RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://repaintnerf.github.io for a better view of our results.Comment: IJCAI 2023 Accepted (Main Track

Characteristics of Local Modulation Beam Propagating through Spatial Filter System

As local defects may significantly harm beam quality and affect safe operation, a systematic analysis of the ability of a spatial filter to alleviate these adverse effects is required. Thus, the evolutional characteristics of a beam modulated by a local defect propagating through a spatial filter system at an image reply plane and a downstream plane are analyzed in detail. Modulation stripes appear at the image reply plane; these are caused by the pinhole cutoff effect. The modulation degree increases with increasing defect size. The maximum intensification factor can reach 3.2 under certain conditions. Thus, the defect size should be restricted to a reasonable size for safe operation with a specified pinhole size. Moreover, a maximal value appears at the downstream plane, and the intensity enhances with increasing defect size. To ensure beam quality, the maximum allowable defect size and angle of the spatial filter should meet special constraints. The maximum allowable defect size is calculated based on practical configuration parameters

Comparison of algorithms for road surface temperature prediction

Purpose - The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately. Design/methodology/approach - Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors. Findings - The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms. Originality/value - This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method

Detecting Protein-Protein Interactions with a Novel Matrix-Based Protein Sequence Representation and Support Vector Machines

Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of proteinprotein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally

Entanglement of spin-orbit qubits induced by Coulomb interaction

Spin-orbit qubit (SOQ) is the dressed spin by the orbital degree of freedom through a strong spin-orbit coupling (SOC). We show that Coulomb interaction between two electrons in quantum dots located separately in two nanowires can efficiently induce quantum entanglement between two SOQs. But to achieve the highest possible value for two SOQs concurrence, strength of SOC and confining potential for the quantum dots should be tuned to an optimal ratio. The physical mechanism to achieve such quantum entanglement is based on the feasibility of the SOQ responding to the external electric field via an intrinsic electric dipole spin resonance

Research on the development mode of underwater offensive and defensive system based on system science system

This paper summarizes the origin, development and interrelation of system science systems, such as system engineering, system engineering and digital engineering. Based on the analysis of the development of underwater attack and defense systems at home and abroad, and in view of the main problems faced by underwater attack and defense systems in the development process, such as complex system operation mechanism and weak technical foundation, this paper expounds the necessity of applying the relevant theories of system science system to the construction and development of underwater attack and defense systems. From the perspective of system engineering, system engineering and digital engineering, the paper puts forward relevant development suggestions of underwater attack and defense system, which has certain military significance and theoretical value for the subsequent research of underwater attack and defense system

Optimizing Crop Systems: Integrating Forage Triticale into the Fallow of Peanut Monoculture in the North China Plain

Integrating a forage crop into the fallow (F) of the peanut (Arachis hypogaea L.) (P) mono-cropping system is a practical approach to provide forage yield and increase the resource use efficiency. However, little information about the comprehensive assessment of water utilization and economic benefits in the crop–livestock system exists for the North China Plain (NCP). This study aims to identify the crop rotation for optimizing water management and enhance economic benefit. The field experiment was performed over three years (2011–2014) to assess production, water utilization, and economic benefits when inserting forage triticale (X Triticosecale Wittmack) (T) into the peanut mono-cropping system. Results showed that replacing the fallow F-P cropping system with forage triticale provided a substantial amount of forage (the average of 9.8 t ha−1 per year) and enhanced the average system productivity by 85.1%. Cultivation of forage triticale during the fallow period decreased the subsequent peanut pod yield by 8.3% due to a 19.3% decline in soil water storage capacity during the sowing stage of peanut. Replacing fallow with forage triticale increased the system net income by 1016.2 US$ ha−1 and the water use efficiency (WUE) by 30.0%, while not affecting the economic efficiency of water use (EEWU), and thus can be recommended as a better option for maintaining relatively high system production, economic benefit, and WUE in NCP

Theoretical Exploration of Properties of Iron-Silicon Interface Constructed by Depositing Fe on Si(111)-(7x7)

Exploring the properties of magnetic metal on the semiconductor surface is of great significance for the application of magnetic recording materials. Herein, DFT calculations are carried out to explore the properties of the iron-silicon interface structures (nFe/DASF) formed by depositing n Fe atoms on the reconstructed Si(111)-(7x7) surface (DASF). The stable nFe/DASF structures are studied in the cases of the adsorption and permeation of Fe atoms on the DASF. In both cases, Fe atoms are not very dispersed and prefer binding with Si atoms rather than the adsorbed Fe atoms, because the Fe-Si interaction is stronger than the Fe-Fe interaction. As the n value increases, the average binding energy (Eb_ave) of Fe generally firstly becomes more negative and then becomes less negative, with the presence of a 7Fe wheel as a stable geometry on the upmost surface. The presence of the 7Fe wheel is attributed to the enhanced Fe-Si interaction in this wheel compared to other geometries. CO adsorption occurs at the central Fe site of the 7Fe wheel which is greatly influenced by the surrounding Si atoms but is little influenced by the additional Fe atoms in the interlayer