Miracle-dz/UniFAR-FTUs huggingface dataset:
QDNN: DNN with Quantum Neural Network Layers

The deep neural network (DNN) became the most important and powerful machine learning method in recent years. In this paper, we introduce a general quantum DNN, which consists of fully quantum structured layers with better representation power than the classical DNN and still keeps the advantages of the classical DNN such as the non-linear activation, the multi-layer structure, and the efficient backpropagation training algorithm. We prove that the quantum structured layer can not be simulated efficiently by classical computers unless universal quantum computing can be classically simulated efficiently and hence our quantum DNN has more representation power than the classical DNN. Moreover, our quantum DNN can be used on near-term noisy intermediate scale quantum (NISQ) processors. A numerical experiment for image classification based on quantum DNN is given, where high accurate rate is achieved.
A NuSTAR view of GRS 1716−249 in the hard and intermediate states

We present a detailed analysis of the spectral properties of the black hole transient GRS 1716−249, based on the archival Swift and NuSTAR observations taken during the outburst of this source in 2016–2017. The first six NuSTAR observations show that the source is in a canonical hard state, where the spectrum is dominated by a power-law continuum. The seventh NuSTAR observation is taken during the intermediate state where both a disc thermal component and a power-law continuum are shown. All of our observations show a broad emission-line feature in the iron band and a Compton hump above 10 keV. We model the broad-band spectra using a high-density disc reflection model, where the soft X-ray emission in the hard state is interpreted as part of the disc reflection component. This model enables us to constrain the disc density parameter of GRS 1716−249 in the range of 1019–1020 cm−3. We only obtain an upper limit of the inner disc radius using high-density disc reflection spectroscopy and the results indicate either a non-truncated disc or a slightly truncated disc with Rin ≲ 20 rg.
Local asymptotic stability of a multi-source power system

We derive a nonlinear multi-source power system model representing synchronous machines or closed-loop DC/AC converters and study its local stability behavior. We reframe the local asymptotic stability problem in terms of small deviation from a well-defined equilibrium set, representing the rotational symmetry of the synchronous steady state manifold. We develop a theoretical framework that builds upon the celebrated Hartman-Grobman theorem to allow for systems with subspace invariance, whose Jacobian matrix, has one zero eigenvalue associated with one-dimensional kernel representing the same invariance. We prove the existence of a homeomorphism that maps linear trajectories approaching the linear subspace into nonlinear trajectories in some neighborhood of the invariance set. Upon projection into the angle and frequency domain, we exploit our key result to show local asymptotic stability of the rotational invariance, determined by the projected level set of a parameterized Lyapunov function, if each generator has sufficient reactive power and resistive damping.
Correlating structural, electronic, and magnetic properties of epitaxial 
VSe2
 thin films

Monolayer films of transition metal dichalcogenides can have very different electronic and structural properties compared to their bulk counterparts. Here, the authors stabilize different structural phases of VSe${}_{2}$ monolayer and bilayer films and study the effects on the electronic properties, charge density wave state, and magnetic properties, using a combination of techniques including scanning tunneling microscopy and first-principles calculations. They find that a new distorted 1$T$ phase can be stabilized in the films that is not found in the bulk compound.
Relatively dominated representations

Anosov representations give a higher-rank analogue of convex cocompactness in a rank-one Lie group which shares many of its good geometric and dynamical properties; geometric finiteness in rank one may be seen as a controlled weakening of convex cocompactness to allow for isolated failures of hyperbolicity. We introduce relatively dominated representations as a relativization of Anosov representations, or in other words a higher-rank analogue of geometric finiteness. We prove that groups admitting relatively dominated representations must be relatively hyperbolic, that these representations induce limit maps with good properties, provide examples, and draw connections to work of Kapovich--Leeb which also introduces higher-rank analogues of geometric finiteness.
The Splash without a Merger

The Milky Way’s progenitor experienced several merger events that left their imprints on the stellar halo, including the Gaia-Sausage/Enceladus. Recently, it has been proposed that this event perturbed the proto-disk and gave rise to a metal-rich ([Fe/H] > −1), low angular momentum (vϕ < 100 km s−1) stellar population. These stars have dynamical and chemical properties different from the accreted stellar halo, but are continuous with the canonical thick disk. In this Letter, we use a hydrodynamical simulation of an isolated galaxy that develops clumps that produce a bimodal thin+thick disk chemistry to explore whether it forms such a population. We demonstrate that clump scattering forms a metal-rich, low angular momentum population, without the need for a major merger. We show that, in the simulation, these stars have chemistry, kinematics, and density distribution in good agreement with those in the Milky Way.
Numerical linear algebra in data assimilation

Data assimilation is a method that combines observations (ie, real world data) of a state of a system with model output for that system in order to improve the estimate of the state of the system and thereby the model output. The model is usually represented by a discretized partial differential equation. The data assimilation problem can be formulated as a large scale Bayesian inverse problem. Based on this interpretation we will derive the most important variational and sequential data assimilation approaches, in particular three‐dimensional and four‐dimensional variational data assimilation (3D‐Var and 4D‐Var) and the Kalman filter. We will then consider more advanced methods which are extensions of the Kalman filter and variational data assimilation and pay particular attention to their advantages and disadvantages. The data assimilation problem usually results in a very large optimization problem and/or a very large linear system to solve (due to inclusion of time and space dimensions). Therefore, the second part of this article aims to review advances and challenges, in particular from the numerical linear algebra perspective, within the various data assimilation approaches.
Reframing SU(1,1) Interferometry

SU(1,1) interferometry, proposed in a classic 1986 paper by Yurke, McCall, and Klauder, involves squeezing, displacing, and then unsqueezing two bosonic modes. It has, over the past decade, been implemented in a variety of experiments. Here, SU(1,1) interferometry is taken apart, to see how and why it ticks. SU(1,1) interferometry arises naturally as the two‐mode version of active‐squeezing‐enhanced, back‐action‐evading measurements aimed at detecting the phase‐space displacement of a harmonic oscillator subjected to a classical force. Truncating an SU(1,1) interferometer, by omitting the second two‐mode squeezer, leaves a prototype that uses the entanglement of two‐mode squeezing to detect and characterize a disturbance on one of the two modes from measurement statistics gathered from both modes.
Harmonic magneto-dielectric study in doped-, double-, and layered-perovskites

Fundamental and harmonic magneto-dielectricity studied for varied perovskite systems-- Pb0.98Gd0. 02(Mg1/3Nb2/3)0.995O3 (A-site co-doped PGMN magneto-relaxor), La0.95Ca0. 05CoO3 (A-site doped spin-state LCCO), and La2NiMnO6 (double-perovskite LNMO multiglass) characterize intricately polarized phases. First-harmonic signal ({\epsilon}2') of magnetically co-doped PGMN manifests finite polarization P(H) below 270K, corroborated by the measured remnant P-E traces. Second-harmonic ({\epsilon}3') reveals the effect of random E-fields causing electrical vitreousity, latter indicated by the divergent timescale of the fundamental response. LCCO features mixed-dipoles phase over appreciable temperature window, affiliated to the coexistent low-spins (LS) and intermediate-spins (IS). Across the 65K-start of IS-to-LS state transition (SST), dc- and ac-conductivities of LCCO exhibit mechanism-changeovers whereas the harmonic susceptibilities evidence IS/LS-interfacial hyper-polarizations. Below the 30K-end of SST, harmonics corroborate the vitreous phase of dipoles in the LS-matrix state. In the LNMO, positive and negative (dual) magneto-dielectricity observed is respectively attributed to the charge-hopping between Ni2+ and Mn4+ ions and the interfacial polarization. Second-harmonic signal here also features dispersion corresponding to the activation energy required for the electron transfer between Ni- and Mn-cations. Results from three different perovskite systems signify the combined importance of first- and second-harmonics, for a detailed understanding of electrical configurations.
PPDM: Parallel Point Detection and Matching for Real-Time Human-Object Interaction Detection

We propose a single-stage Human-Object Interaction (HOI) detection method that has outperformed all existing methods on HICO-DET dataset at 37 fps on a single Titan XP GPU. It is the first real-time HOI detection method. Conventional HOI detection methods are composed of two stages, i.e., human-object proposals generation, and proposals classification. Their effectiveness and efficiency are limited by the sequential and separate architecture. In this paper, we propose a Parallel Point Detection and Matching (PPDM) HOI detection framework. In PPDM, an HOI is defined as a point triplet < human point, interaction point, object point>. Human and object points are the center of the detection boxes, and the interaction point is the midpoint of the human and object points. PPDM contains two parallel branches, namely point detection branch and point matching branch. The point detection branch predicts three points. Simultaneously, the point matching branch predicts two displacements from the interaction point to its corresponding human and object points. The human point and the object point originated from the same interaction point are considered as matched pairs. In our novel parallel architecture, the interaction points implicitly provide context and regularization for human and object detection. The isolated detection boxes unlikely to form meaningful HOI triplets are suppressed, which increases the precision of HOI detection. Moreover, the matching between human and object detection boxes is only applied around limited numbers of filtered candidate interaction points, which saves much computational cost. Additionally, we build a new application-oriented database named as HOI-A, which serves as a good supplement to the existing datasets.
ALMA Observations Reveal No Preferred Outflow-filament and Outflow-magnetic Field Orientations in Protoclusters

