arXiv subjects

Guilherme Ramos

Publications and source records attributed to Guilherme Ramos.

18 recordsLinked to original sources

arXiv2025

Stabilizing Fractional Dynamical Networks Suppresses Epileptic Seizures

Medically uncontrolled epileptic seizures affect nearly 15 million people worldwide, resulting in enormous economic and psychological burdens. Treatment of medically refractory epilepsy is essential for patients to achieve remission, improve psychological functioning, and enhance social and vocational outcomes. Here, we show a state-of-the-art method that stabilizes fractional dynamical networks modeled from intracranial EEG data, effectively suppressing seizure activity in 34 out of 35 total spontaneous episodes from patients at the University of Pennsylvania and the Mayo Clinic. We perform a multi-scale analysis and show that the fractal behavior and stability properties of these data distinguish between four epileptic states: interictal, pre-ictal, ictal, and post-ictal. Furthermore, the simulated controlled signals exhibit substantial amplitude reduction ($49\%$ average). These findings highlight the potential of fractional dynamics to characterize seizure-related brain states and demonstrate its capability to suppress epileptic activity.

q-bio.QM↗

arXiv2025

Average consensus with resilience and privacy guarantees without losing accuracy

This paper addresses the challenge of achieving private and resilient average consensus among a group of discrete-time networked agents without compromising accuracy. State-of-the-art solutions to attain privacy and resilient consensus entail an explicit trade-off between the two with an implicit compromise on accuracy. In contrast, in the present work, we propose a methodology that avoids trade-offs between privacy, resilience, and accuracy. We design a methodology that, under certain conditions, enables non-faulty agents, i.e., agents complying with the established protocol, to reach average consensus in the presence of faulty agents, while keeping the non-faulty agents' initial states private. For privacy, agents strategically add noise to obscure their original state, while later withdrawing a function of it to ensure accuracy. Besides, and unlikely many consensus methods, our approach does not require each agent to compute the left-eigenvector of the dynamics matrix associated with the eigenvalue one. Moreover, the proposed framework has a polynomial time complexity relative to the number of agents and the maximum quantity of faulty agents. Finally, we illustrate our method with examples covering diverse faulty agents scenarios.

math.OC↗

arXiv2024

Distributed Graph Augmentation Protocols to Achieve Strong Connectivity in Multi-Agent Networks

In multi-agent systems, strong connectivity of the communication network is often crucial for establishing consensus protocols, which underpin numerous applications in decision-making and distributed optimization. However, this connectivity requirement may not be inherently satisfied in geographically distributed settings. Consequently, we need to find the minimum number of communication links to add to make the communication network strongly connected. To date, such problems have been solvable only through centralized methods. This paper introduces a fully distributed algorithm that efficiently identifies an optimal set of edge additions to achieve strong connectivity, using only local information. The majority of the communication between agents is local (according to the digraph structure), with only a few steps requiring communication among non-neighboring agents to establish the necessary additional communication links. A comprehensive empirical analysis of the algorithm's performance on various random communication networks reveals its efficiency and scalability.

math.OC↗

arXiv2022

Regulating Group Exposure for Item Providers in Recommendation

Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. Then, we envision a scenario wherein platform owners seek to control the degree of exposure to such groups in the recommendation process. To support this scenario, we rely on disparate exposure measures that characterize the gap between the share of recommendations given to groups and the target level of exposure pursued by the platform owners. We then propose a re-ranking procedure that ensures desired levels of exposure are met. Experiments show that, while supporting certain groups of providers by rendering them with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility.

cs.IR↗

arXiv2022

Robust Reputation Independence in Ranking Systems for Multiple Sensitive Attributes

Ranking systems have an unprecedented influence on how and what information people access, and their impact on our society is being analyzed from different perspectives, such as users' discrimination. A notable example is represented by reputation-based ranking systems, a class of systems that rely on users' reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes. To safeguard that a given sensitive user's attribute does not systematically affect the reputation of that user, prior work has operationalized a reputation independence constraint on this class of systems. In this paper, we uncover that guaranteeing reputation independence for a single sensitive attribute is not enough. When mitigating biases based on one sensitive attribute (e.g., gender), the final ranking might still be biased against certain demographic groups formed based on another attribute (e.g., age). Hence, we propose a novel approach to introduce reputation independence for multiple sensitive attributes simultaneously. We then analyze the extent to which our approach impacts on discrimination and other important properties of the ranking system, such as its quality and robustness against attacks. Experiments on two real-world datasets show that our approach leads to less biased rankings with respect to multiple users' sensitive attributes, without affecting the system's quality and robustness.

