A Scale Normalization Algorithm Based on MR-GDS for Archaeological Fragments Reassembly

2019 ◽  
pp. 289-299
Author(s):  
Congli Yin ◽  
Pengbo Zhou ◽  
Mingquan Zhou ◽  
Zhongke Wu ◽  
Guoguang Du
Keyword(s):  
Scale Normalization

Lack of Rating Scale Normalization and a Socioeconomically Advantaged Population Limits the Generalizability of Preadolescent Transgender Findings

PEDIATRICS ◽  
2016 ◽  
Vol 138 (1) ◽  
pp. e20161203A-e20161203A ◽  
Author(s):  
A. J. McKean ◽  
J. L. Vande Voort ◽  
P. E. Croarkin
Keyword(s):  
Rating Scale ◽  
Scale Normalization

scholarly journals SafeNet: Scale-normalization and Anchor-based Feature Extraction Network for Person Re-identification

2018 ◽  
Author(s):  
Kun Yuan ◽  
Qian Zhang ◽  
Chang Huang ◽  
Shiming Xiang ◽  
Chunhong Pan
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Spatial Distributions ◽  
Body Parts ◽  
Retrieval Task ◽  
Multi Scale ◽  
Aspect Ratios ◽  
Scale Normalization ◽  
Full Body

Person Re-identification (ReID) is a challenging retrieval task that requires matching a person's image across non-overlapping camera views. The quality of fulfilling this task is largely determined on the robustness of the features that are used to describe the person. In this paper, we show the advantage of jointly utilizing multi-scale abstract information to learn powerful features over full body and parts. A scale normalization module is proposed to balance different scales through residual-based integration. To exploit the information hidden in non-rigid body parts, we propose an anchor-based method to capture the local contents by stacking convolutions of kernels with various aspect ratios, which focus on different spatial distributions. Finally, a well-defined framework is constructed for simultaneously learning the representations of both full body and parts. Extensive experiments conducted on current challenging large-scale person ReID datasets, including Market1501, CUHK03 and DukeMTMC, demonstrate that our proposed method achieves the state-of-the-art results.

scholarly journals A Baseline for Cross-Database 3D Human Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3769
Author(s):  
Michał Rapczyński ◽  
Philipp Werner ◽  
Sebastian Handrich ◽  
Ayoub Al-Hamadi
Keyword(s):  
Pose Estimation ◽  
Real Life ◽  
Human Pose Estimation ◽  
3D Pose Estimation ◽  
Multiple Datasets ◽  
In The Wild ◽  
Scale Normalization ◽  
Human Pose ◽  
Future Work ◽  
3D Human Pose Estimation

Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions (“in-the-wild”). To advance towards this goal, we investigated the commonly used datasets HumanEva-I, Human3.6M, and Panoptic Studio, discussed their biases (that is, their limitations in diversity), and illustrated them in cross-database experiments (for which we used a surrogate for roughly estimating in-the-wild performance). For this purpose, we first harmonized the differing skeleton joint definitions of the datasets, reducing the biases and systematic test errors in cross-database experiments. We further proposed a scale normalization method that significantly improved generalization across camera viewpoints, subjects, and datasets. In additional experiments, we investigated the effect of using more or less cameras, training with multiple datasets, applying a proposed anatomy-based pose validation step, and using OpenPose as the basis for the 3D pose estimation. The experimental results showed the usefulness of the joint harmonization, of the scale normalization, and of augmenting virtual cameras to significantly improve cross-database and in-database generalization. At the same time, the experiments showed that there were dataset biases that could not be compensated and call for new datasets covering more diversity. We discussed our results and promising directions for future work.

scholarly journals Modeling of levothyroxine in newborns and infants with congenital hypothyroidism: challenges and opportunities of a rare disease multi-center study

2021 ◽  
Author(s):  
Gilbert Koch ◽  
Britta Steffens ◽  
Stephanie Leroux ◽  
Verena Gotta ◽  
Johannes Schropp ◽  
...  
Keyword(s):  
Rare Disease ◽  
Congenital Hypothyroidism ◽  
Laboratory Data ◽  
Thyroid Stimulating Hormone ◽  
Normalization Method ◽  
Reference Ranges ◽  
Challenges And Opportunities ◽  
Pediatric Patient Population ◽  
Scale Normalization ◽  
Multi Center Study

