Miracle-dz/UniFAR-FTUs huggingface dataset:
Nondestructive current localization upon high-current nanosecond switching of an avalanche transistor

Very good quantitative agreement was found between the experimental and simulated switching transients of a Si avalanche transistor at extreme currents. Two-dimensional (2-D) simulations were performed using the device simulator ATLAS (Silvaco Inc.). Marked current localization was found, which was of a nondestructive character with nanosecond current pulses due to a very significant reduction in the residual voltage across the transistor at high current densities and specific location of the region of intensive heat generation. The device operates reliably at a sufficiently low repetition rate (of a few kilohertz) despite the very high local temperature (/spl sim/750/spl deg/ K) found near the n/sup +/ collector at the end of the switching transient.
Optimization of Grant-Free NOMA With Multiple Configured-Grants for mURLLC

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness

Randomized smoothing is currently a state-of-the-art method to construct a certifiably robust classifier from neural networks against $\ell_2$-adversarial perturbations. Under the paradigm, the robustness of a classifier is aligned with the prediction confidence, i.e., the higher confidence from a smoothed classifier implies the better robustness. This motivates us to rethink the fundamental trade-off between accuracy and robustness in terms of calibrating confidences of a smoothed classifier. In this paper, we propose a simple training scheme, coined SmoothMix, to control the robustness of smoothed classifiers via self-mixup: it trains on convex combinations of samples along the direction of adversarial perturbation for each input. The proposed procedure effectively identifies over-confident, near off-class samples as a cause of limited robustness in case of smoothed classifiers, and offers an intuitive way to adaptively set a new decision boundary between these samples for better robustness. Our experimental results demonstrate that the proposed method can significantly improve the certified $\ell_2$-robustness of smoothed classifiers compared to existing state-of-the-art robust training methods.
Deep learning based on mixed-variable physics informed neural network for solving fluid dynamics without simulation data

Deep learning method has attracted tremendous attention to handle fluid dynamics in recent years. However, the deep learning method requires much data to guarantee the generalization ability and the data of fluid dynamics are deficient. Recently, physics informed neural network (PINN) is popular to solve the fluid flow problems, which basic concept is to embed the governing equation and continuity equation into loss function, with the requirement of less dataset for obtaining a reliable neural network. In this paper, the mixed-variable PINN method, which convert the governing equation into continuum and constitutive formulations, is proposed to solve the fluid dynamics (flow past cylinder) without any labeled data. The initial/boundary conditions with penalty factors are also embedded into the loss function to become a well-imposed problem. The results show that mixed-variable PINN has better predictive ability to construct the flow field than traditional PINN scheme. Furthermore, the transfer learning method is adopted to is solve the fluid solutions with different Reynold numbers with less computational cost. The results also demonstrate that the transfer learning method can well simulate the different Reynolds number in a short time.
J01020100-7122208: an accreted evolved blue straggler that wasn't ejected from a supermassive black hole

J01020100-7122208 is a star whose origin and nature still challenges us. It was first believed to be a yellow super giant ejected from the Small Magellanic Cloud, but it was more recently claimed to be a red giant accelerated by the Milky Way's central black hole. In order to unveil its nature, we analysed photometric, astrometric and high resolution spectroscopic observations to estimate the orbit, age, and 16 elemental abundances. Our results show that this star has a retrograde and highly-eccentric orbit, $e=0. 914_{-0.020}^{+0.016}$. Correspondingly, it likely crossed the Galactic disk at $550\;\mathrm{pc}$ from the Galactic centre. We obtained a spectroscopic mass and age of $1.09\pm0.10$ $M_\odot$ and $4.51\pm1.44$ Gyr respectively. Its chemical composition is similar to the abundance of other retrograde halo stars. We found that the star is enriched in europium, having [Eu/Fe] = 0.93 $\pm$ 0.24, and is more metal-poor than reported in the literature, with [Fe/H] = -1.30 $\pm$ 0.10. This information was used to conclude that J01020100-7122208 is likely not a star ejected from the central black of the Milky Way or from the Small Magellanic Cloud. Instead, we propose that it is simply a halo star which was likely accreted by the Milky Way in the distant past but its mass and age suggest it is probably an evolved blue straggler.
Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach

This paper proposes a novel approach to predict and determine whether the average taxi- out time at an airport will exceed a pre-defined threshold within the next hour of operations. Prior work in this domain has focused exclusively on predicting taxi-out times on a flight-by-flight basis, which requires significant efforts and data on modeling taxiing activities from gates to runways. Learning directly from surface radar information with minimal processing, a computer vision-based model is proposed that incorporates airport surface data in such a way that adaptation-specific information (e.g., runway configuration, the state of aircraft in the taxiing process) is inferred implicitly and automatically by Artificial Intelligence (AI).
Exploring Unsupervised Learning Methods for Automated Protocol Analysis

The ability to analyse and differentiate network protocol traffic is crucial for network resource management to provide differentiated services by Telcos. Automated Protocol Analysis (APA) is crucial to significantly improve efficiency and reduce reliance on human experts. There are numerous automated state-of-the-art unsupervised methods for clustering unknown protocols in APA. However, many such methods have not been sufficiently explored using diverse test datasets. Thus failing to demonstrate their robustness to generalise. This study proposed a comprehensive framework to evaluate various combinations of feature extraction and clustering methods in APA. It also proposed a novel approach to automate selection of dataset dependent model parameters for feature extraction, resulting in improved performance. Promising results of a novel field-based tokenisation approach also led to our proposal of a novel automated hybrid approach for feature extraction and clustering of unknown protocols in APA. Our proposed hybrid approach performed the best in 7 out of 9 of the diverse test datasets, thus displaying the robustness to generalise across diverse unknown protocols. It also outperformed the unsupervised clustering technique in state-of-the-art open-source APA tool, NETZOB in all test datasets.
Propagating irreversibility fronts in cyclically sheared suspensions

The interface separating a liquid from its vapor phase is diffuse: the composition varies continuously from one phase to the other over a finite length. Recent experiments on dynamic jamming fronts in two dimensions [Waitukaitis et al., Europhysics Letters 102, 44001 (2013)] identified a diffuse interface between jammed and unjammed discs. In both cases, the thickness of the interface diverges as a critical transition is approached. We investigate the generality of this behavior using a third system: a model of cyclically-sheared non-Brownian suspensions. As we sediment the particles towards a boundary, we observe a diffuse traveling front that marks the interface between irreversible and reversible phases. We argue that the front width is linked to a diverging correlation lengthscale in the bulk, which we probe by studying avalanches near criticality. Our results show how diffuse interfaces may arise generally when an incompressible phase is brought to a critical point.
LEGWORK: A Python Package for Computing the Evolution and Detectability of Stellar-origin Gravitational-wave Sources with Space-based Detectors

We present LEGWORK (LISA Evolution and Gravitational Wave Orbit Kit), an open-source Python package for making predictions about stellar-origin gravitational-wave sources and their detectability in LISA or other space-based gravitational-wave detectors. LEGWORK can be used to evolve the orbits of sources due to gravitational-wave emission, calculate gravitational-wave strains (using post-Newtonian approximations), compute signal-to-noise ratios, and visualize the results. It can be applied to a variety of potential sources, including binaries consisting of white dwarfs, neutron stars, and black holes. Although we focus on double compact objects, in principle LEGWORK can be used for any system with a user-specified orbital evolution, such as those affected by a third object or gas drag. We optimized the package to make it efficient for use in population studies, which can contain tens of millions of sources. This paper describes the package and presents several potential use cases. We explain in detail the derivations of the expressions behind the package as well as identify and clarify some discrepancies currently present in the literature. We hope that LEGWORK will enable and accelerate future studies triggered by the rapidly growing interest in gravitational-wave sources.
Quantum Technologies Need a Quantum Energy Initiative

Quantum technologies are currently the object of high expectations from governments and private companies, as they hold the promise to shape safer and faster ways to extract, exchange and treat information. However, despite its major potential impact for industry and society, the question of their energetic footprint has remained in a blind spot of current deployment strategies. In this Perspective, I argue that quantum technologies must urgently plan for the creation and structuration of a transverse quantum energy initiative, connecting quantum thermodynamics, quantum information science, quantum physics and engineering. Such initiative is the only path towards energy efficient, sustainable quantum technologies, and to possibly bring out an energetic quantum advantage.
Exploiting self-organized criticality in strongly stratified turbulence

