A unified constitutive model for asymmetric tension and compression creep-ageing behaviour of naturally aged Al-Cu-Li alloy

A set of unified constitutive equations is presented that predict the asymmetric tension and compression creep behaviour and recently observed double primary creep of pre-stretched/naturally aged aluminium-cooper-lithium alloy AA2050-T34. The evolution of the primary micro- and macro-variables related to the precipitation hardening and creep deformation of the alloy during creep age forming (CAF) are analysed and modelled. Equations for the yield strength evolution of the alloy, including an initial reversion and subsequent strengthening, are proposed based on a theory of concurrent dissolution, re-nucleation and growth of precipitates during artificial ageing. We present new observations of so-called double primary creep during the CAF process. This phenomenon is then predicted by introducing effects of interacting microstructures, including evolving precipitates, diffusing solutes and dislocations, into the sinh-law creep model. In addition, concepts of threshold creep stress σth and a microstructure-dependant creep variable H, which behave differently under different external stress directions, are proposed and incorporated into the creep model. This enables prediction of the asymmetric tension and compression creep-ageing behaviour of the alloy. Quantitative transmission electron microscopy (TEM) and related small-angle X-ray scattering (SAXS) analysis have been carried out for selected creep-aged samples to assist the development and calibration of the constitutive model. A good agreement has been achieved between the experimental results and the model. The model has the potential to be applied to creep age forming of other heat-treatable aluminium alloys

Exploring the Referral and Usage of Science Fiction in HCI Literature

Research on science fiction (sci-fi) in scientific publications has indicated the usage of sci-fi stories, movies or shows to inspire novel Human-Computer Interaction (HCI) research. Yet no studies have analysed sci-fi in a top-ranked computer science conference at present. For that reason, we examine the CHI main track for the presence and nature of sci-fi referrals in relationship to HCI research. We search for six sci-fi terms in a dataset of 5812 CHI main proceedings and code the context of 175 sci-fi referrals in 83 papers indexed in the CHI main track. In our results, we categorize these papers into five contemporary HCI research themes wherein sci-fi and HCI interconnect: 1) Theoretical Design Research; 2) New Interactions; 3) Human-Body Modification or Extension; 4) Human-Robot Interaction and Artificial Intelligence; and 5) Visions of Computing and HCI. In conclusion, we discuss results and implications located in the promising arena of sci-fi and HCI research.Comment: v1: 20 pages, 4 figures, 3 tables, HCI International 2018 accepted submission v2: 20 pages, 4 figures, 3 tables, added link/doi for Springer proceedin

The J-triplet Cooper pairing with magnetic dipolar interactions

Recently, cold atomic Fermi gases with the large magnetic dipolar interaction have been laser cooled down to quantum degeneracy. Different from electric-dipoles which are classic vectors, atomic magnetic dipoles are quantum-mechanical matrix operators proportional to the hyperfine-spin of atoms, thus provide rich opportunities to investigate exotic many-body physics. Furthermore, unlike anisotropic electric dipolar gases, unpolarized magnetic dipolar systems are isotropic under simultaneous spin-orbit rotation. These features give rise to a robust mechanism for a novel pairing symmetry: orbital p-wave (L=1) spin triplet (S=1) pairing with total angular momentum of the Cooper pair J=1. This pairing is markedly different from both the $^3$He-B phase in which J=0 and the $^3$He-$A$ phase in which $J$ is not conserved. It is also different from the p-wave pairing in the single-component electric dipolar systems in which the spin degree of freedom is frozen

Single machine scheduling to minimize batch delivery and job earliness penalties

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Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification

Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet.Comment: ECCV'2

Topological Quantum Phase Transition in Synthetic Non-Abelian Gauge Potential

The method of synthetic gauge potentials opens up a new avenue for our understanding and discovering novel quantum states of matter. We investigate the topological quantum phase transition of Fermi gases trapped in a honeycomb lattice in the presence of a synthetic non- Abelian gauge potential. We develop a systematic fermionic effective field theory to describe a topological quantum phase transition tuned by the non-Abelian gauge potential and ex- plore its various important experimental consequences. Numerical calculations on lattice scales are performed to compare with the results achieved by the fermionic effective field theory. Several possible experimental detection methods of topological quantum phase tran- sition are proposed. In contrast to condensed matter experiments where only gauge invariant quantities can be measured, both gauge invariant and non-gauge invariant quantities can be measured by experimentally generating various non-Abelian gauges corresponding to the same set of Wilson loops

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Data Availability Statement: This study utilizes the publicly available dataset, from https:// physionet.org, accessed on 22 June 2020.Copyright: © 2022 by the authors. In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were de-tected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhyth-mia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.Ministry of Science and Technology, Taiwan (grant number: MOST 110-2221-E-155-004-MY2)

Delivery of a baby with severe combined immunodeficiency at 31 weeks gestation following an extreme preterm prelabour spontaneous rupture of the membranes: a case report

<p>Abstract</p> <p>Introduction</p> <p>If left untreated, severe combined immunodeficiency can lead to an acute susceptibility to infection. The intrauterine environment is sterile until the amniotic membranes rupture. The vaginal flora then ascends into the genital tract, thus increasing the risk of chorioamnionitis. An extremely premature and prolonged membrane rupture is associated with a dismal prognosis for an immunocompetent preterm fetus. There are no case reports to date that detail the outcome of an immunocompromised preterm baby following prolonged rupture of membranes.</p> <p>Case presentation</p> <p>We present the case of a 32-year-old Indian woman who delivered a 31-week gestational baby who had a severe combined immunodeficiency following premature prelabour prolonged rupture of the membranes at the 14^thweek of gestation.</p> <p>Conclusion</p> <p>Extreme preterm prelabour spontaneous rupture of membranes in an underlying condition of severe combined immunodeficiency does not necessarily lead to an unfavourable outcome.</p

Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience