RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments

Intention-oriented object detection aims to detect desired objects based on specific intentions or requirements. For instance, when we desire to "lie down and rest", we instinctively seek out a suitable option such as a "bed" or a "sofa" that can fulfill our needs. Previous work in this area is limited either by the number of intention descriptions or by the affordance vocabulary available for intention objects. These limitations make it challenging to handle intentions in open environments effectively. To facilitate this research, we construct a comprehensive dataset called Reasoning Intention-Oriented Objects (RIO). In particular, RIO is specifically designed to incorporate diverse real-world scenarios and a wide range of object categories. It offers the following key features: 1) intention descriptions in RIO are represented as natural sentences rather than a mere word or verb phrase, making them more practical and meaningful; 2) the intention descriptions are contextually relevant to the scene, enabling a broader range of potential functionalities associated with the objects; 3) the dataset comprises a total of 40,214 images and 130,585 intention-object pairs. With the proposed RIO, we evaluate the ability of some existing models to reason intention-oriented objects in open environments.Comment: NeurIPS 2023 D&B accepted. See our project page for more details: https://reasonio.github.io

Traversing double-well potential energy surfaces: photoinduced concurrent intralayer and interlayer structural transitions in XTe2 (X=Mo, W)

Manipulating crystal structure and the corresponding electronic properties in quantum materials provides opportunities for the exploration of exotic physics and practical applications. Here, by ultrafast electron diffraction, structure factor calculation and TDDFT-MD simulations, we report the photoinduced concurrent intralayer and interlayer structural transitions in the Td and 1T' phase of XTe2 (X=Mo, W). Concomitant with the interlayer structural transition by shear displacement, the ultrafast suppression of the intralayer Peierls distortion within 0.3 ps is demonstrated and attributed to Mo-Mo (W-W) bond stretching. We discuss the modification of multiple quantum electronic states associated with the intralayer and interlayer structural transitions, such as the topological band inversion and the higher-order topological state. The twin structure and the stacking fault in XTe2 are identified by the ultrafast structural response. Our work elucidates the pathway of the photoinduced intralayer and interlayer structural transitions in atomic and femtosecond spatiotemporal scale. Moreover, the concurrent intralayer and interlayer structural transitions reveals the traversal of all double-well potential energy surfaces (DWPES) by laser excitation in material system, which may be an intrinsic mechanism in the field of photoexcitation-driven symmetry engineering, beyond the single DWPES transition model and the order-disorder transition model

The development trend and prospect of automobile energy-saving standard system under the goal of peak carbon dioxide emissions

This paper conducts research on the development trend of automobile energy-saving standard system under China’s goal of peak carbon dioxide emissions by 2030. The research first sorted out the carbon dioxide emission standards and regulations of major automobile developed countries in the world, systematically analyzed the current status of China's automobile energy-saving standard system, and proposed the key problems at this stage. With the goal of peak carbon dioxide emissions as the core, the key tasks for the next phase of the construction of the automotive energy-saving standard system are proposed, including comprehensively promoting the formulation of fuel consumption standards for passenger cars and commercial vehicles from 2025 to 2030, and accelerating the construction of NEV energy-saving standard system

The Roads to Haploid Embryogenesis

Although zygotic embryogenesis is usually studied in the field of seed biology, great attention has been paid to the methods used to generate haploid embryos due to their applications in crop breeding. These mainly include two methods for haploid embryogenesis: in vitro microspore embryogenesis and in vivo haploid embryogenesis. Although microspore culture systems and maize haploid induction systems were discovered in the 1960s, little is known about the molecular mechanisms underlying haploid formation. In recent years, major breakthroughs have been made in in vivo haploid induction systems, and several key factors, such as the matrilineal (MTL), baby boom (BBM), domain of unknown function 679 membrane protein (DMP), and egg cell-specific (ECS) that trigger in vivo haploid embryo production in both the crops and Arabidopsis models have been identified. The discovery of these haploid inducers indicates that haploid embryogenesis is highly related to gamete development, fertilization, and genome stability in ealry embryos. Here, based on recent efforts to identify key players in haploid embryogenesis and to understand its molecular mechanisms, we summarize the different paths to haploid embryogenesis, and we discuss the mechanisms of haploid generation and its potential applications in crop breeding. Although these haploid-inducing factors could assist egg cells in bypassing fertilization to initiate embryogenesis or trigger genome elimination in zygotes after fertilization to form haploid embryos, the fertilization of central cells to form endosperms is a prerequisite step for haploid formation. Deciphering the molecular and cellular mechanisms for haploid embryogenesis, increasing the haploid induction efficiency, and establishing haploid induction systems in other crops are critical for promoting the application of haploid technology in crop breeding, and these should be addressed in further studies