We present a statistical study of the orientation of outflows with respect to large-scale filaments and magnetic fields. Although filaments are widely observed toward Galactic star-forming regions, the exact role of filaments in star formation is unclear. Studies toward low-mass star-forming regions revealed both preferred and random orientations of outflows with respect to the filament long axes, while outflows in massive star-forming regions are mostly oriented perpendicular to the host filaments and parallel to the magnetic fields at similar physical scales. Here, we explore outflows in a sample of 11 protoclusters in H ii regions, a more evolved stage compared to infared dark clouds, using Atacama Large Millimeter/submillimeter Array CO (3–2) line observations. We identify a total of 105 outflow lobes in these protoclusters. Among the 11 targets, 7 are embedded within parsec-scale filamentary structures detected in 13CO line and 870 μm continuum emissions. The angles between outflow axes and corresponding filaments (γFil) do not show any hint of preferred orientations (i.e., orthogonal or parallel as inferred in numerical models) with respect to the position angle of the filaments. Identified outflow lobes are also not correlated with the magnetic fields and Galactic plane position angles. Outflows associated with filaments aligned along the large-scale magnetic fields are also randomly oriented. Our study presents the first statistical results of outflow orientation with respect to large-scale filaments and magnetic fields in evolved massive star-forming regions. The random distribution suggests a lack of alignment of outflows with filaments, which may be a result of the evolutionary stage of the clusters.
Supervariable and BRST Approaches to a Toy Model of Reparameterization Invariant Theory

We apply the geometrical supervariable approach to derive the appropriate quantum Becchi-Rouet-Stora-Tyutin (BRST) and anti-BRST symmetries for the toy model of a free scalar relativistic particle by exploiting the classical reparameterization symmetry of this theory. The supervariable approach leads to the derivation of an (anti-)BRST invariant Curci-Ferrari (CF)-type restriction which is the hallmark of a quantum theory (discussed within the framework of BRST formalism). We derive the conserved and off-shell nilpotent (anti-)BRST charges and prove their absolute anticommutativity property by using the virtues of CF-type restriction of our present theory. We establish the sanctity of the existence of CF-type restriction (i) by considering the (anti-)BRST symmetries for the coupled (but equivalent) Lagrangians, and (ii) by proving the symmetry invariance of the Lagrangians within the framework of supervariable approach. We capture the off-shell nilpotency and absolute anticommutativity of the conserved (anti-)BRST charges within the framework of (anti-)chiral supervariable approach (ACSA) to BRST formalism. One of the novel observations of our present endeavor is the derivation of CF-type restriction by using the modified Bonora-Tonin (BT) supervariable approach (while deriving the (anti-)BRST symmetries for the target spacetime and/or momenta variables) and by symmetry considerations of the Lagrangians of the theory. The rest of the (anti-)BRST symmetries for the other variables of our theory are derived by using the ACSA to BRST formalism
NAS evaluation is frustratingly hard

Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue. While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all. As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others. Our first contribution is a benchmark of 8 NAS methods on 5 datasets. To overcome the hurdle of comparing methods with different search spaces, we propose using a method’s relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols. Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline. We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline. These experiments highlight that: (i) the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures; (ii) the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings; (iii) the hand-designed macrostructure (cells) is more important than the searched micro-structure (operations); and (iv) the depth-gap is a real phenomenon, evidenced by the change in rankings between 8 and 20 cell architectures. To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls, e.g. difficulties in reproducibility and comparison of search methods. We provide the code used for our experiments at link-to-come.
Infrared Properties of Asymptotic Giant Branch Stars in Our Galaxy and the Magellanic Clouds

