Miracle-dz/UniFAR-FTUs huggingface dataset:
The path to Z And-type outbursts: The case of V426 Sagittae (HBHA 1704-05)

Context. The star V426 Sge (HBHA 1704-05), originally classified as an emission-line object and a semi-regular variable, brightened at the beginning of August 2018, showing signatures of a symbiotic star outburst. Aims. We aim to confirm the nature of V426 Sge as a classical symbiotic star, determine the photometric ephemeris of the light minima, and suggest the path from its 1968 symbiotic nova outburst to the following 2018 Z And-type outburst. Methods. We re-constructed an historical light curve (LC) of V426 Sge from approximately the year 1900, and used original low- (R ∼ 500–1500; 330–880 nm) and high-resolution (R ∼ 11 000–34 000; 360–760 nm) spectroscopy complemented with Swift-XRT and UVOT, optical UBVRCIC and near-infrared JHKL photometry obtained during the 2018 outburst and the following quiescence. Results. The historical LC reveals no symbiotic-like activity from ∼1900 to 1967. In 1968, V426 Sge experienced a symbiotic nova outburst that ceased around 1990. From approximately 1972, a wave-like orbitally related variation with a period of 493.4 ± 0.7 days developed in the LC. This was interrupted by a Z And-type outburst from the beginning of August 2018 to the middle of February 2019. At the maximum of the 2018 outburst, the burning white dwarf (WD) increased its temperature to ≳2 × 105 K, generated a luminosity of ∼7 × 1037 (d/3.3 kpc)2 erg s−1 and blew a wind at the rate of ∼3 × 10−6 M⊙ yr−1. Our spectral energy distribution models from the current quiescent phase reveal that the donor is a normal M4-5 III giant characterised with Teff ∼ 3400 K, RG ∼ 106 (d/3.3 kpc) R⊙ and LG ∼ 1350 (d/3.3 kpc)2 L⊙ and the accretor is a low-mass ∼0.5 M⊙ WD. Conclusions. During the transition from the symbiotic nova outburst to the quiescent phase, a pronounced sinusoidal variation along the orbit develops in the LC of most symbiotic novae. The following eventual outburst is of Z And-type, when the accretion by the WD temporarily exceeds the upper limit of the stable burning. At this point the system becomes a classical symbiotic star.
Type A Behavior, hostility and race in hospitalized patients with and without coronary heart disease

The primary purpose of this study was to determine whether a group of coronary heart disease patients could be distinguished from a hospitalized control group on the basis of their responses to the Jenkins Activity Survey and a modified Cook Medley Hostility Scale. Of further interest was whether the race of the subjects would have a significant effect on their responses to these two psychological instruments. One hundred and twenty-two hospitalized patients were surveyed and assigned to one of four groups based on their race and he~lth status. The four groups were divided as follows: White heart disease, 45 patients; White control, 38 patients; Black heart disease, 22 patients; and Black control, 17 patients. The coronary heart disease patients were recovering from a recent, documented myocardial infarction. The control group patients were recovering from surgery and medical procedures and had no history of heart problems. The omnibus MANOVA indicated that the independent variable of heart disease did not have a significant effect on the JAS and Ho test scores. Specifically, the heart disease patients did not score significantly different on the JAS and Ho scales. Race did have a significant main effect on the mean scores of the JAS and Ho. The subsequent step-down analysis showed that the Black patients scored significantly higher than the White patients on the Hostility scale. However, after the dependent variables were adjusted for the effects of age, socioeconomic status, smoking history, and family history of myocardial infarction through the use of MANCOVA, the subsequent results indicated that the race of the patient did not have a significant effect on their scores on the JAS and Ho. This research illustrates the importance of analyzing ii the demographic variables in studies that examine the association between coronary heart disease and psychological variables.
Audio Impairment Recognition using a Correlation-Based Feature Representation

Audio impairment recognition is based on finding noise in audio files and categorising the impairment type. Recently, significant performance improvement has been obtained thanks to the usage of advanced deep learning models. However, feature robustness is still an unresolved issue and it is one of the main reasons why we need powerful deep learning architectures. In the presence of a variety of musical styles, handcrafted features are less efficient in capturing audio degradation characteristics and they are prone to failure when recognising audio impairments and could mistakenly learn musical concepts rather than impairment types. In this paper, we propose a new representation of hand-crafted features that is based on the correlation of feature pairs. We experimentally compare the proposed correlation-based feature representation with a typical raw feature representation used in machine learning and we show superior performance in terms of compact feature dimensionality and improved computational speed in the test stage whilst achieving comparable accuracy.
Depth Enables Long-Term Memory for Recurrent Neural Networks

