Miracle-dz/UniFAR-FTUs huggingface dataset:
Early-type galaxy density profiles from IllustrisTNG – II. Evolutionary trend of the total density profile

We study the evolutionary trend of the total density profile of early-type galaxies (ETGs) in IllustrisTNG. To this end, we trace ETGs from z = 0 to 4 and measure the power-law slope γ′ of the total density profile for their main progenitors. We find that their slopes γ′ steepen on average during z ∼ 4–2, then becoming shallower until z = 1, after which they remain almost constant, aside from a residual trend of becoming shallower towards z = 0. We also compare to a statistical sample of ETGs at different redshifts, selected based on their luminosity profiles and stellar masses. Due to different selection effects, the average slopes of the statistical samples follow a modified evolutionary trend. They monotonically decrease since z = 3, and after z ≈ 1, they remain nearly invariant with a mild increase towards z = 0. These evolutionary trends are mass dependent for both samples, with low-mass galaxies having in general steeper slopes than their more massive counterparts. Galaxies that transitioned to ETGs more recently have steeper mean slopes as they tend to be smaller and more compact at any given redshift. By analysing the impact of mergers and AGN feedback on the progenitors’ evolution, we conjecture a multiphase path leading to isothermality in ETGs: dissipation associated with rapid wet mergers tends to steepen γ′ from z = 4 to 2, whereas subsequent AGN feedback (especially in the kinetic mode) makes γ′ shallower again from z = 2 to 1. Afterwards, passive evolution from z = 1 to 0, mainly through gas-poor mergers, mildly decreases γ′ and maintains the overall mass distribution close to isothermal.
Topological Constraints in Eukaryotic Genomes and How They Can Be Exploited to Improve Spatial Models of Chromosomes

Several orders of magnitude typically separate the contour length of eukaryotic chromosomes and the size of the nucleus where they are confined. The ensuing topological constraints can slow down the relaxation dynamics of genomic filaments to the point that mammalian chromosomes are never in equilibrium over a cell's lifetime. In this opinion article, we revisit these out-of-equilibrium effects and discuss how their inclusion in physical models can enhance the spatial reconstructions of interphase eukaryotic genomes from phenomenological constraints collected during interphase.
KRNET: Image Denoising with Kernel Regulation Network

One popular strategy for image denoising is to design a generalized regularization term that is capable of exploring the implicit prior underlying data observation. Convolutional neural networks (CNN) have shown the powerful capability to learn image prior information through a stack of layers defined by a combination of kernels (filters) on the input. However, existing CNN-based methods mainly focus on synthetic gray-scale images. These methods still exhibit low performance when tackling multi-channel color image denoising. In this paper, we optimize CNN regularization capability by developing a kernel regulation module. In particular, we propose a kernel regulation network-block, referred to as KR-block, by integrating the merits of both large and small kernels, that can effectively estimate features in solving image denoising. We build a deep CNN-based denoiser, referred to as KRNET, via concatenating multiple KR-blocks. We evaluate KRNET on additive white Gaussian noise (AWGN), multi-channel (MC) noise, and realistic noise, where KRNET obtains significant performance gains over state-of-the-art methods across a wide spectrum of noise levels.
Neutrinos from the gamma-ray source eHWC J1825-134: Predictions for 
Km3
 detectors

The eHWC J1825-134 source is located in the southern sky and has been recently detected by the HAWC observatory. It presents an hard spectral index and its gamma-ray flux extends up to energies close to 100 TeV without significant suppression. Amongst the HAWC sources, it is the most luminous in the multi-TeV domain and therefore is one of the first that should be searched for with a neutrino telescope in the northern hemisphere. Using an updated effective area for the forthcoming KM3NeT detector, we study the possibility to detect this source within ten years of its running time. We conclude that about a 4 to 5 sigma detection has to be expected after ten years of observations, depending on the details of the considered scenario.
Experimental Observation of Nonreciprocal Waves in a Resonant Metamaterial Beam

