Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind morphology around cool evolved stars in binaries

Context. The late evolutionary phase of low- and intermediate-mass stars is strongly constrained by their mass-loss rate, which is orders of magnitude higher than during the main sequence. The wind surrounding these cool expanded stars frequently shows nonspherical symmetry, which is thought to be due to an unseen companion orbiting the donor star. The imprints left in the outflow carry information about the companion and also the launching mechanism of these dust-driven winds. Aims. We study the morphology of the circumbinary envelope and identify the conditions of formation of a wind-captured disk around the companion. Long-term orbital changes induced by mass loss and mass transfer to the secondary are also investigated. We pay particular attention to oxygen-rich, that is slowly accelerating, outflows in order to look for systematic differences between the dynamics of the wind around carbon and oxygen-rich asymptotic giant branch (AGB) stars. Methods. We present a model based on a parametrized wind acceleration and a reduced number of dimensionless parameters to connect the wind morphology to the properties of the underlying binary system. Thanks to the high performance code MPI-AMRVAC, we ran an extensive set of 72 three-dimensional hydrodynamics simulations of a progressively accelerating wind propagating in the Roche potential of a mass-losing evolved star in orbit with a main sequence companion. The highly adaptive mesh refinement that we used, enabled us to resolve the flow structure both in the immediate vicinity of the secondary, where bow shocks, outflows, and wind-captured disks form, and up to 40 orbital separations, where spiral arms, arcs, and equatorial density enhancements develop. Results. When the companion is deeply engulfed in the wind, the lower terminal wind speeds and more progressive wind acceleration around oxygen-rich AGB stars make them more prone than carbon-rich AGB stars to display more disturbed outflows, a disk-like structure around the companion, and a wind concentrated in the orbital plane. In these configurations, a large fraction of the wind is captured by the companion, which leads to a significant shrinking of the orbit over the mass-loss timescale, if the donor star is at least a few times more massive than its companion. In the other cases, an increase of the orbital separation is to be expected, though at a rate lower than the mass-loss rate of the donor star. Provided the companion has a mass of at least a tenth of the mass of the donor star, it can compress the wind in the orbital plane up to large distances. Conclusions. The grid of models that we computed covers a wide scope of configurations: We vary the terminal wind speed relative to the orbital speed, the extension of the dust condensation region around the cool evolved star relative to the orbital separation, and the mass ratio, and we consider a carbon-rich and an oxygen-rich donor star. It provides a convenient frame of reference to interpret high-resolution maps of the outflows surrounding cool evolved stars.
Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Abstract In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.
Gaussian speaker embedding learning for text-independent speaker verification

The x-vector maps segments of arbitrary duration to vectors of fixed dimension using deep neural network. Combined with the probabilistic linear discriminant analysis (PLDA) backend, the x-vector/PLDA has become the dominant framework in text-independent speaker verification. Nevertheless, how to extract the x-vector appropriate for the PLDA backend is a key problem. In this paper, we propose a Gaussian noise constrained network (GNCN) to extract xvector, which adopts a multi-task learning strategy with the primary task classifying the speakers and the auxiliary task just fitting the Gaussian noises. Experiments are carried out using the SITW database. The results demonstrate the effectiveness of our proposed method
The Large-scale Behavior in the Disk of δ Scorpii from 2000 to 2018

We model the circumstellar disk of δ Sco using the three-dimensional Monte Carlo radiative transfer code HDUST in order to quantify the large-scale changes in the disk through the years 2000–2018, and to see if these changes can be attributed to the secondary star affecting the disk throughout its orbit. We determine our best-fitting models through matching simulated observations to actual Hα spectroscopy and V-band photometric observations. Our modeling results confirm previous findings that the disk of δ Sco was forming early in the century. We also find a period of disk dissipation when the companion is at apastron, as well as a significant growth of the disk between 2009 and 2011, prior to the periastron of 2011. Due to the steady-state nature of the disk after 2011, it is difficult to say whether the variations seen are due to the effect of the close passage of the binary companion.
Stress diffusion interactions in an elastoplastic medium in the presence of geometric discontinuity

Abstract This paper aims to study the effect of stress-diffusion interactions and its effect on the localization of the plastic strain in an elastoplastic material using a fully coupled chemo-mechanical system. The transient coupled system is solved using a finite element formulation in an open-source finite element solver FEniCS. We investigate the role of geometric discontinuities in scenarios involving diffusing species, namely, a plate with a notch/hole/void and particle with a void/hole/core. We also study the effect of stress concentrations and plastic yielding on the diffusion-deformation.
Partial MDS Codes with Local Regeneration

Partial MDS (PMDS) and sector-disk (SD) codes are classes of erasure codes that combine locality with strong erasure correction capabilities. We construct PMDS and SD codes where each local code is a bandwidth-optimal regenerating MDS code. The constructions require significantly smaller field size than the only other construction known in literature.
Appendix for “Learning Overlapping Representations for the Estimation of Individualized Treatment Effects”

