Miracle-dz/UniFAR-FTUs huggingface dataset:
On properties of weighted Hardy constant for means

For a given weighted mean $\mathscr{M}$ defined on a subinterval of $\mathbb{R}_+$ and a sequence of weights $\lambda=(\lambda_n)_{n=1}^\infty$ we define a Hardy constant $\mathscr H(\lambda)$ as the smallest extended real number such that $$ \sum_{n=1}^\infty \lambda_n \mathscr{M}\big((x_1,\dots,x_n),(\lambda_1,\dots,\lambda_n)\big) \le \mathscr H(\lambda) \cdot \sum_{n=1}^\infty \lambda_n x_n \text{ for all }x \in \ell^1(\lambda). $$ The aim of this note is to present a comprehensive study of the mapping $\mathscr H$. For example we prove that it is lower semicontinuous in the pointwise topology. Moreover we show that whenever $\mathscr{M}$ is a monotone and Jensen-concave mean which is continuous in its weights then $\mathscr H$ is monotone with respect to the partitioning of the vector. Finally we deliver some sufficient conditions for $\lambda$ to validate the equality $\mathscr H(\lambda)=\sup \mathscr H$ for every symmetric and monotone mean.
The impact of incorrect social information on collective wisdom in human groups

A major problem resulting from the massive use of social media is the potential spread of incorrect information. Yet, very few studies have investigated the impact of incorrect information on individual and collective decisions. We performed experiments in which participants had to estimate a series of quantities, before and after receiving social information. Unbeknownst to them, we controlled the degree of inaccuracy of the social information through ‘virtual influencers’, who provided some incorrect information. We find that a large proportion of individuals only partially follow the social information, thus resisting incorrect information. Moreover, incorrect information can help improve group performance more than correct information, when going against a human underestimation bias. We then design a computational model whose predictions are in good agreement with the empirical data, and sheds light on the mechanisms underlying our results. Besides these main findings, we demonstrate that the dispersion of estimates varies a lot between quantities, and must thus be considered when normalizing and aggregating estimates of quantities that are very different in nature. Overall, our results suggest that incorrect information does not necessarily impair the collective wisdom of groups, and can even be used to dampen the negative effects of known cognitive biases.
Strong biases in retrieved atmospheric composition caused by day–night chemical heterogeneities

Most planets currently amenable to transit spectroscopy are close enough to their host stars to exhibit a relatively strong day to night temperature gradient. For hot planets this leads to a chemical composition dichotomy between the two hemispheres. In the extreme case of ultra-hot Jupiters, some species, such as molecular hydrogen and water, are strongly dissociated on the day side while others, such as carbon monoxide, are not. However, most current retrieval algorithms rely on 1D forward models that are unable to reproduce this effect. We thus investigate how the 3D structure of the atmosphere biases the abundances retrieved using commonly used algorithms. We study the case of Wasp-121b as a prototypical ultra-hot Jupiter. We use the simulations of this planet performed with the Substellar and Planetary Atmospheric Radiation and Circulation global climate model and generate transmission spectra that fully account for the 3D structure of the atmosphere with Pytmosph3R. These spectra are then analyzed using the TauREx retrieval code. We find that the ultra-hot Jupiter transmission spectra exhibit muted H2O features that originate on the night side where the temperature, hence the scale-height, is smaller than on the day side. However, the spectral features of molecules present on the day side are boosted by both its high temperature and low mean molecular weight. As a result, the retrieved parameters are strongly biased compared to the ground truth. In particular the [CO]/[H2O] is overestimated by one to three orders of magnitude. This must be kept in mind when using the retrieval analysis to infer the C/O of a planet’s atmosphere. We also discuss whether indicators can allow us to infer the 3D structure of an observed atmosphere. Finally, we show that Wide Field Camera 3 fromHubbleSpace Telescope transmission data of Wasp-121b are compatible with the day–night thermal and compositional dichotomy predicted by models.
Systematic Phase Curve Study of Known Transiting Systems from Year One of the TESS Mission

