Measurement in the Age of Information

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Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this letter, we account for this by c ...

Curiosity refers to the intrinsic desire of humans and animals to explore the unknown, even when there is no apparent reason to do so. Thus far, no single, widely accepted definition or framework for curiosity has emerged, but there is growing consensus th ...

The management of existing civil infrastructure is becoming more crucial as a large share of bridges is approaching their theoretical end of service duration. Structural performance monitoring aims to verify bridge safety at a given time, and it should be ...

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

While systems designers are increasingly turning to hardware accelerators for performance gains, realizing these gains is painstaking and error-prone. It can take several person-months to determine if a given accelerator is a good fit for a given piece of ...

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...

Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

Fuzzing has emerged as the most broadly used testing technique to discover bugs. Effective fuzzers rely on coverage to prioritize inputs that exercise new program areas. Edge-based code coverage of the Program Under Test (PUT) is the most commonly used cov ...

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability wil ...

Information derived from experiences is incorporated into the brain as changes to ensembles of cells, termed engram cells, which allow memory storage and recall. The mechanism by which those changes hold specific information is unclear. Here, we test the h ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

Smartphones enable understanding human behavior with activity recognition to support peoples daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous ...

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 ...

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of ...

Measurement in the Age of Information

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Data-driven approaches for non-invasive cuffless blood pressure monitoring

Perspective Aware Road Obstacle Detection

Curiosity-driven exploration: foundations in neuroscience and computational modeling

Framework to evaluate the value of monitoring-technique information for structural performance monitoring

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Interpretable Representation Learning and Evaluation for Abstractive Summarization

The Case for Performance Interfaces for Hardware Accelerators

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Arvin: Greybox Fuzzing Using Approximate Dynamic CFG Analysis

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Aiming beyond slight increases in accuracy

Engram cell connectivity as a mechanism for information encoding and memory function

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Robustness of Local Predictions in Atomistic Machine Learning Models

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning