Miracle-dz/UniFAR-FTUs huggingface dataset:
Interlaced Sparse Self-Attention for Semantic Segmentation

In this paper, we present a so-called interlaced sparse self-attention approach to improve the efficiency of the \emph{self-attention} mechanism for semantic segmentation. The main idea is that we factorize the dense affinity matrix as the product of two sparse affinity matrices. There are two successive attention modules each estimating a sparse affinity matrix. The first attention module is used to estimate the affinities within a subset of positions that have long spatial interval distances and the second attention module is used to estimate the affinities within a subset of positions that have short spatial interval distances. These two attention modules are designed so that each position is able to receive the information from all the other positions. In contrast to the original self-attention module, our approach decreases the computation and memory complexity substantially especially when processing high-resolution feature maps. We empirically verify the effectiveness of our approach on six challenging semantic segmentation benchmarks.
HR 10: a main-sequence binary with circumstellar envelopes around both components

Context. This paper is framed within a large project devoted to studying the presence of circumstellar material around main sequence stars, and looking for exocometary events. The work concentrates on HR 10 (A2 IV/V), known for its conspicuous variability in the circumstellar narrow absorption features of Ca II K and other lines, so far interpreted as β Pic-like phenomena, within the falling evaporating body scenario. Aims. The main goal of this paper is to carry out a thorough study of HR 10 to find the origin of the observed variability, determine the nature of the star, its absolute parameters, and evolutionary status. Methods. Interferometric near-infrared (NIR) observations, multi-epoch high-resolution optical spectra spanning a time baseline of more than 32 yr, and optical and NIR photometry, together with theoretical modelling, were used to tackle the above objectives. Results. Our results reveal that HR 10 is a binary. The narrow circumstellar absorption features superimposed on the photospheric Ca II K lines – and lines of other species – can be decomposed into two or more components, the two deep ones tracing the radial velocity of the individual stars, which implies that their origin cannot be ascribed to transient exocometary events, their variability being fully explained by the binarity of the object. There does not appear to be transient events associated with potential exocomets. Each individual star holds its own circumstellar shell and there are no traces of a circumbinary envelope. Finally, the combined use of the interferometric and radial velocity data leads to a complete spectrometric and orbital solution for the binary, the main parameters being: an orbital period of 747.6 days, eccentricities of the orbits around the centre of mass 0.25 (HR 10-A), 0.21 (HR 10-B) and a mass ratio of q = MB∕MA = 0.72–0.84. The stars are slightly off the main sequence, the binary being ~530 Myr old.
Magnetic stray fields in nanoscale magnetic tunnel junctions

The magnetic stray field is an unavoidable consequence of ferromagnetic devices and sensors leading to a natural asymmetry in magnetic properties. Such asymmetry is particularly undesirable for magnetic random access memory applications where the free layer (FL) can exhibit bias. Using atomistic dipole–dipole calculations we numerically simulate the stray magnetic field emanating from the magnetic layers of a magnetic memory device with different geometries. We find that edge effects dominate the overall stray magnetic field in patterned devices and that a conventional synthetic antiferromagnet structure is only partially able to compensate the field at the FL position. A granular reference layer is seen to provide near-field flux closure while additional patterning defects add significant complexity to the stray field in nanoscale devices. Finally we find that the stray field from a nanoscale antiferromagnet is surprisingly non-zero arising from the imperfect cancellation of magnetic sublattices due to edge defects. Our findings provide an outline of the role of different layer structures and defects in the effective stray magnetic field in nanoscale magnetic random access memory devices and demonstrate that atomistic calculations provide a useful tool to study the stray field effects arising from a wide range of defects.
Soft and hard X-ray dips in the light curves of γ Cassiopeiae

The available six archival XMM–Newton observations of the anomalous X-ray emitter γ Cas (B0.5 IVe) have been surveyed for the presence of soft X-ray ‘dips’ in X-ray light curves. In addition to discovering such events in the soft band (≤2 keV), we show that sometimes they are accompanied by minor, nearly simultaneous dips in the hard X-ray band. Herein, we investigate how these occurrences can be understood in the ‘magnetic star–disc interaction’ hypothesis proposed in the literature to explain the hard, variable X-ray emission of this Be star. In this scenario, the soft X-ray dips are interpreted as transits by comparatively dense, soft X-ray-absorbing blobs that move across the lines of sight to the surface of the Be star. We find that these blobs have similar properties as the ‘cloudlets’ responsible for migrating subfeatures in UV and optical spectral lines and therefore may be part of a common distribution of co-rotating occulters. The frequencies, amplitudes, and longevities of these dips vary widely. Additionally, the most recent spectra from 2014 July suggest that the ‘warm’ (kT ≈ 0.6–4 keV) plasma sources responsible for some of the soft flux are much more widely spread over the Be star’s surface than the hot plasma sites that dominate the flux at all X-ray energies. We finally call attention to a sudden drop in all X-ray energies of the 2014 light curve of γ Cas and a similar sudden drop in a light curve of the ‘analog’ HD 110432. We speculate that these could be related to appearances of particularly strong soft X-ray dips several hours earlier.
The Preliminary Evaluation of a Hypervisor-based Virtualization Mechanism for Intel Optane DC Persistent Memory Module

