Asynchronous Model Research Articles

Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs

This paper proposes a federated supervised learning framework over a general peer-to-peer network with agents that act in a variational Bayesian fashion. The proposed framework consists of local agents where each of which keeps a local “posterior probability distribution” over the parameters of a global model; the updating of the posterior over time happens in a local fashion according to two subroutines of: 1) variational model training given (a batch of) local labeled data, and 2) asynchronous communication and model aggregation with the 1-hop neighbors. Inspired by the popular federated learning (model averaging), the framework allows the training data to remain distributed on mobile devices while utilizing a peer-to-peer model aggregation in a social network. The proposed framework is shown to allow for a systematic treatment of model aggregation over any arbitrary connected graph with consistent (in general, non-iid) local labeled data. Specifically, under mild technical conditions, the proposed algorithm allows agents with local data to learn a shared model explaining the global training data in a decentralized fashion over an arbitrary peering/connectivity graph. Furthermore, the rate of convergence is characterized and shown to be a function of each individual agent’s data quality weighted by its eigenvector centrality. Empirically, the proposed methodology is shown to work well with efficient variation Bayesian inference techniques to train Bayesian neural networks in a decentralized manner even when the local data batches are not identically distributed.

A design in system architecture based on mobile cloud computing for a virtual try-on solution

Cloud computing is an emerging technology in this digital century. It provides an excellent but low-cost source for database storage, computing power, applications, and services through an internet-delivered cloud platform. Thanks to the cost savings in investing and maintaining physical data centers, as well as the stability of Quality of Service (QoS), it has no restrictions on company size or sector for enterprises to shift their operations to a cloud platform. The fashion industry, particularly the fashion e-commerce sector, is a case study in leveraging the cloud platform via a technology called “Virtual try-on” (VTO). VTO solution allows fashion brands to increase the shopping experience, however, requires installing and maintaining a bulky system for implementation. There are different methods and approaches to design architectures using cloud computing, however, there have not been many studies addressing tasks related to machine learning that uses the high Graphics Processing Unit (GPU) encountered in VTO solutions. To design a scheduler that could optimize the system performance while lowering operational expenses in VTO solutions, this research proposes a system to (1) handle synchronous model and asynchronous model separately and clearly, (2) perform multi-layered task processing architecture by hashing task ID and implementing a queue management system. This method would satisfy three major requirements: (1) Avoid complex hardware requirements for users, (2) Ensure the system stability and the ease of horizontal and vertical extension, and (3) Protect user information privacy.

Asynchronous network-based model and algorithm for sentiment analysis of online public opinions

PurposeA novel asynchronous network-based model is proposed in this paper for the sentiment analysis of online public opinions. This new model provides a new approach to analyze the evolution characteristics of online public opinion sentiments in complex environment.Design/methodology/approachFirstly, a new sentiment analysis model is proposed based on the asynchronous network theory. Then the graphical evaluation and review technique is employed and extended to design the model-based sentiment analysis algorithms. Finally, simulations and real-world case studies are given to show the effectiveness of the proposed model.FindingsThe dynamics of online public opinion sentiments are determined by both personal preferences to certain topics and the complex interactive influences of environmental factors. The application of appropriate quantitative models can improve the prediction of public opinion sentiment.Practical implicationsThe proposed model-based algorithms provide simple but effective ways to explore the complex dynamics of online public opinions. Case studies highlight the role of government agencies in shaping sentiments of public opinions on social topics.Originality/valueThis paper proposes a new asynchronous network model for the dynamic sentiment analysis of online public opinions. It extends the previous static models and provides a new way to extract opinion evolution patterns in complex environment. Applications of the proposed model provide some new insights into the online public opinion management.

