Miracle-dz/UniFAR-FTUs huggingface dataset:
A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R. For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.
Mass Segregation as a New Indicator of Binary, IMBH, and Stellar-mass Black Hole Systems in Globular Clusters

This study presents the mass segregation of 35 Galactic globular clusters (GCs) through the radial profile of the mean mass of main-sequence stars. Most of them are found to be mass segregated due to two-body relaxation effects. The GCs with a larger core to half-mass–radius ratio tend to be less mass segregated. Recent simulations show that energy sources can quench mass segregation in relaxed GCs. Combining mass segregation and core binary fractions (f c ), we find a moderate anticorrelation between them when f c is below 0.15. Four GCs that have a small amount of mass segregation are selected as possible intermediate-mass black hole holders. However, none of them are strong candidates considering their half-mass relaxation times.
Nonlinear Simulation of Ionospheric Irregularities at Mars

Motivated by the observations and linear theory analysis of ionospheric irregularities at Mars, we performed numerical simulations of the nonlinear evolution of the electromagnetic gradient drift instability in the Martian ionospheric dynamo region. The seeding source of ionospheric irregularities is perturbation zonal neutral wind. We found that the perturbation electric fields induced by the gradient drift instability can convect lower density plasma into higher density plasma at higher altitudes. Then, the associated perturbation magnetic field and electric field can cause the velocity shear of the plasma, which induced the Kelvin–Helmholtz instability at higher altitude. The Kelvin–Helmholtz instabilities furthermore lead to smaller-scale irregularities in plasma density, magnetic field, and electric field in the Martial ionosphere. Key points: (1) Nonlinear simulation of small-scale ionospheric irregularities at Mars was present. (2) Ionospheric irregularities at Mars can be seeded by the perturbation neutral winds. (3) Model results are comparable to linear theory analysis and satellite observations.
Erratum: “The Spectral Evolution of the First Galaxies. III. Simulated James Webb Space Telescope Spectra of Reionization-epoch Galaxies with Lyman-continuum Leakage” (2017, ApJ, 836, 78)

Erik Zackrisson , Christian Binggeli, Kristian Finlator , Nickolay Y. Gnedin, Jan-Pieter Paardekooper, Ikkoh Shimizu, Akio K. Inoue, Hannes Jensen, Genoveva Micheva , Sadegh Khochfar , and Claudio Dalla Vecchia 1 Department of Physics and Astronomy, Uppsala University, Box 515, SE-751 20 Uppsala, Sweden; erik.zackrisson@physics.uu. se 2 New Mexico State University, MSC 4500, Las Cruces, NM 88003, USA 3 Particle Astrophysics Center, Fermi National Accelerator Laboratory, Batavia, IL 60510, USA 4 Department of Astronomy & Astrophysics, The University of Chicago, Chicago, IL 60637 USA 5 Kavli Institute for Cosmological Physics and Enrico Fermi Institute, The University of Chicago, Chicago, IL 60637 USA 6 Universität Heidelberg, Zentrum für Astronomie, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2, D-69120 Heidelberg, Germany 7 Department of Earth and Space Science, Osaka University, 1-1 Machikaneyama, Toyonaka, Osaka 560-0043, Japan 8 College of General Education, Osaka Sangyo University, 3-1-1, Nakagaito, Daito, Osaka 574-8530, Japan 9 University of Michigan, Department of Astronomy, 311 West Hall, 1085 S. University Ave, Ann Arbor, MI 48109-1107 USA 10 Institute for Astronomy, University of Edinburgh, Royal Observatory, Edinburgh, EH9 3HJ, UK 11 Instituto de Astrofísica de Canarias, C/ Vía Láctea s/n, E-38205 La Laguna, Tenerife, Spain 12 Departamento de Astrofísica, Universidad de La Laguna, Av. del Astrofísico Francisco Sánchez s/n, E-38206 La Laguna, Tenerife, Spain Received 2021 January 29; published 2021 February 17
A full relativistic thin disc – the physics of the plunging region and the value of the stress at the ISCO