AbstractModeling of retrospectively collected multi-center data of a rare disease in pediatrics is challenging because laboratory data can stem from several decades measured with different assays. Here we present a retrospective pharmacometrics (PMX) based data analysis of the rare disease congenital hypothyroidism (CH) in newborns and infants. Our overall aim is to develop a model that can be applied to optimize dosing in this pediatric patient population since suboptimal treatment of CH during the first 2 years of life is associated with a reduced intelligence quotient between 10 and 14 years. The first goal is to describe a retrospectively collected dataset consisting of 61 newborns and infants with CH up to 2 years of age. Overall, 505 measurements of free thyroxine (FT4) and 510 measurements of thyrotropin or thyroid-stimulating hormone were available from patients receiving substitution treatment with levothyroxine (LT4). The second goal is to introduce a scale/location-scale normalization method to merge available FT4 measurements since 34 different postnatal age- and assay-specific laboratory reference ranges were applied. This method takes into account the change of the distribution of FT4 values over time, i.e. a transformation from right-skewed towards normality during LT4 treatment. The third goal is to develop a practical and useful PMX model for LT4 treatment to characterize FT4 measurements, which is applicable within a clinical setting. In summary, a time-dependent normalization method and a practical PMX model are presented. Since there is no on-going or planned development of new pharmacological approaches for CH, PMX based modeling and simulation can be leveraged to personalize dosing with the goal to enhance longer-term neurological outcome in children with the rare disease CH.

Consistent scale normalization for object perception

2021 ◽  
Author(s):  
Zewen He ◽  
He Huang ◽  
Yudong Wu ◽  
Xuebing Yang ◽  
Wensheng Zhang
Keyword(s):  
Object Perception ◽  
Scale Normalization

scholarly journals Development of a Medium-Throughput Targeted LCMS Assay to Detect Endogenous Cellular Levels of Malonyl-CoA to Screen Fatty Acid Synthase Inhibitors

2015 ◽  
Vol 21 (2) ◽  
pp. 111-116 ◽  
Author(s):  
Philip J. Hopcroft ◽  
David I. Fisher
Keyword(s):  
Fatty Acid ◽  
Mammalian Cells ◽  
Fatty Acid Synthase ◽  
De Novo ◽  
Acid Synthesis ◽  
Structure Activity ◽  
Malonyl Coa ◽  
Scale Normalization ◽  
Development Structure ◽  
Screening Cascade

The fatty acid synthase (FAS) enzyme in mammalian cells is a large multidomain protein responsible for de novo synthesis of fatty acids. The steps catalyzed by FAS involve the condensation of acetyl-CoA and malonyl-CoA moieties in the presence of NADPH until palmitate is formed. Inhibition of FAS causes an accumulation of intracellular malonyl-CoA, as this metabolite is essentially committed to fatty acid synthesis once formed. Detection of intracellular metabolites for screening can be problematic due to a lack of appropriate tools, but here we describe a targeted liquid chromatography–mass spectroscopy (LCMS) method to directly measure endogenous levels of malonyl-CoA to drive a drug development structure–activity relationship (SAR) screening cascade. Our process involves preparation of samples at 96-well scale, normalization postpermeabilization via use of a whole-well imaging platform, and the LCMS detection methodology. The assay is amenable to multiplexing cellular endpoints, has a typical Z′ of >0.6, and has high reproducibility of EC50 values.

scholarly journals Web-Scale Normalization of Geospatial Metadata Based on Semantics-Aware Data Sources

2017 ◽  
Vol 6 (11) ◽  
pp. 354 ◽  
Author(s):  
Cristiano Fugazza ◽  
Paolo Tagliolato ◽  
Luca Frigerio ◽  
Paola Carrara
Keyword(s):  
Data Sources ◽  
Geospatial Metadata ◽  
Scale Normalization
Sign in / Sign up

Export Citation Format

Share Document