Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft excess in the quiescent Be/X-ray pulsar RX J0812.4–3114

We report a 72 ks XMM–Newton observation of the Be/X-ray pulsar (BeXRP) RX J0812.4–3114 in quiescence ($L_X \approx 1.6\times 10^{33}\, \mathrm{erg\, s^{-1}}$). Intriguingly, we find a two-component spectrum, with a hard power-law (Γ ≈ 1.5) and a soft blackbody-like excess below ≈1 keV. The blackbody component is consistent in kT with a prior quiescent Chandra observation reported by Tsygankov et al. and has an inferred blackbody radius of ≈10 km, consistent with emission from the entire neutron star (NS) surface. There is also mild evidence for an absorption line at $\approx 1$ and/or $\approx 1.4\, \mathrm{keV}$. The hard component shows pulsations at P ≈ 31.908 s (pulsed fraction 0.84 ± 0.10), agreeing with the pulse period seen previously in outbursts, but no pulsations were found in the soft excess (pulsed fraction $\lesssim \! 31\, {\rm per\, cent}$). We conclude that the pulsed hard component suggests low-level accretion on to the NS poles, while the soft excess seems to originate from the entire NS surface. We speculate that, in quiescence, the source switches between a soft thermal-dominated state (when the propeller effect is at work) and a relatively hard state with low-level accretion, and use the propeller cut-off to estimate the magnetic field of the system to be $\lesssim\! 8.4\times 10^{11}\, \mathrm{G}$. We compare the quiescent thermal LX predicted by the standard deep crustal heating model to our observations and find that RX J0812.4–3114 has a high thermal LX, at or above the prediction for minimum cooling mechanisms. This suggests that RX J0812.4–3114 either contains a relatively low-mass NS with minimum cooling, or that the system may be young enough that the NS has not fully cooled from the supernova explosion.
Hom-Poisson-Nijenhuis structures on Hom-Lie algebroids and Hom-Dirac structures on Hom-Courant algebroids

In this paper, we develop the theory of Hom-Lie algebroids, Hom-Lie bialgebroids and Hom-Courant algebroids introduced by Cai, Liu and Sheng. Specifically, we introduce the notions of Hom-Poisson, Hom-Nijenhuis and Hom-Poisson-Nijenhuis structures on a Hom-Lie algebroid and the notion of Hom-Dirac structures on a Hom-Courant algebroid. We show that these structures satisfy similar properties to structures non "Hom-"version. For example, there exists the hierarchy of a Hom-Poisson-Nijenhuis structure and we have a relation between Hom-Dirac structures and Maurer-Cartan type equation. Moreover we show that there exists a one-to-one correspondence between the pairs consisting of a Poisson structure on $M$ and a Poisson isomorphism for it.
Photoinduced interfacial chiral modes in threefold topological semimetal

We investigate the chiral electronic modes at the interface between two regions of a threefold topological semimetal, which is illuminated by left and right handed elliptically polarized waves. The radiation effects on the band structure of semimetal is analyzed by using Floquet theory. Two distinct solutions of the interface modes are found with the chirality depending on the phase of the irradiation. We also consider the anomalous Hall response which is attributed to the transition between dispersionless flat band and conic bands.
Homotopy Invariance of Convolution Products

The purpose of this paper is to show that various convolution products are fully homotopical, meaning that they preserve weak equivalences in both variables without any cofibrancy hypothesis. We establish this property for diagrams of simplicial sets indexed by the category of finite sets and injections and for tame $M$-simplicial sets, with $M$ the monoid of injective self-maps of the positive natural numbers. We also show that a certain convolution product studied by Nikolaus and the first author is fully homotopical. This implies that every presentably symmetric monoidal $\infty$-category can be represented by a symmetric monoidal model category with a fully homotopical monoidal product.
A Regularized Convolutional Neural Network for Semantic Image Segmentation

Convolutional neural networks (CNNs) have achieved prominent performance in a series of image processing problems. CNNs become the first choice for dense classification problems such as semantic segmentation. However, CNNs predict the class of each pixel independently in semantic segmentation tasks, spatial regularity of the segmented objects is still a problem for these methods. Especially when given few training data, CNN could not perform well in the details, isolated and scattered small regions often appear in all kinds of CNN segmentation results. In this paper, we propose a method to add spatial regularization to the segmented objects. In our method, the spatial regularization such as total variation (TV) can be easily integrated into CNN network and it produces smooth edges and eliminate isolated points. We apply our proposed method to Unet and Segnet, which are well-established CNNs for image segmentation, and test them on WBC and CamVid datasets, respectively. The results show that the details of predictions are well improved by regularized networks.
Development of email classifier in Brazilian Portuguese using feature selection for automatic response

