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Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries

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Related publications (36)

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Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Danya Li

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-av ...

Elsevier2023

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Seyedbehzad Bozorgtabar, Devavrat Tomar

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

IEEE2023

Robust discrete choice models with t-distributed kernel errors

Michel Bierlaire

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

2023

The end of urban sprawl? Internal migration across the rural-urban continuum in Switzerland, 1966-2018

Mathias Lerch

In high-income countries, migration redistributed populations from congested city centres into the sparsely populated outskirts, raising challenges to environmental and population health and the conservation of biodiversity. We evaluate whether this periur ...

2023

Transformational IoT sensing for air pollution and thermal exposures

Dusan Licina

Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, buil ...

FRONTIERS MEDIA SA2022

Amygdala subdivisions exhibit aberrant whole-brain functional connectivity in relation to stress intolerance and psychotic symptoms in 22q11.2DS

Dimitri Nestor Alice Van De Ville, Farnaz Delavari

The amygdala is a key region in emotional regulation, which is often impaired in psychosis. However, it is unclear if amygdala dysfunction directly contributes to psychosis, or whether it contributes to psychosis through symptoms of emotional dysregulation ...

SPRINGERNATURE2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

Association of neighbourhood disadvantage and individual socioeconomic position with all-cause mortality: a longitudinal multicohort analysis

Stéphane Joost, Idris Guessous

Background Few studies have examined the interactions between individual socioeconomic position and neighbourhood deprivation and the findings so far are heterogeneous. Using a large sample of diverse cohorts, we investigated the interaction effect of neig ...

ELSEVIER SCI LTD2022

The Future of Civic Technologies for the Involvement of Citizens in Urban Planning: 3D Urban Participatory e-Planning in the Spotlight

Thibaud Nicolas Chassin

The development of sustainable urban projects is crucial to address the challenges of the growing population living in cities. Facing complex decision-making and increasing expectations from inhabitants, local institutions are gradually becoming open to co ...

EPFL2022

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Soumaya Elkantassi

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

EPFL2023

The effects of time-restricted eating and weight loss on bone metabolism and health: a 6-month randomized controlled trial

Nicholas Edward Phillips

Objective This study explored the impact of time-restricted eating (TRE) versus standard dietary advice (SDA) on bone health. Methods Adults with >= 1 component of metabolic syndrome were randomized to TRE (ad libitum eating within 12 hours) or SDA (food p ...

WILEY2022

Real-time model calibration with deep reinforcement learning

Olga Fink

The real-time, and accurate inference of model parameters is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes. However, f ...

2022

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Ahmed Salaheldin Ahmed Shama

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmark ...

EPFL2022

Plant sterols and cholesterol metabolism are associated with five-year cognitive decline in the elderly population

Mehdi Gholam, Christopher Clark

Dysregulations in cholesterol metabolism are associated with neurodegenerative and vascular pathologies, and dementia. Diet-derived plant sterols (phytosterols) have cholesterol-lowering, anti-inflammatory, and antioxidant properties and may interfere with ...

CELL PRESS2023