Miracle-dz/UniFAR-FTUs huggingface dataset:
A definitive merger-AGN connection at z ∼ 0 with CFIS: mergers have an excess of AGN and AGN hosts are more frequently disturbed

The question of whether galaxy mergers are linked to the triggering of active galactic nuclei (AGN) continues to be a topic of considerable debate. The issue can be broken down into two distinct questions: 1) Can galaxy mergers trigger AGN? 2) Are galaxy mergers the dominant AGN triggering mechanism? A complete picture of the AGN-merger connection requires that both of these questions are addressed with the same dataset. In previous work, we have shown that galaxy mergers selected from the Sloan Digital Sky Survey (SDSS) show an excess of both optically-selected, and mid-IR colour-selected AGN, demonstrating that the answer to the first of the above questions is affirmative. Here, we use the same optical and mid-IR AGN selection to address the second question, by quantifying the frequency of morphological disturbances in low surface brightness r-band images from the Canada France Imaging Survey (CFIS). Only ~30 per cent of optical AGN host galaxies are morphologically disturbed, indicating that recent interactions are not the dominant trigger. However, almost 60 per cent of mid-IR AGN hosts show signs of visual disturbance, indicating that interactions play a more significant role in nuclear feeding. Both mid-IR and optically selected AGN have interacting fractions that are a factor of two greater than a mass and redshift matched non-AGN control sample, an excess that increases with both AGN luminosity and host galaxy stellar mass.
Lagrangian dynamics of the coupled field-medium state of light

In the recently introduced mass-polariton (MP) theory of light (Partanen et al 2017 Phys. Rev. A 95 063850), the optical force of light drives in a medium forward an atomic mass density wave. In this work, we present the Lagrangian formulation of the MP theory starting directly from the principle of least action and the well-known Lagrangian densities of the electromagnetic field and the medium within the special theory of relativity. The Lagrangian densities and the resulting Euler–Lagrange equations lead directly and without any further postulates to the unique expression of the optical Abraham force that dynamically couples the electromagnetic field and the medium in the MP theory of light. The field-medium coupling is symmetric and bi-directional and it fulfills the law of action and counteraction. The coupled dynamical equations also enable the exact description of the very small kinetic energy of the medium as a part of the total energy of the coupled state of light. Thus, the Lagrangian formulation of the present work is a complementary approach to Lorentz covariance properties of the MP theory discussed in our recent work (Partanen and Tulkki 2019 Phys. Rev. A 99 033852). We show how the coupled dynamical equations of the field and the medium can be solved analytically for a Gaussian light pulse. It is astonishing how the simple analytic results for the dynamical equations, the optical force, and the stress-energy-momentum tensor of the MP theory follow ab initio from the Lagrangian densities that have been well known for almost a century.
Estimating ensemble flows on a hidden Markov chain

We propose a new framework to estimate the evolution of an ensemble of indistinguishable agents on a hidden Markov chain using only aggregate output data. This work can be viewed as an extension of the recent developments in optimal mass transport and Schrödinger bridges to the finite state space hidden Markov chain setting. The flow of the ensemble is estimated by solving a maximum likelihood problem, which has a convex formulation at the infinite-particle limit, and we develop a fast numerical algorithm for it. We illustrate in two numerical examples how this framework can be used to track the flow of identical and indistinguishable dynamical systems.
Simulating the interstellar medium and stellar feedback on a moving mesh: implementation and isolated galaxies

We introduce the Stars and MUltiphase Gas in GaLaxiEs – SMUGGLE model, an explicit and comprehensive stellar feedback model for the moving-mesh code arepo. This novel sub-resolution model resolves the multiphase gas structure of the interstellar medium and self-consistently generates gaseous outflows. The model implements crucial aspects of stellar feedback including photoionization, radiation pressure, energy, and momentum injection from stellar winds and from supernovae. We explore this model in high-resolution isolated simulations of Milky Way like disc galaxies. Stellar feedback regulates star formation to the observed level and naturally captures the establishment of a Kennicutt–Schmidt relation. This result is achieved independent of the numerical mass and spatial resolution of the simulations. Gaseous outflows are generated with average mass loading factors of the order of unity. Strong outflow activity is correlated with peaks in the star formation history of the galaxy with evidence that most of the ejected gas eventually rains down on to the disc in a galactic fountain flow that sustains late-time star formation. Finally, the interstellar gas in the galaxy shows a distinct multiphase distribution with a coexistence of cold, warm, and hot phases.
Floating Wigner crystal with no boundary charge fluctuations

