Controlled polarization rotation of an optical field in multi-Zeeman-sublevel atoms

We investigate, both theoretically and experimentally, the phenomenon of polarization rotation of a weak, linearly-polarized optical (probe) field in an atomic system with multiple three-level electromagnetically induced transparency (EIT) sub-systems. The polarization rotation angle can be controlled by a circularly-polarized coupling beam, which breaks the symmetry in number of EIT subsystems seen by the left- and right-circularly-polarized components of the weak probe beam. A large polarization rotation angle (up to 45 degrees) has been achieved with a coupling beam power of only 15 mW. Detailed theoretical analyses including different transition probabilities in different transitions and Doppler-broadening are presented and the results are in good agreements with the experimentally measured results.Comment: 28pages, 12figure

Research on Solving Systems of Nonlinear Equations Based on Improved PSO

Solving systems of nonlinear equations is perhaps one of the most difficult problems in all of numerical computations, especially in a diverse range of engineering applications. The convergence and performance characteristics can be highly sensitive to the initial guess of the solution for most numerical methods such as Newton’s method. However, it is very difficult to select reasonable initial guess of the solution for most systems of nonlinear equations. Besides, the computational efficiency is not high enough. Aiming at these problems, an improved particle swarm optimization algorithm (imPSO) is proposed, which can overcome the problem of selecting reasonable initial guess of the solution and improve the computational efficiency. The convergence and performance characteristics of this method are demonstrated through some standard systems. The results show that the improved PSO for solving systems of nonlinear equations has reliable convergence probability, high convergence rate, and solution precision and is a successful approach in solving systems of nonlinear equations

MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis

We present MeshSegmenter, a simple yet effective framework designed for zero-shot 3D semantic segmentation. This model successfully extends the powerful capabilities of 2D segmentation models to 3D meshes, delivering accurate 3D segmentation across diverse meshes and segment descriptions. Specifically, our model leverages the Segment Anything Model (SAM) model to segment the target regions from images rendered from the 3D shape. In light of the importance of the texture for segmentation, we also leverage the pretrained stable diffusion model to generate images with textures from 3D shape, and leverage SAM to segment the target regions from images with textures. Textures supplement the shape for segmentation and facilitate accurate 3D segmentation even in geometrically non-prominent areas, such as segmenting a car door within a car mesh. To achieve the 3D segments, we render 2D images from different views and conduct segmentation for both textured and untextured images. Lastly, we develop a multi-view revoting scheme that integrates 2D segmentation results and confidence scores from various views onto the 3D mesh, ensuring the 3D consistency of segmentation results and eliminating inaccuracies from specific perspectives. Through these innovations, MeshSegmenter offers stable and reliable 3D segmentation results both quantitatively and qualitatively, highlighting its potential as a transformative tool in the field of 3D zero-shot segmentation. The code is available at \url{https://github.com/zimingzhong/MeshSegmenter}.Comment: The paper was accepted by ECCV202

mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar

Millimeter Wave (mmWave) Radar is gaining popularity as it can work in adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has explored the possibility of reconstructing 3D skeletons or meshes from the noisy and sparse mmWave Radar signals. However, it is unclear how accurately we can reconstruct the 3D body from the mmWave signals across scenes and how it performs compared with cameras, which are important aspects needed to be considered when either using mmWave radars alone or combining them with cameras. To answer these questions, an automatic 3D body annotation system is first designed and built up with multiple sensors to collect a large-scale dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images in different scenes and skeleton/mesh annotations for humans in the scenes. With this dataset, we train state-of-the-art methods with inputs from different sensors and test them in various scenarios. The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected. Further, analysis of the dataset and the results shadow insights on improving the reconstruction from the mmWave radar and the combination of signals from different sensors.Comment: ACM Multimedia 2022, Project Page: https://chen3110.github.io/mmbody/index.htm

