scholarly journals An Optimal Rewiring Strategy for Cooperative Multiagent Social Learning

2019 ◽  
Vol 33 ◽  
pp. 10049-10050
Author(s):  
Hongyao Tang ◽  
Jianye Hao ◽  
Li Wang ◽  
Tim Baarslag ◽  
Zan Wang
Keyword(s):  
Social Learning ◽  
Multiagent Systems ◽  
Real World ◽  
Large Scale ◽  
Dynamic Environment ◽  
Multiagent Coordination ◽  
Learning Framework ◽  
Repeated Interaction ◽  
Long Run ◽  
Network Topologies

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and static social learning framework. However, two aspects of dynamics in real-world MASs are currently missing. First, the network topologies can dynamically change during the course of interaction. Second, the interaction utilities between each pair of agents may not be identical and not known as a prior. Both issues mentioned above increase the difficulty of coordination. In this paper, we consider the multiagent social learning in a dynamic environment in which agents can alter their connections and interact with randomly chosen neighbors with unknown utilities beforehand. We propose an optimal rewiring strategy to select most beneficial peers to maximize the accumulated payoffs in long-run interactions. We empirically demonstrate the effects of our approach in large-scale MASs.

scholarly journals P055: EMS boot camp: a real-world, real-time educational experience for emergency medicine residents

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S96-S97
Author(s):  
C. Farrell ◽  
S. Teed ◽  
N. Costain ◽  
M.A. Austin ◽  
A. Willmore ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Patient Care ◽  
Real Time ◽  
Real World ◽  
Large Scale ◽  
Clinical Care ◽  
Dynamic Environment ◽  
Educational Experience ◽  
Boot Camp ◽  
Medical Control

Introduction/Innovation Concept: In 2014, Eastern Ontario paramedic services, their medical director staff and area community colleges developed an EMS Boot Camp experience to orient Queen’s University and the University of Ottawa emergency medicine residents to the role of paramedics and the challenges they face in the field. Current EMS ride-alongs and didactic classroom sessions were deemed ineffective at adequately preparing residents to provide online medical control. From those early discussions came the creation of a real-world, real-time (RWRT) educational experience. Methods: Specific challenges unique to paramedicine are difficult to communicate to a medical control physician at the other end of a telephone. The goal of this one-day educational experience is for residents to gain insight into the complexity and time sensitive nature of delivering medical care in the field. Residents are immersed as responding paramedics in a day of intense RWRT simulation exercises reflecting the common paramedic logistical challenges to delivering patient care in an uncontrolled and dynamic environment. Curriculum, Tool, or Material: Scenarios, run by paramedic students, are overseen by working paramedics from participating paramedic services. Residents learn proper use of key equipment found on an Ontario ambulance while familiarize themselves with patient care standards and medical directives. Scenarios focus on prehospital-specific clinical care issues; performing dynamic CPR in a moving vehicle, extricating a bariatric patient with limited personnel, large scale multi-casualty triage as well as other time sensitive, high risk procedures requiring online medical control approval (i.e. chest needle thoracostomy). Conclusion: EMS Boot Camp dispels preconceived biases regarding “what it’s really like” to deliver high quality prehospital clinical care. When providing online medical control in the future, the residents will be primed to understand and expect certain challenges that may arise. The educational experience fosters collaboration between prehospital and hospital-based providers. The sessions provide a reproducible, standardized experience for all participants; something that cannot be guaranteed with traditional EMS ride-alongs. Future sessions will evaluate participant satisfaction and self-efficacy with the use of a standard evaluation form including pre/post self-evaluations.

scholarly journals Discovering the Influences of Complex Network Effects on Recovering Large Scale Multiagent Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yang Xu ◽  
Pengfei Liu ◽  
Xiang Li ◽  
Wei Ren
Keyword(s):  
Complex Network ◽  
Multiagent Systems ◽  
Network Topology ◽  
System Performance ◽  
Large Scale ◽  
Network Effects ◽  
Multiagent System ◽  
Distributed Coordination ◽  
Key Factor ◽  
Network Topologies

Building efficient distributed coordination algorithms is critical for the large scale multiagent system design, and the communication network has been shown as a key factor to influence system performance even under the same coordination protocol. Although many distributed algorithm designs have been proved to be feasible to build their functions in the large scale multiagent systems as claimed, the performances may not be stable if the multiagent networks were organized with different complex network topologies. For example, if the network was recovered from the broken links or disfunction nodes, the network topology might have been shifted. Therefore, their influences on the overall multiagent system performance are unknown. In this paper, we have made an initial effort to find how a standard network recovery policy, MPLS algorithm, may change the network topology of the multiagent system in terms of network congestion. We have established that when the multiagent system is organized as different network topologies according to different complex network attributes, the network shifts in different ways. Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes.

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

A Heuristic Algorithm for Solving a Large-scale Real-world Territory Design Problem

Omega ◽  
2021 ◽  
pp. 102442
Author(s):  
Lin Zhou ◽  
Lu Zhen ◽  
Roberto Baldacci ◽  
Marco Boschetti ◽  
Ying Dai ◽  
...  
Keyword(s):  
Heuristic Algorithm ◽  
Real World ◽  
Design Problem ◽  
Large Scale ◽  
Territory Design

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals Monoclonal Antibodies Targeting CGRP: From Clinical Studies to Real-World Evidence—What Do We Know So Far?

2021 ◽  
Vol 14 (7) ◽  
pp. 700
Author(s):  
Theodoros Mavridis ◽  
Christina I. Deligianni ◽  
Georgios Karagiorgis ◽  
Ariadne Daponte ◽  
Marianthi Breza ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
Real World ◽  
Large Scale ◽  
Randomized Clinical Trials ◽  
Clinical Investigation ◽  
Phase Iii ◽  
Real World Evidence ◽  
Symptomatic Management ◽  
Repurposed Drugs ◽  
Cephalic Pain

Now more than ever is the time of monoclonal antibody use in neurology. In headaches, disease-specific and mechanism-based treatments existed only for symptomatic management of migraines (i.e., triptans), while the standard prophylactic anti-migraine treatments consist of non-specific and repurposed drugs that share limited safety profiles and high risk for interactions with other medications, resulting in rundown adherence rates. Recent advances in headache science have increased our understanding of the role of calcitonin gene relate peptide (CGRP) and pituitary adenylate cyclase-activating polypeptide (PACAP) pathways in cephalic pain neurotransmission and peripheral or central sensitization, leading to the development of monoclonal antibodies (mAbs) or small molecules targeting these neuropeptides or their receptors. Large scale randomized clinical trials confirmed that inhibition of the CGRP system attenuates migraine, while the PACAP mediated nociception is still under scientific and clinical investigation. In this review, we provide the latest clinical evidence for the use of anti-CGRP in migraine prevention with emphasis on efficacy and safety outcomes from Phase III and real-world studies.

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