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TORCH is a time-of-flight detector concept using Cherenkov light to provide charged particle identification up to 10 GeV/c. The concept and design are described and performance in simulation is quantified.

The rnn package provides components for implementing a wide range of Recurrent Neural Networks. It is built withing the framework of the Torch distribution for use with the nn package. The components have evolved from 3 iterations, each adding to the flexibility and capability of the package. All component modules inherit either the AbstractRecurrent or AbstractSequencer classes. Strong unit testing, continued backwards compatibility and access to supporting material are the principles followed during its development. The package is compared against existing implementations of two published papers.

In this paper, a thermal DC plasma torch was designed and developed for plasma spraying of a titanium nitride layer. Thermal plasma spray torch is a kind of non-transferred torch which can produce a high energy and high temperature plasma arc jet. Ti powders were injected into the plasma jet interacted with nitrogen plasma, resulted in a thick and hard layer of titanium nitride. Coating sample produced by this method was analysed with Raman spectroscopy confirming titanium nitride production. To study the thickness of the layer optical microscopy was used. It was found that the resulted layer is thicker than the layer synthesized by other deposition methods and it mainly consists of pure titanium nitride.

Deep Neural Networks (DNNs) have demonstrated remarkable performance across various domains, including computer vision and natural language processing. However, they often struggle to accurately quantify the uncertainty of their predictions, limiting their broader adoption in critical real-world applications. Uncertainty Quantification (UQ) for Deep Learning seeks to address this challenge by providing methods to improve the reliability of uncertainty estimates. Although numerous techniques have been proposed, a unified tool offering a seamless workflow to evaluate and integrate these methods remains lacking. To bridge this gap, we introduce Torch-Uncertainty, a PyTorch and Lightning-based framework designed to streamline DNN training and evaluation with UQ techniques and metrics. In this paper, we outline the foundational principles of our library and present comprehensive experimental results that benchmark a diverse set of UQ methods across classification, segmentation, and regression tasks. Our library is available at https://github.com/ENSTA-U2IS-AI/Torch-Uncertainty

The TORCH time-of-flight detector is designed to provide particle identification in the momentum range 2-10 GeV/c over large areas. The detector exploits prompt Cherenkov light produced by charged particles traversing a 10 mm thick quartz plate. The photons propagate via total internal reflection and are focused onto a detector plane comprising position-sensitive Micro-Channel Plate Photo-Multiplier Tubes (MCP-PMT) detectors. The goal is to achieve a single-photon timing resolution of 70 ps, giving a timing precision of 15 ps per charged particle by combining the information from around 30 detected photons. The MCP-PMT detectors have been developed with a commercial partner (Photek Ltd, UK), leading to the delivery of a square tube of active area 53 $\times$ 53mm$^2$ with a granularity of 8 $\times$ 128 pixels equivalent. A large-scale demonstrator of TORCH, having a quartz plate of dimensions 660 $\times$ 1250 $\times$ 10 mm$^3$ and read out by a pair of MCP-PMTs with custom readout electronics, has been verified in a test beam campaign at the CERN PS. Preliminary results indicate that the required performance is close to being achieved. The anticipated performance of a full-scale

Neural Radiance Fields (NeRF) give rise to learning-based 3D reconstruction methods widely used in industrial applications. Although prevalent methods achieve considerable improvements in small-scale scenes, accomplishing reconstruction in complex and large-scale scenes is still challenging. First, the background in complex scenes shows a large variance among different views. Second, the current inference pattern, $i.e.$, a pixel only relies on an individual camera ray, fails to capture contextual information. To solve these problems, we propose to enlarge the ray perception field and build up the sample points interactions. In this paper, we design a novel inference pattern that encourages a single camera ray possessing more contextual information, and models the relationship among sample points on each camera ray. To hold contextual information,a camera ray in our proposed method can render a patch of pixels simultaneously. Moreover, we replace the MLP in neural radiance field models with distance-aware convolutions to enhance the feature propagation among sample points from the same camera ray. To summarize, as a torchlight, a ray in our proposed method achieves rendering a patc

Deep Neural Networks (DNN) have become a central method in ecology. Most current deep learning (DL) applications rely on one of the major deep learning frameworks, in particular Torch or TensorFlow, to build and train DNN. Using these frameworks, however, requires substantially more experience and time than typical regression functions in the R environment. Here, we present 'cito', a user-friendly R package for DL that allows specifying DNNs in the familiar formula syntax used by many R packages. To fit the models, 'cito' uses 'torch', taking advantage of the numerically optimized torch library, including the ability to switch between training models on the CPU or the graphics processing unit (GPU) (which allows to efficiently train large DNN). Moreover, 'cito' includes many user-friendly functions for model plotting and analysis, including optional confidence intervals (CIs) based on bootstraps for predictions and explainable AI (xAI) metrics for effect sizes and variable importance with CIs and p-values. To showcase a typical analysis pipeline using 'cito', including its built-in xAI features to explore the trained DNN, we build a species distribution model of the African elephan

Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon the torch package, and simplifies the definition, training, and evaluation of neural networks for both tabular data and generic tensors (e.g., images) for classification and regression. The package implements predefined architectures, and torch models can easily be converted to mlr3 learners. It also allows users to define neural networks as graphs. This representation is based on the graph language defined in mlr3pipelines and allows users to define the entire modeling workflow, including preprocessing, data augmentation, and network architecture, in a single graph. Through its integration into the mlr3 ecosystem, the package allows for convenient resampling, benchmarking, preprocessing, and more. We explain the package's design and features and show how to customize and extend it to new problems. Furthermore, we demonstrate the package's capabilities using three use cases, namely hyperparameter tuning, fine-tuning, and defining architectures for multimodal data. Finally, we present so

Microwave plasmas are a promising technology for energy-efficient CO$_{2}$ valorization via conversion of CO$_{2}$ into CO and O$_2$ using renewable energies. A 2.45 GHz microwave plasma torch with swirling CO$_{2}$ gas flow is studied in a large pressure (60-1000~mbar) and flow (5-100~slm) range. Two different modes of the plasma torch, depending on the operating pressure and microwave input power, are described: at pressures below 120~mbar the plasma fills most of the plasma torch volume whereas at pressures of about 120~mbar an abrupt contraction of the plasma in the center of the resonator is observed along with an increase of the gas temperature from 3000~K to 6000~K. The CO outflow is found to be proportional to the plasma effective surface and exhibits no significant dependence on the actual CO$_{2}$ flow injected into the reactor but only on the input power at certain pressure. Thermal dissociation calculations show that, even at the lowest pressures of this study, the observed conversion and energy efficiency are compatible with a thermal dissociation mechanism.

The post-discharge of an RF plasma torch supplied with helium and oxygen gases is characterized by mass spectrometry, optical emission spectroscopy and electrical measurements. We have proved the existence of a dc current in the post-discharge (1--20 A), attributed to the Penning ionization of atmospheric nitrogen and oxygenated species. The mechanisms ruling this dc current are investigated through experiments in which we discuss the influence of the O2 flow rate, the He flow rate and the distance separating the plasma torch from a material surface located downstream.