Random convex programs for distributed multi-agent consensus

We consider convex optimization problems with N randomly drawn convex constraints. Previous work has shown that the tails of the distribution of the probability that the optimal solution subject to these constraints will violate the next random constraint, can be bounded by a binomial distribution. In this paper we extend these results to the violation probability of convex combinations of optimal solutions of optimization problems with random constraints and different cost objectives. This extension has interesting applications to distributed multi-agent consensus algorithms in which the decision vectors of the agents are subject to random constraints and the agents' goal is to achieve consensus on a common value of the decision vector that satisfies the constraints. We give explicit bounds on the tails of the probability that the agents' decision vectors at an arbitrary iteration of the consensus protocol violate further constraint realizations. In a numerical experiment we apply these results to a model predictive control problem in which the agents aim to achieve consensus on a control sequence subject to random terminal constraints

Towards a Hand Exoskeleton for a Smart EVA Glove

In this paper we investigate the key factors associated with the realization of a hand exoskeleton that could be embedded in an astronaut glove for EVA (Extra Vehicular Activities). Such a project poses several and varied problems, mainly due to the complex structure of the human hand and to the extreme environment in which the glove operates. This work provides an overview of existing exoskeletons and their related technologies and lays the ground for the forthcoming prototype realization, by presenting a preliminary analysis of possible solutions in terms of mechanical structure, actuators and sensors

From Uncertainty Data to Robust Policies for Temporal Logic Planning

We consider the problem of synthesizing robust disturbance feedback policies for systems performing complex tasks. We formulate the tasks as linear temporal logic specifications and encode them into an optimization framework via mixed-integer constraints. Both the system dynamics and the specifications are known but affected by uncertainty. The distribution of the uncertainty is unknown, however realizations can be obtained. We introduce a data-driven approach where the constraints are fulfilled for a set of realizations and provide probabilistic generalization guarantees as a function of the number of considered realizations. We use separate chance constraints for the satisfaction of the specification and operational constraints. This allows us to quantify their violation probabilities independently. We compute disturbance feedback policies as solutions of mixed-integer linear or quadratic optimization problems. By using feedback we can exploit information of past realizations and provide feasibility for a wider range of situations compared to static input sequences. We demonstrate the proposed method on two robust motion-planning case studies for autonomous driving

Certification of Bounds of Non-linear Functions: the Templates Method

The aim of this work is to certify lower bounds for real-valued multivariate functions, defined by semialgebraic or transcendental expressions. The certificate must be, eventually, formally provable in a proof system such as Coq. The application range for such a tool is widespread; for instance Hales' proof of Kepler's conjecture yields thousands of inequalities. We introduce an approximation algorithm, which combines ideas of the max-plus basis method (in optimal control) and of the linear templates method developed by Manna et al. (in static analysis). This algorithm consists in bounding some of the constituents of the function by suprema of quadratic forms with a well chosen curvature. This leads to semialgebraic optimization problems, solved by sum-of-squares relaxations. Templates limit the blow up of these relaxations at the price of coarsening the approximation. We illustrate the efficiency of our framework with various examples from the literature and discuss the interfacing with Coq.Comment: 16 pages, 3 figures, 2 table

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Intermittent antegrade warm cardioplegia reduces oxidative stress and improves metabolism of the ischemic-reperfused human myocardium

AbstractThe aim of this study was to compare the effect of intermittent antegrade warm blood cardioplegia and intermittent antegrade cold blood cardioplegia on myocardial metabolism and free radical generation of the ischemic-reperfused human myocardium. Thirty patients undergoing mitral valve procedures were randomly allocated to two groups: group 1 (15 patients) received warm blood cardioplegia and group 2 (15 patients), cold blood cardioplegia. Myocardial metabolism was assessed before aortic clamping, 1 minute after crossclamp removal, and after 20 minutes of reperfusion, by collecting blood simultaneously from the radial artery and coronary sinus. All samples were analyzed for lactate, creatine kinase, reduced and oxidized glutathione, ascorbic acid, fluorescent products of lipid peroxidation, and leukocyte activation (elastase). In all patients, early reperfusion was associated with significant coronary sinus lactate release. In group 2, but not in group 1, significant coronary sinus release of reduced and oxidized glutathione, fluorescent products of lipid peroxidation, and creatine kinase was also found; moreover, arterial-coronary sinus difference of ascorbic acid content was increased only in group 2, suggesting a transmyocardial consumption of this antioxidant vitamin. After 20 minutes of reperfusion, coronary sinus lactate release was no longer present in group 1, whereas significant production was still evident in group 2. In this group, significant coronary sinus release of fluorescent products of lipoperoxidation and reduced and oxidized glutathione was also observed at this time. No significant release of elastase from the coronary sinus was noted in the two groups throughout the study. The left ventricular stroke work index measured at the end of the study indicated a better functional recovery in group 1 than in group 2. In conclusion, intermittent antegrade warm blood cardioplegia protects the myocardium from ischemia-reperfusion injury better than intermittent antegrade cold blood cardioplegia; this phenomenon may be partly due to the decreased tissue oxidant burden mediated by intermittent warm blood cardioplegia. (J THORAC CARDIOVASC SURG 1995;109:787-95

