Miracle-dz/UniFAR-FTUs huggingface dataset:
A Kleene Theorem for Higher-Dimensional Automata

We prove a Kleene theorem for higher-dimensional automata (HDAs). It states that the languages they recognise are precisely the rational subsumption-closed sets of interval pomsets. The rational operations include a gluing composition, for which we equip pomsets with interfaces. For our proof, we introduce HDAs with interfaces as presheaves over labelled precube categories and use tools inspired by algebraic topology, such as cylinders and (co)ﬁbrations. HDAs are a general model of non-interleaving concurrency, which subsumes many other models in this ﬁeld. Interval orders are used as models for concurrent or distributed systems where events extend in time. Our tools and techniques may therefore yield templates for Kleene theorems in various models and applications.
The Long Filament of PSR J2030+4415

New X-ray and optical observations shed light on the remarkable X-ray filament of the gamma-ray pulsar PSR J2030+4415. Images of the associated Hα bow shock’s evolution over the past decade compared with its velocity structure provide an improved kinematic distance of ∼0.5 kpc. These velocities also imply that the pulsar spin axis lies ∼15° from the proper motion axis, which is close to the plane of the sky. The multi-bubble shock structure indicates that the bow shock standoff was compressed to a small value ∼20–30 yr ago when the pulsar broke through the bow shock to its present bubble. This compression allowed multi-TeV pulsar e ± to escape to the external interstellar medium (ISM), lighting up an external magnetic field structure as the filament. The narrow filament indicates excellent initial confinement and the full 15′ (2.2 pc = 7 lt-yr) projected length of the filament indicates rapid e ± propagation to its end. Spectral variation along the filament suggests that the injected particle energy evolved during the breakthrough event.
Is local H 0 at odds with dark energy EFT?

Local H_0 determinations currently fall in a window between H 0 ∼ 70 km/s/Mpc (TRGB) and H 0 ∼ 76 km/s/Mpc (Tully-Fisher). In contrast, BAO data calibrated in an early ΛCDM universe are largely consistent with Planck-ΛCDM, H 0 ∼ 67.5 km/s/Mpc. Employing a generic two parameter family of evolving equations of state (EoS) for dark energy (DE) w DE(z) and mock BAO data, we demonstrate that if i) w DE(z = 0) < -1 and ii) integrated DE density less than ΛCDM, then H 0 increases. EoS that violate these conditions at best lead to modest H 0 increases within 1σ. Tellingly, Quintessence and K-essence satisfy neither condition, whereas coupled Quintessence can only satisfy ii). Beyond these seminal DE Effective Field Theories (EFTs), we turn to explicit examples. Working model agnostically in an expansion in powers of redshift z, we show that Brans-Dicke/f(R) and Kinetic Gravity Braiding models within the Horndeski class can lead to marginal and modest increases in H 0, respectively. We confirm that as far as increasing H 0 is concerned, no DE EFT model can outperform the phenomenological two parameter family of the DE models. Evidently, the late universe may no longer be large enough to accommodate H 0, BAO and DE described by EFT.
The Voice: The Shareholders' Motives Behind Corporate Donations During COVID-19 (preprint)/ en

What motivates shareholders to become prosocial activists? We exploit the onset of the COVID-19 pandemic as a natural experiment to study activism at S&P500 corporations. Prominent individual and family shareholders supported covid-related corporate donations to bolster their reputations as, for the public, they are synonymous with the donating firms. Image gains, instead, do not pass through to institutional shareholders;also, due to the sinking stock market, these shareholders preferred to donate themselves rather than support donations at the firms in their portfolios. Our results point to media attention as a critical channel to incentivize institutional shareholders to support prosocial decisions.
Output Regulation of Linear Aperiodic Sampled-Data Systems

This paper deals with the output regulation problem of a linear time-invariant system in the presence of sporadically available measurement streams. A regulator with a continuous intersample injection term is proposed, where the intersample injection is provided by a linear dynamical system and the state of which is reset with the arrival of every new measurement updates. The resulting system is augmented with a timer triggering an instantaneous update of the new measurement and the overall system is then analyzed in a hybrid system framework. With the Lyapunov based stability analysis, we offer sufficient conditions to ensure the objectives of the output regulation problem are achieved under intermittency of the measurement streams. Then, from the solution to linear matrix inequalities, a numerically tractable regulator design procedure is presented. Finally, with the help of an illustrative example, the effectiveness of the theoretical results are validated.
Ultrafast Emergence of Ferromagnetism in Antiferromagnetic FeRh in High Magnetic Fields

