Modular reservoir computing networks for imitation learning of multiple robot behaviors

Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use Reservoir Computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir, projects the input to a dynamic temporal space. The reservoir states are mapped into a readout output layer which is the solely part being trained using standard linear regression. In this paper, we use a two layered modular structure, where the first layer comprises two RC networks, each one for learning primitive behaviors, namely, obstacle avoidance and target seeking. The second layer is composed of one RC network for behavior combination and coordination. The hierarchical RC network learns by examples given by simple controllers which implement the primitive behaviors. We use a simulation model of the e-puck robot which has distance sensors and a camera that serves as input for our system. The experiments show that, after training, the robot learns to coordinate the Goal Seeking (GS) and the Object Avoidance (OA) behaviors in unknown environments, being able to capture targets and navigate efficiently

Deferral of Coronary Revascularization in Patients With Reduced Ejection Fraction Based on Physiological Assessment: Impact on Long-Term Survival

Background Deferring revascularization in patients with nonsignificant stenoses based on fractional flow reserve (FFR) is associated with favorable clinical outcomes up to 15 years. Whether this holds true in patients with reduced left ventricular ejection fraction is unclear. We aimed to investigate whether FFR provides adjunctive clinical benefit compared with coronary angiography in deferring revascularization of patients with intermediate coronary stenoses and reduced left ventricular ejection fraction. Methods and Results Consecutive patients with reduced left ventricular ejection fraction (≤50%) undergoing coronary angiography between 2002 and 2010 were screened. We included patients with at least 1 intermediate coronary stenosis (diameter stenosis ≥40%) in whom revascularization was deferred based either on angiography plus FFR (FFR guided) or angiography alone (angiography guided). The primary end point was the cumulative incidence of all-cause death at 10 years. The secondary end point (incidence of major adverse cardiovascular and cerebrovascular events) was a composite of all-cause death, myocardial infarction, any revascularization, and stroke. A total of 840 patients were included (206 in the FFR-guided group and 634 in the angiography-guided group). Median follow-up was 7 years (interquartile range, 3.22-11.08 years). After 1:1 propensity-score matching, baseline characteristics between the 2 groups were similar. All-cause death was significantly lower in the FFR-guided group compared with the angiography-guided group (94 [45.6%] versus 119 [57.8%]; hazard ratio [HR], 0.65 [95% CI, 0.49-0.85]; P<0.01). The rate of major adverse cardiovascular and cerebrovascular events was lower in the FFR-guided group (123 [59.7%] versus 139 [67.5%]; HR, 0.75 [95% CI, 0.59-0.95]; P=0.02). Conclusions In patients with reduced left ventricular ejection fraction, deferring revascularization of intermediate coronary stenoses based on FFR is associated with a lower incidence of death and major adverse cardiovascular and cerebrovascular events at 10 years

Modular reservoir computing networks for imitation learning of multiple robot behaviors

Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use Reservoir Computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir, projects the input to a dynamic temporal space. The reservoir states are mapped into a readout output layer which is the solely part being trained using standard linear regression. In this paper, we use a two layered modular structure, where the first layer comprises two RC networks, each one for learning primitive behaviors, namely, obstacle avoidance and target seeking. The second layer is composed of one RC network for behavior combination and coordination. The hierarchical RC network learns by examples given by simple controllers which implement the primitive behaviors. We use a simulation model of the e-puck robot which has distance sensors and a camera that serves as input for our system. The experiments show that, after training, the robot learns to coordinate the Goal Seeking (GS) and the Object Avoidance (OA) behaviors in unknown environments, being able to capture targets and navigate efficiently

Long-term clinical outcome after fractional flow reserve-guided treatment in patients with angiographically equivocal left main coronary artery stenosis

Significant left main coronary artery stenosis is an accepted indication for surgical revascularization. The potential of angiography to evaluate the hemodynamic severity of a stenosis is limited. The aims of the present study were to assess the long-term clinical outcome of patients with an angiographically equivocal left main coronary artery stenosis in whom the revascularization strategy was based on fractional flow reserve (FFR) and to determine the relationship between quantitative coronary angiography and FFR

Hyperemic hemodynamic characteristics of serial coronary lesions assessed by pullback pressure gradients

OBJECTIVES To characterize hemodynamics of serial coronary stenoses using fractional flow reserve (FFR) pullbacks and the pullback pressure gradients (PPG) index. BACKGROUND The cross-talk between stenoses within the same coronary artery makes the prediction of the functional contribution of each lesion challenging. METHODS AND RESULTS One-hundred seventeen patients undergoing coronary angiography for stable angina were prospectively recruited. Serial lesions were defined as two or more narrowings with visual diameter stenosis >50% on conventional angiography. Motorized FFR pullback tracings were obtained at 1 mm/s. Pullbacks were visually adjudicated as presenting two, one, and no focal pressure drops. The pattern of disease (i.e., focal or diffuse) was quantified using the PPG index. Twenty-five vessels presented serial lesions (mean PPG 0.48 ± 0.17). Two, one or no focal pressure drops were observed in 40% (n = 10; PPG 0.59 ± 0.17), 52% (n = 13; PPG 0.44 ± 0.12) and 8% of cases (n = 2; PPG 0.27 ± 0.01; p-value = 0.01). Distal FFR was similar between vessels with two, one and no focal pressure drops in the pullback curve (p-value = 0.27). The PPG index independently predicted the presence of two focal pressure drops in the pullback curve (p = 0.04). CONCLUSIONS FFR pullbacks in serial coronary lesions exhibit three distinct functional patterns. High PPG was associated with pullback curves presenting two pressure drops. The PPG provides a quantitative assessment of the pattern of coronary artery disease in cases with serial lesions and might be useful to assess the appropriateness of percutaneous revascularization