Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of a Local Sample of Gamma-Ray Bursts Consistent with a Magnetar Giant Flare Origin

Cosmological gamma-ray bursts (GRBs) are known to arise from distinct progenitor channels: short GRBs mostly from neutron star mergers and long GRBs from a rare type of core-collapse supernova (CCSN) called collapsars. Highly magnetized neutron stars called magnetars also generate energetic, short-duration gamma-ray transients called magnetar giant flares (MGFs). Three have been observed from the Milky Way and its satellite galaxies, and they have long been suspected to constitute a third class of extragalactic GRBs. We report the unambiguous identification of a distinct population of four local (<5 Mpc) short GRBs, adding GRB 070222 to previously discussed events. While identified solely based on alignment with nearby star-forming galaxies, their rise time and isotropic energy release are independently inconsistent with the larger short GRB population at >99.9% confidence. These properties, the host galaxies, and nondetection in gravitational waves all point to an extragalactic MGF origin. Despite the small sample, the inferred volumetric rates for events above 4 × 1044 erg of Gpc−3 yr−1 make MGFs the dominant gamma-ray transient detected from extragalactic sources. As previously suggested, these rates imply that some magnetars produce multiple MGFs, providing a source of repeating GRBs. The rates and host galaxies favor common CCSN as key progenitors of magnetars.
Discrete Knowledge Graph Embedding based on Discrete Optimization

This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods. The convergence of DKGE can be guaranteed theoretically. Extensive experiments demonstrate that DKGE achieves superior accuracy than classical hashing functions that map the effective continuous embeddings into discrete codes. Besides, DKGE reaches comparable accuracy with much lower computational complexity and storage compared to many continuous graph embedding methods.
Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes

In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images. Our Placement Quality Network (PQ-Net) estimates the object pose and the quality for each automatically generated grasp pose for multiple objects simultaneously at 92 fps in a single forward pass of a neural network. All grasping and placement trials are executed in a physics simulation and the gained experience is transferred to the real world using domain randomization. We demonstrate that our policy successfully transfers to the real world. PQ-Net outperforms other model-free approaches in terms of grasping success rate and automatically scales to new objects of arbitrary symmetry without any human intervention.
Analyticity, renormalization, and evolution of the soft-quark function

We compute the renormalization and evolution of the soft-quark function that appears in the factorization theorem for Higgs-boson decays to two photons through a b-quark loop. Our computation confirms a conjecture by Liu, Mecaj, Neubert, Wang, and Fleming for the form of the renormalization and evolution of the soft-quark function in order αs. We also work out the analyticity structure of the soft-quark function by making use of light-cone perturbation theory. ∗ gtb@anl.gov † chodigi@gmail.com ‡ jungil@korea.ac. kr § xiangpeng.wang@anl.gov 1 ar X iv :2 10 1. 04 87 2v 1 [ he pph ] 1 3 Ja n 20 21
Constraining Galaxy Halos from the Dispersion and Scattering of Fast Radio Bursts and Pulsars

Fast radio bursts (FRBs) can be scattered by ionized gas in their local environments, host galaxies, intervening galaxies along their lines of sight (LOS), the intergalactic medium, and the Milky Way. The relative contributions of these different media depend on their geometric configuration and the internal properties of the gas. When these relative contributions are well understood, FRB scattering is a powerful probe of density fluctuations along the LOS. The precise scattering measurements for FRB 121102 and FRB 180916 allow us to place an upper limit on the amount of scattering contributed by the Milky Way halo to these FRBs. The scattering time τ∝(F˜×DM2)Aτ , where DM is the dispersion measure, F˜ quantifies electron density variations with F˜=0 for a smooth medium, and the dimensionless constant A τ quantifies the difference between the mean scattering delay and the 1/e scattering time typically measured. A likelihood analysis of the observed scattering and halo DM constraints finds that F˜ is at least an order of magnitude smaller in the halo than in the Galactic disk. The maximum pulse broadening from the halo is τ ≲ 12 μs at 1 GHz. We compare our analysis of the Milky Way halo with other galaxy halos by placing limits on the scattering contributions from halos intersecting the LOS to FRB 181112 and FRB 191108. Our results are consistent with halos making negligible or very small contributions to the scattering times of these FRBs.
Structural and transport properties of highly Ru-deficient SrRu0.7O3 thin films prepared by molecular beam epitaxy: Comparison with stoichiometric SrRuO3

