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1. Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes

Author

McCart, Jonathan D., Sedler, Andrew R., Versteeg, Christopher, Mifsud, Domenick, Rigotti-Thompson, Mattia, and Pandarinath, Chethan

Subjects
Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Recent advances in recording technology have allowed neuroscientists to monitor activity from thousands of neurons simultaneously. Latent variable models are increasingly valuable for distilling these recordings into compact and interpretable representations. Here we propose a new approach to neural data analysis that leverages advances in conditional generative modeling to enable the unsupervised inference of disentangled behavioral variables from recorded neural activity. Our approach builds on InfoDiffusion, which augments diffusion models with a set of latent variables that capture important factors of variation in the data. We apply our model, called Generating Neural Observations Conditioned on Codes with High Information (GNOCCHI), to time series neural data and test its application to synthetic and biological recordings of neural activity during reaching. In comparison to a VAE-based sequential autoencoder, GNOCCHI learns higher-quality latent spaces that are more clearly structured and more disentangled with respect to key behavioral variables. These properties enable accurate generation of novel samples (unseen behavioral conditions) through simple linear traversal of the latent spaces produced by GNOCCHI. Our work demonstrates the potential of unsupervised, information-based models for the discovery of interpretable latent spaces from neural data, enabling researchers to generate high-quality samples from unseen conditions., Comment: 16 pages, 5 figures

Published
2024

2. Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking

Author

Rioux, Gabriel, Nitsure, Apoorva, Rigotti, Mattia, Greenewald, Kristjan, and Mroueh, Youssef

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory
Abstract

Stochastic dominance is an important concept in probability theory, econometrics and social choice theory for robustly modeling agents' preferences between random outcomes. While many works have been dedicated to the univariate case, little has been done in the multivariate scenario, wherein an agent has to decide between different multivariate outcomes. By exploiting a characterization of multivariate first stochastic dominance in terms of couplings, we introduce a statistic that assesses multivariate almost stochastic dominance under the framework of Optimal Transport with a smooth cost. Further, we introduce an entropic regularization of this statistic, and establish a central limit theorem (CLT) and consistency of the bootstrap procedure for the empirical statistic. Armed with this CLT, we propose a hypothesis testing framework as well as an efficient implementation using the Sinkhorn algorithm. We showcase our method in comparing and benchmarking Large Language Models that are evaluated on multiple metrics. Our multivariate stochastic dominance test allows us to capture the dependencies between the metrics in order to make an informed and statistically significant decision on the relative performance of the models., Comment: 27 pages, 2 figures

Published
2024

3. Distributional Preference Alignment of LLMs via Optimal Transport

Author

Melnyk, Igor, Mroueh, Youssef, Belgodere, Brian, Rigotti, Mattia, Nitsure, Apoorva, Yurochkin, Mikhail, Greenewald, Kristjan, Navratil, Jiri, and Ross, Jerret

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper Alignment via Optimal Transport (AOT), a novel method for distributional preference alignment of LLMs. AOT aligns LLMs on unpaired preference data by making the reward distribution of the positive samples stochastically dominant in the first order on the distribution of negative samples. We introduce a convex relaxation of this first-order stochastic dominance and cast it as an optimal transport problem with a smooth and convex cost. Thanks to the one-dimensional nature of the resulting optimal transport problem and the convexity of the cost, it has a closed-form solution via sorting on empirical measures. We fine-tune LLMs with this AOT objective, which enables alignment by penalizing the violation of the stochastic dominance of the reward distribution of the positive samples on the reward distribution of the negative samples. We analyze the sample complexity of AOT by considering the dual of the OT problem and show that it converges at the parametric rate. Empirically, we show on a diverse set of alignment datasets and LLMs that AOT leads to state-of-the-art models in the 7B family of models when evaluated with Open LLM Benchmarks and AlpacaEval.

Published
2024

4. Hospital-Initiated Smoking Cessation Among Patients Admitted with Behavioral Health Conditions

Author

Cruvinel, Erica, Mussulman, Laura, Scheuermann, Taneisha, Shergina, Elena, He, Jianghua, Sherman, Scott, Harrington, Kathleen, Rigotti, Nancy A, Tindle, Hilary, Zhu, Shu-Hong, and Richter, Kimber

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Behavioral and Social Science, Prevention, Clinical Research, Patient Safety, Clinical Trials and Supportive Activities, Tobacco, Cancer, Tobacco Smoke and Health, Substance Misuse, Cardiovascular, Respiratory, Good Health and Well Being, Humans, Smoking Cessation, Male, Female, Middle Aged, Retrospective Studies, Adult, Hospitalization, Mental Disorders, Aged, smoking cessation, mental health, mood disorders, schizophrenia spectrum and other psychotic disorders, substance-related disorders, hospitals, Clinical Sciences, General & Internal Medicine, Clinical sciences, Health services and systems, Public health
Abstract

BackgroundSmoking rates among people living with behavioral health conditions (BHC) range from 30 to 65% and are 2-4 times higher than rates found in the general population. Starting tobacco treatment during a hospital stay is effective for smoking cessation, but little is known regarding treatment response among inpatients with BHC.ObjectiveThis study pooled data across multiple clinical trials to determine the relative success in quitting among participants with BHC compared to other study participants.ParticipantsAdults who smoke (≥ 18 years old) from five hospital-based smoking cessation randomized clinical trials.DesignA retrospective analysis using data from the electronic health record to identify participants with primary diagnoses related to BHC. Recruitment and data analysis were conducted from 2011 to 2016. We used propensity score matching to pair patients with BHC to those with similar characteristics and logistic regression to determine differences between groups.MeasuresThe main outcome was self-reported 30-day abstinence 6 months post-discharge.ResultsOf 6612 participants, 798 patients had a BHC-related primary diagnosis. The matched sample included 642 pairs. Nearly 1 in 3 reported using tobacco medications after hospitalization, with no significant difference between patients with and without BHC (29.3% vs. 31.5%; OR (95% CI) = 0.90 (0.71, 1.14), p = 0.40). Nearly 1 in 5 patients with BHC reported abstinence at 6 months; however, their odds of abstinence were 30% lower than among people without BHC (OR (95% CI) = 0.70 (0.53,0.92), p = 0.01).ConclusionWhen offered tobacco treatment, hospitalized patients with BHC were as likely as people without BHC to accept and engage in treatment. However, patients with BHC were less likely to report abstinence compared to those without BHC. Hospitals are a feasible and promising venue for tobacco treatment among inpatients with BHC. More studies are needed to identify treatment approaches that help people with BHC achieve long-term abstinence.

Published
2024

6. Outline-Guided Object Inpainting with Diffusion Models

Author

Pobitzer, Markus, Janicki, Filip, Rigotti, Mattia, and Malossi, Cristiano

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4, I.5
Abstract

Instance segmentation datasets play a crucial role in training accurate and robust computer vision models. However, obtaining accurate mask annotations to produce high-quality segmentation datasets is a costly and labor-intensive process. In this work, we show how this issue can be mitigated by starting with small annotated instance segmentation datasets and augmenting them to effectively obtain a sizeable annotated dataset. We achieve that by creating variations of the available annotated object instances in a way that preserves the provided mask annotations, thereby resulting in new image-mask pairs to be added to the set of annotated images. Specifically, we generate new images using a diffusion-based inpainting model to fill out the masked area with a desired object class by guiding the diffusion through the object outline. We show that the object outline provides a simple, but also reliable and convenient training-free guidance signal for the underlying inpainting model that is often sufficient to fill out the mask with an object of the correct class without further text guidance and preserve the correspondence between generated images and the mask annotations with high precision. Our experimental results reveal that our method successfully generates realistic variations of object instances, preserving their shape characteristics while introducing diversity within the augmented area. We also show that the proposed method can naturally be combined with text guidance and other image augmentation techniques.