Non-volatile memory (NVM) technologies, being accessible in the same manner as DRAM, are considered indispensable for expanding main memory capacities. Intel Optane DCPMM is a long-awaited product that drastically increases main memory capacities. However, a substantial performance gap exists between DRAM and DCPMM. In our experiments, the read/write latencies of DCPMM were 400% and 407% higher than those of DRAM, respectively. The read/write bandwidths were 37% and 8% of those of DRAM. This performance gap in main memory presents a new challenge to researchers; we need a new system software technology supporting emerging hybrid memory architecture. In this paper, we present RAMinate, a hypervisor-based virtualization mechanism for hybrid memory systems, and a key technology to address the performance gap in main memory systems. It provides great flexibility in memory management and maximizes the performance of virtual machines (VMs) by dynamically optimizing memory mappings. Through experiments, we confirmed that even though a VM has only 1% of DRAM in its RAM, the performance degradation of the VM was drastically alleviated by memory mapping optimization. The elapsed time to finish the build of Linux Kernel in the VM was 557 seconds, which was only 13% increase from the 100% DRAM case (i.e., 495 seconds). When the optimization mechanism was disabled, the elapsed time increased to 624 seconds (i.e. 26% increase from the 100% DRAM case).
Subkiloparsec Imaging of Lyα Emission in a Low-mass, Highly Ionized, Gravitationally Lensed Galaxy at z = 1.84

Low-mass, low-metallicity galaxies at low to moderate (z ≲ 3) redshifts offer the best opportunity for detailed examination of the interplay between massive stars, ionizing radiation and gas in sources similar to those that likely reionized the universe. We present new narrowband Hubble Space Telescope observations of emission and the adjacent ultraviolet (UV) continuum in the low-mass (M⋆ = 2 × 108 ), low-metallicity (Z ∼ 1/20 Z⊙), and highly ionized gravitationally lensed galaxy SL2S J02176−0513 at z = 1.844. The galaxy has strong emission with photometric equivalent width , at odds with the escape fraction of 10%. However, the spectroscopic profile suggests the presence of broad absorption underlying the emission, and the total equivalent width is consistent with the escape fraction once this underlying absorption is included. The emission is more spatially extended than the UV continuum, and the 0. ″14 spatial resolution of HST coupled with the magnification of gravitational lensing enables us to examine the distribution of and the UV continuum on subkiloparsec scales. We find that the peaks of the emission and the UV continuum are offset by 650 pc, and there is no emission arising from the region with the strongest UV light. Our combined spectroscopic and imaging data imply a significant range in neutral hydrogen column density across the object. These observations offer indirect support for a model in which ionizing radiation escapes from galaxies through channels with low column density of neutral gas.
Neutron Star Merger Afterglows: Population Prospects for the Gravitational Wave Era

Following the historical observation of GW170817 and its electromagnetic follow-up, new neutron star merger afterglows are expected to be observed as counterparts to gravitational wave signals during the next science runs of the gravitational interferometer network. The diversity of the observed population of afterglows of these future events is subject to various factors, which are (i) intrinsic, such as the energy of the ejecta, (ii) environmental, such as the ambient medium density or (iii) observational, such as the viewing angle and distance of the source. Through prescribing a population of mergers and modelling their afterglows, we study the diversity of those events to be observed jointly in gravitational waves and electromagnetic bands. In the future, observables of detected events such as viewing angle, distance, afterglow peak flux or proper motion will form distributions which together with predictions from our study will provide insight on neutron star mergers and their environments.
Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation

Unsupervised Domain Adaptation aims to learn a model on a source domain with labeled data in order to perform well on unlabeled data of a target domain. Current approaches focus on learning \textit{Domain Invariant Representations}. It relies on the assumption that such representations are well-suited for learning the supervised task in the target domain. We rather believe that a better and minimal assumption for performing Domain Adaptation is the \textit{Hidden Covariate Shift} hypothesis. Such approach consists in learning a representation of the data such that the label distribution conditioned on this representation is domain invariant. From the Hidden Covariate Shift assumption, we derive an optimization procedure which learns to match an estimated joint distribution on the target domain and a re-weighted joint distribution on the source domain. The re-weighting is done in the representation space and is learned during the optimization procedure. We show on synthetic data and real world data that our approach deals with both \textit{Target Shift} and \textit{Concept Drift}. We report state-of-the-art performances on Amazon Reviews dataset \cite{blitzer2007biographies} demonstrating the viability of this approach.
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement

Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing rPPG approaches rely on analyzing very fine details of facial videos, which are prone to be affected by video compression. Here we propose a two-stage, end-to-end method using hidden rPPG information enhancement and attention networks, which is the first attempt to counter video compression loss and recover rPPG signals from highly compressed videos. The method includes two parts: 1) a Spatio-Temporal Video Enhancement Network (STVEN) for video enhancement, and 2) an rPPG network (rPPGNet) for rPPG signal recovery. The rPPGNet can work on its own for robust rPPG measurement, and the STVEN network can be added and jointly trained to further boost the performance especially on highly compressed videos. Comprehensive experiments are performed on two benchmark datasets to show that, 1) the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, 2) it also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications.
The Wang-Landau Algorithm as Stochastic Optimization and its Acceleration

We show that the Wang-Landau algorithm can be formulated as a stochastic gradient descent algorithm minimizing a smooth and convex objective function, of which the gradient is estimated using Markov chain Monte Carlo iterations. The optimization formulation provides us another way to establish the convergence rate of the Wang-Landau algorithm, by exploiting the fact that almost surely the density estimates (on the logarithmic scale) remain in a compact set, upon which the objective function is strongly convex. The optimization viewpoint motivates us to improve the efficiency of the Wang-Landau algorithm using popular tools including the momentum method and the adaptive learning rate method. We demonstrate the accelerated Wang-Landau algorithm on a two-dimensional Ising model and a two-dimensional ten-state Potts model.
Multiply Robust Learning of the Average Treatment Effect with an Invalid Instrumental Variable

Instrumental variable (IV) methods have been widely used to identify causal effects in the presence of unmeasured confounding. A key IV identification condition known as the exclusion restriction states that the IV cannot have a direct effect on the outcome which is not mediated by the exposure in view. In the health and social sciences, such an assumption is often not credible. As a result, possible violation of the exclusion restriction can seldom be ruled out in practice. To address this concern, we consider identification conditions of the population average treatment effect (ATE) without requiring the exclusion restriction. We also propose novel semiparametric estimators in multiple observed data models targeting the ATE, and a multiply robust locally efficient estimator that is consistent in the union of these models. We illustrate the proposed methods through simulations and an econometric application evaluating the causal effect of 401(k) participation on savings.
Is BERT Really Robust? Natural Language Attack on Text Classiﬁcation and Entailment

Machine learning algorithms are often vulnerable to adversarial examples that have imper-ceptible alterations from the original counter-parts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present the TextFooler, a general attack framework, to generate natural adversarial texts. By successfully applying it to two fundamental natural language tasks, text classiﬁcation and textual entailment, against various target models, convolutional and recurrent neural networks as well as the most powerful pre-trained BERT, we demonstrate the advantages of this framework in three ways: (i) effective—it outperforms state-of-the-art attacks in terms of success rate and perturbation rate; (ii) utility-preserving—it preserves semantic content and grammaticality, and remains correctly classiﬁed by humans; and (iii) efﬁcient—it generates adversarial text with computational complexity linear in the text length.
C15
: From halo effective field theory structure to the study of transfer, breakup, and radiative-capture reactions

Aside from being a one-neutron halo nucleus, $^{15}$C is interesting because it is involved in reactions of relevance for several nucleosynthesis scenarios. The aim of this work is to analyze various reactions involving $^{15}$C, using a single structure model based on Halo EFT. To develop a Halo-EFT model of $^{15}$C at NLO, we first extract the ANC of its ground state by analyzing $^{14}$C(d,p)$^{15}$C transfer data at low energy. Using this Halo-EFT description, we study the $^{15}$C Coulomb breakup at high (605AMeV) and intermediate (68AMeV) energies using eikonal models with a consistent treatment of nuclear and Coulomb interactions at all orders, and proper relativistic corrections. Finally, we study the $^{14}$C(n,$\gamma$)$^{15}$C radiative capture. Our theoretical cross sections are in good agreement with experimental data for all reactions, thereby assessing the robustness of the $^{15}$C Halo-EFT model. Since a simple NLO description is enough to reproduce all data, the only nuclear-structure observables that matter are the binding energy and its ANC, showing that all the reactions considered are purely peripheral. In particular, it confirms the ANC value obtained for the $^{15}$C ground state: 1.59$\pm$0.06fm$^{-1}$. Our model provides also a new estimate of the radiative-capture cross section at astrophysical energy (23.3keV): 4.66$\pm$0.14$\mu$b. Including a Halo-EFT description of $^{15}$C within precise models of reactions is confirmed to be an excellent way to relate the nucleus reaction cross sections and structure. Its systematic expansion enables us to deduce which nuclear-structure observables are actually probed in the collision. From this, we can infer valuable information on both the structure of $^{15}$C and its synthesis through the $^{14}$C(n,$\gamma$)$^{15}$C radiative capture at astrophysical energies.
Distributed Dense Subgraph Detection and Low Outdegree Orientation

