H∞ Control for Observer-Based Non-Negative Edge Consensus of Discrete-Time Networked Systems

This article is concerned with observer-based non-negative edge-consensus (OBNNEC) problems of networked discrete-time systems with or without actuator saturation. An algorithm which only uses actual outputs of neighboring edges is proposed by means of an H_∞ control method and modified algebraic Riccati equation (MARE)-based technique. The observer matrix and feedback matrix are constructed by solving the MARE and linear matrix inequality (LMI), respectively. Then, sufficient conditions for guaranteeing bounded inputs and non-negative edge states are derived. In addition, the low-gain characteristic of the MARE-based method is instrumental in guaranteeing non-negative edge states and deriving the feasible observer matrix and feedback matrix. Finally, two examples are shown to demonstrate the obtained theoretical results.

Microstructure and mechanical properties of dissimilar NiTi and 304 stainless steel joints produced by ultrasonic welding

Superelastic NiTi alloy and 304 stainless steel (304 SS) were joined with a Cu interlayer by ultrasonic spot welding (USW) using different welding energy inputs. The surface morphology, interfacial microstructure, mechanical properties, and fracture mechanisms of the dissimilar NiTi/304 SS USWed joints were studied. The results showed that the surface oxidation intensified with increasing ultrasonic welding energy due to mutual rubbing between tools and sheets. The weld interface microstructure exhibited voids or unbonded zones at low energy inputs, while an intimate contact was established at the joining interface when applying a higher energy input of 750 J. With increasing energy input to 750 J, the weld interface shows two interfaces due to the behavior of plastic flow of Cu interlayer. The lap-shear load of the joints first increased, achieving a maximum value of ∼690 N at an energy input of 750 J, and then decreased with further increase in welding energy. Interfacial failure was observed at NiTi/Cu interface at all energy inputs, and no intermetallic compounds were found on the fracture surfaces of both the NiTi/Cu and Cu/304 SS interfaces.

Intralayer Synchronization of Multiplex Dynamical Networks via Pinning Impulsive Control

These days, the synchronization of multiplex networks is an emerging and important research topic. Grounded framework and theory about synchronization and control on multiplex networks are yet to come. This article studies the intralayer synchronization on a multiplex network (i.e., a set of networks connected through interlayer edges), via the pinning impulsive control method. The topologies of different layers are independent of each other, and the individual dynamics of nodes in different layers are different as well. Supra-Laplacian matrices are adopted to represent the topological structures of multiplex networks. Two cases are considered according to impulsive sequences of multiplex networks: 1) pinning controllers are applied to all the layers simultaneously at the instants of a common impulse sequence and 2) pinning controllers are applied to each layer at the instants of distinct impulse sequences. Using the Lyapunov stability theory and the impulsive control theory, several intralayer synchronization criteria for multiplex networks are obtained, in terms of the supra-Laplacian matrix of network topology, self-dynamics of nodes, impulsive intervals, and the pinning control effect. Furthermore, the algorithms for implementing pinning schemes at every impulsive instant are proposed to support the obtained criteria. Finally, numerical examples are presented to demonstrate the effectiveness and correctness of the proposed schemes.

Circ_0008717 promotes renal cell carcinoma progression by upregulating FBXO17 via targeting miR-217

BACKGROUND

Renal cell carcinoma (RCC) is a common lethal urological malignancy. Circular RNAs (circRNAs) are summarized to play important roles in cancer development. The objective of this study was to investigate the role and action mechanism of circ_0008717 in RCC.

METHODS

The expression of circ_0008717, miR-217 and F-box protein 17 (FBXO17) mRNA was detected by real-time quantitative polymerase chain reaction (qRT-PCR). Cell proliferation was examined using cell counting kit-8 (CCK-8) assay and EdU assay. Cell apoptosis was assessed by flow cytometry assay. Cell migration and cell invasion were investigated using transwell assay. Glycolysis progression was assessed according to the levels of glucose uptake and lactate production. The expression of glycolysis-related proteins and FBXO17 protein was quantified by western blot. The targets were analyzed by the bioinformatics tools (starBase and circinteractome) and validated by dual-luciferase reporter assay, RNA pull-down assay and RNA immunoprecipitation (RIP) assay. Xenograft model was established to monitor the role of circ_0008717 in vivo.

RESULTS

Circ_0008717 was upregulated in RCC tissues and cells. Silencing circ_0008717 suppressed RCC cell proliferation, migration, invasion and glycolysis but promoted cell apoptosis. MiR-217 was a target of circ_0008717 and bound to FBXO17 3'UTR. The expression of FBXO17 was positively regulated by circ_0008717 but impaired by miR-217 reintroduction. The inhibitory effects of circ_0008717 knockdown on RCC cell malignant behaviors were reversed by miR-217 inhibition or FBXO17 overexpression. Circ_0008717 knockdown inhibited tumor growth in vivo by regulating miR-217 and FBXO17.