Published
2024

7. On the generalization capacity of neural networks during generic multimodal reasoning

Author

Ito, Takuya, Dan, Soham, Rigotti, Mattia, Kozloski, James, and Campbell, Murray

Subjects
Computer Science - Machine Learning
Abstract

The advent of the Transformer has led to the development of large language models (LLM), which appear to demonstrate human-like capabilities. To assess the generality of this class of models and a variety of other base neural network architectures to multimodal domains, we evaluated and compared their capacity for multimodal generalization. We introduce a multimodal question-answer benchmark to evaluate three specific types of out-of-distribution (OOD) generalization performance: distractor generalization (generalization in the presence of distractors), systematic compositional generalization (generalization to new task permutations), and productive compositional generalization (generalization to more complex tasks structures). We found that across model architectures (e.g., RNNs, Transformers, Perceivers, etc.), models with multiple attention layers, or models that leveraged cross-attention mechanisms between input domains, fared better. Our positive results demonstrate that for multimodal distractor and systematic generalization, either cross-modal attention or models with deeper attention layers are key architectural features required to integrate multimodal inputs. On the other hand, neither of these architectural features led to productive generalization, suggesting fundamental limitations of existing architectures for specific types of multimodal generalization. These results demonstrate the strengths and limitations of specific architectural components underlying modern neural models for multimodal reasoning. Finally, we provide Generic COG (gCOG), a configurable benchmark with several multimodal generalization splits, for future studies to explore., Comment: ICLR 2024

Published
2024

8. BRAND: a platform for closed-loop experiments with deep network models

Author

Ali, Yahia H, Bodkin, Kevin, Rigotti-Thompson, Mattia, Patel, Kushant, Card, Nicholas S, Bhaduri, Bareesh, Nason-Tomaszewski, Samuel R, Mifsud, Domenick M, Hou, Xianda, Nicolas, Claire, Allcroft, Shane, Hochberg, Leigh R, Yong, Nicholas Au, Stavisky, Sergey D, Miller, Lee E, Brandman, David M, and Pandarinath, Chethan

Subjects
Engineering, Biomedical and Clinical Sciences, Neurosciences, Biomedical Engineering, Networking and Information Technology R&D (NITRD), Neurological, Industry, Innovation and Infrastructure, Humans, Neural Networks, Computer, Brain-Computer Interfaces, brain-computer interface, closed-loop, artificial neural network, real-time, brain–computer interface, Clinical Sciences, Biomedical engineering
Abstract

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved

Published
2024

9. Unraveling the Key Components of OOD Generalization via Diversification

Author

Benoit, Harold, Jiang, Liangze, Atanov, Andrei, Kar, Oğuzhan Fatih, Rigotti, Mattia, and Zamir, Amir

Subjects
Computer Science - Machine Learning
Abstract

Supervised learning datasets may contain multiple cues that explain the training set equally well, i.e., learning any of them would lead to the correct predictions on the training data. However, many of them can be spurious, i.e., lose their predictive power under a distribution shift and consequently fail to generalize to out-of-distribution (OOD) data. Recently developed "diversification" methods (Lee et al., 2023; Pagliardini et al., 2023) approach this problem by finding multiple diverse hypotheses that rely on different features. This paper aims to study this class of methods and identify the key components contributing to their OOD generalization abilities. We show that (1) diversification methods are highly sensitive to the distribution of the unlabeled data used for diversification and can underperform significantly when away from a method-specific sweet spot. (2) Diversification alone is insufficient for OOD generalization. The choice of the used learning algorithm, e.g., the model's architecture and pretraining, is crucial. In standard experiments (classification on Waterbirds and Office-Home datasets), using the second-best choice leads to an up to 20\% absolute drop in accuracy. (3) The optimal choice of learning algorithm depends on the unlabeled data and vice versa i.e. they are co-dependent. (4) Finally, we show that, in practice, the above pitfalls cannot be alleviated by increasing the number of diverse hypotheses, the major feature of diversification methods. These findings provide a clearer understanding of the critical design factors influencing the OOD generalization abilities of diversification methods. They can guide practitioners in how to use the existing methods best and guide researchers in developing new, better ones., Comment: ICLR 2024

Published
2023

12. Risk Aware Benchmarking of Large Language Models

Author

Nitsure, Apoorva, Mroueh, Youssef, Rigotti, Mattia, Greenewald, Kristjan, Belgodere, Brian, Yurochkin, Mikhail, Navratil, Jiri, Melnyk, Igor, and Ross, Jerret

Subjects
Computer Science - Machine Learning, Mathematics - Statistics Theory, Quantitative Finance - Risk Management, Statistics - Machine Learning
Abstract

We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical relative testing based on first and second order stochastic dominance of real random variables. We show that the second order statistics in this test are linked to mean-risk models commonly used in econometrics and mathematical finance to balance risk and utility when choosing between alternatives. Using this framework, we formally develop a risk-aware approach for foundation model selection given guardrails quantified by specified metrics. Inspired by portfolio optimization and selection theory in mathematical finance, we define a metrics portfolio for each model as a means to aggregate a collection of metrics, and perform model selection based on the stochastic dominance of these portfolios. The statistical significance of our tests is backed theoretically by an asymptotic analysis via central limit theorems instantiated in practice via a bootstrap variance estimate. We use our framework to compare various large language models regarding risks related to drifting from instructions and outputting toxic content., Comment: ICML 2024

Published
2023

13. Assessment of the COVID-19 impact on the Brazilian Unified Health System (SUS) financing: an analysis of the financing dynamics of 2020 and 2021

Author

Ana Carolina Costa Corrêa, Maria Luísa Oliveira Rigotti, Hugo Dilhermando Souza Lacerda, and Bruno Pérez Ferreira

Subjects
Pandemic, Unified Health System (SUS), ARIMAX, Financing, COVID-19, Spending policy, Public aspects of medicine, RA1-1270
Abstract

Abstract Background After the establishment of the public health emergency of international concern in 2020, health systems worldwide and in Brazil observed the need to apply more extraordinary logistical efforts and possibly resources to combat the imminent pandemic. Methods Using the historical series of public expenditures of the National Health Fund (FNS), 2015 to 2021, the number of confirmed cases of COVID-19, and a seasonal ARIMAX model, we sought to assess how the increase in the new virus infections affected the systematic financing of the SUS in Brazil. Results There were signs of seasonality and an increasing trend in the expenditure variable, which in practical terms, only indicated that the resource contributions followed an increasing trajectory already underway before the advent of the pandemic. The 1% increase in COVID-19 cases, with a one-month lag, contributes to the 0.062% increase in the variation in FNS expenditures but a decrease of 0.058% with a two-month lag. Conclusion The tests showed no evidence to confirm a positive shift on FNS spending growth trajectory due to the increase of COVID-19 cases, only observing a significant increase one month after the occurrence of COVID cases, probably due to their worsening after this period, which was followed by a similar and comparable decrease in percentage of growth in the following month.

