arXiv subjects

Antonio Fernández Anta

Publications and source records attributed to Antonio Fernández Anta.

At least 19 recordsLinked to original sources

arXiv2026

On the Decidability of Distributed Tasks with Output Sets under Asynchrony and Any Number of Crashes

This paper studies the decidability of task problems, i.e., distributed problems expressed as sets of distributed tasks. Specifically, we introduce a new class of task problems called Set of Output Sets (SOS) problems. An SOS problem $Π_O$ is defined by a set $O$ (called SOS), and requires that the set of sets of distinct output values produced across all executions corresponds exactly to $O$. We then demonstrate that this class of problems is decidable: there is a procedure determining whether any SOS problem is solvable asynchronously under $f$ crashes. The decision rule is as follows. Every SOS problem is solvable when $f=0$. For $f > 0$, an SOS problem is solvable if and only if the graph $G=(O,\subset)$ is connected. In this graph, each vertex is an output set in $O$, and two vertices are linked by an edge whenever one output set includes the other. One of the surprising implications of our results is that, replacing validity by a completeness property (which guarantees that all output sets of size at most $k$ are produced), $k$-set agreement is solvable under any number of crashes $f \geq 0$ for $k>1$, and unsolvable under $f>0$ crashes only for $k=1$ (consensus). Finally, we study a novel family of problems called $d$-disagreement, which requires the system to always produce $d$ different output values, and we show that its implementability condition is related to the harmonic series.

cs.DC↗

arXiv2026

Variational Approach for Uniform Quantum Permutation Generators

Uniform permutation generation is a fundamental task in both classical and quantum computation, with applications ranging from cryptography to quantum optimization and quantum error correction. Existing exact quantum constructions typically require all-to-all qubit connectivity and quadratic circuit depth. We develop a variational quantum circuit framework for uniform permutation generation under connectivity constraints, in which the circuit architecture is determined by the underlying interaction graph and the variational parameters are optimized to enforce the target permutation statistics. In particular, we present explicit controlled-SWAP-based unitary constructions that achieve exact uniformity with quadratic circuit size and linear depth $O(n)$ on linear nearest-neighbor topologies. Our approach, therefore, removes the need for all-to-all connectivity while improving the depth of previous exact constructions by a factor. We further prove that a quantum Beneš-like architecture is intrinsically non-uniform. Despite its logarithmic depth and ability to realize any permutation it cannot generate a uniform distribution over permutations for any choice of variational parameters. These results clarify the role of circuit topology in exact permutation generation and identify variational quantum circuits as a natural framework for hardware-constrained uniform sampling. More broadly, this work suggests that exact uniform permutation generation is a strictly stronger requirement than mere permutation realizability, and lays the groundwork for a formal complexity separation between the two.

quant-ph↗

arXiv2026

What Causes COVID-19 Fear? General Drivers of Fear During a Health Crisis

The COVID-19 pandemic triggered not only a global health crisis but also an infodemic, where exposure to heterogeneous information sources influenced public emotional responses. In this work, we investigate the determinants of self-reported fear of infection using data from the Delphi US CTIS survey. In particular, we analyze how demographic variables, epidemiological conditions, and exposure to different information sources shape fear levels. We introduce a Probabilistic Causal Model to estimate causal relationship strengths, identifying the variables that most strongly influence fear. Our results indicate that exposure to information sources accounts for a greater proportion of the variance in fear than demographic and epidemiological variables do. We further compute the Average Treatment Effect to quantify the impact of different information sources on fear. After causal adjustment, institutional and expert-driven sources are associated with increased fear levels, whereas politicians, religious leaders, and alternative information channels are associated with reduced fear. These findings highlight both the central role of the information ecosystem in shaping emotional responses during public health crises and the value of causal inference approaches for studying behavioral responses to pandemics.