Medical Prior Knowledge Guided Automatic Detection of Coronary Arteries Calcified Plaque with Cardiac CT

Calcified plaque in coronary arteries is one major cause and prediction of future coronary artery disease risk. Therefore, the detection of calcified plaque in coronary arteries is exceptionally significant in clinical for slowing coronary artery disease progression. At present, the Convolutional Neural Network (CNN) is exceedingly popular in natural images’ object detection field. Therefore, CNN in the object detection field of medical images also has a wide range of applications. However, many current calcified plaque detection methods in medical images are based on improving the CNN model algorithm, not on the characteristics of medical images. In response, we propose an automatic calcified plaque detection method in non-contrast-enhanced cardiac CT by adding medical prior knowledge. The training data merging with medical prior knowledge through data augmentation makes the object detection algorithm achieve a better detection result. In terms of algorithm, we employ a deep learning tool knows as Faster R-CNN in our method for locating calcified plaque in coronary arteries. To reduce the generation of redundant anchor boxes, Region Proposal Networks is replaced with guided anchoring. Experimental results show that the proposed method achieved a decent detection performance

An improved U-Net-based in situ root system phenotype segmentation method for plants

The condition of plant root systems plays an important role in plant growth and development. The Minirhizotron method is an important tool to detect the dynamic growth and development of plant root systems. Currently, most researchers use manual methods or software to segment the root system for analysis and study. This method is time-consuming and requires a high level of operation. The complex background and variable environment in soils make traditional automated root system segmentation methods difficult to implement. Inspired by deep learning in medical imaging, which is used to segment pathological regions to help determine diseases, we propose a deep learning method for the root segmentation task. U-Net is chosen as the basis, and the encoder layer is replaced by the ResNet Block, which can reduce the training volume of the model and improve the feature utilization capability; the PSA module is added to the up-sampling part of U-Net to improve the segmentation accuracy of the object through multi-scale features and attention fusion; a new loss function is used to avoid the extreme imbalance and data imbalance problems of backgrounds such as root system and soil. After experimental comparison and analysis, the improved network demonstrates better performance. In the test set of the peanut root segmentation task, a pixel accuracy of 0.9917 and Intersection Over Union of 0.9548 were achieved, with an F1-score of 95.10. Finally, we used the Transfer Learning approach to conduct segmentation experiments on the corn in situ root system dataset. The experiments show that the improved network has a good learning effect and transferability

Truncated Expression of a Carboxypeptidase A from Bovine Improves Its Enzymatic Properties and Detoxification Efficiency of Ochratoxin A

Ochratoxin A (OTA) is a toxic secondary metabolite produced mainly by Penicillium spp. and Aspergillus spp. and commonly found in foodstuffs and feedstuffs. Carboxypeptidase A (CPA) can hydrolyze OTA into the non-toxic product ochratoxin α, with great potential to realize industrialized production and detoxify OTA in contaminated foods and feeds. This study constructed a P. pastoris expression vector of mature CPA (M-CPA) without propeptide and signal peptide. The results showed that the degradation rate of OTA by M-CPA was up to 93.36%. Its optimum pH was 8, the optimum temperature was 40 °C, the value of Km was 0.126 mmol/L, and the maximum reaction rate was 0.0219 mol/min. Compared with commercial CPA (S-CPA), the recombinant M-CPA had an improve stability, for which its optimum temperature increased by 10 °C and stability at a wide range pH, especially at pH 3–4 and pH 11. M-CPA could effectively degrade OTA in red wine. M-CPA has the potential for industrial applications, such as can be used as a detoxification additive for foods and feeds