Published
2024
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14. DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Author

Ivankay, Adam, Rigotti, Mattia, and Frossard, Pascal

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Along with the successful deployment of deep neural networks in several application domains, the need to unravel the black-box nature of these networks has seen a significant increase recently. Several methods have been introduced to provide insight into the inference process of deep neural networks. However, most of these explainability methods have been shown to be brittle in the face of adversarial perturbations of their inputs in the image and generic textual domain. In this work we show that this phenomenon extends to specific and important high stakes domains like biomedical datasets. In particular, we observe that the robustness of explanations should be characterized in terms of the accuracy of the explanation in linking a model's inputs and its decisions - faithfulness - and its relevance from the perspective of domain experts - plausibility. This is crucial to prevent explanations that are inaccurate but still look convincing in the context of the domain at hand. To this end, we show how to adapt current attribution robustness estimation methods to a given domain, so as to take into account domain-specific plausibility. This results in our DomainAdaptiveAREstimator (DARE) attribution robustness estimator, allowing us to properly characterize the domain-specific robustness of faithful explanations. Next, we provide two methods, adversarial training and FAR training, to mitigate the brittleness characterized by DARE, allowing us to train networks that display robust attributions. Finally, we empirically validate our methods with extensive experiments on three established biomedical benchmarks., Comment: Accepted at 61st Annual Meeting of the Association for Computational Linguistics (ACL) 2023

Published
2023

16. Comparison of “IN-REC-SUR-E” and LISA in preterm neonates with respiratory distress syndrome: a randomized controlled trial (IN-REC-LISA trial)

Author

Vento, Giovanni, Paladini, Angela, Aurilia, C., Ozdemir, S. Alkan, Carnielli, V. P., Cools, F., Costa, S., Cota, F., Dani, C., Davis, P. G., Fattore, S., Fè, C., Finer, N., Fusco, F. P., Gizzi, C., Herting, E., Jian, M., Lio, A., Lista, G., Mosca, F., Nobile, S., Perri, A., Picone, S., Pillow, J. J., Polglase, G., Pasciuto, T., Pastorino, R., Tana, M., Tingay, D., Tirone, C., van Kaam, A. H., Ventura, M. L., Aceti, A., Agosti, M., Alighieri, G., Ancora, G., Angileri, V., Ausanio, G., Aversa, S., Balestri, E., Baraldi, E., Barbini, M. C., Barone, C., Beghini, R., Bellan, C., Berardi, A., Bernardo, I., Betta, P., Binotti, M., Bizzarri, B., Borgarello, G., Borgione, S., Borrelli, A., Bottino, R., Bracaglia, G., Bresesti, I., Burattini, I., Cacace, C., Calzolari, F., Campagnoli, M. F., Capasso, L., Capozza, M., Capretti, M. G., Caravetta, J., Carbonara, C., Cardilli, V., Carta, M., Castoldi, F., Castronovo, A., Cavalleri, E., Cavigioli, F., Cecchi, S., Chierici, V., Cimino, C., Cocca, F., Cocca, C., Cogo, P., Coma, M., Comito, V., Condò, V., Consigli, C., Conti, R., Corradi, M., Corsello, G., Corvaglia, L. T., Costa, A., Coscia, A., Cresi, F., Crispino, F., D’Amico, P., De Cosmo, L., De Maio, C., Del Campo, G., Di Credico, S., Di Fabio, S., Di Nicola, P., Di Paolo, A., Di Valerio, S., Distilo, A., Duca, V., Falcone, A., Falsaperla, R., Fasolato, V. A., Fatuzzo, V., Favini, F., Ferrarello, M. P., Ferrari, S., Nastro, F. Fiori, Forcellini, C. A., Fracchiolla, A., Gabriele, A., Galdo, F., Gallini, F., Gangemi, A., Gargano, G., Gazzolo, D., Gentile, M. P., Ghirardello, S., Giardina, F., Giordano, L., Gitto, E., Giuffrè, M., Grappone, L., Grasso, F., Greco, I., Grison, A., Guglielmino, R., Guidotti, I., Guzzo, I., La Forgia, N., La Placa, S., La Torre, G., Lago, P., Lanciotti, L., Lavizzari, A., Leo, F., Leonardi, V., Lestingi, D., Li, J., Liberatore, P., Lodin, D., Lubrano, R., Lucente, M., Luciani, S., Luvarà, D., Maffei, G., Maggio, A., Maggio, L., Maiolo, K., Malaigia, L., Mangili, G., Manna, A., Maranella, E., Marciano, A., Marcozzi, P., Marletta, M., Marseglia, L., Martinelli, D., Martinelli, S., Massari, S., Massenzi, L., Matina, F., Mattia, L., Mescoli, G., Migliore, I. V., Minghetti, D., Mondello, I., Montano, S., Morandi, G., Mores, N., Morreale, S., Morselli, I., Motta, M., Napolitano, M., Nardo, D., Nicolardi, A., Nider, S., Nigro, G., Nuccio, M., Orfeo, L., Ottaviano, C., Paganin, P., Palamides, S., Palatta, S., Paolillo, P., Pappalardo, M. G., Pasta, E., Patti, L., Paviotti, G., Perniola, R., Perotti, G., Perrone, S., Petrillo, F., Piazza, M. S., Piccirillo, A., Pierro, M., Piga, E., Pingitore, G. A., Pisu, S., Pittini, C., Pontiggia, F., Pontrelli, G., Primavera, A., Proto, A., Quartulli, L., Raimondi, F., Ramenghi, L., Rapsomaniki, M., Ricotti, A., Rigotti, C., Rinaldi, M., Risso, F. M., Roma, E., Romanini, E., Romano, V., Rosati, E., Rosella, V., Rulli, I., Salvo, V., Sanfilippo, C., Sannia, A., Saporito, A., Sauna, A., Scapillati, E., Schettini, F., Scorrano, A., Mantelli, S. Semeria, Sepporta, V., Sindico, P., Solinas, A., Sorrentino, E., Spaggiari, E., Staffler, A., Stella, M., Termini, D., Terrin, G., Testa, A., Tina, G., Tirantello, M., Tomasini, B., Tormena, F., Travan, L., Trevisanuto, D., Tuling, G., Tulino, V., Valenzano, L., Vedovato, S., Vendramin, S., Villani, P. E., Viola, S., Viola, V., Vitaliti, G., Vitaliti, M., Wanker, P., Yang, Y., Zanetta, S., and Zannin, E.

Published
2024
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20. Primary membranous nephropathy in the Italian region of Emilia Romagna: results of a multicenter study with extended follow-up

Author

Albertazzi, Vittorio, Fontana, Francesco, Giberti, Stefania, Aiello, Valeria, Battistoni, Sara, Catapano, Fausta, Graziani, Romina, Cimino, Simonetta, Scichilone, Laura, Forcellini, Silvia, De Fabritiis, Marco, Sara, Signorotti, Delsante, Marco, Fiaccadori, Enrico, Mosconi, Giovanni, Storari, Alda, Mandreoli, Marcora, Bonucchi, Decenzio, Buscaroli, Andrea, Mancini, Elena, Rigotti, Angelo, La Manna, Gaetano, Gregorini, Mariacristina, Donati, Gabriele, Cappelli, Gianni, and Scarpioni, Roberto

Published
2024
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21. Adaptive Conformal Regression with Jackknife+ Rescaled Scores

Author

Deutschmann, Nicolas, Rigotti, Mattia, and Martinez, Maria Rodriguez

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Conformal regression provides prediction intervals with global coverage guarantees, but often fails to capture local error distributions, leading to non-homogeneous coverage. We address this with a new adaptive method based on rescaling conformal scores with an estimate of local score distribution, inspired by the Jackknife+ method, which enables the use of calibration data in conformal scores without breaking calibration-test exchangeability. Our approach ensures formal global coverage guarantees and is supported by new theoretical results on local coverage, including an a posteriori bound on any calibration score. The strength of our approach lies in achieving local coverage without sacrificing calibration set size, improving the applicability of conformal prediction intervals in various settings. As a result, our method provides prediction intervals that outperform previous methods, particularly in the low-data regime, making it especially relevant for real-world applications such as healthcare and biomedical domains where uncertainty needs to be quantified accurately despite low sample data., Comment: 24 pages, 7 figures