We investigate the infrared properties of asymptotic giant branch (AGB) stars in our Galaxy and the Magellanic Clouds using various infrared observational data and theoretical models. We use catalogs for the sample of 4996 AGB stars in our Galaxy and about 39,000 AGB stars in the Magellanic Clouds from the available literature. For each object in the sample, we cross-identify the 2MASS, Wide-field Infrared Survey Explorer, and Spitzer counterparts. To compare the physical properties of O- and C-rich AGB stars in our Galaxy and the Magellanic Clouds, we present IR two-color diagrams (2CDs) using various photometric data. We perform radiative transfer model calculations for AGB stars using various possible parameters of central stars and dust shells. Using the dust opacity functions of amorphous silicate and carbon, the theoretical dust shell models can roughly reproduce the observations of AGB stars on various IR 2CDs. Compared with our Galaxy, we find that the Magellanic Clouds are deficient in AGB stars with thick dust shells. Compared with the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC) is more deficient in AGB stars with thick dust shells. This could be because the Magellanic Clouds are more metal-poor than our Galaxy and the LMC is more metal-rich than the SMC. We also present the IR properties of known pulsating variables. Investigating the magnitude distributions at mid-IR (MIR) bands for AGB stars in the Magellanic Clouds, we find that the SMC is more deficient in bright AGB stars at MIR bands compared with the LMC.
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework

This paper develops a Pontryagin differentiable programming (PDP) methodology, which establishes a unified framework to solve a broad class of learning and control tasks. The PDP methodology distinguishes from existing methods by two novel techniques: first, we differentiate the Pontryagin's Maximum Principle, and this allows us to obtain analytical gradient of a trajectory with respect to a tunable parameter of a system, thus enabling end-to-end learning of system dynamics, policy, or/and control objective function; and second, we propose an auxiliary control system in backward pass of the PDP framework, and show that the output of the auxiliary control system is exactly the gradient of the system trajectory with respect to the parameter, which can be iteratively obtained using control tools. We investigate three learning modes of the PDP: inverse reinforcement learning, system identification, and control/planning, respectively. We demonstrate the capability of the PDP in each learning mode using various high-dimensional systems, including multilink robot arm, 6-DoF maneuvering UAV, and 6-DoF rocket powered landing.
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our proposed method deeply integrates both 3D voxel Convolutional Neural Network (CNN) and PointNet-based set abstraction to learn more discriminative point cloud features. It takes advantages of efficient learning and high-quality proposals of the 3D voxel CNN and the flexible receptive fields of the PointNet-based networks. Specifically, the proposed framework summarizes the 3D scene with a 3D voxel CNN into a small set of keypoints via a novel voxel set abstraction module to save follow-up computations and also to encode representative scene features. Given the high-quality 3D proposals generated by the voxel CNN, the RoI-grid pooling is proposed to abstract proposal-specific features from the keypoints to the RoI-grid points via keypoint set abstraction. Compared with conventional pooling operations, the RoI-grid feature points encode much richer context information for accurately estimating object confidences and locations. Extensive experiments on both the KITTI dataset and the Waymo Open dataset show that our proposed PV-RCNN surpasses state-of-the-art 3D detection methods with remarkable margins.
From matter to galaxies: general relativistic bias for the one-loop bispectrum

We write down the Lagrangian bias expansion in general relativity up to 4th order in terms of operators describing the curvature of an early-time hypersurface for comoving observers. They can be easily expanded in synchronous or comoving gauges. This is necessary for the computation of the one-loop halo bispectrum, where relativistic effects can be degenerate with a primordial non-Gaussian signal. Since the bispectrum couples scales, an accurate prediction of the squeezed limit behavior needs to be both non-linear and relativistic. We then evolve the Lagrangian bias operators in time in comoving gauge, obtaining non-local operators analogous to what is known in the Newtonian limit. Finally, we show how to renormalize the bias expansion at an arbitrary time and find that this is crucial in order to cancel unphysical 1/k2 divergences in the large-scale power spectrum and bispectrum that could be mistaken for a contamination to the non-Gaussian signal.
Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

HighlightsCorn damage detection was possible using advanced deep learning and computer vision techniques trained with images of simulated corn lodging.RetinaNet and YOLOv2 both worked well at identifying regions of lodged corn.Automating crop damage identification could provide useful information to producers and other stakeholders from visual-band UAS imagery.Abstract. Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect areas of damaged corn from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which is best suited for corn damage detection. Simulated corn lodging was used to create a training and analysis data set. An unmanned aerial system equipped with an RGB camera was used for image acquisition. Three popular object detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability to detect damaged areas. Average precision (AP) was used to compare object detectors. RetinaNet and YOLOv2 demonstrated robust capability for corn damage identification, with AP ranging from 98.43% to 73.24% and from 97.0% to 55.99%, respectively, across all conditions. Faster R-CNN did not perform as well as the other two models, with AP between 77.29% and 14.47% for all conditions. Detecting corn damage at later growth stages was more difficult for all three object detectors. Keywords: Computer vision, Faster R-CNN, RetinaNet, Severe weather, Smart farming, YOLO.