A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry

Mercury (Hg) is a global persistent contaminant. Modeling studies are useful means of synthesizing a current understanding of the Hg cycle. Previous studies mainly use coarse-resolution models, which makes it impossible to analyze the role of turbulence in the Hg cycle and inaccurately describes the transport of kinetic energy. Furthermore, all of them are coupled with offline biogeochemistry, and therefore they cannot respond to short-term variability in oceanic Hg concentration. In our approach, we utilize a high-resolution ocean model (MITgcm-ECCO2, referred to as “high-resolution-MITgcm”) coupled with the concurrent simulation of biogeochemistry processes from the Darwin Project (referred to as “online”). This integration enables us to comprehensively simulate the global biogeochemical cycle of Hg with a horizontal resolution of 1/5∘. The finer portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes demonstrate the effects of turbulence that are neglected in previous models. Ecological events such as algal blooms can cause a sudden enhancement of phytoplankton biomass and chlorophyll concentrations, which can also result in a dramatic change in particle-bound Hg (HgaqP) sinking flux simultaneously in our simulation. In the global estuary region, including riverine Hg input in the high-resolution model allows us to reveal the outward spread of Hg in an eddy shape driven by fine-scale ocean currents. With faster current velocities and diffusion rates, our model captures the transport and mixing of Hg from river discharge in a more accurate and detailed way and improves our understanding of Hg cycle in the ocean.</p

CT-NeRF: Incremental Optimizing Neural Radiance Field and Poses with Complex Trajectory

Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like BARF have introduced camera pose optimization within NeRF, their applicability is limited to simple trajectory scenes. Existing methods struggle while tackling complex trajectories involving large rotations. To address this limitation, we propose CT-NeRF, an incremental reconstruction optimization pipeline using only RGB images without pose and depth input. In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure. Further, we instantiate the consistency between poses as a reprojected geometric image distance constraint resulting from pixel-level correspondences between input image pairs. Through the incremental reconstruction, CT-NeRF enables the recovery of both camera poses and scene structure and is capable of handling scenes with complex trajectories. We evaluate the performance of CT-NeRF on two real-world datasets, NeRFBuster and Free-Dataset, which feature complex trajectories. Results show CT-NeRF outperforms existing methods in novel view synthesis and pose estimation accuracy

Spatial identification of conservation priority areas for urban ecological land: An approach based on water ecosystem services

How to effectively prevent land degradation and ecosystem deterioration in the process of urbanization has been the focus of land degradation researches in urban areas. Urban ecological land can be defined as the natural base on which a city relies to ecologically survive. It closely links the social economy with the natural eco‐environment, providing an important integrated approach to resolve the contradiction between urban expansion and natural ecosystems conservation in the process of urbanization. The research question addressed in this study is how to accurately identify the conservation priority areas for urban ecological land. Taking Zhuhai City, located in China, as an example, an approach based on seven kinds of water ecosystem services was put forward, combining social demand and natural supply for the services to determine service targets and conservation priority areas. The results showed that the conservation priority areas in Zhuhai City covered 868 km2, accounting for 51.03% of the total land area, which were mainly covered by woodlands or paddy fields and fish ponds. In addition, by synthesizing ecological importance and ecological sensitivity, management zones for urban ecological land were delineated, including 510 km2 of primary control areas and 358 km2 of secondary control areas. In the supply and demand view of water ecosystem services, this study put forward an integrated ecosystem‐based approach for conservation priority area identification of urban ecological land, aiming to prevent land degradation and achieve urban ecological sustainability

Exosome delivery to the testes for dmrt1 suppression: a powerful tool for sex-determining gene studies