Published
2023

22. Auditing and Generating Synthetic Data with Controllable Trust Trade-offs

Author

Belgodere, Brian, Dognin, Pierre, Ivankay, Adam, Melnyk, Igor, Mroueh, Youssef, Mojsilovic, Aleksandra, Navratil, Jiri, Nitsure, Apoorva, Padhi, Inkit, Rigotti, Mattia, Ross, Jerret, Schiff, Yair, Vedpathak, Radhika, and Young, Richard A.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate unbiased, privacy-preserving data while maintaining fidelity to the original data. However, assessing the trustworthiness of synthetic datasets and models is a critical challenge. We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases like education, healthcare, banking, and human resources, spanning different data modalities such as tabular, time-series, vision, and natural language. This holistic assessment is essential for compliance with regulatory safeguards. We introduce a trustworthiness index to rank synthetic datasets based on their safeguards trade-offs. Furthermore, we present a trustworthiness-driven model selection and cross-validation process during training, exemplified with "TrustFormers" across various data types. This approach allows for controllable trustworthiness trade-offs in synthetic data creation. Our auditing framework fosters collaboration among stakeholders, including data scientists, governance experts, internal reviewers, external certifiers, and regulators. This transparent reporting should become a standard practice to prevent bias, discrimination, and privacy violations, ensuring compliance with policies and providing accountability, safety, and performance guarantees., Comment: submitted

Published
2023

23. Comparison of 'IN-REC-SUR-E' and LISA in preterm neonates with respiratory distress syndrome: a randomized controlled trial (IN-REC-LISA trial)

Author

Giovanni Vento, Angela Paladini, C. Aurilia, S. Alkan Ozdemir, V. P. Carnielli, F. Cools, S. Costa, F. Cota, C. Dani, P. G. Davis, S. Fattore, C. Fè, N. Finer, F. P. Fusco, C. Gizzi, E. Herting, M. Jian, A. Lio, G. Lista, F. Mosca, S. Nobile, A. Perri, S. Picone, J. J. Pillow, G. Polglase, T. Pasciuto, R. Pastorino, M. Tana, D. Tingay, C. Tirone, A. H. van Kaam, M. L. Ventura, A. Aceti, M. Agosti, G. Alighieri, G. Ancora, V. Angileri, G. Ausanio, S. Aversa, E. Balestri, E. Baraldi, M. C. Barbini, C. Barone, R. Beghini, C. Bellan, A. Berardi, I. Bernardo, P. Betta, M. Binotti, B. Bizzarri, G. Borgarello, S. Borgione, A. Borrelli, R. Bottino, G. Bracaglia, I. Bresesti, I. Burattini, C. Cacace, F. Calzolari, M. F. Campagnoli, L. Capasso, M. Capozza, M. G. Capretti, J. Caravetta, C. Carbonara, V. Cardilli, M. Carta, F. Castoldi, A. Castronovo, E. Cavalleri, F. Cavigioli, S. Cecchi, V. Chierici, C. Cimino, F. Cocca, C. Cocca, P. Cogo, M. Coma, V. Comito, V. Condò, C. Consigli, R. Conti, M. Corradi, G. Corsello, L. T. Corvaglia, A. Costa, A. Coscia, F. Cresi, F. Crispino, P. D’Amico, L. De Cosmo, C. De Maio, G. Del Campo, S. Di Credico, S. Di Fabio, P. Di Nicola, A. Di Paolo, S. Di Valerio, A. Distilo, V. Duca, A. Falcone, R. Falsaperla, V. A. Fasolato, V. Fatuzzo, F. Favini, M. P. Ferrarello, S. Ferrari, F. Fiori Nastro, C. A. Forcellini, A. Fracchiolla, A. Gabriele, F. Galdo, F. Gallini, A. Gangemi, G. Gargano, D. Gazzolo, M. P. Gentile, S. Ghirardello, F. Giardina, L. Giordano, E. Gitto, M. Giuffrè, L. Grappone, F. Grasso, I. Greco, A. Grison, R. Guglielmino, I. Guidotti, I. Guzzo, N. La Forgia, S. La Placa, G. La Torre, P. Lago, L. Lanciotti, A. Lavizzari, F. Leo, V. Leonardi, D. Lestingi, J. Li, P. Liberatore, D. Lodin, R. Lubrano, M. Lucente, S. Luciani, D. Luvarà, G. Maffei, A. Maggio, L. Maggio, K. Maiolo, L. Malaigia, G. Mangili, A. Manna, E. Maranella, A. Marciano, P. Marcozzi, M. Marletta, L. Marseglia, D. Martinelli, S. Martinelli, S. Massari, L. Massenzi, F. Matina, L. Mattia, G. Mescoli, I. V. Migliore, D. Minghetti, I. Mondello, S. Montano, G. Morandi, N. Mores, S. Morreale, I. Morselli, M. Motta, M. Napolitano, D. Nardo, A. Nicolardi, S. Nider, G. Nigro, M. Nuccio, L. Orfeo, C. Ottaviano, P. Paganin, S. Palamides, S. Palatta, P. Paolillo, M. G. Pappalardo, E. Pasta, L. Patti, G. Paviotti, R. Perniola, G. Perotti, S. Perrone, F. Petrillo, M. S. Piazza, A. Piccirillo, M. Pierro, E. Piga, G. A. Pingitore, S. Pisu, C. Pittini, F. Pontiggia, G. Pontrelli, A. Primavera, A. Proto, L. Quartulli, F. Raimondi, L. Ramenghi, M. Rapsomaniki, A. Ricotti, C. Rigotti, M. Rinaldi, F. M. Risso, E. Roma, E. Romanini, V. Romano, E. Rosati, V. Rosella, I. Rulli, V. Salvo, C. Sanfilippo, A. Sannia, A. Saporito, A. Sauna, E. Scapillati, F. Schettini, A. Scorrano, S. Semeria Mantelli, V. Sepporta, P. Sindico, A. Solinas, E. Sorrentino, E. Spaggiari, A. Staffler, M. Stella, D. Termini, G. Terrin, A. Testa, G. Tina, M. Tirantello, B. Tomasini, F. Tormena, L. Travan, D. Trevisanuto, G. Tuling, V. Tulino, L. Valenzano, S. Vedovato, S. Vendramin, P. E. Villani, S. Viola, V. Viola, G. Vitaliti, M. Vitaliti, P. Wanker, Y. Yang, S. Zanetta, and E. Zannin

Subjects
Preterm infants, Lung recruitment, HFOV, INRECSURE, LISA, Surfactant, Medicine (General), R5-920
Abstract

Abstract Background Surfactant is a well-established therapy for preterm neonates affected by respiratory distress syndrome (RDS). The goals of different methods of surfactant administration are to reduce the duration of mechanical ventilation and the severity of bronchopulmonary dysplasia (BPD); however, the optimal administration method remains unknown. This study compares the effectiveness of the INtubate-RECruit-SURfactant-Extubate (IN-REC-SUR-E) technique with the less-invasive surfactant administration (LISA) technique, in increasing BPD-free survival of preterm infants. This is an international unblinded multicenter randomized controlled study in which preterm infants will be randomized into two groups to receive IN-REC-SUR-E or LISA surfactant administration. Methods In this study, 382 infants born at 24+0–27+6 weeks’ gestation, not intubated in the delivery room and failing nasal continuous positive airway pressure (nCPAP) or nasal intermittent positive pressure ventilation (NIPPV) during the first 24 h of life, will be randomized 1:1 to receive IN-REC-SUR-E or LISA surfactant administration. The primary outcome is a composite outcome of death or BPD at 36 weeks’ postmenstrual age. The secondary outcomes are BPD at 36 weeks’ postmenstrual age; death; pulse oximetry/fraction of inspired oxygen; severe intraventricular hemorrhage; pneumothorax; duration of respiratory support and oxygen therapy; pulmonary hemorrhage; patent ductus arteriosus undergoing treatment; percentage of infants receiving more doses of surfactant; periventricular leukomalacia, severe retinopathy of prematurity, necrotizing enterocolitis, sepsis; total in-hospital stay; systemic postnatal steroids; neurodevelopmental outcomes; and respiratory function testing at 24 months of age. Randomization will be centrally provided using both stratification and permuted blocks with random block sizes and block order. Stratification factors will include center and gestational age (24+0 to 25+6 weeks or 26+0 to 27+6 weeks). Analyses will be conducted in both intention-to-treat and per-protocol populations, utilizing a log-binomial regression model that corrects for stratification factors to estimate the adjusted relative risk (RR). Discussion This trial is designed to provide robust data on the best method of surfactant administration in spontaneously breathing preterm infants born at 24+0–27+6 weeks’ gestation affected by RDS and failing nCPAP or NIPPV during the first 24 h of life, comparing IN-REC-SUR-E to LISA technique, in increasing BPD-free survival at 36 weeks’ postmenstrual age of life. Trial registration ClinicalTrials.gov NCT05711966. Registered on February 3, 2023.