Use of a hybrid teledermatology model in an Australian tertiary hospital during the COVID-19 pandemic

To the Editor: The COVID-19 pandemic has led to the adoption of teledermatology by health services across the world. There is increasing support for the use of teledermatology in the outpatient setting; however, its role in inpatient and emergency settings is less established. We reported the use of teledermatology in inpatient and emergency settings at the Dermatology Department, St George Hospital, a tertiary center in Sydney, Australia. Inpatient and emergency consultations are conducted at our institution by 1 of 2 staff dermatologists per day and 1 hospital-based dermatology resident. A hybrid model of inpatient teledermatology consultations was established prior to the COVID-19 pandemic. All referrals would be seen face to face by the dermatology resident to ensure relevant history, and high-quality clinical and dermatoscopic photographs would be taken. The referrals would then be summarized and sent to the on-call dermatologist, who would provide an impression and plan for the resident to enact. For severe or life-threatening cases, the on-call dermatologist would also attend the face-to-face consultation. This system involved both synchronous and asynchronous models depending on the triaging of consultations performed by the dermatology resident. To avoid the exposure of the on-call dermatologists to COVID-19, which would subsequently shut down the provision of dermatology services to both outpatient and inpatient or emergency services, this hybrid model of inpatient teledermatology was maintained during the pandemic, and outpatient clinics were converted to teledermatology for large proportions of 2020 and 2021. Using this hybrid approach, our dermatology service avoided any shut down periods due to the exposure of the 2 on-call dermatologists to COVID-19. The main exposure site for COVID-19 was the emergency department, and this was the main source of inpatient referrals (Fig 1). There was no statistically significant difference (P > 0.05, Student t test) in the number of inpatient consultations seen between pre-COVID-19 2019 (n = 295; mean, 27 consultations per month; SD, 10.9) and 2020 (n = 305; mean, 25 consultations per month; SD, 6.3) or 2021 (n = 323; mean, 27 consultations per month; SD, 8.1).1Ho G. Blake S.C. Sheriff T. Daniel B.S. Murrell D.F. Impact of COVID-19 on inpatient dermatology consults in an Australian tertiary hospital.Australas J Dermatol. 2021; 62: 427-428Crossref PubMed Scopus (3) Google Scholar This may have been due to the establishment of the hybrid model of inpatient teledermatology. In 2021, 70% of inpatient consultations had a treatment plan provided to the referring team within the same day of referral. The role of teledermatology to increase the efficiency of inpatient consultations was described prior to the COVID-19 pandemic, with reduction in the time taken for inpatient medical teams to receive advice from dermatology consultants.2Sharma P. Kovarik C.L. Lipoff J.B. Teledermatology as a means to improve access to inpatient dermatology care.J Telemed Telecare. 2016; 22: 304-310Crossref PubMed Scopus (25) Google Scholar Concordance in investigations and treatment plans between teleconsultations and face-to-face consultations has also been described.3Keller J.J. Johnson J.P. Latour E. Inpatient teledermatology: diagnostic and therapeutic concordance among a hospitalist, dermatologist and teledermatologist using store-and-forward teledermatology.J Am Acad Dermatol. 2020; 82: 1262-1267Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar,4Gabel C.K. Nguyen E. Karmouta R. Liu K.J. Zhou G. Alloo A. Use of teledermatology by dermatology hospitalists is effective in the diagnosis and management of inpatient disease.J Am Acad Dermatol. 2021; 84: 1547-1553Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar The COVID-19 pandemic has seen wider adoption of teledermatology services.5Loh C.H. Tam S.Y. Oh C.C. Teledermatology in the COVID-19 pandemic: a systematic review.JAAD Int. 2021; 5: 54-64Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar The experience of our department supports the use of teledermatology for efficient delivery of care that has been stable throughout the COVID-19 pandemic. Our experience supports use of a hybrid model of teledermatology rather than a virtual model and can be applied in other parts of the world. None disclosed.

C-Lop: Accurate contention-based modeling of MPI concurrent communication

MPI communication optimization is a crucial stage to optimize high-performance applications. As a formal analysis of MPI communication, the communication performance models have made some achievements in improving the efficiency of collective algorithms and optimizing communication scheduling. However, previous models are difficult to model asynchronous concurrent communication and do not take into account numerous contention factors. In this paper, we present C-Lop, an incremental MPI performance model based on τ-Lop. Firstly, C-Lop proposes a method for asynchronous modeling of concurrent communication. As the only model that considers concurrent transmission, τ-Lop describes the cost of the all processes as a whole without distinguishing the cost of each process. Here, C-Lop uses the idea of asynchronous modeling that describe the cost of the system by averaging the communication cost per process. It can describe the communication cost for some systems with out-of-sync communication more accurately. Moreover, C-Lop introduces the parameter C to represent the contention, and considers the contention of concurrent transmissions on network-on-chip, data reuse, and contention of noncommunication processes to make a more accuracy estimation. Furthermore, parameter C can be customized to fit more application scenarios. In addition, we evaluate several common collective algorithms, a matrix multiplication algorithm (SUMMA), and two kinds of communication in a three-dimensional multi-grid application on the Tianhe-3 prototype, and results show that C-Lop outperforms the competition.