Space-time-varying materials pledge to deliver nonreciprocal dispersion in linear systems by inducing an artificial momentum bias. Although such a paradigm eliminates the need for actual motion of the medium, experimental realization of space-time structures with dynamically changing material properties has been elusive. In this letter, we present an elastic metamaterial that exploits stiffness variations in an array of geometrically phase-shifted resonators -- rather than external material stimulation -- to induce a temporal modulation. We experimentally demonstrate that the resulting bias breaks time-reversal symmetry in the resonant metamaterial, and achieves a nonreciprocal tilt of dispersion modes within dynamic modulation regimes.
A survey for high-mass eclipsing binaries

We report results from a search for Galactic high-mass eclipsing binaries. The photometric monitoring campaign was performed in Sloan r and i with the robotic twin refractor RoBoTT at the Universitätssternwarte Bochum in Chile and complemented by Johnson UBV data. Comparison with the SIMBAD data base reveals 260 variable high-mass stars. Based on well-sampled light curves, we discovered 35 new eclipsing high-mass systems and confirm the properties of six previously known systems. For all objects, we provide the first light curves and determine orbital periods through the Lafler–Kinman algorithm. Apart from GSC 08173-0018 and Pismis 24-13 ($P = 19.47\, d$ and $20.14\, d$) and the exceptional short-period system TYC 6561-1765-1 ($P = 0.71\, d$), all systems have orbital periods between 1 and 9 d. We model the light curves of 26 systems within the framework of the Roche geometry and calculate fundamental parameters for each system component. The Roche lobe analysis indicates that 14 systems have a detached geometry, while 12 systems have a semidetached geometry; seven of them are near-contact systems. The deduced mass ratios q = M2/M1 reach from 0.4 to 1.0 with an average value of 0.8. The similarity of masses suggests that these high-mass binaries were created during the star formation process rather than by tidal capture.
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction

In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of the vehicle. The long term goal of this work is to design a fully autonomous system that acts and reacts as a defensive human driver would — predicting future events and reacting to mitigate risk. We focus on pedestrian-vehicle interactions because of the high risk of harm to the pedestrian if their actions are miss-predicted. Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames. The second stage is a deep spatiotemporal network that utilizes the predicted frames of the first stage to predict the pedestrian’s future action. Our system achieves state-of-the-art accuracy on the Joint Attention for Autonomous Driving (JAAD) dataset on both future frames prediction, with a pixel-wise prediction l1 error of 1.12, and pedestrian behavior prediction with an average precision of 86.7.
On Radon Measures Invariant Under Horospherical Flows on Geometrically Infinite Quotients

We consider a locally finite (Radon) measure on $ SO^+(d,1)/ \Gamma $ invariant under a horospherical subgroup of $ SO^+(d,1) $ where $ \Gamma $ is a discrete, but not necessarily geometrically finite, subgroup. We show that whenever the measure does not observe any additional invariance properties then it must be supported on a set of points with geometrically degenerate trajectories under the corresponding contracting $ 1 $-parameter diagonalizable flow (geodesic flow).
S2k – Guideline on the Therapy of Acne

Alexander Nast, Christiane Bayerl, Claudia Borelli, Klaus Degitz, Thomas Dirschka, Ricardo Erdmann, Joachim Fluhr, Uwe Gieler, Roland Hartwig, Eva-Maria Meigel, Siegfried Möller, Falk Ochsendorf, Maurizio Podda, Thomas Rabe, Berthold Rzany, Adel Sammain, Susanne Schink, Christos Zouboulis, Harald Gollnick (1) Division of Evidence Based Medicine (dEBM), Department of Dermatology, Venerology and Allergology, Charité – Universitätsmedizin Berlin, Berlin (2) Department of Dermatology and Allergology, Wilhelm Fresenius Klinik, Wiesbaden (3) Department of Dermatology and Allergology, Ludwig-Maximilians University Munich, Munich (4) Dermatologist in private practice, Munich (5) Dermatologist in private practice, Wuppertal (6) Department of Dermatology, Venerology and Allergology, Charité – Universitätsmedizin Berlin, Berlin (7) Department of Psychosomatic Medicine and Psychotherapy, Justus-Liebig University Giessen, Giessen (8) Dermatologist in private practice, Cuxhaven (9) Dermatologist in private practice, Hamburg (10) Dermatologist in private practice, Wiesbaden (11) Department of Dermatology and Venerology, Johann Wolfgang Goethe University, Frankfurt / Main (12) Department of Dermatology, Darmstadt City Hospital, Darmstadt. (13) Department of Gynecologic Endocrinology and Reproductive Medicine, Heidelberg University Womens Hospital, Heidelberg (14) Patient representative, medical student, Charité – University Medical School Berlin, Berlin (15) Department of Dermatology, Venerology and Allergology / Immunological Center, Dessau City Hospital, Dessau / Laboratory for Biogerontology, Dermatopharmacology and Dermatoendocrinology, Institute of Clinical Pharmacology and Toxicology, Benjamin Franklin Campus, Charité – University Medical School Berlin, Berlin (16) Department of Dermatology and Venerology, Otto-von-Guericke University, Magdeburg
An Alternative Surrogate Loss for PGD-based Adversarial Testing

Adversarial testing methods based on Projected Gradient Descent (PGD) are widely used for searching norm-bounded perturbations that cause the inputs of neural networks to be misclassified. This paper takes a deeper look at these methods and explains the effect of different hyperparameters (i.e., optimizer, step size and surrogate loss). We introduce the concept of MultiTargeted testing, which makes clever use of alternative surrogate losses, and explain when and how MultiTargeted is guaranteed to find optimal perturbations. Finally, we demonstrate that MultiTargeted outperforms more sophisticated methods and often requires less iterative steps than other variants of PGD found in the literature. Notably, MultiTargeted ranks first on MadryLab's white-box MNIST and CIFAR-10 leaderboards, reducing the accuracy of their MNIST model to 88.36% (with $\ell_\infty$ perturbations of $\epsilon = 0.3$) and the accuracy of their CIFAR-10 model to 44.03% (at $\epsilon = 8/255$). MultiTargeted also ranks first on the TRADES leaderboard reducing the accuracy of their CIFAR-10 model to 53.07% (with $\ell_\infty$ perturbations of $\epsilon = 0.031$).
Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets

Phenomenon-specific “adversarial” datasets have been recently designed to perform targeted stress-tests for particular inference types. Recent work (Liu et al., 2019a) proposed that such datasets can be utilized for training NLI and other types of models, often allowing to learn the phenomenon in focus and improve on the challenge dataset, indicating a “blind spot” in the original training data. Yet, although a model can improve in such a training process, it might still be vulnerable to other challenge datasets targeting the same phenomenon but drawn from a different distribution, such as having a different syntactic complexity level. In this work, we extend this method to drive conclusions about a model’s ability to learn and generalize a target phenomenon rather than to “learn” a dataset, by controlling additional aspects in the adversarial datasets. We demonstrate our approach on two inference phenomena – dative alternation and numerical reasoning, elaborating, and in some cases contradicting, the results of Liu et al. Our methodology enables building better challenge datasets for creating more robust models, and may yield better model understanding and subsequent overarching improvements.
Using Supervised Learning to Classify Metadata of Research Data by Discipline of Research

Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records, which is published alongside this paper. These data allow to reproducibly assess classification approaches, such as tree-based models and neural networks. According to our experiments with 20 base classes (multi-label classification), multi-layer perceptron models perform best with a f1-macro score of 0.760 closely followed by Long Short-Term Memory models (f1-macro score of 0.755). A possible application of the trained classification models is the quantitative analysis of trends towards interdisciplinarity of digital scholarly output or the characterization of growth patterns of research data, stratified by discipline of research. Both applications perform at scale with the proposed models which are available for re-use.
Analysis of the H.E.S.S. public data release with ctools