A key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ability to model intricate long-term temporal dependencies. However, a well established measure of RNNs long-term memory capacity is lacking, and thus formal understanding of the effect of depth on their ability to correlate data throughout time is limited. Specifically, existing depth efficiency results on convolutional networks do not suffice in order to account for the success of deep RNNs on data of varying lengths. In order to address this, we introduce a measure of the network's ability to support information flow across time, referred to as the Start-End separation rank, which reflects the distance of the function realized by the recurrent network from modeling no dependency between the beginning and end of the input sequence. We prove that deep recurrent networks support Start-End separation ranks which are combinatorially higher than those supported by their shallow counterparts. Thus, we establish that depth brings forth an overwhelming advantage in the ability of recurrent networks to model long-term dependencies, and provide an exemplar of quantifying this key attribute. We empirically demonstrate the discussed phenomena on common RNNs through extensive experimental evaluation using the optimization technique of restricting the hidden-to-hidden matrix to being orthogonal. Finally, we employ the tool of quantum Tensor Networks to gain additional graphic insights regarding the complexity brought forth by depth in recurrent networks.
The effect of ram-pressure stripping on dwarf galaxies

Ram-pressure stripping (RPS) is a well observed phenomenon of massive spiral galaxies passing through the hot intracluster medium (ICM) of galaxy clusters. For dwarf galaxies (DGs) within a cluster, the transformation from gaseous to gas-poor systems by RPS is not easily observed and must happen in the outskirts of clusters. In a few objects in close by galaxy clusters and the field, RPS has been observed. Since cluster early-type DGs also show a large variety of internal structures (unexpected central gas reservoirs, blue stellar cores, composite radial stellar profiles), we aim in this study to investigate how ram pressure (RP) affects the interstellar gas content and therefore the star formation (SF) activity. Using a series of numerical simulations, we quantify the dependence of the stripped-off gas on the velocity of the infalling DGs and on the ambient ICM density. We demonstrated that SF can be either suppressed or triggered by RP depending on the ICM density and the DGs mass. Under some conditions, RP can compress the gas, so that it is unexpectedly retained in the central DG region and forms stars. When gas clouds are still bound against stripping but lifted from a thin disc and fall back, their new stars form an ellipsoidal (young) stellar population already with a larger velocity dispersion without the necessity of harassment. Most spectacularly, star clusters can form downstream in stripped-off massive gas clouds in the case of strong RP. We compare our results to observations.
A Framework for Generating Explanations from Temporal Personal Health Data

Whereas it has become easier for individuals to track their personal health data (e.g., heart rate, step count, food log), there is still a wide chasm between the collection of data and the generation of meaningful explanations to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work towards striving closer to their health goals. We aim to bridge the gap between data collection and explanation generation by mining the data for interesting behavioral findings that may provide hints about a user's tendencies. Our focus is on improving the explainability of temporal personal health data via a set of informative summary templates, or "protoforms. " These protoforms span both evaluation-based summaries that help users evaluate their health goals and pattern-based summaries that explain their implicit behaviors. In addition to individual users, the protoforms we use are also designed for population-level summaries. We apply our approach to generate summaries (both univariate and multivariate) from real user data and show that our system can generate interesting and useful explanations.
Monaural Speech Enhancement with Recursive Learning in the Time Domain

In this paper, we propose a type of neural network with recursive learning in the time domain called RTNet for monaural speech enhancement, where the proposed network consists of three principal components. The ﬁrst part is called stage recurrent neural network, which is proposed to effectively aggregate the deep feature dependencies across different stages with a memory mechanism and also remove the interference stage-by-stage. The second part is the convolutional auto-encoder. The third part consists of a series of concatenated gated linear units, which are capable of facilitating the information ﬂow and gradually increasing the receptive ﬁelds. Recursive learning is adopted to signiﬁcantly improve the parameter efﬁciency and therefore, the number of trainable parameters is effectively reduced without sacriﬁcing its performance. The experiments are conducted on TIMIT corpus. Experimental results demonstrate that the proposed network achieves consistently better performance in both PESQ and STOI scores than two advanced time domain-based baselines in different conditions. The code is provided at https://github.com/Andong-Li-speech/ RTNet .
Private Function Computation for Noncolluding Coded Databases

