Miracle-dz/UniFAR-FTUs huggingface dataset:
A Catalog of 204 Offset and Dual Active Galactic Nuclei (AGNs): Increased AGN Activation in Major Mergers and Separations under 4 kpc

During galaxy mergers, gas and dust are driven toward the centers of merging galaxies, triggering enhanced star formation and supermassive black hole (SMBH) growth. Theory predicts that this heightened activity peaks at SMBH separations <20 kpc; if sufficient material accretes onto one or both of the SMBHs for them to become observable as active galactic nuclei (AGNs) during this phase, they are known as offset and dual AGNs, respectively. To better study these systems, we have built the ACS-AGN Merger Catalog, a large catalog (N = 204) of uniformly selected offset and dual AGN observed by the Hubble Space Telescope at 0.2 < z < 2.5 with separations <20 kpc. Using this catalog, we answer many questions regarding SMBH−galaxy coevolution during mergers. First, we confirm predictions that the AGN fraction peaks at SMBH pair separations <10 kpc; specifically, we find that the fraction increases significantly at pair separations of <4 kpc. Second, we find that AGNs in mergers are preferentially found in major mergers and that the fraction of AGNs found in mergers follows a logarithmic relation, decreasing as merger mass ratio increases. Third, we do not find that mergers (nor the major or minor merger subpopulations) trigger the most luminous AGNs. Finally, we find that nuclear column density, AGN luminosity, and host galaxy star formation rate have no dependence on SMBH pair separation or merger mass ratio in these systems, nor do the distributions of these values differ significantly from that of the overall AGN population.
Digital-Analog Quantum Simulations Using the Cross-Resonance Effect

Digital-analog quantum computation aims to reduce the currently infeasible resource requirements needed for near-term quantum information processing by replacing a series of two-qubit gates with a unitary transformation generated by the systems underlying Hamiltonian. Inspired by this paradigm, we consider superconducting architectures and extend the cross-resonance effect, up to first order in perturbation theory, from a pair of qubits to 1D chains and 2D square lattices. In an appropriate reference frame, this results in a purely two-local Hamiltonian comprised of non-commuting interactions. By augmenting the analog Hamiltonian dynamics with single-qubit gates, we generate new families of locally transformed analog Hamiltonians. Toggling between these Hamiltonians, as needed, we design unitary sequences simulating the dynamics of Ising, $XY$, and Heisenberg spin models. Our dynamics simulations are Trotter error-free for the Ising and $XY$ models in 1D. We also show that the Trotter errors for 2D $XY$ and 1D Heisenberg chains are reduced, with respect to a digital decomposition, by a constant factor. Our Hamiltonian toggling techniques could be extended to derive new analog Hamiltonians which may be of use in more complex digital-analog quantum simulations for various models of interacting spins.
Reliability Enhancement for VR Delivery in Mobile-Edge Empowered Dual-Connectivity μWave-mmWave HetNets

The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz ($\mu$Wave) networks. In this paper, we propose a dual-connectivity $\mu$Wave-mmWave heterogeneous network architecture empowered by mobile edge capability. The core idea of the proposed architecture is to utilize the complementary advantages of $\mu$Wave links and mmWave links to conduct a collaborative edge resource design, which aims to improve the reliability of VR delivery. From the perspective of stochastic geometry, we analyze the reliability of VR delivery and theoretically demonstrate that $\mu$Wave links can be used to enhance the reliability of VR delivery despite the large mmWave bandwidth. Based on our analytical work, we formulate a joint caching and computing optimization problem with the goal to maximize the reliability of VR delivery. By analyzing the coupling caching and computing strategies at HMDs, $\mu$Wave and mmWave base stations (BSs), we further transform the problem into a multiple-choice multi-dimension knapsack problem. A best-first branch and bound algorithm and a difference of convex programming algorithm are proposed to obtain the optimal and sub-optimal solution, respectively. Numerical results demonstrate the performance improvement using the proposed algorithms, and reveal that caching more monocular videos at $\mu$Wave BSs and more stereoscopic videos at mmWave BSs can improve the VR delivery reliability efficiently.
Positivity preserving high order schemes for angiogenesis models

Abstract Hypoxy induced angiogenesis processes can be described by coupling an integrodifferential kinetic equation of Fokker–Planck type with a diffusion equation for the angiogenic factor. We propose high order positivity preserving schemes to approximate the marginal tip density by combining an asymptotic reduction with weighted essentially non oscillatory and strong stability preserving time discretization. We capture soliton-like solutions representing blood vessel formation and spread towards hypoxic regions.
Finding Prerequisite Relations between Concepts using Textbook