Abstract A multiscale reduced description of turbulent free shear flows in the presence of strong stabilizing density stratification is derived via asymptotic analysis of the Boussinesq equations in the simultaneous limits of small Froude and large Reynolds numbers. The analysis explicitly recognizes the occurrence of dynamics on disparate spatiotemporal scales, yielding simplified partial differential equations governing the coupled evolution of slow large-scale hydrostatic flows and fast small-scale isotropic instabilities and internal waves. The dynamics captured by the coupled reduced equations is illustrated in the context of two-dimensional strongly stratified Kolmogorov flow. A noteworthy feature of the reduced model is that the fluctuations are constrained to satisfy quasilinear (QL) dynamics about the comparably slowly varying large-scale fields. Crucially, this QL reduction is not invoked as an ad hoc closure approximation, but rather is derived in a physically relevant and mathematically consistent distinguished limit. Further analysis of the resulting slow–fast QL system shows how the amplitude of the fast stratified-shear instabilities is slaved to the slowly evolving mean fields to ensure the marginal stability of the latter. Physically, this marginal stability condition appears to be compatible with recent evidence of self-organized criticality in both observations and simulations of stratified turbulence. Algorithmically, the slaving of the fluctuation fields enables numerical simulations to be time-evolved strictly on the slow time scale of the hydrostatic flow. The reduced equations thus provide a solid mathematical foundation for future studies of three-dimensional strongly stratified turbulence in extreme parameter regimes of geophysical relevance and suggest avenues for new sub-grid-scale parametrizations.
Probing the electromagnetic response of dielectric antennas by vortex electron beams

Focused beams of electrons, which act as both sources, and sensors of electric fields, can be used to characterise the electric response of complex photonic systems by locally probing the induced optical near fields. This functionality can be complemented by embracing the recently developed vortex electron beams (VEBs), made up of electrons with orbital angular momentum, which could, in addition, probe induced magnetic near fields. In this work, we revisit the theoretical description of this technique, dubbed vortex Electron Energy-Loss Spectroscopy (v-EELS). We map the fundamental, quantum-mechanical picture of the scattering of the VEB electrons to the intuitive classical models, which treat the electron beams as a superposition of linear electric and magnetic currents. We then apply this formalism to characterise the optical response of dielectric nanoantennas with v-EELS. Our calculations reveal that VEB electrons probe electric or magnetic modes with different efficiency, which can be adjusted by changing either beam vorticity or acceleration voltage to determine the nature of the probed excitations. We also study a chirally-arranged nanostructure, which in the interaction with electron vortices produces dichroism in electron energy loss spectra. Our theoretical work establishes VEBs as versatile probes that could provide information on optical excitations otherwise inaccessible with conventional electron beams.
A Machine Learning Model for Predicting Progressive Crack Extension based on DCPD Fatigue Data

Time history data collected from a Direct Current Potential Drop (DCPD) fatigue experiment at a range of temperatures was used to train a Bidirectional Long-Short Term Memory Neural Network (BiLSTM) model. The model was trained on high sampling rate experimental data from crack initiation up through the Paris regime. The BiLSTM model was able to predict the progressive crack extension at intermediate temperatures and stress intensities. The model was able to reproduce crack jumps and overall crack progression. The BiLSTM model demonstrated the potential to be used as a tool for future investigation into fundamental mechanisms such as high-temperature oxidation and new damage models.
GRB 191016A: A highly collimated gamma-ray burst jet with magnetised energy injection

Long gamma-ray burst GRB 191016A was a bright and slow rising burst that was detected by the Swift satellite and followed up by ground based Liverpool Telescope (LT). LT follow-up started 2411-s after the Swift Burst Alert Telescope (BAT) trigger using imager IO:O around the time of the late optical peak. From 3987 − 7687-s, we used the LT polarimeter RINGO3 to make polarimetric and photometric observations of the GRB simultaneously in the+, ' and bands. The combined optical light curve shows an initial late peak followed by a decline until 6147-s, 6087-s, and 5247-s for , ' and + filters respectively followed by a flattening phase. There is evidence of polarization at all phases including polarization (% = 14.6 ± 7.2%) which is coincident with the start of the flattening phase. The combination of the light curve morphology and polarization measurement favours an energy injection scenario where slower magnetised ejecta from the central engine catches up with the decelerating blast wave. We calculate the minimum energy injection to be Δ / > 0.36. At a later time combining the optical light curve from BOOTES (reported via GCN) and IO:O we see evidence of a jet break with jet opening angle 2◦.
Improving Person Re-Identification with Temporal Constraints

