RWNeRF:Robust Watermarking Scheme for Neural Radiance Fields Based on Invertible Neural Networks

As neural radiance fields continue to advance in 3D content representation,the copyright issues surrounding 3D models oriented towards implicit representation become increasingly pressing.In response to this challenge,this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network watermarking.Leveraging 2D image watermarking technology for 3D scene protection,the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from images rendered by neural radiance fields through the reverse process,thereby ensuring copyright protection for both the neural radiance fields and associated 3D scenes.However,challenges such as information loss during rendering processes and deliberate tampering necessitate the design of an image quality enhancement module to increase the scheme’s robustness.This module restores distorted images through neural network processing before watermark extraction.Additionally,embedding watermarks in each training image enables watermark information extraction from multiple viewpoints.Our proposed watermarking method achieves a PSNR(Peak Signal-to-Noise Ratio)value exceeding 37 dB for images containing watermarks and 22 dB for recovered watermarked images,as evaluated on the Lego,Hotdog,and Chair datasets,respectively.These results demonstrate the efficacy of our scheme in enhancing copyright protection.

MarkNeRF:Watermarking for Neural Radiance Field

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)models.We employ an embedding network to integrate the watermark into the images within the training set.Then,theNeRFmodel is utilized for 3Dmodeling.For copyright verification,a secret image is generated by inputting a confidential viewpoint into NeRF.On this basis,design an extraction network to extract embedded watermark images fromconfidential viewpoints.In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s copyright.The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages.This approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency regularization.The NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel.In addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity field.The velocity uncertainties are also evaluated through ensemble learning.The effectiveness of the proposed algorithm is demonstrated through numerical examples.The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

Seizures dynamics in a neural field model of cortical-thalamic circuitry

The focus of this study is to explore the mechanisms during seizure behavior using a physiologically motivated by corticothalamic circuity. The model is based on the assumption that, the inhibitory projects from thalamus reticular nucleus(TRN) to specific relay nuclei(SRN) are mediated by GABAA and GABAB receptors which react different time scales in synaptic transmission.Secondly, we include the effects of slow modulation on the threshold current of TRN population that were found to generate bursting behavior. Our model can reproduce healthy and pathological dynamics including wake, spindle, deep sleep, and also seizure states. In addition, contour maps are used to explore the transition of different activity states. It is worthy to point out seizure duration is significantly affected by a time-varying delay as illustrated in our numerical simulation. Finally, a reduced model ignoring the cerebral cortex mass can also capture the feature of spike wave discharge as generated in the full network.

The Effects of High-intensity Pulsed Electromagnetic Field on Proliferation and Differentiation of Neural Stem Cells of Neonatal Rats in vitro

The effects of high-intensity pulsed electromagnetic stimulation （HIPEMS） on proliferation and differentiation of neonatal rat neural stem cells in vitro were investigated. Neural stem cells derived from neonatal rats were exposed to 0.1 Hz, 0.5–10 Tesla （T） [8 groups of B–I, respectively], 5 stimuli of HIPEMF. The sham exposure controls were correspondingly established. Inverted phase contrast microscope was used to observe the cultured cells, MTT assay to detect the viability of the cells as expressed by absorbance （A） value, and flow cytometry to measure differentiation of neural stem cells. The results showed that A values of neural stem cells in both 3.0 T and 4.0 T groups were significantly higher than the other groups 24 to 168 h post HPEMS, indicating a strong promotion of the growth of neural stem cells （P〈0.05）. The A values of neural stem cells in the 6.0 T, 8.0 T, and 10.0 T groups were lower than the sham exposure control group, indicating a restraint of the growth of neural stem cells. The rate of neuron-specific enolase-positive neurons revealed by flow cytometry in HPEMS groups was the same as that in control group （P〉0.05）. It was suggested that 0.1 Hz, 5 pulses stimulation of HPEMS within certain scale of intensity （0.5–10.0 T）, significantly promoted the growth of neural stem cells with the rational intensity being 4.0 T.

Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm

This paper presents the speed control of a separately excited DC motor using Neural Network (NN) controller in field weakening region. In armature control, speed controller has been used in outer loop while current controller in inner loop is used. The function of NN is to predict the field current that realizes the field weakening to drive the motor over rated speed. The parameters of NN are optimized by the Social Spider Optimization (SSO) algorithm. The system has been implemented using MATLAB/SIMULINK software. The simulation results show that the proposed method gives a good performance and is feasible to be applied instead of others conventional combined control methods.

Predicting formation lithology from log data by using a neural network

In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.

Decentralized PID neural network control for a quadrotor helicopter subjected to wind disturbance

A decentralized PID neural network(PIDNN) control scheme was proposed to a quadrotor helicopter subjected to wind disturbance. First, the dynamic model that considered the effect of wind disturbance was established via Newton-Euler formalism.For quadrotor helicopter flying at low altitude in actual situation, it was more susceptible to be influenced by the turbulent wind field.Therefore, the turbulent wind field was generated according to Dryden model and taken into consideration as the disturbance source of quadrotor helicopter. Then, a nested loop control approach was proposed for the stabilization and navigation problems of the quadrotor subjected to wind disturbance. A decentralized PIDNN controller was designed for the inner loop to stabilize the attitude angle. A conventional PID controller was used for the outer loop in order to generate the reference path to inner loop. Moreover, the connective weights of the PIDNN were trained on-line by error back-propagation method. Furthermore, the initial connective weights were identified according to the principle of PID control theory and the appropriate learning rate was selected by discrete Lyapunov theory in order to ensure the stability. Finally, the simulation results demonstrate that the controller can effectively resist external wind disturbances, and presents good stability, maneuverability and robustness.