The ctools open-source software package was developed for the scientific analysis of astronomical data from Imaging Air Cherenkov Telescopes (IACTs), such as H.E. S.S. , VERITAS, MAGIC, and the future Cherenkov Telescope Array (CTA). To date, the software has been mainly tested using simulated CTA data; however, upon the public release of a small set of H.E. S.S. observations of the Crab nebula, MSH 15–52, RX J1713.7–3946, and PKS 2155–304 validation using real data is now possible. We analysed the data of the H.E. S.S. public data release using ctools version 1.6 and compared our results to those published by the H.E. S.S. Collaboration for the respective sources. We developed a parametric background model that satisfactorily describes the expected background rate as a function of reconstructed energy and direction for each observation. We used that model, and tested all analysis methods that are supported by ctools, including novel unbinned and joint or stacked binned analyses of the measured event energies and reconstructed directions, and classical On-Off analysis methods that are comparable to those used by the H.E. S.S. Collaboration. For all analysis methods, we found a good agreement between the ctools results and the H.E. S.S. Collaboration publications considering that they are not always directly comparable due to differences in the datatsets and event processing software. We also performed a joint analysis of H.E. S.S. and Fermi-LAT data of the Crab nebula, illustrating the multi-wavelength capacity of ctools. The joint Crab nebula spectrum is compatible with published literature values within the systematic uncertainties. We conclude that the ctools software is mature for the analysis of data from existing IACTs, as well as from the upcoming CTA.
Improving Word Representations: A Sub-sampled Unigram Distribution for Negative Sampling

Word2Vec is the most popular model for word representation and has been widely investigated in literature. However, its noise distribution for negative sampling is decided by empirical trials and the optimality has always been ignored. We suggest that the distribution is a sub-optimal choice, and propose to use a sub-sampled unigram distribution for better negative sampling. Our contributions include: (1) proposing the concept of semantics quantification and deriving a suitable sub-sampling rate for the proposed distribution adaptive to different training corpora; (2) demonstrating the advantages of our approach in both negative sampling and noise contrastive estimation by extensive evaluation tasks; and (3) proposing a semantics weighted model for the MSR sentence completion task, resulting in considerable improvements. Our work not only improves the quality of word vectors but also benefits current understanding of Word2Vec.
Discovering the Compositional Structure of Vector Representations with Role Learning Networks

How can neural networks perform so well on compositional tasks even though they lack explicit compositional representations? We use a novel analysis technique called ROLE to show that recurrent neural networks perform well on such tasks by converging to solutions which implicitly represent symbolic structure. This method uncovers a symbolic structure which, when properly embedded in vector space, closely approximates the encodings of a standard seq2seq network trained to perform the compositional SCAN task. We verify the causal importance of the discovered symbolic structure by showing that, when we systematically manipulate hidden embeddings based on this symbolic structure, the model’s output is changed in the way predicted by our analysis.
Absolute Properties of the Eclipsing γ Dor Star V404 Lyrae

We present the first high-resolution spectra for the eclipsing binary V404 Lyr showing γ Dor pulsations, which we use to study its absolute properties. By fitting models to the disentangling spectrum of the primary star, we found that it has an effective temperature of Teff,1 = 7330 ± 150 K and a rotational velocity of v1 sin i = 148 ± 18 km s−1. The simultaneous analysis of our double-lined radial velocities and the pulsation-subtracted Kepler data gives us accurate stellar and system parameters of V404 Lyr. The masses, radii, and luminosities are M1 = 2.17 ± 0.06 M⊙, R1 = 1.91 ± 0.02 R⊙, and L1 = 9.4 ± 0.8 L⊙ for the primary, and M2 = 1.42 ± 0.04 M⊙, R2 = 1.79 ± 0.02 R⊙, and L2 = 2.9 ± 0.2 L⊙ for the secondary. The tertiary component orbiting the eclipsing pair has a mass of M3b = 0.71 ± 0.15 M⊙ in an orbit of P3b = 642 ± 3 days, e3b = 0.21 ± 0.04, and a3b = 509 ± 2 R⊙. The third light of l3 = 4.1% ± 0.2% could be partly attributable to the K-type circumbinary object. By applying a multiple frequency analysis to the eclipse-subtracted light residuals, we detected 45 frequencies with signal-to-noise amplitude ratios larger than 4.0. Identified as independent pulsation modes, seven frequencies (f1 − f6, f9), their new pulsation constants, and the location in the Hertzsprung–Russell diagram indicate that the pulsating primary is a γ Dor-type variable star.
Nemytskii operators between Stepanov almost periodic or almost automorphic function spaces

