Asynchronous Networks Research Articles

Asynchronous Generative Adversarial Network for Asymmetric Unpaired Image-to-Image Translation

The unpaired image-to-image translation aims to translate input images from one source domain to some desired outputs in a target domain by learning from unpaired training data. Cycle-consistency constraint provides a general principle to estimate and measure forward and backward mapping functions between two domains. In many cases, the information entropy of images from the two domains is not equal, resulting in an information-rich domain and an information-poor domain. However, existing solutions based on cycle-consistency either completely discard the information asymmetry between the two domains (a common choice), which leads to inferior translation performance for the asymmetric unpaired image-to-image translation, or have to rely on special task-specific designs and introduce extra loss components. These elaborative designs especially for the relatively harder translation direction from the information-poor domain to the information-rich domain (poor-to-rich translation) require extra labor and are limited to some specific tasks. In this paper, we propose a novel asynchronous generative adversarial network named Async-GAN, which provides a model-agnostic framework for easily turning symmetrical models into powerful asymmetric counterparts that can handle asymmetric unpaired image-to-image translation much better. The key innovation is to iteratively build gradually improving intermediate domains for generating pseudo paired training samples, which provide stronger full supervision for assisting the poor-to-rich translation. Extensive experiments on various asymmetric unpaired translation tasks demonstrate the superiority of the proposal. Furthermore, the proposed training framework could be extended to various Cycle-GAN solutions and achieve a performance gain.

Consensus-Based Distributed Batch Estimation in Asynchronous Wireless Sensor Networks

In this paper, to address the distributed estimation problem over an asynchronous wireless sensor network (aWSN), an average consensus-based distributed batch estimation (DBE) method is proposed. The DBE seeks to update the global posterior with a predefined global update period (GUP) and is implemented with a local filter (LF) and a fusion filter (FF). For LF, we develop two different asynchronous batch estimation approaches to align and compute the asynchronous local posteriors of multiple nodes in an aWSN. At FF, an average consensus filter is adopted to compute the global posterior via a proposed DBE fusion rule. Numerical results show that the proposed DBE method has high target-tracking accuracy and is robust to strong asynchronism. Besides, the optimality of DBE fusion can be approximately achieved with a sufficiently large number of particles and consensus iterations.

Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks

In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently. Therefore, a co-design of the self-triggered policy and asynchronous distributed filter is developed to ensure consensus of the state estimates. Finally, a numerical example is given to illustrate the effectiveness of the consensus filtering approach.

An Extended Spatiotemporal Contextual Learning and Memory Network Model for Hardware Implementation

A spatiotemporal contextual learning and memory network (STCLMN) model was proposed based on the hippocampal CA1 network using the difference equations suitable for digital computer simulations. However, the inherent capability of handling and processing of continuous-time asynchronous signals (spikes) cannot be fully employed in the STCLMN model. Furthermore, digital circuit implementation of the STCLMN is not suitable for real-time edge applications, wherein small size, low power consumption, and real-time learning capability are necessary. Therefore, this study extends the original STCLMN model using a set of differential equations to overcome the above problems encountered by STCLMN for asynchronous circuit implementation. The minimum accuracy required for a compact and low-power hardware implementation of the proposed model is then investigated. The validity and feasibility of the proposed model are confirmed through numerical simulations and experiments with a small mixed analog/digital circuit based on the investigated accuracy. In this study, LIF neurons are used because of their ease of circuit implementation. The proposed eSTCLMN model would enable an efficient circuit implementation of an asynchronous spiking neural network with spatiotemporal contextual one-shot learning and memory.

Target Control of Asynchronous Boolean Networks.

We study the target control of asynchronous Boolean networks, to identify interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with these three types of perturbations. We compare our methods with the stable motif-based control method on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.

Decentralized Stream Runtime Verification for Timed Asynchronous Networks

Decentralized Stream Runtime Verification for Timed Asynchronous Networks

Distributed Sampling Rate Offset Estimation Over Acoustic Sensor Networks Based on Asynchronous Network Newton Optimization

Sampling rate synchronization is an inevitable issue in distributed acoustic sensor networks. In this paper, an analytical sampling rate offset (SRO) estimation approach is first proposed, and then, it is extended to a distributed method that suitable for acoustic sensor networks with arbitrary communication graphs. Specifically, a linear-phase drift model in the short-time Fourier transform domain is used to approximate the SRO between each pair of microphone nodes. Next, after unwrapping the temporally averaged phase information, SROs are recovered analytically via a new weighted-sum criterion. Based on this, a distributed cost function is established at each node to obtain the SROs of all nodes simultaneously in a distributed manner. Finally, a state-of-the-art distributed algorithm named asynchronous network Newton optimization is adopted to carry out the distributed SRO estimation. The proposed method can effectively estimate the SROs among acoustic sensor nodes in noisy and reverberant environments. Compared with the existing approaches, it does not require an external central processor, and only local communications among nodes are needed. Experimental results confirm the validity of the proposed method.

Design and Development of Low Power Clock and Data Recovery Circuit for Asynchronous Network on Chips

Today, the current buffered router Synchronous Network on Chip architecture consumes significant chip area and power. Therefore, based on biased routing, buffer-less routers have recently been predicted as a possible solution, but they suffer from contiguous port assignments, slow critical paths, and increased latency. Asynchronous Network on Chip architecture emerges as the best option for avoiding glitches and consuming less power. A clock and data recovery circuit is used to recover the clock signal from the router-generated data and reduce power consumption in a Multiprocessor System on Chip. This paper proposes a clock and data recovery circuit design for an asynchronous Network on Chip. The proposed 4x4 Mesh router architecture implemented in this paper can process 64-bit of data samples with a depth of 64. When comparing the proposed architecture with the existing NoC architecture, the proposed architecture has shown a power reduction of 5times. The proposed architecture has consumed a total power of 0.103W.

Efficient practical Byzantine consensus using random linear network coding

Using random linear network coding (RLNC) in asynchronous networks with one-to-many information flow has already been proven to be a valid approach to maximize the channel capacities. Message-based consensus protocols such as practical Byzantine fault tolerance (pBFT) adhere partially to said scenario. Protocol phases with many-to-many communication, however, still suffer from quadratic growth in the number of required transmissions to reach consensus. We show that an enhancement in the data transmission behavior in the quadratic phases is possible through combining RLNC with pBFT as one hybrid protocol. We present several experiments conducted on random network topologies. We conclude that using RLNC-based data transmission offers a significantly better performance under specific circumstances, which depend on the number of participating network nodes and the chosen coding parameters. Applying the same approach to other combinations of message-based consensus and network coding protocols promises not only a gain in performance, but may also improve robustness and security and open up new application scenarios for RLNC, e.g., running it on the application layer.

Professional Factors Associated with Case Resolution without Referrals of Orofacial Pain Cases to Secondary Dental Care by Telehealth in Brazil: A Cross-Sectional Study in 2019 and 2020.

This study aimed to identify professional factors associated with case resolution without a referral of orofacial pain to secondary health care by Brazilian Primary Health Care (PHC) practitioners who demanded asynchronous teleconsulting, stratified by year, in 2019 and 2020 (the COVID-19 Pandemic burst). A cross-sectional study employed secondary databases from asynchronous teleconsulting Telehealth Brazil Networks from January 2019 to December 2020. The outcome was the dichotomous variable "If referral to secondary care was avoided." As covariates: sex, healthcare professions, and category of orofacial pain doubts. A negative binomial regression model estimated each covariate's unadjusted and adjusted PR (95%CI) and p values, stratified for 2019 and 2020. There was a difference in descriptive factors associated with case resolution without a referral from 2019 to 2020. Females prevailed in both years, and the total demand decreased to a third from 2019 to 2020. The rate of resoluteness decreased by 19.1%. In 2019, nurses (PR = 0.69 CI 95% 0.57-0.83) and other professionals (PR = 0.84 CI 95% 0.73-0.97) showed less frequency of case resolution without a referral than did general dentists. In 2020, oral-cavity-related doubts (PR = 1.18 CI 95% 1.06-1.32) and temporomandibular disorders (PR = 1.33 95% 1.15-1.54) surpassed other causes of orofacial pain in case resolution without a referral, and female professionals avoided referrals more frequently than men (PR = 1.24 CI 95% 1.21-1.38). In conclusion, in 2019, oral cavity doubts and the PHC profession influenced the case resolution. Female professionals and oral cavity doubts scored the higher case resolution without a referral for the service in 2020.

