OD-NeRF: Efficient Training of On-the-Fly Dynamic Neural Radiance Fields

Dynamic neural radiance fields (dynamic NeRFs) have demonstrated impressive results in novel view synthesis on 3D dynamic scenes. However, they often require complete video sequences for training followed by novel view synthesis, which is similar to playing back the recording of a dynamic 3D scene. In contrast, we propose OD-NeRF to efficiently train and render dynamic NeRFs on-the-fly which instead is capable of streaming the dynamic scene. When training on-the-fly, the training frames become available sequentially and the model is trained and rendered frame-by-frame. The key challenge of efficient on-the-fly training is how to utilize the radiance field estimated from the previous frames effectively. To tackle this challenge, we propose: 1) a NeRF model conditioned on the multi-view projected colors to implicitly track correspondence between the current and previous frames, and 2) a transition and update algorithm that leverages the occupancy grid from the last frame to sample efficiently at the current frame. Our algorithm can achieve an interactive speed of 6FPS training and rendering on synthetic dynamic scenes on-the-fly, and a significant speed-up compared to the state-of-the-art on real-world dynamic scenes

GNeSF: Generalizable Neural Semantic Fields

3D scene segmentation based on neural implicit representation has emerged recently with the advantage of training only on 2D supervision. However, existing approaches still requires expensive per-scene optimization that prohibits generalization to novel scenes during inference. To circumvent this problem, we introduce a generalizable 3D segmentation framework based on implicit representation. Specifically, our framework takes in multi-view image features and semantic maps as the inputs instead of only spatial information to avoid overfitting to scene-specific geometric and semantic information. We propose a novel soft voting mechanism to aggregate the 2D semantic information from different views for each 3D point. In addition to the image features, view difference information is also encoded in our framework to predict the voting scores. Intuitively, this allows the semantic information from nearby views to contribute more compared to distant ones. Furthermore, a visibility module is also designed to detect and filter out detrimental information from occluded views. Due to the generalizability of our proposed method, we can synthesize semantic maps or conduct 3D semantic segmentation for novel scenes with solely 2D semantic supervision. Experimental results show that our approach achieves comparable performance with scene-specific approaches. More importantly, our approach can even outperform existing strong supervision-based approaches with only 2D annotations. Our source code is available at: https://github.com/HLinChen/GNeSF.Comment: NeurIPS 202

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Research on Lightning Performance and Protective Measures of ±800 kV UHVDC Power Transmission Line

[Introduction] Lightning strike is the primary cause of failures of ±800 kV UHVDC power transmission lines, and the lightning protection assessment of power transmission lines is critical to the safe and stable operation of the system. [Method] Based on a ±800 kV power transmission line project under construction in China, the conductor and ground wire types were selected according to the design and use conditions of the project, and the typical towers were determined according to the altitude and meteorological area distribution along the line. After comprehensive consideration of the distribution ratio of ground inclination, soil resistivity, thunderstorm days, meteorological zone, etc. of towers along the line, the lightning protection performance of the line was evaluated and specific lightning protection measures were proposed in respect of the lighting characteristics of the project. In addition, the calculation differences between the two methods regarding terrain considerations in the EGM were compared. [Result] The calculation results show that the comprehensive lightning flashover rate doesn’t meet the requirements of design reference value and mainly protect against shielding failure. Thunderstorm days and the terrain conditions on the positive side of tower are the key factors for lightning protection. [Conclusion] After adopting the -15° tower protection angle, the line located in lightning areas with C level and above whose positive side ground inclination angle is ≥25° can meet the lightning protection requirements that the lightning flashover rate is not more than 0.10 fl/(100 km·a·40 d). The lightning protection performance and actual operating data of this project and adjacent ±800 kV as-built lines are compared and analyzed, which verifies the rationality of the calculation method and results in this paper

HODN: Disentangling Human-Object Feature for HOI Detection

The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances currently. However, these methods ignore the relationship among humans, objects, and interactions: 1) human features are more contributive than object ones to interaction prediction; 2) interactive information disturbs the detection of objects but helps human detection. In this paper, we propose a Human and Object Disentangling Network (HODN) to model the HOI relationships explicitly, where humans and objects are first detected by two disentangling decoders independently and then processed by an interaction decoder. Considering that human features are more contributive to interaction, we propose a Human-Guide Linking method to make sure the interaction decoder focuses on the human-centric regions with human features as the positional embeddings. To handle the opposite influences of interactions on humans and objects, we propose a Stop-Gradient Mechanism to stop interaction gradients from optimizing the object detection but to allow them to optimize the human detection. Our proposed method achieves competitive performance on both the V-COCO and the HICO-Det datasets. It can be combined with existing methods easily for state-of-the-art results.Comment: Accepted by TMM 202