In this paper we introduce an image-based person where some methods achiever re-identification dataset collected across five non-overlapping camera views in the large and busy airport in Dublin, Ireland. Unlike all publicly available image-based datasets, our dataset contains timestamp information in addition to frame number, and camera and person IDs. Also our dataset has been fully anonymized to comply with modern data privacy regulations. We apply state-of-the-art person re-identification models to our dataset and show that by leveraging the available timestamp information we are able to achieve a significant gain of 37.43% in mAP and a gain of 30.22% in Rank1 accuracy. We also propose a Bayesian temporal re-ranking post-processing step, which further adds a 10.03% gain in mAP and 9.95% gain in Rank1 accuracy metrics. This work on combining visual and temporal information is not possible on other image-based person re-identification datasets. We believe that the proposed new dataset will enable further development of person re-identification research for challenging real-world applications.
Intracavity Rydberg Superatom for Optical Quantum Engineering: Coherent Control, Single-Shot Detection, and Optical 
π
 Phase Shift

We demonstrate a new versatile building block for optical quantum technologies, based on an intracavity Rydberg-blockaded atomic ensemble acting as a single two-level superatom. We coherently control its state and optically detect it in a single shot with a $95\%$ efficiency. Crucially, we demonstrate a superatom-state-dependent $\pi$ phase rotation on the light reflected from the cavity. Together with the state manipulation and detection, it is a key ingredient for implementing deterministic photonic entangling gates and for generating highly non-classical light states.
Polarization of fluorescence lines: tracing magnetic field from circumstellar medium to early universe

Fluorescence emission lines are broadly applied in observation for diffuse medium in the universe. They are normally observed around strong pumping source, tracing the gas in circumstellar medium, reflection nebula, and H II regions, etc. They reside in UV/optical and infrared bands and hence could be directly observed with ground-base telescopes. In this letter, we demonstrate the polarization of fluorescence lines as a magnetic field tracer arising from ground state atomic alignment in diffuse medium, including our solar system, supernova remnants (SNRs), as well as quasi-stellar object (QSO) host galaxies. Two types of fluorescence emissions are considered: the primary fluorescence from the excited states; and the secondary fluorescence from the metastable state (forbidden lines). We find that the synergy of these lines could measure three-dimensional magnetic direction: the polarizations of the primary fluorescence lines could reveal the magnetic polar angle along the line-of-sight, whereas the polarization of forbidden lines traces the plane-of-sky magnetic direction. The expected degree of polarization is P > 10%. Polarizations of both types of fluorescence emissions have shown strong potential for observations, and are applicable to measure magnetic field within and beyond our galaxy.
Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records

Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals - only one in four people who suffer from dementia are diagnosed. Even when a diagnosis is made, it may not be entered as a structured International Classification of Diseases (ICD) diagnosis code in a patient's charts. Information relevant to cognitive impairment (CI) is often found within electronic health records (EHR), but manual review of clinician notes by experts is both time consuming and often prone to errors. Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data. We developed natural language processing (NLP) tools to identify patients with cognitive impairment and demonstrate that linguistic context enhances performance for the cognitive impairment classification task. We fine-tuned our attention based deep learning model, which can learn from complex language structures, and substantially improved accuracy (0.93) relative to a baseline NLP model (0.84). Further, we show that deep learning NLP can successfully identify dementia patients without dementia-related ICD codes or medications.
The Increased Effect of Elections and Changing Prime Ministers on Topics Discussed in the Australian Federal Parliament between 1901 and 2018

Politics and discussion in parliament is likely to be influenced by the party in power and associated election cycles. However, little is known about the extent to which these events affect discussion and how this has changed over time. We systematically analyse how discussion in the Australian Federal Parliament changes in response to two types of political events: elections and changed prime ministers. We use a newly constructed dataset of what was said in the Australian Federal Parliament from 1901 through to 2018 based on extracting and cleaning available public records. We reduce the dimensionality of discussion in this dataset by using a correlated topic model to obtain a set of comparable topics over time. We then relate those topics to the Comparative Agendas Project, and then analyse the effect of these two types of events using a Bayesian hierarchical Dirichlet model. We find that: changes in prime minister tend to be associated with topic changes even when the party in power does not change; and the effect of elections has been increasing since the 1980s, regardless of whether the election results in a change of prime minister.