Closed-Loop Neural Interfaces with Embedded Machine Learning

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The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Neuroprostheses have been used clinically for decades, to help restore or preserve brain functions, when pharmaceutical treatments are inefficient. Although great progress in the field has been made over the years to interface with the nervous system, surf ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

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 ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

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 ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

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 ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

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 ...

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

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 ...

Closed-Loop Neural Interfaces with Embedded Machine Learning

Chattez avec Graph Search

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Investigating neural resource allocation in the sensorimotor control of extra limbs

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Robust machine learning for neuroscientific inference

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Aggregating Spatial and Photometric Context for Photometric Stereo

Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

Sequential Reasoning with Socially Caused Beliefs

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

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

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

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

Small-scale robotic devices for medical interventions in the brain

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