Update Rule Research Articles

Dynamical interplay between random Boolean networks and awareness propagation

The stability of random Boolean networks (RBNs) has aroused continuous attention due to its profound applications in genetic regulatory. With extensive research on the joint influence of network topology and update rules, the interaction between RBN stability and other dynamic processes remains to be investigated. In this paper we propose an interactive multiplex model and study the dynamical interplay between RBN stability and awareness propagation. Theoretical analysis reveals that the stability of RBN is enhanced by awareness propagation. Specifically, its transition point between stable and unstable phases is determined by the main eigenvalue of H, which is closely related to the prevalence of awareness propagation and coupling strength between two layers. In addition, we illustrate the existence of a meta-critical point, from which awareness propagates and thus inhibits chaotic behaviors in RBN. Our work sheds light on the interaction between RBN stability and awareness propagation, and provides theoretical guidance to intervention of real Boolean systems.

A perspective–retrospective analysis of diversity in signature-based open-source network intrusion detection systems

The signature-based network intrusion detection systems (IDSs) entail relying on a pre-established signatures and IP addresses that are frequently updated to keep up with the rapidly evolving threat landscape. To effectively evaluate the efficacy of these updates, a comprehensive, long-term assessment of the IDSs’ performance is required. This article presents a perspective–retrospective analysis of the Snort and Suricata IDSs using rules that were collected over a 4-year period. The study examines how these IDSs perform when monitoring malicious traffic using rules from the past, as well as how they behave when monitoring the same traffic using updated rules in the future. To accomplish this, a set of Snort Subscribed and Suricata Emerging Threats rules were collected from 2017 to 2020, and a labeled PCAP data from 2017 to 2018 was analyzed using past and future rules relative to the PCAP date. In addition to exploring the evolution of Snort and Suricata IDSs, the study also analyses the functional diversity that exists between these IDSs. By examining the evolutionary behavior of signature-based IDSs and their diverse configurations, the research provides valuable insights into how their performance can be impacted. These insights can aid security architects in combining and layering IDSs in a defence-in-depth deployment.

Non-negative Tucker decomposition with graph regularization and smooth constraint for clustering

Non-negative Tucker decomposition (NTD) and its graph regularized extensions are the most popular techniques for representing high-dimensional non-negative data, which are typically found in a low-dimensional sub-manifold of ambient space, from a geometric perspective. Therefore, the performance of the graph-based NTD methods relies heavily on the low-dimensional representation of the original data. However, most existing approaches treat the last factor matrix in NTD as a low-dimensional representation of the original data. This treatment leads to the loss of the original data’s multi-linear structure in the low-dimensional subspace. To remedy this defect, we propose a novel graph regularized Lp smooth NTD (GSNTD) method for high-dimensional data representation by incorporating graph regularization and an Lp smoothing constraint into NTD. The new graph regularization term constructed by the product of the core tensor and the last factor matrix in NTD, and it is used to uncover hidden semantics while maintaining the intrinsic multi-linear geometric structure of the data. The addition of the Lp smoothing constraint to NTD may produce a more accurate and smoother solution to the optimization problem. The update rules and the convergence of the GSNTD method are proposed. In addition, a randomized variant of the GSNTD algorithm based on fiber sampling is proposed. Finally, the experimental results on four standard image databases show that the proposed method and its randomized variant have better performance than some other state-of-the-art graph-based regularization methods for image clustering.

Gradient-aware learning for joint biases: Label noise and class imbalance

Data biases such as class imbalance and label noise always exist in large-scale datasets in real-world. These problems bring huge challenges to deep learning methods. Some previous works adopted loss re-weighting, sample re-weighting, or data-dependent regularization to mitigate the influence of these training biases. But these methods usually pay more attention to class imbalance problem when both the class imbalance and label noise exist in training set simultaneously. These methods may overfit noisy labels, which leads to a great degradation in performance. In this paper, we propose a gradient-aware learning method for the combination of the two biases. During the training process, we update only a part of crucial parameters regularly and rectify the update direction of the rest redundant parameters. This update rule is conducted both in the encoder and classifier of the deep network to decouple label noise and class imbalance implicitly. The experimental results verify the effectiveness of the proposed method on synthetic and real-world data biases.