cs.IR↗

arXiv2021

Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed estimation, and control. A key advantage of these protocols is that agents exchange and reveal their state information only to their neighbors. Yet, it can raise privacy concerns in situations where the agents' states contain sensitive information. In this paper, we propose a novel (noiseless) privacy preserving distributed algorithms for multi-agent systems to reach an average consensus. The main idea of the algorithms is that each agent runs a (small) network with a crafted structure and dynamics to form a network of networks (i.e., the connection between the newly created networks and their interconnections respecting the initial network connections). Together with a re-weighting of the dynamic parameters dictating the inter-agent dynamics and the initial states, we show that it is possible to ensure that the value of each node converges to the consensus value of the original network. Furthermore, we show that, under mild assumptions, it is possible to craft the dynamics such that the design can be achieved in a distributed fashion. Finally, we illustrate the proposed algorithm with examples.

math.OC↗

arXiv2021

Fractional cyber-neural systems -- a brief survey

Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Ultimately, advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people's quality of life.

math.OC↗

arXiv2021

Minimum jointly structural input and output selection for strongly connected networks

In this paper, given a linear time-invariant strongly connected network, we study the problem of determining the minimum number of state variables that need to be simultaneously actuated and measured to ensure structural controllability and observability, respectively. This problem is fundamental in the design of multi-agent systems, where there are economic constraints in the decision of which agents to equip with a more costly on-board system that will allow the agent to have both actuation and sensing capabilities. Despite the combinatorial nature of this problem, we present a solution that couples the design of both structural controllability and structural observability counterparts to address it with polynomial-time complexity.

math.OC↗

arXiv2021

Minimum Structural Sensor Placement for Switched Linear Time-Invariant Systems and Unknown Inputs

In this paper, we study the structural state and input observability of continuous-time switched linear time-invariant systems and unknown inputs. First, we provide necessary and sufficient conditions for their structural state and input observability that can be efficiently verified in $O((m(n+p))^2)$, where $n$ is the number of state variables, $p$ is the number of unknown inputs, and $m$ is the number of modes. Moreover, we address the minimum sensor placement problem for these systems by adopting a feed-forward analysis and by providing an algorithm with a computational complexity of $ O((m(n+p)+α)^{2.373})$, where $α$ is the number of target strongly connected components of the system's digraph representation. Lastly, we explore different assumptions on both the system and unknown inputs (latent space) dynamics that add more structure to the problem, and thereby, enable us to render algorithms with lower computational complexity, which are suitable for implementation in large-scale systems.

eess.SY↗

arXiv2021

A Discrete-time Reputation-based Resilient Consensus Algorithm for Synchronous or Asynchronous Communications

We tackle the problem of a set of agents achieving resilient consensus in the presence of attacked agents. We present a discrete-time reputation-based consensus algorithm for synchronous and asynchronous networks by developing a local strategy where, at each time, each agent assigns a reputation (between zero and one) to each neighbor. The reputation is then used to weigh the neighbors' values in the update of its state. Under mild assumptions, we show that: (i) the proposed method converges exponentially to the consensus of the regular agents; (ii) if a regular agent identifies a neighbor as an attacked node, then it is indeed an attacked node; (iii) if the consensus value of the normal nodes differs from that of any of the attacked nodes' values, then the reputation that a regular agent assigns to the attacked neighbors goes to zero. Further, we extend our method to achieve resilience in the scenarios where there are noisy nodes, dynamic networks and stochastic node selection. Finally, we illustrate our algorithm with several examples, and we delineate some attacking scenarios that can be dealt by the current proposal but not by the state-of-the-art approaches.

eess.SY↗

arXiv2021

A scalable distributed dynamical systems approach to compute the strongly connected components and diameter of networks

Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learning and control theory problems. On the one hand, SCCs are used in solving the 2-satisfiability problem, which has applications in clustering, scheduling, and visualization. On the other hand, the diameter has applications in network learning and discovery problems enabling efficient internet routing and searches, as well as identifying faults in the power grid. In this paper, we leverage consensus-based principles to find the SCCs in a scalable and distributed fashion with a computational complexity of $\mathcal{O}\left(Dd_{\text{in-degree}}^{\max}\right)$, where $D$ is the (finite) diameter of the network and $d_{\text{in-degree}}^{\max}$ is the maximum in-degree of the network. Additionally, we prove that our algorithm terminates in $D+1$ iterations, which allows us to retrieve the diameter of the network. We illustrate the performance of our algorithm on several random networks, including Erdős-Rényi, Barabási-Albert, and \mbox{Watts-Strogatz} networks.

