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A comparison of pivotal sampling and unequal probability sampling with replacement

STATISTICS & PROBABILITY LETTERS 2017年 121卷 1-5页

作者： Chauvet, Guillaume Ruiz-Gazen, Anne ENSAI IRMAR Campus Ker Lann F-35170 Bruz France Toulouse Sch Econ 21 Allee Brienne F-31000 Toulouse France

We prove that any implementation of pivotal sampling is more efficient than multinomial sampling. This yields the weak consistency of the Horvitz Thompson estimator and the existence of a conservative variance estimat... 详细信息

关键词： Cube method Mean-square consistency sampling algorithm Unequal probabilities Variance estimator With-replacement sampling

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sampling Exchangeable and Hierarchical Marshall-Olkin Distributions

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2013年第4期42卷 619-632页

作者： Mai, Jan-Frederik Scherer, Matthias Assenagon Credit Management Munich Germany Tech Univ Munich HVB Inst Math Finance D-85748 Garching Germany

The seminal Marshall-Olkin distribution is given by the following d-dimensional frailty model: independent exponentially distributed random variables represent the arrival times of shocks that destroy subgroups of a vector with d components. sampling the resulting random vector of extinction times along the lines of this stochastic representation is conceptually straightforward. Firstly, all possible shocks are sampled. Secondly, the extinction time of each component is computed. However, implementing this sampling scheme involves 2 d - 1 random variables and requires finding the minimum of a set with 2 d-1 elements for each component. Thus, the overall effort is exponentially increasing in the number of considered components, preventing the general model from being used in high-dimensional simulation studies. This problem is overcome for the subfamily of exchangeable Marshall-Olkin distributions, for which a sampling algorithm with polynomial effort in d, an upper bound is ??(d 3), is presented. In a second step, it is shown how hierarchical models are constructed from exchangeable structures and how models with alternative univariate marginal laws are obtained. The presented algorithm is then adapted to sample such structures as well. Finally, a small case study in the context of portfolio credit risk is presented.

关键词： Lack-of-memory property Marshall-Olkin distribution sampling algorithm 62D05 62H99

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sampling Random Spanning Trees Faster Than Matrix Multiplication 2017

Sampling Random Spanning Trees Faster Than Matrix Multiplica...

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49th Annual ACM-SIGACT Symposium on Theory of Computing (STOC)

作者： Durfee, David Kyng, Rasmus Peebles, John Rao, Anup B. Sachdeva, Sushant Georgia Tech Atlanta GA 30332 USA Yale Univ New Haven CT 06520 USA MIT Cambridge MA 02139 USA Google Mountain View CA USA

ISBN: (纸本)9781450345286

We present an algorithm that, with high probability, generates a random spanning tree from an edge-weighted undirected graph in (O) over tilde (n(5/3)m(1/3)) time. The tree is sampled from a distribution where the probability of each tree is proportional to the product of its edge weights. This improves upon the previous best algorithm due to Colbourn et al. that runs in matrix multiplication time, O(n(omega)). For the special case of unweighted graphs, this improves upon the best previously known running time of (O) over tilde (min{n(omega), m root n, m(4/3)}) form m >> n(7/4) (Colbourn et al. '96, Kelner-Madry '09, Madry et al. '15). The effective resistance metric is essential to our algorithm, as in the work of Madry et al., but we eschew determinant-based and random walk-based techniques used by previous algorithms. Instead, our algorithm is based on Gaussian elimination, and the fact that effective resistance is preserved in the graph resulting from eliminating a subset of vertices (called a Schur complement). As part of our algorithm, we show how to compute epsilon-approximate effective resistances for a set S of vertex pairs via approximate Schur complements in (O) over tilde (m + (n + vertical bar S vertical bar)is an element of(-2)) time, without using the Johnson-Lindenstrauss lemma which requires (O) over tilde (min {(m +vertical bar S vertical bar)is an element of(-2), m + n is an element of(-4) + vertical bar S vertical bar is an element of(-2)}) time. We combine this approximation procedure with an error correction procedure for handling edges where our estimate isn't suffciently accurate.