The risks of a form-based approach to exclusionary abuses of dominance – an economic perspective

The European Commission has initiated the process of changing how exclusionary abuses of dominance are evaluated, moving towards a more ‘form-based’ approach compared to the current ‘effects-based’ approach. This article explains that, if not implemented correctly, its planned approach risks worsening consumer outcomes. This article outlines the Commission’s existing approach to taking enforcement action against exclusionary abuses, and the two actions taken by the Commission to develop this approach. Then, it explores three cases where the false positives that arise from a form-based approach, rather than an effects-based approach, risk worsening outcomes for consumers. It suggests that, while an effects-based approach has its limitations, the Commission should ensure that it does not err too far into adopting a completely form-based approach as it develops updated rules on exclusionary abuses.

Exploring Multi‐Bit Logic In‐Memory with Memristive HfO2‐Based Ferroelectric Tunnel Junctions

AbstractThe increasing demand for data movement and energy consumption in physically separate von Neumann architectures, where the processor and memory are distinct entities, highlights the severity of the memory‐wall problem. Thus, memristor‐based logic‐in‐memory (LiM) has garnered significant interest as it is a paradigm that enables both logic and memory functionalities to be performed within a single device. Ferroelectric tunnel junctions (FTJs) have the advantages of low energy consumption and high scalability; thus, they are particularly appropriate for use as analog memristors in LiMs. Precise and accurate programming of the analog resistance states in FTJs is essential to achieve an efficient memristor‐based LiM design. However, existing switching models for FTJs cannot completely consider the ferroelectric domain switching, which challenges multi‐bit LiM operations. This particularly applies in cases involving updates from one intermediate resistance state (IRS) to another IRS, such as transitioning from State 01 to 10 in 2‐bit LiM. In this study, a novel phenomenon associated with domain switching is observed in the IRS of an HZO FTJ device and applied it to an LiM implementation. These findings can contribute to the operational accuracy and update rules of not only FTJ‐based LiMs but also other FTJ applications, such as crossbar arrays.

AdaInject: Injection-Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks

The convolutional neural networks (CNNs) are generally trained using stochastic gradient descent (SGD) based optimization techniques. The existing SGD optimizers generally suffer with the overshooting of the minimum and oscillation near minimum. In this paper, we propose a new approach, hereafter referred as AdaInject, for the gradient descent optimizers by injecting the second order moment into the first order moment. Specifically, the short-term change in parameter is used as a weight to inject the second order moment in the update rule. The AdaInject optimizer controls the parameter update, avoids the overshooting of the minimum and reduces the oscillation near minimum. The proposed approach is generic in nature and can be integrated with any existing SGD optimizer. The effectiveness of the AdaInject optimizer is explained intuitively as well as through some toy examples. We also show the convergence property of the proposed injection based optimizer. Further, we depict the efficacy of the AdaInject approach through extensive experiments in conjunction with the state-of-the-art optimizers, namely AdamInject, diffGradInject, RadamInject, and AdaBeliefInject on four benchmark datasets. Different CNN models are used in the experiments. A highest improvement in the top-1 classification error rate of 16.54% is observed using diffGradInject optimizer with ResNeXt29 model over the CIFAR10 dataset. Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach.

A Stochastic Binary Vertex-Triggering Resetting Algorithm for Global Synchronization of Pulse-Coupled Oscillators

In this paper, we propose a novel stochastic binary resetting algorithm for networks of pulse-coupled oscillators (or, simply, agents) to reach global synchronization. The algorithm is simple to state: Every agent in a network oscillates at a common frequency. Upon completing an oscillation, an agent generates a Bernoulli random variable to decide whether it sends pulses to all of its out-neighbours or it stays quiet. Upon receiving a pulse, an agent resets its state by following a binary phase update rule. We show that such an algorithm can guarantee <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global</i> synchronization of the agents almost surely as long as the underlying information flow topology is a rooted directed graph. The proof of the result relies on the use of a stochastic hybrid dynamical system approach. Toward the end of the paper, we present numerical demonstrations for the validity of the result and, also, numerical studies about the units of time needed to reach synchronization for networks with various information flow topologies.

