Dynamic Bayesian Collective Awareness Models for a Network of Ego-Things

A novel approach is proposed for multimodal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet-of-Things (IoT) node equipped with machine learning capabilities; CA aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multisensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching dynamic Bayesian network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A growing neural gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely, distributed Markov jump particle filter (D-MJPF), employed for joint state estimation and abnormality detection. The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, first the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analyzed for the collective abnormality estimation of the agents. Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well

Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles

The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness (CA) in a network of intelligent agents. A specific collective self-awareness functionality is considered, namely, agent-centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent . Data-driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent , is used to allow the agents in the network to reach synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithm’s reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Performances of the DBN-based abnormality detection models under different channel and source conditions are discussed. The effects of distances among agents and of the delays and packet losses are analyzed in different scenario categories (urban, suburban, and rural). Real data se..

Contemporary characteristics and outcomes in chagasic heart failure compared with other nonischemic and ischemic cardiomyopathy

Background: Chagas’ disease is an important cause of cardiomyopathy in Latin America. We aimed to compare clinical characteristics and outcomes in patients with heart failure (HF) with reduced ejection fraction caused by Chagas’ disease, with other etiologies, in the era of modern HF therapies. Methods and Results: This study included 2552 Latin American patients randomized in the PARADIGM-HF (Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure) and ATMOSPHERE (Aliskiren Trial to Minimize Outcomes in Patients With Heart Failure) trials. The investigator-reported etiology was categorized as Chagasic, other nonischemic, or ischemic cardiomyopathy. The outcomes of interest included the composite of cardiovascular death or HF hospitalization and its components and death from any cause. Unadjusted and adjusted Cox proportional hazards models were performed to compare outcomes by pathogenesis. There were 195 patients with Chagasic HF with reduced ejection fraction, 1300 with other nonischemic cardiomyopathy, and 1057 with ischemic cardiomyopathy. Compared with other etiologies, Chagasic patients were more often female, younger, and had lower prevalence of hypertension, diabetes mellitus, and renal impairment (but had higher prevalence of stroke and pacemaker implantation) and had worse health-related quality of life. The rates of the composite outcome were 17.2, 12.5, and 11.4 per 100 person-years for Chagasic, other nonischemic, and ischemic patients, respectively—adjusted hazard ratio for Chagasic versus other nonischemic: 1.49 (95% confidence interval, 1.15–1.94; P=0.003) and Chagasic versus ischemic: 1.55 (1.18–2.04; P=0.002). The rates of all-cause mortality were also higher. Conclusions: Despite younger age, less comorbidity, and comprehensive use of conventional HF therapies, patients with Chagasic HF with reduced ejection fraction continue to have worse quality of life and higher hospitalization and mortality rates compared with other etiologies. Clinical Trial Registration: PARADIGM-HF: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01035255; ATMOSPHERE: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00853658