We propose and test a specific correction factor which improves both the resampling and projection algorithm for approximating the minimum volume ellipsoid (MVE) estimator. Simulations show that a high-breakdown-point...
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We propose and test a specific correction factor which improves both the resampling and projection algorithm for approximating the minimum volume ellipsoid (MVE) estimator. Simulations show that a high-breakdown-point GM estimator, based among other things on these improved MVE-estimates of location and scatter (i) is little less efficient than OLS if data are free from outlying observations, but in most cases is much more efficient if outliers corrupt the data, (ii) is always more efficient than Rousseeuw's least median of squares (LMS) estimator, and (iii) is always superior to both LMS and OLS if both precision and efficiency are considered. (C) 1997 Elsevier Science B.V.
The Lovasz Local Lemma (LLL) is a cornerstone principle in the probabilistic method of combinatorics, and a seminal algorithm of Moser and Tardos (2010) provides an efficient randomized algorithm to implement it. This...
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The Lovasz Local Lemma (LLL) is a cornerstone principle in the probabilistic method of combinatorics, and a seminal algorithm of Moser and Tardos (2010) provides an efficient randomized algorithm to implement it. This can be parallelized to give an algorithm that uses polynomially many processors and runs in O(log(3) n) time on an EREW PRAM, stemming from O(log n) adaptive computations of a maximal independent set (MIS). Chung et al. (2014) developed faster local and parallel algorithms, potentially running in time O(log(2) n), but these algorithms require more stringent conditions than the LLL. We give a new parallel algorithm that works under essentially the same conditions as the original algorithm of Moser and Tardos but uses only a single MIS computation, thus running in O(log(2) n) time on an EREW PRAM. This can be derandomized to give an NC algorithm running in time O(log(2) n) as well, speeding up a previous NC LLL algorithm of Chandrasekaran et al. (2013). We also provide improved and tighter bounds on the runtimes of the sequential and parallel resampling-based algorithms originally developed by Moser and Tardos. These apply to any problem instance in which the tighter Shearer LLL criterion is satisfied.
Frailty syndrome is prevalent among the elderly, often linked to chronic diseases and resulting in various adverse health outcomes. Existing research has predominantly focused on predicting individual frailty-related ...
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Frailty syndrome is prevalent among the elderly, often linked to chronic diseases and resulting in various adverse health outcomes. Existing research has predominantly focused on predicting individual frailty-related outcomes. However, this paper takes a novel approach by framing frailty as a multi-label learning problem, aiming to predict multiple adverse outcomes simultaneously. In the context of multi-label classification, dealing with imbalanced label distribution poses inherent challenges to multi-label prediction. To address this issue, our study proposes a hybrid resampling approach tailored for handling imbalance problems in the multi-label scenario. The proposed resampling technique and prediction tasks were applied to a high-dimensional real-life medical dataset comprising individuals aged 65 years and above. Several multi-label algorithms were employed in the experiment, and their performance was evaluated using multi-label metrics. The results obtained through our proposed approach revealed that the best-performing prediction model achieved an average precision score of 83%. These findings underscore the effectiveness of our method in predicting multiple frailty outcomes from a complex and imbalanced multi-label dataset.
The resampling algorithm of Moser and Tardos is a powerful approach to develop constructive versions of the Lovasz Local Lemma. We generalize this to partial resampling: When a bad event holds, we resample an appropri...
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The resampling algorithm of Moser and Tardos is a powerful approach to develop constructive versions of the Lovasz Local Lemma. We generalize this to partial resampling: When a bad event holds, we resample an appropriately random subset of the variables that define this event rather than the entire set, as in Moser and Tardos. This is particularly useful when the bad events are determined by sums of random variables. This leads to several improved algorithmic applications in scheduling, graph transversals, packet routing, and so on. For instance, we settle a conjecture of Szabo and Tardos (2006) on graph transversals asymptotically and obtain improved approximation ratios for a packet routing problem of Leighton, Maggs, and Rao (1994).
