Learning Part Segmentation from Synthetic Animals

Semantic part segmentation provides an intricate and interpretable understanding of an object, thereby benefiting numerous downstream tasks. However, the need for exhaustive annotations impedes its usage across diverse object types. This paper focuses on learning part segmentation from synthetic animals, leveraging the Skinned Multi-Animal Linear (SMAL) models to scale up existing synthetic data generated by computer-aided design (CAD) animal models. Compared to CAD models, SMAL models generate data with a wider range of poses observed in real-world scenarios. As a result, our first contribution is to construct a synthetic animal dataset of tigers and horses with more pose diversity, termed Synthetic Animal Parts (SAP). We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution. Concretely, we examine three Syn-to-Real adaptation methods but observe relative performance drop due to the innate difference between the two tasks. To address this, we propose a simple yet effective method called Class-Balanced Fourier Data Mixing (CB-FDM). Fourier Data Mixing aligns the spectral amplitudes of synthetic images with real images, thereby making the mixed images have more similar frequency content to real images. We further use Class-Balanced Pseudo-Label Re-Weighting to alleviate the imbalanced class distribution. We demonstrate the efficacy of CB-FDM on SynRealPart over previous methods with significant performance improvements. Remarkably, our third contribution is to reveal that the learned parts from synthetic tiger and horse are transferable across all quadrupeds in PartImageNet, further underscoring the utility and potential applications of animal part segmentation

Effects of Dietary Cholesterol Levels on the Growth, Molt Performance, and Immunity of Juvenile Swimming Crab, Portunus trituberculatus

The effects of dietary cholesterol levels on growth, molt performance, and immunity of juvenile swimming crab Portunus trituberculatus, were investigated at four cholesterol levels (0.2%-1.4%) of purified diets. Each diet was fed in triplicate to 18 crabs per replicate for 50 days. Crabs fed the diet with 1.0% cholesterol showed significantly higher (P<0.05) specific growth rate (SGR) than the other groups, who suffered from relatively lower molt death syndrome (MDS). Cholesterol content in the serum, whole body, and hepatopancreas increased in relation to dietary cholesterol. Muscle lipid content was significantly higher (P<0.05) in crabs fed the diet with 0.2% cholesterol compared to the other treatments. Crabs fed moderate dietary cholesterol levels showed higher alkaline phosphatase (AKP) or acid phosphatase (ACP) levels than those fed 0.2% or 1.4% cholesterol diets. The present study also showed that dietary cholesterol supplementation generally increased serum superoxide dismutase (SOD) activity. Overall, moderate dietary cholesterol (1.0 %) enhanced the performance of growth, survival, molting, and immunity of juvenile swimming crab P. trituberculatus

Learning Generalizable Feature Fields for Mobile Manipulation

An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects. The latter requires capturing intricate geometry while understanding fine-grained semantics, whereas the former involves capturing the complexity inherit to an expansive physical scale. In this work, we present GeFF (Generalizable Feature Fields), a scene-level generalizable neural feature field that acts as a unified representation for both navigation and manipulation that performs in real-time. To do so, we treat generative novel view synthesis as a pre-training task, and then align the resulting rich scene priors with natural language via CLIP feature distillation. We demonstrate the effectiveness of this approach by deploying GeFF on a quadrupedal robot equipped with a manipulator. We evaluate GeFF's ability to generalize to open-set objects as well as running time, when performing open-vocabulary mobile manipulation in dynamic scenes.Comment: Preprint. Project website is at: https://geff-b1.github.io

Ovarian Transcriptome Analysis of Portunus trituberculatus Provides Insights into Genes Expressed during Phase III and IV Development.

Enhancing the production of aquatic animals is crucial for fishery management and aquaculture applications. Ovaries are specialized tissues that play critical roles in producing oocytes and hormones. Significant biochemical changes take place during the sexual maturation of Portunus trituberculatus, but the genetics of this process has not been extensively studied. Transcriptome sequencing can be used to determine gene expression changes within specific periods. In the current study, we used transcriptome sequencing to produce a comprehensive transcript dataset for the ovarian development of P. trituberculatus. Approximately 100 million sequencing reads were generated, and 126,075 transcripts were assembled. Functional annotation of the obtained transcripts revealed important pathways in ovarian development, such as those involving the vitellogenin gene. Also, we performed deep sequencing of ovaries in phases III and IV of sexual maturation in P. trituberculatus. Differential analysis of gene expression identified 506 significantly differentially expressed genes, which belong to 20 pathway, transporters, development, transcription factors, metabolism of other amino acids, carbohydrate and lipid, solute carrier family members, and enzymes. Taken together, our study provides the first comprehensive transcriptomic resource for P. trituberculatus ovaries, which will strengthen understanding of the molecular mechanisms underlying the sexual maturation process and advance molecular nutritional studies of P. trituberculatus

Gene Ontology classification of assembled gene s in the <i>P</i>. <i>trituberculatus</i> ovary transcriptome.

<p>Gene Ontology classification of assembled gene s in the <i>P</i>. <i>trituberculatus</i> ovary transcriptome.</p

Summary of ovary transcriptome sequencing results of <i>Portunus trituberculatus</i>.

<p>Summary of ovary transcriptome sequencing results of <i>Portunus trituberculatus</i>.</p

Sequence length distribution of transcripts assembled from Illumina reads.

<p>Sequence length distribution of transcripts assembled from Illumina reads.</p

Nucleotide sequences of primers used for qPCR amplification.

<p>Nucleotide sequences of primers used for qPCR amplification.</p

KEGG map in assemble result.

<p>KEGG map in assemble result.</p