Miracle-dz/UniFAR-FTUs huggingface dataset:
Discarding orbital decay in WASP-19b after one decade of transit observations★†

We present a empirical study of orbital decay for the exoplanet WASP-19b, based on mid-time measurements of 74 complete transits (12 newly obtained by our team and 62 from the literature), covering a 10-year baseline. A linear ephemeris best represents the mid-transit times as a function of epoch. Thus, we detect no evidence of the shortening of WASP-19b’s orbital period and establish an upper limit of its steady changing rate, $\dot{P}=-2.294$ ms yr−1, and a lower limit for the modified tidal quality factor $Q^{\prime }_{\star } = (1.23 \pm 0.231) \times 10^{6}$. Both are in agreement with previous works. This is the first estimation of $Q^{\prime }_{\star }$ directly derived from the mid-times of WASP-19b obtained through homogeneously analyzed transit measurements. Additionally, we do not detect periodic variations in the transit timings within the measured uncertainties in the mid-times of transit. We are therefore able to discard the existence of planetary companions in the system down to a few M⊕ in the first order mean-motion resonances 1:2 and 2:1 with WASP-19b, in the most conservative case of circular orbits. Finally, we measure the empirical $Q^{\prime }_{\star }$ values of 15 exoplanet host stars which suggest that stars with Teff ≲ 5600K dissipate tidal energy more efficiently than hotter stars. This tentative trend needs to be confirmed with a larger sample of empirically measured $Q^{\prime }_{\star }$.
Eikonal black hole ringings in generalized energy-momentum squared gravity

In the scope of black hole spectroscopy, several attempts have been made in the past decades in order to test black holes or gravitational theories via black hole quasinormal modes. In the eikonal approximation, the quasinormal modes are generically associated with the photon ring of the black hole. This correspondence is valid for most cases in general relativity, but may not be true in other theories of gravity. In this paper, we consider the generalized energy-momentum squared gravity in which matter fields are non-minimally coupled to geometry. We investigate the axial perturbations of the charged black holes in this model, without assuming any explicit expression of the action functional. After obtaining the modified Klein-Gordon equation and the modified Maxwell equations, we perturb the gravitational equations and the modified Maxwell equations to derive the coupled master equations of the axial perturbations. In the presence of the non-minimal coupling between matter and geometry, the correspondence between the eikonal quasinormal modes and the photon ring is not satisfied in general. Also, the two coupled fields of the axial perturbations are found to propagate independently and they do not share the same quasinormal frequencies in the eikonal limit.
Finding Mixed Strategy Nash Equilibrium for Continuous Games through Deep Learning

Nash equilibrium has long been a desired solution concept in multi-player games, especially for those on continuous strategy spaces, which have attracted a rapidly growing amount of interests due to advances in research applications such as the generative adversarial networks. Despite the fact that several deep learning based approaches are designed to obtain pure strategy Nash equilibrium, it is rather luxurious to assume the existence of such an equilibrium. In this paper, we present a new method to approximate mixed strategy Nash equilibria in multi-player continuous games, which always exist and include the pure ones as a special case. We remedy the pure strategy weakness by adopting the pushforward measure technique to represent a mixed strategy in continuous spaces. That allows us to generalize the Gradient-based Nikaido-Isoda (GNI) function to measure the distance between the players' joint strategy profile and a Nash equilibrium. Applying the gradient descent algorithm, our approach is shown to converge to a stationary Nash equilibrium under the convexity assumption on payoff functions, the same popular setting as in previous studies. In numerical experiments, our method consistently and significantly outperforms recent works on approximating Nash equilibrium for quadratic games, general blotto games, and GAMUT games.
The soft X-ray excess: NLS1s versus BLS1s

The soft X-ray excess – the excess of X-rays below 2 keV with respect to the extrapolation of the hard X-ray spectral continuum model – is a very common feature among type 1 active galactic nuclei (AGNs); yet the nature of the soft X-ray excess is still poorly understood and hotly debated. To shed some light on this issue, we have measured in a model-independent way the soft excess strength in a flux-limited sample of broad- and narrow-line Seyfert 1 galaxies (BLS1s and NLS1s) that are matched in X-ray luminosity but different in terms of the black hole mass and the accretion rate values, with NLS1s being characterized by smaller MBH and larger $\dot{m}$ values. Our analysis, in agreement with previous studies carried out with different AGN samples, indicates that: (1) a soft excess is ubiquitously detected in both BLS1s and NLS1s; (2) the strength of the soft excess is significantly larger in the NLS1 sample, compared to the BLS1 sample; and (3) combining the two samples, the strength of the soft excess appears to positively correlate with the photon index as well as with the accretion rate, whereas there is no correlation with the black hole mass. Importantly, our work also reveals the lack of an anticorrelation between the soft excess strength and the luminosity of the primary X-ray component, predicted by the absorption and reflection scenarios. Our findings suggest that the soft excess is consistent with being produced by a warm Comptonization component. Larger, more complete samples of NLS1s and BLS1s are needed to confirm these conclusions.
Spectral Algorithm for Low-rank Multitask Regression

Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learning. We focus on matrix regression tasks where the rank of the weight matrix is constrained to reduce sample complexity. We introduce the common mechanism regression (CMR) model which assumes a shared left low-rank component across all tasks, but allows an individual per-task right low-rank component. This dramatically reduces the number of samples needed for accurate estimation. The problem of jointly recovering the common and the local components has a non-convex bi-linear structure. We overcome this hurdle and provide a provably beneficial non-iterative spectral algorithm. Appealingly, the solution has favorable behavior as a function of the number of related tasks and the small number of samples available for each one. We demonstrate the efficacy of our approach for the challenging task of remote river discharge estimation across multiple river sites, where data for each task is naturally scarce. In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model. We also show the benefit of the approach on the markedly different setting of image classification where the common component can be interpreted as the shared convolution filters.
Meta Learning for End-To-End Low-Resource Speech Recognition

In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and meta-learned the initialization parameters from many pretraining languages to achieve fast adaptation on unseen target language, via recently proposed model-agnostic meta learning algorithm (MAML). We evaluated the proposed approach using six languages as pretraining tasks and four languages as target tasks. Preliminary results showed that the proposed method, MetaASR, significantly outperforms the state-of-the-art multitask pretraining approach on all target languages with different combinations of pretraining languages. In addition, since MAML’s model-agnostic property, this paper also opens new research direction of applying meta learning to more speech-related applications.
Unveiling the cause of hybrid morphology radio sources (HyMoRS)

Hybrid morphology radio sources (HyMoRS) are a rare group of radio galaxies in which differing Fanaroff & Riley morphologies (FR I/II) are observed for each of the two lobes. While they potentially provide insights into the formation of lobe structure, particle acceleration, and the FR dichotomy, previous work on HyMoRS has mainly been limited to low-resolution studies, searches for new candidates, and milliarcsecond-scale VLBI observations of the core region. In this paper, we use new multi-array configuration Very Large Array (VLA) observations between 1 and 8 GHz to determine the morphology of HyMoRS on arcsecond scales and perform the first well-resolved spectral study of these unusual sources. We find that while the apparent FR I lobe is centre brightened, this is the result of a compact acceleration region resembling a hotspot with a spectrum more consistent with an FR II (‘strong-flavour’) jet. We find that the spectra of the apparent FR I lobes are not similar to their classical counterparts and are likely the result of line-of-sight mixing of plasma across a range of spectral ages. We consider possible mechanisms that could lead to the formation of HyMoRS under such conditions, including environment asymmetry and restarted sources, concluding through the use of simple modelling that HyMoRS are the result of orientation effects on intrinsically FR II sources with lobes non-parallel to the inner jet.
Factors influencing patients' hope in stroke and spinal cord injury

Aims: The purpose of this research is to help health-care professionals (HCPs) consider how hope is promoted and challenged during the rehabilitation of patients who have had a stroke or spinal cord injury (SCI). Background: Stroke and SCI are two chronic neurological conditions that require significant neurological rehabilitation. During the process of neurological rehabilitation, several internal, external or environmental factors can influence a patient's hope. While research has identified the importance of hope, it has not explicitly identified which factors influence patients' hope. Neither has existing research provided an evidence-based understanding of how health professionals can use strategies to help maintain patients' feelings of hope during rehabilitation. Methods: A qualitative narrative review was undertaken. Following a systematic search, 17 qualitative articles were identified that met the inclusion criteria. Thematic analysis was used to enable information to emerge regarding the strategies that promote hope and the factors that challenge hope. Results: The results included five hope-generating factors and four factors that challenged hope. Conclusions: This article identified several factors that influenced the hope of patients who have had a stroke or SCI. Hope is influenced by personal, social and situational factors, and many of these factors can have both positive and negative effects. Therefore, HCPs need to understand how to implement these ideas effectively. The current findings consider this. [ABSTRACT FROM AUTHOR]
Quoting Caravaggio: Contemporary Art, Preposterous History

