Randomized Solutions to Convex Programs with Multiple Chance Constraints

The scenario-based optimization approach (`scenario approach') provides an intuitive way of approximating the solution to chance-constrained optimization programs, based on finding the optimal solution under a finite number of sampled outcomes of the uncertainty (`scenarios'). A key merit of this approach is that it neither assumes knowledge of the uncertainty set, as it is common in robust optimization, nor of its probability distribution, as it is usually required in stochastic optimization. Moreover, the scenario approach is computationally efficient as its solution is based on a deterministic optimization program that is canonically convex, even when the original chance-constrained problem is not. Recently, researchers have obtained theoretical foundations for the scenario approach, providing a direct link between the number of scenarios and bounds on the constraint violation probability. These bounds are tight in the general case of an uncertain optimization problem with a single chance constraint. However, this paper shows that these bounds can be improved in situations where the constraints have a limited `support rank', a new concept that is introduced for the first time. This property is typically found in a large number of practical applications---most importantly, if the problem originally contains multiple chance constraints (e.g. multi-stage uncertain decision problems), or if a chance constraint belongs to a special class of constraints (e.g. linear or quadratic constraints). In these cases the quality of the scenario solution is improved while the same bound on the constraint violation probability is maintained, and also the computational complexity is reduced.Comment: This manuscript is the preprint of a paper submitted to the SIAM Journal on Optimization and it is subject to SIAM copyright. SIAM maintains the sole rights of distribution or publication of the work in all forms and media. If accepted, the copy of record will be available at http://www.siam.or

Computationally efficient stochastic MPC: A probabilistic scaling approach

In recent years, the increasing interest in stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, which has restricted their applicability to slow-dynamics and high-performing systems. To reduce the computational burden, in this paper we extend the probabilistic scaling approach to obtain a low-complexity inner approximation of chance-constrained sets. This approach provides probabilistic guarantees at a lower computational cost than other schemes for which the sample complexity depends on the design space dimension. To design candidate simple approximating sets, which approximate the shape of the probabilistic set, we introduce two possibilities: i) fixed-complexity polytopes, and ii) ell_{p-norm based sets. Once the candidate approximating set is obtained, it is scaled around its center so to enforce the expected probabilistic guarantees. The resulting scaled set is then exploited to enforce constraints in the classical SMPC framework. The computational gain obtained with respect to the scenario approach is demonstrated via simulations, where the objective is the control of a fixed-wing UAV performing a crop-monitoring mission over a sloped vineyard

COVID-19: Open-data resources for monitoring, modeling, and forecasting the epidemic

We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources

Effects of intrauterine devices on proteins in the uterine lavage fluid of mares

Intrauterine devices block luteolysis in cyclic mares, but the underlying mechanism is unknown. To clarify the mechanisms, the protein profile of the endometrial secretome was analyzed using two-dimensional difference gel electrophoresis (2D-DIGE). Twenty-seven mares were classified according to whether they were inseminated (AI) or had an intrauterine device (IUD), a water-filled plastic sphere, inserted into the uterus on Day 3 after ovulation. Uterine lavage fluids were collected on Day 15 from pregnant inseminated mares (AI-P; n = 8), non-pregnant inseminated mares (AI-N; n = 4), and mares with IUD (n = 15). The IUD group was further divided into prolonged (IUD-P; n = 7) and normal luteal phase (IUD-N; n = 8) groups on the basis of ultrasound examinations, serum levels of progesterone and PGFM on Days 14 and 15, and COX-2 results on Day 15. Four mares from each group were selected for the 2D-DIGE analyses. Ten proteins had significantly different abundance among the groups, nine of the proteins were identified. Malate dehydrogenase 1, increased sodium tolerance 1, aldehyde dehydrogenase 1A1, prostaglandin reductase 1, albumin and hemoglobin were highest in pregnant mares; T-complex protein 1 was highest in non-pregnant mares; and annexin A1 and 6-phosphogluconolactonase were highest in IUD mares. The results suggest that the mechanism behind the intrauterine devices is likely related to inflammation.Peer reviewe

Discrimination of aging wines with alternative oak products and micro-oxygenation by FTIR-ATR

The use of alternative oak wood products (AOP), such as chips, cubes and staves, among other, from different geographical origins is a common practice for wine aging, where the micro-oxygenation (MOX, adding small doses of oxygen constantly over time) is essential to obtain a final wine more stable in time and with similar characteristics of barrel-aged wine. The aim of this work was to identify if spectroscopic techniques allow to discriminate wines aged with alternative oak products (chips and staves) from different oak woods (American, French and Spanish) and a floating micro-oxygenation (20 µg·L−1) after 10 years of bottling and compared to those aged in barrels. The spectral information and analysis were performed in a FTIR-ATR, with 128 scans per spectrum at a spectral resolution of 8 cm-1 in the wavenumber range from 4,000 to 450 cm-1. Principal component analyses of spectral information were performed using the Unscrambler® X. The results indicate that with this technique it is possible to clearly separate the wines aged by the three systems (chips, staves and barrels) in the case of American oak. In the case of French oak, wines aged in chips were clearly differentiated from wines aged in staves with those aged in barrels between the two. It is also possible to clearly separate aged wines with different Spanish oak systems. The application of FTIR-ATR appears to be a powerful technique for discriminating the quality of wines aged by different AOPs and wood barrels from different geographical origins

