VO2 oscillators coupling for Neuromorphic Computation

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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 ...

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPAHM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent ...

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

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 ...

Semi-structured networks (SSNs) merge the structures familiar from additive models with deep neural networks, allowing the modeling of interpretable partial feature effects while capturing higher-order non-linearities at the same time. A significant challe ...

Ion cyclotron (IC) wave assisted breakdown has the potential to increase the robustness of plasma initiation during the ITER pre-fusion operation phase. Studies were performed at JET at ITER relevant loop electric field, E-loop ? 0.33 Vm(-1), and a range o ...

Federated Learning (FL) has gained considerable attention recently, as it allows clients to cooperatively train a global machine learning model without sharing raw data. However, its performance can be compromised due to the high heterogeneity in clients' ...

We report on the design and characterization of two inductive proximity sensors based on LC oscillators operating at 500 MHz. The distance from the target is obtained by measuring the oscillation frequency variation. Both sensors contain an off-chip LC res ...

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO3 into an ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Neuromorphic computing requires electronic systems that can perform massively parallel computational tasks with low energy consumption. Such systems have traditionally been based on complementary metal-oxide-semiconductor circuits, but further advances in ...

In engineering, oscillatory instabilities and resonances are often considered undesirable flow features and measures are taken to avoid them. This may include avoiding certain parametric regions or implementing control and mitigation strategies. However, t ...

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 ...

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour tow ...

VO2 oscillators coupling for Neuromorphic Computation

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Aggregating Spatial and Photometric Context for Photometric Stereo

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

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ICRH assisted breakdown study on JET

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500 MHz high resolution proximity sensors with fully integrated digital counter

Learnable latent embeddings for joint behavioural and neural analysis

Hydrogen-induced tunable remanent polarization in a perovskite nickelate

Random matrix methods for high-dimensional machine learning models

Neuromorphic computing based on halide perovskites

Self-sustained dynamics and forced resonant oscillations in flows: cross-junction jets and sloshing liquids

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Expectation consistency for calibration of neural networks