Imaging through multimode fibers using deep learning: The effects of intensity versus holographic recording of the speckle pattern

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In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

What changes with age? Results in reinforcement learning (RL) are mixed. Some studies found deteriorated RL performance in older participants compared to younger controls whereas other studies did not. Daniel et al. (J Neurosci 40(5):1084–1096, 2020. 10.15 ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Purpose: Although clinical 7T MRI offers various advantages compared to lower field strengths, achieving spatially uniform flip angle distributions remains a challenge. Sampling Perfection with Application optimized Contrast using different flip angle Evol ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

The visualization and manipulation of proteins in live cells are critical for studying complex biological processes. Self-labeling proteins do so by enabling the specific and covalent attachment of synthetic probes, offering unprecedented flexibility in th ...

The diversity of experimentally observed nonlinear coherent and partially coherent optical structures, including breathing, zero-dispersion, and switching wave states in driven optical microresonators, is mainly provided by cavity dispersion engineering. R ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Animals exhibit remarkable locomotion skills despite significant sensorimotor delays and operating in uncertain environments. Moreover, mammals acquire these skills within minutes of birth. From the Cambrian explosion to the present day, vertebrate motor c ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

This paper reflects on the pedagogy used for a series of student workshops on building with reused materials at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and presents the results of its 7 th edition, which focused on floor systems. During these w ...

Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost, placement constraints, or inability to directly measure certain critical pa ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

Imaging through multimode fibers using deep learning: The effects of intensity versus holographic recording of the speckle pattern

Chattez avec Graph Search

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Intact reinforcement learning in healthy ageing

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Individually optimized dynamic parallel transmit pulses for 3D high‐resolution SPACE imaging at 7T

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Exploiting Covalent Chemical Labeling with Self-Labeling Proteins

Coherent Dissipative Structures in Arbitrary Dispersion-Engineered Gallium Phosphide Fabry-Perot Resonators

Network time series forecasting in photovoltaics power production

A Bio-Inspired Learning and Control Framework for Cross-Embodiment and Cross-Task Locomotion

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

On the number of regions of piecewise linear neural networks

Pedagogy of an interdisciplinary, hands-on workshop to design and build floor systems with reused materials

Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Robust NAS under adversarial training: benchmark, theory, and beyond