cs.SI↗

arXiv2026

Exploiting Multi-Core Parallelism in Blockchain Validation and Construction

Blockchain validators can reduce block processing time by exploiting multi-core CPUs, but deterministic execution must preserve a given total order while respecting transaction conflicts and per-block runtime limits. This paper systematically examines how validators can exploit multi-core parallelism during both block construction and execution without violating blockchain semantics. We formalize two validator-side optimization problems: (i) executing an already ordered block on $p$ cores to minimize makespan while ensuring equivalence to sequential execution; and (ii) selecting and scheduling a subset of mempool transactions under a runtime limit $B$ to maximize validator reward. For both, we develop exact Mixed-Integer Linear Programming (MILP) formulations that capture conflict, order, and capacity constraints, and propose fast deterministic heuristics that scale to realistic workloads. Using Ethereum mainnet traces and including a Solana-inspired declared-access baseline (Sol) for ordered-block scheduling and a simple reward-greedy baseline (RG) for block construction, we empirically quantify the trade-offs between optimality and runtime.

cs.DC↗

arXiv2025

Error Bounds for the Network Scale-Up Method

Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. This method involves querying a subset of network nodes about the number of their neighbours belonging to the hidden sub-population. In general, NSUM assumes that the social network topology and the hidden sub-population distribution are well-behaved; hence, the NSUM estimate is close to the actual value. However, bounds on NSUM estimation errors have not been analytically proven. This paper provides analytical bounds on the error incurred by the two most popular NSUM estimators. These bounds assume that the queried nodes accurately provide their degree and the number of neighbors belonging to the hidden population. Our key findings are twofold. First, we show that when an adversary designs the network and places the hidden sub-population, then the estimate can be a factor of $Ω(\sqrt{n})$ off from the real value (in a network with $n$ nodes). Second, we also prove error bounds when the underlying network is randomly generated, showing that a small constant factor can be achieved with high probability using samples of logarithmic size $O(\log{n})$. We present improved analytical bounds for Erdos-Renyi and Scale-Free networks. Our theoretical analysis is supported by an extensive set of numerical experiments designed to determine the effect of the sample size on the accuracy of the estimates in both synthetic and real networks.

cs.DC↗

arXiv2025

Tight Conditions for Binary-Output Tasks under Crashes

This paper explores necessary and sufficient system conditions to solve distributed tasks with binary outputs (\textit{i.e.}, tasks with output values in $\{0,1\}$). We focus on the distinct output sets of values a task can produce (intentionally disregarding validity and value multiplicity), considering that some processes may output no value. In a distributed system with $n$ processes, of which up to $t \leq n$ can crash, we provide a complete characterization of the tight conditions on $n$ and $t$ under which every class of tasks with binary outputs is solvable, for both synchronous and asynchronous systems. This output-set approach yields highly general results: it unifies multiple distributed computing problems, such as binary consensus and symmetry breaking, and it produces impossibility proofs that hold for stronger task formulations, including those that consider validity, account for value multiplicity, or move beyond binary outputs.

cs.DC↗

arXiv2025

Setchain Algorithms for Blockchain Scalability

Setchain has been proposed to increase blockchain scalability by relaxing the strict total order requirement among transactions. Setchain organizes elements into a sequence of sets, referred to as epochs, so that elements within each epoch are unordered. In this paper, we propose and evaluate three distinct Setchain algorithms, that leverage an underlying block-based ledger. Vanilla is a basic implementation that serves as a reference point. Compresschain aggregates elements into batches, and compresses these batches before appending them as epochs in the ledger. Hashchain converts batches into fixed-length hashes which are appended as epochs in the ledger. This requires Hashchain to use a distributed service to obtain the batch contents from its hash. To allow light clients to safely interact with only one server, the proposed algorithms maintain, as part of the Setchain, proofs for the epochs. An epoch-proof is the hash of the epoch, cryptographically signed by a server. A client can verify the correctness of an epoch with $f+1$ epoch-proofs (where $f$ is the maximum number of Byzantine servers assumed). All three Setchain algorithms are implemented on top of the CometBFT blockchain application platform. We conducted performance evaluations across various configurations, using clusters of four, seven, and ten servers. Our results show that the Setchain algorithms reach orders of magnitude higher throughput than the underlying blockchain, and achieve finality with latency below 4 seconds.

cs.DC↗

arXiv2025

A Secure Sequencer and Data Availability Committee for Rollups (Extended Version)

