On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

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The paper details the process of developing the ITER Plasma Control System (PCS), that is, how to design and deploy it systematically, in the most efficient and effective manner. The integrated nature of the ITER PCS, with its multitude of coupled control ...

Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...

In droplet microfluidic devices with suction-based flow control, the microchannel geometry and suction pressure at the outlet govern the dynamic properties of the two phases that influence the droplet generation. Therefore, it is critical to understand the ...

Magnonics is a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern informa ...

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 ...

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we d ...

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 ...

Metal Forming is a basic and essential industrial process to provide materials for constructing complex products. To design an efficient metal forming process, the functional requirements and operational performance are two important aspects to be consider ...

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 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 ...

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 ...

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 ...

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Tradition ...

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 ...

The construction industry plays a major role in the high levels of greenhouse gas emissions, resource consumption, and waste generation observed nowadays. Key to the circular economy, structural component reuse arises as a promising solution to divert cons ...

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 ...

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 ...

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 ...

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 ...

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

Chat with Graph Search

Strategy to systematically design and deploy the ITER plasma control system: A system engineering and model-based design approach

Oligolithic Cross-task Optimizations across Isolated Workloads

Modified capillary number to standardize droplet generation in suction-driven microfluidics

The 2024 Magnonics Roadmap

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

Dynamical simulation via quantum machine learning with provable generalization

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Safe Deep Neural Networks

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

Gibbs sampling the posterior of neural networks

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Deep Learning Theory Through the Lens of Diagonal Linear Networks

RE:SLAB — a load bearing system for open-ended component reuse in building structures

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

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

Random matrix methods for high-dimensional machine learning models

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

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants