On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

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Primary diamines are valuable yet challenging to synthesize due to issues such as product and intermediate condensation and catalyst poisoning. To address these problems, effective synthesis systems must be explored. Here, 2,5-bis(aminomethyl)furan (BAMF), ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

We present a new setup for resonant inelastic hard x-ray scattering at the Bernina beamline of SwissFEL with energy, momentum, and temporal resolution. The compact R = 0.5 m Johann-type spectrometer can be equipped with up to three crystal analyzers and al ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Nuclear power is a powerful technology that plays an important role in the fight against climate change, and research is continuously engaged in studies that could further improve its safety. After the Fukushima accident, Accident Tolerant Fuels research h ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

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 ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

The time-honored Allen -Feldman theory of heat transport in glasses is generally assumed to predict a finite value for the thermal conductivity, even if it neglects the anharmonic broadening of vibrational normal modes. We demonstrate that the harmonic app ...

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

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 ...

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 past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Energy piles represent an innovative technology that can help provide sustainable geothermal heating or cooling energy for thermal conditioning purposes. In hot-dominated climates, the interest is to inject heat in the ground and extract energy for space-c ...

In this work, a tool to design district heating networks (DHN) is presented and applied to the city of Lausanne as a case study. The evaluation of the buildings’ heat/cooling demand is performed using a Geographic Information System (GIS) database, built f ...

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Chattez avec Graph Search

Targeted Conversion of Biomass into Primary Diamines via Carbon Shell-Confined Cobalt Nanoparticles

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

A setup for hard x-ray time-resolved resonant inelastic x-ray scattering at SwissFEL

Safe Deep Neural Networks

Innovative thermophysical characterization of an ATF composite cladding: the case of silicon carbide

Gibbs sampling the posterior of neural networks

On the number of regions of piecewise linear neural networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Machine Learning for Modeling Stock Returns

Unearthing the foundational role of anharmonicity in heat transport in glasses

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Topics in statistical physics of high-dimensional machine learning

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Performance of energy piles foundation in hot-dominated climate: A case study in Dubai

Decomposition method to evaluate district heating/cooling network potential at urban scale