33 research outputs found
PDO-eCNNs: Partial Differential Operator Based Equivariant Spherical CNNs
Spherical signals exist in many applications, e.g., planetary data, LiDAR
scans and digitalization of 3D objects, calling for models that can process
spherical data effectively. It does not perform well when simply projecting
spherical data into the 2D plane and then using planar convolution neural
networks (CNNs), because of the distortion from projection and ineffective
translation equivariance. Actually, good principles of designing spherical CNNs
are avoiding distortions and converting the shift equivariance property in
planar CNNs to rotation equivariance in the spherical domain. In this work, we
use partial differential operators (PDOs) to design a spherical equivariant
CNN, PDO-eCNN, which is exactly rotation equivariant in the
continuous domain. We then discretize PDO-eCNNs, and analyze
the equivariance error resulted from discretization. This is the first time
that the equivariance error is theoretically analyzed in the spherical domain.
In experiments, PDO-eCNNs show greater parameter efficiency
and outperform other spherical CNNs significantly on several tasks.Comment: Accepted by AAAI202
High-Quality Entity Segmentation
Dense image segmentation tasks e.g., semantic, panoptic) are useful for image
editing, but existing methods can hardly generalize well in an in-the-wild
setting where there are unrestricted image domains, classes, and image
resolution and quality variations. Motivated by these observations, we
construct a new entity segmentation dataset, with a strong focus on
high-quality dense segmentation in the wild. The dataset contains images
spanning diverse image domains and entities, along with plentiful
high-resolution images and high-quality mask annotations for training and
testing. Given the high-quality and -resolution nature of the dataset, we
propose CropFormer which is designed to tackle the intractability of
instance-level segmentation on high-resolution images. It improves mask
prediction by fusing high-res image crops that provide more fine-grained image
details and the full image. CropFormer is the first query-based Transformer
architecture that can effectively fuse mask predictions from multiple image
views, by learning queries that effectively associate the same entities across
the full image and its crop. With CropFormer, we achieve a significant AP gain
of on the challenging entity segmentation task. Furthermore, CropFormer
consistently improves the accuracy of traditional segmentation tasks and
datasets. The dataset and code will be released at
http://luqi.info/entityv2.github.io/.Comment: The project webiste: http://luqi.info/entityv2.github.io
Molecular Characterization and Tissue Localization of an F-Box Only Protein from Silkworm, Bombyx mori
The eukaryotic F-box protein family is characterized by an F-box motif that has been shown to be critical for the controlled degradation of regulatory proteins. We identified a
gene encoding an F-box protein from a cDNA library of silkworm pupae, which has an
ORF of 1821 bp, encoding a predicted 606 amino acids. Bioinformatic analysis on the
amino acid sequence shows that BmFBXO21 has a low degree of similarity to proteins
from other species, and may be related to the regulation of cell-cycle progression. We
have detected the expression pattern of BmFBXO21 mRNA and protein and performed
immunohistochemistry at three different levels. Expression was highest in the spinning
stage, and in the tissues of head, epidermis, and genital organs
Relevance of PUFA-derived metabolites in seminal plasma to male infertility
AimThis study aims to investigate the biological effects of polyunsaturated fatty acid (PUFA)-derived metabolites in seminal plasma on male fertility and to evaluate the potential of PUFA as a biomarker for normozoospermic male infertility.MethodsFrom September 2011 to April 2012, We collected semen samples from 564 men aged 18 to 50 years old (mean=32.28 years old)ch., residing in the Sandu County, Guizhou Province, China. The donors included 376 men with normozoospermia (fertile: n=267; infertile: n=109) and 188 men with oligoasthenozoospermia (fertile: n=121; infertile: n=67). The samples thus obtained were then analyzed by liquid chromatography-mass spectrometry (LC-MS) to detect the levels of PUFA-derived metabolites in April 2013. Data were analyzed from December 1, 2020, to May 15, 2022.ResultsOur analysis of propensity score-matched cohorts revealed that the concentrations of 9/26 and 7/26 metabolites differed significantly between fertile and infertile men with normozoospermia and oligoasthenozoospermia, respectively (FDR < 0.05). In men with normozoospermia, higher levels of 7(R)-MaR1 (HR: 0.4 (95% CI [0.24, 0.64]) and 11,12-DHET (0.36 (95% CI [0.21, 0.58]) were significantly associated with a decreased risk of infertility, while higher levels of 17(S)-HDHA (HR: 2.32 (95% CI [1.44, 3.79]), LXA5 (HR: 8.38 (95% CI [4.81, 15.24]), 15d-PGJ2 (HR: 1.71 (95% CI [1.06, 2.76]), and PGJ2 (HR: 2.28 (95% CI [1.42, 3.7]) correlated with an increased risk of infertility. Our ROC model using the differentially expressed metabolites showed the value of the area under the curve to be 0.744.ConclusionThe PUFA-derived metabolites 7(R)-MaR1, 11,12-DHET, 17(S)-HDHA, LXA5, and PGJ2 might be considered as potential diagnostic biomarkers of infertility in normozoospermic men
NICE 2023 Zero-shot Image Captioning Challenge
In this report, we introduce NICE
project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and
outcomes of NICE challenge 2023. This project is designed to challenge the
computer vision community to develop robust image captioning models that
advance the state-of-the-art both in terms of accuracy and fairness. Through
the challenge, the image captioning models were tested using a new evaluation
dataset that includes a large variety of visual concepts from many domains.
There was no specific training data provided for the challenge, and therefore
the challenge entries were required to adapt to new types of image descriptions
that had not been seen during training. This report includes information on the
newly proposed NICE dataset, evaluation methods, challenge results, and
technical details of top-ranking entries. We expect that the outcomes of the
challenge will contribute to the improvement of AI models on various
vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai
Effect of P to B concentration ratio on soft magnetic properties in FeSiBPCu nanocrystalline alloys
The effects of P to B concentration ratio on magnetic properties and microstructure in annealed Fe82.65Si2B14-xPxCu1.35 (x = 1-6) soft magnetic alloys prepared by melt spinning were investigated. The proper substitution of B with P was found to be effective in decreasing grain size in the alloys annealed at 793 K for 120 s. The coercive force H-c markedly decreases from 67.1 to 1.1 A/m and the saturation magnetic flux density B-s shows a slightly decreasing trend with increasing P content from x = 1 to 5. The nanocrystalline Fe82.65Si2B9P5Cu1.35 alloy, with an average grain size of 15 nm, shows a combination of high B-s and excellent soft magnetic properties. (C) 2012 American Institute of Physics. [doi:10.1063/1.3672082
Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
The correspondence between residual networks and dynamical systems motivates researchers to unravel the physics of ResNets with well-developed tools in numeral methods of ODE systems. The Runge-Kutta-Fehlberg method is an adaptive time stepping that renders a good trade-off between the stability and efficiency. Can we also have an adaptive time stepping for ResNets to ensure both stability and performance? In this study, we analyze the effects of time stepping on the Euler method and ResNets. We establish a stability condition for ResNets with step sizes and weight parameters, and point out the effects of step sizes on the stability and performance. Inspired by our analyses, we develop an adaptive time stepping controller that is dependent on the parameters of the current step, and aware of previous steps. The controller is jointly optimized with the network training so that variable step sizes and evolution time can be adaptively adjusted. We conduct experiments on ImageNet and CIFAR to demonstrate the effectiveness. It is shown that our proposed method is able to improve both stability and accuracy without introducing additional overhead in inference phase