We propose herein an algorithm to resample the Raman spectra which aims to correct the predicted error induced by spectral shift. The same set of training data for both human and nonhuman blood Raman spectra was appli...
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We propose herein an algorithm to resample the Raman spectra which aims to correct the predicted error induced by spectral shift. The same set of training data for both human and nonhuman blood Raman spectra was applied to build the model using partial least squares (PLS) method. Several kinds of Raman spectra of the blood samples originating from human, goose, monkey, chicken, sheep and duck without spectral shift were used to validate the model. The Raman spectra of the same blood samples with a shift of 9.18 cm(-1) were used to illustrate the influence of the spectral shift to the model. To demonstrate the effectiveness of the algorithm proposed, Raman spectra with spectral shift were resampled. The results show that the resampling algorithm can improve both the stability and accuracy of the model and reduce the impact of the spectral shift, which is promising for quantitative or qualitative analysis in Raman spectroscopy for complex samples.
This paper presents a particle filter processor for mobile positioning systems. The proposed particle filter employs a selective sampling scheme to reduce the required iteration of particle filtering and improve posit...
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ISBN:
(纸本)9781467344364;9781467344357
This paper presents a particle filter processor for mobile positioning systems. The proposed particle filter employs a selective sampling scheme to reduce the required iteration of particle filtering and improve positioning accuracy. Moreover, a threshold IMH resampling algorithm is also proposed to enhance the hardware utilization rate and processing latency. This study also designs and implements the particle filering processor with a flexible particle number and sampling threshold according different channel conditions. The proposed threshold IMH resampling not only improves the hardware utilization to more than 95% but also reduces the processing latency of the conventional IMH resampling algorithm. The chip implementation results show that the particle filter, when deployed in base-station, can track the location of 100 mobile users at 180 km/hr speed with less than 10-meter root-mean-square (RMS) error.
Compute Unified Device Architecture (CUDA) is a mature parallel computing architecture, which can significantly accelerate performance of the computation intensive algorithm. In this paper, FastSLAM algorithm based on...
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Compute Unified Device Architecture (CUDA) is a mature parallel computing architecture, which can significantly accelerate performance of the computation intensive algorithm. In this paper, FastSLAM algorithm based on the probability model is further studied and the resampling algorithm for the path estimation is improved. In the resampling phase, resampling rules are redesigned and the previous data limitations are broken for the purpose of parallelization. We propose the FastSLAM algorithm based on CUDA, which accelerates robot localization and mapping. The experiment results show that FastSLAM_CUDA can achieve a significant speedup over the FastSLAM with many particles.
Traditional GNSS receiver usually uses Least Squares(LS) method or Kalman filtering to calculate the position. This paper proposes a new method that uses particle filtering for positioning calculation. The proposed pa...
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Traditional GNSS receiver usually uses Least Squares(LS) method or Kalman filtering to calculate the position. This paper proposes a new method that uses particle filtering for positioning calculation. The proposed particle filtering bases on LS method, but resolves the low positioning accuracy problem of LS. It also overcomes the Kalman filtering's drawback that need to know noise properties and receiver's dynamic model previously. The paper gives detailed implementation of the particle filter that is applicable to positioning calculation in GNSS receiver. Based on a suitably built model, the filter uses a modified resampling algorithm, and selects reasonable particle number and proposal distribution according to simulation test. These effectively reduce the calculation and improve the performance of the filter. Simulation results with GSS6700 simulator, based on the digital Intermediate Frequency(IF) signals, show that compared with traditional LS method and Kalman filtering, the proposed particle filtering achieves much higher positioning accuracy.
An improved resampling algorithm for S estimators reduces the number of times the objective function is evaluated and increases the speed of convergence. With this algorithm, S estimates can be computed in less time t...
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In the generation of a caricatured profile of a human face, it is most important to recognize the contour of each facial part. Notably, in the extraction of personal features from the portrait and their caricatured ex...
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