A novel recombinant pseudorabies virus expressing parvovirus VP2 gene: Immunogenicity and protective efficacy in swine

<p>Abstract</p> <p>Background</p> <p>Porcine parvovirus (PPV) VP2 gene has been successfully expressed in many expression systems resulting in self-assembly of virus-like particles (VLPs) with similar morphology to the native capsid. Here, a pseudorabies virus (PRV) system was adopted to express the PPV VP2 gene.</p> <p>Methods</p> <p>A recombinant PRV SA215/VP2 was obtained by homologous recombination between the vector PRV viral DNA and a transfer plasmid. Then recombinant virus was purified with plaque purification, and its identity confirmed by PCR amplification, Western blot and indirect immunofluorescence (IFA) analyses. Electronic microscopy of PRV SA215/VP2 confirmed self-assembly of both pseudorabies virus and VLPs from VP2 protein.</p> <p>Results</p> <p>Immunization of piglets with recombinant virus elicited PRV-specific and PPV-specific humoral immune responses and provided complete protection against a lethal dose of PRV challenges. Gilts immunized with recombinant viruses induced PPV-specific antibodies, and significantly reduced the mortality rate of (1 of 28) following virulent PPV challenge compared with the control (7 of 31). Furthermore, PPV virus DNA was not detected in the fetuses of recombinant virus immunized gilts.</p> <p>Conclusions</p> <p>In this study, a recombinant PRV SA215/VP2 virus expressing PPV VP2 protein was constructed using PRV SA215 vector. The safety, immunogenicity, and protective efficacy of the recombinant virus were demonstrated in piglets and primiparous gilts. This recombinant PRV SA215/VP2 represents a suitable candidate for the development of a bivalent vaccine against both PRV and PPV infection.</p

Triangle Counting Rule: An Approach to Forecast the Magnetic Properties of Benzenoid Polycyclic Hydrocarbons

Open-shell benzenoid polycyclic hydrocarbons (BPHs) are promising materials for future quantum applications. However, the search and realization of open-shell BPHs with desired properties is a challenging task due to the gigantic chemical space of BPHs, requiring new strategies for both theoretical understanding and experimental advancement. In this work, by building a structure database of BPHs through graphical enumeration, performing data-driven analysis, and combining tight-binding and mean-field Hubbard calculations, we discovered that the number of the internal vertices of the BPH graphs is closely correlated to their open-shell characters. We further established a series of simple rules, the triangle counting rule (TCR), to predict the magnetic ground state of BPHs. These findings not only provide a database of open-shell BPHs, but also extend the well-known Lieb's theorem and Ovchinnikov's rule and provide a straightforward method for designing open-shell carbon nanostructures. These insights may aid in the exploration of emerging quantum phases and the development of magnetic carbon materials for technology applications.Comment: 6 pages, 4 figure

Surface Modification of Ammonium Polyphosphate for Enhancing Flame-Retardant Properties of Thermoplastic Polyurethane.

Currently, the development of efficient and environmentally friendly flame-retardant thermoplastic polyurethane (TPU) composite materials has caused extensive research. Ammonium polyphosphate (APP) is used as a general intumescent flame retardant to improve the flame retardancy of TPU. In this paper, we developed a functionalized APP flame retardant (APP-Cu@PDA). Adding only 5 wt% of APP-Cu@PDA into TPU can significantly improve the flame-retardant’s performance of the composite material, reflected by a high LOI value of 28% with a UL-94 test of V-0 rating. Compared with pure TPU, the peak heat release rate, total heat release, peak smoke release rate, and total smoke release were reduced by 82%, 25%, 50%, and 29%, respectively. The improvements on the flame-retardant properties of the TPU/5%APP-Cu@PDA composites were due to the following explanations: Cu2+-chelated PDA has a certain catalytic effect on the carbonization process, which can promote the formation of complete carbon layers and hinder the transfer of heat and oxygen. In addition, after adding 5% APP-Cu@PDA, the tensile strength and elongation at the break of TPU composites did not decrease significantly. In summary, we developed a new flame-retardant APP-Cu@PDA, which has better flame-retardant properties than many reported TPU composites, and its preparation process is simple and environmentally friendly. This process can be applied to the industrial production of flame retardants in the future.post-print4370 K