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检索条件"主题词=Algorithm design and analysis"

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On the Local Structure of Stable Clustering Instances 58

On the Local Structure of Stable Clustering Instances

引用

58th IEEE Annual Symposium on Foundations of Computer Science (FOCS)

作者： Cohen-Addad, Vincent Schwiegelshohn, Chris Univ Copenhagen Copenhagen Denmark Sapienza Univ Rome Rome Italy

ISBN: (纸本)9781538634646

We study the classic k-median and k-means clustering objectives in the beyond-worst-case scenario. We consider three well-studied notions of structured data that aim at characterizing real-world inputs: Distribution Stability (introduced by Awasthi, Blum, and Sheffet, FOCS 2010) Spectral Separability (introduced by Kumar and Kannan, FOCS 2010) Perturbation Resilience (introduced by Bilu and Linial, ICS 2010) We prove structural results showing that inputs satisfying at least one of the conditions are inherently "local". Namely, for any such input, any local optimum is close both in term of structure and in term of objective value to the global optima. As a corollary we obtain that the widely-used Local Search algorithm has strong performance guarantees for both the tasks of recovering the underlying optimal clustering and obtaining a clustering of small cost. This is a significant step toward understanding the success of local search heuristics in clustering applications.

关键词： Clustering algorithms Approximation algorithms algorithm design and analysis Stability analysis Extraterrestrial measurements Resilience

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Noise-Blind Image Deblurring 30

Noise-Blind Image Deblurring

引用

30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jin, Meiguang Roth, Stefan Favaro, Paolo Univ Bern Bern Switzerland Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9781538604571

We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional integrals in closed form. It avoids the degeneracy of Maximum a-Posteriori (MAP) estimates and leads to an effective noise-adaptive scheme. Moreover, we drastically accelerate our algorithm by using Majorization Minimization (MM) without introducing any approximation or boundary artifacts. We further speed up convergence by turning our algorithm into a neural network termed GradNet, which is highly parallelizable and can be efficiently trained. We demonstrate that our noise-blind formulation can be integrated with different priors and significantly improves existing deblurring algorithms in the noise-blind and in the known-noise case. Furthermore, GradNet leads to state-of-the-art performance across different noise levels, while retaining high computational efficiency.

关键词： Noise level Image restoration Kernel Bayes methods Estimation Robustness algorithm design and analysis

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Investigating Grammar-based design of Multi-objective Particle Swarm Optimization algorithm 6

Investigating Grammar-based Design of Multi-objective Partic...

引用

6th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Remes de Lima, Ricardo Henrique Ramirez Pozo, Aurora Trinidad Univ Fed Parana Comp Sci Dept Curitiba Parana Brazil

ISBN: (纸本)9781538624074

The importance of automatic algorithms design has been pointed out by researchers. In general, automatically designed algorithms can outperform tailored made ones. This is the case of the Particle Swarm Optimization algorithm (PSO) that has many components that can be chosen such as the velocity equation, etc. Motivated by the success reported by the automatic design of the PSO, this study investigates Multi-objective PSO (MOPSO), an extension of PSO that deals with multi-objective problems. Furthermore, this study presents a framework based on the use of a context-free grammar to guide the design of the MOPSO. The grammar allows the use of different components and parameters from various MOPSOs. Further, the framework offers two design methods: Grammatical Evolution (GE) and Iterated Race (IRACE). Likewise, a set of experiments is made to evaluate the framework using a set of Multi-objective problems, quality indicators and statistical tests. The set of experiments includes the evaluation of: two versions of the grammar, GE against IRACE and a comparison with the Speed-constrained PSO (SMPSO), a well-known multi-objective algorithm.

关键词： algorithm design and analysis Grammar Particle swarm optimization Training Genetics Production Reactive power

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Online Resource Allocation for Arbitrary User Mobility in Distributed Edge Clouds 37

Online Resource Allocation for Arbitrary User Mobility in Di...

引用

37th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Wang, Lin Jiao, Lei Li, Jun Muehlhaeuser, Max Tech Univ Darmstadt Darmstadt Germany Univ Oregon Eugene OR 97403 USA

ISBN: (纸本)9781538617915

As clouds move to the network edge to facilitate mobile applications, edge cloud providers are facing new challenges on resource allocation. As users may move and resource prices may vary arbitrarily, resources in edge clouds must be allocated and adapted continuously in order to accommodate such dynamics. In this paper, we first formulate this problem with a comprehensive model that captures the key challenges, then introduce a gap-preserving transformation of the problem, and propose a novel online algorithm that optimally solves a series of subproblems with a carefully designed logarithmic objective, finally producing feasible solutions for edge cloud resource allocation over time. We further prove via rigorous analysis that our online algorithm can provide a parameterized competitive ratio, without requiring any a priori knowledge on either the resource price or the user mobility. Through extensive experiments with both real-world and synthetic data, we further confirm the effectiveness of the proposed algorithm. We show that the proposed algorithm achieves near-optimal results with an empirical competitive ratio of about 1.1, reduces the total cost by up to 4x compared to static approaches, and outperfiffms the online greedy one-shot optimizations by up to 70%.

