书评

暂无评论

NPJ digital medicine 2024年第1期7卷 240页

作者： Gregory Holste Mingquan Lin Ruiwen Zhou Fei Wang Lei Liu Qi Yan Sarah H Van Tassel Kyle Kovacs Emily Y Chew Zhiyong Lu Zhangyang Wang Yifan Peng Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA. Department of Surgery University of Minnesota Minneapolis MN USA. Center for Biostatistics and Data Science Washington University School of Medicine St. Louis MO USA. Department of Obstetrics & Gynecology Columbia University Irving Medical Center New York NY USA. Israel Englander Department of Ophthalmology Weill Cornell Medicine New York NY USA. Division of Epidemiology and Clinical Applications National Eye Institute National Institutes of Health (NIH) Bethesda MD USA. National Center for Biotechnology Information (NCBI) National Library of Medicine (NLM) National Institutes of Health (NIH) Bethesda MD USA. Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA. atlaswang@utexas.edu. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. yip4002@med.cornell.edu.

Modelling a secure support vector machine classifier for private data

学校读者我要写书评

暂无评论

International Journal of Information and computer Security 2018年第1期10卷 25-40页

作者： Sumana, M. Hareesha, K.S. Department of Information Science and Engineering MSRIT Bangalore Karnataka560054 India Department of Computer Applications Manipal Institute of Technology Manipal Karnataka576104 India

Privacy preserving data mining engrosses in drawing out information from distributed data without disclosing sensitive information to collaborating sites. This paper aims on the construction of a vertically distributed privacy preserving support vector machine classifier. The learning model is build for datasets, where one of the collaborating parties comprises the dependent attribute. Furthermore, the amount of privacy, computation speed and the accuracy of our classifier outperform other benchmark algorithms. Privacy of the perceptive attributes values of the cooperating sites are retained while performing secure computations. Collaborative classification is performed using these attributes. The site with the dependent attribute is the master site that initiates the process of secure computation to identify support vectors. Homomorphic property is used to protectively compute the data matrix on records/tuples available at sites. The recommended nonlinear privacy preserving classifier provides an accuracy equivalent to the non-privacy undistributed SVM classifier which uses all the attributes directly. © 2018 Inderscience Enterprises Ltd.

关键词： Vectors

Extending Network Security by Multi Model Encryption Standards for Dynamic Networks Using Fuzzy Logic Technique

学校读者我要写书评

暂无评论

SSRN

SSRN 2018年

作者： Vetrivel, K. Shanmugam, S. Gurumurthy, Sasikumar Department of Computer Applications Anna University Regional Campus Coimbatore India Department of Computer Science and Engineering Anna University Regional Campus Coimbatore India Department of Computer Science and Systems Enigneering Sree Vidyanikethan Engineering Collge Tirupati India

The modern internet technology paves the way for the network users to access various services which can be accessed through mobile devices, PDA and many more. The loosely coupled architecture of network communication has more chance of information threat and many form of network threats. The service provider has the responsibility to secure user’s personalized information and there are many security measures has been implemented to encrypt and decrypt the network data. There are many approaches has been discussed for the security of information flow of the network and suffers with the problem of key dimension and frequency of retransmission of keys due to packet drops. Based on the literature , we propose a novel service orient multi model encryption standards (SMME) to provide efficient security in dynamic networks. The proposed method uses block based encryption approach to transfer large size streams and the encryption keys are generated at each session. The same key will be used for the session and will expire at the end of session. Sometimes the protocol needs exchange of keys between the user and the service provider. This situation is handled by applying fuzzy based key generation with the support of stream cipher which uses number of keys to perform encryption and decryption of the original text. The proposed method can be more applicable in broadcast networks where the subscriber has to posses the key to access the information. The subscriber will be provided with the key for the channel only if he is registered. The exchange of subscription key could be performed using stream cipher method and the rest will be handled using block based cipher methods. With the possession of subscription, the user can access the channel stream using the block based approach. © 2018, The Authors. All rights reserved.

关键词： Network security