Ultrafast heating of FeRh by a femtosecond laser pulse launches a magneto-structural phase transition from an antiferromagnetic to a ferromagnetic state. Aiming to reveal the ultrafast kinetics of this transition, we studied magnetization dynamics with the help of the magneto-optical Kerr effect in a broad range of temperatures (from 4 K to 400 K) and magnetic fields (up to 25 T). Three different types of ultrafast magnetization dynamics were observed and, using a numerically calculated H-T phase diagram, the differences were explained by different initial states of FeRh corresponding to a (i) collinear antiferromagnetic, (ii) canted antiferromagnetic and (iii) ferromagnetic alignment of spins. We argue that ultrafast heating of FeRh in the canted antiferromagnetic phase launches practically the fastest possible emergence of magnetization in this material. The magnetization emerges on a time scale of 2 ps, which corresponds to the earlier reported time-scale of the structural changes during the phase transition.
Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes With Point-Level Annotations

Deep learning algorithms have obtained great success in semantic segmentation of very high-resolution (VHR) remote sensing images. Nevertheless, training these models generally requires a large amount of accurate pixel-wise annotations, which is very laborious and time-consuming to collect. To reduce the annotation burden, this paper proposes a consistency-regularized region-growing network (CRGNet) to achieve semantic segmentation of VHR remote sensing images with point-level annotations. The key idea of CRGNet is to iteratively select unlabeled pixels with high confidence to expand the annotated area from the original sparse points. However, since there may exist some errors and noises in the expanded annotations, directly learning from them may mislead the training of the network. To this end, we further propose the consistency regularization strategy, where a base classifier and an expanded classifier are employed. Specifically, the base classifier is supervised by the original sparse annotations, while the expanded classifier aims to learn from the expanded annotations generated by the base classifier with the region-growing mechanism. The consistency regularization is thereby achieved by minimizing the discrepancy between the predictions from both the base and the expanded classifiers. We find such a simple regularization strategy is yet very useful to control the quality of the region-growing mechanism. Extensive experiments on two benchmark datasets demonstrate that the proposed CRGNet significantly outperforms the existing state-of-the-art methods. Codes and pre-trained models are available online (https://github.com/YonghaoXu/CRGNet).
A Study on the Development of Indicator for the Level Diagnosis of Big Data-Utilizing companies

In recent years, more data is being generated for the activation of the SNS, the spread of Smartphones and the development of IT technology. Therefore, it is to collect large amounts of data, analyze and ensure meaningful information has become important. The use of these data are formed on the global trend. Big data so-called, has attracted attention as a source of new business. Big Data can then give us the opportunity to be able to create a new customer and diversify the business. So, many companies have investment and effort for big data utilization. However, technology, infrastructure, human resources is different for each of the companies. Therefore, it is necessary to diagnose the level of big data utilization companies. In this study, through a literature review of existing, we derived the success factors for the big data utilization. And developed a diagnostic indicator that allows success factors derived, can be used to determine levels of big data utilization of the company. In addition, as a development of diagnostic indicators, were carried out case studies to diagnose company. Through this study, it will be an opportunity to be able to be reflected in the strategy of big data utilization company.
On quantum states over time

In 2017, D. Horsman, C. Heunen, M. Pusey, J. Barrett and R. Spekkens proved that there is no physically reasonable assignment that takes a quantum channel and an initial state and produces a joint state on the tensor product of the input and output spaces. The interpretation was that there is a clear distinction between space and time in the quantum setting that is not visible classically, where in the latter, one can freely use Bayes’ theorem to go between joint states and marginals with noisy channels. In this paper, we prove that there actually is such a physically reasonable assignment, bypassing the no-go result of Horsman et al., and we illustrate that this is achievable by restricting the domain of their assignment to a domain which represents the given data more faithfully.
Virtual photon emission in leptonic decays of charged pseudoscalar mesons

We study the radiative leptonic decays $P\to\ell\nu_\ell\,\ell^{\prime\,+}\ell^{\prime\,-}$, where $P$ is a pseudoscalar meson and $\ell$ and $\ell^\prime$ are charged leptons. In such decays the emitted photon is off-shell and, in addition to the"point-like"contribution in which the virtual photon is emitted either from the lepton or the meson treated as a point-like particle, four structure-dependent (SD) form factors contribute to the amplitude. We present a strategy for the extraction of the SD form factors and implement it in an exploratory lattice computation of the decay rates for the four channels of kaon decays ($\ell,\ell^\prime=e,\mu$). It is the SD form factors which describe the interaction between the virtual photon and the internal hadronic structure of the decaying meson, and in our procedure we separate the SD and point-like contributions to the amplitudes. We demonstrate that the form factors can be extracted with good precision and, in spite of the unphysical quark masses used in our simulation ($m_\pi\simeq 320\,$MeV and $m_K\simeq 530\,$MeV), the results for the decay rates are in reasonable semiquantitative agreement with experimental data (for the channels where these exist). Following this preparatory work, the emphasis of our future work will be on obtaining results at physical quark masses and on the control of the systematic uncertainties associated with discretisation and finite-volume errors.
Mr. Bayes and the classics: a suggested interpretation