基于离散Hopfield神经网络的我国农口国家工程技术研究中心综合实力评价研究

农口国家工程技术研究中心是所有涉农国家工程技术研究中心的总称。在明确我国农口国家工程技术研究中心职能定位、梳理其发展现状的基础上,基于离散Hopfield神经网络方法,实现对农口国家工程技术研究中心综合实力的分级评价,并根据结果对评价指标提出修正建议。

Neural Regeneration Research

Neural Regeneration Research （NRR; ISSN 1673-5374） is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers,

Neural Regeneration Research

Instructions to Authors GENERAL INFORMATION Neural Regeneration Research （NRR; ISSN 1673-5374）is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration. NRR aims to publish timely, innovative and creative basic and clinical research with the highest standards in neural regeneration research. NRR publishes a diverse variety of topics in neural regeneration, including brain, spinal cord and peripheral nerve injury, traditional Chinese medicine, acupuncture and moxibustion, stem cells, tissue engineering, inflammation, gliat scar, gene therapy, biological factors, neurorehabilitation, neuroimaging, neurodegenerative diseases, neuroplasticity and neurogenesis

Neural Regeneration Research

GENERAL INFORMATIONNeural Regeneration Research （NRR; ISSN 1673-5374） is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural reaeneration.

Neural Regeneration Research

Neural Regeneration Research （NRR, ISSN 1673-5374） is an open access, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, published 36 issues per year.

Texture Segmentation Based on Image Model and Neural Network

This paper presents a texture segmentation approach which is based on the Markov random field model (MRF) and feed forward neural network.Image texture is modeled by the second order Gauss MRF model, and the least square error estimation is employed for the solution of model parameters. To perform texture segmentation, we introduced an improved BP algorithm to get faster learning speed. Experiment shows that better segmentation results can be obtained than the traditional Euclidean distance method.

Neural Regeneration Research

GENERAL INFORMATION Neural Regeneration Research （NRR; ISSN 1673-5374） is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration.

Neural Regeneration Research

Instructions to Authors GENERAL INFORMATION Neural Regeneration Research （NRR; ISSN 1673-5374） is an open-access （www.nrronline.org）, peer-reviewed intemational journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical tdal papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration. NRR aims to publish timely, innovative and creative basic and clinical research with the highest standards in neural regeneration research. NRR publishes a diverse variety of topics in neural regeneration, including brain, spinal cord and peripheral nerve injury, traditional Chinese medicine, acupuncture and moxibustion, stem cells, tissue engineering, inflammation, glial scar, gene therapy, biological factors, neurorehabilitation, neuroimaging, neurodegenerative diseases, neuroplasticity and neurogenesis.

Neural Regeneration Research

GENERAL INFORMATIONNeural Regeneration Research （NRR; ISSN 1673-5374） is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration. NRR aims to publish timely, innovative and creative basic and clinical research with the highest standards in neural regeneration research. NRR publishes a diverse variety of topics in neural regeneration, including brain, spinal cord and peripheral nerve injury, traditional Chinese medicine, acupuncture and moxibustion, stem cells, tissue engineering, inflammation, glial scar, gene therapy, biological factors, neurorehabilitation, neuroimaging, neurodegenerative diseases, neuroplasticity and neurogenesis

Neural Regeneration Research

Instructions to AuthorsNeural Regeneration Research （NRR; ISSN 1673-5374） is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration. NRR aims to publish timely, innovative and creative basic and clinical research with the highest standards in neural regeneration research. NRR Dub）ishes a diverse vadetv of tnni~ in n~.llr^l rp.n~_np_r^finn

Neural Regeneration Research

Neural Regeneration Research （NRR; ISSN 1673-5374）is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration. NRR aims to publish timely, innovative and creative basic and clinical research with the highest standards in neural regeneration research. NRR publishes a diverse variety of topics in neural regeneration, including brain, spinal cord and peripheral nerve injury, traditional Chinese medicine, acupuncture and moxibustion, stem cells, tissue engineering, inflammation, glial scar, gene therapy, biological factors, neurorehabilitation, neuroimaging, neurodegenerative diseases, neuroplasticity and neurogenesis

Neural Regeneration Research

Neural Regeneration Research （NRR; ISSN 1673-5374） is an open-access （www.nrronline.org）, peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration. NRR aims to publish timely, innovative and creative basic and clinical research with the highest standards in neural regeneration research. NRR publishes a diverse variety of topics in neural regeneration, including brain, spinal cord and peripheral nerve injury, traditional Chinese medicine, acupuncture and moxibustion, stem cells, tissue engineering, inflammation, glial scar, gene therapy, biological factors, neurorehabilitation, neuroimaging, neurodegenerative diseases, neuroplasticity and neurogenesis.