Teaching during COVID-19: reflections of early-career science teachers

The unique circumstances of the COVID-19 pandemic required that instruction be shifted online through asynchronous, synchronous, or hybrid models of instruction. This created a need for many K-12 teachers to dramatically rethink how teaching and learning occurred in their classrooms. In this study, we investigate the experiences of early-career science teachers who were in their first year of teaching when the pandemic struck. Using a comparative case study and an analytical framework focused on technology-related leader practices, we explore the unique opportunities for technology-based leadership that emerged for early-career teachers during the pandemic. We posit that the circumstances of the COVID-19 pandemic presented novel opportunities for early-career teachers to assume leadership roles that were embedded within the classroom teaching experience, which created unique opportunities for innovation and leadership in teaching.

State estimation of distribution network based on hybrid measurement combined with multi-source asynchronous data

At present, the redundancy of real-time measurement in distribution network is low and the mixed measurement is incompatible, Hybrid measurement combined with multi-source asynchronous data such as SCADASCADA/ smart distribution transformer information acquisition system (SDTIAS), anew state estimation based on SCADASCADA and SDTIAS is proposed in this paper. Firstly, SDTIAS and SCADA measurements are matched and synchronized; Then, the quadratic multi-source asynchronous measurement model is constructed, the quadratic equality constraint is established, the unified modeling of multi-source asynchronous measurement is realized; Finally, the proposed model and algorithm are simulated and verified in the standard test system, The results show the error seven times lower than traditional methods, and the calculation efficiency (100 Statistics) is increased by 35%, indicating better accuracy and higher efficiency.

Review of a pivoted fully online flipped learning modality to promote reflection for early career teaching staff development

The professional development of early career teaching staff (academics and researchers) in learning and teaching practices has attracted significant attention internationally over the past two decades. Institutional agendas, national accreditation and international recognition schemes have been established to provide frameworks for the promotion of effective teaching practices through professional development programmes. Recently, though, the Covid-19 pandemic has challenged the flexibility of staff development frameworks based on how they are administered and the impact that they have on the professional needs of early career teaching staff.This study takes a critical stance on a face-to-face teaching programme that is pivoted to a fully online flipped modality to accommodate the rapid transition to remote learning and teaching due to the Covid-19 pandemic. The study is written as a case study from a medical school that became fully immersed in a global crisis and took steps to maintain provision in the development of teaching staff at a time when both clinical and teaching roles took precedence and plans for professional development became secondary.Utilising a combination of asynchronous and synchronous online engagement models, the paper presents a conceptual framework to foster reflective practice and reviews the workflow of promoting flexible engagement and accommodating participant-led development priorities. Analysis of assessment results by comparing pre- and post-pivot participant performances indicate a statistically significant improvement in the flipped learning version of the programme.Drawing from the theory of situated learning as a tool of investigating professional learning in the context of a developing global crisis, the paper reviews the role of social systems in facilitating flexible learning at a distance through a series of authentic collaborative activities that are designed to support the sharing of practice, promotion of reflection and to inform future approaches to learning and teaching methods.Keywords: online flipped learning; situated learning; critical reflection lenses; staff development; Covid-19Part of the Special Issue Technology and educational ‘pivoting’ in the wake of the Covid-19 pandemic <https://doi.org/10.21428/8c225f6e.0a9292af>

Vector control system design for asynchronous motors

In order to decouple the excitation and torque components of the stator current so that the asynchronous motor speed control system approximates a DC speed control system. Firstly, the asynchronous motor model is transformed in coordinates, and then the vector control idea is proposed to construct a vector control system for the asynchronous motor. At the same time, a simulation model of the vector control system of an asynchronous motor was created on the Matlab platform and the speed waveform was recorded. The analysis of the experimental data showed that the speed response of the vector control system was fast and smooth, which improved the performance of the asynchronous motor speed control system.