We investigate structural and transport properties of highly Ru-deficient SrRu0.7O3 thin films prepared by molecular beam epitaxy on (001) SrTiO3 substrates. To distinguish the influence of the two types of disorders in the films—Ru vacancies within lattices and disorders near the interface—SrRu0.7O3 thin films with various thicknesses (t = 1–60 nm) were prepared. It was found that the influence of the former dominates the electrical and magnetic properties when t ≥ 5–10 nm while that of the latter does when t ≤ 5–10 nm. Structural characterizations revealed that the crystallinity, in terms of the Sr and O sublattices, of SrRu0.7O3 thin films is as high as that of the ultrahigh-quality SrRuO3 ones. The Curie temperature (TC) analysis elucidated that SrRu0.7O3 (TC ≈ 140 K) is a material distinct from SrRuO3 (TC ≈ 150 K). Despite the large Ru deficiency (∼30%), the SrRu0.7O3 films showed metallic conduction when t ≥ 5 nm. In high-field magnetoresistance measurements, the fascinating phenomenon of Weyl fermion transport was not observed for the SrRu0.7O3 thin films irrespective of thickness, which is in contrast to the stoichiometric SrRuO3 films. The (magneto)transport properties suggest that a picture of carrier scattering due to the Ru vacancies is appropriate for SrRu0.7O3 and also that proper stoichiometry control is a prerequisite to utilizing the full potential of SrRuO3 as a magnetic Weyl semimetal and two-dimensional spin-polarized system. Nevertheless, the large tolerance in Ru composition (∼30%) to metallic conduction is advantageous for some practical applications where SrRu1−xO3 is used as an epitaxial conducting layer.
Linear Representation Meta-Reinforcement Learning for Instant Adaptation

This paper introduces Fast Linearized Adaptive Policy (FLAP), a new meta-reinforcement learning (meta-RL) method that is able to extrapolate well to out-of-distribution tasks without the need to reuse data from training, and adapt almost instantaneously with the need of only a few samples during testing. FLAP builds upon the idea of learning a shared linear representation of the policy so that when adapting to a new task, it suffices to predict a set of linear weights. A separate adapter network is trained simultaneously with the policy such that during adaptation, we can directly use the adapter network to predict these linear weights instead of updating a meta-policy via gradient descent, such as in prior meta-RL methods like MAML, to obtain the new policy. The application of the separate feed-forward network not only speeds up the adaptation run-time significantly, but also generalizes extremely well to very different tasks that prior Meta-RL methods fail to generalize to. Experiments on standard continuous-control meta-RL benchmarks show FLAP presenting significantly stronger performance on out-of-distribution tasks with up to double the average return and up to 8X faster adaptation run-time speeds when compared to prior methods.
Simultaneous p-adic Diophantine approximation

Abstract The aim of this paper is to develop the theory of weighted Diophantine approximation of rational numbers to p-adic numbers. Firstly, we establish complete analogues of Khintchine’s theorem, the Duffin–Schaeffer theorem and the Jarník–Besicovitch theorem for ‘weighted’ simultaneous Diophantine approximation in the p-adic case. Secondly, we obtain a lower bound for the Hausdorff dimension of weighted simultaneously approximable points lying on p-adic manifolds. This is valid for very general classes of curves and manifolds and have natural constraints on the exponents of approximation. The key tools we use in our proofs are the Mass Transference Principle, including its recent extension due to Wang and Wu in 2019, and a Zero-One law for weighted p-adic approximations established in this paper.
Ultralow-threshold thin-film lithium niobate optical parametric oscillator

Materials with strong χ (2) optical nonlinearity, especially lithium niobate, play a critical role in building optical parametric oscillators (OPOs). However, chip-scale integration of low-loss χ (2) materials remains challenging and limits the threshold power of on-chip χ (2) OPO. Here we report the ﬁrst on-chip lithium niobate optical parametric oscillator at the telecom wavelengths using a quasi-phase matched, high-quality microring resonator, whose threshold power ( ∼ 30 µ W) is 400 times lower than that in previous χ (2) integrated photonics platforms. An on-chip power conversion eﬃciency of 11% is obtained at a pump power of 93 µ W. The OPO wavelength tuning is achieved by varying the pump frequency and chip temperature. With the lowest power threshold among all on-chip OPOs demonstrated so far, as well as advantages including high conversion eﬃciency, ﬂexibility in quasi-phase matching and device scalability, the thin-ﬁlm lithium niobate OPO opens new opportunities for chip-based tunable classical and quantum light sources and provides an potential platform for realizing photonic neural networks.
Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization

The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space. Usually, such design relies on the prior definition of design parameters and places restrictions on synthesizing novel shapes. In this work, we propose a data-driven shape encoding and generating method, which automatically learns representations from existing airfoils and uses the learned representations to generate new airfoils. The representations are then used in the optimization of synthesized airfoil shapes based on their aerodynamic performance. Our model is built upon VAEGAN, a neural network that combines Variational Autoencoder with Generative Adversarial Network and is trained by the gradient-based technique. Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm. Our experiments show that the learned features encode shape information thoroughly and comprehensively without predefined design parameters. By interpolating/extrapolating feature vectors or sampling from Gaussian noises, the model can automatically synthesize novel airfoil shapes, some of which possess competitive or even better aerodynamic properties as compared to airfoils used for model training purposes. By optimizing shapes on the learned latent domain via a genetic algorithm, synthesized airfoils can evolve to target aerodynamic properties. This demonstrates an efficient learning-based airfoil design framework that encodes and optimizes the airfoil on the latent domain and synthesizes promising airfoil candidates for required aerodynamic performance.
CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions

Natural Language (NL) descriptions can be the most convenient or the only way to interact with systems built to understand and detect city scale traffic patterns and vehicle-related events. In this paper, we extend the widely adopted CityFlow Benchmark with natural language descriptions for vehicle targets and introduce the CityFlow-NL Benchmark. The CityFlow-NL contains more than 5,000 unique and precise NL descriptions of vehicle targets, making it the largest-scale tracking with NL descriptions dataset to our knowledge. Moreover, the dataset facilitates research at the intersection of multi-object tracking, retrieval by NL descriptions, and temporal localization of events.
OGLE-2019-BLG-0960 Lb: the Smallest Microlensing Planet

We report the analysis of OGLE-2019-BLG-0960, which contains the smallest mass-ratio microlensing planet found to date (q = 1.2–1.6 × 10−5 at 1σ). Although there is substantial uncertainty in the satellite parallax measured by Spitzer, the measurement of the annual parallax effect combined with the finite source effect allows us to determine the mass of the host star (M L = 0.3–0.6 M ⊙), the mass of its planet (m p = 1.4–3.1 M ⊕), the projected separation between the host and planet (a ⊥ = 1.2–2.3 au), and the distance to the lens system (D L = 0.6–1.2 kpc). The lens is plausibly the blend, which could be checked with adaptive optics observations. As the smallest planet clearly below the break in the mass-ratio function, it demonstrates that current experiments are powerful enough to robustly measure the slope of the mass-ratio function below that break. We find that the cross-section for detecting small planets is maximized for planets with separations just outside of the boundary for resonant caustics and that sensitivity to such planets can be maximized by intensively monitoring events whenever they are magnified by a factor A > 5. Finally, an empirical investigation demonstrates that most planets showing a degeneracy between (s > 1) and (s < 1) solutions are not in the regime ( ∣logs∣≫0 ) for which the “close”/“wide” degeneracy was derived. This investigation suggests that there is a link between the “close”/“wide” and “inner/outer” degeneracies and also that the symmetry in the lens equation goes much deeper than symmetries uncovered for the limiting cases.
Summation by parts and truncation error matching on hyperboloidal slices

We examine stability of summation by parts (SBP) numerical schemes that use hyperboloidal slices to include future null infinity in the computational domain. This inclusion serves to mitigate outer boundary effects and, in the future, will help reduce systematic errors in gravitational waveform extraction. We also study a setup with truncation error matching. Our SBP-Stable scheme guarantees energy-balance for a class of linear wave equations at the semidiscrete level. We develop also specialized dissipation operators. The whole construction is made at second order accuracy in spherical symmetry, but could be straightforwardly generalized to higher order or spectral accuracy without symmetry. In a practical implementation we evolve first a scalar field obeying the linear wave equation and observe, as expected, long term stability and norm convergence. We obtain similar results with a potential term. To examine the limitations of the approach we consider a massive field, whose equations of motion do not regularize, and whose dynamics near null infinity, which involve excited incoming pulses that can not be resolved by the code, is very different to that in the massless setting. We still observe excellent energy conservation, but convergence is not satisfactory. Overall our results suggest that compactified hyperboloidal slices are likely to be provably effective whenever the asymptotic solution space is close to that of the wave equation.
Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems

Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. To address this issue, in this paper, we propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage. To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly. Specifically, three deep learning assisted calibrated beam training schemes are proposed. The first scheme adopts convolution neural network to implement the prediction based on the instantaneous received signals of wide beam training. We also perform the additional narrow beam training based on the predicted probabilities for further beam direction calibrations. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise. To further reduce the overhead of wide beam training, our third scheme, an adaptive beam training strategy, selects partial wide beams to be trained based on the prior received signals. Two criteria, namely, optimal neighboring criterion and maximum probability criterion, are designed for the selection. Furthermore, to handle mobile scenarios, auxiliary LSTM is introduced to calibrate the directions of the selected wide beams more precisely. Simulation results demonstrate that our proposed schemes achieve significantly higher beamforming gain with smaller beam training overhead compared with the conventional and existing deep-learning based counterparts.
Theory of competing Chern-Simons orders and emergent phase transitions