We modify the "floating crystal" trial state for the classical Homogeneous Electron Gas (also known as Jellium), in order to suppress the boundary charge fluctuations that are known to lead to a macroscopic increase of the energy. The argument is to melt a thin layer of the crystal close to the boundary and consequently replace it by an incompressible fluid. With the aid of this trial state we show that three different definitions of the ground state energy of Jellium coincide. In the first point of view the electrons are placed in a neutralizing uniform background. In the second definition there is no background but the electrons are submitted to the constraint that their density is constant, as is appropriate in Density Functional Theory. Finally, in the third system each electron interacts with a periodic image of itself, that is, periodic boundary conditions are imposed on the interaction potential. "
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

In this work, we consider one challenging training time attack by modifying training data with bounded perturbation, hoping to manipulate the behavior (both targeted or non-targeted) of any corresponding trained classifier during test time when facing clean samples. To achieve this, we proposed to use an auto-encoder-like network to generate the pertubation on the training data paired with one differentiable system acting as the imaginary victim classifier. The perturbation generator will learn to update its weights by watching the training procedure of the imaginary classifier in order to produce the most harmful and imperceivable noise which in turn will lead the lowest generalization power for the victim classifier. This can be formulated into a non-linear equality constrained optimization problem. Unlike GANs, solving such problem is computationally challenging, we then proposed a simple yet effective procedure to decouple the alternating updates for the two networks for stability. The method proposed in this paper can be easily extended to the label specific setting where the attacker can manipulate the predictions of the victim classifiers according to some predefined rules rather than only making wrong predictions. Experiments on various datasets including CIFAR-10 and a reduced version of ImageNet confirmed the effectiveness of the proposed method and empirical results showed that, such bounded perturbation have good transferability regardless of which classifier the victim is actually using on image data.
A Multi-epoch X-Ray Study of the Spiral Galaxy NGC 7331

X-ray point sources in galaxies are dominated by X-ray binaries (XRBs) that are variables or transients, and whether their variability would alter the X-ray luminosity functions (XLFs) is still in debate. Here we report on NGC 7331 as an example to test this with seven Chandra observations. Their detection limit is 7 × 1037 erg s−1 in the energy range 0.3–8.0 keV by assuming a power-law (PL) spectral model with a photon index of 1.7. We detected 55 X-ray sources. Thirteen of them are variables, of which three are transients, and some of the sources possess a bimodal feature in their luminosity–hardness ratio, which is often observed among XRBs. Nine more ultraluminous X-ray sources are found in comparison with previous studies and eight are likely to be low-mass or high-mass XRBs. Twenty-one optical counterpart candidates are found based on the Hubble Space Telescope images, but we cannot rule out the possibility of positional coincidence. The spectral analysis of SN 2014C shows a trend of increasing soft photons and decreasing hydrogen column densities as its outer shell expands. We fit the seven incompleteness-corrected XLFs to both a PL and a PL with an exponential cut-off (PLC) model using the Bayesian method, which is used for the first time in XLF fitting. The hierarchical PLC model can describe the XLF of NGC 7331 best with a slope of ∼0.5 and a luminosity cut-off around 8 × 1038 erg s−1. This study proves that multi-epoch observations decrease the deviation due to the variable luminous sources in XLFs.
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible and natural modeling tool to model such complex relationships. The obvious existence of such complex relationships in many real-world networks naturaly motivates the problem of learning with hypergraphs. A popular learning paradigm is hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph. Motivated by the fact that a graph convolutional network (GCN) has been effective for graph-based SSL, we propose HyperGCN, a novel GCN for SSL on attributed hypergraphs. Additionally, we show how HyperGCN can be used as a learning-based approach for combinatorial optimisation on NP-hard hypergraph problems. We demonstrate HyperGCN's effectiveness through detailed experimentation on real-world hypergraphs.
What Would You Expect? Anticipating Egocentric Actions With Rolling-Unrolling LSTMs and Modality Attention

Egocentric action anticipation consists in understanding which objects the camera wearer will interact with in the near future and which actions they will perform. We tackle the problem proposing an architecture able to anticipate actions at multiple temporal scales using two LSTMs to 1) summarize the past, and 2) formulate predictions about the future. The input video is processed considering three complimentary modalities: appearance (RGB), motion (optical flow) and objects (object-based features). Modality-specific predictions are fused using a novel Modality ATTention (MATT) mechanism which learns to weigh modalities in an adaptive fashion. Extensive evaluations on two large-scale benchmark datasets show that our method outperforms prior art by up to +7% on the challenging EPIC-Kitchens dataset including more than 2500 actions, and generalizes to EGTEA Gaze+. Our approach is also shown to generalize to the tasks of early action recognition and action recognition. Our method is ranked first in the public leaderboard of the EPIC-Kitchens egocentric action anticipation challenge 2019. Please see the project web page for code and additional details: http://iplab.dmi. unict.it/rulstm.
The Effects of Computer-Assisted Instruction on the Mathematics Performance and Classroom Behavior of Children With ADHD

The present study examines the effects of computer-assisted instruction (CAI) on the mathematics performance and classroom behavior of three second-through fourth-grade students with ADHD. A controlled case study is used to evaluate the effects of the computer software on participants’ mathematics performance and on-task behavior. Participants’ mathematics achievement improve and their on-task behavior increase during the CAI sessions relative to independent seatwork conditions. In addition, students and teachers consider CAI to be an acceptable intervention for some students with ADHD who are having difficulty with mathematics. Implications of these results for practice and research are discussed.
Acoustic-to-Word Models with Conversational Context Information

Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are typically built at a sentence level, and thus it may not capture important conversational context information. The recent progress in end-to-end speech recognition enables integrating context with other available information (e.g., acoustic, linguistic resources) and directly recognizing words from speech. In this work, we present a direct acoustic-to-word, end-to-end speech recognition model capable of utilizing the conversational context to better process long conversations. We evaluate our proposed approach on the Switchboard conversational speech corpus and show that our system outperforms a standard end-to-end speech recognition system.
Measuring Average Treatment Effect from Heavy-tailed Data

Heavy-tailed metrics are common and often critical to product evaluation in the online world. While we may have samples large enough for Central Limit Theorem to kick in, experimentation is challenging due to the wide confidence interval of estimation. We demonstrate the pressure by running A/A simulations with customer spending data from a large-scale Ecommerce site. Solutions are then explored. On one front we address the heavy tail directly and highlight the often ignored nuances of winsorization. In particular, the legitimacy of false positive rate could be at risk. We are further inspired by the idea of robust statistics and introduce Huber regression as a better way to measure treatment effect. On another front covariates from pre-experiment period are exploited. Although they are independent to assignment and potentially explain the variation of response well, concerns are that models are learned against prediction error rather than the bias of parameter. We find the framework of orthogonal learning useful, matching not raw observations but residuals from two predictions, one towards the response and the other towards the assignment. Robust regression is readily integrated, together with cross-fitting. The final design is proven highly effective in driving down variance at the same time controlling bias. It is empowering our daily practice and hopefully can also benefit other applications in the industry.
From Subkautz Digraphs to Cyclic Kautz Digraphs

The Kautz digraphs K(d, ℓ) are a well-known family of dense digraphs, widely studied as a good model for interconnection networks. Closely related to these, the cyclic Kautz digraphs CK(d, ℓ) were recently introduced by Böhmová, Huemer and the author, and some of its distance-related parameters were fixed. In this paper we propose a new approach to the cyclic Kautz digraphs by introducing the family of the subKautz digraphs sK(d, ℓ), from where the cyclic Kautz digraphs can be obtained as line digraphs. This allows us to give exact formulas for the distance between any two vertices of both sK(d, ℓ) and CK(d, ℓ). Moreover, we compute the diameter and the semigirth of both families, also providing efficient routing algorithms to find the shortest path between any pair of vertices. Using these parameters, we also prove that sK(d, ℓ) and CK(d, ℓ) are maximally vertex-connected and super-edge-connected. Whereas K(d, ℓ) are optimal with respect to the diameter, we show that sK(d, ℓ) and CK(d, ℓ) are optimal with respect to the mean distance, whose exact values are given for both families when ℓ = 3. Finally, we provide a lower bound on the girth of CK(d, ℓ) and sK(d, ℓ).
Revealing the galaxy–halo connection in IllustrisTNG

We use the IllustrisTNG (TNG) simulations to explore the galaxy–halo connection as inferred from state-of-the-art cosmological, magnetohydrodynamical simulations. With the high-mass resolution and large volume achieved by combining the 100 Mpc (TNG100) and 300 Mpc (TNG300) volumes, we establish the mean occupancy of central and satellite galaxies and their dependence on the properties of the dark matter haloes hosting them. We derive best-fitting HOD parameters from TNG100 and TNG300 for target galaxy number densities of $\bar{n}_g = 0.032\,$ and $\bar{n}_g = 0.016\, h^3$ Mpc−3, respectively, corresponding to a minimum galaxy stellar mass of $M_\star \sim 1.9\times 10^9\, $ and $M_\star \sim 3.5\times 10^9\, {\rm M}_\odot$, respectively, in hosts more massive than $10^{11}\, {\rm M}_\odot$. Consistent with previous work, we find that haloes located in dense environments, with low concentrations, later formation times, and high angular momenta are richest in their satellite population. At low mass, highly concentrated haloes and those located in overdense regions are more likely to contain a central galaxy. The degree of environmental dependence is sensitive to the definition adopted for the physical boundary of the host halo. We examine the extent to which correlations between galaxy occupancy and halo properties are independent and demonstrate that HODs predicted by halo mass and present-day concentration capture the qualitative dependence on the remaining halo properties. At fixed halo mass, concentration is a strong predictor of the stellar mass of the central galaxy, which may play a defining role in the fate of the satellite population. The radial distribution of satellite galaxies, which exhibits a universal form across a wide range of host halo mass, is described accurately by the best-fitting NFW density profile of their host haloes.
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defenses for graph data. In this paper, we propose both attack and defense techniques. For attack, we show that the discreteness problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defense, we observe that the adversarially manipulated graph for the targeted attack differs from normal graphs statistically. Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations. Our experiments on a number of datasets show the effectiveness of the proposed methods.
Oral swirl samples - a robust source of microRNA protected by extracellular vesicles.

BACKGROUND MicroRNAs are small non-coding RNAs which are dysregulated in disease states, such as oral cancer. Extracellular vesicles, a potential source of microRNA, are found in saliva. OBJECTIVE To demonstrate that a quantifiable amount of microRNA can be isolated from oral swirl samples. Additionally, we hypothesized that extracellular vesicles may protect contained microRNA from degradation in these samples. METHOD A polyethylene glycol-based precipitation was used for extracellular vesicle enrichment of oral swirl samples. Comparison was made between samples treated with and without RNase. Further, samples from three subjects were exposed to a range of conditions over 7 days and assessed for presence of microRNA by reverse-transcription quantitative PCR. Extracellular vesicles from samples were identified under transmission electron microscopy. RESULTS An adequate quantity of microRNA for qPCR analysis was extractable from samples despite exposure to conditions under which degradation of RNA would be expected. CONCLUSION A technique was developed to isolate an adequate quantity of microRNA for analysis from oral swirl samples. Extracellular vesicle-associated microRNA may be protected from degradation. This technique moves towards chairside application of translational microRNA research in the field of oral cancer prognostics.
Numerical investigation of the dynamics of linear spin 
s
 fields on a Kerr background: Late-time tails of spin 
s=±1,±2
 fields

The time evolution of linear fields of spin $s = \pm 1$ and $s = \pm 2$ on Kerr black hole spacetimes are investigated by solving the homogeneous Teukolsky equation numerically. The applied numerical setup is based on a combination of conformal compactification and the hyperbolic initial value problem. The evolved basic variables are expanded in terms of spin-weighted spherical harmonics which allows us to evaluate all the angular derivatives analytically, whereas the evolution of the expansion coefficients, in the time-radial section, is determined by applying the method of lines implemented in a fourth order accurate finite differencing stencil. Concerning the initialization, in all of our investigations single mode excitations---either static or purely dynamical type initial data---are applied. Within this setup the late time tail behavior is investigated. Due to the applied conformal compactification the asymptotic decay rates are determined at three characteristic locations---in the domain of outer communication, at the event horizon and at future null infinity---simultaneously. Recently introduced new type of `energy' and `angular momentum' balance relations are also applied in order to demonstrate the feasibility and robustness of the developed numerical schema, and also to verify the proper implementation of the underlying mathematical model.