EPCFormer: Expression Prompt Collaboration Transformer for Universal Referring Video Object Segmentation

Audio-guided Video Object Segmentation (A-VOS) and Referring Video Object Segmentation (R-VOS) are two highly-related tasks, which both aim to segment specific objects from video sequences according to user-provided expression prompts. However, due to the challenges in modeling representations for different modalities, contemporary methods struggle to strike a balance between interaction flexibility and high-precision localization and segmentation. In this paper, we address this problem from two perspectives: the alignment representation of audio and text and the deep interaction among audio, text, and visual features. First, we propose a universal architecture, the Expression Prompt Collaboration Transformer, herein EPCFormer. Next, we propose an Expression Alignment (EA) mechanism for audio and text expressions. By introducing contrastive learning for audio and text expressions, the proposed EPCFormer realizes comprehension of the semantic equivalence between audio and text expressions denoting the same objects. Then, to facilitate deep interactions among audio, text, and video features, we introduce an Expression-Visual Attention (EVA) mechanism. The knowledge of video object segmentation in terms of the expression prompts can seamlessly transfer between the two tasks by deeply exploring complementary cues between text and audio. Experiments on well-recognized benchmarks demonstrate that our universal EPCFormer attains state-of-the-art results on both tasks. The source code of EPCFormer will be made publicly available at https://github.com/lab206/EPCFormer.Comment: The source code will be made publicly available at https://github.com/lab206/EPCForme

SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection

Transformer-based methods have demonstrated superior performance for monocular 3D object detection recently, which aims at predicting 3D attributes from a single 2D image. Most existing transformer-based methods leverage both visual and depth representations to explore valuable query points on objects, and the quality of the learned query points has a great impact on detection accuracy. Unfortunately, existing unsupervised attention mechanisms in transformers are prone to generate low-quality query features due to inaccurate receptive fields, especially on hard objects. To tackle this problem, this paper proposes a novel Supervised Scale-aware Deformable Attention (SSDA) for monocular 3D object detection. Specifically, SSDA presets several masks with different scales and utilizes depth and visual features to adaptively learn a scale-aware filter for object query augmentation. Imposing the scale awareness, SSDA could well predict the accurate receptive field of an object query to support robust query feature generation. Aside from this, SSDA is assigned with a Weighted Scale Matching (WSM) loss to supervise scale prediction, which presents more confident results as compared to the unsupervised attention mechanisms. Extensive experiments on the KITTI benchmark demonstrate that SSDA significantly improves the detection accuracy, especially on moderate and hard objects, yielding state-of-the-art performance as compared to the existing approaches. Our code will be made publicly available at https://github.com/mikasa3lili/SSD-MonoDETR.Comment: Code will be made publicly available at https://github.com/mikasa3lili/SSD-MonoDET

Biodiversity Conservation in Rice Paddies in China: Toward Ecological Sustainability

Rice paddies are artificial wetlands that supply people with food and provide wildlife with habitats, breeding areas, shelters, feeding grounds and other services, and rice paddies play an important part in agricultural ecological systems. However, modern agricultural practices with large-scale intensive farming have significantly accelerated the homogenization of the paddy field ecosystem. Modern agriculture mostly relies on chemically-driven modern varieties and irrigation to ensure high production, resulting in the deterioration and imbalance of the ecosystem. Consequently, outbreaks of diseases, insects and weeds have become more frequent in paddy fields. This paper describes the current situation of rice paddy biodiversity in China and analyzes the community characteristics of arthropods and weedy plants. Meanwhile, we discuss how biodiversity was affected by modern agriculture changes, which have brought about a mounting crisis threatening to animals and plants once common in rice paddies. Measures should be focused to firstly preventing further deterioration and, then, also, promoting restoration processes. Ecological sustainability can be achieved by restoring paddy field biodiversity through protecting the ecological environment surrounding the paddy fields, improving paddy cropping patterns, growing rice with less agricultural chemicals and chemical fertilizers, constructing paddy systems with animals and plants and promoting ecological education and public awareness

Preparation and Spectrum Characterization of a High Quality Linear Variable Filter