Published
2024
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24. Estimating the Adversarial Robustness of Attributions in Text with Transformers

Author

Ivankay, Adam, Rigotti, Mattia, Girardi, Ivan, Marchiori, Chiara, and Frossard, Pascal

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Explanations are crucial parts of deep neural network (DNN) classifiers. In high stakes applications, faithful and robust explanations are important to understand and gain trust in DNN classifiers. However, recent work has shown that state-of-the-art attribution methods in text classifiers are susceptible to imperceptible adversarial perturbations that alter explanations significantly while maintaining the correct prediction outcome. If undetected, this can critically mislead the users of DNNs. Thus, it is crucial to understand the influence of such adversarial perturbations on the networks' explanations and their perceptibility. In this work, we establish a novel definition of attribution robustness (AR) in text classification, based on Lipschitz continuity. Crucially, it reflects both attribution change induced by adversarial input alterations and perceptibility of such alterations. Moreover, we introduce a wide set of text similarity measures to effectively capture locality between two text samples and imperceptibility of adversarial perturbations in text. We then propose our novel TransformerExplanationAttack (TEA), a strong adversary that provides a tight estimation for attribution robustness in text classification. TEA uses state-of-the-art language models to extract word substitutions that result in fluent, contextual adversarial samples. Finally, with experiments on several text classification architectures, we show that TEA consistently outperforms current state-of-the-art AR estimators, yielding perturbations that alter explanations to a greater extent while being more fluent and less perceptible.

Published
2022

25. Recycling of LDPE-PVC blends from cable waste: Mechanical characterization and performance optimization

Author

Daniele Rigotti and Alessandro Pegoretti

Subjects
PVC recycling, Cable waste, Mechanical properties, Optimization, Sustainable materials, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

The recycling of polyvinylchloride (PVC) from waste electrical cables in blends with low-density polyethylene (LDPE) is here explored, focusing on the material's mechanical and physical responses. Factors influencing these responses were identified, and reasonable ranges of variability were estimated for each factor. A comprehensive test plan was subsequently devised to develop optimal PVC/LDPE blends. The samples were produced by melt-blending the polymers in a counter-rotating internal mixer followed by compression molding. The effects of suitable coupling additives were also evaluated to enhance phases adhesion. The study investigated the impact of various processing parameters on thermomechanical properties using ANOVA, while a specific focus on optimizing material toughness was pursued. This optimization aimed to maximize both elastic modulus and elongation at break, through the application of genetic algorithms. Introducing a 5 % compatibilizer significantly enhances the interface, resulting in a complex fracture surface facilitated by its presence. Higher mixing temperatures promote better dispersion and distribution of PVC and the compatibilizer within the LDPE matrix, yielding a more uniform and interconnected structure. Increased PVC content correlates with reduced elastic modulus in the blend. The inclusion of a compatibilizer plays a vital role in counteracting the negative effects of PVC particles on stiffness, acting as a bridge to enhance interactions with the matrix and thereby improving interfacial adhesion and overall structure. The study offers insights into enhancing the recyclability of PVC from waste cables and optimizing the mechanical performance of the resultant materials.

Published
2024
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26. Active Learning for Imbalanced Civil Infrastructure Data

Author

Frick, Thomas, Antognini, Diego, Rigotti, Mattia, Giurgiu, Ioana, Grewe, Benjamin, and Malossi, Cristiano

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we are working towards fully automating the visual inspections to support the prioritization of maintenance activities. To that end we combine recent advances in drone technology and deep learning. Unfortunately, annotation costs are incredibly high as our proprietary civil engineering dataset must be annotated by highly trained engineers. Active learning is, therefore, a valuable tool to optimize the trade-off between model performance and annotation costs. Our use-case differs from the classical active learning setting as our dataset suffers from heavy class imbalance and consists of a much larger already labeled data pool than other active learning research. We present a novel method capable of operating in this challenging setting by replacing the traditional active learning acquisition function with an auxiliary binary discriminator. We experimentally show that our novel method outperforms the best-performing traditional active learning method (BALD) by 5% and 38% accuracy on CIFAR-10 and our proprietary dataset respectively.

Published
2022

27. Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures

Author

Janouskova, Klara, Rigotti, Mattia, Giurgiu, Ioana, and Malossi, Cristiano

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Labeling images for visual segmentation is a time-consuming task which can be costly, particularly in application domains where labels have to be provided by specialized expert annotators, such as civil engineering. In this paper, we propose to use attribution methods to harness the valuable interactions between expert annotators and the data to be annotated in the case of defect segmentation for visual inspection of civil infrastructures. Concretely, a classifier is trained to detect defects and coupled with an attribution-based method and adversarial climbing to generate and refine segmentation masks corresponding to the classification outputs. These are used within an assisted labeling framework where the annotators can interact with them as proposal segmentation masks by deciding to accept, reject or modify them, and interactions are logged as weak labels to further refine the classifier. Applied on a real-world dataset resulting from the automated visual inspection of bridges, our proposed method is able to save more than 50\% of annotators' time when compared to manual annotation of defects.

Published
2022

28. Compositional generalization through abstract representations in human and artificial neural networks

Author

Ito, Takuya, Klinger, Tim, Schultz, Douglas H., Murray, John D., Cole, Michael W., and Rigotti, Mattia

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study the computational properties associated with compositional generalization in both humans and artificial neural networks (ANNs) on a highly compositional task. First, we identified behavioral signatures of compositional generalization in humans, along with their neural correlates using whole-cortex functional magnetic resonance imaging (fMRI) data. Next, we designed pretraining paradigms aided by a procedure we term {\em primitives pretraining} to endow compositional task elements into ANNs. We found that ANNs with this prior knowledge had greater correspondence with human behavior and neural compositional signatures. Importantly, primitives pretraining induced abstract internal representations, excellent zero-shot generalization, and sample-efficient learning. Moreover, it gave rise to a hierarchy of abstract representations that matched human fMRI data, where sensory rule abstractions emerged in early sensory areas, and motor rule abstractions emerged in later motor areas. Our findings give empirical support to the role of compositional generalization in human behavior, implicate abstract representations as its neural implementation, and illustrate that these representations can be embedded into ANNs by designing simple and efficient pretraining procedures., Comment: NeurIPS 2022. 24 pages (10 pages main text), 17 figures (8 main text figures)

Published
2022

31. Conditional reciprocal stressor–strain effects in university students: a cross-lagged panel study in Germany

Author

Jennifer L. Reichel, Lina M. Mülder, Pavel Dietz, Sebastian Heller, Antonia M. Werner, Markus Schäfer, Lisa Schwab, Stephan Letzel, and Thomas Rigotti

Subjects
Stressor effect, Strain effect, University student, Emotional exhaustion, Depression, Well-being, Medicine, Science
Abstract

Abstract University students worldwide are facing increasing mental health challenges. Traditional stress models, like the Job/Study Demand-Resources Model, link stressors directly to strain. Yet, recent studies suggest the influence of strain on stressors may be even stronger. Our research explored these reciprocal dynamics among university students, considering social support and mindfulness as potential moderators. We conducted a two-wave panel study with 264 university students. We ran separate cross-lagged panel structural equation analyses for three key health outcomes—emotional exhaustion, depression, and well-being—each paired with perceived study stressors, specifically workload and work complexity. Findings revealed significant stressor and strain effects, with social support notably moderating the impact of emotional exhaustion on workload. These insights challenge traditional stress theories and underscore the importance of mental health support and effective stress management strategies for students, emphasizing the need for proactive mental health initiatives in academic environments.