The widely used Novikov-Thorne relativistic thin disc equations are only valid down to the radius of the innermost stable circular orbit (ISCO). This leads to an undetermined boundary condition at the ISCO, known as the inner stress of the disc, which sets the luminosity of the disc at the ISCO and introduces considerable ambiguity in accurately determining the mass, spin and accretion rate of black holes from observed spectra. We resolve this ambiguity by self-consistently extending the relativistic disc solution through the ISCO to the black hole horizon by calculating the inspiral of an average disc particle subject to turbulent disc forces, using a new particle-in-disc technique. Traditionally it has been assumed that the stress at the ISCO is zero, with material plunging approximately radially into the black hole at close to the speed of light. We demonstrate that in fact the inspiral is less severe, with several (∼ 4 − 17) orbits completed before the horizon. This leads to a small non-zero stress and luminosity at and inside the ISCO, with a local surface temperature at the ISCO between ∼ 0.15 and 0.3 times the maximum surface temperature of the disc, in the case where no dynamically important net magnetic field is present. For a range of disc parameters we calculate the value of the inner stress/surface temperature, which is required when fitting relativistic thin disc models to observations. We resolve a problem in relativistic slim disc models in which turbulent heating becomes inaccurate and falls to zero inside the plunging region.
Graph Partitioning into Hamiltonian Subgraphs on a Quantum Annealer

We demonstrate that a quantum annealer can be used to solve the NP-complete problem of graph partitioning into subgraphs containing Hamiltonian cycles of constrained length. We present a method to find a partition of a given directed graph into Hamiltonian subgraphs with three or more vertices, called vertex 3-cycle cover. We formulate the problem as a quadratic unconstrained binary optimisation and run it on a D-Wave Advantage quantum annealer. We test our method on synthetic graphs constructed by adding a number of random edges to a set of disjoint cycles. We show that the probability of solution is independent of the cycle length, and a solution is found for graphs up to 4000 vertices and 5200 edges, close to the number of physical working qubits available on the quantum annealer.
Adiabaticity parameters for the categorization of light-matter interaction: From weak to strong driving

We investigate theoretically and numerically the light-matter interaction in a two-level system (TLS) as a model system for excitation in a solid-state band structure. We identify five clearly distinct excitation regimes, categorized with well known adiabaticity parameters: (1) the perturbative multiphoton absorption regime for small driving field strengths, and four light field-driven regimes, where intraband motion connects different TLS: (2) the impulsive Landau-Zener (LZ) regime, (3) the non-impulsive LZ regime, (4) the adiabatic regime and (5) the adiabatic-impulsive regime for large electric field strengths. This categorization is tremendously helpful to understand the highly complex excitation dynamics in any TLS, in particular when the driving field strength varies, and naturally connects Rabi physics with Landau-Zener physics. In addition, we find an insightful analytical expression for the photon orders connecting the perturbative multiphoton regime with the light field-driven regimes. Moreover, in the adiabatic-impulsive regime, adiabatic motion and impulsive LZ transitions are equally important, leading to an inversion symmetry breaking of the TLS when applying few cycle laser pulses. This categorization allows a deep understanding of driven TLS in a large variety of settings ranging from cold atoms and molecules to solids and qubits, and will help to find optimal driving parameters for a given purpose.
HYPER^2: Hyperbolic Poincare Embedding for Hyper-Relational Link Prediction

Link Prediction, addressing the issue of completing KGs with missing facts, has been broadly studied. However, less light is shed on the ubiquitous hyper-relational KGs. Most existing hyper-relational KG embedding models still tear an n-ary fact into smaller tuples, neglecting the indecomposability of some n-ary facts. While other frameworks work for certain arity facts only or ignore the significance of primary triple. In this paper, we represent an n-ary fact as a whole, simultaneously keeping the integrity of n-ary fact and maintaining the vital role that the primary triple plays. In addition, we generalize hyperbolic Poincar\'e embedding from binary to arbitrary arity data, which has not been studied yet. To tackle the weak expressiveness and high complexity issue, we propose HYPER^2 which is qualified for capturing the interaction between entities within and beyond triple through information aggregation on the tangent space. Extensive experiments demonstrate HYPER^2 achieves superior performance to its translational and deep analogues, improving SOTA by up to 34.5\% with relatively few dimensions. Moreover, we study the side effect of literals and we theoretically and experimentally compare the computational complexity of HYPER^2 against several best performing baselines, HYPER^2 is 49-61 times quicker than its counterparts.
Characterizing the UAV-to-Machine UWB Radio Channel in Smart Factories