Supervisory control of discrete event systems under asynchronous spiking neuron P systems

Asynchronous spiking neural P systems with multiple channels (SNP systems) operate in an asynchronous pattern, in which the firing of enabled rules is not obligatory. Asynchronous models are one of the most potential research carriers of supervisory control. However, a systematic understanding and process of leveraging SNPs to address control issues in discrete event systems are still lacking. This paper extends a type of SNP formalization, called vector-SNP, to explore the system’s intrinsic stability properties without configuration attention by introducing two classes of structural feature vectors: N-invariants and R-invariants. Then, a typical style of specifications, namely neuron mutual exclusion inequality constraints (NMEICS), is advanced for supervisory control using SNPs. An NMEIC is enforced by explicitly adding a supervisor (group of observation and control monitors represented by SNPs) to an SNP system. On top of that, the supervisor construct algorithm that utilizes the linear algebraic calculations is established to obtain the supervisors for pre-designed control constraints. Finally, we evaluate the proposed supervisory control theory using SNPs on an automated manufacturing system. Experimental results achieve maximally permissive controlled behaviors that reserve all feasible legal states.

Evaluation of a technical information systems module for distance learning during the COVID-19 pandemic

PurposeIn the wake of the pandemic (COVID-19) throughout the United States, many educators had to promptly switch to online modality to continue to provide education to students with safety through physical distancing requirements. This study mainly focuses on delivering an Information Systems module aligned with the information systems curriculum model during a pandemic.Design/methodology/approachThe authors used data collection techniques from student assignments and course tasks. These data are used for the purpose of academic assessment for the Information Systems program. The student performance is assessed on a 5-point scale (1 being low and 5 being high) for the synchronous and the asynchronous tasks related to the course. The authors compared the student performance during the pandemic to the pre-pandemic semester.FindingsThis study revealed that the technical module of an information systems course can be successfully delivered during a pandemic in a remote session. However, the authors found that there is a decline in the student performance in synchronous tasks and asynchronous tasks. But the decline of the student performance in the synchronous tasks is greater than that of the asynchronous tasks. The result of this study helps the Information Systems program with their assessment and to improve their course delivery during a pandemic.Originality/valueIn this paper, the authors examine the delivery of a technical module in the field of information systems via online learning models. The authors particularly examine the synchronous and asynchronous online learning models in the delivery of the technical module. The lessons learned from transitioning to the online modality can help universities better prepare for the future during unprecedented times.

Supporting undergraduate students\u2019 developing water literacy during a global pandemic: a longitudinal study

To prepare students to address water-related challenges, undergraduate STEM education must provide them with opportunities to learn and reason about water issues. Water in Society is an introductory-level, innovative, and interdisciplinary undergraduate course offered annually at a large midwestern university. The course focuses on both disciplinary concepts and civic engagement, and is designed around a variety of interactive, research-based practices to support students’ learning, engagement with authentic data, scientific models and modeling, and collaboration and learning among peers. This study aims to evaluate, “how have student outcomes and perceptions changed over five years of the course?”. The results are based on data from students (n = 326) in five consecutive years of the course, during which time the course transitioned from a face-to-face model to fully asynchronous online model due, in part, to impacts of the COVID-19 pandemic. The particularly rapid and abrupt transition between 2020 and 2021 in response to COVID-19 led to many course changes, including modes of communication between instructors and students and opportunities for collaboration. Here, multiple measures are used to evaluate students’ learning about water concepts, model-based reasoning about socio-hydrologic systems, and perceptions of the course across all five years. By the end of each iteration of the course, students improved their knowledge of hydrologic concepts, independent of the course format or other student-level variables. However, results also show that students’ performance on complex socio-hydrologic systems modeling tasks, as well as their overall satisfaction with the course, decreased in Year 5 when the course was fully online. Results provide insight into efforts to move undergraduate STEM courses online and specific evidence of the COVID-19 pandemic’s impacts on undergraduate STEM teaching and learning about water.

Hybrid Synchronous–Asynchronous Execution of Reconfigurable PLC Programs in Edge Computing

In edge computing, heterogeneous automation tasks, such as networked motion control and reconfiguration management, have diverse weights on determinism and flexibility. Such heterogeneities escalate the demands on the runtime environments (RTEs) of programmable logic controllers to provide the best-suited rather than one-size-fits-all execution policies for reconfigurable programs. This article presents the proposal and implementation of a novel IEC 61499-based RTE capable of offering hybrid synchronous and asynchronous execution models for function block (FB) based programs. We also construct a runtime reconfigurable FB-type repository by realizing a just-in-time FB-type definitions compiler in the RTE. Finally, we evaluate and compare the proposed RTE and state-of-the-art works by running a set of synthetic benchmark programs and a realistic networked motion control kernel and application. Experiment results from the benchmark programs have shown a maximal 40% faster execution speed in our RTE. Furthermore, our RTE can ensure fewer jitters during distributed motion control with a 1-ms cycle time on a realistic motion control testbed.

Adaptive event-triggered resilient control of industrial cyber physical systems under asynchronous data injection attack

A two sides asynchronous Adaptive Event-triggered Resilient Control Scheme (AERCS) is proposed for ICPS which suffering asynchronous data injection attacks. For adapting to changes of the harsh environmental, the event trigger threshold is dynamically optimized with the external interference and attacks to ensure the system with a certain resilience. Based on an asynchronous event-triggered timing model, a control strategy of asynchronous event-triggered control is constructed by being combined with the dynamic output feedback control for different attack scenarios. Based on the feedback control theory, a novel Lyapunov-Krasovskii functional is constructed to analyze the stability and H∞ performance of system by fully utilizing the attack information, the changed system information caused by network delays and the adaptive threshold. Moreover, the sufficient condition of co-design of dynamic controller gain and event trigger parameter matrix are given. The simulation experiments are conducted by using Matlab tool with the inverted pendulum as the plant to evaluate the stability of ICPS. Simulation results show that AERCS can not only ensure the desired stability of the system, but also effectively reduce the number of times for transmitting data through the wireless network.

The “Socialized Architecture”: A Software Engineering Approach for a New Cloud

Today, the cloud means a revolution within the Internet revolution. However, an oligopoly sustaining the cloud may not be the best solution, since ethical problems such as privacy or even transferring data sovereignty could eventually happen. Our research, coined as the "socialized architecture," presents a novel disruptive approach to completely transform the cloud as we know it today. The approach follows ideas already working in the field of volunteer computing, since it tries to socialize spare computing power in the infraused hardware that institutions and normal people own. However, our solution is completely different to current ones, since it does not create hyper-specialized muscles in client machines. The solution is new since it proposes a software engineering approach for developing “socialized services”, which, leveraging an asynchronous interaction model, creates a network of lightweight microservices that can be dynamically allocated and replicated through the network. The use of state-of-the-art patterns, such as Command Query Responsibility Segregation, helps to isolate domain events and persistence needs, while an API Gateway addresses communication. All previous ideas were tested through a complete and functional proof of concept, which is a prototype called Circle implementing a social network. Circle has been useful to expose problems that need to be addressed. The results of the assessment confirm, in our view, that it is worth to start this new field of work.

Synchronous and asynchronous teledermatology: A narrative review of strengths and limitations

The policy changes prompted by the COVID-19 pandemic caused synchronous models (primarily video visits) to supplant asynchronous models (store-and-forward or shared digital photographs) as the default and predominant modality of teledermatology care. Here, we call attention to the unique strengths and limitations of these models in terms of clinical utility, accessibility, and cost-effectiveness. Strengths of synchronous visits include direct physician-patient interaction and current reimbursement parity; limitations include variable video image quality, technological difficulties, and accessibility barriers. Strengths of asynchronous visits include greater convenience, especially for clinicians, and potential for image quality superior to video; limitations include less direct physician-patient communication, barriers to follow-up, and limited reimbursement. Both synchronous and asynchronous models have been shown to be cost-effective. Teledermatology is positioned to play a prominent role in patient care post-pandemic. Moving forward, dermatologists are challenged to optimize teledermatology use in order to improve outcomes, efficiency, and workflows to meet diverse patient needs. Future directions will depend on sustainable reimbursement of both teledermatology formats by government and private payers.