Nest predation research:Recent findings and future perspectives

Nest predation is a key source of selection for birds that has attracted increasing attention from ornithologists. The inclusion of new concepts applicable to nest predation that stem from social information, eavesdropping or physiology has expanded our knowledge considerably. Recent methodological advancements now allow focus on all three players within nest predation interactions: adults, offspring and predators. Indeed, the study of nest predation now forms a vital part of avian research in several fields, including animal behaviour, population ecology, evolution and conservation biology. However, within nest predation research there are important aspects that require further development, such as the comparison between ecological and evolutionary antipredator responses, and the role of anthropogenic change. We hope this review of recent findings and the presentation of new research avenues will encourage researchers to study this important and interesting selective pressure, and ultimately will help us to better understand the biology of birds

MPC for tracking with optimal closed-loop performance

Abstract-This paper deals with the tracking problem for constrained linear systems using a model predictive control (MPC) law. As it is well known, MPC provides a control law suitable for regulating a constrained linear system to a given target steady state. Asymptotic stability and constraint fulfilment for any finite prediction horizon is typically ensured by means of a suitable choice of the terminal cost and constraint. However, when the target operating point changes, the feasibility of the controller may be lost and the controller fails to track the reference. Recently, a novel MPC formulation has been proposed to solve this problem, ensuring feasibility and asymptotic convergence to any admissible steady state. On the other hand, this control law can not ensure the local optimality of the proposed controller, which is a desirable property of predictive controllers. In this paper, this controller is extended considering a generalized offset cost function. Sufficient conditions on this function are given to ensure the local optimality property. Besides, this novel formulation allows to consider as target operation points, states which may be not equilibrium points of the linear systems. In this case, it is proved in this paper that the proposed control law steers the system to an admissible steady state (different to the target) which is optimal with relation to the offset cost function. Thanks to the proposed generalization, the offset cost function could be chosen according to some steady performance criterium. Therefore, the proposed controller for tracking achieves an optimal closed-loop performance during the transient as well as an optimal steady state in case of not admissible target. These properties are illustrated in an example

Method for performing cerebral perfusion-weighted MRI in neonates

Cerebral perfusion-weighted imaging (PWI) in neonates is known to be technically difficult and there are very few published studies on its use in preterm infants. In this paper, we describe one convenient method to perform PWI in neonates, a method only recently used in newborns. A device was used to manually inject gadolinium contrast material intravenously in an easy, quick and reproducible way. We studied 28 newborn infants, with various gestational ages and weights, including both normal infants and those suffering from different brain pathologies. A signal intensity-time curve was obtained for each infant, allowing us to build perfusion maps. This technique offered a fast and easy method to manually inject a bolus gadolinium contrast material, which is essential in performing PWI in neonates. Cerebral PWI is technically feasible and reproducible in neonates of various gestational age and with various pathologies

Effects of climate variation on bird escape distances modulate community responses to global change

Warm thanks to Jacqui Sykoff for substantially improving the readability of former versions of the manuscript. GM was supported by the Hungarian Ministry for Innovation and Technology within the framework of the Thematic Excellence Programme 2020 (TKP2020-IKA-12, TKP2020-NKA-16).. KT was supported by institutional research funding IUT (34-8) of the Estonian Ministry of Education and Research. This paper is a contribution to the project URBILAND (PID2019-107423GA-I00/SRA 1013039/501100011033), funded by the Spanish Research Agency.Climate and land use are rapidly changing environmental conditions. Behavioral responses to such global perturbations can be used to incorporate interspecific interactions into predictive models of population responses to global change. Flight initiation distance (FID) reflects antipredator behaviour defined as the distance at which an individual takes flight when approached by a human, under standardized conditions. This behavioural trait results from a balance between disturbance, predation risk, food availability and physiological needs, and it is related to geographical range and population trends in European birds. Using 32,145 records of flight initiation distances for 229 bird species during 2006-2019 in 24 European localities, we show that FIDs decreased with increasing temperature and precipitation, as expected if foraging success decreased under warm and humid conditions. Trends were further altered by latitude, urbanisation and body mass, as expected if climate effects on FIDs were mediated by food abundance and need, differing according to position in food webs, supporting foraging models. This provides evidence for a role of behavioural responses within food webs on how bird populations and communities are affected by global change.Hungarian Ministry for Innovation and Technology TKP2020-IKA-12 TKP2020-NKA-16Ministry of Education and Research, Estonia 34-8Spanish Research Agency PID2019-107423GA-I00/SRA 1013039/50110001103

A decomposition algorithm for feedback min-max model predictive control

Abstract-An algorithm for solving feedback min-max model predictive control for discrete time uncertain linear systems with constraints is presented in the paper. The algorithm solves the corresponding multi-stage min-max linear optimization problem. It is based on applying recursively a decomposition technique to solve the min-max problem via a sequence of low complexity linear programs. It is proved that the algorithm converges to the optimal solution in finite time. Simulation results are provided to compare the proposed algorithm with other approaches