A prerequisite is anything that you need to know or understand first before attempting to learn or understand something new. In the current work, we present a method of finding prerequisite relations between concepts using related textbooks. Previous researchers have focused on finding these relations using Wikipedia link structure through unsupervised and supervised learning approaches. In the current work, we have proposed two methods, one is statistical method and another is learning-based method. We mine the rich and structured knowledge available in the textbooks to find the content for those concepts and the order in which they are discussed. Using this information, proposed statistical method estimates explicit as well as implicit prerequisite relations between concepts. During experiments, we have found performance of proposed statistical method is better than the popular RefD method, which uses Wikipedia link structure. And proposed learning-based method has shown a significant increase in the efficiency of supervised learning method when compared with graph and text-based learning-based approaches.
Pseudoinverse-free randomized block iterative methods for consistent and inconsistent linear systems

Randomized iterative methods have attracted much attention in recent years because they can approximately solve large-scale linear systems of equations without accessing the entire coefficient matrix. In this paper, we propose two novel pseudoinverse-free randomized block iterative methods for solving consistent and inconsistent linear systems. Our methods require two user-defined random matrices: one for row sampling and the other for column sampling. The well-known doubly stochastic Gauss--Seidel, randomized Kaczmarz, randomized coordinate descent, and randomized extended Kaczmarz methods are special cases of our methods corresponding to specially selected random matrices. Because our methods allow for a much wider selection of these two random matrices, a number of new specific methods can be obtained. We prove the linear convergence (in the mean square sense) of our methods. Numerical experiments for linear systems with synthetic and real-world coefficient matrices demonstrate the efficiency of some special cases of our methods. We remark that due to the pseudoinverse-free nature our methods can be easily implemented for parallel computation to yield further computational gains.
Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association

We propose a self-supervised method for incrementally refining visual descriptors to improve performance in the task of object-level visual data association. Our method optimizes deep descriptor generators online, by fine-tuning a widely available network pre-trained for image classification. We show that earlier layers in the network outperform later-stage layers for the data association task while also allowing for a 94% reduction in the number of parameters, enabling the online optimization. We show that choosing positive examples separated by large temporal distances and negative examples close in the descriptor space improves the quality of the learned descriptors for the multi-object tracking task. Finally, we demonstrate a MOTA score of 21.25% on the 2D-MOT-2015 dataset using visual information alone, outperforming methods that incorporate motion information.
Escherization with Generalized Distance Functions Focusing on Local Structural Similarity

The Escherization problem involves finding a closed figure that tiles the plane that is most similar to a given goal figure. In Koizumi and Sugihara's formulation of the Escherization problem, the tile and goal figures are represented as $n$-point polygons where the similarity between them is measured based on the difference in the positions between the corresponding points. This paper presents alternative similarity measures (distance functions) suitable for this problem. The proposed distance functions focus on the similarity of local structures in several different manners. The designed distance functions are incorporated into a recently developed framework of the exhaustive search of the templates for the Escherization problem. Efficient exhaustive and incomplete search algorithms for the formulated problems are also developed to obtain results within a reasonable computation time. Experimental results showed that the proposed algorithms found satisfactory tile shapes for fairly complicated goal figures in a reasonable computation time.
Synchronization Instability of Inverter-Based Generation During Asymmetrical Grid Faults

The transient stability of traditional power systems is concerned with the ability of generators to stay synchronized with the positive-sequence voltage of the network, whether for symmetrical or asymmetrical faults. In contrast, both positive- and negative-sequence synchronizations should be of concern for inverter-based generation (IBG) under asymmetrical faults. This is because the latest grid codes stipulate that IBG should inject dual-sequence current when riding through asymmetrical faults. Currently, much less is known about the synchronization stability during asymmetrical faults. This significantly differs from the positive-sequence synchronization alone because the coupled dual-sequence synchronization is involved. This paper aims to fill this gap. Considering the sequence coupling under asymmetrical faults, the dual-sequence synchronization model of IBG is developed. Based on the model, the conditions that steady-state equilibrium points should follow are identified. The conditions throw light on the possible types of synchronization instability, including the positive-sequence dominated instability and the negative-sequence dominated one. For different types of instability, the dominant factors are analyzed quantitatively, which are reflected by the limit on the current injection amplitude. Exceeding the limit will lead to the loss of both positive- and negative-sequence synchronizations. The model and the analysis are verified by simulations and hardware-in-the-loop experiments.
Understanding the Distributional Aspects of Microcredit Expansions.

