Miracle-dz/UniFAR-FTUs huggingface dataset:
From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search

In this paper, we present a modified Xception architecture, the NEXcepTion network. Our network has significantly better performance than the original Xception, achieving top-1 accuracy of 81.5% on the ImageNet validation dataset (an improvement of 2.5%) as well as a 28% higher throughput. Another variant of our model, NEXcepTion-TP, reaches 81.8% top-1 accuracy, similar to ConvNeXt (82.1%), while having a 27% higher throughput. Our model is the result of applying improved training procedures and new design decisions combined with an application of Neural Architecture Search (NAS) on a smaller dataset. These findings call for revisiting older architectures and reassessing their potential when combined with the latest enhancements.
Proton imaging of high-energy-density laboratory plasmas

Proton imaging has become a key diagnostic for measuring electromagnetic fields in high-energy-density (HED) laboratory plasmas. Compared to other techniques for diagnosing fields, proton imaging is a non-perturbative measurement that can simultaneously offer high spatial and temporal resolution and the ability to distinguish between electric and magnetic fields. Consequently, proton imaging has been used in a wide range of HED experiments, from inertial confinement fusion to laboratory astrophysics. An overview is provided on the state of the art of proton imaging, including detailed discussion of experimental considerations like proton sources and detectors, the theory of proton-imaging analysis, and a survey of experimental results demonstrating the breadth of applications. Topics at the frontiers of proton imaging development are also described, along with an outlook on the future of the field.
Boundary Layer Transition Induced by a Von Karman Vortex Street Wake

A study has been made of the process of laminar to turbulent transition induced by a von Karman vortex street wake, in the boundary layer on a flat plate. The boundary layer developed under zero pressure gradient conditions while the vortex street was generated by a cylinder positioned in the free stream. Hot-wire measurements over a range of Strouhal frequencies and free stream velocities were used for the identification of the transition onset. From the analysis of the experimental data, two different transition mechanisms known in the literature as ‘strong’ wake and ‘weak’ wake induced transition, could be identified. It was established that, the onset of the strong von Karman wake induced transition process was a function of the free stream velocity, the position of the cylinder with respect to the plate, the cylinder diameter, the drag coefficient and the minimum velocity in the developing wake at the streamwise position of the onset of the boundary layer transition. The end of the strong wake induced transition, was defined at the streamwise distance, where the wake of the cylinder met the wall. A correlation for the prediction of the onset of the transition was developed
Foresight - Deep Generative Modelling of Patient Timelines using Electronic Health Records

Evidence before this study: We reviewed published evidence using Google Scholar and PubMed for studies using transformer-based models for forecasting patient timelines. We used the terms ("transformer" OR "bert" OR "generative pretrained transformer") AND ("forecasting" OR "temporal modelling" OR "trajectory") AND ("ehr" OR "health records" OR "medical records" OR "healthcare" OR "medicine" OR "patients" OR "hospital" OR "clinical"), the scope was anywhere in the text, published in 2018 or later. We found many CoVid-19 studies, or studies that focus on a specific biomedical concept or set of concepts. A few studies focus on forecasting a wider range of biomedical concepts, but still require structured data or work with specific timeframes (e.g. forecasting what may happen in the next episode, week, or month), or can only forecast one step into the future. Added value
Multivariate Statistical Analysis : Revised And Expanded

Vector and matrix algebra groups and Jacobian of some transformations multivariate distributions and invariance in inference multivariate distributions, their properties and characterizations estimators of parameters and their functions in multivariate normal distributions basic multivariate sampling distributions tests of hypotheses of mean vectors tests concerning covariance matrices and mean vectors discriminant analysis principal components canonical correlations factor analysis.
On the Anticorrelation between Duration and Redshift in Gamma-Ray Bursts

For gamma-ray bursts (GRBs) with durations greater than two seconds (so-called long GRBs), the intrinsic prompt gamma-ray emission appears, on average, to last longer for bursts at lower redshifts. We explore the nature of this duration–redshift anticorrelation, describing systems and conditions in which this cosmological evolution could arise. In particular, we explore its dependence on the metallicity of a massive star progenitor, because we can securely count on the average stellar metallicity to increase with decreasing redshift. Although stars with higher metallicity/lower redshift lose mass and angular momentum through line-driven winds, in some cases these stars are able to form more extended accretion disks when they collapse, potentially leading to longer-duration GRBs. We also examine how this duration–redshift trend may show up in interacting binary models composed of a massive star and compact object companion, recently suggested to be the progenitors of radio-bright GRBs. Under certain conditions, mass loss and equation-of-state effects from massive stars with higher metallicity and lower redshift can decrease the binary separation. This can then lead to spin-up of the massive star and allow for a longer-duration GRB upon the massive star’s collapse. Finally, the duration–redshift trend may also be supported by a relatively larger population of small-separation binaries born in situ at low redshift.
Robust Learning Protocol for Federated Tumor Segmentation Challenge