In this work, the results of Ultra-Wideband air-to-ground measurements carried out in a real-world factory environment are presented and discussed. With intelligent industrial deployments in mind, we envision a scenario where the Unmanned Aerial Vehicle can be used as a supplementary tool for factory operation, optimization and control. Measurements address narrow band and wide band characterization of the wireless radio channel, and can be used for link budget calculation, interference studies and time dispersion assessment in real factories, without the usual limitation for both radio terminals to be close to ground. The measurements are performed at different locations and different heights over the 3.1–5.3 GHz band. Some fundamental propagation parameters values are determined vs. distance, height and propagation conditions. The measurements are complemented with, and compared to, conventional ground-to-ground measurements with the same setup. The conducted measurement campaign gives an insight for realizing wireless applications in smart connected factories, including UAV-assisted applications.
Learning-based Compression for Material and Texture Recognition

Learning-based image compression was shown to achieve a competitive performance with state-of-the-art transform-based codecs. This motivated the development of new learning-based visual compression standards such as JPEG-AI. Of particular interest to these emerging standards is the development of learning-based image compression systems targeting both humans and machines. This paper is concerned with learning-based compression schemes whose compressed-domain representations can be utilized to perform visual processing and computer vision tasks directly in the compressed domain. Such a characteristic has been incorporated as part of the scope and requirements of the new emerging JPEG-AI standard. In our work, we adopt the learning-based JPEG-AI framework for performing material and texture recognition using the compressed-domain latent representation at varing bit-rates. For comparison, performance results are presented using compressed but fully decoded images in the pixel domain as well as original uncompressed images. The obtained performance results show that even though decoded images can degrade the classification performance of the model trained with original images, retraining the model with decoded images will largely reduce the performance gap for the adopted texture dataset. It is also shown that the compressed-domain classification can yield a competitive performance in terms of Top-1 and Top-5 accuracy while using a smaller reduced-complexity classification model.
A General 3D Space-Time-Frequency Non-Stationary THz Channel Model for 6G Ultra-Massive MIMO Wireless Communication Systems

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.
Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT

We classify seven months' worth of Belgian COVID-related Tweets using multilingual BERT and relate them to their governments' COVID measures. We classify Tweets by their stated opinion on Belgian government curfew measures (too strict, ok, too loose). We examine the change in topics discussed and views expressed over time and in reference to dates of related events such as implementation of new measures or COVID-19 related announcements in the media.
A new anomaly observed in 4He supporting the existence of the hypothetical X17 particle

Recently, we observed an anomalous peak-like excess of internal e+e− pairs at around 140° for the M1 transition depopulating the 18.15 MeV isoscalar 1+ state in 8Be. The deviation from the theoretical prediction can be described by GEANT simulations assuming the creation and subsequent decay of a new, light boson with a mass of 16.7 MeV/c2. In order to reduce the possible systematic errors from the experimenntal data, we re-investigated the 8Be anomaly with an improved setup and confirmed the anomaly within the statistical uncertainties. We also studied the angular correlation of the electron-positron pairs created in the M0 transition depopulating the 21.01 MeV 0− state in 4He, and observed an anomalous excess of e+e− pairs at a significantly smaller angle of 115°. Since the transition energy was higher in this case, the observed anomaly could be described by assuming the creation and subsequent decay of the same light particle in the simulations.
Upper and Lower Bounds for Deterministic Approximate Objects