Blockchains face a scalability limitation, partly due to the throughput limitations of consensus protocols, especially when aiming to obtain a high degree of decentralization. Layer 2 Rollups (L2s) are a faster alternative to conventional blockchains. L2s perform most computations offchain using minimally blockchains (L1) under-the-hood to guarantee correctness. A sequencer is a service that receives offchain L2 transaction requests, batches these transactions, and commits compressed or hashed batches to L1. Using hashing needs less L1 space, which is beneficial for gas cost, but requires a data availability committee (DAC) service to translate hashes into their corresponding batches of transaction requests. The behavior of sequencers and DACs influence the evolution of the L2 blockchain, presenting a potential security threat and delaying L2 adoption. We propose in this paper fraud-proof mechanisms, arbitrated by L1 contracts, to detect and generate evidence of dishonest behavior of the sequencer and DAC. We study how these fraud-proofs limit the power of adversaries that control different number of sequencer and DACs members, and provide incentives for their honest behavior. We designed these fraud-proof mechanisms as two player games. Unlike the generic fraud-proofs in current L2s (designed to guarantee the correct execution of transactions), our fraud-proofs are over pred-etermined algorithms that verify the properties that determine the correctness of the DAC. Arbitrating over concrete algorithms makes our fraud-proofs more efficient, easier to understand, and simpler to prove correct. We provide as an artifact a mechanization in LEAN4 of our fraud-proof games, including (1) the verified strategies that honest players should play to win all games as well as (2) mechanisms to detect dishonest claims.

cs.CR↗

arXiv2025

A mathematical model of vaccine hesitancy: Analysing the impact of political trends and the interaction across age and education groups in the USA

Vaccination against the SARS-CoV-2 disease has significantly reduced its mortality rate and spread. However, despite its availability, a considerable proportion of the public has either refused or delayed getting vaccinated. This reluctance is known as vaccine hesitancy. The aim of this paper is to present a mathematical model to investigate how social interaction can impact vaccine hesitancy. The model describes the temporal transitions between different vaccination classes of the population (those vaccinated, those who are not yet vaccinated but agree to be vaccinated, and those who refuse). We apply the model to state and national survey data from the USA to estimate model parameters that quantify the rates at which public opinion on vaccination changes. Moreover, we investigate how political trends and demographic factors, such as age and education, impact these parameters. Our results show that state-level political affiliation, age, and educational level shape opinions on vaccination and have a strong influence on the temporal dynamics of attitude changes.

q-bio.PE↗

arXiv2025

Auditable Shared Objects: From Registers to Synchronization Primitives

Auditability allows to track operations performed on a shared object, recording who accessed which information. This gives data owners more control on their data. Initially studied in the context of single-writer registers, this work extends the notion of auditability to other shared objects, and studies their properties. We start by moving from single-writer to multi-writer registers, and provide an implementation of an auditable $n$-writer $m$-reader read / write register, with $O(n+m)$ step complexity. This implementation uses $(m+n)$-sliding registers, which have consensus number $m+n$. We show that this consensus number is necessary. The implementation extends naturally to support an auditable load-linked / store-conditional (LL/SC) shared object. LL/SC is a primitive that supports efficient implementation of many shared objects. Finally, we relate auditable registers to other access control objects, by implementing an anti-flickering deny list from auditable registers.

cs.DC↗

arXiv2025

Auditing without Leaks Despite Curiosity

\textit{Auditing} data accesses helps preserve privacy and ensures accountability by allowing one to determine who accessed (potentially sensitive) information. A prior formal definition of register auditability was based on the values returned by read operations, \emph{without accounting for cases where a reader might learn a value without explicitly reading it or gain knowledge of data access without being an auditor}. This paper introduces a refined definition of auditability that focuses on when a read operation is \emph{effective}, rather than relying on its completion and return of a value. Furthermore, we formally specify the constraints that \textit{prevent readers from learning values they did not explicitly read or from auditing other readers' accesses.} Our primary algorithmic contribution is a wait-free implementation of a \emph{multi-writer, multi-reader register} that tracks effective reads while preventing unauthorized audits. The key challenge is ensuring that a read is auditable as soon as it becomes effective, which we achieve by combining value access and access logging into a single atomic operation. Another challenge is recording accesses without exposing them to readers, which we address using a simple encryption technique (one-time pad). We extend this implementation to an \emph{auditable max register} that tracks the largest value ever written. The implementation deals with the additional challenge posed by the max register semantics, which allows readers to learn prior values without reading them. The max register, in turn, serves as the foundation for implementing an \emph{auditable snapshot} object and, more generally, \emph{versioned types}. These extensions maintain the strengthened notion of auditability, appropriately adapted from multi-writer, multi-reader registers.

