Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

Fasting produces antidepressant-like effects via activating mammalian target of rapamycin complex 1 signaling pathway in ovariectomized mice

Recent studies have shown that a 9-hour fast in mice reduces the amount of time spent immobile in the forced swimming test.Howeve r,whether 9-hour fasting has therapeutic effects in female mice with depressive symptoms has not been established.Therefore,in this study,we simulated perimenopausal depression via an ovariectomy in mice,and subjected them to a single 9-hour fasting 7 days later.We found that the ovariectomy increased the time spent immobile in the forced swimming test,inhibited expression of the mammalian target of rapamycin complex 1 signaling pathway in the hippocampus and prefro ntal cortex,and decreased the density of dendritic spines in the hippocampus.The 9-hour acute fasting alleviated the above-mentioned phenomena.Furthermore,all of the antidepressant-like effects of 9-hour fasting were reve rsed by an inhibitor of the mammalian to rget of rapamycin complex 1.Electrophysiology data showed a remarkable increase in long-term potentiation in the hippocampal CA1 of the ovariectomized mice subjected to fasting compared with the findings in the ovariectomized mice not subjected to fasting.These findings show that the antidepressant-like effects of 9-hour fasting may be related to the activation of the mammalian target of the rapamycin complex 1 signaling pathway and synaptic plasticity in the mammalian hippocampus.Thus,fasting may be a potential treatment for depression.

Complex-Valued Neural Networks:A Comprehensive Survey

Complex-valued neural networks(CVNNs)have shown their excellent efficiency compared to their real counterparts in speech enhancement,image and signal processing.Researchers throughout the years have made many efforts to improve the learning algorithms and activation functions of CVNNs.Since CVNNs have proven to have better performance in handling the naturally complex-valued data and signals,this area of study will grow and expect the arrival of some effective improvements in the future.Therefore,there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of CVNNs.In this paper,we discuss and summarize the recent advances based on their learning algorithms,activation functions,which is the most challenging part of building a CVNN,and applications.Besides,we outline the structure and applications of complex-valued convolutional,residual and recurrent neural networks.Finally,we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs.

The Complex System Modeling Method Based on Uniform Design and Neural Network

In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.

Studies on the Transition Emission of Eu^（2＋） Ion in Complex Fluorides Using Neural Network

Quasi-Newton, fully-connected, and feed forward neural network were used to study the correlation between the spectral structure of the Eu2+ ion in complex fluorides and the structures of complex fluorides. The experimental results show that the neural network architecture (number of layers and number of nodes in each layer),the intial weights. and the organization of the data were all important factors affecting the performance of the neural network. The performance of the neural network was enhanced by adapting a test set to monitor the training process. Once trained, the neural network correctly classified 100% of the training set and 96.3% of the test set. These results offer tremendous improvement over previous pattern recognition methods.

Synthesization of high-capacity auto-associative memories using complex-valued neural networks

In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.

Complex Valued Recurrent Neural Network: From Architecture to Training

Recurrent Neural Networks were invented a long time ago, and dozens of different architectures have been published. In this paper we generalize recurrent architectures to a state space model, and we also generalize the numbers the network can process to the complex domain. We show how to train the recurrent network in the complex valued case, and we present the theorems and procedures to make the training stable. We also show that the complex valued recurrent neural network is a generalization of the real valued counterpart and that it has specific advantages over the latter. We conclude the paper with a discussion of possible applications and scenarios for using these networks.

Predicting Model for Complex Production Process Based on Dynamic Neural Network

Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.

The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network

A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.

Learning Dynamics of the Complex-Valued Neural Network in the Neighborhood of Singular Points

In this paper, the singularity and its effect on learning dynamics in the complex-valued neural network are elucidated. It has learned that the linear combination structure in the updating rule of the complex-valued neural network increases the speed of moving away from the singular points, and the complex-valued neural network cannot be easily influenced by the singular points, whereas the learning of the usual real-valued neural network can be attracted in the neighborhood of singular points, which causes a standstill in learning. Simulation results on the learning dynamics of the three-layered real-valued and complex-valued neural networks in the neighborhood of singularities support the analytical results.

Lyapunov-Based Dynamic Neural Network for Adaptive Control of Complex Systems

In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.

APPLICATION OF ARTIFICIAL NEURAL NETWORK IN COMPLEX SYSTEMS OF REGIONAL SUSTAINABLE DEVELOPMENT

Meeting the challenge of sustainable development requires substantial advances in understanding the interaction of natural and human systems. The dynamics of regional sustainable development could be addressed in the context of complex system thinking. Three features of complex systems are that they are uncertain, non-linear and self-organizing. Modeling regional development requires a consideration of these features. This paper discusses the feasibility of using the artificial neural networt(ANN) to establish an adjustment prediction model for the complex systems of sustainable development (CSSD). Shanghai Municipality was selected as the research area to set up the model, from which reliable prediction data were produced in order to help regional development planning. A new approach, which could help to manage regional sustainable development, is then explored.

Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginarytype activation functions

In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.

Vitamin B complex and vitamin B12 levels after peripheral nerve injury

The aim of the present study was to evaluate whether tissue levels of vitamin B complex and vitamin B12 were altered after crush-induced peripheral nerve injury in an experimental rat model. A total of 80 male Wistar rats were randomized into one control（n = 8） and six study groups（1, 6, 12, 24 hours, 3, and 7 days after experimental nerve injury; n = 12 for each group）. Crush-induced peripheral nerve injury was performed on the sciatic nerves of rats in six study groups. Tissue samples from the sites of peripheral nerve injury were obtained at 1, 6, 12, 24 hours, 3 and 7 days after experimental nerve injury. Enzyme-linked immunosorbent assay results showed that tissue levels of vitamin B complex and vitamin B12 in the injured sciatic nerve were significantly greater at 1 and 12 hours after experimental nerve injury, while they were significantly lower at 7 days than in control group. Tissue level of vitamin B_（12） in the injured sciatic nerve was significantly lower at 1, 6, 12 and 24 hours than in the control group. These results suggest that tissue levels of vitamin B complex and vitamin B12 vary with progression of crush-induced peripheral nerve injury, and supplementation of these vitamins in the acute period may be beneficial for acceleration of nerve regeneration.

Networked Knowledge and Complex Networks:An Engineering View

Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.

Neurochemical mechanism of the gastrointestinal interdigestive migrating motor complex in rats with acute inflammatory stomach ache

The normal gastrointestinal interdigestive migrating motor complex cycle was interrupted, and paroxysmal contraction appeared after formaldehyde-induced stomach ache. Activities of nitric oxide synthase, acetylcholinesterase and vasoactive intestinal peptide neurons were significantly reduced, whereas activities of calcitonin gene-related peptide neurons were significantly increased in the pyloric sphincter muscular layer, myenteric nerve plexus and submucous nerve plexus. Electroacupuncture at Zusanfi （ST36） suppressed paroxysmal contraction in rats with formaldehyde-induced stomach ache, and neurons in the enteric nervous system were normal. These results indicated that nitrergic neurons, cholinergic neurons, vasoactive intestinal peptide neurons and calcitonin gene-related peptide neurons in the enteric nervous system may be involved in changes to the gastrointestinal interdigestive migrating motor complex following stomach ache, and that electroacupuncture can regulate this process.

Pinning control of a generalized complex dynamical network model

This paper investigates the local and global synchronization of a generalized complex dynamical network model with constant and delayed coupling. Without assuming symmetry of the couplings, we proved that a single controller can pin the generalized complex network to a homogenous solution. Some previous synchronization results are generalized. In this paper, we first discuss how to pin an array of delayed neural networks to the synchronous solution by adding only one controller. Next, by using the Lyapunov functional method, some sufficient conditions are derived for the local and global synchronization of the coupled systems. The obtained results are expressed in terms of LMIs, which can be efficiently checked by the Matlab LMI toolbox. Finally, an example is given to illustrate the theoretical results.

Design of complex FIR filters with arbitrary magnitude and group delay responses

To design approximately linear-phase complex coefficient finite impulse response （FIR） digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter＇s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.

Analysis on Design of Kohonen-network System Based on Classification of Complex Signals

The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.

Neurotoxicity of prenatal alcohol exposure on medullary pre-B?tzinger complex neurons in neonatal rats

Prenatal alcohol exposure disrupts the development of normal fetal respiratory function, but whether it perturbs respiratory rhythmical discharge activity is unclear. Furthermore, it is unknown whether the 5-hydroxytryptamine 2A receptor（5-HT2AR） is involved in the effects of prenatal alcohol exposure. In the present study, pregnant female rats received drinking water containing alcohol at concentrations of 0%, 1%, 2%, 4%, 8% or 10%（v/v） throughout the gestation period. Slices of the medulla from 2-day-old neonatal rats were obtained to record respiratory rhythmical discharge activity. 5-HT2 AR protein and m RNA levels in the pre-B？tzinger complex of the respiratory center were measured by western blot analysis and quantitative RT-PCR, respectively. Compared with the 0% alcohol group, respiratory rhythmical discharge activity in medullary slices in the 4%, 8% and 10% alcohol groups was decreased, and the reduction was greatest in the 8% alcohol group. Respiratory rhythmical discharge activity in the 10% alcohol group was irregular. Thus, 8% was the most effective alcohol concentration at attenuating respiratory rhythmical discharge activity. These findings suggest that prenatal alcohol exposure attenuates respiratory rhythmical discharge activity in neonatal rats by downregulating 5-HT2 AR protein and m RNA levels.