Compute Canada

BackgroundMany studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy.MethodsWe used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between cortical sources of EEG during video watching using the corrected imaginary part of phase-locking values.ResultsOur main analysis found no significant association between functional connectivity and ASD, showing only significant effects for age, sex, age-sex interaction, and site. Given these null results, we performed an exploratory analysis and observed, at 12 months, a negative correlation between functional connectivity and ADOS calibrated severity scores for restrictive and repetitive behaviors (RRB).LimitationsThe small sample of ASD participants inherent to sibling studies limits diagnostic group comparisons. Also, results from our secondary exploratory analysis should be considered only as potential relationships to further explore, given their increased vulnerability to false positives.ConclusionsThese results are inconclusive concerning an association between EEG functional connectivity and ASD in infancy. Exploratory analyses provided preliminary support for a relationship between RRB and functional connectivity specifically, but these preliminary observations need corroboration on larger samples.

We show how quantum-inspired 2d tensor networks can be used to efficiently and accurately simulate the largest quantum processors from IBM, namely Eagle (127 qubits), Osprey (433 qubits) and Condor (1121 qubits). We simulate the dynamics of a complex quantum many-body system -- specifically, the kicked Ising experiment considered recently by IBM in Nature 618, p. 500-505 (2023) -- using graph-based Projected Entangled Pair States (gPEPS), which was proposed by some of us in PRB 99, 195105 (2019). Our results show that simple tensor updates are already sufficient to achieve very large unprecedented accuracy with remarkably low computational resources for this model. Apart from simulating the original experiment for 127 qubits, we also extend our results to 433 and 1121 qubits, and for evolution times around 8 times longer, thus setting a benchmark for the newest IBM quantum machines. We also report accurate simulations for infinitely-many qubits. Our results show that gPEPS are a natural tool to efficiently simulate quantum computers with an underlying lattice-based qubit connectivity, such as all quantum processors based on superconducting qubits.

The XML-based GNDS has been emerging as the state-of-art format for nuclear data. However, the XML file format has complicated structures, and the encoding and decoding of which bring a handful of computing burdens. In this work, the so-called GNDS-JSON format is proposed using JSON instead of XML as a variant data format to the coming GNDS 2.0 standard. With reduced data structural complexity, the data structure of this new format matches naturally with the syntax of most programming languages. Without providing users vendor APIs, the data infrastructure is thinned and entitles users to a nonintrusive integration of data access programming modules, while not sacrificing user experience. Moreover, compared to existing JSON schema for the GNDS data in the XML format, a new JSON schema is proposed for direct validation of data in the GNDS-JSON format. As an initial attempt, a set of minimal JSON schema has been demonstrated, which is enough for the description of the neutron reaction data of 364 (out of 372) materials from BROND 3.1, 272 materials from the CENDL 3.2, 557 materials from the ENDF/B-VIII.0, 550 (out of 562) materials from JEFF 3.3, 406 materials from JENDL 4.0, 795 materials from JENDL 5.0, and 628 (out of 2,813) materials from TENDL 2021. The set of new JSON schema, converted data files in GNDS-JSON and example data accessing codes are opensourced for community feedback. A resonance cross section reconstruction code is in-house developed and verified again Fudge to demonstrate the application of the GNDS-JSON format in nuclear data processing.

IntroductionCybersickness is an array of symptoms associated with exposure to three‐dimensional visualization technology (3DVT) environments, such as virtual reality (VR). It is thought that cybersickness is a type of motion sickness caused by a mismatch in sensory and vestibular input when using these modalities. Symptoms of cybersickness are often akin to those of traditional motion sickness, such as headache and nausea. In the literature, as many as 40‐60% of VR users report symptoms of cybersickness, though in our laboratory approximately 20% of users report symptoms of cybersickness. Our past research has shown that the physical environment is often preferred by students, and more effective for learning anatomy, compared to 3DVT environments. We hypothesize that the preference for, and effectiveness of, the physical environment over 3DVT may be a result of cybersickness due to the isolation of the learner from the physical environment. However, no direct measurements of cybersickness or comparisons between 3DVT environments in anatomy are available.MethodsOur study involves comparisons of cybersickness during anatomy learning between three 3D modalities: a VR Oculus Quest 2TM headset, an autostereoscopic screen (AlioscopyTM), and a 3D‐printed physical model. Undergraduate students, with no formal anatomy training, will be randomized via a Latin square design to view one of three skeletal models (human hemipelvis, zygomatic bone, or calcaneus) in each of the modalities. Participants may not touch the model but may rotate it along the horizontal plane using an XboxTM controller. Participants will have four minutes to learn ten bony landmarks on the viewed model and will then take an untimed, recognition‐based test for landmarks learned before progressing onto the next modality. Cybersickness will be assessed following testing in each modality, via self‐reports on the simulation sickness questionnaire (SSQ).ResultsWe hypothesise that reported cybersickness will be highest for the VR displayed on the head‐mounted display of the Oculus Quest 2TM which entirely covers the visual field, compared to learning through the AlioscopyTM screen or the physical model due to the relative lack of isolation of the learner from the physical environment in the last two treatment groups.ConclusionWith the rapid development of 3DVT for use in anatomical education, cybersickness is an important issue in evaluating the quality of 3D modalities. The results of this study will allow educators and students to make informed decisions about the use of 3DVT in anatomy thus preventing uncomfortable physical symptoms associated with learning using 3DVT and improving learning as a result.