cs.DC↗

arXiv2025

A Decentralized Sequencer and Data Availability Committee for Rollups Using Set Consensus

Blockchains face a scalability challenge due to the intrinsic throughput limitations of consensus protocols and the limitation in block sizes due to decentralization. An alternative to improve the number of transactions per second is to use Layer 2 (L2) rollups. L2s perform most computations offchain using blockchains (L1) minimally under-the-hood to guarantee correctness. A sequencer receives offchain L2 transaction requests, batches them, and commits compressed or hashed batches to L1. Hashing offers much better compression but requires a data availability committee (DAC) to translate hashes back into their corresponding batches. Current L2s consist of a centralized sequencer which receives and serializes all transactions and an optional DAC. Centralized sequencers can undesirably influence L2s evolution. We propose in this paper a fully decentralized implementation of a service that combines (1) a sequencer that posts hashes to the L1 blockchain and (2) the data availability committee that reverses the hashes. We call the resulting service a (decentralized) arranger. Our decentralized arranger is based on Set Byzantine Consensus (SBC), a service where participants can propose sets of values and consensus is reached on a subset of the union of the values proposed. We extend SBC for our fully decentralized arranger. Our main contributions are (1) a formal definition of arrangers; (2) two implementations, one with a centralized sequencer and another with a fully decentralized algorithm, with their proof of correctness; and (3) empirical evidence that our solution scales by implementing all building blocks necessary to implement a correct server.

cs.DC↗

arXiv2024

AMECOS: A Modular Event-based Framework for Concurrent Object Specification

In this work, we introduce a modular framework for specifying distributed systems that we call AMECOS. Specifically, our framework departs from the traditional use of sequential specification, which presents limitations both on the specification expressiveness and implementation efficiency of inherently concurrent objects, as documented by Castañeda, Rajsbaum and Raynal in CACM 2023. Our framework focuses on the interactions between the various system components, specified as concurrent objects. Interactions are described with sequences of object events. This provides a modular way of specifying distributed systems and separates legality (object semantics) from other issues, such as consistency. We demonstrate the usability of our framework by (i) specifying various well-known concurrent objects, such as registers, shared memory, message-passing, reliable broadcast, and consensus, (ii) providing hierarchies of ordering semantics (namely, consistency hierarchy, memory hierarchy, and reliable broadcast hierarchy), and (iii) presenting a novel axiomatic proof of the impossibility of the well-known Consensus problem.

cs.DC↗

arXiv2024

Fast and Secure Decentralized Optimistic Rollups Using Setchain

Modern blockchains face a scalability challenge due to the intrinsic throughput limitations of consensus protocols. Layer 2 optimistic rollups (L2) are a faster alternative that offer the same interface in terms of smart contract development and user interaction. Optimistic rollups perform most computations offchain and make light use of an underlying blockchain (L1) to guarantee correct behavior, implementing a cheaper blockchain on a blockchain solution. With optimistic rollups, a sequencer calculates offchain batches of L2 transactions and commits batches (compressed or hashed) to the L1 blockchain. The use of hashes requires a data service to translate hashes into their corresponding batches. Current L2 implementations consist of a centralized sequencer (central authority) and an optional data availability committee (DAC). In this paper, we propose a decentralized L2 optimistic rollup based on Setchain, a decentralized Byzantine-tolerant implementation of sets. The main contribution is a fully decentralized "arranger" where arrangers are a formal definition combining sequencers and DACs. We prove our implementation correct and show empirical evidence that our solution scales. A final contribution is a system of incentives (payments) for servers that implement the sequencer and data availability committee protocols correctly, and a fraud-proof mechanism to detect violations of the protocol.

