EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos

Reconstructing soft tissues from stereo endoscope videos is an essential prerequisite for many medical applications. Previous methods struggle to produce high-quality geometry and appearance due to their inadequate representations of 3D scenes. To address this issue, we propose a novel neural-field-based method, called EndoSurf, which effectively learns to represent a deforming surface from an RGBD sequence. In EndoSurf, we model surface dynamics, shape, and texture with three neural fields. First, 3D points are transformed from the observed space to the canonical space using the deformation field. The signed distance function (SDF) field and radiance field then predict their SDFs and colors, respectively, with which RGBD images can be synthesized via differentiable volume rendering. We constrain the learned shape by tailoring multiple regularization strategies and disentangling geometry and appearance. Experiments on public endoscope datasets demonstrate that EndoSurf significantly outperforms existing solutions, particularly in reconstructing high-fidelity shapes. Code is available at https://github.com/Ruyi-Zha/endosurf.git.Comment: MICCAI 2023 (Early Accept); Ruyi Zha and Xuelian Cheng made equal contributions. Corresponding author: Ruyi Zha ([email protected]

A Robust Method for Speech Emotion Recognition Based on Infinite Student’s t

Speech emotion classification method, proposed in this paper, is based on Student’s t-mixture model with infinite component number (iSMM) and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model), speech emotion model based on Student’s t-mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, t-mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus) and two acting (DES and EMO-DB) universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters

Network-Based Gene Expression Biomarkers for Cold and Heat Patterns of Rheumatoid Arthritis in Traditional Chinese Medicine

In Traditional Chinese Medicine (TCM), patients with Rheumatoid Arthritis (RA) can be classified into two main patterns: cold-pattern and heat-pattern. This paper identified the network-based gene expression biomarkers for both cold- and heat-patterns of RA. Gene expression profilings of CD4+ T cells from cold-pattern RA patients, heat-pattern RA patients, and healthy volunteers were obtained using microarray. The differentially expressed genes and related networks were explored using DAVID, GeneSpring software, and the protein-protein interactions (PPI) method. EIF4A2, CCNT1, and IL7R, which were related to the up-regulation of cell proliferation and the Jak-STAT cascade, were significant gene biomarkers of the TCM cold pattern of RA. PRKAA1, HSPA8, and LSM6, which were related to fatty acid metabolism and the I-κB kinase/NF-κB cascade, were significant biomarkers of the TCM heat-pattern of RA. The network-based gene expression biomarkers for the TCM cold- and heat-patterns may be helpful for the further stratification of RA patients when deciding on interventions or clinical trials

Microarray-based gene expression profiles in multiple tissues of the domesticated silkworm, Bombyx mori

Using a genome-wide oligonucleotide microarray, gene expression was surveyed in multiple silkworm tissues on day 3 of the fifth instar, providing a new resource for annotating the silkworm genome

Modelling the Effects of Climatic Factors on the Biomass and Rodent Distribution in a Tibetan Grassland Region in China

To identify the main climatic factors from 2007 to 2009 that influence biomass and rodent distribution, 576 fixed sample plots within 81 million km2 of different climatic grassland in Tibet were monitored. The aboveground biomass, the total burrows, the active burrows, the burrow index, and the rodent density in the plots were measured yearly in October. The monthly precipitation and the average temperatures from April to November were obtained for four successive years (2006-2009). Correlative and modelling analyses between the aboveground biomass, the rodent density, and the climatic factors were performed. The results showed that biomass and rodent density were significantly correlated with the climatic factors. Using ridge regression analyses, models of the biomass and rodent density with respect to the monthly precipitations and average temperatures of the previous year were developed. The raw testing data demonstrated that the models can be used approximately to predict biomass and rodent density

Practical Speech Emotion Recognition Based on Online Learning: From Acted Data to Elicited Data

We study the cross-database speech emotion recognition based on online learning. How to apply a classifier trained on acted data to naturalistic data, such as elicited data, remains a major challenge in today’s speech emotion recognition system. We introduce three types of different data sources: first, a basic speech emotion dataset which is collected from acted speech by professional actors and actresses; second, a speaker-independent data set which contains a large number of speakers; third, an elicited speech data set collected from a cognitive task. Acoustic features are extracted from emotional utterances and evaluated by using maximal information coefficient (MIC). A baseline valence and arousal classifier is designed based on Gaussian mixture models. Online training module is implemented by using AdaBoost. While the offline recognizer is trained on the acted data, the online testing data includes the speaker-independent data and the elicited data. Experimental results show that by introducing the online learning module our speech emotion recognition system can be better adapted to new data, which is an important character in real world applications

An analytic model of the Gruneisen parameter at all densities

We model the density dependence of the Gruneisen parameter as gamma(rho) = 1/2 + gamma_1/rho^{1/3} + gamma_2/rho^{q}, where gamma_1, gamma_2, and q>1 are constants. This form is based on the assumption that gamma is an analytic function of V^{1/3}, and was designed to accurately represent the experimentally determined low-pressure behavior of gamma. The numerical values of the constants are obtained for 20 elemental solids. Using the Lindemann criterion with our model for gamma, we calculate the melting curves for Al, Ar, Ni, Pd, and Pt and compare them to available experimental melt data. We also determine the Z (atomic number) dependence of gamma_1. The high-compression limit of the model is shown to follow from a generalization of the Slater, Dugdale-MacDonald, and Vashchenko-Zubarev forms for the dependence of the Gruneisen parameter.Comment: 14 Pages, LaTeX, 5 eps figues; changes in the tex