Among the emerging nonvolatile memory (NVM) technologies, some resistive memories, including phase change memory (PCM), spin-transfer torque magnetic random access memory (STT-RAM), and metal-oxide resistive RAM (ReRA...
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Among the emerging nonvolatile memory (NVM) technologies, some resistive memories, including phase change memory (PCM), spin-transfer torque magnetic random access memory (STT-RAM), and metal-oxide resistive RAM (ReRAM), have been considered as promising replacements of conventional dynamic RAM (DRAM) to build future main memory systems. Main memory databases can benefit from their nice features, such as their low leakage power and nonvolatility, the high density of PCM, the good read performance and low read energy consumption of STT-RAM, and the low cost of ReRAM's crossbar architecture. However, they also have some disadvantages, such as their long write latency, high write energy, and limited lifetime, which bring challenges to database algorithm design for NVM-based memory systems. In this paper, we focus on the design of the ubiquitous B+-tree, aiming to make it NVM-friendly. We present a basic cost model for NVM-based memory systems which distinguishes writes from reads, and propose detailed CPU cost and memory access models for search, insert, and delete operations on a B+-tree. Based on the proposed models, we analyze the CPU costs and memory behaviors of the existing NVM-friendly B+-tree schemes, and find that they suffer from three issues. To address these issues we propose three different schemes. Experimental results show that our schemes can efficiently improve the performance, reduce the memory energy consumption, and extend the lifetime for NVM-based memory systems.
The focus of this special issue is optimization methods for problems whose solutions have simple *** structures include sparsity (the solution has very few nonzero entries),low-rankness (the solution is a matrix of ve...
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The focus of this special issue is optimization methods for problems whose solutions have simple *** structures include sparsity (the solution has very few nonzero entries),low-rankness (the solution is a matrix of very low rank),consensus (the solution is a set of identical vectors),and *** authors of the articles in this special issue use certain functions and constraints to ensure their solutions to have these *** functions are typically nonsmooth,and such constraints involve all components of variables,thus posing a challenge to algorithm *** algorithms using (sub)gradients and projections are either non-applicable or performing ***,the authors study new methods for better efficiency.
This paper addresses the makespan minimization problem in scheduling flexible job shops whenever there exist separable sequence-dependent setup times. An extension to the neighborhood search functions of Mastrolilli a...
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This paper addresses the makespan minimization problem in scheduling flexible job shops whenever there exist separable sequence-dependent setup times. An extension to the neighborhood search functions of Mastrolilli and Gambradella, developed for the flexible job shop scheduling problem (FJSP), is provided. It is shown that under certain conditions such an extension is viable. Accordingly, a randomized neighborhood search function is introduced, and its best search parameters are determined experimentally using modified FJSP benchmark instances. A tabu search approach utilizing the proposed neighborhood search function is then developed, and experimentations are conducted using the modified instances to benchmark it against a lower bound. Experimental results show that on average, the tabu search approach is capable of achieving optimality gaps of below 10% for instances with low average setup time to processing time ratios. (C) 2015 Elsevier Inc. All rights reserved.
In this paper, we show the first comprehensive experimental study on mobile RFID reading performance based on a relatively large number of tags. By making a number of observations regarding the tag reading performance...
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In this paper, we show the first comprehensive experimental study on mobile RFID reading performance based on a relatively large number of tags. By making a number of observations regarding the tag reading performance, we build a model to depict how various parameters affect the reading performance. Through our model, we have designed very efficient algorithms to maximize the time-efficiency and energy-efficiency by adjusting the reader's power and moving speed. Our experiments show that our algorithms can reduce the total scanning time by 50 percent and the total energy consumption by 83 percent compared to the prior solutions.
In this paper, a Supervised Adaptive Learning-based Fuzzy Controller (ALFC) with Neural Network Identification and Convex Parameterization is designed to identify and control the unmanned vehicle in an autonomous park...