cs.CR↗

arXiv2023

Nowcasting Temporal Trends Using Indirect Surveys

Indirect surveys, in which respondents provide information about other people they know, have been proposed for estimating (nowcasting) the size of a \emph{hidden population} where privacy is important or the hidden population is hard to reach. Examples include estimating casualties in an earthquake, conditions among female sex workers, and the prevalence of drug use and infectious diseases. The Network Scale-up Method (NSUM) is the classical approach to developing estimates from indirect surveys, but it was designed for one-shot surveys. Further, it requires certain assumptions and asking for or estimating the number of individuals in each respondent's network. In recent years, surveys have been increasingly deployed online and can collect data continuously (e.g., COVID-19 surveys on Facebook during much of the pandemic). Conventional NSUM can be applied to these scenarios by analyzing the data independently at each point in time, but this misses the opportunity of leveraging the temporal dimension. We propose to use the responses from indirect surveys collected over time and develop analytical tools (i) to prove that indirect surveys can provide better estimates for the trends of the hidden population over time, as compared to direct surveys and (ii) to identify appropriate temporal aggregations to improve the estimates. We demonstrate through extensive simulations that our approach outperforms traditional NSUM and direct surveying methods. We also empirically demonstrate the superiority of our approach on a real indirect survey dataset of COVID-19 cases.

cs.SI↗

arXiv2023

Improving Blockchain Scalability with the Setchain Data-type

Blockchain technologies are facing a scalability challenge, which must be overcome to guarantee a wider adoption of the technology. This scalability issue is due to the use of consensus algorithms to guarantee the total order of the chain of blocks and of the transactions within each block. However, total order is often not fully necessary, since important advanced applications of smart-contracts do not require a total order among all operations. A much higher scalability can potentially be achieved if a more relaxed order can be exploited. In this paper, we propose a novel distributed concurrent data type, called Setchain, which improves scalability significantly. A Setchain implements a grow-only set whose elements are not ordered, unlike conventional blockchain operations. When convenient, the Setchain allows forcing a synchronization barrier that assigns permanently an epoch number to a subset of the latest elements added, agreed by consensus. Therefore, two operations in the same epoch are not ordered, while two operations in different epochs are ordered by their respective epoch number. We present different Byzantine-tolerant implementations of Setchain, prove their correctness and report on an empirical evaluation of a prototype implementation. Our results show that Setchain is orders of magnitude faster than consensus-based ledgers, since it implements grow-only sets with epoch synchronization instead of total order. Since Setchain barriers can be synchronized with the underlying blockchain, Setchain objects can be used as a sidechain to implement many decentralized solutions with much faster operations than direct implementations on top of blockchains. Finally, we also present an algorithm that encompasses in a single process the combined behavior of Byzantine servers, which simplifies correctness proofs by encoding the general attacker in a concrete implementation.

cs.DC↗

arXiv2022

Multivariate Functional Outlier Detection using the FastMUOD Indices

We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects outliers by computing, for each curve, an amplitude, magnitude and shape index meant to target the corresponding types of outliers. Some methods adapting FastMUOD to outlier detection in multivariate functional data are then proposed. These include applying FastMUOD on the components of the multivariate data and using random projections. Moreover, these techniques are tested on various simulated and real multivariate functional datasets. Compared with the state of the art in multivariate functional OD, the use of random projections showed the most effective results with similar, and in some cases improved, OD performance.

stat.ME↗

arXiv2022

Setchain: Improving Blockchain Scalability with Byzantine Distributed Sets and Barriers

Blockchain technologies are facing a scalability challenge, which must be overcome to guarantee a wider adoption of the technology. This scalability issue is mostly caused by the use of consensus algorithms to guarantee the total order of the chain of blocks (and of the operations within each block). However, total order is often overkilling, since important advanced applications of smart-contracts do not require a total order of all the operations. Hence, if a more relaxed partial order (instead of a total order) is allowed under certain safety conditions, a much higher scalability can be achieved. In this paper, we propose a distributed concurrent data type, called Setchain, that allows implementing this partial order and increases significantly blockchain scalability. A Setchain implements a grow-only set object whose elements are not totally ordered, unlike conventional blockchain operations. When convenient, the Setchain allows forcing a synchronization barrier that assigns permanently an epoch number to a subset of the latest elements added. With the Setchain, operations in the same epoch are not ordered, while operations in different epochs are. We present different Byzantine-tolerant implementations of Setchain, prove their correctness and report on an empirical evaluation of a direct implementation. Our results show that Setchain is orders of magnitude faster than consensus-based ledgers to implement grow-only sets with epoch synchronization. Since the Setchain barriers can be synchronized with block consolidation, Setchain objects can be used as a sidechain to implement many smart contract solutions with much faster operations than on basic blockchains.

cs.DC↗