To meet the requirements for lightweight, miniaturized dispersive optical systems for space applications, linear variable filters with a high transmittance and spatial dispersion coefficient are proposed. The filters were produced with dual ion beam sputtering, where a single layer thickness variation was achieved with a deposition rate adjustment based on a linear variable correction formula. A linear variable trend matching method was used to correct the film thickness based on the reduction of the mismatch error between two materials: Ta2O5 and SiO2. The influence of the spectral and spatial measuring average effects was addressed by sampling the spot size optimization. This paper presents an all-dielectric linear variable filter that operates between 520 and 1000 nm, with an excellent linear dependence of 40 nm/mm over 12 mm. The linear variable filter possessed a 2.5% bandwidth, and its transmittance was found to be >80% at the central wavelength of the band, with a 0.1% transmittance in the cut-off region. These results indicate great potential for optical devices for space applications, and the developed process has good reproducibility and stability

The Waterlogging Process Model in the Paddy Fields of Flat Irrigation Districts

Flat, low-lying agricultural areas such as irrigation districts in southern China have been increasingly vulnerable to flood inundation disasters because of the increased runoff associated with urbanization and climate change. In this study, we developed a waterlogging process simulation model comprising two parts: runoff generation module and runoff confluence module. An improved tank model and hydrodynamic model based on Saint–Venant equations were adopted in the runoff generation and confluence module, respectively. The results show that the model’s relative error and root mean square error are 2.1% and 0.17 mm/h, and the Nash coefficient of the model is 0.91. The relative error of river level simulation was within 5%, and the Nash coefficient was higher than 0.9. The proposed waterlogging simulation model could be a valuable tool for describing the process of waterlogging generation, accumulation, and confluence in the studied irrigation district or other regions with similar climatic conditions

Further analysis of the near-plug gas flow conditions in micro gas-solid fluidized beds

This work presents a theoretical analysis of the near-plug gas flow conditions in micro gas-solid fluidized beds based on the simple one-dimensional dispersion model. The analysis is validated by comparison with the experimental results reported in the literature. It concludes that gas flows can be considered near-plug flow only when: 1) the gas RTD curve is symmetrically shaped; 2) the product of RTD peak height E(t)(h) and variance sigma(t) (i.e., E(t)(h)sigma(t)) approximates 0.4; and 3) E(t)h is greater than 1.0 s(-1) (dimensionless E-theta,E-max > 2.82) or sigma(2)(t) 0.02). This work further improves the criterion for the near-plug flow of gas in micro fluidized beds originally proposed by Geng et al. (CEJ, 351 (2018) 110-118).(C) 2022 Elsevier B.V. All rights reserved

Further analysis of the near-plug gas flow conditions in micro gas-solid fluidized beds

This work presents a theoretical analysis of the near-plug gas flow conditions in micro gas-solid fluidized beds based on the simple one-dimensional dispersion model. The analysis is validated by comparison with the experimental results reported in the literature. It concludes that gas flows can be considered near-plug flow only when: 1) the gas RTD curve is symmetrically shaped; 2) the product of RTD peak height E(t)(h) and variance sigma(t) (i.e., E(t)(h)sigma(t)) approximates 0.4; and 3) E(t)h is greater than 1.0 s(-1) (dimensionless E-theta,E-max > 2.82) or sigma(2)(t) 0.02). This work further improves the criterion for the near-plug flow of gas in micro fluidized beds originally proposed by Geng et al. (CEJ, 351 (2018) 110-118).(C) 2022 Elsevier B.V. All rights reserved

Optimized combustion temperature in the facile synthesis of Ni/Al2O3 catalyst for CO2 methanation

Ni/Al2O3 catalysts (noted as Ni/Al-350, Ni/Al-500, and Ni/Al-700, respectively) were synthesized by a facile solution combustion method but at different combustion temperatures (350 °C, 500 °C, and 700 °C, respectively). For comparison, Ni/Al2O3 (noted as Ni/Al-100) was also prepared by the impregnation method and applied in the methanation reaction. The Ni/Al2O3 synthesized at the combustion temperature of 350 °C showed better performance than the others at higher combustion temperatures, and the activity for CO2 methanation followed an order of Ni/Al-350 > Ni/Al-500 > Ni/Al-700 > Ni/Al-100. The increase in the combustion temperature decreased active sites for the H2 and CO2 adsorption, which could be associated with the reduced Ni dispersion and basic sites, thus lowering its activity and increasing its carbon deposition rate