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ISBN:
(纸本)9781467386838
In this paper, a Supervised Adaptive Learning-based Fuzzy Controller (ALFC) with Neural Network Identification and Convex Parameterization is designed to identify and control the unmanned vehicle in an autonomous parking system. The objective is to achieve robust learning and control while maintaining a low implementation cost. The proposed algorithm design incorporates the following learning and control theorems - non-linear system identification using neural network, fuzzy logic, supervised adaptive learning as well as multiple model based convex parameterization. To demonstrate the algorithm in a more straight forward manner, we are using a real nonlinear unmanned autonomous driving system as an example to apply the algorithm and showing the superior performance of controller. In the autonomous driving system, the proposed method can be used for both estimating and further controlling a desired vehicle speed and steering wheel turning. With a supervised adaptive learning-based method, robustness can be also assured under various operating environments regardless of unpredictable disturbances. The convex parameterization further improves the speed of convergence of the adaptive learning process for the Fuzzy controller by using the multiple models concept. Last but not least, comparative experiments have also demonstrated that systems equipped with the new algorithm are able to achieve faster and smoother convergence.
The diverse world of machine learning applications has given rise to a plethora of algorithms and optimization methods, finely tuned to the specific regression or classification task at hand. We reduce the complexity ...
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ISBN:
(纸本)9781510838819
The diverse world of machine learning applications has given rise to a plethora of algorithms and optimization methods, finely tuned to the specific regression or classification task at hand. We reduce the complexity of algorithm design for machine learning by reductions: we develop reductions that take a method developed for one setting and apply it to the entire spectrum of smoothness and strong-convexity in applications. Furthermore, unlike existing results, our new reductions are optimal and more practical. We show how these new reductions give rise to new and faster running times on training linear classifiers for various families of loss functions, and conclude with experiments showing their successes also in practice.
We present a study on minimizing non-renewable energy for the Internet. The classification of renewable and nonrenewable energy brings in several challenges. First, it is necessary to understand how the routing system...
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ISBN:
(纸本)9781467399548
We present a study on minimizing non-renewable energy for the Internet. The classification of renewable and nonrenewable energy brings in several challenges. First, it is necessary to understand how the routing system can distinguish the two types of energy in the power supply. Second, the routing problem changes due to renewable energy;and so do the algorithm designs and analysis. We first clarify the model of how routers can distinguish renewable and non-renewable energy supporting their power supply. This cannot be determined by the routing system alone, and involves modeling the energy generation and supply of the grid. We then present the router power consumption model, which has a fixed startup power and a dynamic traffic-dependent power. We formulate a minimum non-renewable energy routing problem, and two special cases representing either the startup power dominates or the traffic-dependent power dominates. We analyze the complexity of these problems, develop optimal and sub-optimal algorithms, and jointly consider QoS requirements such as path stretch. We evaluate our algorithms using real data from both National and European centers. As compared to the algorithms minimizing the total energy, our algorithms can reduce the non-renewable energy consumption for more than 20% under realistic assumptions.
A randomized on-line algorithm is given for competitiveness less than 1901 against the previously best known competitiveness of IN uses a new approach and defines a potential in the 2-server problem on the line, with ...
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A randomized on-line algorithm is given for competitiveness less than 1901 against the previously best known competitiveness of IN uses a new approach and defines a potential in the 2-server problem on the line, with oblivious adversary. This improves the 155/78 approximate to 1.987 for the problem. The algorithm terms of isolation indices from T-theory. (C) 2015 Elsevier B.V. All rights reserved.
Executing irregular, data-intensive workloads on multithreaded architectures can result in performance losses and scalability problems. Codesigning algorithms and architectures can realize high performance on irregula...
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Executing irregular, data-intensive workloads on multithreaded architectures can result in performance losses and scalability problems. Codesigning algorithms and architectures can realize high performance on irregular applications. A codesign study reveals four key lessons learned from implementing matching algorithms on various platforms.
Background: Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental desig...
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Background: Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory. Results: We propose a new methodology for dynamical modeling of genomic networks that can utilize the interaction knowledge provided in public databases. The model assigns discrete states for physical entities, sets priorities among interactions based on information provided in the database, and updates each interaction based on associated node states. Whenever uncertainty in dynamics arises, it explores all possible outcomes. By using the proposed model, biologists can study regulation networks that are too complex for manual analysis. Conclusions: The proposed approach can be effectively used for constructing dynamical models of interaction-based genomic networks without requiring a complete knowledge of all parameters affecting the network dynamics, and thus based on a small set of available data.
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