Can pro-environmental behavior increase farmers’ income?—Evidence from arable land quality protection practices in China

In China, agricultural non-point source pollution is one of the key factors limiting farmers’ income growth, and pro-environmental behavior can address agricultural surface pollution. Based on field survey data from 591 farmers in Xinjiang, China, this study empirically estimates the impact of pro-environmental behavior on farmers’ income growth. The results show that pro-environmental behavior plays a significant positive role in increasing farmers’ income, and the positive effect continues in the long run. Specifically, pro-environmental behavior can optimize the allocation of agricultural production factors, thus resulting in farmers’ income growth. The mechanism analysis shows that pro-environmental behavior affects farmers’ income growth by promoting the increase in the size of arable land and farmers’ willingness to transfer their land in the future. These findings indicate that a sound reward–punishment system for pro-environmental behavior should be established; training on pro-environmental behavior should be strengthened, and a mechanism for linking the benefits of pro-environmental behavior among stakeholders should be constructed

HBT characterization and modeling for nonlinear microwave circuit design

Ph.DDOCTOR OF PHILOSOPH

Molecular Conformation Generation via Shifting Scores

Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule. Generating molecular conformation via diffusion requires learning to reverse a noising process. Diffusion on inter-atomic distances instead of conformation preserves SE(3)-equivalence and shows superior performance compared to alternative techniques, whereas related generative modelings are predominantly based upon heuristical assumptions. In response to this, we propose a novel molecular conformation generation approach driven by the observation that the disintegration of a molecule can be viewed as casting increasing force fields to its composing atoms, such that the distribution of the change of inter-atomic distance shifts from Gaussian to Maxwell-Boltzmann distribution. The corresponding generative modeling ensures a feasible inter-atomic distance geometry and exhibits time reversibility. Experimental results on molecular datasets demonstrate the advantages of the proposed shifting distribution compared to the state-of-the-art.Comment: 18 pages, 7 figure

Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.Comment: ICCV 2023 camera-ready, Project Page: https://me.kiui.moe/nerf2mes

Design and Performance Research on Dual Layer Cement Based Absorber Reinforced with Graphene Nanosheets and Manganese-zinc Ferrite

Dual layer cement-based absorber is synthesized by mixing with graphene nanosheets and manganese-zinc ferrite, to study the effect of absorbing filler content on the mechanical properties, microstructure, electrical resistivity and reflectivity of the paste. The microstructure of the absorber is seen by Scanning Electron Microscope (SEM) images, Fourier Transform Infrared (FTIR) spectroscopy, X-Ray Diffraction (XRD) curves of the absorber. The results show that graphene nanosheets significantly reduce the electrical resistivity of paste, increasing its mechanical properties by improving its pore structure. SEM images indicate that graphene nanosheets promote the increase and coarsening of cement hydration products and produce a large number of dense bulk crystals. Furthermore, reflectivity measurements show that the minimum reflectivity of – 14.1 dB is obtained in the range of 2 ~ 18 GHz and the effective bandwidth of 16 GHz is obtained when reflectivity is less than – 7 dB. This study provides a new method for the preparation of dual layer cement-based absorber

An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage. In this paper, we propose an efficient NeRF-based framework that enables real-time synthesizing of talking portraits and faster convergence by leveraging the recent success of grid-based NeRF. Our key insight is to decompose the inherently high-dimensional talking portrait representation into three low-dimensional feature grids. Specifically, a Decomposed Audio-spatial Encoding Module models the dynamic head with a 3D spatial grid and a 2D audio grid. The torso is handled with another 2D grid in a lightweight Pseudo-3D Deformable Module. Both modules focus on efficiency under the premise of good rendering quality. Extensive experiments demonstrate that our method can generate realistic and audio-lips synchronized talking portrait videos, while also being highly efficient compared to previous methods.Comment: Project page: https://me.kiui.moe/radnerf

Five Cases Report of Solid Tumor Synchronously with Hematologic Malignancy

The reported incidence of synchronous multiple primary cancer (SMPC) is rare, and it is even less common to observe synchronous solid tumor with a hematological malignancy. We report five cases of solid tumor presented synchronously with hematological malignancy, all observed within a 2 year period at the oncology department of a university hospital in Shanghai, China. These individual cases included lung adenocarcinoma with chronic myelogenous leukemia, colon cancer with solitary plasmocytoma, gastric adenocarcinoma with diffuse large B cell non-Hodgkin's lymphoma, lung adenocarcinoma with multiple myeloma, and colon cancer with diffuse large B cell non-Hodgkin's lymphoma. It is challenging to therapeutically control the biological behavior of concurrent multiple primary tumors, and there is no standard treatment for such rare conditions. In this paper we discuss these five cases of SMPC and their treatments

Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation

Creating the photo-realistic version of people sketched portraits is useful to various entertainment purposes. Existing studies only generate portraits in the 2D plane with fixed views, making the results less vivid. In this paper, we present Stereoscopic Simplified Sketch-to-Portrait (SSSP), which explores the possibility of creating Stereoscopic 3D-aware portraits from simple contour sketches by involving 3D generative models. Our key insight is to design sketch-aware constraints that can fully exploit the prior knowledge of a tri-plane-based 3D-aware generative model. Specifically, our designed region-aware volume rendering strategy and global consistency constraint further enhance detail correspondences during sketch encoding. Moreover, in order to facilitate the usage of layman users, we propose a Contour-to-Sketch module with vector quantized representations, so that easily drawn contours can directly guide the generation of 3D portraits. Extensive comparisons show that our method generates high-quality results that match the sketch. Our usability study verifies that our system is greatly preferred by user.Comment: Project Page on https://hangz-nju-cuhk.github.io