Based on the Chern-Simons fermionization of spin-1 / 2 operators, we propose a systematic framework to investigate the competition between emergent phases in frustrated two-dimensional XY quantum magnets. Application of the method to the antiferromagnetic honeycomb spin-1 / 2 J 1 J 2 XY model reveals an unconventional phase transition between two Chern-Simons orders: the Chern-Simons superconductor and the exciton insulator of Chern-Simons fermions. We show that in the spin language, this transition translates to the transition from the planar N´eel state to the non-uniform chiral spin-liquid that was proposed earlier in the literature. Namely, the Chern-Simons superconductor describes the planar N´eel state, while the Chern-Simons exciton insulator corresponds to the non-uniform chiral spin-liquid. These results are further conﬁrmed by our high-precision tensor network calculation, which provides the ﬁrst numerical evidence for the transition from N´eel order to a non-uniform chiral spin-liquid. We argue that the developed method can be applied to other frustrated quantum magnets of XXZ type and can detect unconventional phase transitions.
The Polar Precursor Method for Solar Cycle Prediction: Comparison of Predictors and Their Temporal Range

The polar precursor method is widely considered to be the most robust physically motivated method to predict the amplitude of an upcoming solar cycle. It uses indicators of the magnetic field concentrated near the poles around the sunspot minimum. Here, we present an extensive analysis of the performance of various such predictors, based on both observational data (Wilcox Solar Observatory (WSO) magnetograms, Mount Wilson Observatory polar faculae counts, and Pulkovo A(t) index) and outputs (polar cap magnetic flux and global dipole moment) of various existing flux transport dynamo models. We calculate Pearson correlation coefficients (r) of the predictors with the next cycle amplitude as a function of time measured from several solar cycle landmarks: setting r = 0.8 as a lower limit for acceptable predictions, we find that observations and models alike indicate that the earliest time when the polar predictor can be safely used is 4 yr after the polar field reversal. This is typically 2–3 yr before the solar minimum and about 7 yr before the predicted maximum, considerably extending the usual temporal scope of the polar precursor method. Reevaluating the predictors another 3 yr later, at the time of the solar minimum, further increases the correlation level to r ≳ 0.9. As an illustration of the result, we determine the predicted amplitude of Cycle 25 based on the value of the WSO polar field at the now official minimum date of 2019 December as 126 ± 3. A forecast based on the value in early 2017, 4 yr after the polar reversal would have only differed from this final prediction by 3.1 ± 14.7%.
Radio observations of massive stars in the Galactic centre: The Arches Cluster

We present high-angular-resolution radio observations of the Arches cluster in the Galactic centre, one of the most massive young clusters in the Milky Way. The data were acquired in two epochs and at 6 and 10 GHz with the Karl G. Jansky Very Large Array. The rms noise reached is three to four times better than during previous observations and we have almost doubled the number of known radio stars in the cluster. Nine of them have spectral indices consistent with thermal emission from ionised stellar winds, one is a confirmed colliding wind binary, and two sources are ambiguous cases. Regarding variability, the radio emission appears to be stable on timescales of a few to ten years. Finally, we show that the number of radio stars can be used as a tool for constraining the age and/or mass of a cluster and also its mass function.
Strong edge colorings of graphs and the covers of Kneser graphs

A proper edge coloring of a graph is strong if it creates no bichromatic path of length three. It is well known that for a strong edge coloring of a k $k$ ‐regular graph at least 2 k − 1 $2k-1$ colors are needed. We show that a k $k$ ‐regular graph admits a strong edge coloring with 2 k − 1 $2k-1$ colors if and only if it covers the Kneser graph K ( 2 k − 1 , k − 1 ) $K(2k-1,k-1)$ . In particular, a cubic graph is strongly 5‐edge‐colorable whenever it covers the Petersen graph. One of the implications of this result is that a conjecture about strong edge colorings of subcubic graphs proposed by Faudree et al. is false.
Contribution to the Study of Sub-Bandgap Photon Absorption in Quantum Dot InAs/AlGaAs Intermediate Band Solar Cells

Intermediate band solar cells (IBSCs) pursue the increase in efficiency by absorbing below-bandgap energy photons while preserving the output voltage. Experimental IBSCs based on quantum dots (QDs) have already demonstrated that both below-bandgap photon absorption and the output voltage preservation are possible. However, the experimental work has also revealed that the below-bandgap absorption of light is weak and insufficient to boost the efficiency of the solar cells. The objective of this article is to contribute to the study of this absorption by manufacturing and characterizing a QD IBSC with a single QD layer with and without light trapping elements. Using 1-D substrate texturing, our results show a three-fold increase in the absorption of below-bandgap energy photons in the lowest energy region of the spectrum, a region not previously explored using this approach. Furthermore, we also measure, at 9K, a distinguished split of quasi-Fermi levels between the conduction and intermediate bands, which is a necessary condition to preserve the output voltage of the cell.