Exosomes are endosome-derived extracellular vesicles about 100 nm in diameter. They are emerging as prom ising delivery platforms due to their advantages in biocompatibility and engineerability. However, research into and applications for engineered exosomes are still limited to a few areas of medicine in mammals. Here, we expanded the scope of their applications to sex-determining gene studies in early vertebrates. An integrated strategy for constructing the exosome-based delivery system was developed for efficient regulation of dmrt1, which is one of the most widely used sex-determining genes in metazoans. By combining classical methods in molecular biology and the latest technology in bioinformatics, isomiR-124a was identified as a dmrt1 inhibitor and was loaded into exosomes and a testis-targeting peptide was used to modify exosomal surface for efficient delivery. Results showed that isomiR-124a was efficiently delivered to the testes by engineered exosomes and revealed that dmrt1 played important roles in maintaining the regular structure and function of testis in juvenile fish. This is the first de novo development of an exosome-based delivery system applied in the study of sex determining gene, which indicates an attractive prospect for the future applications of engineered exosomes in exploring more extensive biological conundrums.info:eu-repo/semantics/publishedVersio

Achieving the Sustainable Development Goals in the post-pandemic era

The COVID-19 pandemic continues to pose substantial challenges to achieving the Sustainable Development Goals (SDGs). Exploring systematic SDG strategies is urgently needed to aid recovery from the pandemic and reinvigorate global SDG actions. Based on available data and comprehensive analysis of the literature, this paper highlights ongoing challenges facing the SDGs, identifies the effects of COVID-19 on SDG progress, and proposes a systematic framework for promoting the achievement of SDGs in the post-pandemic era. Progress towards attaining the SDGs was already lagging behind even before the onset of the COVID-19 pandemic. Inequitable distribution of food–energy–water resources and environmental crises clearly threaten SDG implementation. Evidently, there are gaps between the vision for SDG realization and actual capacity that constrain national efforts. The turbulent geopolitical environment, spatial inequities, and trade-offs limit the effectiveness of SDG implementation. The global public health crisis and socio-economic downturn under COVID-19 have further impeded progress toward attaining the SDGs. Not only has the pandemic delayed SDG advancement in general, but it has also amplified spatial imbalances in achieving progress, undermined connectivity, and accentuated anti-globalization sentiment under lockdowns and geopolitical conflicts. Nevertheless, positive developments in technology and improvement in environmental conditions have also occurred. In reflecting on the overall situation globally, it is recommended that post-pandemic SDG actions adopt a “Classification–Coordination–Collaboration” framework. Classification facilitates both identification of the current development status and the urgency of SDG achievement aligned with national conditions. Coordination promotes domestic/international and inter-departmental synergy for short-term recovery as well as long-term development. Cooperation is key to strengthening economic exchanges, promoting technological innovation, and building a global culture of sustainable development that is essential if the endeavor of achieving the SDGs is to be successful. Systematic actions are urgently needed to get the SDG process back on track.publishedVersio

Simultaneous extraction and determination of alkaloids and organic acids in Uncariae Ramulas Cum Unicis by vortex-assisted matrix solid phase dispersion extraction coupled with UHPLC-MS/MS

A simple and efficient vortex-assisted matrix solid phase dispersion with a ultra-high-performance liquid chromatography-triple quadrupole mass spectrometer (VA-MSPD-UHPLC-MS/MS) was applied for simultaneous extraction and determination of seven alkaloids and three organic acids from Uncariae Ramulas Cum Unicis. The optimal extraction conditions of the target components were obtained by Box-Behnken design (BBD) combined with response surface methodology (RSM). The results of the method validation showed that this analytical method displayed good linearity with a correlation coefficient (r) no lower than 0.9990. The recoveries of ten active ingredients from Uncariae Ramulas Cum Unicis ranged from 95.9% to 103% (RSD ≤ 2.77%). The RSDs of intra-day and inter-day precisions were all below 2.97%. The present method exhibited not only lower solvent and sample usage, but also shorter sample processing and analysis time. Consequently, the developed VA-MSPD-UHPLC-MS/MS method could be successfully and effectively used for the extraction and analysis of ten active components from Uncariae Ramulas Cum Unicis