Acceleration of Functional Maturation and Differentiation of Neonatal Porcine Islet Cell Monolayers Shortly In Vitro Cocultured with Microencapsulated Sertoli Cells

The limited availability of cadaveric human donor pancreata as well as the incomplete success of the Edmonton protocol for human islet allografts fasten search for new sources of insulin the producing cells for substitution cell therapy of insulin-dependent diabetes mellitus (T1DM). Starting from isolated neonatal porcine pancreatic islets (NPIs), we have obtained cell monolayers that were exposed to microencapsulated monolayered Sertoli cells (ESCs) for different time periods (7, 14, 21 days). To assess the development of the cocultured cell monolayers, we have studied either endocrine cell phenotype differentiation markers or c-kit, a hematopoietic stem cell marker, has recently been involved with growth and differentiation of β-cell subpopulations in human as well as rodent animal models. ESC which were found to either accelerate maturation and differentiation of the NPIs β-cell phenotype or identify an islet cell subpopulation that was marked positively for c-kit. The insulin/c-kit positive cells might represent a new, still unknown functionally immature β-cell like element in the porcine pancreas. Acceleration of maturation and differentiation of our NPI cell monolayers might generate a potential new opportunity to develop insulin-producing cells that may suite experimental trials for cell therapy of T1DM

Impact of rehabilitation on fatigue in post-COVID-19 patients: a systematic review and meta-analysis

The post-COVID-19 syndrome may affect patients after the COVID-19 post-acute phase. In particular, the 69% of patients reported persistent fatigue at the discharge. To date, no clear data are available regarding the most effective rehabilitative approaches for the treatment of this condition. Thus, this systematic review aimed to evaluate the rehabilitation treatment’s efficacy on fatigue in post-COVID-19 patients. We systematically searched PubMed, Scopus, and Web of Science databases to find longitudinal study designs presenting: post-COVID-19 patients as participants; a rehabilitative approach aimed to reduce post-COVID-19 syndrome as intervention; and fatigue intensity assessed through an evaluation tool that quantified the perceived exertion (i.e., fatigue severity scale, FSS; Borg Scale (BS); Borg Category Ratio 10, CR10; Checklist Individual Strength (CIS) fatigue scale; FACIT (Functional Assessment of Chronic Illness Therapy) fatigue scale). The present systematic review protocol was registered on PROSPERO (registration number CRD42021284058). Out of 704 articles, 6 studies were included. Nearly all patients showed COVID-19-related fatigue, and after the rehabilitation treatment, only 17% of subjects reported the persistency of symptoms. The overall effect size reported a −1.40 decrease in Borg Category Ratio 10 with a SE of 0.05 and a 95% CI between −1.50 and −1.30 (p < 0.001). The present systematic review and meta-analysis underlines the rehabilitation role in the fatigue reduction in patients affected by post-COVID-19 syndrome

Short term outcomes of total arterial coronary revascularization in patients above 65 years: a propensity score analysis

<p>Abstract</p> <p>Background</p> <p>Despite the advantages of bilateral mammary coronary revascularization, many surgeons are still restricting this technique to the young patients. The objective of this study is to demonstrate the safety and potential advantages of bilateral mammary coronary revascularization in patients older than 65 years.</p> <p>Methods</p> <p>Group I included 415 patients older than 65 years with exclusively bilateral mammary revascularization. Using a propensity score we selected 389 patients (group II) in whom coronary bypass operations were performed using the left internal mammary artery and the great saphenous vein.</p> <p>Results</p> <p>The incidence of postoperative stroke was higher in group II (1.5% vs. 0%, P = 0.0111). The amount of postoperative blood loss was higher in group I (908 ± 757 ml vs. 800 ± 713 ml, P = 0.0405). There were no other postoperative differences between both groups.</p> <p>Conclusion</p> <p>Bilateral internal mammary artery revascularization can be safely performed in patients older than 65 years. T-graft configuration without aortic anastomosis is particularly beneficial in this age group since it avoids aortic manipulation, which is an important risk factor for postoperative stroke.</p

Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction

Current bundle adjustment solvers such as the Levenberg-Marquardt (LM) algorithm are limited by the bottleneck in solving the Reduced Camera System (RCS) whose dimension is proportional to the camera number. When the problem is scaled up, this step is neither efficient in computation nor manageable for a single compute node. In this work, we propose a stochastic bundle adjustment algorithm which seeks to decompose the RCS approximately inside the LM iterations to improve the efficiency and scalability. It first reformulates the quadratic programming problem of an LM iteration based on the clustering of the visibility graph by introducing the equality constraints across clusters. Then, we propose to relax it into a chance constrained problem and solve it through sampled convex program. The relaxation is intended to eliminate the interdependence between clusters embodied by the constraints, so that a large RCS can be decomposed into independent linear sub-problems. Numerical experiments on unordered Internet image sets and sequential SLAM image sets, as well as distributed experiments on large-scale datasets, have demonstrated the high efficiency and scalability of the proposed approach. Codes are released at https://github.com/zlthinker/STBA.Comment: Accepted by ECCV 202