Margarida Carvalho - Academia.edu (original) (raw)

Papers by Margarida Carvalho

arXiv (Cornell University), Mar 17, 2020

Physiological signals, such as the electrocardiogram and the phonocardiogram are very often corru... more Physiological signals, such as the electrocardiogram and the phonocardiogram are very often corrupted by noisy sources. Usually, artificial intelligent algorithms analyze the signal regardless of its quality. On the other hand, physicians use a completely orthogonal strategy. They do not assess the entire recording, instead they search for a segment where the fundamental and abnormal waves are easily detected, and only then a prognostic is attempted. Inspired by this fact, a new algorithm that automatically selects an optimal segment for a post-processing stage, according to a criteria defined by the user is proposed. In the process, a Neural Network is used to compute the output state probability distribution for each sample. Using the aforementioned quantities, a graph is designed, whereas state transition constraints are physically imposed into the graph and a set of constraints are used to retrieve a subset of the recording that maximizes the likelihood function, proposed by the user. The developed framework is tested and validated in two applications. In both cases, the system performance is boosted significantly, e.g in heart sound segmentation, sensitivity increases 2.4% when compared to the standard approaches in the literature.

arXiv (Cornell University), Jul 6, 2020

Learning heuristics for combinatorial optimization problems through graph neural networks have re... more Learning heuristics for combinatorial optimization problems through graph neural networks have recently shown promising results on some classic NP-hard problems. These are single-level optimization problems with only one player. Multilevel combinatorial optimization problems are their generalization, encompassing situations with multiple players taking decisions sequentially. By framing them in a multi-agent reinforcement learning setting, we devise a value-based method to learn to solve multilevel budgeted combinatorial problems involving two players in a zero-sum game over a graph. Our framework is based on a simple curriculum: if an agent knows how to estimate the value of instances with budgets up to B, then solving instances with budget B + 1 can be done in polynomial time regardless of the direction of the optimization by checking the value of every possible afterstate. Thus, in a bottom-up approach, we generate datasets of heuristically solved instances with increasingly larger budgets to train our agent. We report results close to optimality on graphs up to 100 nodes and a 185× speedup on average compared to the quickest exact solver known for the Multilevel Critical Node problem, a max-min-max trilevel problem that has been shown to be at least Σ p 2-hard.

arXiv (Cornell University), Nov 20, 2019

The goal of a kidney exchange program (KEP) is to maximize number of transplants within a pool of... more The goal of a kidney exchange program (KEP) is to maximize number of transplants within a pool of incompatible patient-donor pairs by exchanging donors. A KEP can be modelled as a maximum matching problem in a graph. A KEP between incompatible patient-donor from pools of several hospitals, regions or countries has the potential to increase the number of transplants. These entities aim is to maximize the transplant benefit for their patients, which can lead to strategic behaviours. Recently, this was formulated as a non-cooperative two-player game and the game solutions (equilibria) were characterized when the entities objective function is the number of their patients receiving a kidney. In this paper, we generalize these results for N-players and discuss the impact in the game solutions when transplant information quality is introduced. Furthermore, the game theory model is analyzed through computational experiments on instances generated through the Canada Kidney Paired Donation Program. These experiments highlighting the importance of using the concept of Nash equilibrium, as well as, the anticipation of the necessity to further research for supporting police makers once measures on transplant quality are available. Keywords Kidney exchange program • Non-cooperative • Nash equilibria • Social welfare • Maximum matching • Graft survival * Parts of this material are based on data and information provided by Canadian Blood Services. However, the analyses, conclusions, opinions and statements expressed herein are those of the authors and not necessarily those of Canadian Blood Services