Published
2024
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32. Cloud-Based Real-Time Molecular Screening Platform with MolFormer

Author

Belgodere, Brian, Chenthamarakshan, Vijil, Das, Payel, Dognin, Pierre, Kurien, Toby, Melnyk, Igor, Mroueh, Youssef, Padhi, Inkit, Rigotti, Mattia, Ross, Jarret, Schiff, Yair, and Young, Richard A.

Subjects
Computer Science - Machine Learning
Abstract

With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based real-time platform that allows users to virtually screen molecules of interest. For this purpose, molecular embeddings inferred from a recently proposed large chemical language model, named MolFormer, are leveraged. The platform currently supports three tasks: nearest neighbor retrieval, chemical space visualization, and property prediction. Based on the functionalities of this platform and results obtained, we believe that such a platform can play a pivotal role in automating chemistry and chemical engineering research, as well as assist in drug discovery and material design tasks. A demo of our platform is provided at \url{www.ibm.biz/molecular_demo}., Comment: Paper accepted at ECML PKDD 2022 demo track

Published
2022

33. Race and sex differences in HDL peroxide content among American adults with and without type 2 diabetes

Author

Flaherty, Shelby M, Wood, Elizabeth K, Ryff, Carol D, Love, Gayle D, Kelesidis, Theodoros, Berkowitz, Loni, Echeverría, Guadalupe, Rivera, Katherine, Rigotti, Attilio, and Coe, Christopher L

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Nutrition and Dietetics, Prevention, Diabetes, Nutrition, Clinical Research, Cardiovascular, Atherosclerosis, Metabolic and endocrine, Black or African American, Case-Control Studies, Cholesterol, HDL, Diabetes Mellitus, Type 2, Female, Humans, Lipid Peroxides, Male, Middle Aged, Racial Groups, Sex Factors, United States, White People, Cholesterol, Dyslipidemia lipid peroxidation, Obesity, HDL function, Race, Other Information and Computing Sciences, Nutrition & Dietetics, Medical biochemistry and metabolomics, Nutrition and dietetics
Abstract

BackgroundHigh-density lipoprotein (HDL) plays a critical role in protection against atherosclerosic and cardiovascular disease (ASCVD). In addition to contributing to clearing excess vascular cholesterol, HDL particles exhibit antioxidative functions, helping to attenuate adverse effects of oxidized low-density lipoproteins. However, these beneficial properties can be undermined by oxidative stress, inflammation, and unhealthy lifestyles and diet, as well as influenced by race and sex. Thus, when assessing cardiovascular risk, it is important to consider multifactorial aspects of HDL, including antioxidant activity rather than just total amount and type of HDL-cholesterol (HDL-C) particles. Because prior research showed HDL peroxide content (HDLperox) can be inversely associated with normal anti-oxidant HDL activity, elevated HDLperox may serve as a bioindicator of HDL dysfunction.MethodsIn this study, data from a large national cohort of Americans was utilized to determine the impact of sex, race, and diabetes status on HDLperox in middle-aged and older adults. A previously developed cell-free fluorometric method was utilized to quantify HDLperox in serum depleted of apo-B containing lipoproteins.ResultsIn keeping with predictions, white men and diabetics exhibited HDLperox in the atypical upper range, suggestive of less functional HDL. White men had higher HDLperox levels than African American males (13.46 ± 6.10 vs. 10.88 ± 5.81, p

Published
2022

34. Sleep disturbances and psychological well-being among military medical doctors of the Swiss Armed Forces: study protocol, rationale and development of a cross-sectional and longitudinal interventional study

Author

Dena Sadeghi-Bahmani, Viola Rigotti, Zeno Stanga, Undine E. Lang, Rebecca K. Blais, Michelle L. Kelley, and Serge Brand

Subjects
sleep disturbances, Swiss Armed Forces, psychological well-being, military medical doctors, eCBTI, active control condition, Public aspects of medicine, RA1-1270
Abstract

BackgroundCompared to civilians and non-medical personnel, military medical doctors are at increased risk for sleep disturbances and impaired psychological well-being. Despite their responsibility and workload, no research has examined sleep disturbances and psychological well-being among the medical doctors (MDs) of the Swiss Armed Forces (SAF). Thus, the aims of the proposed study are (1) to conduct a cross-sectional study (labeled ‘Survey-Study 1’) of sleep disturbances and psychological well-being among MDs of the SAF; (2) to identify MDs who report sleep disturbances (insomnia severity index >8), along with low psychological well-being such as symptoms of depression, anxiety and stress, but also emotion regulation, concentration, social life, strengths and difficulties, and mental toughness both in the private/professional and military context and (3) to offer those MDs with sleep disturbances an evidence-based and standardized online interventional group program of cognitive behavioral therapy for insomnia (eCBTi) over a time lapse of 6 weeks (labeled ‘Intervention-Study 2’).MethodAll MDs serving in the SAF (N = 480) will be contacted via the SAF-secured communication system to participate in a cross-sectional survey of sleep disturbances and psychological well-being (‘Survey-Study 1’). Those who consent will be provided a link to a secure online survey that assesses sleep disturbances and psychological well-being (depression, anxiety, stress, coping), including current working conditions, job-related quality of life, mental toughness, social context, family/couple functioning, substance use, and physical activity patterns. Baseline data will be screened to identify those MDs who report sleep disturbances (insomnia severity index >8); they will be re-contacted, consented, and randomly assigned either to the eCBTi or the active control condition (ACC) (‘Intervention-Study 2’). Individuals in the intervention condition will participate in an online standardized and evidence-based group intervention program of cognitive behavioral therapy for insomnia (eCBTi; once the week for six consecutive weeks; 60–70 min duration/session). Participants in the ACC will participate in an online group counseling (once the week for six consecutive weeks; 60–70 min duration/session), though, the ACC is not intended as a bona fide psychotherapeutic intervention. At the beginning of the intervention (baseline), at week 3, and at week 6 (post-intervention) participants complete a series of self-rating questionnaires as for the Survey-Study 1, though with additional questionnaires covering sleep-related cognitions, experiential avoidance, and dimensions of self-awareness.Expected outcomesSurvey-Study 1: We expect to describe the prevalence rates of, and the associations between sleep disturbances (insomnia (sleep quality); sleep onset latency (SOL); awakenings after sleep onset (WASO)) and psychological well-being among MDs of the SAF; we further expect to identify specific dimensions of psychological well-being, which might be rather associated or non-associated with sleep disturbances.Intervention-Study 2: We expect several significant condition-by-time-interactions. Such that participants in the eCBTi will report significantly greater improvement in sleep disturbances, symptoms of depression, anxiety, stress reduction both at work and at home (family related stress), and an improvement in the overall quality of life as compared to the ACC over the period of the study.ConclusionThe study offers the opportunity to understand the prevalence of sleep disturbances, including factors of psychological well-being among MDs of the SAF. Further, based on the results of the Intervention-Study 2, and if supported, eCBTi may be a promising method to address sleep disturbances and psychological well-being among the specific context of MDs in the SAF.