Various poverty reduction strategies are being implemented in the pursuit of eliminating extreme poverty. One such strategy is increased access to microcredit in poor areas around the world. Microcredit, typically defined as the supply of small loans to underserved entrepreneurs that originally aimed at displacing expensive local money-lenders, has been both praised and criticized as a development tool (Banerjee et al., 2015b). This paper presents an analysis of heterogeneous impacts from increased access to microcredit using data from three randomised trials. In the spirit of recognising that in general the impact of a policy intervention varies conditional on an unknown set of factors, particular, we investigate whether heterogeneity presents itself as groups of winners and losers, and whether such subgroups share characteristics across RCTs. We find no evidence of impacts, neither average nor distributional, from increased access to microcredit on consumption levels. In contrast, the lack of average effects on profits seems to mask heterogeneous impacts. The findings are, however, not robust to the specific machine learning algorithm applied. Switching from the better performing Elastic Net to the worse performing Random Forest leads to a sharp increase in the variance of the estimates. In this context, methods to evaluate the relative performing machine learning algorithm developed by Chernozhukov et al. (2019) provide a disciplined way for the analyst to counter the uncertainty as to which algorithm to deploy.
WiFi-Based Channel Impulse Response Estimation and Localization via Multi-Band Splicing

Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study the problem of channel impulse response (CIR) estimation from commodity WiFi channel state information (CSI). The accuracy of a CIR estimation method in this setup is limited by both the available channel bandwidth as well as various CSI distortions induced by the underlying hardware. We propose a multi-band splicing method that increases channel bandwidth by combining CSI data across multiple frequency bands. In order to compensate for the CSI distortions, we develop a per-band processing algorithm that is able to estimate the distortion parameters and remove them to yield the “clean” CSI. This algorithm incorporates the atomic norm denoising sparse recovery method to exploit channel sparsity. Splicing clean CSI over M frequency bands, we use orthogonal matching pursuit (OMP) as an estimation method to recover the sparse CIR with high (M-fold) resolution. Unlike previous works in the literature, our method does not appeal to any limiting assumption on the CIR (other than the widely accepted sparsity assumption) or any ad hoc processing for distortion removal. We show, empirically, that the proposed method outperforms the state of the art in terms of localization accuracy.
CLIPPER: A Graph-Theoretic Framework for Robust Data Association

We present CLIPPER (Consistent LInking, Pruning, and Pairwise Error Rectification), a framework for robust data association in the presence of noise and outliers. We formulate the problem in a graph-theoretic framework using the notion of geometric consistency. State-of-the-art techniques that use this framework utilize either combinatorial optimization techniques that do not scale well to large-sized problems, or use heuristic approximations that yield low accuracy in high-noise, high-outlier regimes. In contrast, CLIPPER uses a relaxation of the combinatorial problem and returns solutions that are guaranteed to correspond to the optima of the original problem. Low time complexity is achieved with an efficient projected gradient ascent approach. Experiments indicate that CLIPPER maintains a consistently low runtime of 15 ms where exact methods can require up to 24 s at their peak, even on small-sized problems with 200 associations. When evaluated on noisy point cloud registration problems, CLIPPER achieves 100% precision and 98% recall in 90% outlier regimes while competing algorithms begin degrading by 70% outliers. In an instance of associating noisy points of the Stanford Bunny with 990 outlier associations and only 10 inlier associations, CLIPPER successfully returns 8 inlier associations with 100% precision in 138 ms. Code is available at https://mit-acl.github. io/clipper.
Megahertz dynamics in skyrmion systems probed with muon-spin relaxation

We present longitudinal-field muon-spin relaxation (LF $\mu$SR) measurements on two systems that stabilize a skyrmion lattice (SkL): Cu$_2$OSeO$_3$, and Co$_x$Zn$_y$Mn$_{20-x-y}$ for $(x,y)~=~(10,10)$, $(8,9)$ and $(8,8)$. We find that the SkL phase of Cu$_2$OSeO$_3$ exhibits emergent dynamic behavior at megahertz frequencies, likely due to collective excitations, allowing the SkL to be identified from the $\mu$SR response. From measurements following different cooling protocols and calculations of the muon stopping site, we suggest that the metastable SkL is not the majority phase throughout the bulk of this material at the fields and temperatures where it is often observed. The dynamics of bulk Co$_8$Zn$_9$Mn$_3$ are well described by $\simeq~2$ GHz excitations that reduce in frequency near the critical temperature, while in Co$_8$Zn$_8$Mn$_4$ we observe similar behavior over a wide range of temperatures, implying that dynamics of this kind persist beyond the SkL phase.
GAN based ball screw drive picture database enlargement for failure classification

The lack of reliable large datasets is one of the biggest difficulties of using modern machine learning methods in the field of failure detection in the manufacturing industry. In order to develop the function of failure classification for ball screw surface, sufficient image data of surface failures is necessary. When training a neural network model based on a small dataset, the trained model may lack the generalization ability and may perform poorly in practice. The main goal of this paper is to generate synthetic images based on the generative adversarial network (GAN) to enlarge the image dataset of ball screw surface failures. Pitting failure and rust failure are two possible failure types on ball screw surface chosen in this paper to represent the surface failure classes. The quality and diversity of generated images are evaluated afterwards using qualitative methods including expert observation, t-SNE visualization and the quantitative method of FID score. To verify whether the GAN based generated images can increase failure classification performance, the real image dataset was augmented and replaced by GAN based generated images to do the classification task. The authors successfully created GAN based images of ball screw surface failures which showed positive effect on classification test performance.
The antiferromagnetic xy model on the triangular lattice: topological singularities

We study the discrete-to-continuum variational limit of the antiferromagnetic XY model on the two-dimensional triangular lattice in the vortex regime. Within this regime, the spin system cannot overcome the energetic barrier of chirality transitions, hence one of the two chirality phases is prevalent. We find the order parameter that describes the vortex structure of the spin field in the majority chirality phase and we compute explicitly the $\Gamma$-limit of the scaled energy, showing that it concentrates on finitely many vortex-like singularities of the spin field.
Compressive Shack-Hartmann Wavefront Sensing based on Deep Neural Networks

The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this paper, we propose a compressive Shack-Hartmann wavefront sensing method. Instead of reconstructing wavefronts with slope measurements of all sub-apertures, our method reconstructs wavefronts with slope measurements of sub-apertures which have spot images with high signal to noise ratio. Besides, we further propose to use a deep neural network to accelerate wavefront reconstruction speed. During the training stage of the deep neural network, we propose to add a drop-out layer to simulate the compressive sensing process, which could increase development speed of our method. After training, the compressive Shack-Hartmann wavefront sensing method can reconstruct wavefronts in high spatial resolution with slope measurements from only a small amount of sub-apertures. We integrate the straightforward compressive Shack-Hartmann wavefront sensing method with image deconvolution algorithm to develop a high-order image restoration method. We use images restored by the high-order image restoration method to test the performance of our the compressive Shack-Hartmann wavefront sensing method. The results show that our method can improve the accuracy of wavefront measurements and is suitable for real-time applications.
Topological correlations and breaking of fermionic antisymmetry of electrons in FQHE

Highly nonlocal interparticle correlations in quantum Hall states of 2D charged system exposed to the perpendicular strong magnetic field are detailed by application of the commensurability condition upon path-integral quantization approach and examined by Monte-Carlo Metropolis simulations in perfect consistence with exact diagonalization of the Coulomb interaction in small models and with experimental data. In this way refined filling rate hierarchy in the lowest Landau level fully explains the experimentally collected features for FQHE at filling ratios predicted by the conventional composite fermion model as well as for those beyond the composite fermion model but also visible in the experiment. The trial wave functions for FQHE states are proposed using a systematic topological method revealing the different symmetry for different correlated states depending on filling fraction. A violation of fermionic antisymmetry for 2D electrons is evidenced for some filling rates related to specific correlations in FQHE.
Topic Modelling Discourse Dynamics in Historical Newspapers

This paper addresses methodological issues in diachronic data analysis for historical research. We apply two families of topic models (LDA and DTM) on a relatively large set of historical newspapers, with the aim of capturing and understanding discourse dynamics. Our case study focuses on newspapers and periodicals published in Finland between 1854 and 1917, but our method can easily be transposed to any diachronic data. Our main contributions are a) a combined sampling, training and inference procedure for applying topic models to huge and imbalanced diachronic text collections; b) a discussion on the differences between two topic models for this type of data; c) quantifying topic prominence for a period and thus a generalization of document-wise topic assignment to a discourse level; and d) a discussion of the role of humanistic interpretation with regard to analysing discourse dynamics through topic models.
Effects of the Background Turbulence on the Relaxation of Ion Temperature Anisotropy in Space Plasmas

Turbulence in space plasmas usually exhibits two regimes separated by a spectral break that divides the so called inertial and kinetic ranges. Large scale magnetic fluctuations are dominated by non-linear MHD wave-wave interactions following a −5/3 or −2 slope power-law spectrum. After the break, at scales in which kinetic effects take place, the magnetic spectrum follows a steeper power-law k−α shape given by a spectral index α > 5/3. Despite its ubiquitousness, the possible effects of a turbulent background spectrum in the quasilinear relaxation of solar wind temperatures are usually not considered. In this work, a quasilinear kinetic theory is used to study the evolution of the proton temperatures in an initially turbulent collisionless plasma composed by cold electrons and bi-Maxwellian protons, in which electromagnetic waves propagate along a background magnetic field. Four wave spectrum shapes are compared with different levels of wave intensity. We show that a sufficient turbulent magnetic power can drive stable protons to transverse heating, resulting in an increase in the temperature anisotropy and the reduction of the parallel proton beta. Thus, stable proton velocity distribution can evolve in such a way as to develop kinetic instabilities. This may explain why the constituents of the solar wind can be observed far from thermodynamic equilibrium and near the instability thresholds.