关键词： Cloud computing Resource management Delays algorithm design and analysis Dynamic scheduling Optimization Servers

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A fast noise level estimation algorithm based on adaptive image segmentation and Laplacian convolution 40

A fast noise level estimation algorithm based on adaptive im...

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40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Turajlic, Emir Univ Sarajevo Fac Elect Engn Sarajevo Bosnia & Herceg

ISBN: (纸本)9789532330922

This paper proposes a fast algorithm for additive white Gaussian noise level estimation from still digital images. The proposed algorithm uses a Laplacian operator to suppress the underlying image signal. In addition, the algorithm performs a non-overlapping block segmentation of images in conjunction with the local averaging to obtain the local noise level estimates. These local noise level estimates facilitate a variable block size image tessellation and adaptive estimation of homogenous image patches. Thus, the proposed algorithm can be described as a hybrid method as it adopts some principal characteristics of both filter-based and block-based methods. The performance of the proposed noise estimation algorithm is evaluated on a dataset of natural images. The results show that the proposed algorithm is able to provide a consistent performance across different image types and noise levels. In addition, it has been demonstrated that the adaptive nature of homogenous block estimation improves the computational efficiency of the algorithm.

关键词： Estimation Noise level Image segmentation Laplace equations AWGN algorithm design and analysis Convolution

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HYBRID METAHEURISTIC EXPERIMENTS OF REAL-TIME ADAPTIVE OPTIMIZATION OF PARAMETRIC SHADING design THROUGH REMOTE DATA TRANSFER

HYBRID METAHEURISTIC EXPERIMENTS OF REAL-TIME ADAPTIVE OPTIM...

引用

Winter Simulation Conference (WSC)

作者： Yi, Hwang Florida Int Univ Dept Architecture Coll Commun Architecture Arts 11200 SW 8th St Miami FL 33199 USA

ISBN: (纸本)9781538634288

The author seeks a practical approach to complement deterministic design optimization in environmental performance-based building design. This study investigates algorithms and scripted processes to test and monitor the dynamic optimal control of building components. To this end, an integration of wireless data transfer equipment (nRF24L01) and a customized metaheuristic hybrid optimization algorithm (Tabubased adaptive pattern search simulated annealing, T-APSSA) through a parametric visual programming language (VPL) interface (Rhino grasshopper (R)) is presented with experimental design of a responsive kinetic shading device. To demonstrate the performance of the algorithmic hybridization and early design integration, T-APSSA is compared to simulated annealing and pattern (direct) search, and two different approaches to daylight-optimized design solutions are tested: a deterministic optimization based on historical weather data and a site-specific adaptive optimization according to real-time monitoring of incident solar radiance. The suggestion of a seamless environmental building design workflow through remote data communication contributes to strengthening intelligent architectural design decisions.

关键词： Buildings algorithm design and analysis Simulated annealing Computer architecture Architecture Data communication

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LSHiForest: A Generic Framework for Fast Tree Isolation based Ensemble Anomaly analysis 33

LSHiForest: A Generic Framework for Fast Tree Isolation base...

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IEEE 33rd International Conference on Data Engineering (ICDE)

作者： Zhang, Xuyun Dou, Wanchun He, Qiang Zhou, Rui Leckie, Christopher Kotagiri, Ramamohanarao Salcic, Zoran Univ Auckland Auckland New Zealand Nanjing Univ Nanjing Jiangsu Peoples R China Swinburne Univ Technol Hawthorn Vic Australia Victoria Univ Melbourne Vic Australia Univ Melbourne Melbourne Vic Australia

ISBN: (纸本)9781509065431

Anomaly or outlier detection is a major challenge in big data analytics because anomaly patterns provide valuable insights for decision-making in a wide range of applications. Recently proposed anomaly detection methods based on the tree isolation mechanism are very fast due to their logarithmic time complexity, making them capable of handling big data sets efficiently. However, the underlying similarity or distance measures in these methods have not been well understood. Contrary to the claims that these methods never rely on any distance measure, we find that they have close relationships with certain distance measures. This implies that the current use of this fast isolation mechanism is only limited to these distance measures and fails to generalise to other commonly-used measures. In this paper, we propose a generic framework named LSHiForest for fast tree isolation based ensemble anomaly analysis with the use of a Locality-Sensitive Hashing (LSH) forest. Being generic, the proposed framework can be instantiated with a diverse range of LSH families, and the fast isolation mechanism can be extended to any distance measures, data types and data spaces where an LSH family is defined. In particular, the instances of our framework with kernelised LSH families or learning based hashing schemes can detect complicated anomalies like local or surrounded anomalies. We also formally show that the existing tree isolation based detection methods are special cases of our framework with the corresponding distance measures. Extensive experiments on both synthetic and real-world benchmark data sets show that the framework can achieve both high time efficiency and anomaly detection quality.

