170 research outputs found
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Recognizing facial action units (AUs) during spontaneous facial displays is a
challenging problem. Most recently, Convolutional Neural Networks (CNNs) have
shown promise for facial AU recognition, where predefined and fixed convolution
filter sizes are employed. In order to achieve the best performance, the
optimal filter size is often empirically found by conducting extensive
experimental validation. Such a training process suffers from expensive
training cost, especially as the network becomes deeper.
This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the
filter sizes and weights of all convolutional layers are learned simultaneously
from the training data along with learning convolution filters. Specifically,
the filter size is defined as a continuous variable, which is optimized by
minimizing the training loss. Experimental results on two AU-coded spontaneous
databases have shown that the proposed OFS-CNN is capable of estimating optimal
filter size for varying image resolution and outperforms traditional CNNs with
the best filter size obtained by exhaustive search. The OFS-CNN also beats the
CNN using multiple filter sizes and more importantly, is much more efficient
during testing with the proposed forward-backward propagation algorithm
NIH Public Access Author Manuscript Biol Psychiatry. Author manuscript; available in PMC 2011 January 1.
Published in final edited form as
Genomic value prediction for quantitative traits under the epistatic model
Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Generalizable 3D part segmentation is important but challenging in vision and
robotics. Training deep models via conventional supervised methods requires
large-scale 3D datasets with fine-grained part annotations, which are costly to
collect. This paper explores an alternative way for low-shot part segmentation
of 3D point clouds by leveraging a pretrained image-language model, GLIP, which
achieves superior performance on open-vocabulary 2D detection. We transfer the
rich knowledge from 2D to 3D through GLIP-based part detection on point cloud
rendering and a novel 2D-to-3D label lifting algorithm. We also utilize
multi-view 3D priors and few-shot prompt tuning to boost performance
significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets
shows that our method enables excellent zero-shot 3D part segmentation. Our
few-shot version not only outperforms existing few-shot approaches by a large
margin but also achieves highly competitive results compared to the fully
supervised counterpart. Furthermore, we demonstrate that our method can be
directly applied to iPhone-scanned point clouds without significant domain
gaps.Comment: CVPR 2023, project page: https://colin97.github.io/PartSLIP_page
Identifying noncoding risk variants using disease-relevant gene regulatory networks.
Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations. Nat Commun 2018 Feb 16; 9(1):702
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