Notation. Let Px,t denote the input data distribution p(x, t), Pt = p(yt|x)p(x, t) the joint factual distribution of x and yt, P1−t = p(yt|x)p(x, 1− t) the joint counterfactual distribution of x and yt. Each instance in the observed (factual) dataset Dt = {(xi,t, yi,t)} i=1, is assumed to be sampled i.i. d from Pt. Dt−1 will be used to denote the unobserved (counterfactual) data set that results from fliping the treatment assignment for each instance. While we assume φ to be deterministic, in the following section we let wt be a vector of weights with prior distribution πt = N (0, λ−1 t I), λt > 0. In this sense, πt defines the hypothesis space F of ft and we write ρ̂t for the posterior distribution of ft, itself a random variable. We write μ(xi|Dt,Θt) and σ(xi|Dt,Θt) for its posterior mean and variance given context xi. Θt includes all hyperparameters (both shared parameters (in φ) and specific parameters to each treatment group).
Practical quantum error mitigation for analog quantum simulation

Analog quantum simulation has been proposed as an efficient approach for probing classically intractable many-body physics. In contrast to digital, gate-based quantum simulation, the analog approach involves configuring the quantum hardware to directly mimic the physics of the target system. This reduces the level of control required and can offer a degree of inherent robustness, but it comes at the cost that known error-handling techniques may be inapplicable. Accumulation of error is thus a major challenge facing medium-scale and large-scale analog quantum simulation with current and near-term noisy quantum (NISQ) devices. Here we propose a hybrid error mitigation scheme that can suppress general local Markovian noise in analog quantum simulators. We employ stochastic error mitigation for physical noise and use Richardson extrapolation to compensate for model estimation error. We numerically test the error mitigation scheme for an Ising-type Hamiltonian with energy relaxation and dephasing noise, and show an improvement of simulation accuracy by two orders. We assess the resource cost of our error mitigation scheme and conclude that analog quantum simulation at scale is feasible with NISQ hardware.
Behaviour of CFRP-Confined Hollow Core Reactive Powder Concrete Columns

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Scalability and high-efficiency of an $(n+1)$-qubit Toffoli gate sphere via blockaded Rydberg atoms

The Toffoli gate serving as a basic building block for reversible quantum computation, has manifested its great potentials in improving the error-tolerant rate in quantum communication. While current route to the creation of Toffoli gate requires implementing sequential single- and two-qubit gates, limited by longer operation time and lower average fidelity. We develop a new theoretical protocol to construct a universal $(n+1)$-qubit Toffoli gate sphere based on the Rydberg blockade mechanism, by constraining the behavior of one central target atom with $n$ surrounding control atoms. Its merit lies in the use of only five $\pi$ pulses independent of the control atom number $n$ which leads to the overall gate time as fast as $\sim$125$n$s and the average fidelity closing to 0.999. The maximal filling number of control atoms can be up to $n=46$, determined by the spherical diameter which is equal to the blockade radius, as well as by the nearest neighbor spacing between two trapped-atom lattices. Taking $n=2,3,4$ as examples we comparably show the gate performance with experimentally accessible parameters, and confirm that the gate errors mainly attribute to the imperfect blockade strength, the spontaneous atomic loss and the imperfect ground-state preparation. In contrast to an one-dimensional-array configuration it is remarkable that the spherical atomic sample preserves a high-fidelity output against the increasing of $n$, shedding light on the study of scalable quantum simulation and entanglement with multiple neutral atoms.
Effects of annotation granularity in deep learning models for histopathological images

Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machine learning, especially deep learning have established robust and accurate classifiers. They are being used to analyze histopathological slides and assist pathologists in diagnosis. Most machine learning systems rely heavily on annotated data sets to gain experiences and knowledge to correctly and accurately perform various tasks such as classification and segmentation. Generally, annotations made in pathology-related datasets have inherited annotation methods from natural scene images. This work investigates different granularity of annotations in histopathological data set including image-wise, bounding box, ellipse-wise, and pixel-wise to verify the influence of annotation in pathological slide on deep learning models. We design corresponding experiments to test classification and segmentation performance of deep learning models based on annotations with different annotation granularity. In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer granularity annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows not only that finer-grained annotation can improve the performance of deep learning models, but also help they extract more accurate phenotypic information from histopathological slides. The accurate and spatially precise acquisitions of phenotypic information can improve the reliability of the model prediction. Intelligence systems trained on granular annotations may help pathologists inspecting certain regions and features in the slide that were mainly used to calculate the prediction. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies.
Central molecular zones in galaxies: 12CO-to-13CO ratios, carbon budget, and X factors

