The Royalflush System for VoxCeleb Speaker Recognition Challenge 2022

In this technical report, we describe the Royalflush submissions for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our submissions contain track 1, which is for supervised speaker verification and track 3, which is for semi-supervised speaker verification. For track 1, we develop a powerful U-Net-based speaker embedding extractor with a symmetric architecture. The proposed system achieves 2.06% in EER and 0.1293 in MinDCF on the validation set. Compared with the state-of-the-art ECAPA-TDNN, it obtains a relative improvement of 20.7% in EER and 22.70% in MinDCF. For track 3, we employ the joint training of source domain supervision and target domain self-supervision to get a speaker embedding extractor. The subsequent clustering process can obtain target domain pseudo-speaker labels. We adapt the speaker embedding extractor using all source and target domain data in a supervised manner, where it can fully leverage both domain information. Moreover, clustering and supervised domain adaptation can be repeated until the performance converges on the validation set. Our final submission is a fusion of 10 models and achieves 7.75% EER and 0.3517 MinDCF on the validation set

Individual Differences in Holistic Processing Predict Face Recognition Ability

Why do some people recognize faces easily and others frequently make mistakes in recognizing faces? Classic behavioral work has shown that faces are processed in a distinctive holistic manner that is unlike the processing of objects. In the study reported here, we investigated whether individual differences in holistic face processing have a significant influence on face recognition. We found that the magnitude of face-specific recognition accuracy correlated with the extent to which participants processed faces holistically, as indexed by the composite-face effect and the whole-part effect. This association is due to face-specific processing in particular, not to a more general aspect of cognitive processing, such as general intelligence or global attention. This finding provides constraints on computational models of face recognition and may elucidate mechanisms underlying cognitive disorders, such as prosopagnosia and autism, that are associated with deficits in face recognition

The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom

Exposure of phthalate has adverse effects on child health. Currently, the field measurement on PAEs concentration in children’s bedrooms were limited, and the test of PAEs is laborious. Based on the data of home detection in 454 residences from March 2013 to December 2014 in Shanghai, the association of PAEs in children's bedroom and building characteristics, residents’ lifestyle and indoor environment characterization were built by Spearman correlation. According to the Spearman correlation coefficient method, the concentration of PAEs, such as residential area was significantly correlated with DMP, BBP and DiBP in children’s bedroom (sig 0), and the use of chemicals was significantly associated with DEP and DiBP in children’s bedroom (sig 0). Then a gradient-boosting decision tree model with higher prediction accuracy is established. The influencing factors of the studied PAEs were determined by comprehensive consideration of the current study and literature review. 11 influencing factors of PAEs concentrations from three aspects were finally established in this study. The training model of GBDT has a reasonable accuracy( R2>0.9). This paper provides a reference for the prediction of PAEs concentration in the residential bedroom and the influence degree of influencing factors

Microstructure, mechanical properties and biocompatibility of laser metal deposited Ti–23Nb coatings on a NiTi substrate

To simultaneously obtain superior superelasticity and biological properties, single- and multi-layer Ti–23Nb coatings were deposited on a cold-rolled NiTi substrate using laser metal deposition (LMD). The microstructure of the single-layer coating consisted of a cellular structure with a grid size of ∼300 μm in the eutectic layer, strip structures and prior β-(Ti, Nb) phases surrounded by the Ti2Ni(Nb) phase in the Ni diffusion zone. In contrast, the microstructure of the multi-layer coating consisted of α′, α′′, and prior β phases, which arise from the partition of Nb. Compared with the NiTi substrate, the Ni ion release concentration of the single-layer coating is reduced by 45% with similar nano-mechanical behavior, i.e. a nanohardness, H, of ∼4.0 GPa, a reduced Young's modulus, E r, of ∼65 GPa, an elastic strain to failure, H/E r, of ∼0.06, a yield stress, H 3/E r 2, of ∼0.016 GPa, and a superelastic strain recovery, η sr, of ∼0.3. The reduction of Ni ion concentration for multi-layer coating after 35 days is even better at up to 62%, but at the cost of a degradation in the mechanical properties. The LMD coatings have a high dislocation density, and their creep is controlled by dislocation movement

Individual Differences in Holistic Processing Predict Face Recognition Ability

Why do some people recognize faces easily and others frequently make mistakes in recognizing faces? Classic behavioral work has shown that faces are processed in a distinctive holistic manner that is unlike the processing of objects. In the study reported here, we investigated whether individual differences in holistic face processing have a significant influence on face recognition. We found that the magnitude of face-specific recognition accuracy correlated with the extent to which participants processed faces holistically, as indexed by the composite-face effect and the whole-part effect. This association is due to face-specific processing in particular, not to a more general aspect of cognitive processing, such as general intelligence or global attention. This finding provides constraints on computational models of face recognition and may elucidate mechanisms underlying cognitive disorders, such as prosopagnosia and autism, that are associated with deficits in face recognition

Human Exposure to Short- and Medium-Chain Chlorinated Paraffins via Mothers’ Milk in Chinese Urban Population

Chlorinated paraffins (CPs) are high production volume synthetic chemicals, found ubiquitously in various environmental matrices. However, little information is available on CP contamination in mothers’ milk. In this study, 1370 urban mothers’ milk samples were collected from 12 Chinese provinces in 2007 and 16 provinces in 2011. CP geographical distribution and congener group profiles were studied to assess the CP levels and figure out the source of exposure in humans. Twenty-eight pooled samples were analyzed for 48 short-chain CP (SCCP) and medium-chain CP (MCCP) congener groups using the GC × GC-ECNI-HRTOFMS method. The median concentrations of SCCPs were 681 and 733 ng/g lipid in 2007 and 2011, respectively; median concentrations of MCCPs were 60.4 and 64.3 ng/g lipid in 2007 and 2011, respectively. Variations of more than 2 orders of magnitude in CP exposure levels were found between different provinces. The levels of CPs increased from 2007 to 2011, which indicates that CP production and use may be an important exposure source. This is the first global comprehensive and large-scale investigation of CPs in mothers’ milk, and it lays foundations for improving our understanding of the metabolism of CPs in humans. The high CP concentrations found in Chinese mothers’ milk should raise concern about potential toxic effects in both mothers and breastfeeding infants