CONCLUSION

Circ_0008717 aggravated the progression of RCC by activating FBXO17 through targeting miR-217, which provided a novel mechanism for circ_0008717 to participate in RCC progression.

MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block

DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.

Few-Shot Domain Adaptation via Mixup Optimal Transport

Unsupervised domain adaptation aims to learn a classification model for the target domain without any labeled samples by transferring the knowledge from the source domain with sufficient labeled samples. The source and the target domains usually share the same label space but are with different data distributions. In this paper, we consider a more difficult but insufficient-explored problem named as few-shot domain adaptation, where a classifier should generalize well to the target domain given only a small number of examples in the source domain. In such a problem, we recast the link between the source and target samples by a mixup optimal transport model. The mixup mechanism is integrated into optimal transport to perform the few-shot adaptation by learning the cross-domain alignment matrix and domain-invariant classifier simultaneously to augment the source distribution and align the two probability distributions. Moreover, spectral shrinkage regularization is deployed to improve the transferability and discriminability of the mixup optimal transport model by utilizing all singular eigenvectors. Experiments conducted on several domain adaptation tasks demonstrate the effectiveness of our proposed model dealing with the few-shot domain adaptation problem compared with state-of-the-art methods.

Output-Feedback Global Consensus of Discrete-Time Multiagent Systems Subject to Input Saturation via Q-Learning Method

This article proposes a Q -learning (QL)-based algorithm for global consensus of saturated discrete-time multiagent systems (DTMASs) via output feedback. According to the low-gain feedback (LGF) theory, control inputs of the saturated DTMASs can avoid the saturation by utilizing the control policies with LGF matrices, which were computed from the modified algebraic Riccati equation (MARE) by requiring the information of system dynamics in most previous works. However, in this article, we first find the lower bound on the real part of Laplacian matrices' nonzero eigenvalues of directed network topologies. Then, we define a test control input and propose a Q -function to derive a QL Bellman equation, which plays an essential part of the QL algorithm. Subsequently, different from the previous works, the output-feedback gain (OFG) matrix of this article can be obtained by limited iterations of the QL algorithm without requiring the information of agent dynamics and network topologies of the saturated DTMASs. Furthermore, the saturated DTMASs can achieve global consensus rather than the semiglobal consensus of the previous results. Finally, the effectiveness of the QL algorithm is confirmed via two simulations.

Generative Mixup Networks for Zero-Shot Learning

Zero-shot learning casts light on lacking unseen class data by transferring knowledge from seen classes via a joint semantic space. However, the distributions of samples from seen and unseen classes are usually imbalanced. Many zero-shot learning methods fail to obtain satisfactory results in the generalized zero-shot learning task, where seen and unseen classes are all used for the test. Also, irregular structures of some classes may result in inappropriate mapping from visual features space to semantic attribute space. A novel generative mixup networks with semantic graph alignment is proposed in this article to mitigate such problems. To be specific, our model first attempts to synthesize samples conditioned with class-level semantic information as the prototype to recover the class-based feature distribution from the given semantic description. Second, the proposed model explores a mixup mechanism to augment training samples and improve the generalization ability of the model. Third, triplet gradient matching loss is developed to guarantee the class invariance to be more continuous in the latent space, and it can help the discriminator distinguish the real and fake samples. Finally, a similarity graph is constructed from semantic attributes to capture the intrinsic correlations and guides the feature generation process. Extensive experiments conducted on several zero-shot learning benchmarks from different tasks prove that the proposed model can achieve superior performance over the state-of-the-art generalized zero-shot learning.

Multiple Mittag-Leffler Stability of Delayed Fractional-Order Cohen-Grossberg Neural Networks via Mixed Monotone Operator Pair

This article mainly investigates the multiple Mittag-Leffler stability of delayed fractional-order Cohen-Grossberg neural networks with time-varying delays. By using mixed monotone operator pair, the conditions of the coexistence of multiple equilibrium points are obtained for fractional-order Cohen-Grossberg neural networks, and these conditions are eventually transformed into algebraic inequalities based on the vertex of the divided region. In particular, when the symbols of these inequalities are determined by the dominant term, several verifiable corollaries are given. And then, the sufficient conditions of the Mittag-Leffler stability are derived for fractional-order Cohen-Grossberg neural networks with time-varying delays. In addition, two numerical examples are provided to illustrate the effectiveness of the theoretical results.

