Exploration and Improvement of Nerf-based 3D Scene Editing Techniques

NeRF's high-quality scene synthesis capability was quickly accepted by scholars in the years after it was proposed, and significant progress has been made in 3D scene representation and synthesis. However, the high computational cost limits intuitive and efficient editing of scenes, making NeRF's development in the scene editing field facing many challenges. This paper reviews the preliminary explorations of scholars on NeRF in the scene or object editing field in recent years, mainly changing the shape and texture of scenes or objects in new synthesized scenes; through the combination of residual models such as GaN and Transformer with NeRF, the generalization ability of NeRF scene editing has been further expanded, including realizing real-time new perspective editing feedback, multimodal editing of text synthesized 3D scenes, 4D synthesis performance, and in-depth exploration in light and shadow editing, initially achieving optimization of indirect touch editing and detail representation in complex scenes. Currently, most NeRF editing methods focus on the touch points and materials of indirect points, but when dealing with more complex or larger 3D scenes, it is difficult to balance accuracy, breadth, efficiency, and quality. Overcoming these challenges may become the direction of future NeRF 3D scene editing technology

The expression of RIPK3 is associated with cell turnover of gastric mucosa in the mouse and human stomach

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Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted

Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network

The expectation to deploy a universal neural network for speech enhancement, with the aim of improving noise robustness across diverse speech processing tasks, faces challenges due to the existing lack of awareness within static speech enhancement frameworks regarding the expected speech in downstream modules. These limitations impede the effectiveness of static speech enhancement approaches in achieving optimal performance for a range of speech processing tasks, thereby challenging the notion of universal applicability. The fundamental issue in achieving universal speech enhancement lies in effectively informing the speech enhancement module about the features of downstream modules. In this study, we present a novel weighting prediction approach, which explicitly learns the task relationships from downstream training information to address the core challenge of universal speech enhancement. We found the role of deciding whether to employ data augmentation techniques as crucial downstream training information. This decision significantly impacts the expected speech and the performance of the speech enhancement module. Moreover, we introduce a novel speech enhancement network, the Plugin Speech Enhancement (Plugin-SE). The Plugin-SE is a dynamic neural network that includes the speech enhancement module, gate module, and weight prediction module. Experimental results demonstrate that the proposed Plugin-SE approach is competitive or superior to other joint training methods across various downstream tasks

Hot Compression Test and Microstructure Evolution in LZ50 Axle Steel

True strain-true stress curves of the LZ50 axle steel were obtained after hot compression tests had been performed on a Gleeble-3800 thermal simulator at strain rates of 0.01, 0.1, 1 and 5 s^(-1) and at deformation temperatures from 850 to 1,150 ℃. Following the data processing, the relationship between the flow stress and the deformation temperature of the material under different true strain conditions was analysed. On this basis and according to the influence of deformation factors, the constitutive equation of the Johnson-Cook flow stress model is established, and the model is modified according to the defects of the model, so that the improved model can effectively predict the mechanical behaviour in the range of high strain rates and temperatures. The dynamic material model (DMM) was used to generate the hot working diagram of the material. Through calculation and analysis, the optimum process area in terms of temperature was found to be in the range from 1,050 to 1,150 ℃ and in terms of strain rate in the rage from 1 to 5 s^(-1). Finally, the microstructure evolution of the compressed specimens under different strain rates and temperatures was studied in the metallographic analysis, which provided a theoretical basis and reference value for later damage