Published
2024
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35. Tobacco smoking, smoking cessation and life expectancy among people with HIV on antiretroviral therapy in South Africa: a simulation modelling study

Author

Acadia M. Thielking, Kieran P. Fitzmaurice, Ronel Sewpaul, Stavroula A. Chrysanthopoulou, Lotanna Dike, Douglas E. Levy, Nancy A. Rigotti, Mark J. Siedner, Robin Wood, A. David Paltiel, Kenneth A. Freedberg, Emily P. Hyle, and Krishna P. Reddy

Subjects
HIV, antiretroviral therapy, tobacco, smoking, smoking cessation, South Africa, Immunologic diseases. Allergy, RC581-607
Abstract

Abstract Introduction As access to effective antiretroviral therapy (ART) has improved globally, tobacco‐related illnesses, including cardiovascular disease, cancer and chronic respiratory conditions, account for a growing proportion of deaths among people with HIV (PWH). We estimated the impact of tobacco smoking and smoking cessation on life expectancy among PWH in South Africa. Methods In a microsimulation model, we simulated 18 cohorts of PWH with virologic suppression, each homogenous by sex, initial age (35y/45y/55y) and smoking status (current/former/never). Input parameters were from data sources published between 2008 and 2022. We used South African data to estimate age‐stratified mortality hazard ratios: 1.2−2.3 (females)/1.1−1.9 (males) for people with current versus never smoking status; and 1.0−1.3 (females)/1.0−1.5 (males) for people with former versus never smoking status, depending on age at cessation. We assumed smoking status remains unchanged during the simulation; people who formerly smoked quit at model start. Simulated PWH face a monthly probability of disengagement from care and virologic non‐suppression. In sensitivity analysis, we varied smoking‐associated and HIV‐associated mortality risks. Additionally, we estimated the total life‐years gained if a proportion of all virologically suppressed PWH stopped smoking. Results Forty‐five‐year‐old females/males with HIV with virologic suppression who smoke lose 5.3/3.7 life‐years compared to PWH who never smoke. Smoking cessation at age 45y adds 3.4/2.4 life‐years. Simulated PWH who continue smoking lose more life‐years from smoking than from HIV (females, 5.3 vs. 3.0 life‐years; males, 3.7 vs. 2.6 life‐years). The impact of smoking and smoking cessation increase as smoking‐associated mortality risks increase and HIV‐associated mortality risks, including disengagement from care, decrease. Model results are most sensitive to the smoking‐associated mortality hazard ratio; varying this parameter results in 1.0−5.1 life‐years gained from cessation at age 45y. If 10−25% of virologically suppressed PWH aged 30−59y in South Africa stopped smoking now, 190,000−460,000 life‐years would be gained. Conclusions Among virologically suppressed PWH in South Africa, tobacco smoking decreases life expectancy more than HIV. Integrating tobacco cessation interventions into HIV care, as endorsed by the World Health Organization, could substantially improve life expectancy.

Published
2024
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36. Evaluation of two study demands-resources-based interventions: a randomized controlled trial

Author

Lorena Sarah Körner, Timo Kortsch, Kerstin Rieder, and Thomas Rigotti

Subjects
study demands-resources framework, online intervention, mindfulness, study crafting, self-undermining, exhaustion, Psychology, BF1-990
Abstract

IntroductionHigher education students experience significant levels of exhaustion in their studies, yet there are limited evidence-based support programs available. Therefore, this study evaluated a novel intervention approach by testing the effectiveness of two online interventions based on the study demands-resources framework. These interventions aimed to balance demands and resources. Derived from the theoretical assumptions of the framework, we hypothesized that the interventions would increase study and personal resources, engagement, and study crafting, and decrease study demands, exhaustion, and self-undermining. Additionally, we hypothesized that demands and resources would mediate the effects of the intervention on engagement, exhaustion, study crafting, and self-undermining.MethodsConducted as a randomized controlled trial with a waitlist control group (n = 71), the study involved participants in two intervention groups who engaged with the interventions for 2 weeks. Intervention group 1 (n = 64) focused on adapting demands, while intervention group 2 (n = 70) focused on increasing resources. The design allowed for a comparison of the effectiveness of these different approaches. Participants completed questionnaires before and after the intervention, and at a 5-week follow-up.ResultsResults of the analyses of variance with repeated measures revealed that the interventions had significant positive effects on the personal resource mindfulness, two study crafting strategies, self-undermining, and exhaustion. Notably, intervention group 2 exhibited more positive outcomes. The hypothesized mediation effects through mindfulness were partially supported.DiscussionThe study demonstrates the considerable potential of interventions based on the study demands-resources framework for higher education institutions in supporting student well-being.

Published
2024
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37. Revealed Incomplete Preferences

Author

Nielsen, Kirby and Rigotti, Luca

Subjects
Economics - General Economics
Abstract

We elicit incomplete preferences over monetary gambles with subjective uncertainty. Subjects rank gambles, and these rankings are used to estimate preferences; payments are based on estimated preferences. About 40\% of subjects express incompleteness, but they do so infrequently. Incompleteness is similar for individuals with precise and imprecise beliefs, and in an environment with objective uncertainty, suggesting that it results from imprecise tastes more than imprecise beliefs. When we force subjects to choose, we observe more inconsistencies and preference reversals. Evidence suggests there is incompleteness that is indirectly revealed -- in up to 98\% of subjects -- in addition to what we directly measure.

Published
2022

38. Endothelial Notch1 signaling in white adipose tissue promotes cancer cachexia

Author

Taylor, Jacqueline, Uhl, Leonie, Moll, Iris, Hasan, Sana Safatul, Wiedmann, Lena, Morgenstern, Jakob, Giaimo, Benedetto Daniele, Friedrich, Tobias, Alsina-Sanchis, Elisenda, De Angelis Rigotti, Francesca, Mülfarth, Ronja, Kaltenbach, Sarah, Schenk, Darius, Nickel, Felix, Fleming, Thomas, Sprinzak, David, Mogler, Carolin, Korff, Thomas, Billeter, Adrian T., Müller-Stich, Beat P., Berriel Diaz, Mauricio, Borggrefe, Tilman, Herzig, Stephan, Rohm, Maria, Rodriguez-Vita, Juan, and Fischer, Andreas

Published
2023
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40. Multifunctional 3D-Printed Thermoplastic Polyurethane (TPU)/Multiwalled Carbon Nanotube (MWCNT) Nanocomposites for Thermal Management Applications

Author

Daniele Rigotti, Andrea Dorigato, and Alessandro Pegoretti

Subjects
thermoplastic polyurethane, phase-change materials, carbon nanotubes, 3D printing, fused filament fabrication, thermal energy storage, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

In this work, multiwalled carbon nanotubes (MWCNTs) were melt-compounded into a novel thermal energy storage system consisting of a microencapsulated paraffin, with a melting temperature of 6 °C (M6D), dispersed within a flexible thermoplastic polyurethane (TPU) matrix. The resulting materials were then processed via Fused Filament Fabrication (FFF), and their thermo-mechanical properties were comprehensively evaluated. After an optimization of the processing parameters, good adhesion between the polymeric layers was obtained. Field-Emission Scanning Electron Microscopy (FESEM) images of the 3D-printed samples highlighted a uniform distribution of the microcapsules within the polymer matrix, without an evident MWCNT agglomeration. The thermal energy storage/release capability provided by the paraffin microcapsules, evaluated through Differential Scanning Calorimetry (DSC), was slightly lowered by the FFF process but remained at an acceptable level (i.e., >80% with respect to the neat M6D capsules). The novelty of this work lies in the successful integration of MWCNTs and PCMs into a TPU matrix, followed by 3D printing via FFF technology. This approach combines the high thermal conductivity of MWCNTs with the thermal energy storage capabilities of PCMs, creating a multifunctional nanocomposite material with unique thermal management properties.