arXiv (Cornell University), Dec 13, 2020

The recently defined class of integer programming games (IPG) models situations where multiple se... more The recently defined class of integer programming games (IPG) models situations where multiple self-interested decision makers interact, with their strategy sets represented by a finite set of linear constraints together with integer requirements. Many real-world problems can suitably be fit in this class, and hence anticipating IPG outcomes is of crucial value for policy makers and regulators. Nash equilibria have been widely accepted as the solution concept of a game. Consequently, their computation provides a reasonable prediction of games outcome. In this paper, we start by showing the computational complexity of deciding the existence of a Nash equilibrium for an IPG. Then, using sufficient conditions for their existence, we develop two general algorithmic approaches that are guaranteed to approximate an equilibrium under mild conditions. We also showcase how our methodology can be changed to determine other equilibria definitions. The performance of our methods is analysed through computational experiments in a knapsack game, a competitive lot-sizing game and a kidney exchange game. To the best of our knowledge, this is the first time that equilibria computation methods for general integer programming games have been designed and computationally tested.

arXiv (Cornell University), Jun 7, 2020

Two-sided markets have become increasingly important during the last years, mostly because of the... more Two-sided markets have become increasingly important during the last years, mostly because of their numerous applications in housing, labor and dating. Decentralized customer-supplier matching platforms face several challenges, specially due to the tradeoff between recommending suitable suppliers to customers and avoiding congestion among attractive suppliers. In this work, we address the challenge of market congestion via the multi-agent assortment optimization problem in the two-sided sequential matching model introduced by Ashlagi et al. (2022). The setting is the following: we (the platform) offer a menu of suppliers to each customer. Then, every customer selects, simultaneously and independently, to match with a supplier or to remain unmatched. Each supplier observes the subset of customers that selected them, and choose either to match a customer or to leave the system. Therefore, a match takes place if both a customer and a supplier sequentially select each other. Each agent's behavior is probabilistic and determined by a discrete choice model. Our goal is to choose an assortment family that maximizes the expected revenue of the matching. Given the hardness of the problem, we show a 1 − 1/e-approximation factor for the heterogeneous setting where customers follow general choice models and suppliers follow a general choice model whose demand function is monotone and submodular. Our approach is flexible enough to allow for different assortment constraints and for a revenue objective function. Furthermore, we design an algorithm that beats the 1 − 1/e barrier and, in fact, is asymptotically optimal when suppliers follow the classic multinomial-logit choice model and are sufficiently selective. We finally provide other results and further insights. Notably, in the unconstrained setting where customers and suppliers follow multinomial-logit models, we design a simple and efficient approximation algorithm that appropriately randomizes over a family of nested-assortments. Also, we analyze various aspects of the matching market model that lead to several operational insights, such as the fact that matching platforms can benefit from allowing the more selective agents to initiate the matchmaking process.

Proceedings of the AAAI Conference on Artificial Intelligence

Kidney transplant is the preferred method of treatment for patients suffering from kidney failure... more Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a donor which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective of maximizing the total number of transplants. Since selecting one optimal solution translates to a decision on who receives a transplant, it has a major effect on the lives of patients. The current practice in selecting an optimal solution does not necessarily ensure fairness in the selection process. In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. We propose the use of randomized policies for selecting an optimal solution in which patients' equal opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a ...

INFORMS Journal on Computing

We study the network pricing problem where the leader maximizes revenue by determining the optima... more We study the network pricing problem where the leader maximizes revenue by determining the optimal amounts of tolls to charge on a set of arcs, under the assumption that the followers will react rationally and choose the shortest paths to travel. Many distinct single-level reformulations of this bilevel optimization program have been proposed; however, their relationship has not been established. In this paper, we aim to build a connection between those reformulations and explore the combination of the path representation with various modeling options, allowing us to generate 12 different reformulations of the problem. Moreover, we propose a new path enumeration scheme, path-based preprocessing, and hybrid framework to further improve performance and robustness when solving the final model. We provide numerical results, comparing all the derived reformulations and confirming the efficiency of the novel dimensionality reduction procedures.

