CORE: Cooperative Reconstruction for Multi-Agent Perception

This paper presents CORE, a conceptually simple, effective and communication-efficient model for multi-agent cooperative perception. It addresses the task from a novel perspective of cooperative reconstruction, based on two key insights: 1) cooperating agents together provide a more holistic observation of the environment, and 2) the holistic observation can serve as valuable supervision to explicitly guide the model learning how to reconstruct the ideal observation based on collaboration. CORE instantiates the idea with three major components: a compressor for each agent to create more compact feature representation for efficient broadcasting, a lightweight attentive collaboration component for cross-agent message aggregation, and a reconstruction module to reconstruct the observation based on aggregated feature representations. This learning-to-reconstruct idea is task-agnostic, and offers clear and reasonable supervision to inspire more effective collaboration, eventually promoting perception tasks. We validate CORE on OPV2V, a large-scale multi-agent percetion dataset, in two tasks, i.e., 3D object detection and semantic segmentation. Results demonstrate that the model achieves state-of-the-art performance on both tasks, and is more communication-efficient.Comment: Accepted to ICCV 2023; Code: https://github.com/zllxot/COR

Multi-level Gated Bayesian Recurrent Neural Network for State Estimation

The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely a multi-level gated Bayesian recurrent neural network specifically designed to state estimation under model mismatches. Firstly, we transform the non-Markov state-space model into an equivalent first-order Markov model with memory. It is a generalized transformation that overcomes the limitations of the first-order Markov property and enables recursive filtering. Secondly, by deriving a data-assisted joint state-memory-mismatch Bayesian filtering, we design a Bayesian multi-level gated framework that includes a memory update gate for capturing the temporal regularities in state evolution, a state prediction gate with the evolution mismatch compensation, and a state update gate with the observation mismatch compensation. The Gaussian approximation implementation of the filtering process within the gated framework is derived, taking into account the computational efficiency. Finally, the corresponding internal neural network structures and end-to-end training methods are designed. The Bayesian filtering theory enhances the interpretability of the proposed gated network, enabling the effective integration of offline data and prior models within functionally explicit gated units. In comprehensive experiments, including simulations and real-world datasets, the proposed gated network demonstrates superior estimation performance compared to benchmark filters and state-of-the-art deep learning filtering methods

Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution

Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs. In this paper, we propose a Multi-granularity Backprojection Transformer termed MBT for RSISR. MBT incorporates the backprojection learning strategy into a Transformer framework. It consists of Scale-aware Backprojection-based Transformer Layers (SPTLs) for scale-aware low-resolution feature learning and Context-aware Backprojection-based Transformer Blocks (CPTBs) for hierarchical feature learning. A backprojection-based reconstruction module (PRM) is also introduced to enhance the hierarchical features for image reconstruction. MBT stands out by efficiently learning low-resolution features without excessive modules for high-resolution processing, resulting in lower computational resources. Experiment results on UCMerced and AID datasets demonstrate that MBT obtains state-of-the-art results compared to other leading methods

IMECE2011-64425 THEORETICAL ANALYSIS ON FILM THICKNESS OF INTERTUBE FALLING-FILM FLOW WITH A COUNTERCURRENT GAS FLOW

ABSTRACT In falling-film type of heat exchangers, gas/vapor usually exists, and its effect on falling-film mode transitions and heat transfer could not be neglected. It could impact the film thickness, which is an important parameter to determine the thin-film heat transfer performance, or even destroy fallingfilm modes and significantly deteriorate the heat transfer. However, there have been very few studies of countercurrent gas flow effects on the film thickness. In this paper, the fallingfilm film thickness with and without liquid-gas interfacial shear stress due to the countercurrent gas flow was studied. A twophase empirical correlation is used to solve the momentum equation. Calculation results were compared with available experimental data in literatures for validation. Reasonable agreement was achieved. Thus, the two-phase correlation for predicting shear stress of a thin film flow inside a vertical rectangular channel has been extended to a new type of flow. Effects of film Reynolds number, gas velocity, and gas-channel equivalent hydraulic diameter on the film thickness were studied. It is shown that the countercurrent gas flow thickened the falling film. The increased film thickness can shift the mode transitional Reynolds number and reduce the heat transfer coefficient, corroborating the conjecture in our earlier work

Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various methods constrain attention to a local window, enhancing its efficiency. Consequently, the receptive fields in a single attention layer are inadequate, leading to insufficient context modeling. Furthermore, while most transform-based approaches reuse shallow features through skip connections, relying solely on these connections treats shallow and deep features equally, impeding the model's ability to characterize them. To address these issues, we propose a novel transformer architecture called Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively enhances global cognition and understanding of the entire image, facilitating efficient integration of features cross-stages. The model incorporates cross-spatial pixel integration attention (CSPIA) to introduce contextual information into a local window, while cross-stage feature fusion attention (CSFFA) adaptively fuses features from the previous stage to improve feature expression in line with the requirements of the current stage. We conducted comprehensive experiments on multiple benchmark datasets, demonstrating the superior performance of our proposed SPIFFNet in terms of both quantitative metrics and visual quality when compared to state-of-the-art methods

Ultrasonication effects on thermal and rheological properties of carbon nanotube suspensions

The preparation of nanofluids is very important to their thermophysical properties. Nanofluids with the same nanoparticles and base fluids can behave differently due to different nanofluid preparation methods. The agglomerate sizes in nanofluids can significantly impact the thermal conductivity and viscosity of nanofluids and lead to a different heat transfer performance. Ultrasonication is a common way to break up agglomerates and promote dispersion of nanoparticles into base fluids. However, research reports of sonication effects on nanofluid properties are limited in the open literature. In this work, sonication effects on thermal conductivity and viscosity of carbon nanotubes (0.5 wt%) in an ethylene glycol-based nanofluid are investigated. The corresponding effects on the agglomerate sizes and the carbon nanotube lengths are observed. It is found that with an increased sonication time/energy, the thermal conductivity of the nanofluids increases nonlinearly, with the maximum enhancement of 23% at sonication time of 1,355 min. However, the viscosity of nanofluids increases to the maximum at sonication time of 40 min, then decreases, finally approaching the viscosity of the pure base fluid at a sonication time of 1,355 min. It is also observed that the sonication process not only reduces the agglomerate sizes but also decreases the length of carbon nanotubes. Over the current experimental range, the reduction in agglomerate size is more significant than the reduction of the carbon nanotube length. Hence, the maximum thermal conductivity enhancement and minimum viscosity increase are obtained using a lengthy sonication, which may have implications on application

Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization

In this era characterized by rapid improvements in the quality of living, people are eager to seek better medical services. However, the medical resource shortage threatens people's daily lives and has become an important factor causing dissatisfaction. Furthermore, as a sub-branch of artificial intelligence, computational intelligence is widely used to solve real-world problems like resource allocation. This paper proposes a medical resource allocation model based on mass customization, considering parameters such as doctors' professional level, patient preferences, and the medical station distribution. This model aims at optimizing and balancing the uneven distribution of medical resources by taking into account the patient requirements and medical costs. Moreover, a genetic algorithm is applied to improve the computational efficiency of the proposed method. The results show that the medical resource allocation model based on mass customization can lead to a higher profit. Suggestions are also discussed for sustainable development in medical service based on mass customization

New and little-known stonefly species of the Rhopalopsole vietnamica ‘western assemblage’ group (Plecoptera, Leuctridae) from China

We examined Chinese stonefly specimens of the Rhopalopsole vietnamica ‘western assemblage’ group. A new species from Hainan Province, R. bawanglinga Li, Li & Yang, sp. nov. is described and illustrated from male and female adults, and it is compared to closely related taxa. The hitherto unknown female of R. hainana Li & Yang, 2010 is described. Morphological evidence is presented for the identity of R. dentiloba Wu, 1973, on the basis of topotypes from Yunnan Province, southwestern China

TransGOP: Transformer-Based Gaze Object Prediction

Gaze object prediction aims to predict the location and category of the object that is watched by a human. Previous gaze object prediction works use CNN-based object detectors to predict the object's location. However, we find that Transformer-based object detectors can predict more accurate object location for dense objects in retail scenarios. Moreover, the long-distance modeling capability of the Transformer can help to build relationships between the human head and the gaze object, which is important for the GOP task. To this end, this paper introduces Transformer into the fields of gaze object prediction and proposes an end-to-end Transformer-based gaze object prediction method named TransGOP. Specifically, TransGOP uses an off-the-shelf Transformer-based object detector to detect the location of objects and designs a Transformer-based gaze autoencoder in the gaze regressor to establish long-distance gaze relationships. Moreover, to improve gaze heatmap regression, we propose an object-to-gaze cross-attention mechanism to let the queries of the gaze autoencoder learn the global-memory position knowledge from the object detector. Finally, to make the whole framework end-to-end trained, we propose a Gaze Box loss to jointly optimize the object detector and gaze regressor by enhancing the gaze heatmap energy in the box of the gaze object. Extensive experiments on the GOO-Synth and GOO-Real datasets demonstrate that our TransGOP achieves state-of-the-art performance on all tracks, i.e., object detection, gaze estimation, and gaze object prediction. Our code will be available at https://github.com/chenxi-Guo/TransGOP.git