关键词： Vegetation Current measurement Big Data algorithm design and analysis Data mining Benchmark testing Feature extraction

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A Data Fusion System for Accurate Precipitation Estimation Using Satellite and Ground Radar Observations: Urban Scale Application in Dallas-Fort Worth Metroplex 32

A Data Fusion System for Accurate Precipitation Estimation U...

引用

32nd General Assembly and Scientific Symposium of the International-Union-of-Radio-Science (URSI GASS)

作者： Chen, Haonan Chandrasekar, V. Cifelli, Robert Xie, Pingping Tan, Haiming Colorado State Univ Ft Collins CO 80523 USA NOAA Earth Syst Res Lab Boulder CO 80305 USA NOAA Climate Predict Ctr College Pk MD 20740 USA

ISBN: (纸本)9789082598704

The space-based precipitation products are commonly used for regional and/or global hydrologic modelling and climate studies. However, the accuracy of onboard satellite measurements is limited due to the spatial temporal sampling limitations, especially for extreme events such as very heavy or light rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation (QPE). Nowadays, ground radars are critical for providing local scale rainfall estimation for operational forecasters to issue watches and warnings, as well as validation of various space measurements and products. This paper introduces a neural network based data fusion mechanism to improve satellite-based precipitation retrievals by incorporating dual-polarization measurements from ground-based dense radar network. The prototype architecture of this fusion system is detailed. Results from urban scale application in Dallas-Fort Worth (DFW) Metroplex are presented.

关键词： Radar polarimetry Rain Real-time systems algorithm design and analysis Probability distribution Probability density function

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MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection

MACORD: Online Adaptive Machine Learning Framework for Silen...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Subasi, Omer Di, Sheng Balaprakash, Prasanna Unsal, Osman Labarta, Jesus Cristal, Adrian Krishnamoorthy, Sriram Cappello, Franck Pacific Northwest Natl Lab Richland WA 99352 USA Barcelona Supercomp Ctr Barcelona Spain Argonne Natl Lab Lemont IL USA CSIC Spanish Natl Res Council IIIA Artificial Intelligence Res Inst Madrid Spain

ISBN: (纸本)9781538623268

Future high-performance computing (HPC) systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are among the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs can be harmful and dangerous in that they cannot be detected by hardware. To remedy this, we propose an online MAchine-learning-based silent data CORruption Detection framework (abbreviated as MACORD) for detecting SDCs in HPC applications. In our study, we comprehensively investigate the prediction ability of a multitude of machine-learning algorithms and enable the detector to automatically select the best-fit algorithms at runtime to adapt to the data dynamics. Because it takes only spatial features (i.e., neighboring data values for each data point in the current time step) into the training data, our learning framework exhibits low memory overhead (less than 1%). Experiments based on real-world scientific applications/benchmarks show that our framework can elevate the detection sensitivity (i.e., recall) up to 99%. Meanwhile the false positive rate is limited to 0.1% in most cases, which is one order of magnitude improvement compared with the latest state-of-the-art spatial technique.

关键词： Training algorithm design and analysis Heuristic algorithms Vegetation Prediction algorithms Machine learning algorithms Mathematical model

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Identifying Mosquito Species using Smart-Phone Cameras

Identifying Mosquito Species using Smart-Phone Cameras

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European Conference on Networks and Communications (EuCNC)

作者： Minakshi, Mona Bharti, Pratool Chellappan, Sriram Univ S Florida Comp Sci & Engn Dept Tampa FL 33620 USA

ISBN: (纸本)9781538638736

Mosquito borne diseases have been amongst the most important healthcare concerns since time. An important component in combating the spread of infections in any geographic region of interest has been to identify the type of species that are prevalent in that region. As of today, dedicated personnel are assigned in most (if not all nations) to trap samples and identify them. Unfortunately, the process of identifying the actual species of mosquito is currently a manual process requiring highly trained personnel to visually inspect each specimen one by one under a microscope to make the identification. In this paper, we propose a system to automate this process. Specifically, we demonstrate results of an experiment we conducted where learning algorithms were designed to process images of captured mosquito samples taken via a smart-phone camera in order to identify the actual species. Using a total sample size of 60 images that included 7 species collected by the Hillsborough County Mosquito and Aquatic Weed Control Unit (in the city of Tampa) our proposed technique using Random Forests achieved an overall accuracy of 83:3% in correctly identifying the species of mosquito with good precision and recall. While our proposed technique will greatly benefit the state-of-the-art in species identification, we also believe that common citizens can also use our proposed system to improve existing mosquito control programs across the globe.

关键词： Feature extraction Image color analysis Cameras algorithm design and analysis Colored noise Diseases Personnel

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