Neuromorphic Electronic Module, focused on the Use of Memristor ECB, for Image Recognition

Currently, the expansion of the range of tasks solved by neural networks occurs mainly due to the complication of their structure, an increase in the number of neurons and synapses. Networks with thousands of neurons and tens to hundreds of thousands of synapses can achieve impressive results in the areas of speech processing, image identification, computer vision, etc., but at the same time, their use and training require significant computing power and energy costs. This is unacceptable for embedded autonomous systems, which, as follows from current trends, will constitute one of the most important applications for neural networks on a chip and must perform their tasks using a minimum number of elements and with minimal energy consumption. In this article a method for increasing the efficiency of hardware implementation and minimizing the number of electronic synaptic elements of asynchronous spiking neural networks (ASNN) in solving image identification problems was developed. To achieve this goal, a theoretical analysis of ASNN architectural solutions was carried out, the number of electronic synaptic elements was minimized due to the decomposition of the image identification problem, an ASNN software model was developed, neuron parameters were optimized and the neural network was learned (the weights of electronic synaptic elements were sat) on a software model. An ASNN electrical circuit was developed and the results of SPICE modeling were obtained. A hardware implementation of an asynchronous spiking neural network on serial electronic components was performed. The effectiveness of the proposed method was demonstrated in the process of optimizing the parameters of neurons and learning the neural network on the developed software model and confirmed by the results of SPICE modeling of the developed electrical circuit of the ASNN and the results of measuring the signals of the neural network implemented on serial electronic components.

Information-theoretic secure rational secret sharing in asynchronous networks for untrusted cloud environments

AbstractToday, cloud storage services increased the popular for data storage in the cloud and retrieve from any location without any time limitations. One of the most important demands required in cloud is secured data transmission in un-trusted cloud applications. Particularly, secure and efficient multiparty communications in Untrusted Cloud Environments (UCE) attract widespread attentions. The equipment used in UCE have the particularity of being heterogeneous and UCE communication environment are asynchronous networks in which multiple users cannot transmit their messages simultaneously. How to ensure secure communication between these heterogeneous intelligent devices is a major challenge for multiparty communication applied in UCE. In such an asynchronous environment, the asynchronous transmission can cause security problems in cryptographic functions. Therefore, how to implement rational secret sharing (RSS) in an asynchronous model of the UCE networks has become a burning research topic. The RSS refers to finding a solution composed of strategies to encourage players in the secret reconstruction to act honestly even players are rational to act for their own interest. If each player plays the game for the best response to the best response of other players, the game is in Nash equilibrium. The objective of an RSS is to achieve the Nash equilibrium state corresponding to the global optima. In this paper, we propose an information-theoretic secure RSS in asynchronous model for UCE. Our design uses Petersen’s VSS to allow every player to divide his share into multiple pieces for other players. Then, shares can be revealed asynchronously. If any player acts maliciously, his share can be recovered by other players. This feature can encourage players to act honestly since any malicious action (i.e., either revealing a fake share or refusing to release one) is useless. Our scheme is practically valuable for secure group-oriented applications in UCE.

Trust-Region Based Stochastic Variational Inference for Distributed and Asynchronous Networks

Trust-Region Based Stochastic Variational Inference for Distributed and Asynchronous Networks

Flexico: An efficient dual-mode consensus protocol for blockchain networks.

Blockchain is a Byzantine fault tolerant (BFT) system wherein decentralized nodes execute consensus protocols to drive the agreement process on new blocks added to a distributed ledger. Generally, two-round communications among [Formula: see text] nodes are required to tolerate up to [Formula: see text] faults in BFT-based consensus networks. This communication pattern corresponds to the worse-case scenario of consensus achievement, even under asynchronous network conditions. Nevertheless, it is not uncommon for a network to operate under better conditions, where a consensus can be reached with a lower communication cost. Hence, with the addition of a faster optimistic path toward an agreement, the idea of dual-mode consensus has been proposed as a promising approach to enhance the performance of asynchronous BFT protocols. However, this opportunity is not completely exploited by existing dual-mode protocols as the fast path can be followed only in a nonfaulty and synchronous network. This article presents a novel dual-mode protocol consisting of fast and backup subprotocols. To create different consensus committees for fast and backup-mode operations, the network contains both active and passive nodes. A consensus can be expedited through a fast-mode operation when majority of the active nodes can communicate synchronously. Under non-ideal conditions, the backup protocol takes over the agreement process from its fast-mode counterpart without starting over the suspended round. The safety and liveness of the proposed protocol are guaranteed with lower communication costs, which balance the trade-off between protocol efficiency and availability.

Cooperative distributed state estimation: resilient topologies against smart spoofers

A network of observers is considered, where through asynchronous (with bounded delay) communications, they cooperatively estimate the states of a linear time-invariant (LTI) system. In such a setting, a new type of adversary might affect the observation process by impersonating the identity of the regular node, which is a violation of communication authenticity. These adversaries also inherit the capabilities of Byzantine nodes, making them more powerful threats called smart spoofers. We show how asynchronous networks are vulnerable to smart spoofing attack. In the estimation scheme considered in this paper, information flows from the sets of source nodes, which can detect a portion of the state variables each, to the other follower nodes. The regular nodes, to avoid being misguided by the threats, distributively filter the extreme values received from the nodes in their neighborhood. Topological conditions based on strong robustness are proposed to guarantee the convergence. Two simulation scenarios are provided to verify the results.

