TODE-Trans: Transparent Object Depth Estimation with Transformer

Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects due to the refraction and reflection of light. In this work, we present a transformer-based transparent object depth estimation approach from a single RGB-D input. We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas. In addition, to better enhance the fine-grained features, a feature fusion module (FFM) is designed to assist coherent prediction. Our empirical evidence demonstrates that our model delivers significant improvements in recent popular datasets, e.g., 25% gain on RMSE and 21% gain on REL compared to previous state-of-the-art convolutional-based counterparts in ClearGrasp dataset. Extensive results show that our transformer-based model enables better aggregation of the object's RGB and inaccurate depth information to obtain a better depth representation. Our code and the pre-trained model will be available at https://github.com/yuchendoudou/TODE.Comment: Submitted to ICRA202

An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles

The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects

2-(3-Methyl-2-nitro­phen­yl)-4,5-dihydro-1,3-oxazole

In the title compound, C10H10N2O3, an inter­mediate in the synthesis of anthranilamide insecticides, all the non-H atoms except the nitro-group O atom lie on a crystallographic mirror plane. The H atoms of the methyl group are disordered over two sets of sites with equal occupancies. In the crystal structure, C—H⋯N links lead to chains of mol­ecules propagating in [100]

Calcium channel α2δ1 proteins mediate trigeminal neuropathic pain states associated with aberrant excitatory synaptogenesis.

To investigate a potential mechanism underlying trigeminal nerve injury-induced orofacial hypersensitivity, we used a rat model of chronic constriction injury to the infraorbital nerve (CCI-ION) to study whether CCI-ION caused calcium channel α2δ1 (Cavα2δ1) protein dysregulation in trigeminal ganglia and associated spinal subnucleus caudalis and C1/C2 cervical dorsal spinal cord (Vc/C2). Furthermore, we studied whether this neuroplasticity contributed to spinal neuron sensitization and neuropathic pain states. CCI-ION caused orofacial hypersensitivity that correlated with Cavα2δ1 up-regulation in trigeminal ganglion neurons and Vc/C2. Blocking Cavα2δ1 with gabapentin, a ligand for the Cavα2δ1 proteins, or Cavα2δ1 antisense oligodeoxynucleotides led to a reversal of orofacial hypersensitivity, supporting an important role of Cavα2δ1 in orofacial pain processing. Importantly, increased Cavα2δ1 in Vc/C2 superficial dorsal horn was associated with increased excitatory synaptogenesis and increased frequency, but not the amplitude, of miniature excitatory postsynaptic currents in dorsal horn neurons that could be blocked by gabapentin. Thus, CCI-ION-induced Cavα2δ1 up-regulation may contribute to orofacial neuropathic pain states through abnormal excitatory synapse formation and enhanced presynaptic excitatory neurotransmitter release in Vc/C2

Transgenic Mice Convert Carbohydrates to Essential Fatty Acids

Transgenic mice (named “Omega mice”) were engineered to carry both optimized fat-1 and fat-2 genes from the roundworm Caenorhabditis elegans and are capable of producing essential omega-6 and omega-3 fatty acids from saturated fats or carbohydrates. When maintained on a high-saturated fat diet lacking essential fatty acids or a high-carbohydrate, no-fat diet, the Omega mice exhibit high tissue levels of both omega-6 and omega-3 fatty acids, with a ratio of ∼1∶1. This study thus presents an innovative technology for the production of both omega-6 and omega-3 essential fatty acids, as well as a new animal model for understanding the true impact of fat on human health