145 citations · 374 across the 29 of their papers we have counts for
29 papers
When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting
Emma Andrews, Gianmarco Mengaldo
Multimodal forecasting models that combine time series with text annotations promise richer prediction through textual context, but how do we know whether a text annotation meaning…
OmniClimate-TC: Physics-Aware Visual Abstractions for Multimedia Reasoning over Tropical Cyclones
Luwei Xiao, Xin Wang, Keane Ong +5
Meteorological reanalysis encodes extreme weather through continuous, physically constrained fields, posing a fundamental challenge for vision-language models (VLMs) whose perceptu…
OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization
Keane Ong, Sabri Boughorbel, Luwei Xiao +9
Socially intelligent AI systems must reason across diverse human behavioral tasks and generalize to new social contexts. However, behavioral data is inherently heterogeneous, compr…
Enhancing Language Models for Robust Greenwashing Detection
Neil Heinrich Braun, Keane Ong, Rui Mao +2
Sustainability reports are critical for ESG assessment, yet greenwashing and vague claims often undermine their reliability. Existing NLP models lack robustness to these practices,…
Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding
Keane Ong, Wei Dai, Carol Li +8
Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observab…
Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation
Keane Ong, Rui Mao, Deeksha Varshney +3
Counterfactual reasoning typically involves considering alternatives to actual events. While often applied to understand past events, a distinct form-forward counterfactual reasoni…
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics
Deeksha Varshney, Keane Ong, Rui Mao +2
Accurate assessments of extreme weather events are vital for research and policy, yet localized and granular data remain scarce in many parts of the world. This data gap limits our…
Leveraging LLMS for Top-Down Sector Allocation In Automated Trading
Ryan Quek Wei Heng, Edoardo Vittori, Keane Ong +3
This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market…
Tell me why: Visual foundation models as self-explainable classifiers
Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1
Visual foundation models (VFMs) have become increasingly popular due to their state-of-the-art performance. However, interpretability remains crucial for critical applications. In…
Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization
Keane Ong, Rui Mao, Deeksha Varshney +2
Sustainability reports are key for evaluating companies' environmental, social and governance, ESG performance, but their content is increasingly obscured by greenwashing - sustain…
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints
Xin Wang, Jianda Chen, Juntao Yang +8
Accurate and efficient climate simulations are crucial for understanding Earth's evolving climate. However, current general circulation models (GCMs) face challenges in capturing u…
ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis
Keane Ong, Rui Mao, Deeksha Varshney +5
Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy. However, this is hindered by the…
Revisiting the robustness of post-hoc interpretability methods
Jiawen Wei, Hugues Turbé, Gianmarco Mengaldo
Post-hoc interpretability methods play a critical role in explainable artificial intelligence (XAI), as they pinpoint portions of data that a trained deep learning model deemed imp…
Explainable Natural Language Processing for Corporate Sustainability Analysis
Keane Ong, Rui Mao, Ranjan Satapathy +4
Sustainability commonly refers to entities, such as individuals, companies, and institutions, having a non-detrimental (or even positive) impact on the environment, society, and th…
ProtoS-ViT: Visual foundation models for sparse self-explainable classifications
Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1
Prototypical networks aim to build intrinsically explainable models based on the linear summation of concepts. Concepts are coherent entities that we, as humans, can recognize and…
A Comprehensive Review on Financial Explainable AI
Wei Jie Yeo, Wihan van der Heever, Rui Mao +3
The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process…
Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package
Marcin Rogowski, Brandon C. Y. Yeung, Oliver T. Schmidt +4
We propose a parallel (distributed) version of the spectral proper orthogonal decomposition (SPOD) technique. The parallel SPOD algorithm distributes the spatial dimension of the d…
FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis
Keane Ong, Wihan van der Heever, Ranjan Satapathy +2
This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis,…
Non-intrusive reduced order models for the accurate prediction of bifurcating phenomena in compressible fluid dynamics
Niccolò Tonicello, Andrea Lario, Gianluigi Rozza +1
The present works is focused on studying bifurcating solutions in compressible fluid dynamics. On one side, the physics of the problem is thoroughly investigated using high-fidelit…
Reduced order modeling for spectral element methods: current developments in Nektar++ and further perspectives
Martin W. Hess, Andrea Lario, Gianmarco Mengaldo +1
In this paper, we present recent efforts to develop reduced order modeling (ROM) capabilities for spectral element methods (SEM). Namely, we detail the implementation of ROM for bo…
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik, Vishwas Rao, Jiali Wang +6
Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical we…
Fully-discrete spatial eigenanalysis of discontinuous spectral element methods: insights into well-resolved and under-resolved vortical flows
Niccolò Tonicello, Rodrigo C Moura, Guido Lodato +1
This study presents a comprehensive spatial eigenanalysis of fully-discrete discontinuous spectral element methods, now generalizing previous spatial eigenanalysis that did not inc…
Neural-network learning of SPOD latent dynamics
Andrea Lario, Romit Maulik, Oliver T. Schmidt +2
We aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary systems using model order reduction via the spectral proper orthogonal decomposition (SPOD). T…
PyParSVD: A streaming, distributed and randomized singular-value-decomposition library
Romit Maulik, Gianmarco Mengaldo
We introduce PyParSVD\footnote{https://github.com/Romit-Maulik/PyParSVD}, a Python library that implements a streaming, distributed and randomized algorithm for the singular value…