Institution profile

Universidade Federal do Rio Grande do Sul

Academic institutionsouthamerica · br
Official website
Research library77linked papers
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Selected work

Representative Papers

Set risk measures

Jul 26, 2024

Traditional risk measures apply only to single random variables, limiting their use in systemic and set-valued risk assessment. Method: This paper introduces **Set-Valued Risk Measures (SRMs)**—real-valued mappings defined on nonempty, closed, bounded, and almost-surely bounded sets of random variables—and develops a tailored axiomatic framework compatible with set operations. Leveraging convex analysis, set-valued functions, the (L^infty) space, and regular finitely additive measures on the unit ball, it establishes a **dual representation theorem for convex SRMs**, fully characterizing them via regular finite additivity. It further defines worst-case SRMs to address systemic risk evaluation and ambiguity- or robustness-aware decision-making. Contribution/Results: The proposed framework provides a rigorous, axiomatically complete foundation for systemic risk measurement and robust portfolio optimization, bridging theoretical depth with practical flexibility in financial applications.

2 citationsRead paper

SemEval-2025 Task 1: AdMIRe -- Advancing Multimodal Idiomaticity Representation

Mar 19, 2025

This work addresses the core challenge of idiom comprehension—particularly non-literal figurative meaning—in multimodal, multilingual settings. To this end, we introduce the first cross-lingual, cross-modal idiom understanding benchmark, comprising two tasks: image–idiom alignment ranking and sequential image prediction. We propose a systematic evaluation framework featuring novel multi-query fusion and a Mixture-of-Experts (MoE) mechanism to mitigate semantic biases in large language models regarding idiomatic expressions. Our approach integrates vision-language models (VLMs) and large language models (LLMs) via multi-query reasoning, expert-weighted ensemble, and semantic smoothing. Experiments demonstrate human-level performance on both tasks, with significant improvements in multilingual idiom–image semantic alignment accuracy and sequential consistency. This work establishes a new paradigm for idiom representation learning in multimodal, multilingual contexts.

1 citationsRead paper
Recent publications

Latest Papers

Spectral graph clustering with inhomogeneous latent geometry

Aug 11, 2026

This work addresses the challenge that conventional spectral clustering struggles to accurately recover communities under non-uniform latent geometric structures, as its dominant eigenvectors are often distorted by geometric interference. Building upon a block-wise latent space model, the authors analyze the spectrum of the adjacency matrix and its associated limiting integral operator, revealing that deeper eigenvectors—beyond the leading ones—encode meaningful community structure. Leveraging this insight, they propose DBSPEC, a density-based spectral clustering algorithm that does not rely on the uniform torus assumption and requires only coarse localization of informative eigenvalues. The method exhibits robustness in settings with poor eigenvalue separation. Theoretical predictions align closely with empirical observations, and DBSPEC demonstrates substantially improved community recovery performance in complex geometric settings.

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