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Natalie was my shy 4-year-old little girl when she started in Star Kidz. I put her into your program in hopes that she would have fun and come out of her shell a bit. Throughout her years of Star Kidz shows, Steve and I would watch her confidence and performance abilities skyrocket.

Class Length: 50 minutes once a week

This is a drop-off class only. In this series of acting, singing, and dancing classes for kids in Harrison, Rye Brook, and White Plains, we introduce them to music as a means of expressing themselves vocally, dramatically and through movement, with the goal of building confidence and poise. For many of these little ones, this class is their first opportunity to hold a working microphone. In each of these areas, we encourage the kids to feel comfortable within themselves to perform, an attribute that helps them in all areas of schooling and in growing up.

Children can pick from a wide variety of songs to sing from their favorite TV shows, Disney movies, Broadway shows and radio hits as well as the time-tested, kid-friendly standbys. Fun and simple vocal exercises like animal sounds and scales get their mouth muscles moving and help convey the concept of pitch.

Throughout the semester, we engage the students in a variety of fun acting exercises and drama games that capture their creative attention. They learn the fundamentals of acting by creating characters, exploring emotions and using their imagination. They learn to work both individually and together as a group. No matter what they’re working on, our focus is always building confidence as well as having a blast!

As for the dance, we customize a routine that follows suit with their developing fine-motor skills. With all aspects of the program, we make great use of repetition to reinforce learning.

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Class Length: 1 hour once a week

Each week in our singing, dancing, acting classes, our kindergartners and 1st-graders rush into the classroom to announce what songs they can’t wait to sing! And now that they’re learning to read, the karaoke screen and printed lyrics reinforce that learning in a fun way.

As with all of our Star Kidz classes, in K/1 we give students the tools they need to prevent bad vocal habits — teaching them that their voice is an instrument and offering a beginner’s understanding of how vocal exercises work mouth muscles and reinforce pitch learning. We continue that work with age-appropriate song choices, happily expanding their repertoire to include Broadway songs from beloved shows, while many enjoy singing from shows they are doing in their school musicals. The result is an excitement that builds all semester long.

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Extracting anthropometric or tailoring measurements from 3D human body scans is important for applications such as virtual try-on, custon clothing, and online sizing. Existing commercial solutions identify anatomical landmarks on high-resolution 3D scans and then compute distances or circumferences on the scan. Landmark detection is sensitive to acquisition noise (e.g. holes) and these methods require subjects to adopt a specific pose. In contrast, we propose a solution that we call. We fit a deformable 3D body model to scan data in one or more poses; this model-based fitting is robust to scan noise. This brings the scan into registration iwth a database of registered body scans. Then, we extract features from the registered model (rather than from the scan); these include, limb lengths, circumferences, and statistical features of global shape. Finally, we learn a mapping from these features to measurements using regularized linear regression. We perform an extensive evaluation using the CAESAR dataset and demonstrate that the accuracy of our method outperforms state-of-art methods.

model-based anthropometry

Details on measurement prediction accuracy:

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Abstract
Extracting anthropometric or tailoring measurements from 3D human body scans is important for applications such as virtual try-on, custom clothing, and online sizing. Existing commercial solutions identify anatomical landmarks on high-resolution 3D scans and then compute distances or circumferences on the scan. Landmark detection is sensitive to acquisition noise (e.g. holes) and these methods require subjects to adopt a specific pose. In contrast, we propose a solution we call model-based anthropometry. We fit a deformable 3D body model to scan data in one or more poses; this model-based fitting is robust to scan noise. This brings the scan into registration with a database of registered body scans. Then, we extract features from the registered model (rather than from the scan); these include, limb lengths, circumferences, and statistical features of global shape. Finally, we learn a mapping from these features to measurements using regularized linear regression. We perform an extensive evaluation using the CAESAR dataset and demonstrate that the accuracy of our method outperforms state-of-the-art methods.

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Description:

To perform many downstream analyses after OTU picking (besides metagenomeSeq’s fitZIG and DESeq OTU differential abundance testing), the OTU matrix must be normalized to account for uneven column (sample) sums that are a result of most modern sequencing techniques. These methods attempt to correct for compositionality too. Rarefying throws away some data by rarefying to a constant sum and throwing away extremely low depth samples.

Even with these new normalization techniques, we would recommend throwing away low depth samples (e.g. less that 1000 sequences/sample). DESeq/DESeq2 outputs negative values for lower abundant OTUs as a result of its log transformation. For most ecologically useful metrics (e.g. UniFrac/Bray Curtis) this presents problems. No good solution exists at the moment for this issue. Note that one is added to the matrix to avoid log(0). It has been shown that clustering results can be highly dependent upon the choice of the pseudocount (e.g. should it be 0.01 instead of 1?), for more information read Costea, P. et al. (2014) “A fair comparison”, Nature Methods.

DESeq/DESeq2 can also have a very slow runtime, especially for larger datasets. In this script, we implement DESeq2’s variance stabilization technique. If you do use these alternatives to rarefying, we would recommend metagenomeSeq’s CSS (cumulative sum scaling) transformation for those metrics that are abundance-based. It is not recommended to use these new methods with presence/absence metrics, for example binary Jaccard or unweighted UniFrac.

For more on metagenomeSeq’s CSS, please see Paulson, JN, et al. ‘Differential abundance analysis for microbial marker-gene surveys’ Nature Methods 2013. For DESeq please see Anders S, Huber W. ‘Differential expression analysis for sequence count data.’ Genome Biology 2010. For DESeq2 please read Love, MI et al. ‘Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2,’ Genome Biology 2014. If you use these methods, please CITE the appropriate reference as well as QIIME. For any of these methods, clustering by sequence depth MUST BE CHECKED FOR as a confounding variable, e.g. by coloring by sequences/sample on a PCoA plot and testing for correlations between taxa abundances and sequencing depth with e.g. adonis in compare_categories.py , or observation_metadata_correlation.py .

Note: If the input BIOM table contains observation metadata (e.g., taxonomy metadata for each OTU), this metadata will not be included in the output normalized BIOM table when using DESeq2. When using CSS the taxonomy metadata will be included in the output normalized table but it may not be in the same format as the input table (e.g., “NA” will be added for missing taxonomic levels). This discrepancy occurs because the underlying R packages used to perform the normalization store taxonomy metadata in a different format.

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