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SimpleFit: a framework for analyzing design trade-offs in Raw architectures

C.A. Moritz, Donald Yeung, A. Agarwal
2001 IEEE Transactions on Parallel and Distributed Systems  
Although the optimal machine configurations obtained vary for different applications, problem sizes, and budgets, the general trends for various applications are similar.  ...  One open challenge in Raw architectures is to determine their optimal grain size and balance.  ...  The authors are grateful to Tom Knight, Jonathan Babb, and Matt Frank for many relevant discussions on cost modeling, and the anonymous reviewers for their very valuable comments and help with earlier  ... 
doi:10.1109/71.940747 fatcat:trzaxxmj7vb63ougqfclbrvshe

Distributed Machine Learning on Mobile Devices: A Survey [article]

Renjie Gu, Shuo Yang, Fan Wu
2019 arXiv   pre-print
It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization  ...  This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information.  ...  Another kind of possible solutions are to develop machine learning optimizers which are not sensitive to the distribution of training set.  ... 
arXiv:1909.08329v1 fatcat:h3xiw2xfyjab5mgn2g4f7merwq

Crowd-ML: A library for privacy-preserving machine learning on smart devices

Jihun Hamm, Jackson Luken, Yani Xie
2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
The library allows researchers and developers to easily implement and deploy customized tasks that use on-device sensors to collect sensitive data for machine learning.  ...  When user-generated data such as audio and video signals are used to train machine learning algorithms, users' privacy must be considered before the learned model is released.  ...  Deciding an optimal noise sequence with the least utility loss is still an open problem.  ... 
doi:10.1109/icassp.2017.7953387 dblp:conf/icassp/HammLX17 fatcat:dqkz7uuyyva47moppvqharste4

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Kwok Chui, Wadee Alhalabi, Sally Pang, Patricia Pablos, Ryan Liu, Mingbo Zhao
2017 Sustainability  
Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered.  ...  To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things  ...  The idea and structure of this manuscript was proposed by Kwok Tai Chui. Wadee Alhalabi advised and guided on structuring and presenting the information clearly.  ... 
doi:10.3390/su9122309 fatcat:7tmwfunpo5a4baqyc73gqkqggy

Model-Agnostic Linear Estimation of Generator Rotor Speeds based on Phasor Measurement Units

Federico Milano, Alvaro Ortega, Antonio J. Conejo
2018 IEEE Transactions on Power Systems  
The dynamic state estimation is formally stated as a convex optimization problem and a thorough discussion of the sensitivity analysis of the optimal solution is provided.  ...  Abstract-The paper focuses on the estimation of the rotor speeds of synchronous machines by means of phasor measurement units.  ...  We will also study how to improve the robustness of the proposed optimization problem through, for example, an EKF technique and consider practical implementation aspects, such as communication issues  ... 
doi:10.1109/tpwrs.2018.2846737 fatcat:h65dzjuvlva7ncwynmnglyuxfa

Editorial: Introduction to the Issue on Distributed Machine Learning for Wireless Communication

Ping Yang, Octavia A. Dobre, Ming Xiao, Marco Di Renzo, Jun Li, Tony Q. S. Quek, Zhu Han
2022 IEEE Journal on Selected Topics in Signal Processing  
problem of joint beam training and data transmission control of delay-sensitive communications over mmWave channels.  ...  Three optimization criteria for MEC networks are proposed based on the requirements of latency and energy consumption, and the optimization problem is solved using a FL optimization framework in which  ... 
doi:10.1109/jstsp.2022.3165356 fatcat:dab46w4tone55oow6gquetnp6m

Presence-Aware Optimum Resource Allocation for Virtual Collaboration Web 3.0 Environments

Michael G. Kallitsis, Robert D. Callaway, Michael Devetsikiotis, George Michailidis
2009 2009 IEEE Globecom Workshops  
Our optimization model involves a variation of the 2D Knapsack problem and a nonlinear programming one.  ...  In this paper, we present a system that optimally and dynamically allocates the available computing resources to virtual machines that support virtual collaboration environments.  ...  MK and MD are supported in part by IBM and CACC.  ... 
doi:10.1109/glocomw.2009.5360709 fatcat:n2p4md6s5zd5vnfhki5qt63k44

Database Virtualization: A New Frontier for Database Tuning and Physical Design

Ahmed A. Soror, Ashraf Aboulnaga, Kenneth Salem
2007 2007 IEEE 23rd International Conference on Data Engineering Workshop  
This has many benefits, but it also introduces new tuning and physical design problems that are of interest to the database research community.  ...  We also discuss the next steps in solving this problem, and present some long-term research directions.  ...  This is a combinatorial optimization problem, and the framework to solve it is presented in Figure 2 .  ... 
doi:10.1109/icdew.2007.4401021 dblp:conf/icde/SororAS07 fatcat:kqoqub67w5ai5bmjeqr2g3f4c4

