Feature mapping space and sample determination for person re-identification

Person re-identification(Re-ID) is integral to intelligent monitoring systems.However,due to the variability in viewing angles and illumination,it is easy to cause visual ambiguities,affecting the accuracy of person re-identification.An approach for person re-identification based on feature mapping space and sample determination is proposed.At first,a weight fusion model,including mean and maximum value of the horizontal occurrence in local features,is introduced into the mapping space to optimize local features.Then,the Gaussian distribution model with hierarchical mean and covariance of pixel features is introduced to enhance feature expression.Finally,considering the influence of the size of samples on metric learning performance,the appropriate metric learning is selected by sample determination method to further improve the performance of person re-identification.Experimental results on the VIPeR,PRID450 S and CUHK01 datasets demonstrate that the proposed method is better than the traditional methods.

Frequentist and Bayesian Sample Size Determination for Single-Arm Clinical Trials Based on a Binary Response Variable: A Shiny App to Implement Exact Methods

Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.

ON-LINE ELECTROLYTIC DISSOLUTION OF SOLID METAL SAMPLE AND DETERMINATION OF COPPER IN ALLOY BY AAS

On-line dissolution of solid metal sample can be carried out by electrolysis under the control of flow injection analyzer(FIA),and the dissolved sample can be transferred to atomic spectrometer for the direct analysis.The hyphenated technique of FIA on-line electrolytic dissolution of alloy and atomic absorption spectrometer(AAS)detection is developed.The research is focused on the effects of electrolyte composition and electrolysis parameters on the sample dissolving,as well as the quantitative analysis of Cu in Al alloy samples.

THE USE OF CHELATING RESIN FOR THE PRE-CONCENTRATION OF TRACE RARE EARTH ELEMENTS IN BIOLOGICAL SAMPLES AND THEIR DETERMINATION

Macroporous amino-carboxvlic chelating resin of acrylic acid series may concentrate trace rare earth in the presence of 0.1 mol/l α-hydroxy-isobutyric acid at pH 4.5.With 0.10 mol/l HCl as eluant rare earth may be eluted quantitatively and separated from Ca,Mg,Fe and Cu.This method is used to determine trace amount of rare earth in human hair,stone (in kidney and bladder),peanut,grape,strawberry,cucumber,pig liver and other biological samples.

DETERMINATION OF ANTIMONY IN WATER SAMPLES BY FLOW-INJECTION HYDRIDE GENERATION ATOMIC ABSORPTION SPECTROMETRY WITH ON-LINE ION-EXCHANGE COLUMN PRECONCENTRATION

On-line ion-exchange separation and preconcentration were combined with flow-injection hydride generation atomic absorption spectrometry (HGAAS) to determine ultra-trace amounts of antimony in water samples. Antimony(Ⅲ) was preconcentrated on a micro-column packed with CPG-8Q chelating ion-exchanger using time-based sample loading and eluted by 4 mol l^(-1) HCl directly into the hydride generation AAS system. A detection limit (3σ) of 0.0015μg l^(-1) Sb(Ⅲ) was obtained on the basis of a 20 fold enrichment and with a sampling frequency of 60h^(-1). The precision was 1.0% r.s.d.(n=11) at the 0.5μg l^(-1) Sb(Ⅲ) level. Recoveries for the analysis of antimony in tap water, snow water and sea water samples were in the range 97-102%.