Research on global path planning of intelligent vehicles based on improved ant colony algorithm

According to the needs of intelligent vehicles, this paper proposes a global path planning algorithm based on improved ant colony algorithm to address the problems of slow convergence speed and multiple path bends in ant colony algorithm. By redefining the heuristic function of ant colony algorithm, adopting fallback and fill mechanisms, setting safe distances, modifying pheromone concentration update rules, and limiting the range of pheromone concentration, the ant colony algorithm is improved. And by deleting redundant points on the path, selecting key turning points and cubic B-spline curves for path smoothing, the planned path is shortest and smooth, which can meet the needs of path following. Through simulation, the improved ant colony algorithm has the advantages of fast convergence and smooth path.

Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming

The concept of updating a probability distribution in the light of new evidence lies at the heart of statistics and machine learning. Pearl's and Jeffrey's rule are two natural update mechanisms which lead to different outcomes, yet the similarities and differences remain mysterious. This paper clarifies their relationship in several ways: via separate descriptions of the two update mechanisms in terms of probabilistic programs and sampling semantics, and via different notions of likelihood (for Pearl and for Jeffrey). Moreover, it is shown that Jeffrey's update rule arises via variational inference. In terms of categorical probability theory, this amounts to an analysis of the situation in terms of the behaviour of the multiset functor, extended to the Kleisli category of the distribution monad.

Quantized feedback control of continuous time Lur'e systems with nested quantization over finite data rate channels

AbstractThis paper studies a control problem of continuous time Lur'e systems involving input quantization (for the output of the controller) and output quantization (for the output of the plant). When the output of one quantization operator becomes directly the input of the other one, the nested quantization is produced, which is difficult for the design of the systems. Based on spherical polar coordinate quantizer, a quantization method with finite data rate is proposed for handling the nested quantization. In addition, both the quantization errors and Lur'e‐type nonlinearity are converted to sector bound uncertainties that are modeled by norm bounded uncertain matrices, which facilitates the design of the systems. Owing to the quantization error the resulting closed‐loop system is described by a discontinuous right‐hand side differential equation and the notion of Krasovskii solution is adopted for the equation. By Krasovskii solution the update rule of the quantization region is designed to ensure the convergence to the origin of the system with the nested quantization and avoid the quantizer saturation. Further, since the quantizers cannot quantize the controller output and the system output directly, we develop an optimalization problem for each quantizer to obtain the estimates of the outputs of the plant and the controller. Finally, a method is presented to achieve the parameters of the controller.

Enhancing coal-gangue object detection using GAN-based data augmentation strategy with dual attention mechanism

Coal separation based on computer vision has attracted substantial attention in recent years. However, developing reliable object detection models relies on large-scale annotated dataset, which in industrial practice is time-consuming and labor-intensive to obtain. In this paper, we propose a novel data augmentation model called dual attention deep convolutional generative adversarial network (DADCGAN) to expand dataset scale and improve object detection. For the first time, the proposed DADCGAN, which adopts DCGAN as its foundation architecture, introduces efficient channel attention and external attention mechanisms to capture essential feature information from the channel and spatial dimensions of images, respectively. Moreover, spectral normalization and two time-scale update rule strategies are incorporated to stabilize the training process. The implementation of our proposed data augmentation strategy includes two steps. First, traditional pixel transformation is used to expand an original small dataset. Then, our GAN-based data augmentation is executed to further expand the dataset by generating synthetic images. Experimental results show that our DADCGAN model achieves the lowest FID value, decreasing the FID by 21.30–71.96 % compared to other baseline GAN models, showcasing its ability to produce more realistic coal-gangue images. Finally, the data augmentation strategies are applied to the YOLOv4 model, enhancing the mAP by 9.26 %, highlighting its significance in enhancing coal-gangue object detection. These results have important implications for the development and implementation of computer vision-based technologies, enabling the realization of cleaner and more efficient coal separation methods.