Event-Based Synchronization for Multiple Neural Networks With Time Delay and Switching Disconnected Topology

This article discusses the synchronization problem for a class of multiple delayed neural networks (MDNNs) with a directed switching topology by using an event-triggering strategy. First, a new differential inequality with delay is shown, which is a generalization of Halanay-type inequalities. Then, the sufficient conditions of event-based synchronization (quasisynchronization) for MDNN with sequentially connected topology are obtained by using this inequality and the iterative method. Meantime, we prove that Zeno behavior can be avoided under the designed event-triggering rules. As an extension, MDNN with jointly connected topology is also discussed. Finally, a numerical example is listed to illustrate the results in theory analysis.

Memristive Circuit Design of Brain-Inspired Emotional Evolution Based on Theories of Internal Regulation and External Stimulation

In this work, a bionic memristive circuit with functions of emotional evolution is proposed by mimicking the emotional circuit in limbic system, which can perform unconscious and conscious emotional evolutions by using theories of internal regulation and external stimulation respectively. Two kinds of memristive models, volatile and non-volatile, play key roles in the process of emotional evolution. That is, the internal regulation is mainly responsible for simulating the unconscious evolution process over time by using the forgetting effect of the volatile memristor. The external stimulation is mainly responsible for using the memristance plasticity of the non-volatile memristor to simulate the evolutionary learning behavior under the action of multi-modal inputs (such as visual, speech and text signals), so as to realize the conscious emotional evolution. A two-dimensional (2D) emotional state space consisted of valence and arousal signals is adopted, the evolution behaviors are performed on the basis of valence and arousal signals in the space, in order to achieve continuous emotional evolution and express the evolved emotions intuitively. Due to the use of memristors, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and constructs a brain-inspired information processing architecture. The simulation results in PSPICE show that a nonlinear mapping relationship between inputs and outputs is constructed through the proposed circuit, which can carry out diversified emotional evolution based on the designed internal regulation and external stimulation evolution circuits.

Neuroadaptive Impulsive Control on Consensus of Uncertain Multiagent Systems Using Continuous and Sampled Information

This article considers the consensus problem of uncertain multiagent systems, which is addressed by neuroadaptive impulsive control schemes. The proposed control schemes indicate that the communication among agents only occurs impulsively, while the dynamics uncertainty is addressed by adaptive schemes using neural networks. Based on such approaches, two specific control schemes are designed. One is that with impulsive feedback, the control scheme uses continuous-time information, which implies that the adaptive process is continuous over time. Another is that by adopting sampled information, the update of all systems, including the feedbacks on agents, the update of neural networks, and the estimation for uncertainty, can be executed only at impulsive instants. The latter case can reduce the energy cost for communication and control, but extra assistant systems are required. The estimation and consensus prove to be achieved with errors if some conditions are fulfilled. Numerical simulations, including a practical system example, are presented.

Tradeoff Analysis Between Control Time and Energy Consumption for Delayed Neural Networks With Discontinuous Activation Functions

This article studies finite-time stabilization of delayed neural networks (DNNs) whose activation functions are discontinuous. Several sufficient conditions for guaranteeing finite-time stabilization of considered DNNs are obtained by constructing appropriate controllers with giving upper bounds of control time. Subsequently, based on the existing definition of energy consumption, the required energy to achieve stabilization is estimated. To quantify the cost of control, an evaluation index function is constructed to analyze the tradeoff between control time and consumed energy. Ultimately, acquired results are verified by simulating two numerical examples.

Global Dissipativity and Quasi-Mittag-Leffler Synchronization of Fractional-Order Discontinuous Complex-Valued Neural Networks

This article is concerned with fractional-order discontinuous complex-valued neural networks (FODCNNs). Based on a new fractional-order inequality, such system is analyzed as a compact entirety without any decomposition in the complex domain which is different from a common method in almost all literature. First, the existence of global Filippov solution is given in the complex domain on the basis of the theories of vector norm and fractional calculus. Successively, by virtue of the nonsmooth analysis and differential inclusion theory, some sufficient conditions are developed to guarantee the global dissipativity and quasi-Mittag-Leffler synchronization of FODCNNs. Furthermore, the error bounds of quasi-Mittag-Leffler synchronization are estimated without reference to the initial values. Especially, our results include some existing integer-order and fractional-order ones as special cases. Finally, numerical examples are given to show the effectiveness of the obtained theories.

Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding

The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.

Event-Triggered Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays

This paper addresses the synchronization of multiple fractional-order recurrent neural networks (RNNs) with time-varying delays under event-triggered communications. Based on the assumption of the existence of strong connectivity or a spanning tree in the communication digraph, two sets of sufficient conditions are derived for achieving event-triggered synchronization. Moreover, an additional condition is derived to preclude Zeno behaviors. As a generalization of existing results, the criteria herein are also applicable to the event-triggered synchronization of multiple integer-order RNNs with or without delays. Two numerical examples are elaborated to illustrate the new results.