关键词： sampling algorithm Graph Sparsification Approximate Schur Complement Random Spanning Trees

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sampling Proper Colorings on Line Graphs Using (1+o(1))Δ Colors 2024

Sampling Proper Colorings on Line Graphs Using (1+o(1))Δ Co...

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56th Annual ACM Symposium on Theory of Computing (STOC)

作者： Wang, Yulin Zhang, Chihao Zhang, Zihan Shanghai Jiao Tong Univ Shanghai Peoples R China SOKENDAI Grad Inst Adv Studies Tokyo Japan

We prove that the single-site Glauber dynamics for sampling proper q-colorings mixes in O Delta(n log n) time on line graphs with n vertices and maximum degree Delta when q > (1 + o(1))Delta. The main tool in our p... 详细信息

ISBN: (纸本)9798400703836

关键词： Markov chain Monte Carlo edge colorings sampling algorithm

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An Adaptive sampling Methodology for Internet Traffic Data Measurement

An Adaptive Sampling Methodology for Internet Traffic Data M...

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International Conference on Communication Software and Networks

作者： Zeng, Bin Zhang, Dafang Li, Wenwei Zhang, Mei Hong, Qiao Hunan Univ Coll Comp & Commun Changsha 410082 Hunan Peoples R China Hunan Univ Software Sch Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9780769535227

Current popular sampling algorithms can't provide accurate approximation of true character under the circumstance of network traffic bursting. In the paper, we develop an adaptive network traffic sampling methodology (MANFC-AT) based on aggregated time-serial evolution trend. The MANFC-AT algorithm adaptively adjusts the time interval between consecutive samples according to the evolution trend of network traffic which is estimated from the obtained samples. Then it is able to approximate the measured traffic more precisely. Compared with current popular sampling algorithms, MANFC-AT algorithm can provide more precise estimation of network traffic, only requires fewer samples, contains more information about network traffic bursting.

关键词： traffic measurement traffic data adnalysis sampling algorithm network measurement

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Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 261-271页

作者： Jinping Sun Qifang Chen Mingchao Xia School of Electrical Engineering Beijing Jiaotong University(BJTU)Beijing 100044China

Line parameters play an important role in the control and management of distribution ***,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution ***,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on *** solve this problem,a data-driven method is *** measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the *** parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional ***,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)***,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)*** simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

关键词： Distribution systems line parameter detection and identification probability density function sampling algorithm the time skew of PMU and SCADA measurements

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S-OPT: A POINTS SELECTION algorithm FOR HYPER-REDUCTION IN REDUCED ORDER MODELS

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2024年第4期46卷 B474-B501页

作者： Lauzon, Jessica t. Cheung, Siu wun Shin, Yeonjong Choi, Youngsoo Copeland, Dylan m. Huynh, Kevin Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA North Carolina State Univ Dept Math Raleigh NC 27695 USA Lawrence Livermore Natl Lab Applicat Simulat & Qual Livermore CA 94550 USA

While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce the points selection algorithm developed by Shin and Xiu [SIAM J. Sci. Comput., 38 (2016), pp. A385-A411] as a hyper-reduction method. The selection algorithm was originally proposed as a stochastic collocation method for uncertainty quantification. Since the algorithm aims at maximizing a quantity S that measures both the column orthogonality and the determinant, we refer to the algorithm as S-OPT. Numerical examples are provided to demonstrate the performance of S-OPT and to compare its performance with a gappy proper orthogonal decomposition (POD) algorithm. We found that using the S-OPT algorithm is shown to predict the full order solutions with higher accuracy than gappy POD especially when the number of sampling points is small, although we note that S-OPT shows slow asymptotic convergence with respect to the number of samples for some applications, e.g., Lagrangian hydrodynamics.