As period, as style, as sensibility, the Baroque remains elusive, its definition subject to dispute. Perhaps this is so in part because baroque vision resists separation of mind and body, form and matter, line and color, image and discourse. In "Quoting Caravaggio", Mieke Bal deploys this insight of entanglement as a form of art analysis, exploring its consequences for both contemporary and historical art, as well as for current conceptions of history. Mieke Bal's primary object of investigation in "Quoting Caravaggio" is not the great 17th-century painter, but rather the issue of temporality in art. In order to retheorize linear notions of influence in cultural production, Bal analyzes the productive relationship between Caravaggio and a number of late-20th-century artists who "quote" the baroque master in their own works. These artists include Andres Serrano, Carrie Mae Weems, Ken Aptekar, David Reed, and Ana Mendieta, among others. Each chapter of "Quoting Caravaggio" shows particular ways in which quotation is vital to the new art but also to the source from which it is derived. Through such dialogue between present and past, Bal argues for a notion of "preposterous history" where works that appear chronologically first operate as an aftereffect caused by the images of subsequent artists. "Quoting Caravaggio" is at once a meditation on history as creative, nonlinear process; a study of the work of Caravaggio and the Baroque; and, a critical exposition of contemporary artistic representation and practice.
Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback.

Here we investigate single-cell stochastic gene expression kinetics in a minimal coupled gene circuit with positive-plus-negative feedback. A triphasic stochastic bifurcation is observed upon increasing the ratio of the positive and negative feedback strengths, which reveals a strong synergistic interaction between positive and negative feedback loops. We discover that coupled positive-plus-negative feedback amplifies gene expression mean but reduces gene expression noise over a wide range of feedback strengths when promoter switching is relatively slow, stabilizing gene expression around a relatively high level. In addition, we study two types of macroscopic limits of the discrete chemical master equation model: the Kurtz limit applies to proteins with large burst frequencies and the Lévy limit applies to proteins with large burst sizes. We derive the analytic steady-state distributions of the protein abundance in a coupled gene circuit for both the discrete model and its two macroscopic limits, generalizing the results obtained by Liu et al. [Chaos 26, 043108 (2016)CHAOEH1054-150010.1063/1.4947202]. We also obtain the analytic time-dependent protein distribution for the classical Friedman-Cai-Xie random bursting model [Friedman, Cai, and Xie, Phys. Rev. Lett. 97, 168302 (2006)PRLTAO0031-900710.1103/PhysRevLett.97. 168302]. Our analytic results are further applied to study the structure of gene expression noise in a coupled gene circuit, and a complete decomposition of noise in terms of five different biophysical origins is provided.
Sequential controlled sensing for composite multihypothesis testing

Abstract The problem of multihypothesis testing with controlled sensing of observations is considered. The distribution of observations collected under each control is assumed to follow a single-parameter exponential family distribution. The goal is to design a policy to find the true hypothesis with minimum expected delay while ensuring that the probability of error is below a given constraint. The decision-maker can reduce the delay by intelligently choosing the control for observation collection in each time slot. A policy for this problem is derived that satisfies given constraints on the error probability, and it is shown that this policy is asymptotically optimal in the sense that it asymptotically achieves an information-theoretic lower bound on the expected delay. Numerical results are provided that illustrate an application of the policy to medical diagnostic inference.
A systematic study of spiral density waves in the accretion discs of cataclysmic variables

Spiral density waves are thought to be excited in the accretion discs of accreting compact objects, including cataclysmic variable stars (CVs). Observational evidence has been obtained for a handful of systems in outburst over the last two decades. We present the results of a systematic study searching for spiral density waves in CVs, and report their detection in two of the sixteen observed systems. While most of the systems observed present asymmetric, non-Keplerian accretion discs during outburst, the presence of ordered structures interpreted as spiral density waves is not as ubiquitous as previously anticipated. From a comparison of systems by their system parameters it appears that inclination of the systems may play a major role, favouring the visibility and/or detection of spiral waves in systems seen at high inclination.
Near-direct bandgap WSe2/ReS2 type-II pn heterojunction for enhanced ultrafast photodetection and high-performance photovoltaics.

PN heterojunctions comprising layered van der Waals (vdW) semiconductors have been used to demonstrate current-rectifiers, photodetectors, and photovoltaic devices. However, a direct or near-direct heterointerface bandgap for enhanced photogeneration in high light-absorbing few-layer vdW materials remains unexplored. In this work, for the first time, density functional theory calculations show that the heterointerface of few-layer group-6 transition metal dichalcogenide (TMD) WSe2 with group-7 ReS2 results in a sizeable (0.7 eV) near-direct type-II bandgap. The interlayer IR bandgap is confirmed through IR photodetection and micro-photoluminescence measurements demonstrate type-II alignment. Few-layer flakes exhibit ultrafast response time (5 μs) at high responsivity (3 A/W) and large photocurrent-generation and responsivity-enhancement at the hetero-overlap region (10-100X). Large open-circuit voltage of 0.64 V and short-circuit current of 2.6 μA enable high output electrical power. Finally, long term air-stability and facile single contact metal fabrication process make the multi-functional few-layer WSe2/ReS2 heterostructure diode technologically promising for next-generation optoelectronics.
Addressing the Sim2Real Gap in Robotic 3-D Object Classification