Imitation dynamics on networks with incomplete information

Imitation is an important learning heuristic in animal and human societies. Previous explorations report that the fate of individuals with cooperative strategies is sensitive to the protocol of imitation, leading to a conundrum about how different styles of imitation quantitatively impact the evolution of cooperation. Here, we take a different perspective on the personal and external social information required by imitation. We develop a general model of imitation dynamics with incomplete information in networked systems, which unifies classical update rules including the death-birth and pairwise-comparison rule on complex networks. Under pairwise interactions, we find that collective cooperation is most promoted if individuals neglect personal information. If personal information is considered, cooperators evolve more readily with more external information. Intriguingly, when interactions take place in groups on networks with low degrees of clustering, using more personal and less external information better facilitates cooperation. Our unifying perspective uncovers intuition by examining the rate and range of competition induced by different information situations.

Online self-learning attitude tracking control of morphing unmanned aerial vehicle based on dual heuristic dynamic programming

This article presents a partially model-free dual heuristic dynamic programming (DHP) control approach to address the online discrete-time attitude tracking problem. The DHP method learns the control policy by solving the translated optimal regulation problem of the augmented system, which is constructed with the tracking error dynamics and the desired trajectory. In the context of the augmented system, a corresponding recursive least squares (RLS) identification strategy has been devised, and the DHP update rules have been derived. The identified augmented system model not only provides dynamic information and predicted states for the actor and critic updates but also incorporates an uncertainty compensation part to reduce the learning complexity and enhance robustness of the controller. It is noteworthy that concurrent learning (CL) is employed to improve the critic convergence by utilizing an identified model-based data generation method. This approach eliminates the need for an offline learning stage and enables flexible controller design based on the available system knowledge. Based on the proposed approach, different attitude control frameworks have been designed for a morphing unmanned aerial vehicle (UAV). In addition to numerical simulations, flight tests have been conducted to further demonstrate the capabilities of the proposed approach, highlighting its effectiveness and advantages.

Privacy Concerns Related to Data Sharing for European Diabetes Devices.

Individuals with diabetes rely on medical equipment (eg, continuous glucose monitoring (CGM), hybrid closed-loop systems) and mobile applications to manage their condition, providing valuable data to health care providers. Data sharing from this equipment is regulated via Terms of Service (ToS) and Privacy Policy documents. The introduction of the Medical Devices Regulation (MDR) and In Vitro Diagnostic Medical Devices Regulation (IVDR) in the European Union has established updated rules for medical devices, including software. This study examines how data sharing is regulated by the ToS and Privacy Policy documents of approved diabetes medical equipment and associated software. It focuses on the equipment approved by the Norwegian Regional Health Authorities. A document analysis was conducted on the ToS and Privacy Policy documents of diabetes medical equipment and software applications approved in Norway. The analysis identified 11 medical equipment and 12 software applications used for diabetes data transfer and analysis in Norway. Only 3 medical equipment (OmniPod Dash, Accu-Chek Insight, and Accu-Chek Solo) were registered in the European Database on Medical Devices (EUDAMED) database, whereas none of their respective software applications were registered. Compliance with General Data Protection Regulation (GDPR) security requirements varied, with some software relying on adequacy decisions (8/12), whereas others did not (4/12). The study highlights the dominance of non-European Economic Area (EEA) companies in medical device technology development. It also identifies the lack of registration for medical equipment and software in the EUDAMED database, which is currently not mandatory. These findings underscore the need for further attention to ensure regulatory compliance and improve data-sharing practices in the context of diabetes management.

Adaptive asynchronous federated learning

Federated Learning enables data owners to train an artificial intelligence model collaboratively while keeping all the training data locally, reducing the possibility of personal data breaches. However, the heterogeneity of local resources and dynamic characteristics of federated learning systems bring new challenges hindering the development of federated learning techniques. To this end, we propose an Adaptive Asynchronous Federated Learning scheme with Momentum, called FedAAM, comprising an adaptive weight allocation algorithm and a novel asynchronous federated learning framework. Firstly, we dynamically allocate weights for the global model update using an adaptive weight allocation strategy that can improve the convergence rate of models in asynchronous federated learning systems. Then, targeting the challenges mentioned previously, we proposed two new asynchronous global update rules based on the differentiated strategy, which is an essential component of the proposed novel federated learning framework. Furthermore, our asynchronous federated learning framework introduces the historical global update direction (i.e., global momentum) into the global update operation, aiming at improving training efficiency. Moreover, we prove that the model under the FedAAM scheme can achieve a sublinear convergence rate. Extensive experiments on real-world datasets demonstrate that the FedAAM scheme outperforms representative synchronous and asynchronous federated learning schemes (i.e., FedAvg and FedAsync) regarding the model’s convergence rate and capacity to deal with dynamic systems.