Journal of Computer and System Sciences

In this work, we analyze a sequential game played in a graph called the Multilevel Critical Node ... more In this work, we analyze a sequential game played in a graph called the Multilevel Critical Node problem (MCN). A defender and an attacker are the players of this game. The defender starts by preventively interdicting vertices (vaccination) from being attacked. Then, the attacker infects a subset of non-vaccinated vertices and, finally, the defender reacts with a protection strategy. We provide the first computational complexity results associated with MCN and its subgames. Moreover, by considering unitary, weighted, undirected and directed graphs, we clarify how the theoretical tractability or intractability of those problems vary. Our findings contribute with new NP-complete, Σ p 2-complete and Σ p 3-complete problems.

Elementos de Zootecnia - Volume 1, 2020

Modo de acesso: World Wide Web Inclui bibliografia 1. Zootecnia 2. Bovinos.3. Suínos I. Título CD... more Modo de acesso: World Wide Web Inclui bibliografia 1. Zootecnia 2. Bovinos.3. Suínos I. Título CDD-636 O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos seus respectivos autores.

ArXiv, 2021

The college admission problem plays a fundamental role in several real-world allocation mechanism... more The college admission problem plays a fundamental role in several real-world allocation mechanisms such as school choice and supply chain stability. The classical framework assumes that the capacity of each college is known and fixed in advance. However, increasing the quota of even a single college would improve the overall cost of the students. In this work, we study the problem of finding the college capacity expansion that achieves the best cost of the students, subject to a cardinality constraint. First, we show that this problem is NP-hard to solve, even under complete and strict preference lists. We provide an integer quadratically constrained programming formulation and study its linear reformulation. We also propose two natural heuristics: A greedy algorithm and an LP-based method. We empirically evaluate the performance of our approaches in a detailed computational study. We observe the practical superiority of the linearized model in comparison with its quadratic counterp...

We aim to investigate a new class of games, where each player’s set of strategies is a union of p... more We aim to investigate a new class of games, where each player’s set of strategies is a union of polyhedra. These are called integer programming games. To motivate our work, we describe some practical examples suitable to be modeled under this paradigm. We analyze the problem of determining whether or not a Nash equilibria exists for an integer programming game, and demonstrate that it is complete for the second level of the polynomial hierarchy.

Not all patients who need kidney transplant can find a donor with compatible characteristics. Kid... more Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the objective of maximizing the total number of transplants. We propose a randomized policy for selecting an optimal solution in which patients’ equity of opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. We empirically demonstrate the advantages of our proposed method over the common practice of using the first optimal solution obtained by a solver.

In this paper, we develop algorithmic approaches for a recently defined class of games, the integ... more In this paper, we develop algorithmic approaches for a recently defined class of games, the integer programming games. Two general methods to approximate an equilibrium are presented and enhanced in order to improve their practical efficiency. Their performance is analysed through computational experiments in a knapsack game and a competitive lot-sizing game. To the best of our knowledge, this is the first time that equilibria computation methods for general integer programming games are build and computationally tested.

Operations Research, 2021

Multilevel programming can provide the right mathematical formulations for modeling sequential de... more Multilevel programming can provide the right mathematical formulations for modeling sequential decision-making problems. In such cases, it is implicit that each level anticipates the optimal reaction of the subsequent ones. Defender–attacker–defender trilevel programs are a particular case of interest that encompasses a fortification strategy, followed by an attack, and a consequent recovery defensive strategy. In “Multilevel Approaches for the Critical Node Problem,” Baggio, Carvalho, Lodi, and Tramontani study a combinatorial sequential game between a defender and an attacker that takes place in a network. The authors propose an exact algorithmic framework. This work highlights the significant improvements that the defender can achieve by taking the three-stage game into account instead of considering fortification and recovery as isolated. Simultaneously, the paper contributes to advancing the methodologies for solving trilevel programs.