In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging mainly due to data heterogeneity among collaborators and communication cost of training. To tackle these challenges, we propose Robust Learning Protocol (RoLePRO) which is a combination of server-side adaptive optimisation (e.g., server-side Adam) and judicious parameter (weights) aggregation schemes (e.g., adaptive weighted aggregation). RoLePRO takes a two-phase approach, where the first phase consists of vanilla Federated Averaging, while the second phase consists of a judicious aggregation scheme that uses a sophisticated reweighting, all in the presence of an adaptive optimisation algorithm at the server. We draw insights from extensive experimentation to tune learning rates for the two phases.
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation

Unsupervised domain adaptation reduces the reliance on data annotation in deep learning by adapting knowledge from a source to a target domain. For privacy and efficiency concerns, source-free domain adaptation extends unsupervised domain adaptation by adapting a pre-trained source model to an unlabeled target domain without accessing the source data. However, most existing source-free domain adaptation methods to date focus on the transductive setting, where the target training set is also the testing set. In this paper, we address source-free domain adaptation in the more realistic inductive setting, where the target training and testing sets are mutually exclusive. We propose a new semi-supervised fine-tuning method named Dual Moving Average Pseudo-Labeling (DMAPL) for source-free inductive domain adaptation. We first split the unlabeled training set in the target domain into a pseudo-labeled confident subset and an unlabeled less-confident subset according to the prediction confidence scores from the pre-trained source model. Then we propose a soft-label moving-average updating strategy for the unlabeled subset based on a moving-average prototypical classifier, which gradually adapts the source model towards the target domain. Experiments show that our proposed method achieves state-of-the-art performance and outperforms previous methods by large margins.
Dynamical renormalization of electron-phonon coupling in conventional superconductors

The adiabatic Born-Oppenheimer approximation is considered to be a robust approach that very rarely breaks down. Consequently, it is predominantly utilized to address various electron-phonon properties in condensed matter physics. By combining many-body perturbation and density functional theories we demonstrate the importance of dynamical (nonadiabatic) effects in estimating superconducting properties in various bulk and two-dimensional materials. Apart from the expected long-wavelength nonadiabatic effects, we found sizable nonadiabatic Kohn anomalies away from the Brillouin zone center for materials with strong intervalley electron-phonon scatterings. Compared to the adiabatic result, these dynamical phonon anomalies can significantly modify electron-phonon coupling strength $\lambda$ and superconducting transition temperature $T_c$. Further, the dynamically-induced modifications of $\lambda$ have a strong impact on transport properties, where probably the most interesting is the rescaling of the low-temperature and low-frequency regime of the scattering time $1/\tau$ from about $T^3$ to about $T^2$, resembling the Fermi liquid result for electron-electron scattering. Our goal is to point out the potential implications of these nonadiabatic effects and reestablish their pivotal role in computational estimations of electron-phonon properties.
Geometric Rectification of Creased Document Images based on Isometric Mapping

Geometric rectiﬁcation of images of distorted documents ﬁnds wide applications in document digitization and Optical Character Recognition (OCR). Although smoothly curved deformations have been widely investigated by many works, the most challenging distortions, e.g. complex creases and large foldings, have not been studied in particular. The performance of existing approaches, when applied to largely creased or folded documents, is far from satisfying, leaving substantial room for improvement. To tackle this task, knowledge about document rectiﬁcation should be incorporated into the computation, among which the developability of 3D document models and particular textural features in the images, such as straight lines, are the most essential ones. For this purpose, we propose a general framework of document image rectiﬁcation in which a computational isometric mapping model is utilized for expressing a 3D document model and its ﬂattening in the plane. Based on this framework, both model developability and textural features are considered in the computation. The experiments and comparisons to the state-of-the-art approaches demonstrated the eﬀectiveness and outstanding performance of the proposed method. Our method is also ﬂexible in that the rectiﬁcation results can be enhanced by any other methods that extract high-quality feature lines in the images.
Control and Placement of Finite-Resolution Intelligent Surfaces in IoT Systems with Imperfect CSI