Boolean networks are discrete dynamical systems where each automaton has its own Boolean function for computing its state according to the configuration of the network. The updating mode then determines how the configuration of the network evolves over time. Many of updating modes from the literature, including synchronous and asynchronous modes, can be defined as the composition of elementary deterministic configuration updates, i.e., by functions mapping configurations of the network. Nevertheless, alternative dynamics have been introduced using ad-hoc auxiliary objects, such as that resulting from binary projections of Memory Boolean networks, or that resulting from additional pseudo-states for Most Permissive Boolean networks. One may wonder whether these latter dynamics can still be classified as updating modes of finite Boolean networks, or belong to a different class of dynamical systems. In this paper, we study the extension of updating modes to the composition of non-deterministic updates, i.e., mapping sets of finite configurations. We show that the above dynamics can be expressed in this framework, enabling a better understanding of them as updating modes of Boolean networks. More generally, we argue that non-deterministic updates pave the way to a unifying framework for expressing complex updating modes, some of them enabling transitions that cannot be computed with elementary and non-elementary deterministic updates.

The given work considers the existing methods of text compression (finding keywords or creating summary) using RAKE, Lex Rank, Luhn, LSA, Text Rank algorithms; image generation; text-to-image and image-to-image translation including GANs (generative adversarial networks). Different types of GANs were described such as StyleGAN, GauGAN, Pix2Pix, CycleGAN, BigGAN, AttnGAN. This work aims to show ways to create illustrations for the text. First, key information should be obtained from the text. Second, this key information should be transformed into images. There were proposed several ways to transform keywords to images: generating images or selecting them from a dataset with further transforming like generating new images based on selected ow combining selected images e.g. with applying style from one image to another. Based on results, possibilities for further improving the quality of image generation were also planned: combining image generation with selecting images from a dataset, limiting topics of image generation.

Logics for social networks have been studied in recent literature. This paper presents a framework based on dynamic term-modal logic (\(\mathsf {DTML}\)), a quantified variant of dynamic epistemic logic (\(\mathsf {DEL}\)). In contrast with \(\mathsf {DEL}\) where it is commonly known to whom agent names refer, \(\mathsf {DTML}\) can represent dynamics with uncertainty about agent identity. We exemplify dynamics where such uncertainty and de re/de dicto distinctions are key to social network epistemics. Technically, we show that \(\mathsf {DTML}\) semantics can represent a popular class of hybrid logic epistemic social network models. We also show that \(\mathsf {DTML}\) can encode previously discussed dynamics for which finding a complete logic was left open. As complete reduction axioms systems exist for \(\mathsf {DTML}\), this yields a complete system for the dynamics in question.

Modelling the turbulent diffusion of thermal energy, momentum, and metals is\nrequired in all galaxy evolution simulations due to the ubiquity of turbulence\nin galactic environments. The most commonly employed diffusion model, the\nSmagorinsky model, is known to be over-diffusive due to its strong dependence\non the fluid velocity shear. We present a method for dynamically calculating a\nmore accurate, locally appropriate, turbulent diffusivity: the dynamic\nlocalised Smagorinsky model. We investigate a set of standard\nastrophysically-relevant hydrodynamical tests, and demonstrate that the dynamic\nmodel curbs over-diffusion in non-turbulent shear flows and improves the\ndensity contrast in our driven turbulence experiments. In galactic discs, we\nfind that the dynamic model maintains the stability of the disc by preventing\nexcessive angular momentum transport, and increases the metal-mixing timescale\nin the interstellar medium. In both our isolated Milky Way-like galaxies and\ncosmological simulations, we find that the interstellar and circumgalactic\nmedia are particularly sensitive to the treatment of turbulent diffusion. We\nalso examined the global gas enrichment fractions in our cosmological\nsimulations, to gauge the potential effect on the formation sites and\npopulation statistics of Population III stars and supermassive black holes,\nsince they are theorised to be sensitive to the metallicity of the gas out of\nwhich they form. The dynamic model is, however, not for galaxy evolution\nstudies only. It can be applied to all astrophysical hydrodynamics simulations,\nincluding those modelling stellar interiors, planetary formation, and star\nformation.\n

In many hospital systems, new technologies that influence patient data require extensive technical testing before implementation into production. Therefore, to implement, an existing High Performance Computing (HPC) Linux node clusters via WestGrid were used to represent a simulation of patient data benchmarked and cross-referenced with current metadata profiles in operational hospital systems at the Vancouver Island Health Authority (VIHA), Victoria, Canada. Over the tested cross-platform, the data were generated, indexed and stored over a Hadoop Distributed File System (HDFS) to noSQL database (HBase) that represented three billion patient records. The study objective to establish an interactive Big Data Platform (BDA) was successful implemented in that Hadoop/MapReduce technologies formed the framework of the platform distributed with HBase (key-value NoSQL database storage) and generated desired hospital-specific metadata at very extremely large volumes. In fact, the framework over generated HBase data files took a week or a month for one billion (10TB) and three billion (30TB), respectively. Further performance tests retrieved results from simulated patient records with Apache tools in Hadoop's ecosystem. At optimized iteration, HDFS ingestion with HBase exhibited sustained database integrity over hundreds of iterations; however, to complete the bulk loading via MapReduce to HBase required a month. Inconsistencies of MapReduce limited the capacity to generate/replicate data to HBase efficiently. Hospital system based on patient encounter database was very difficult and data profiles were fully representative of complex patient-to-hospital relationships. Our platform is important to lead discovery of useful big data technologies across multiple hospital systems.