Object classification with 3D data is an essential component of any scene understanding method. It has gained significant interest in a variety of communities, most notably in robotics and computer graphics. While the advent of deep learning has progressed the field of 3D object classification, most work using this data type are solely evaluated on CAD model datasets. Consequently, current work does not address the discrepancies existing between real and artificial data. In this work, we examine this gap in an indoor service robotic context by specifically addressing the problem of classification when transferring from artificial CAD models to real reconstructed objects. This is performed by training on ModelNet (CAD models) and evaluating on ScanNet (objects extracted from reconstructed rooms). We show that standard methods do not perform well in this task. We thus introduce a method that carefully samples object parts that are reproducible under various transformations and hence robust. Using graph convolution to classify the composed graph of parts, our method improves upon the baseline. Code is publicly available at https://rgit.acin. tuwien.ac. at/jb.weibel/cad2real_object_clf.
The importance of relational coordination for integrated care delivery to older patients in the hospital

Aim: This study investigated relational coordination among professionals providing healthcare to hospitalized elderly and assessed its impact on integrated care delivery. Background: Previous studies have shown that relational coordination is positively associated with the delivery of acute, emergency and trauma care. According to this theory, coordination that occurs through frequent, high-quality communication supported by relationships of shared goals, shared knowledge, and mutual respect enable an organization to achieve better task integration. However, the effect of relational coordination on integrated care delivery to hospitalized elderly remains unknown. Methods: This cross-sectional study was part of an examination of integrated care delivery to hospitalized elderly. Data were collected using questionnaires distributed to hospital professionals (192 respondents; 44% response rate). Relational coordination was measured by the Relational Coordination survey, which included questions about communication and relationships with others involved in delivering care to hospitalized older patients. The Assessment of Chronic Illness Care (ACIC) assess the integration of several components of the Chronic Care Model (CCM; selfmanagement support, delivery system design, decision support, and clinical information systems). Since chronic illness care for hospitalized elderly contains several interacting components, we generalized the ACIC as a measure of integrated care delivery for hospitalized older patients.
Interrupted and Cascaded Permutation Invariant Training for Speech Separation

Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem. With PIT selecting the minimum cost label assignments dynamically, very few studies considered the separation problem to be optimizing both the model parameters and the label assignments, but focused on searching for good model architecture and parameters. In this paper, we investigate instead for a given model architecture the various flexible label assignment strategies for training the model, rather than directly using PIT. Surprisingly, we discover a significant performance boost compared to PIT is possible if the model is trained with fixed label assignments and a good set of labels is chosen. With fixed label training cascaded between two sections of PIT, we achieved the state-of-the-art performance on WSJ0-2mix without changing the model architecture at all.
A framework for deep energy-based reinforcement learning with quantum speed-up

In the past decade, deep learning methods have seen tremendous success in various supervised and unsupervised learning tasks such as classification and generative modeling. More recently, deep neural networks have emerged in the domain of reinforcement learning as a tool to solve decision-making problems of unprecedented complexity, e.g., navigation problems or game-playing AI. Despite the successful combinations of ideas from quantum computing with machine learning methods, there have been relatively few attempts to design quantum algorithms that would enhance deep reinforcement learning. This is partly due to the fact that quantum enhancements of deep neural networks, in general, have not been as extensively investigated as other quantum machine learning methods. In contrast, projective simulation is a reinforcement learning model inspired by the stochastic evolution of physical systems that enables a quantum speed-up in decision making. In this paper, we develop a unifying framework that connects deep learning and projective simulation, opening the route to quantum improvements in deep reinforcement learning. Our approach is based on so-called generative energy-based models to design reinforcement learning methods with a computational advantage in solving complex and large-scale decision-making problems.
Motivic Donaldson-Thomas Invariants of Parabolic Higgs Bundles and Parabolic Connections on a Curve

Let $X$ be a smooth projective curve over a field of characteristic zero and let $D$ be a non-empty set of rational points of $X$. We calculate the motivic classes of moduli stacks of semistable parabolic bundles with connections on $(X,D)$ and motivic classes of moduli stacks of semistable parabolic Higgs bundles on $(X,D)$. As a by-product we give a criteria for non-emptiness of these moduli stacks, which can be viewed as a version of the Deligne-Simpson problem.