The Unlocking of High-Pressure Science with Broadband Neutron Spectroscopy at the ISIS Pulsed Neutron & Muon Source

Following significant instrument upgrades and parallel methodological developments over the past decade, the TOSCA neutron spectrometer at the ISIS Pulsed Neutron & Muon Source in the United Kingdom has developed a rich and growing scientific community spanning a broad range of non-traditional areas of neutron science, including chemical catalysis, gas adsorption & storage, and new materials for energy and sustainability. High-pressure science, however, has seen little to no representation to date owing to previous limitations in capability. Herein, we explore for the first time the viability of rapid high-pressure measurements in the gigapascal regime, capitalizing from the orders-of-magnitude increase in incident flux afforded by a recent upgrade of the primary-beam path. In particular, we show that spectroscopic measurements up to pressures of 2 GPa over an unprecedented energy-transfer range are now possible within the hour timescale. In addition, we have designed and commissioned a dedicated set of high-pressure vessels, with a view to foster and support the further growth and development of an entirely new user community on TOSCA

A robust comparison of dynamical scenarios in a glass-forming liquid

We use Bayesian inference methods to provide fresh insights into the sub-nanosecond dynamics of glycerol, a prototypical glass-forming liquid. To this end, quasielastic neutron scattering data as a function of temperature have been analyzed using a minimal set of underlying physical assumptions. On the basis of this analysis, we establish the unambiguous presence of three distinct dynamical processes in glycerol, namely, translational diffusion of the molecular centre of mass and two additional localized and temperature-independent modes. The neutron data also provide access to the characteristic length scales associated with these motions in a model-independent manner, from which we conclude that the faster (slower) localized motions probe longer (shorter) length scales. Careful Bayesian analysis of the entire scattering law favors a heterogeneous scenario for the microscopic dynamics of glycerol, where molecules undergo either the faster and longer or the slower and shorter localized motions.Peer ReviewedPostprint (author's final draft

FIVA: Facial Image and Video Anonymization and Anonymization Defense

In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA. Our proposed method is able to maintain the same face anonymization consistently over frames with our suggested identity-tracking and guarantees a strong difference from the original face. FIVA allows for 0 true positives for a false acceptance rate of 0.001. Our work considers the important security issue of reconstruction attacks and investigates adversarial noise, uniform noise, and parameter noise to disrupt reconstruction attacks. In this regard, we apply different defense and protection methods against these privacy threats to demonstrate the scalability of FIVA. On top of this, we also show that reconstruction attack models can be used for detection of deep fakes. Last but not least, we provide experimental results showing how FIVA can even enable face swapping, which is purely trained on a single target image.Comment: Accepted to ICCVW 2023 - DFAD 202

Image-Based Fire Detection in Industrial Environments with YOLOv4

Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.Comment: Accepted for publication at ICPRA

Synthetic Data for Object Classification in Industrial Applications

One of the biggest challenges in machine learning is data collection. Training data is an important part since it determines how the model will behave. In object classification, capturing a large number of images per object and in different conditions is not always possible and can be very time-consuming and tedious. Accordingly, this work explores the creation of artificial images using a game engine to cope with limited data in the training dataset. We combine real and synthetic data to train the object classification engine, a strategy that has shown to be beneficial to increase confidence in the decisions made by the classifier, which is often critical in industrial setups. To combine real and synthetic data, we first train the classifier on a massive amount of synthetic data, and then we fine-tune it on real images. Another important result is that the amount of real images needed for fine-tuning is not very high, reaching top accuracy with just 12 or 24 images per class. This substantially reduces the requirements of capturing a great amount of real data.Comment: Accepted for publication at ICPRA

Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers

Workplace injuries are common in today's society due to a lack of adequately worn safety equipment. A system that only admits appropriately equipped personnel can be created to improve working conditions. The goal is thus to develop a system that will improve workers' safety using a camera that will detect the usage of Personal Protective Equipment (PPE). To this end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system into an entry control point where workers must present themselves to obtain access to a restricted area. Combined with facial identity recognition, the system would ensure that only authorized people wearing appropriate equipment are granted access. A novelty of this work is that we increase the number of classes to five objects (hardhat, safety vest, safety gloves, safety glasses, and hearing protection), whereas most existing works only focus on one or two classes, usually hardhats or vests. The AI model developed provides good detection accuracy at a distance of 3 and 5 meters in the collaborative environment where we aim at operating (mAP of 99/89%, respectively). The small size of some objects or the potential occlusion by body parts have been identified as potential factors that are detrimental to accuracy, which we have counteracted via data augmentation and cropping of the body before applying PPE detection.Comment: Accepted for publication at ICPRA

Bayesian Inference in MANTID - An Update

In the context of neutron science, Bayesian inference methods have been recently implemented within the MANTID framework [Monserrat D et al. 2015 J. Phys. Conf. Ser. 663 012009 (2015)]. In this contribution, we highlight the advantages of this software package for robust data analysis and subsequent model selection. To this end, we use the celebrated Rosenbrock function to illustrate its merits and strengths relative to classical fitting algorithms. We also introduce the latest additions implemented in MANTID, with a view to increasing its user friendliness as well as stimulating wider use. These include simulated-annealing schemes to reduce the need for initial guesses, as well as new options for multidimensional fitting. © Published under licence by IOP Publishing Ltd.Peer ReviewedPostprint (published version

Detecting Molecular Rotational Dynamics Complementing the Low-Frequency Terahertz Vibrations in a Zirconium-Based Metal-Organic Framework

We show clear experimental evidence of co-operative terahertz (THz) dynamics observed below 3 THz (~100 cm-1), for a low-symmetry Zr-based metal-organic framework (MOF) structure, termed MIL-140A [ZrO(O2C-C6H4-CO2)]. Utilizing a combination of high-resolution inelastic neutron scattering and synchrotron radiation far-infrared spectroscopy, we measured low-energy vibrations originating from the hindered rotations of organic linkers, whose energy barriers and detailed dynamics have been elucidated via ab initio density functional theory (DFT) calculations. For completeness, we obtained Raman spectra and characterized the alterations to the complex pore architecture caused by the THz rotations. We discovered an array of soft modes with trampoline-like motions, which could potentially be the source of anomalous mechanical phenomena, such as negative linear compressibility and negative thermal expansion. Our results also demonstrate coordinated shear dynamics (~2.5 THz), a mechanism which we have shown to destabilize MOF crystals, in the exact crystallographic direction of the minimum shear modulus (Gmin).Comment: 10 pages, 6 figure

Gamma background characterization on VESUVIO: before and after the moderator upgrade

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