math.OC↗

arXiv2020

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Online educational platforms are playing a primary role in mediating the success of individuals' careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform values, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we formalize educational principles that model recommendations' learning properties, and a novel fairness metric that combines them in order to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a large-scale course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. Our study moves a step forward in operationalizing the ethics of human learning in recommendations, a core unit of intelligent educational systems.

cs.IR↗

arXiv2020

Structural Systems Theory: an overview of the last 15 years

In this paper, we provide an overview of the research conducted in the context of structural systems since the latest survey by Dion et al. in 2003. We systematically consider all the papers that cite this survey as well as the seminal work in this field that took place on and after the publication of the later survey, are published in peer-reviewed venues and in English. Structural systems theory deals with parametric systems where parameters might be unknown and, therefore, addresses the study of systems properties that depend only on the system's structure (or topology) described by the inter-dependencies between state variables. Remarkably, structural systems properties hold generically (i.e., almost always) under the assumption that parameters are independent. Therefore, it constitutes an approach to assess necessary conditions that systems should satisfy. In recent years, structural systems theory was applied to design systems that attain such properties, as well as to endure resilient/security and privacy properties. Furthermore, structural systems theory enables the formulation of such topics as combinatorial optimization problems, which allow us to understand their computational complexity and find algorithms that are efficiently deployed in the context of large-scale systems. In particular, we present an overview of how structural systems theory has been used in the context of linear time-invariant systems, as well as other dynamical models, for which a brief description of the different problem statements and solutions approaches are presented. Next, we describe recent variants of structural systems theory, as well as different applications of the classical and new approaches. Finally, we provide an overview of recent and future directions in this field.

math.OC↗

arXiv2020

Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities

Recent literature on ranking systems (RS) has considered users' exposure when they are the object of the ranking. Although items are the object of reputation-based RS, users have a central role also in this class of algorithms. Indeed, when ranking the items, user preferences are weighted by how relevant this user is in the platform (i.e., their reputation). In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences. We consider two demographic attributes, i.e., gender and age, and show that DR systematically occurs. Then, we propose mitigation, which ensures that reputation is independent of the users' sensitive attributes. Experiments on real-world data show that our approach can overcome DR and also improve ranking effectiveness.

cs.IR↗

arXiv2020

A Robust Reputation-based Group Ranking System and its Resistance to Bribery

The spread of online reviews and opinions and its growing influence on people's behavior and decisions, boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.

cs.IR↗

arXiv2017

Recommendation via matrix completion using Kolmogorov complexity

A usual way to model a recommendation system is as a matrix completion problem. There are several matrix completion methods, typically using optimization approaches or collaborative filtering. Most approaches assume that the matrix is either low rank, or that there are a small number of latent variables that encode the full problem. Here, we propose a novel matrix completion algorithm for recommendation systems, without any assumptions on the rank and that is model free, i.e., the entries are not assumed to be a function of some latent variables. Instead, we use a technique akin to information theory. Our method performs hybrid neighborhood-based collaborative filtering using Kolmogorov complexity. It decouples the matrix completion into a vector completion problem for each user. The recommendation for one user is thus independent of the recommendation for other users. This makes the algorithm scalable because the computations are highly parallelizable. Our results are competitive with state-of-the-art approaches on both synthetic and real-world dataset benchmarks.

cs.IR↗

arXiv2017

Robust reputation-based ranking on multipartite rating networks

The spread of online reviews, ratings and opinions and its growing influence on people's behavior and decisions boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business, governments, and others. We propose a new reputation-based ranking system utilizing multipartite rating subnetworks, that clusters users by their similarities, using Kolmogorov complexity. Our system is novel in that it reflects a diversity of opinions/preferences by assigning possibly distinct rankings, for the same item, for different groups of users. We prove the convergence and efficiency of the system and show that it copes better with spamming/spurious users, and it is more robust to attacks than state-of-the-art approaches.

cs.IR↗

arXiv2016

The Robust Minimal Controllability Problem

In this paper, we address two minimal controllability problems, where the goal is to determine a minimal subset of state variables in a linear time-invariant system to be actuated to ensure controllability under additional constraints. First, we study the problem of characterizing the sparsest input matrices that assure controllability when the autonomous dynamics' matrix is simple. Secondly, we build upon these results to describe the solutions to the robust minimal controllability problem, where the goal is to determine the sparsest input matrix ensuring controllability when specified number of inputs fail. Both problems are NP-hard, but under the assumption that the dynamics' matrix is simple, we show that it is possible to reduce these two problems to set multi-covering problems. Consequently, these problems share the same computational complexity, i.e., they are NP-complete, but polynomial algorithms to approximate the solutions of a set multi-covering problem can be leveraged to obtain close-to-optimal solutions to either of the minimal controllability problems.

math.OC↗