Relaxing the sequential specification of shared objects has been proposed as a promising approach to obtain implementations with better complexity. In this paper, we study the step complexity of relaxed variants of two common shared objects: max registers and counters. In particular, we consider the $k$-multiplicative-accurate max register and the k-multiplicative-accurate counter, where read operations are allowed to err by a multiplicative factor of $k$ (for some $k\in \mathbb{N}$). More accurately, reads are allowed to return an approximate value $x$ of the maximum value $v$ previously written to the max register, or of the number $v$ of increments previously applied to the counter, respectively, such that $v/k\leq x\leq v. k$. We provide upper and lower bounds on the complexity of implementing these objects in a wait-free manner in the shared memory model.
ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion

We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net. Specifically, the Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a shared latent space, which can capture detailed shape prior. Then we design an iterative refinement unit to generate complete shapes with fine-grained details by integrating prior information. Experiments are conducted on the PCN dataset and the Completion3D benchmark, demonstrating the state-of-the-art performance of the proposed ASFM-Net. Our method achieves the 1st place in the leaderboard of Completion3D and outperforms existing methods with a large margin, about 12%. The codes and trained models are released publicly at https://github.com/Yan-Xia/ASFM-Net.
Modelling the broad-band emission of 3C 454.3

The results of a long-term multiwavelength study of the powerful flat spectrum radio quasar 3C 454.3 using Fermi-LAT and Swift XRT/UVOT data are reported. In the γ-ray band, Fermi-LAT observations show several major flares when the source flux was > 10−5 photon cm−2 s−1; the peak γ-ray flux above 141.6 MeV, (9.22 ± 1.96) × 10−5 photon cm−2 s−1 observed on MJD 55519.33, corresponds to 2.15 × 1050 erg s−1 isotropic γ-ray luminosity. The analysis of Swift XRT and UVOT data revealed a flux increase, although with smaller amplitudes, also in the X-ray and optical/UV bands. The X-ray emission of 3C 454.3 is with a hard spectral index of ΓX = 1.16 − 1.75, and the flux in the flaring states increased up to (1.80 ± 0.18) × 10−10erg cm−2 s−1. Through combining the analyzed data, it was possible to assemble 362 high-quality and quasi-simultaneous spectral energy distributions of 3C 454.3 in 2008-2018 which all were modeled within a one-zone leptonic scenario assuming the emission region is within the broad line region, involving synchrotron, synchrotron self-Compton and external Compton mechanisms. Such an extensive modeling is the key for constraining the underlying emission mechanisms in the 3C 454.3 jet and allows to derive the physical parameters of the jet and investigate their evolution in time. The modeling suggests that during the flares, along with the variation of emitting electron parameters, the Doppler boosting factor increased substantially implying that the emission in these periods has most likely originated in a faster moving region.
A Model-Based Iterative Learning Approach for Diffuse Optical Tomography

Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a ‘model-based’ learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.
Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization

We evaluate a large-scale set of interventions to increase demand for immunization in Haryana, India. The policies under consideration include the two most frequently discussed tools--reminders and incentives--as well as an intervention inspired by the networks literature. We cross-randomize whether (a) individuals receive SMS reminders about upcoming vaccination drives; (b) individuals receive incentives for vaccinating their children; (c) influential individuals (information hubs, trusted individuals, or both) are asked to act as "ambassadors" receiving regular reminders to spread the word about immunization in their community. By taking into account different versions (or "dosages") of each intervention, we obtain 75 unique policy combinations. We develop a new statistical technique--a smart pooling and pruning procedure--for finding a best policy from a large set, which also determines which policies are effective and the effect of the best policy. We proceed in two steps. First, we use a LASSO technique to collapse the data: we pool dosages of the same treatment if the data cannot reject that they had the same impact, and prune policies deemed ineffective. Second, using the remaining (pooled) policies, we estimate the effect of the best policy, accounting for the winner's curse. The key outcomes are (i) the number of measles immunizations and (ii) the number of immunizations per dollar spent. The policy that has the largest impact (information hubs, SMS reminders, incentives that increase with each immunization) increases the number of immunizations by 44% relative to the status quo. The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1%.