A Parallel Immune Algorithm Based on Fine-Grained Model with GPU-Acceleration

Jianming Li, Lihua Zhang, Linlin Liu
2009 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)  
Fine-grained parallel ant colony optimization algorithm (FGACO), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size  ...  is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers.  ...  data communication is hardly acceptable in most parallel machines; (2) Parallel machine equipments are relatively more difficult to use, manage and maintain; and (3) some people may not have access to  ... 
doi:10.1109/icicic.2009.44 fatcat:7u2krti2hbcm5bhai3vmahy4d4

Sensitivity analysis of tree scheduling on two machines with communication delays

Frédéric Guinand, Aziz Moukrim, Eric Sanlaville
2004 Parallel Computing  
The paper compares the optimal makespans with and without communication delays.  ...  The results are used to obtain sensitivity bounds for algorithms providing optimal schedules for graphs with unit execution and communication times (UECT UECT).  ...  Section 3 compares the two classical problems with and without communications. Within Section 4 tight relative and absolute bounds are presented for the twomachine problem.  ... 
doi:10.1016/s0167-8191(03)00091-7 fatcat:ktz2slienngrvph3zmkmgjcxpe

Preface

Alexandre d'Aspremont, Francis Bach, Inderjit S. Dhillon, Bin Yu
2010 Mathematical programming  
At the same time, machine learning provides optimization with an ever larger array of new problems and challenging data sets: 1 penalized least-squares and the NETFLIX problem being two recent examples  ...  realistically large statistical problems, and statisticians to consider sophisticated optimization algorithms.  ...  A. d'Aspremont First-order methods are often the only option when solving large-scale machine learning problems and Yurii Nestrov presents here a "Barrier subgradient method" and derives a new affine-invariant  ... 
doi:10.1007/s10107-010-0424-0 fatcat:vg2jfbgvt5b2hnat7emkej7miy

Optimal dataset allocation in distributed heterogeneous clouds

Min Sang Yoon, Ahmed E. Kamal
2014 2014 IEEE Globecom Workshops (GC Wkshps)  
Processing time includes virtual machine processing time, communication time, and data transfer time.  ...  If we place datasets far from each other, this increases the communication and data transfer time. The cost objective includes virtual machine cost, communication cost, and data transfer cost.  ...  We therefore formulated the assignment problem as a dual objective optimization problem, and introduced an algorithm for finding the Pareto front of optimal solutions.  ... 
doi:10.1109/glocomw.2014.7063389 dblp:conf/globecom/YoonK14 fatcat:sunf3rdk4bavpm6fyn525dpqkq

Guest Editorial: Robust Design and Analysis of Electric Machines and Drives

Gerd Bramerdorfer, Andrea Cavagnino, Seungdeog Choi, Gang Lei, David Lowther, Stjepan Stipetic, Jan Sykulski, Yongchang Zhang, Jian Guo Zhu
2020 IEEE transactions on energy conversion  
Scholnick-Philippidis and K. Capaldo, for their technical insights and timely assistance. GERD  ...  ACKNOWLEDGMENT The Guest Editor-in-Chief and Guest Associate Editors would like to express their gratitude to the IEEE Power and Energy Society for the support received and specifically thank the contributing  ...  Satisfaction Problem r Parameter Design Process for a High-Speed Permanent Magnet Machine under Multiphysics Constraints r Robust Design Optimization of a Five-Phase PM Hub Motor for Fault-Tolerant Operation  ... 
doi:10.1109/tec.2020.3035940 fatcat:rqyp2zq3fzfurmiodyva7gdd2u

Automated Machine Learning for Future Networks Including 5G

Shagufta Henna, Alan Davy
2019 IEEE Technology Policy and Ethics  
Popular scenarios of 5G under this release include enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra-Reliable and Low Latency Communications (URLLC) [1] .  ...  The proposed framework can optimize the learning performance based on the strict use-case requirements.  ...  Some are sensitive to the data type in terms of statistics and visualizations and less sensitive to missing data. Others are sensitive to outliers and may result in poor predictions.  ... 
doi:10.1109/ntpe.2019.9778120 fatcat:ovhz4k6nrvapfeavi6zosqckqm

An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation

Jiafu Su, Xu Chen, Fengting Zhang, Na Zhang, Fei Li
2021 Journal of Theoretical and Applied Electronic Commerce Research  
On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in  ...  support vector machine theory.  ...  the Chongqing humanities and social sciences research project (20SKGH110).  ... 
doi:10.3390/jtaer16050088 fatcat:flx66rukcjgoblpkihrmykqnwy
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