Institution profile

University of Turin

Academic institutioneurope · it
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Research library185linked papers
Opportunities0open roles
Selected work

Representative Papers

Hidden assumptions of integer ratio analyses in bioacoustics and music

Feb 06, 2025

Widely adopted integer-ratio analyses in bioacoustics and music rhythm research suffer from critical methodological flaws: inadequate modeling of temporal noise, sensitivity of statistical inference to ratio formulation choices, and longstanding neglect of null-hypothesis appropriateness. Method: We formally characterize the temporal properties of empirically observed rhythmic ratios and introduce the first general-purpose statistical testing framework for integer ratios—flexibly accommodating arbitrary null hypotheses. This framework integrates mathematical modeling, probabilistic distribution analysis, and rigorous hypothesis testing to systematically identify the sources of statistical bias in prevailing approaches. Contribution/Results: The framework rectifies long-overlooked methodological shortcomings and provides a standardized, reproducible testing protocol. It substantially enhances statistical robustness and cross-study comparability in cross-species rhythmic analyses and music cognition research, enabling principled inference about rhythmic structure beyond ad hoc ratio assessments.

3 citations1 influentialRead paper

Designing a Token Economy: Incentives, Governance, and Tokenomics

Feb 10, 2026

This study addresses the absence of a systematic, reusable, and empirically grounded end-to-end approach that integrates incentive mechanisms, governance structures, and tokenomics in current token economic designs. To bridge this gap, the paper proposes the Token Economic Design Method (TEDM), which, for the first time, unifies these three dimensions into a structured and actionable design framework, with explicit emphasis on sociotechnical context and early-stage design considerations. Developed through the design science research paradigm and informed by qualitative synthesis, co-design case studies, and expert interviews, TEDM was empirically validated through its application to the Currynomics stablecoin ecosystem and subsequent expert evaluation. The results demonstrate that TEDM effectively supports the analysis and construction of tokenized ecosystems, offering practical and reusable design guidance.

3 citationsRead paper

What Are They Filtering Out? A Survey of Filtering Strategies for Harm Reduction in Pretraining Datasets

Feb 17, 2025arXiv.org

Pretraining data filtering strategies intended to reduce harmful content inadvertently exacerbate representational underrepresentation of marginalized groups, thereby amplifying demographic bias at the data level. Method: We systematically reviewed 55 English-language large language model technical reports to construct the first integrated data governance evaluation framework balancing safety and fairness. Through controlled experiments and quantitative bias analysis across mainstream filtering strategies, we measured their impact on group-level representation. Contribution/Results: Our analysis reveals that such strategies reduce text associated with disadvantaged groups by 12.7%–38.4% on average—significantly worsening representational disparity. This study provides the first empirical evidence refuting the “safety implies fairness” assumption in AI governance. We propose a co-optimization paradigm that jointly addresses content safety and equitable group representation, advocating for fairness-aware data curation in foundation model development.

2 citationsRead paper

A mirror descent approach to maximum likelihood estimation in latent variable models

Jan 27, 2025

Standard Expectation-Maximization (EM) algorithms are ill-suited for statistical models with discrete latent variables due to their reliance on differentiability and continuous latent spaces. Method: We propose the first unified framework integrating Mirror Descent (MD) with Sequential Monte Carlo (SMC) for joint parameter estimation and posterior inference. Our approach jointly minimizes a variational functional over both the parameter space and the space of probability measures, enabling maximum likelihood estimation (MLE) without requiring latent variable continuity. Contribution/Results: This work breaks EM’s dependence on latent variable continuity, marking the first application of MD to MLE in discrete latent variable models, with rigorous convergence guarantees established. Experiments demonstrate significant improvements over standard EM across multiple discrete latent variable tasks; on real-valued latent variable benchmarks, our method matches state-of-the-art performance, validating both theoretical soundness and empirical robustness.

2 citationsRead paper

Toward Scalable Normalizing Flows for the Hubbard Model

Jan 26, 2026

This work investigates the effective scaling of normalizing flows to larger lattice sizes and lower temperatures in the Hubbard model for efficient learning of its Boltzmann distribution. Addressing the scalability bottleneck of normalizing flows in strongly correlated fermionic systems, we integrate stochastic normalizing flows with nonequilibrium Markov chain Monte Carlo (MCMC) methods, systematically analyzing their stability, computational efficiency, and resource requirements in the low-temperature, large-scale limit. Our study is the first to reveal the scaling behavior of normalizing flows under these challenging conditions and establishes a stable, scalable pathway for generative modeling of strongly correlated quantum systems.

1 citationsRead paper
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