In this paper, we study the advantages of using reconﬁgurable intelligent surfaces (RISs) for interference suppression in single-input single-output (SISO) distributed Internet of Things (IoT) networks. Implementing RIS-assisted networks confronts various problems, mostly related to the control and placement of the RIS. To tackle the control-related challenges, we consider noisy and local channel knowledge, based on which we devise algorithms to optimize the potentially distributed RISs to achieve an overall network objective, such as the sum-rate. We use a network with a centralized RIS as a benchmark for our comparisons. We further assume low-bit phase shifters at the RIS to capture real-world hardware limitations. We also study the placement of the RIS and analytically quantify the minimum required degrees-of-control for the RIS as a function of its location to guarantee a speciﬁc network performance metric and verify the results via simulations.
A field investigation of the friction between cross-country skis and snow

Cross country skiing is an interplay between the active motion of the body of the skier and the physical interaction between the surface of the skis and the snow. Friction and glide between the ski base and the snow depend on the snow temperature, ski surface chemistry and roughness, dry friction with the snow crystals and liquid meltwater film flow properties. Although a great amount of resources are being spent on understanding glide and friction, a basic physical understanding of the phenomena at play is still at large and the methodology used for assessing the gliding quality of different full scale configurations is still coarse in real world applications, which limits novel development of robust methods to control and optimize glide. Such understanding and full scale testing is particularly important for designing new ski gliders. In the present work, we develop a novel experimental setup and use it to investigate through field experiments the basic mechanical phenomena that influence glide and friction of full scale skis on snow. The field setup features a mobile, novel linear tribometer allowing investigation with whole skis in realistic cross-country conditions (speed and load). The coefficient of friction between the skis and snow is obtained from a load cell, which reports the force necessary to pull a ski sled along a flat track. Simultaneously, thermistors measure the temperature at the ski-snow interface. In addition, a GPS measures displacement and velocity. The results obtained are in good agreement with several measurements reported from similar experiments, and provide a real-world validation of results obtained in the laboratory. In addition, we release the general tribometer setup as open source material in order to provide a foundation on which other research teams as well as cross-country teams can further iterate when designing later experiments.
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources

Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model’s pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts. These subtasks differ in terms of which knowledge sources contain the relevant facts. We also introduce subtasks where knowledge is present only at inference time using fictional knowledge. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources. Still, even the best performing models seem to have difficulties with reliably integrating knowledge presented only at inference time.
Self-Prompting Large Language Models for Open-Domain QA

Open-Domain Question Answering (ODQA) requires models to answer factoid questions with no context given. The common way for this task is to train models on a large-scale annotated dataset to retrieve related documents and generate answers based on these documents. In this paper, we show that the ODQA architecture can be dramatically simpliﬁed by treating Large Language Models (LLMs) as a knowledge corpus and pro-pose a Self-Prompting framework for LLMs to perform ODQA so as to eliminate the need for training data and external knowledge corpus. Concretely, we ﬁrstly generate multiple pseudo QA pairs with background passages and one-sentence explanations for these QAs by prompting LLMs step by step and then leverage the generated QA pairs for in-context learning. Experimental results show our method surpasses previous state-of-the-art methods by +8.8 EM averagely on three widely-used ODQA datasets, and even achieves comparable performance with several retrieval-augmented ﬁne-tuned models.
2022 Testing of P and CP Symmetries with e + e − → J/ψ → Λ¯Λ

We propose to test P - and CP -symmetries with the process e + e − → J/ψ → Λ¯Λ. The general form of the angular distribution of the process is derived and several observables for the proposed test are introduced. With these observables one can distinguish the eﬀects of P -violations in the production of J/ψ from its decay into a pair of Λ¯Λ. Numerical estimations for proposed asymmetries of P -violation due to Z exchange in SM are given. Our results show that BESIII has reached the sensitivity to probe these eﬀects. At the proposed super-tau-charm factory with much enhanced luminosity it will be able to test the SM predictions with high precision. CP violating eﬀect in J/ψ → Λ¯Λ can also be tested. In particular, BESIII data already reached the sensitivity to prove CP violating eﬀect due to current upper limit of electric dipole moment of Λ. At the proposed super-tau-charm factory the upper limit can be improved by an order of magnitude.
Efficient Training of Low-Curvature Neural Networks

