Children's Geographies; 10/01/2022
(AN 159948781); ISSN: 14733285
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An internship with Arm gives you exposure to real work and insight into the Arm innovations that shape extraordinary. Students who thrive at Arm take their love of learning beyond their experience of formal education and develop new ideas. This is the energy that interests us.

Internships at Arm will give you the opportunity to put theory into practice through exciting, intellectually challenging, real-world projects that enrich your personal and technical development while enhancing your future career opportunities.

This internship position is within Machine Learning Group in Arm which works on key technologies for the future of computing. Working on the cutting edge of Arm IP, this Group creates technology that powers the next generation of mobile apps, portable devices, home automation, smart cities, self-driving cars, and much more.

When applying, please make sure to include your most up to date academic transcript.

For a sneak peek what it’s like to work in Arm Lund, please have a look at the following video: http://bit.ly/2kxWMXp

The Role

You will work alongside experienced engineers within one of the IP development teams in Arm and be given real project tasks and will be supported by experienced engineers. Examples of previous project tasks are:

• Developing and trialing new processes for use by the design/verification teams.
• Investigating alternative options for existing design or verification implementations.
• Help to develop a hardware platform that can guide out customers to the best solution.
• Implement complex logic using Verilog to bridge a gap in a system.
• Develop bare metal software to exercise design functionality.
• Verify a complex design, from unit to full SoC level.
• Help to take a platform to silicon.

Arm's Machine Learning Group is seeking for a highly motivated and creative Graduate Software Engineer to join the Cambridge-based applied ML team.

From research, to proof-of-concept development, to deployment on ARM IPs, joining this team, would be a phenomenal opportunity to contribute to the full life-cycle of machine learning projects and understand how state-of-the-art machine learning is used to solve real word problems.

Working closely with field experts in a truly multi-discipline environment, you will have the chance to explore existing or build new machine learning techniques, while helping unpick the complex world of use-cases that are applied on high end mobile phones, TVs, and laptops.

About the role

Your role would be to understand, develope and implement these use case, collaborating with Arm's system architects, and working with our marketing groups to ensure multiple Arm products are molded to work well for machine learning. Also, experience deploying inference in a mobile or embedded environment would be ideal. Knowledge of the theory and concepts involved in ML is also needed, so fair comparisons of different approaches can be made.

As an in depth technical role, you will need to understand the complex applications you analyse in detail and communicate them in their simplest form to help include them in product designs, where you will be able to influence both IP and system architecture.

The post Machine Learning Technology in Predicting Relapse and Implementing Peer Recovery Intervention Before Drug Use Occurs was curated by information for practice.

Current Directions in Psychological Science, Ahead of Print. The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities […]

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Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

New software capabilities in RMCProfile allow researchers to study the structure of materials by combining machine learning interatomic potentials and reverse Monte Carlo.

This paper describes real-time statistical analysis of liquid jet images for SFX experiments at the European XFEL. This analysis forms one part of the automated jet re-alignment system for SFX experiments at the SPB/SFX instrument of European XFEL.

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

During beam time at a research facility, alignment and optimization of instrumentation, such as spectrometers, is a time-intensive task and often needs to be performed multiple times throughout the operation of an experiment. Despite the motorization of individual components, automated alignment solutions are not always available. In this study, a novel approach that combines optimisers with neural network surrogate models to significantly reduce the alignment overhead for a mobile soft X-ray spectrometer is proposed. Neural networks were trained exclusively using simulated ray-tracing data, and the disparity between experiment and simulation was obtained through parameter optimization. Real-time validation of this process was performed using experimental data collected at the beamline. The results demonstrate the ability to reduce alignment time from one hour to approximately five minutes. This method can also be generalized beyond spectrometers, for example, towards the alignment of optical elements at beamlines, making it applicable to a broad spectrum of research facilities.

Transition-metal nitro­gen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

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How do galaxies such as our Milky Way come into existence? How do they grow and change over time? The science behind galaxy formation has long been a puzzle, but a University of Arizona-led team of scientists is one step closer to finding answers, thanks to supercomputer simulations. Observing real galaxies in space can only provide snapshots in time, so researchers who study how galaxies evolve over billions of years need to use computer simulations. Traditionally, astronomers have used simulations to invent theories of galaxy formation and test them, but they have had to proceed one galaxy at a time. Peter Behroozi of the university's Steward Observatory and colleagues overcame this hurdle by generating millions of different universes on a supercomputer, each according to different physical theories for how galaxies form. The findings challenge fundamental ideas about the role dark matter plays in galaxy formation, the evolution of galaxies over time and the birth of stars. The study is the first to create self-consistent universes that are exact replicas of the real ones -- computer simulations that each represent a sizeable chunk of the actual cosmos, containing 12 million galaxies and spanning the time from 400 million years after the Big Bang to the present day. The results from the "UniverseMachine," as the authors call their approach, have helped resolve the long-standing paradox of why galaxies cease to form new stars even when they retain plenty of hydrogen gas, the raw material from which stars are forged. The research is partially funded by NSF's Division of Physics through grants to UC Santa Barbara's Kavli Institute for Theoretical Physics and the Aspen Center for Physics.

Image credit: NASA/ESA/J. Lotz and the HFF Team/STScI

SEB Embedded has selected Thought Machine’s cloud-native core banking system, Vault Core, as the foundation for its latest service offering.

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Machine learning algorithms can sometimes do a great job with a little help from human expertise, at least in the field of materials science. In many specialized areas of science, engineering and medicine, researchers are turning to machine learning algorithms to analyze data sets that have grown too large for humans to understand. In materials science, success with this effort could accelerate the design of next-generation advanced functional materials, where development now usually depends on old-fashioned trial and error. By themselves, however, data analytics techniques borrowed from other research areas often fail to provide the insights needed to help materials scientists and engineers choose which of many variables to adjust -- and the techniques can't account for dramatic changes such as the introduction of a new chemical compound into the process. In a new study, researchers explain a technique known as dimensional stacking, which shows that human experience still has a role to play in the age of machine intelligence. The machines gain an edge at solving a challenge when the data to be analyzed are intelligently organized based on human knowledge of what factors are likely to be important and related. "When your machine accepts strings of data, it really does matter how you are putting those strings together," said Nazanin Bassiri-Gharb, the paper's corresponding author and a scientist at the Georgia Institute of Technology. "We must be mindful that the organization of data before it goes to the algorithm makes a difference. If you don't plug the information in correctly, you will get a result that isn't necessarily correlated with the reality of the physics and chemistry that govern the materials."

Image credit: Rob Felt/Georgia Tech

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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