The main hypothesis about Thomas Bayes’s intentions to write his famous {\it Essay} on probability is that he wanted to refute the arguments of David Hume against the reliability of the occurrence of miracles, published in 1748. In this paper we argue that it was not Bayes’s intention to rebut Hume but that his interest on the ``inverse problem'' came about as result of his study of the second edition of Abraham De Moivre’s book, {\it The Doctrine of Chances}, published in 1738. A possible communication of Bayes’s breakthrough might have annoyed De Moivre, leading to a response written for Bayes in the third edition of De Moivre’s book, published in 1756. Among other points, the response claims that De Moivre was the first to solve the mentioned inverse problem. Under this perspective Richard Price’s letter, written as preface to Bayes’s essay, has a new interpretation, appearing also as a defense of Bayes premiership on a satisfactory or proper solution.
Progressive Update Guided Interdependent Networks for Single Image Dehazing

Images with haze of different varieties often pose a significant challenge to dehazing. Therefore, guidance by estimates of haze parameters related to the variety would be beneficial, and their progressive update jointly with haze reduction will allow effective dehazing. To this end, we propose a multi-network dehazing framework containing novel interdependent dehazing and haze parameter updater networks that operate in a progressive manner. The haze parameters, transmission map and atmospheric light, are first estimated using dedicated convolutional networks that allow color-cast handling. The estimated parameters are then used to guide our dehazing module, where the estimates are progressively updated by novel convolutional networks. The updating takes place jointly with progressive dehazing using a network that invokes inter-step dependencies. The joint progressive updating and dehazing gradually modify the haze parameter values toward achieving effective dehazing. Through different studies, our dehazing framework is shown to be more effective than image-to-image mapping and predefined haze formation model based dehazing. The framework is also found capable of handling a wide variety of hazy conditions wtih different types and amounts of haze and color casts. Our dehazing framework is qualitatively and quantitatively found to outperform the state-of-the-art on synthetic and real-world hazy images of multiple datasets with varied haze conditions.
Locally Sparse Neural Networks for Tabular Biomedical Data

Tabular datasets with low-sample-size or many variables are prevalent in biomedicine. Practitioners in this domain prefer linear or tree-based models over neural networks since the latter are harder to interpret and tend to overfit when applied to tabular datasets. To address these neural networks' shortcomings, we propose an intrinsically interpretable network for heterogeneous biomedical data. We design a locally sparse neural network where the local sparsity is learned to identify the subset of most relevant features for each sample. This sample-specific sparsity is predicted via a \textit{gating} network, which is trained in tandem with the \textit{prediction} network. By forcing the model to select a subset of the most informative features for each sample, we reduce model overfitting in low-sample-size data and obtain an interpretable model. We demonstrate that our method outperforms state-of-the-art models when applied to synthetic or real-world biomedical datasets using extensive experiments. Furthermore, the proposed framework dramatically outperforms existing schemes when evaluating its interpretability capabilities. Finally, we demonstrate the applicability of our model to two important biomedical tasks: survival analysis and marker gene identification.
Super-Resolution of Real-World Faces

Real low-resolution (LR) face images contain degradations which are too varied and complex to be captured by known downsampling kernels and signal-independent noises. So, in order to successfully super-resolve real faces, a method needs to be robust to a wide range of noise, blur, compression artifacts etc. Some of the recent works attempt to model these degradations from a dataset of real images using a Generative Adversarial Network (GAN). They generate synthetically degraded LR images and use them with corresponding real high-resolution(HR) image to train a super-resolution (SR) network using a combination of a pixel-wise loss and an adversarial loss. In this paper, we propose a two module super-resolution network where the feature extractor module extracts robust features from the LR image, and the SR module generates an HR estimate using only these robust features. We train a degradation GAN to convert bicubically downsampled clean images to real degraded images, and interpolate between the obtained degraded LR image and its clean LR counterpart. This interpolated LR image is then used along with it's corresponding HR counterpart to train the super-resolution network from end to end. Entropy Regularized Wasserstein Divergence is used to force the encoded features learnt from the clean and degraded images to closely resemble those extracted from the interpolated image to ensure robustness.
A Survey on Safety-Critical Scenario Generation for Autonomous Driving – A Methodological Perspective