Automatic email categorization is an important application of text classification. We study the automatic reply of email business messages in Brazilian Portuguese. We present a novel corpus containing messages from a real application, and baseline categorization experiments using Naive Bayes and support Vector Machines. We then discuss the effect of lemmatization and the role of part-of-speech tagging filtering on precision and recall. Support Vector Machines classification coupled with nonlemmatized selection of verbs, nouns and adjectives was the best approach, with 87.3% maximum accuracy. Straightforward lemmatization in Portuguese led to the lowest classification results in the group, with 85.3% and 81.7% precision in SVM and Naive Bayes respectively. Thus, while lemmatization reduced precision and recall, part-of-speech filtering improved overall results.
A prey–predator model with three interacting species

We consider a class of discrete time prey–predator models with three interacting species defined on the two-dimensional simplex. For some choices of parameters of the operator describing the evolution of the relative frequencies, we show that the trajectories approach a heteroclinic cycle and that the ergodic hypothesis does not hold. Moreover, we prove that any order Cesàro mean of the trajectories diverges. For another class of parameters, we show that all orbits starting from the interior of the simplex converge to the unique fixed point of the operator while for the remaining choices of parameters all orbits converge to one of the vertices of the simplex. Contrary to many authors, we study discrete time models but we include a speed function f in the dynamics which allows us to approximate the continuous-time case arbitrarily well when f is small.
Dust Transport and Processing in Centrifugally Driven Protoplanetary Disk Winds

There is evidence that protoplanetary disks—including the protosolar one—contain crystalline dust grains on spatial scales where the dust temperature is lower than the threshold value for their formation through thermal annealing of amorphous interstellar silicates. We interpret these observations in terms of an extended, magnetocentrifugally driven disk wind that transports grains from the inner disk—where they are thermally processed by the stellar radiation after being uplifted from the disk surfaces—to the outer disk regions. For any disk radius r, there is a maximum grain size amax(r) that can be uplifted from that location: grains of size a ≪ amax are carried away by the wind, whereas those with a ≲ amax reenter the disk at larger radii. A significant portion of the reentering grains converge to—and subsequently accumulate in—a narrow region just beyond rmax(a), the maximum radius from which grains of size a can be uplifted. We show that this model can account for the inferred crystallinity fractions in classical T Tauri and Herbig Ae disks and for their indicated near constancy after being established early in the disk evolution. It is also consistent with the reported radial gradients in the mean grain size, crystallinity, and crystal composition. In addition, this model yields the properties of the grains that remain embedded in the outflows from protoplanetary disks and naturally explains the inferred persistence of small grains in the surface layers of these disks.
FindeR: Accelerating FM-Index-Based Exact Pattern Matching in Genomic Sequences through ReRAM Technology

Genomics is the critical key to enabling precision medicine, ensuring global food security and enforcing wildlife conservation. The massive genomic data produced by various genome sequencing technologies presents a significant challenge for genome analysis. Because of errors from sequencing machines and genetic variations, approximate pattern matching (APM) is a must for practical genome analysis. Recent work proposes FPGA, ASIC and even process-in-memory-based accelerators to boost the APM throughput by accelerating dynamic-programming-based algorithms (e.g., Smith-Waterman). However, existing accelerators lack the efficient hardware acceleration for the exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation and compression. State-of-the-art genome analysis adopts the FM-Index that augments the space-efficient BWT with additional data structures permitting fast EPM operations. But the FM-Index is notorious for poor spatial locality and massive random memory accesses. In this paper, we propose a ReRAM-based process-in-memory architecture, FindeR, to enhance the FM-Index EPM search throughput in genomic sequences. We build a reliable and energy-efficient Hamming distance unit to accelerate the computing kernel of FM-Index search using commodity ReRAM chips without introducing extra CMOS logic. We further architect a full-fledged FM-Index search pipeline and improve its search throughput by lightweight scheduling on the NVDIMM. We also create a system library for programmers to invoke FindeR to perform EPMs in genome analysis. Compared to state-of-the-art accelerators, FindeR improves the FM-Index search throughput by 83% ~ 30K× and throughput per Watt by 3.5×~42.5K×.
The Nonbinary Fraction: Looking Towards the Future of Gender Equity in Astronomy

Gender equity is one of the biggest issues facing the field of astrophysics, and there is broad interest in addressing gender disparities within astronomy. Many studies of these topics have been performed by professional astronomers who are relatively unfamiliar with research in fields such as gender studies and sociology. As a result, they adopt a normative view of gender as a binary choice of 'male' or 'female', leaving astronomers whose genders do not fit within that model out of such research entirely. Reductive frameworks of gender and an overemphasis on quantification as an indicator of gendered phenomena are harmful to people of marginalized genders, especially those who live at the intersections of multiple axes of marginalization such as race, disability, and socioeconomic status. In order for the astronomy community to best serve its marginalized members as we move into the next decade, a new paradigm must be developed. This paper aims to address the future of gender equity in astronomy by recommending better survey practices and institutional policies based on a more complex approach to gender.
Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation

Network reconfiguration is an effective strategy for different purposes of distribution systems (DSs), e.g., resilience enhancement. In particular, DS automation, distributed generation integration and microgrid (MG) technology development, etc., are empowering much more flexible reconfiguration and operation of the system, e.g., DSs or MGs with flexible boundaries. However, the formulation of DS reconfiguration-related optimization problems to include those new flexibilities is non-trivial, especially for the issue of topology, which has to be radial. That is, the existing methods of formulating the radiality constraints can cause under-utilization of DS flexibilities. Thus, in this work, we propose a new method for radiality constraints formulation that fully enables the topological and some other related flexibilities of DSs, so that the reconfiguration-related optimization problems can have extended feasibility and enhanced optimality. Graph-theoretic supports are provided to certify its theoretical validity. As integer variables are involved, we also analyze the issues of tightness and compactness. The proposed radiality constraints are specifically applied to post-disturbance MG formation, which is involved in many resilience-oriented DS service restoration and infrastructure recovery problems. The resulting new MG formation model, which allows more flexible merge and/or separation of the sub-grids, etc., establishes superiority over the models in the literature. Demonstrative case studies are conducted on two test systems.
Growth Equation of the General Fractional Calculus

We consider the Cauchy problem ( D ( k ) u ) ( t ) = λ u ( t ) , u ( 0 ) = 1 , where D ( k ) is the general convolutional derivative introduced in the paper (A. N. Kochubei, Integral Equations Oper. Theory 71 (2011), 583–600), λ > 0 . The solution is a generalization of the function t ↦ E α ( λ t α ) , where 0 < α < 1 , E α is the Mittag–Leffler function. The asymptotics of this solution, as t → ∞ , are studied.
Understanding black hole evaporation using explicitly computed Penrose diagrams

Rigorously computed Penrose diagrams are plotted for a semiclassical model of black hole formation and evaporation, in which black holes form by the accretion of infalling spherical shells of matter and subsequently evaporate by emitting spherical shells of Hawking radiation. The method allows arbitrary interior solutions of the form $ds^2=-f(r)\,dt^2+f(r)^{-1}\,dr^2+r^2\,d\Omega^2$, including singular and nonsingular models. Matter dynamics are visualized by explicitly plotting proper densities and pressures in the diagrams, as well as by tracking the location of trapped surfaces and energy condition violations. The most illustrative model accurately approximates the standard time evolution for black hole thermal evaporation; its time dependence and causal structure are analyzed by inspection of the rigorous diagram. The resulting insights contradict some common intuitions and assumptions, and we point out some examples in the literature with assumptions that do not hold up in our more detailed model. Based on the new diagrams, we argue for an improved understanding of the Hawking radiation process, propose an improved definition of "black hole" in the presence of evaporation, and suggest some implications regarding information preservation and unitarity.
Perturbed Greedy on Oblivious Matching Problems

We study the maximum matching problem in the oblivious setting, i.e. the edge set of the graph is unknown to the algorithm. The existence of an edge is revealed upon the probe of this pair of vertices. Further, if an edge exists between two probed vertices, then the edge must be included in the matching irrevocably. For unweighted graphs, the \textsf{Ranking} algorithm by Karp et al.~(STOC 1990) achieves approximation ratios $0.696$ for bipartite graphs and $0.526$ for general graphs. For vertex-weighted graphs, Chan et al. (TALG 2018) proposed a $0.501$-approximate algorithm. In contrast, the edge-weighted version only admits the trivial $0.5$-approximation by Greedy. In this paper, we propose the \textsf{Perturbed Greedy} algorithm for the edge-weighted oblivious matching problem and prove that it achieves a $0.501$ approximation ratio. Besides, we show that the approximation ratio of our algorithm on unweighted graphs is $0.639$ for bipartite graphs, and $0.531$ for general graphs. The later improves the state-of-the-art result by Chan et al. (TALG 2018). Furthermore, our algorithm can be regarded as a robust version of the \textsf{Modified Randomized Greedy} (MRG) algorithm. By implication, our $0.531$ approximation ratio serves as the first analysis of the MRG algorithm beyond the $(1/2+\epsilon)$ regime.
Structural and elastic properties of amorphous carbon from simulated quenching at low rates