We present ground-based measurements of 126 nearby galaxy centers in 12CO and 92 in 13CO in various low-J transitions. More than 60 galaxies were measured in at least four lines. The average relative intensities of the first four J 12CO transitions are 1.00:0.92:0.70:0.57. In the first three J transitions, the average 12CO-to-13CO intensity ratios are 13.0, 11.6, and 12.8, with individual values in any transition ranging from 5 to 25. The sizes of central CO concentrations are well defined in maps, but poorly determined by multi-aperture photometry. On average, the J = 1−0 12CO fluxes increase linearly with the size of the observing beam. CO emission covers only a quarter of the HI galaxy disks. Using radiative transfer models (RADEX), we derived model gas parameters. The assumed carbon elemental abundances and carbon gas depletion onto dust are the main causes of uncertainty. The new CO data and published [CI] and [CII] data imply that CO, C°, and C+ each represent about one-third of the gas-phase carbon in the molecular interstellar medium. The mean beam-averaged molecular hydrogen column density is N(H2) = (1.5 ± 0.2)×1021 cm−2. Galaxy center CO-to-H2 conversion factors are typically ten times lower than the “standard” Milky Way X° disk value, with a mean X(CO) = (1.9 ± 0.2)×1019 cm−2/K km s−1 and a dispersion 1.7. The corresponding [CI]-H2 factor is five times higher than X(CO), with X[CI] = (9 ± 2)×1019 cm−2/K km s−1. No unique conversion factor can be determined for [CII]. The low molecular gas content of galaxy centers relative to their CO intensities is explained in roughly equal parts by high central gas-phase carbon abundances, elevated gas temperatures, and large gas velocity dispersions relative to the corresponding values in galaxy disks.
Knowledge Representations in Technical Systems - A Taxonomy

The recent usage of technical systems in human-centric environments leads to the question, how to teach technical systems, e.g., robots, to understand, learn, and perform tasks desired by the human. Therefore, an accurate representation of knowledge is essential for the system to work as expected. This article mainly gives insight into different knowledge representation techniques and their categorization into various problem domains in artificial intelligence. Additionally, applications of presented knowledge representations are introduced in everyday robotics tasks. By means of the provided taxonomy, the search for a proper knowledge representation technique regarding a specific problem should be facilitated.
An Index Coding Approach to Caching With Uncoded Cache Placement

Caching is an efficient way to reduce network traffic congestion during peak hours, by storing some content at the user’s local cache memory, even without knowledge of user’s later demands. Maddah-Ali and Niesen proposed a two-phase (placement phase and delivery phase) coded caching strategy for broadcast channels with cache-aided users. This paper investigates the same model under the constraint that content is placed uncoded within the caches, that is, when bits of the files are simply copied within the caches. When the cache contents are uncoded and the users’ demands are revealed, the caching problem can be connected to an index coding problem. This paper focuses on deriving fundamental performance limits for the caching problem by using tools for the index coding problem that were either known or are newly developed in this work. First, a converse bound for the caching problem under the constraint of uncoded cache placement is proposed based on the “acyclic index coding converse bound. ” This converse bound is proved to be achievable by the Maddah-Ali and Niesen’s scheme when the number of files is not less than the number of users, and by a newly derived index coding achievable scheme otherwise. The proposed index coding achievable scheme is based on distributed source coding and strictly improves on the widely used “composite (index) coding” achievable bound and its improvements, and is of independent interest. An important consequence of the findings of this paper is that advancements on the coded caching problem posed by Maddah-Ali and Niesen are thus only possible by considering strategies with coded placement phase. A recent work by Yu et al has however shown that coded cache placement can at most half the network load compared to the results presented in this paper.
Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

Abstract An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency and provide the risk bound for the proposed estimator under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival probability. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome clinical data. Supplementary materials for this article are available online.
Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention

Acquiring information about the road lane structure is a crucial step for autonomous navigation. To this end, several approaches tackle this task from different perspectives such as lane marking detection or semantic lane segmentation. However, to the best of our knowledge, there is yet no purely vision based end-to-end solution to answer the precise question: How to estimate the relative number or "ID" of the current driven lane within a multi-lane road or a highway? In this work, we propose a real-time, vision-only (i.e. monocular camera) solution to the problem based on a dual left-right convention. We interpret this task as a classification problem by limiting the maximum number of lane candidates to eight. Our approach is designed to meet low-complexity specifications and limited runtime requirements. It harnesses the temporal dimension inherent to the input sequences to improve upon high-complexity state-of-the-art models. We achieve more than 95% accuracy on a challenging test set with extreme conditions and different routes.
Collective oscillations of Majorana neutrinos in strong magnetic fields and self-induced flavor equilibrium

We study collective oscillations of Majorana neutrinos in some of the most extreme astrophysical sites such as neutron star merger remnants and magneto-rotational core-collapse supernovae which include dense neutrino media in the presence of strong magnetic fields. We show that neutrinos can reach flavor equilibrium if neutrino transition magnetic moment μν is strong enough, namely when μν/μB≳10-14–10-15 with μB being the Bohr magneton. This sort of flavor equilibrium, which is not necessarily flavor equipartition, can occur on (short) scales determined by the strength of the magnetic term in the Hamiltonian. Our findings can have interesting implications for the physics of such violent astrophysical environments.