Private computation in a distributed storage system (DSS) is a generalization of the private information retrieval (PIR) problem. In such setting a user wishes to compute a function of $f$ messages stored in noncolluding coded databases while revealing no information about the desired function to the databases. We consider the problem of private polynomial computation (PPC). In PPC, a user wishes to compute a multivariate polynomial of degree at most $g$ over $f$ variables (or messages) stored in multiple databases. First, we consider the private computation of polynomials of degree $g=1$, i.e., private linear computation (PLC) for coded databases. In PLC, a user wishes to compute a linear combination over the $f$ messages while keeping the coefficients of the desired linear combination hidden from the database. For a linearly encoded DSS, we present a capacity-achieving PLC scheme and show that the PLC capacity, which is the ratio of the desired amount of information and the total amount of downloaded information, matches the maximum distance separable coded capacity of PIR for a large class of linear storage codes. Then, we consider private computation of higher degree polynomials, i.e., $g>1$. For this setup, we construct two novel PPC schemes. In the first scheme we consider Reed-Solomon coded databases with Lagrange encoding, which leverages ideas from recently proposed star-product PIR and Lagrange coded computation. The second scheme considers the special case of coded databases with systematic Lagrange encoding. Both schemes yield improved rates compared to the best known schemes from the literature for a small number of messages, while asymptotically, as $f\rightarrow \infty$, the systematic scheme gives a significantly better computation rate compared to all known schemes up to some storage code rate that depends on the maximum degree of the candidate polynomials.
A Joint Approach to Compound Splitting and Idiomatic Compound Detection

Applications such as machine translation, speech recognition, and information retrieval require efficient handling of noun compounds as they are one of the possible sources for out of vocabulary words. In-depth processing of noun compounds requires not only splitting them into smaller components (or even roots) but also the identification of instances that should remain unsplitted as they are of idiomatic nature. We develop a two-fold deep learning-based approach of noun compound splitting and idiomatic compound detection for the German language that we train using a newly collected corpus of annotated German compounds. Our neural noun compound splitter operates on a sub-word level and outperforms the current state of the art by about 5%
Non-local network dynamics via fractional graph Laplacians

We introduce non-local dynamics on directed networks through the construction of a fractional version of a non-symmetric Laplacian for weighted directed graphs. Furthermore, we provide an analytic treatment of fractional dynamics for both directed and undirected graphs, showing the possibility of exploring the network employing random walks with jumps of arbitrary length. We also provide some examples of the applicability of the proposed dynamics, including consensus over multi-agent systems described by directed networks.
Learning reduced systems via deep neural networks with memory

We present a general numerical approach for constructing governing equations for unknown dynamical systems when only data on a subset of the state variables are available. The unknown equations for these observed variables are thus a reduced system of the complete set of state variables. Reduced systems possess memory integrals, based on the well known Mori-Zwanzig (MZ) formulism. Our numerical strategy to recover the reduced system starts by formulating a discrete approximation of the memory integral in the MZ formulation. The resulting unknown approximate MZ equations are of finite dimensional, in the sense that a finite number of past history data are involved. We then present a deep neural network structure that directly incorporates the history terms to produce memory in the network. The approach is suitable for any practical systems with finite memory length. We then use a set of numerical examples to demonstrate the effectiveness of our method.
Addressing via N-body simulations the distribution of the satellite tidal debris in the Milky Way environment.

We study the distribution of the Milky Way satellites stellar and dark matter debris. For the first time we address the question of the tidal disruption of satellites in simulations by utilising simultaneously a) a realistic set of orbits extracted from cosmological simulations, b) a three component host galaxy with live halo, disc and bulge components, and c) satellites from hydrodynamical simulations. We analyse the statistical properties of the satellite debris of all massive galaxies reaching the inner Milky Way on a timescale of 2 Gyr. Up to 80$\%$ of the dark matter is stripped from the satellites, while this happens for up to 30$\%$ of their stars. The stellar debris ends mostly in the inner Milky Way halo, whereas the dark matter debris shows a flat mass distribution over the full main halo. The dark matter debris follows a density profile with inner power law index $\alpha_{\rm DM}=-0.66$ and outer index $\beta_{\rm DM}=2.94$, while for stars $\alpha_{*}=-0.44$ and $\beta_{*}=6.17$. In the inner 25 kpc, the distribution of the stellar debris is flatter than that of the dark matter debris and the orientations of their short axes differ significantly. Changing the orientation of the stellar disc by 90$^{\rm{o}}$ has only a minor impact on the distribution of the satellite debris. Our results indicate that the dark matter is more easily stripped than stars from the Milky Way satellites. The structure of the debris is dominated by the satellite orbital properties. The radial profiles, the flattening and the orientation of the stellar and dark matter debris are significantly different, which prevents the prediction of the dark matter distribution from the observed stellar component.
Sparsest Continuous Piecewise-Linear Representation of Data

