Cosmic constraint on the unified model of dark sectors with or without a cosmic string fluid in the varying gravitational constant theory

Observations indicate that most of the universal matter are invisible and the gravitational constant $G(t)$ maybe depends on the time. A theory of the variational $G$ (VG) is explored in this paper, with naturally producing the useful dark components in universe. We utilize the observational data: lookback time data, model-independent gamma ray bursts, growth function of matter linear perturbations, type Ia supernovae data with systematic errors, CMB and BAO to restrict the unified model (UM) of dark components in VG theory. Using the best-fit values of parameters with the covariance matrix, constraints on the variation of $G$ are $(\frac{G}{G_{0}})_{z=3.5}\simeq 1.0015^{+0.0071}_{-0.0075}$ and $(\frac{\dot{G}}{G})_{today}\simeq -0.7252^{+2.3645}_{-2.3645}\times 10^{-13} yr^{-1}$, the small uncertainties around constants. Limit on the equation of state of dark matter is $w_{0dm}=0.0072^{+0.0170}_{-0.0170}$ with assuming $w_{0de}=-1$ in unified model, and dark energy is $w_{0de}=-0.9986^{+0.0011}_{-0.0011}$ with assuming $w_{0dm}=0$ at prior. Restriction on UM parameters are $B_{s}=0.7442^{+0.0137+0.0262}_{-0.0132-0.0292}$ and $\alpha=0.0002^{+0.0206+0.0441}_{-0.0209-0.0422}$ with $1\sigma$ and $2\sigma$ confidence level. In addition, the effect of a cosmic string fluid on unified model in VG theory are investigated. In this case it is found that the $\Lambda$CDM ($\Omega_{s}=0$, $\beta=0$ and $\alpha=0$) is included in this VG-UM model at $1\sigma$ confidence level, and the larger errors are given: $\Omega_{s}=-0.0106^{+0.0312+0.0582}_{-0.0305-0.0509}$ (dimensionless energy density of cosmic string), $(\frac{G}{G_{0}})_{z=3.5}\simeq 1.0008^{+0.0620}_{-0.0584}$ and $(\frac{\dot{G}}{G})_{today}\simeq -0.3496^{+26.3135}_{-26.3135}\times 10^{-13}yr^{-1}$.Comment: 17 pages,4 figure

Technology in an Alternative Modernity

This essay tries to defend a general embracing-controlling-stance on modern technology on the basis of the analysis of technology and a synthesized theory about the relationship between technology and culture. The task is carried out in the framework of an alternative modernity theory, in a cross-cultural context. China and specific technologies are used to illustrate the central ideas as case studies

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future

A new approach of CMT seam welding deformation forecasting based on GA-BPNN

Welding deformation affects the quality of the welded parts. In this paper, by introducing improved back propagation neural network (BPNN), a cold metal transfer (CMT) welding deformation prediction model for aluminum-steel hybrid sheets is established. Before applying BPNN, important parameters affecting welding deformation were obtained by orthogonal test and gray relational grade theory. The accuracy of welding deformation prediction of BPNN is improved by genetic algorithm. The results show that compared with the prediction method based on traditional theory, the deformation prediction model based on GA-BPNN has higher accuracy. Predicted results were applied to the aluminum-steel CMT seam welding in the form of inverse deformation, and the deformation of the welded plate was significantly improved

Collaboration Helps Camera Overtake LiDAR in 3D Detection

Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major challenge lies in precise depth estimation due to the lack of direct 3D measurements in the input. Many previous methods attempt to improve depth estimation through network designs, e.g., deformable layers and larger receptive fields. This work proposes an orthogonal direction, improving the camera-only 3D detection by introducing multi-agent collaborations. Our proposed collaborative camera-only 3D detection (CoCa3D) enables agents to share complementary information with each other through communication. Meanwhile, we optimize communication efficiency by selecting the most informative cues. The shared messages from multiple viewpoints disambiguate the single-agent estimated depth and complement the occluded and long-range regions in the single-agent view. We evaluate CoCa3D in one real-world dataset and two new simulation datasets. Results show that CoCa3D improves previous SOTA performances by 44.21% on DAIR-V2X, 30.60% on OPV2V+, 12.59% on CoPerception-UAVs+ for AP@70. Our preliminary results show a potential that with sufficient collaboration, the camera might overtake LiDAR in some practical scenarios. We released the dataset and code at https://siheng-chen.github.io/dataset/CoPerception+ and https://github.com/MediaBrain-SJTU/CoCa3D.Comment: Accepted by CVPR2