International Journal of Production Economics, 2018

A game merging the lot-sizing problem with a Cournot competition model is for the first time theo... more A game merging the lot-sizing problem with a Cournot competition model is for the first time theoretically studied. Each player is a producer with her own production facility, modeled as an uncapacitated lot-sizing problem (i.e., production incurs setup and variable costs and inventories are allowed). A Cournot competition is played in each time period (market) with each player deciding the quantity of product to place on it. The market price of that product in each time period depends on the total quantity placed in the market. We show that this game is potential with possibly multiple pure Nash equilibria. If the game has a single period, we prove that an equilibrium can be found in polynomial time, but it is weakly NP-hard to find an optimal pure Nash equilibrium (with respect to a given equilibrium refinement). If the game has no constant production and no inventory costs, we prove that a pure Nash equilibrium can be computed in polynomial time.

Revista de Enfermagem Referência, 2017

Enquadramento: A recolha de informação através de entrevistas é uma estratégia de investigação co... more Enquadramento: A recolha de informação através de entrevistas é uma estratégia de investigação comum, existindo vasta literatura sobre a realização e a análise das mesmas. Contrariamente, a transcrição tem sido um tema secundarizado, embora não irrelevante. Transcrever consiste na transformação de um discurso oral num texto escrito com significado, que possa ser analisado e que contenha as informações relevantes da entrevista. Objetivos: Abordam-se e discutem-se as questões conceptuais, pragmáticas e desafios inerentes à transcrição de entrevistas, numa perspetiva integradora. Principais tópicos em análise: O que se entende por transcrever? Que tipos de transcrições existem? O que é que se transcreve e como é que transcreve uma entrevista? Que cuidados devem ser salvaguardados quando transcrevermos? Quais são as principais dificuldades de se transcrever? Conclusão: Reconhecendo a inexistência de protocolos ou regras universais para transcrever, importa que os investigadores explicitem as suas práticas e decisões, as quais poderão influenciar a análise de dados.

Marine Biology Research, 2017

The shanny Lipophrys pholis is an intertidal fish commonly found in Portuguese coastal waters. Sp... more The shanny Lipophrys pholis is an intertidal fish commonly found in Portuguese coastal waters. Spawning takes place from early autumn to mid spring, after which demersal eggs hatch and larvae disperse along the coast. Two to three months later, young juveniles return to the tide pools to settle. However, information on fish movement, habitat connectivity and population structure is scarce for this species. One hundred and twenty early juveniles (16-35 mm) were collected in April 2014 from six rocky beaches along the western and south Portuguese coasts (Agudela, Cabo do Mundo, Boa Nova, Peniche, Sines and Olhos de Água). δ 18 O and δ 13 C were determined by isotope-ratio mass spectrometry. Data were analysed to determine whether isotopic signatures could be used to assess the degree of separation between individuals collected from different locations. Mean δ 13 C and δ 18 O values ranged from −0.02‰ to 1.14‰ and −7.77‰ to −6.62‰, respectively. Both seawater temperature and salinity caused differences in otolith δ 18 O among the four main sampling areas. The variation among areas in δ 13 C was most likely related to slight differences in the diet, growth and metabolism of fish. The distinct isotopic signatures, at least for the northern and central areas, suggested low levels of connectivity across large spatial scales during the juvenile stage. Furthermore, similar isotopic signatures within the same area indicated some degree of larval oceanic retention at short spatial scales. This study suggests that stable isotope records in otoliths could provide information about the home residency, movements and habitat connectivity of intertidal fishes.

INFORMS Journal on Computing, 2016

We consider a bilevel integer programming model that extends the classic 0–1 knapsack problem in ... more We consider a bilevel integer programming model that extends the classic 0–1 knapsack problem in a very natural way. The model describes a Stackelberg game where the leader’s decision interdicts a subset of the knapsack items for the follower. As this interdiction of items substantially increases the difficulty of the problem, it prevents the application of the classical methods for bilevel programming and of the specialized approaches that are tailored to other bilevel knapsack variants. Motivated by the simple description of the model, by its complexity, by its economic applications, and by the lack of algorithms to solve it, we design a novel viable way for computing optimal solutions. Finally, we present extensive computational results that show the effectiveness of the new algorithm on instances from the literature and on randomly generated instances.

Applied Categorical Structures, 2015