Printing surface charge as a new paradigm to program droplet transport

Directed, long-range and self-propelled transport of droplets on solid surfaces, especially on water repellent surfaces, is crucial for many applications from water harvesting to bio-analytical devices. One appealing strategy to achieve the preferential transport is to passively control the surface wetting gradients, topological or chemical, to break the asymmetric contact line and overcome the resistance force. Despite extensive progress, the directional droplet transport is limited to small transport velocity and short transport distance due to the fundamental trade-off: rapid transport of droplet demands a large wetting gradient, whereas long-range transport necessitates a relatively small wetting gradient. Here, we report a radically new strategy that resolves the bottleneck through the creation of an unexplored gradient in surface charge density (SCD). By leveraging on a facile droplet printing on superamphiphobic surfaces as well as the fundamental understanding of the mechanisms underpinning the creation of the preferential SCD, we demonstrate the self-propulsion of droplets with a record-high velocity over an ultra-long distance without the need for additional energy input. Such a Leidenfrost-like droplet transport, manifested at ambient condition, is also genetic, which can occur on a variety of substrates such as flexible and vertically placed surfaces. Moreover, distinct from conventional physical and chemical gradients, the new dimension of gradient in SCD can be programmed in a rewritable fashion. We envision that our work enriches and extends our capability in the manipulation of droplet transport and would find numerous potential applications otherwise impossible.Comment: 11 pages, 4 figure

Prognostic value of HMGN family expression in acute myeloid leukemia

Aim: The objective of this work was to investigate the prognostic role of the HMGN family in acute myeloid leukemia (AML). Methods: A total of 155 AML patients with HMGN1-5 expression data from the Cancer Genome Atlas database were enrolled in this study. Results: In the chemotherapy-only group, patients with high HMGN2 expression had significantly longer event-free survival (EFS) and overall survival (OS) than those with low expression (all p < 0.05), whereas high HMGN5 expressers had shorter EFS and OS than the low expressers (all p < 0.05). Multivariate analysis identified that high HMGN2 expression was an independent favorable prognostic factor for patients who only received chemotherapy (all p < 0.05). HMGN family expression had no impact on EFS and OS in AML patients receiving allogeneic hematopoietic stem cell transplantation. Conclusion: High HMGN2/5 expression is a potential prognostic indicator for AML

Event-Centric Query Expansion in Web Search

In search engines, query expansion (QE) is a crucial technique to improve search experience. Previous studies often rely on long-term search log mining, which leads to slow updates and is sub-optimal for time-sensitive news searches. In this work, we present Event-Centric Query Expansion (EQE), a novel QE system that addresses these issues by mining the best expansion from a significant amount of potential events rapidly and accurately. This system consists of four stages, i.e., event collection, event reformulation, semantic retrieval and online ranking. Specifically, we first collect and filter news headlines from websites. Then we propose a generation model that incorporates contrastive learning and prompt-tuning techniques to reformulate these headlines to concise candidates. Additionally, we fine-tune a dual-tower semantic model to function as an encoder for event retrieval and explore a two-stage contrastive training approach to enhance the accuracy of event retrieval. Finally, we rank the retrieved events and select the optimal one as QE, which is then used to improve the retrieval of event-related documents. Through offline analysis and online A/B testing, we observe that the EQE system significantly improves many metrics compared to the baseline. The system has been deployed in Tencent QQ Browser Search and served hundreds of millions of users. The dataset and baseline codes are available at https://open-event-hub.github.io/eqe .Comment: ACL 2023 Industry Trac

Combining machine learning and human judgment in author disambiguation

ABSTRACT Author disambiguation in digital libraries becomes increasingly difficult as the number of publications and consequently the number of ambiguous author names keep growing. The fully automatic author disambiguation approach could not give satisfactory results due to the lack of signals in many cases. Furthermore, human judgment on the basis of automatic algorithms is also not suitable because the automatically disambiguated results are often mixed and not understandable for humans. In this paper, we propose a Labeling Oriented Author Disambiguation approach, called LOAD, to combine machine learning and human judgment together in author disambiguation. LOAD exploits a framework which consists of high precision clustering, high recall clustering, and top dissimilar clusters selection and ranking. In the framework, supervised learning algorithms are used to train the similarity functions between publications and a clustering algorithm is further applied to generate clusters. To validate the effectiveness and efficiency of the proposed LOAD approach, comprehensive experiments are conducted. Comparing to conventional author disambiguation algorithms, the LOAD yields much more accurate results to assist human labeling. Further experiments show that the LOAD approach can save labeling time dramatically