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Matching individuals across non-overlapping camera networks, known as person re-identification, is a fundamentally challenging problem due to the large visual appearance changes caused by variations of viewpoints, lighting, and occlusion. Approaches in literature can be categorized into two streams: The first stream is to develop reliable features against realistic conditions by combining several visual features in a pre-defined way; the second stream is to learn a metric from training data to ensure strong inter-class differences and intra-class similarities. However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is an unsolved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification. In this paper, we propose two effective structured learning based approaches which explore the adaptive effects of visual features in recognizing persons in different benchmark data sets. Our framework is built on the basis of multiple low-level visual features with an optimal ensemble of their metrics. We formulate two optimization algorithms, CMC  triplet and CMC  top , which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. The more standard CMC  triplet formulation works on the triplet information by maximizing the relative distance between a matched pair and a mismatched pair in each triplet unit. The CMC  top formulation, modeled on a structured learning of maximizing the correct identification among top candidates, is demonstrated to be more beneficial to person re-identification by directly optimizing an objective closer to the actual testing criteria. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results by improving the rank-1 recognition rates from 40% to 61% on the iLIDS benchmark, 16% to 22% on the PRID2011 benchmark, 43% to 50% on the VIPeR benchmark, 34% to 55% on the CUHK01 benchmark and 21% to 68% on the CUHK03 benchmark. [ABSTRACT FROM AUTHOR]

Canonical correlation analysis (CCA) is a popular method that has been widely used in information fusion. However, CCA requires that the data from two views must be paired, which is hard to satisfy in the real applications, moreover, it only considers the correlated information of the paired data. Thus, it cannot be used when there are only a little paired data or no paired data. In this paper, we propose a novel method named Canonical Principal Angles Correlation Analysis (CPACA) which does not need paired data during training stage. It makes classic CCA escape from the limitation of paired information. Its objective function can be constructed as follows: First, the correlation of two views is represented by the similarity between two subspace spanned by the principal components, which makes CPACA favorably compare with CCA in the case of limited paired data; Second, in order to increase the discriminative information of CPACA, we utilize manifold regularization to exploit the geometry of the marginal distribution. To optimize the objective function, we propose a new method to calculate the projected vectors. The experimental results show that the performance of CPACA is superior to that of traditional CCA and its variants. [ABSTRACT FROM AUTHOR]

Impaired neurodevelopment of children has become a growing public concern; however, the associations between metals exposure and neurocognitive function have remained largely unknown. We systematically evaluated the associations of multiple metals exposure during pregnancy and childhood on the neurodevelopment of children aged 2–3 years. We measured 22 metals in the serum and urine among703 mother-child pairs from the Guangxi Birth Cohort Study. The neurocognitive development of children was assessed by the Gesell Development Diagnosis Scale (GDDS; Chinese version). Multiple linear regression models were used to evaluate the relationship between the metals (selected by elastic net regression) and the outcomes. The Bayesian kernel machine regression (BKMR) was used to evaluate the possible joint effect between the multiple metal mixture and the outcomes. Prenatal aluminum (Al) exposure was negatively associated with the fine motor developmental quotient (DQ) (β = −1.545, 95%CI: 2.231, −0.859), adaption DQ (β = −1.182, 95%CI: 1.632, −0.732), language DQ (β = −1.284, 95% CI: 1.758, −0.809), and social DQ (β = −1.729, 95% CI: 2.406, −1.052) in the multi-metal model. Prenatal cadmium (Cd) exposure was negatively associated with gross motor DQ (β = −2.524, 95% CI: 4.060, −0.988), while postpartum Cd exposure was negatively associated with language DQ (β = −1.678, 95% CI: 3.227, −0.129). In stratified analyses, infants of different sexes had different sensitivities to metal exposure, and neurobehavioral development was more significantly affected by metal exposure in the first and second trimester. BKMR analysis revealed a negative joint effect of the Al, Cd, and selenium (Se) on the language DQ score; postpartum Cd exposure played a major role in this relationship. Prenatal exposure to Al, Ba, Cd, molybdenum (Mo), lead (Pb), antimony (Sb), and strontium (Sr), and postpartum exposure to cobalt (Co), Cd, stannum (Sn), iron (Fe), nickel (Ni), and Se are associated with neurological development of infants. The first and second trimester might be the most sensitive period when metal exposure affects neurodevelopment. • Metal exposure during fetal life and childhood have both been considered. • Association of mixture exposure to 22 metals on infant neurodevelopment were explored. • Exposure to Al during pregnancy impairs neurodevelopment in young children. • Prenatal Ba exposure, postpartum Se exposure were positively with neurodevelopment. [ABSTRACT FROM AUTHOR]