An Extensible Framework for Open Heterogeneous Collaborative Perception

Collaborative perception aims to mitigate the limitations of single-agent perception, such as occlusions, by facilitating data exchange among multiple agents. However, most current works consider a homogeneous scenario where all agents use identity sensors and perception models. In reality, heterogeneous agent types may continually emerge and inevitably face a domain gap when collaborating with existing agents. In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception, while ensuring high perception performance and low integration cost? To address this problem, we propose HEterogeneous ALliance (HEAL), a novel extensible collaborative perception framework. HEAL first establishes a unified feature space with initial agents via a novel multi-scale foreground-aware Pyramid Fusion network. When heterogeneous new agents emerge with previously unseen modalities or models, we align them to the established unified space with an innovative backward alignment. This step only involves individual training on the new agent type, thus presenting extremely low training costs and high extensibility. To enrich agents' data heterogeneity, we bring OPV2V-H, a new large-scale dataset with more diverse sensor types. Extensive experiments on OPV2V-H and DAIR-V2X datasets show that HEAL surpasses SOTA methods in performance while reducing the training parameters by 91.5% when integrating 3 new agent types. We further implement a comprehensive codebase at: https://github.com/yifanlu0227/HEALComment: Accepted by ICLR 2024. The code and data are open-sourced at https://github.com/yifanlu0227/HEA

Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection

Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have expanded the SNNs to more challenging object detection tasks, they often suffer from high latency and low detection accuracy, making them difficult to deploy on latency sensitive mobile platforms. Furthermore, the conversion method from Artificial Neural Networks (ANNs) to SNNs is hard to maintain the complete structure of the ANNs, resulting in poor feature representation and high conversion errors. To address these challenges, we propose two methods: timesteps compression and spike-time-dependent integrated (STDI) coding. The former reduces the timesteps required in ANN-SNN conversion by compressing information, while the latter sets a time-varying threshold to expand the information holding capacity. We also present a SNN-based ultra-low latency and high accurate object detection model (SUHD) that achieves state-of-the-art performance on nontrivial datasets like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30% mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based object detection model to date that achieves ultra low timesteps to complete the lossless conversion.Comment: 14 pages, 10 figure

A New Method for Predicting Well Pattern Connectivity in a Continental Fluvial-delta Reservoir

The features of bad flow unit continuity and multiple layers emphesize the importance of a well pattern design for the development of a fluvial-delta reservoir. It is proposed a method to predict well pattern connectivity (WPC) based on the exploration and evaluation of wells. Moreover, the method helps evaluate the risk of well placement. This study initially establishes the parameters for characterizing the lateral and vertical flow unit distributions. Then, extensive statistics on the mature oil-field sands of synthetic geological models are obtained to generate the prediction model of WPC which will reveal the correlation among WPC, flow unit distribution, and well spacing (WS). Finally, a case study is conducted to validate the proposed method for predicting WPC. The procedure of the method is comprised of two steps. The first step is to calculate the parameters which characterize the vertical sand body distribution of the target formation by using the well drilling and logging information. The second step is to integrate the calculated parameters and designed WS into the proposed formula to forecast WPC. The new method of WPC prediction has the advantage of integrating the static and dynamic information of similar mature oil fields with each other. By utilizing the model, making an important decision on well pattern design and a reservoir production forecast in the newly discovered continental fluvial-delta reservoir would be reasonable