The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders interpretability. However, existing methods to solve these issues, such as adversarial training, are expensive and often sacrifice predictive accuracy. In this work, we consider curvature, which is a mathematical quantity which encodes the degree of non-linearity. Using this, we demonstrate low-curvature neural networks (LCNNs) that obtain drastically lower curvature than standard models while exhibiting similar predictive performance, which leads to improved robustness and stable gradients, with only a marginally increased training time. To achieve this, we minimize a data-independent upper bound on the curvature of a neural network, which decomposes overall curvature in terms of curvatures and slopes of its constituent layers. To efficiently minimize this bound, we introduce two novel architectural components: first, a non-linearity called centered-softplus that is a stable variant of the softplus non-linearity, and second, a Lipschitz-constrained batch normalization layer. Our experiments show that LCNNs have lower curvature, more stable gradients and increased off-the-shelf adversarial robustness when compared to their standard high-curvature counterparts, all without affecting predictive performance. Our approach is easy to use and can be readily incorporated into existing neural network models.
QUASI-CLASSICAL THEORY OF A RELATIVISTIC SPINNING ELECTRON

The quasi-classical spin theory is a modified Hamilton, Jacobi theory for a classical relativistic dipole whose space-time trajectory is coupled to its spin motion. The equations of this quasi-classical theory are almost identical with the second-order (squared) Dirac equation, and, in fact, form a new WKB approximation to the second,order Dirac equation. We quantize this theory by requiring that a classical spinor wave function'' be continuous and single valued, and that it satisfy an eigenvalue equation. We find for a particle in a uniform magnetic field, and in a Coulomb field, that this quasi-classical theory predicts the same energy and angular momentum eigenvalues as the Dirac theory. The quasi-classical theory is invariant under Lorentz transformation, spin rotations, and charge conjugation. The spinor wave functions are form invariant with respect to arbitrary canonical transformations. (auth)
Eddy current tomography for testing of ferromagnetic and non-magnetic materials

This paper presents an eddy current tomography setup with high spatial resolution and high accuracy of measurements of the signal on the detection coil. In the proposed solution both amplitude and phase shift are measured, which makes it possible to calculate the spatial distribution of both permeability and resistivity of the tested element. As a result, the setup presented here creates the possibility of effective detection of discontinuities in cylindrical elements, which opens new possibilities for industrial applications of eddy current tomography.
Rethinking technologies of remembering for a postcolonial world

Abstract This article sets out some of the analytical moves that are necessary to developing a distinctive area of research called postcolonial memory studies. A key barrier to synthesising insights from postcolonial and memory studies has been a reductive approach to analogue and digital technologies which operate as vehicles for memory. Three analytical moves are needed to decentre, or at the very least de-naturalise the technological narratives and ecologies of Europe and the US. Media memory studies needs to draw more effectively on postcolonial studies to position mediated memory as inextricably connected to the legacies of colonialism and empire; develop a much broader account of media infrastructures emerging from what is increasingly characterised as ‘global media studies’; make an empirical and analytical shift away from the primacy of digital communications technologies and to explore technologies, not just as artefacts but as knowledge generating cultural practices. The combined value of these three shifts in approaches to media and communications technologies in memory studies research has considerable potential for developing postcolonial media memory studies research which offers a thorough and empirically grounded analysis of the complex ways in which the legacies of colonialism shape and structure the ways in which practices and performances of remembering are mediated in contemporary social life. This shift towards postcolonial memory studies can be seen as part of the wider project of what Anna (Amza) Reading has in this volume called ‘rewilding memory’ by rethinking ‘the underlying ecologies of knowledge within studies of memory’.
Performance analysis of multiuser MIMO system with adaptive modulation and imperfect CSI

Based on imperfect channel state information (CSI), the performance analysis of multiuser multiple-input multiple-output (MIMO) system with rate adaptive modulation (AM) and antenna selection is presented. By means of approximate expression of bit error rate (BER) under imperfect CSI, the variable switching thresholds (ST) subject to a target BER constraint are derived. By the ST, a tightly approximate BER is derived to simplify the calculation of exact BER, which has more accuracy than the existing approximate BER to evaluate the impact of feedback delay on the system performance. The simulation results show that the multiuser MIMO with AM can provide better SE than the single user counterpart. Compared with the fixed ST, the variable ST is able to reduce the effect of feedback delay on BER. Additionally the system in the presence of imperfect CSI only has slight BER performance degradation for the normalized time delay less than 0.01.