We study the superposition operators (also called Nemytskii operators) between spaces of almost periodic (respectively almost automorphic) functions in the sense of Stepanov. We state new results on the superposition, notably we give a necessary and sufficient condition for that these operators are well-defined and continuous.
Separable Convolutional Eigen-Filters (SCEF): Building Efficient CNNs Using Redundancy Analysis

The high model complexity of deep learning algorithms enables remarkable learning capacity in many application domains. However, a large number of trainable parameters comes with a high cost. For example, during both the training and inference phases, the numerous trainable parameters consume a large amount of resources, such as CPU/GPU cores, memory and electric power. In addition, from a theoretical statistical learning perspective, the high complexity of the network can result in a high variance in its generalization performance. One way to reduce the complexity of a network without sacrificing its accuracy is to define and identify redundancies in order to remove them. In this work, we propose a method to observe and analyze redundancies in the weights of a 2D convolutional (Conv2D) network. Based on the proposed analysis, we construct a new layer called Separable Convolutional Eigen-Filters (SCEF) as an alternative parameterization to Conv2D layers. A SCEF layer can be easily implemented using depthwise separable convolution, which are known to be computationally effective. To verify our hypothesis, experiments are conducted on the CIFAR-10 and ImageNet datasets by replacing the Conv2D layers with SCEF and the results have shown an increased accuracy using about 2/3 of the original parameters and reduce the number of FLOPs to 2/3 of the original net. Implementation-wise, our method is highly modular, easy to use, fast to process and does not require any additional dependencies.
Holographic superfluid solitons with backreaction

Solitons are important nonperturbative excitations in superfluids. For holographic superfluids, we numerically construct ``dark'' solitons that have the symmetry-restored phase at their core. A central point is that we include the gravitational backreaction of the matter fields, which becomes important at low temperatures. We study in detail the properties of these solitons under variation of the backreaction strength via tuning the gravitational constant. In particular, the depletion fraction of the particle number density at the core of the solitons is carefully investigated. In agreement with the probe-limit analysis, the depletion fraction shows the same qualitative behavior as in Bogoliubov--de Gennes theory, even if the backreaction is included. We find that the depletion decreases with increasing backreaction strength. Moreover, the inclusion of backreaction enables us to obtain the effective energy density of solitons within holography, which together with an evaluation of the surface tension leads to a simple physical explanation for the snake instability of dark solitons.
Maximum principles for nonlocal parabolic Waldenfels operators

As a class of Lévy type Markov generators, nonlocal Waldenfels operators appear naturally in the context of investigating stochastic dynamics under Lévy fluctuations and constructing Markov processes with boundary conditions (in particular the construction with jumps). This work is devoted to prove the weak and strong maximum principles for ‘parabolic’ equations with nonlocal Waldenfels operators. Applications in stochastic differential equations with [Formula: see text]-stable Lévy processes are presented to illustrate the maximum principles.
Enhancing Recurrent Neural Networks with Sememes

Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications. However, most existing studies exploit sememes in specific tasks and few efforts are made to utilize sememes more fundamentally. In this paper, we propose to incorporate sememes into recurrent neural networks (RNNs) to improve their sequence modeling ability, which is beneficial to all kinds of downstream tasks. We design three different sememe incorporation methods and employ them in typical RNNs including LSTM, GRU and their bidirectional variants. For evaluation, we use several benchmark datasets involving PTB and WikiText-2 for language modeling, SNLI for natural language inference. Experimental results show evident and consistent improvement of our sememe-incorporated models compared with vanilla RNNs, which proves the effectiveness of our sememe incorporation methods. Moreover, we find the sememe-incorporated models have great robustness and outperform adversarial training in defending adversarial attack. All the code and data of this work will be made available to the public.