We generate representative structural models of amorphous carbon (a-C) from constant-volume quenching from the liquid with subsequent relaxation of internal stresses in molecular dynamics simulations using empirical and machine-learning interatomic potentials. By varying volume and quench rate we generate structures with a range of density and amorphous morphologies. We find that all a-C samples show a universal relationship between hybridization, bulk modulus and density despite having distinctly different cohesive energies. Differences in cohesive energy are traced back to slight changes in the distribution of bond-angles that is likely linked to thermal stability of these structures.
Speech Bandwidth Extension with Wavenet

Large-scale mobile communication systems tend to contain legacy transmission channels with narrowband bottlenecks, resulting in characteristic ‘telephone-quality’ audio. While higher quality codecs exist, due to the scale and heterogeneity of the networks, transmitting higher sample rate audio with modern high-quality audio codecs can be difficult in practice. This paper proposes an approach where a communication node can instead extend the bandwidth of a band-limited incoming speech signal that may have been passed through a low-rate codec. To this end, we propose a WaveNet-based model conditioned on a log-mel spectrogram representation of a bandwidth-constrained speech audio signal of 8 kHz and audio with artifacts from GSM full-rate (FR) compression to reconstruct the higher-resolution signal. In our experimental MUSHRA evaluation, we show that a model trained to upsample to 24kHz speech signals from audio passed through the 8kHz GSM-FR codec is able to reconstruct audio only slightly lower in quality to that of the Adaptive Multi-Rate Wideband audio codec (AMR-WB) codec at 16kHz, and closes around half the gap in perceptual quality between the original encoded signal and the original speech sampled at 24kHz. We further show that when the same model is passed 8kHz audio that has not been compressed, is able to again reconstruct audio of slightly better quality than 16kHz AMR-WB, in the same MUSHRA evaluation.
Cross-Domain Complementary Learning with Synthetic Data for Multi-Person Part Segmentation

The success of supervised deep learning depends on the training labels. However, data labeling at pixel-level is very expensive, and people have been exploring synthetic data as an alternative. Even though it is easy to generate labels for synthetic data, the quality gap makes it challenging to transfer knowledge from synthetic data to real data. In this paper, we propose a novel technique, called cross-domain complementary learning that takes advantage of the rich variations of real data and the easily obtainable labels of synthetic data to learn multi-person part segmentation on real images without any human-annotated segmentation labels. To make sure the synthetic data and real data are aligned in a common latent space, we use an auxiliary task of human pose estimation to bridge the two domains. Without any real part segmentation training data, our method performs comparably to several supervised state-of-the-art approaches which require real part segmentation training data on Pascal-Person-Parts and COCO-DensePose datasets. We further demonstrate the generalizability of our method on predicting novel keypoints in the wild where no real data labels are available for the novel keypoints.
The structure of k-planar graphs

Dujmovic et al. (FOCS 2019) recently proved that every planar graph is a subgraph of the strong product of a graph of bounded treewidth and a path. This tool has been used to solve longstanding problems on queue layouts, non-repetitive colouring, $p$-centered colouring, and implicit graph encoding. We generalise this result for $k$-planar graphs, where a graph is $k$-planar if it has a drawing in the plane in which each edge is involved in at most $k$ crossings. In particular, we prove that every $k$-planar graph is a subgraph of the strong product of a graph of treewidth $O(k^5)$ and a path. This is the first result of this type for a non-minor-closed class of graphs. It implies, amongst other results, that $k$-planar graphs have non-repetitive chromatic number upper-bounded by a function of $k$. All these results generalise for drawings of graphs on arbitrary surfaces. In fact, we work in a much more general setting based on so-called shortcut systems that are of independent interest.
A Wasserstein-Type Distance in the Space of Gaussian Mixture Models

In this paper we introduce a Wasserstein-type distance on the set of Gaussian mixture models. This distance is defined by restricting the set of possible coupling measures in the optimal transport problem to Gaussian mixture models. We derive a very simple discrete formulation for this distance, which makes it suitable for high dimensional problems. We also study the corresponding multi-marginal and barycenter formulations. We show some properties of this Wasserstein-type distance, and we illustrate its practical use with some examples in image processing.
A hidden Markov model based keyword recognition system

A speaker-independent hidden Markov model (HMM) keyword recognizer (KWR) based on a continuous-speech-recognition model is presented. The baseline keyword recognition system is described, and techniques for dealing with nonkeyword speech and linear channel effects are discussed. The training of acoustic models to provide an explicit representation of nonvocabulary speech is investigated. A likelihood ratio scoring procedure is used to account for sources of variability affecting keyword likelihood scores. An acoustic class-dependent spectral normalization procedure is used to provide explicit compensation for linear channel effects. Keyword recognition results for a standard conversational speech task with a 20-keyword vocabulary reach 82% probability of detection at a false alarm rate of 12 false alarms per keyword per hour. <<ETX>>