A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs

People's opinions evolve over time as they interact with their friends, family, colleagues, and others. In the study of dynamics on networks, one often encodes interactions between people in the form of dyadic relationships, but many social interactions in real life are polyadic (i.e., they involve three or more people). In this paper, we extend an asynchronous bounded-confidence model (BCM) on graphs, in which nodes are connected pairwise by edges, to hypergraphs. We show that our hypergraph BCM converges to consensus under a wide range of initial conditions for the opinions of the nodes. We show that, under suitable conditions, echo chambers can form on hypergraphs with community structure. We also observe that the opinions of individuals can sometimes jump from one cluster to another in a single time step, a phenomenon (which we call opinion jumping) that is not possible in standard dyadic BCMs. We also show that there is a phase transition in the convergence time on the complete hypergraph when the variance $\sigma^2$ of the initial distribution equals the confidence bound $c$. Therefore, to determine the convergence properties of our hypergraph BCM when the variance and the number of hyperedges are both large, it is necessary to use analytical methods instead of relying only on Monte Carlo simulations.

RTL Conversion Method From Pipelined Synchronous RTL Models Into Asynchronous Ones

In this paper, we propose a conversion method from pipelined synchronous Register Transfer Level (RTL) models into pipelined asynchronous RTL models with bundled-data implementation. To know data-path resources controlled by each pipeline stage, the proposed method generates a control data flow graph (CDFG) from synchronous RTL models. After generating the CDFG, the proposed method generates asynchronous RTL models by analyzing each pipeline stage on the CDFG, assigning asynchronous control modules, and connecting the control modules to the data-path resources. In addition, we also propose optimization methods during the conversion. In the experiment, we converted four pipelined synchronous RTL models into pipelined asynchronous ones. In addition, we performed logic synthesis for the converted asynchronous RTL models to check the quality of the asynchronous RTL models. The synthesized asynchronous circuits without the optimization methods could reduce the energy consumption by 1.47&#x0025; on average compared to synchronous circuits. Moreover, the optimization methods could reduce the energy consumption by 15.12&#x0025; on average compared to synchronous circuits. Furthermore, the optimization methods reduced the energy consumption by up to 34.72&#x0025; compared to asynchronous circuits without the optimization methods.

Mixed Hegselmann-Krause dynamics

&lt;p style='text-indent:20px;'&gt;The original Hegselmann-Krause (HK) model consists of a set of &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; agents that are characterized by their opinion, a number in &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ [0, 1] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Each agent, say agent &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ i $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, updates its opinion &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ x_i $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; by taking the average opinion of all its neighbors, the agents whose opinion differs from &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ x_i $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; by at most &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ \epsilon $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. There are two types of HK models: the synchronous HK model and the asynchronous HK model. For the synchronous model, all the agents update their opinion simultaneously at each time step, whereas for the asynchronous HK model, only one agent chosen uniformly at random updates its opinion at each time step. This paper is concerned with a variant of the HK opinion dynamics, called the mixed HK model, where each agent can choose its degree of stubbornness and mix its opinion with the average opinion of its neighbors at each update. The degree of the stubbornness of agents can be different and/or vary over time. An agent is not stubborn or absolutely open-minded if its new opinion at each update is the average opinion of its neighbors, and absolutely stubborn if its opinion does not change at the time of the update. The particular case where, at each time step, all the agents are absolutely open-minded is the synchronous HK model. In contrast, the asynchronous model corresponds to the particular case where, at each time step, all the agents are absolutely stubborn except for one agent chosen uniformly at random who is absolutely open-minded. We first show that some of the common properties of the synchronous HK model, such as finite-time convergence, do not hold for the mixed model. We then investigate conditions under which the asymptotic stability holds, or a consensus can be achieved for the mixed model.&lt;/p&gt;

Parallel multiple DNA sequence alignment using genetic algorithm and asynchronous advantage actor critic model

Multiple sequence alignment plays a crucial role in bioinformatics field. In this process, sequences are compared by establishing residue-residue correspondence among related sequences. In this paper, we present a novel framework that uses genetic algorithm and asynchronous advantage actor critic model to look for the optimal multiple DNA sequence alignment. In the first step, we generate initial alignments by applying the T-COFFEE method. Then, we recursively use a genetic algorithm to find the optimal fragmentation of the pre-aligned sequences. At this stage, the obtained short sub alignments are introduced to the A3C model for parallel alignments. So, the optimal multiple alignment of the original sequences represents the fusion of sub-alignments. To evaluate the performances of the proposed approach, we used the Balibase 3.0 benchmark and compared to TCOFFEE, MAFFT and RLALIGN methods. The obtained experimental results show that the proposed approach can achieve solutions of competitive quality.