Sector hopes updated sufficiency rules will boost access to services

Revised government guidance on assessment and delivery of council youth work aims to make it clearer what level of provision is needed, but experts warn little will change if it is not backed with extra funding

Frequent asymmetric migrations suppress natural selection in spatially structured populations.

Natural microbial populations often have complex spatial structures. This can impact their evolution, in particular the ability of mutants to take over. While mutant fixation probabilities are known to be unaffected by sufficiently symmetric structures, evolutionary graph theory has shown that some graphs can amplify or suppress natural selection, in a way that depends on microscopic update rules. We propose a model of spatially structured populations on graphs directly inspired by batch culture experiments, alternating within-deme growth on nodes and migration-dilution steps, and yielding successive bottlenecks. This setting bridges models from evolutionary graph theory with Wright-Fisher models. Using a branching process approach, we show that spatial structure with frequent migrations can only yield suppression of natural selection. More precisely, in this regime, circulation graphs, where the total incoming migration flow equals the total outgoing one in each deme, do not impact fixation probability, while all other graphs strictly suppress selection. Suppression becomes stronger as the asymmetry between incoming and outgoing migrations grows. Amplification of natural selection can nevertheless exist in a restricted regime of rare migrations and very small fitness advantages, where we recover the predictions of evolutionary graph theory for the star graph.

The study for optimization strategies on the performance of DCGAN

Since Deep Convolutional Generative Adversarial Networks (DCGAN) was proposed, it has been perceived as a model with difficulty in training due to several factors. To solve this problem, dozens of optimization strategies were presented, but none of them was compared with the others. In this paper, the author chose three representative methods, namely one-label smoothing, the two Time-Scale Update Rule (TTUR), and the Earth-Mover Distance (EMD) or Wasserstein-1 to make a comparison of the optimization effect on the DCGAN model. To be specific, these three approaches were adopted respectively while using MNIST and Fashion-MNIST as datasets. One-side label smoothing was designed to prevent overconfidence in the model by adding a penalty term in the discriminator. TTUR was a simpler update strategy that could help the model find the stationary local Nash equilibrium under mild assumptions. EMD was an alternative loss function that enabled the model to distinguish the difference while the real distribution and generated distribution were not overlapped. Contrast experiments were conducted both vertically and horizontally. The author applied these three methods with the same dataset and the same method with different datasets in order to compare the time of the model collapse, the trend of loss in line graphs, and the impact of different datasets on results. Experimental results indicated that both one-label smoothing and TTUR postponed the model collapse while EMD completely get rid of it. Furthermore, generated images may lose texture information when using more complicated datasets.

An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

The ant colony algorithm faces dimensional catastrophe problems when solving the large-scale traveling salesman problem, which leads to unsatisfactory solution quality and convergence speed. To solve this problem, an adaptive ant colony optimization for large-scale traveling salesman problem (AACO-LST) is proposed. First, AACO-LST improves the state transfer rule to make it adaptively adjust with the population evolution, thus accelerating its convergence speed; then, the 2-opt operator is used to locally optimize the part of better ant paths to further optimize the solution quality of the proposed algorithm. Finally, the constructed adaptive pheromone update rules can significantly improve the search efficiency and prevent the algorithm from falling into local optimal solutions or premature stagnation. The simulation based on 45 traveling salesman problem instances shows that AACO-LST improves the solution quality by 79% compared to the ant colony system (ACS), and in comparison with other algorithms, the PE of AACO-LST is not more than 1% and the Err is not more than 2%, which indicates that AACO-LST can find high-quality solutions with high stability. Finally, the convergence speed of the proposed algorithm was tested. The data shows that the average convergence speed of AACO-LST is more than twice that of the comparison algorithm. The relevant code can be found on our project homepage.