关键词： reduced order modeling nonlinear model reduction Galerkin projection hyper-reduction sampling algorithm

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Unsupervised model for aspect categorization and implicit aspect extraction

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第6期64卷 1625-1651页

作者： AL-Janabi, Omar Mustafa Malim, Nurul Hashimah Ahamed Hassain Cheah, Yu-N Univ Sains Sch Comp Sci George Town 11800 Pulau Pinang Malaysia

People's ability to quickly convey their thoughts, or opinions, on various services or items has improved as Web 2.0 has evolved. This is to look at the public perceptions expressed in the reviews. Aspect-based sentiment analysis (ABSA) deemed to receive a set of texts (e.g., product reviews or online reviews) and identify the opinion-target (aspect) within each review. Contemporary aspect-based sentiment analysis systems, like the aspect categorization, rely predominantly on lexicon-based, or manually labelled seeds that is being incorporated into the topic models. And using either handcrafted rules or pre-labelled clues for performing implicit aspect detection. These constraints are restricted to a particular domain or language which is domain-dependent. In this work, we first propose a novel unsupervised probabilistic model Topic-seeds Latent Dirichlet Allocation (TSLDA) that leverages semantic regularities for the articulation of explicit aspect-categories. Then, based on the articulated categories, a distributed vector is used for the identification of implicit aspects. The experimental results show that our approach outperforms baseline methods for different domain-data with minimal configurations. Specifically, utilizing the RI measure, our proposed TSLDA outperformed multiple clustering and topic models by an average of 0.83% in diverse domain-data, and roughly 0.89% using the Precision metric for implicit aspect detection.

关键词： Topic-seeds Topic modelling Word embedding Aspect categorization sampling algorithm Implicit aspects

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Geometrically stable tracking for depth images based 3D reconstruction on mobile devices

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 143卷 222-232页

作者： Liu, Yangdong Gao, Wei Hu, Zhanyi Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

With the development of hardwares such as mobile devices and portable depth cameras, on-line 3D reconstruction on the mobile devices with depth streams as input turns to be possible and promising. Most existing systems use volumetric representation methods to fuse the depth images and use ICP algorithm to estimate the poses of cameras. However, ICP tracker suffers from large drift in scenes containing insufficient geometric information. To deal with this problem, we propose a stability based sampling method which select different number of point-pairs in different windows according to their geometric stability. In addition, we fuse the ICP tracker with the IMU information through an analysis of the condition number. Then we apply the stability based sampling method to the spatially hashed volumetric representation. Qualitative and quantitative evaluations of tracking accuracy and 3D reconstruction results show that our method outperforms the current state-of-the-art systems, especially in scenes lacking sufficient geometric information. In total, our method achieves frame rates 20 Hz on an Apple iPad Air 2 and 200 Hz on a Nvidia GeForce GTX 1060 GPU. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Visual-SLAM Dense mapping ICP algorithm sampling algorithm On-line Mobile device

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Understanding Negative Proposals in Generic Few-Shot Object Detection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 5818-5829页

作者： Yan, Bowei Lang, Chunbo Cheng, Gong Han, Junwei Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China

Recently, Few-Shot Object Detection (FSOD) has received considerable research attention as a strategy for reducing reliance on extensively labeled bounding boxes. However, current approaches encounter significant challenges due to the intrinsic issue of incomplete annotation while building the instance-level training benchmark. In such cases, the instances with missing annotations are regarded as background, resulting in erroneous training gradients back-propagated through the detector, thereby compromising the detection performance. To mitigate this challenge, we introduce a simple and highly efficient method that can be plugged into both meta-learning-based and transfer-learning-based methods. Our method incorporates two innovative components: Confusing Proposals Separation (CPS) and Affinity-Driven Gradient Relaxation (ADGR). Specifically, CPS effectively isolates confusing negatives while ensuring the contribution of hard negatives during model fine-tuning;ADGR then adjusts their gradients based on the affinity to different category prototypes. As a result, false-negative samples are assigned lower weights than other negatives, alleviating their harmful impacts on the few-shot detector without the requirement of additional learnable parameters. Extensive experiments conducted on the PASCAL VOC and MS-COCO datasets consistently demonstrate that our method significantly outperforms both the baseline and recent FSOD methods. Furthermore, its versatility and efficiency suggest the potential to become a stronger new baseline in the field of FSOD. Code is available at https://***/Ybowei/UNP.

关键词： Deep learning few-shot object detection few-shot learning incomplete annotation sampling algorithm

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