Published
2024
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41. Inadequate Intake of Choline and Essential Fatty Acids in Latin American Childbearing-Age Women as a Regional Pre-Conceptional Disadvantage: ELANS Results

Author

Marianella Herrera-Cuenca, Martha Cecilia Yépez García, Lilia Yadira Cortés Sanabria, Pablo Hernández, Guillermo Ramírez, Maura Vásquez, Yaritza Sifontes, Georgina Gómez, María Reyna Liria-Domínguez, Attilio Rigotti, Mauro Fisberg, Irina Kovalkys, and Maritza Landaeta-Jiménez

Subjects
choline, essential fatty acids, women of childbearing age, diet, Latin America, ELANS, Nutrition. Foods and food supply, TX341-641
Abstract

Background/Objectives: Choline and essential fatty acids (EFA) are vital for fetal brain development, supporting pregnancy, and maintaining hormonal balance. They also promote overall health. The childbearing years present a window of opportunity to increase the intake of these key nutrients and develop healthy dietary habits. The aims of this study were to evaluate the intake of choline and EFA in women of childbearing age (15–49 years old), identify their food sources and determine if supplements containing choline and EFA were available across the Estudio Latinoamericano de Nutrición y Salud (ELANS) countries. Methods: Survey data were collected for the ELANS, including participants from Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela (n = 9218; 15–65 years old). Women of childbearing age were extracted from the largest database (n = 3704). Results: In general, choline intake was inadequate in all countries, while EFA intake was normal or above requirements. Chile had the lowest intake of choline, and Colombia had the highest. The results showed that some countries had more inadequate choline intake than others. Consuming a larger quantity of eggs helped reduce choline inadequacy, as did including eggs and fish in the diet. The intake of EFA, including ALA, EPA, and DHA, showed variability. The contributions of EPA and DHA were lower than that of ALA, and the results differed by age group. Conclusions: choline intake is inadequate, and EFA intake is variable among women of childbearing age in the ELANS study. More awareness and education are needed to achieve better intake of these nutrients.

Published
2024
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42. Assessing Response Rates and Sleep Disorder Prevalence: Insights from a Propranolol Treatment Study for Infantile Haemangiomas

Author

Francesca Opri, Roberta Opri, Marco Zaffanello, and Erika Rigotti

Subjects
children, electronic questionnaire, infantile haemangiomas, infants, propranolol, sleep disturbances, Pediatrics, RJ1-570
Abstract

Background: Infantile haemangiomas (IHs) sometimes require treatment with propranolol. Sleep disturbances are the most frequently reported side effects. Monitoring adverse drug events necessitates repeated hospital visits, which can be challenging during a pandemic. Objectives: To explore the effectiveness of a new electronic questionnaire in identifying sleep disturbances related to treatment with propranolol and potential confounding factors. To evaluate the response rate to the questionnaire. To report the proportion of patients on propranolol with sleep disturbances. Methods: In an observational, prospective cohort study, caregivers provided clinical information during ambulatory visits and via an electronic questionnaire after an 8-week treatment course with propranolol and at the time of treatment interruption. Adverse drug reaction reporting forms were assessed for causality. Results: The questionnaire response rate was 91%, and the completion rate was 100%. A total of 59% of patients experienced sleep disturbances during propranolol treatment, which were considered adverse reactions. Sleep disorders were frequent during sleep regression phases and in subjects who fell asleep during physical contact with caregivers or bed-sharing with parents. Conclusion: The application of this questionnaire allows for identifying adverse sleep events associated with propranolol in IHs and potential confounders. Counselling on sleep hygiene is recommended before treatment onset.

Published
2024
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43. Moral Hazard with Heterogeneous Beliefs

Author

Dumav, Martin, Khan, Urmee, and Rigotti, Luca

Subjects
Economics - Theoretical Economics
Abstract

We study a model of moral hazard with heterogeneous beliefs where each of agent's actions gives rise to a pair of probability distributions over output levels, one representing the beliefs of the agent and the other those of the principal. The agent's relative optimism or pessimism dictates whether the contract is high-powered (i.e. with high variability between wage levels) or low-powered. When the agent is sufficiently more optimistic than the principal, the trade-off between risk-sharing and incentive provision may be eliminated. Using Monotone Likelihood Ratio ranking to model disagreement in the parties' beliefs, we show that incentives move in the direction of increasing disagreement. In general, the shape of the wage scheme is sensitive to the differences in beliefs. Thereby, key features of optimal incentive contracts under common beliefs do not readily generalize to the case of belief heterogeneity.

Published
2021

48. HAPLN1 potentiates peritoneal metastasis in pancreatic cancer

Author

Wiedmann, Lena, De Angelis Rigotti, Francesca, Vaquero-Siguero, Nuria, Donato, Elisa, Espinet, Elisa, Moll, Iris, Alsina-Sanchis, Elisenda, Bohnenberger, Hanibal, Fernandez-Florido, Elena, Mülfarth, Ronja, Vacca, Margherita, Gerwing, Jennifer, Conradi, Lena-Christin, Ströbel, Philipp, Trumpp, Andreas, Mogler, Carolin, Fischer, Andreas, and Rodriguez-Vita, Juan

Published
2023
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49. NEGRITUDE E DIFERENÇA: UMA PROPOSTA DE ABORDAGEM TEÓRICA

Author

Renildo Nascimento Santos, Vinicius Ramos Rigotti, and Francisco Ramos de Farias

Subjects
Negritude, Filosofia da Diferença, Diáspora, Identidade, Colonialidade, Education, Social Sciences
Abstract

No presente trabalho, é constituída uma análise, em caráter preliminar, da contextualização do conceito de Negritude privilegiando a abordagem teórica do político, poeta e ensaísta Aimé Césaire como um dos fundadores do movimento que deu origem a essa categoria de análise. Em complementariedade, são apresentadas algumas considerações sobre a Filosofia da Diferença, sobretudo partindo das ideias do filósofo Gilles Deleuze. O principal objetivo é verificar se a Negritude pode ser entendida como uma afirmação da Diferença. Esse esforço intelectual faz parte de um projeto de pesquisa mais amplo sobre a diáspora africana e suas consequências na contemporaneidade brasileira que se encontra ainda na fase de recorte teórico e delimitação espacial dos fenômenos a serem estudados.

Published
2024

50. The association between health behaviours and academic performance moderated by trait mindfulness amongst university students: an observational study

Author

Sebastian Heller, Jennifer L. Reichel, Lina M. Mülder, Markus Schäfer, Lisa Schwab, Antonia M. Werner, Stephan Letzel, Thomas Rigotti, and Pavel Dietz

Subjects
health behaviour, academic performance, mindfulness, health communication, health promotion, Public aspects of medicine, RA1-1270
Abstract

ObjectivesTo target health communication at less health-conscious groups, evidence on health behaviours’ effects on non-health-related outcomes – such as academic performance – is necessary. Recent research has highlighted the associations of various health behaviours on academic performance of university students. However, there is a lack of research investigating the most predominant health behaviours simultaneously and their association with academic performance, as well as the factors that potentially influence the direction or strength of these associations. Therefore, this study investigated (I) which of the predominant health behaviours (physical activity, healthy diet, sleep, sedentary behaviour, alcohol consumption, smoking, drug use) are most associated with academic performance and (II) whether the personal resource of trait mindfulness moderates these associations.MethodsAn online survey was conducted amongst university students during the 2021 summer semester. Group differences in academic performance regarding health behaviours were analysed using ANOVA (N = 1,049). A first linear regression model (N = 571), considering all selected health behaviours simultaneously, assessed their association with academic performance. A second model (N = 540) assessed interaction effects of health behaviours and trait mindfulness. Separate regressions assessed each interaction’s association with academic performance.ResultsSleep, fruit and vegetable consumption, and gender were significantly associated with academic performance. The second model showed no significant interaction effects.ConclusionTargeting sleep and fruit and vegetable consumption might be the most promising strategies for elevating students’ academic performance, thereby enabling health communication strategies to reach groups driven by performance improvements rather than health benefits.

Published
2024
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