The densest subgraph problem, introduced in the 80s by Picard and Queyranne as well as Goldberg, is a classic problem in combinatorial optimization with a wide range of applications. The lowest outdegree orientation problem is known to be its dual problem. We study both the problem of finding dense subgraphs and the problem of computing a low outdegree orientation in the distributed settings. Suppose $G=(V,E)$ is the underlying network as well as the input graph. Let $D$ denote the density of the maximum density subgraph of $G$. Our main results are as follows. Given a value $\tilde{D} \leq D$ and $0 < \epsilon < 1$, we show that a subgraph with density at least $(1-\epsilon)\tilde{D}$ can be identified deterministically in $O((\log n) / \epsilon)$ rounds in the LOCAL model. We also present a lower bound showing that our result for the LOCAL model is tight up to an $O(\log n)$ factor. In the CONGEST model, we show that such a subgraph can be identified in $O((\log^3 n) / \epsilon^3)$ rounds with high probability. Our techniques also lead to an $O(diameter + (\log^4 n)/\epsilon^4)$-round algorithm that yields a $1-\epsilon$ approximation to the densest subgraph. This improves upon the previous $O(diameter /\epsilon \cdot \log n)$-round algorithm by Das Sarma et al. [DISC 2012] that only yields a $1/2-\epsilon$ approximation. Given an integer $\tilde{D} \geq D$ and $\Omega(1/\tilde{D}) < \epsilon < 1/4$, we give a deterministic, $\tilde{O}((\log^2 n) /\epsilon^2)$-round algorithm in the CONGEST model that computes an orientation where the outdegree of every vertex is upper bounded by $(1+\epsilon)\tilde{D}$. Previously, the best deterministic algorithm and randomized algorithm by Harris [FOCS 2019] run in $\tilde{O}((\log^6 n)/ \epsilon^4)$ rounds and $\tilde{O}((\log^3 n) /\epsilon^3)$ rounds respectively and only work in the LOCAL model.
Dark Matter Haloes and Subhaloes

The development of methods and algorithms to solve the N-body problem for classical, collisionless, non-relativistic particles has made it possible to follow the growth and evolution of cosmic dark matter structures over most of the universe’s history. In the best-studied case—the cold dark matter or CDM model—the dark matter is assumed to consist of elementary particles that had negligible thermal velocities at early times. Progress over the past three decades has led to a nearly complete description of the assembly, structure, and spatial distribution of dark matter haloes, and their substructure in this model, over almost the entire mass range of astronomical objects. On scales of galaxies and above, predictions from this standard CDM model have been shown to provide a remarkably good match to a wide variety of astronomical data over a large range of epochs, from the temperature structure of the cosmic background radiation to the large-scale distribution of galaxies. The frontier in this field has shifted to the relatively unexplored subgalactic scales, the domain of the central regions of massive haloes, and that of low-mass haloes and subhaloes, where potentially fundamental questions remain. Answering them may require: (i) the effect of known but uncertain baryonic processes (involving gas and stars), and/or (ii) alternative models with new dark matter physics. Here we present a review of the field, focusing on our current understanding of dark matter structure from N-body simulations and on the challenges ahead.
Gender Balance and Inclusion Within The Australian Space Community: An Overview Of Delegates At The 2018 Australian Space Research Conference

A significant gender disparity is widely known to occur within the fields of science, technology, engineering and mathematics (STEM). Women are both under-represented in their participation in Australian STEM education (compared to their fraction within the population at large), and face a much higher attrition rate from STEM subjects throughout secondary and tertiary education. Even within STEM careers the proportion of women frequently generally decreases with increasing position of seniority. In 2015 organisers and members of the Australian Space Research Community began to analyse the data from participants of the Australian Space Research Conference, in order to derive statistics relating to the gender balance of the conference. In this paper, the data from the most recent conference (held on the Gold Coast, in September 2018) is analysed, considering the gender demographics of delegates, presenters (within numerous categories), and awards. This year, we also present the dimensions of career type and academic level of conference attendees. The resulting trends are compared to those of other national space research conferences - the Scientific Meeting of the Astronomical Society of Australia. The results show that the representation of females dropped to the lowest levels since formal data collection began in 2015, with females comprising 23% of conference attendees, and 21% of all session presentations (posters and talks combined). Gender parity was achieved however amongst the invited Plenary presentations. Recommendations for removing some of the barriers to female participation in the conference and encouraging greater gender balance and diversity in future are discussed.