We study the problem of interpolating one-dimensional data with total variation regularization on the second derivative, which is known to promote piecewise-linear solutions with few knots. In a first scenario, we consider the problem of exact interpolation. We thoroughly describe the form of the solutions of the underlying constrained optimization problem, including the sparsest piecewise-linear solutions, i.e., with the minimum number of knots. Next, we relax the exact interpolation requirement, and consider a penalized optimization problem with a strictly convex data-fidelity cost function. We show that the underlying penalized problem can be reformulated as a constrained problem, and thus that all our previous results still apply. We propose a simple and fast two-step algorithm to reach a sparsest solution of this constrained problem.
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks

Deep neural networks have been successfully applied to solving the video-based person re-identification problem with impressive results reported. The existing networks for person re-id are designed to extract discriminative features that preserve the identity information. Usually, whole video frames are fed into the neural networks and all the regions in a frame are equally treated. This may be a suboptimal choice because many regions, e.g., background regions in the video, are not related to the person. Furthermore, the person of interest may be occluded by another person or something else. These unrelated regions may hinder person re-identification. In this paper, we introduce a novel gating mechanism to deep neural networks. Our gating mechanism will learn which regions are helpful for person re-identification and let these regions pass the gate. The unrelated background regions or occluding regions are filtered out by the gate. In each frame, the color channels and optical flow channels provide quite different information. To better leverage such information, we generate one gate using the color channels and another gate using the optical flow channels. These two gates are combined to provide a more reliable gate with a novel fusion method. Experimental results on two major datasets demonstrate the performance improvements due to the proposed gating mechanism.
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging

Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a large amount of annotated data, which is time-consuming and costly. Though, semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabeled data along with a small amount of labeled data in the training process. Recently, MixUp regularizer has been successfully introduced to semi-supervised learning methods showing superior performance. MixUp augments the model with new data points through linear interpolation of the data at the input space. We argue that this option is limited. Instead, we propose ROAM, a \textit{random layer mixup}, which encourages the network to be less confident for interpolated data points at randomly selected space. ROAM generates more data points that have never seen before, and hence it avoids over-fitting and enhances the generalization ability. We conduct extensive experiments to validate our method on publicly available datasets on whole-brain image segmentation (MALC) and Lung and Infection segmentation in COVID-19. ROAM achieves state-of-the-art (SOTA) results in fully supervised (89.5\%) and semi-supervised (87.0\%) settings with a relative improvement of up to 2.40\% and 16.50\%, respectively for the whole-brain segmentation. Similarly, ROAM achieves superior performance for COVID-19 segmentation beating SOTA SSL settings.
Unsteady control of supersonic turbulent cavity flow based on resolvent analysis

Abstract We use resolvent analysis to develop a physics-based, open-loop, unsteady control strategy to attenuate pressure fluctuations in turbulent flow over a rectangular cavity with a length-to-depth ratio of $6$ at a Mach number of $1.4$ and a Reynolds number based on cavity depth of $10\,000$. Large-eddy simulations (LES) of the baseline uncontrolled flow reveal the dominance of Rossiter modes II and IV that generate high-amplitude unsteadiness via trailing-edge impingement and oblique shock waves that obstruct the free stream. To suppress the oscillations, we introduce three-dimensional unsteady blowing along the cavity leading edge. We leverage resolvent analysis as a linear model with respect to the baseline flow to guide the selections of the optimal spanwise wavenumber and frequency of the unsteady actuation input for a fixed momentum coefficient of 0.02. Instead of choosing the most amplified resolvent forcing modes, we seek a disturbance that yields sustained amplification of the primary response mode-based kinetic energy distribution over the entire cavity length. This necessary but not sufficient guideline for effective mean flow modification is evaluated using LES of the controlled cavity flows. The most effective control case reduces the pressure root mean square level up to $52\,\%$ along cavity walls relative to the baseline and is approximately twice that achievable by comparable steady blowing. Dynamic mode decomposition on the controlled flows confirms that the optimal actuation input indeed suppresses the formation of the large-scale Rossiter modes. It is expected that the present flow control guideline derived from resolvent analysis will also be applicable at higher Reynolds numbers with the aid of physical insights and further validation.