—Autonomous driving systems have witnessed a great development during the past years thanks to the advance in sensing and decision-making. One critical obstacle for their massive deployment in the real world is the evaluation of safety. Most existing driving systems are still trained and evaluated on naturalistic scenarios that account for the vast majority of daily life or heuristically-generated adversarial ones. However, the large population of cars requires an extremely low collision rate, indicating safety-critical scenarios collected in the real world would be rare. Thus, methods to artiﬁcially generate artiﬁcial scenarios becomes critical to manage the risk and reduce the cost. In this survey, we focus on the algorithms of safety-critical scenario generation. We ﬁrst provide a comprehensive taxonomy of existing algorithms by dividing them into three categories: data-driven generation, adversarial generation, and knowledge-based generation. Then, we discuss useful tools for scenario generation, including simulation platforms and packages. Finally, we extend our discussion to ﬁve main challenges of current works – ﬁdelity, efﬁciency, diversity, transferability, controllability – and the research opportunities lighted up by these challenges.
The impact of molecular data on the phylogenetic position of the putative oldest crown crocodilian and the age of the clade

The use of molecular data for living groups is vital for interpreting fossils, especially when morphology-only analyses retrieve problematic phylogenies for living forms. These topological discrepancies impact on the inferred phylogenetic position of many fossil taxa. In Crocodylia, morphology-based phylogenetic inferences differ fundamentally in placing Gavialis basal to all other living forms, whereas molecular data consistently unite it with crocodylids. The Cenomanian Portugalosuchus azenhae was recently described as the oldest crown crocodilian, with affinities to Gavialis, based on morphology-only analyses, thus representing a potentially important new molecular clock calibration. Here, we performed analyses incorporating DNA data into these morphological datasets, using scaffold and supermatrix (total evidence) approaches, in order to evaluate the position of basal crocodylians, including Portugalosuchus. Our analyses incorporating DNA data robustly recovered Portugalosuchus outside Crocodylia (as well as thoracosaurs, planocraniids and Borealosuchus spp.), questioning the status of Portugalosuchus as crown crocodilian and any future use as a node calibration in molecular clock studies. Finally, we discuss the impact of ambiguous fossil calibration and how, with the increasing size of phylogenomic datasets, the molecular scaffold might be an efficient (though imperfect) approximation of more rigorous but demanding supermatrix analyses.
A data-driven fault propagation analysis method

It is important to analyze the fault propagation mechanism of large scale automatic system which is comprised of many tightly connected subsystems.Most existed fault propagation analysis methods are bothered by "knowledge explosion" problem,which is an obstacle for the application of these methods.A knowledge-guided data driven fault propagation analysis method is proposed in this paper. Firstly,by using correlation analysis,it is proved that correlation between the designated component(DC)of faults occurred in input and output system can tell some fault propagation information.Secondly,the fault propagation relation matrix is determined by comparing the correlation between input DC and output DC of typical faulty data. The main criterion is to set the corresponding element in the fault propagation relation matrix to be 1 when the correlation coefficient between input DC and output DC is larger.Thirdly,according to the sampling data of input DC and output DC of the case when a common fault is occurred,a DC regress model is established to predict the fault imperil level for the output system.Finally,simulation study shows its efficiency for knowledge-guided data driven fault propagation analysis method.
Viscous froth model applied to the motion and topological transformations of two-dimensional bubbles in a channel: three-bubble case

The viscous froth model is used to predict rheological behaviour of a two-dimensional (2D) liquid-foam system. The model incorporates three physical phenomena: the viscous drag force, the pressure difference across foam films and the surface tension acting along them with curvature. In the so-called infinite staircase structure, the system does not undergo topological bubble neighbour-exchange transformations for any imposed driving back pressure. Bubbles then flow out of the channel of transport in the same order in which they entered it. By contrast, in a simple single bubble staircase or so-called lens system, topological transformations do occur for high enough imposed back pressures. The three-bubble case interpolates between the infinite staircase and simple staircase/lens. To determine at which driving pressures and at which velocities topological transformations might occur, and how the bubble areas influence their occurrence, steady-state propagating three-bubble solutions are obtained for a range of bubble sizes and imposed back pressures. As an imposed back pressure increases quasi-statically from equilibrium, complex dynamics are exhibited as the systems undergo either topological transformations, reach saddle-node bifurcation points, or asymptote to a geometrically invariant structure which ceases to change as the back pressure is further increased.
The European defence fund: a step towards a single market for defence?

ABSTRACT The European Defence Fund (EDF) is considered a game changer for European defence. The unprecedented co-funding of research and development expenses of cutting-edge defence systems is functional to strengthen the European Defence Technological and Industrial Base (EDTIB) and the EU’s Strategic Autonomy. This paper analyses the factors that enabled a partial shift from intergovernmental to supranational governance in the European defence market, applying the lens of the new intergovernmentalism theory. The cost of non-cooperation in defence and the Brexit impact will be taken into account to explain the observed shift. The paper highlights the role of the Commission in the integration of the European Defence Agency’s activities with those of the newly created DG for Defence Industry and Space (DG DEFIS). It also examines the extent to which diverging strategic industrial interests of Member States and of industrial players seek to prevent a deeper integration of the European defence market.