When switching makes impossible synchronization possible

Synchronization of dynamical networks is of great significance in diverse scientific fields. In this paper, the focus is on the networks in which the synchronization stability region is bounded. We represent that blinking of determined links can lead to reaching a synchronous state for specific asynchronous networks. The requisite for the primary network is that it must have only one normalized coupling parameter (the first or the last) out of the stability region found by the master stability function. Then, the proper links that their substitution causes the other coupling parameter (the last or the first) to fall out of the stability region should be recognized. Finally, switching the identified links periodically can synchronize the network. This phenomenon is represented on two networks of Rӧssler and Lorenz oscillators.

An Interpolatory Algorithm for Distributed Set Membership Estimation in Asynchronous Networks

This article addresses distributed estimation problems over asynchronous networks in a set membership framework. The agents in the network asynchronously collect and process measurements, communicate over a possibly time-varying and unbalanced directed graph and may have nonnegligible computation times. Measurements are affected by bounded errors so that they define feasible sets containing the unknown parameters to be estimated. The proposed algorithm requires each agent to compute a weighted average of its estimate and those of its neighbors and to project it onto a local feasible set. By assuming convexity of the measurement sets, the local estimates are shown to converge to a common point belonging to the global feasible set.

Coresets based asynchronous network slimming

Pruning is effective to reduce neural networks’ parameters and accelerate inferences, facilitating deep learning in resource-limited scenarios. This paper proposes an asynchronous pruning method for multi-branch networks on the basis of our previous work on channel coresets constructions, to achieve module-level pruning. Firstly, this paper accelerates coreset based pruning by batch sampling with a sampling probability decided on our-designed importance function. Secondly, this paper gives asynchronous pruning solutions with an in-place distillation of feature maps for deployment on multi-branch networks such as ResNet and SqueezeNet. Thirdly, this paper provides an extension to neuron pruning by grouping weights as channels. During tests on sensitivity of different layers to channel pruning, our method outperforms comparison schemes on object detection networks, indicating advantages of data-independent channel selections in maintaining precision. As shown in tests of asynchronous pruning solutions on multi-branch classification networks, our method further decreases FLOPs with a small accuracy decline on ResNet and acquires a small accuracy increment on SqueezeNet. In tests on neuron pruning, our method achieves an accuracy comparable to existing coreset based pruning methods by two solutions of precision recovery.

AEON.py: Python library for attractor analysis in asynchronous Boolean networks.

AEON.py is a Python library for the analysis of the long-term behaviour in very large asynchronous Boolean networks. It provides significant computational improvements over the state-of-the-art methods for attractor detection. Furthermore, it admits the analysis of partially specified Boolean networks with uncertain update functions. It also includes techniques for identifying viable source-target control strategies and the assessment of their robustness with respect to parameter perturbations. All relevant results are available in Supplementary Materials. The tool is accessible through https://github.com/sybila/biodivine-aeon-py. Supplementary data are available at Bioinformatics online.

Byzantine Fault Tolerance For Distributed Ledgers Revisited

The problem of Byzantine Fault Tolerance (BFT) has received a lot of attention in the last 30 years. Due to the popularity of Proof of Stake (PoS) blockchains in recent years, several BFT protocols have been deployed in the large scale of Internet environment. We analyze several popular BFT protocols such as Capser FFG/CBC-FBC for Ethereum 2.0 and GRANDPA for Polkadot. Our analysis shows that the security models for these BFT protocols are slightly different from the models commonly accepted in the academic literature. For example, we show that, if the adversary has a full control of the message delivery order in the underlying network, then none of the BFT protocols for Ethereum blockchain 2.0 and Polkadot blockchain could achieve liveness even in a synchronized network. Though it is not clear whether a practical adversary could actually control and re-order the underlying message delivery system (at Internet scale) to mount these attacks, it raises an interesting question on security model gaps between academic BFT protocols and deployed BFT protocols in the Internet scale. With these analysis, this article proposes a Casper CBC-FBC style binary BFT protocol and shows its security in the traditional academic security model with complete asynchronous networks. For partial synchronous networks, we propose a multi-value BFT protocol BDLS based on the seminal DLS protocol and show that it is one of the most efficient practical BFT protocols at large scale networks in the traditional academic BFT security model. The implementation of BDLS is available at https://github.com/yonggewang/bdls . Finally, we propose a multi-value BFT protocol XP for complete asynchronous networks and show its security in the traditional academic BFT security model.