We present a systematic phase curve analysis of known transiting systems observed by the Transiting Exoplanet Survey Satellite (TESS) during year one of the primary mission. Using theoretical predictions for the amplitude of the planetary longitudinal atmospheric brightness modulation, stellar ellipsoidal distortion and Doppler boosting, as well as brightness considerations to select targets with likely detectable signals, we applied a uniform data processing and light-curve modeling framework to fit the full-orbit phase curves of 22 transiting systems with planet-mass or brown dwarf companions, including previously published systems. Statistically significant secondary eclipse depths and/or atmospheric brightness modulation amplitudes were measured for HIP 65A, WASP-18, WASP-19, WASP-72, WASP-100, WASP-111, WASP-121, and WASP-122/KELT-14. For WASP-100b, we found marginal evidence that the brightest region of the atmosphere is shifted eastward away from the substellar point. We detected significant ellipsoidal distortion signals in the light curves of HIP 65A, TOI-503, WASP-18, and WASP-30, with HIP 65A, TOI-503 and WASP-18 also exhibiting Doppler boosting. The measured amplitudes of these signals agree with the predictions of theoretical models. Combining the optical secondary eclipse depths with previously published Spitzer 3.6 and 4.5 μm measurements, we derived dayside brightness temperatures and visible-light geometric albedos for a subset of the analyzed systems. We also calculated updated transit ephemerides combining the transit timings from the TESS light curves with previous literature values.
Gimme Signals: Discriminative signal encoding for multimodal activity recognition

We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture. Our focus was to find an approach that generalizes well across different sensor modalities without specific adaptions while still achieving good results. We apply our method to 4 action recognition datasets containing skeleton sequences, inertial and motion capturing measurements as well as Wi-Fi fingerprints that range up to 120 action classes. Our method defines the current best CNN-based approach on the NTU RGB+D 120 dataset, lifts the state of the art on the ARIL Wi-Fi dataset by +6.8%, improves the UTD-MHAD inertial baseline by +14.4%, the UTD-MHAD skeleton baseline by +0.5% and achieves 96.1% on the Simitate motion capturing data (80/20 split). We further demonstrate experiments on both, modality fusion on a signal level and signal reduction to prevent the representation from overloading.
No missing photons for reionization: moderate ionizing photon escape fractions from the FIRE-2 simulations

We present the escape fraction of hydrogen ionizing photons (fesc) from a sample of 34 high-resolution cosmological zoom-in simulations of galaxies at z ≥ 5 in the Feedback in Realistic Environments project, post-processed with a Monte Carlo radiative transfer code for ionizing radiation. Our sample consists of 8500 haloes in Mvir ∼ 108–$10^{12}\, M_{\odot }$ (M* ∼ 104–$10^{10}\, M_{\odot }$) at z = 5–12. We find the sample average 〈fesc〉increases with halo mass for Mvir ∼ 108–$10^{9.5}\, M_{\odot }$, becomes nearly constant for 109.5–$10^{11}\, M_{\odot }$, and decreases at ${\gtrsim}10^{11}\, M_{\odot }$. Equivalently, 〈fesc〉 increases with stellar mass up to $M_{\ast }\sim 10^8\, M_{\odot }$ and decreases at higher masses. Even applying single-star stellar population synthesis models, we find a moderate 〈fesc〉 ∼ 0.2 for galaxies at $M_{\ast }\sim 10^8\, M_{\odot }$. Nearly half of the escaped ionizing photons come from stars 1–3 Myr old and the rest from stars 3–10 Myr old. Binaries only have a modest effect, boosting 〈fesc〉 by ∼25–35 per cent and the number of escaped photons by 60–80 per cent. Most leaked ionizing photons are from vigorously star-forming regions that usually contain a feedback-driven kpc-scale superbubble surrounded by a dense shell. The shell is forming stars while accelerated, so new stars formed earlier in the shell are already inside the shell. Young stars in the bubble and near the edge of the shell can fully ionize some low-column-density paths pre-cleared by feedback, allowing a large fraction of their ionizing photons to escape. The decrease of 〈fesc〉 at the high-mass end is due to dust attenuation, while at the low-mass end, 〈fesc〉 decreases owing to inefficient star formation and hence feedback. At fixed mass, 〈fesc〉 tends to increase with redshift. Although the absolute 〈fesc〉does not fully converge with resolution in our simulations, the mass- and redshift-dependence of 〈fesc〉 is likely robust. Our simulations produce sufficient ionizing photons for cosmic reionization.
Efficient Rule Learning with Template Saturation for Knowledge Graph Completion

The logic-based methods that learn ﬁrst-order rules from knowledge graphs (KGs) for knowledge graph completion (KGC) task are desirable in that the learnt models are inductive, interpretable and transferable. The challenge in such rule learners is that the expressive rules are often buried in vast rule space, and the procedure of identifying expressive rules by measuring rule quality is costly to execute. Therefore, optimizations on rule generation and evaluation are in need. In this work, we propose a novel bottom-up probabilistic rule learner that features: 1. ) a two-stage procedure for optimized rule generation where the system ﬁrst generalizes paths sampled from a KG into template rules that contain no constants until a certain degree of template saturation is achieved and then specializes template rules into instantiated rules that contain constants; 2. ) a grouping technique for optimized rule evaluation where structurally similar instantiated rules derived from the same template rules are put into the same groups and evaluated collectively over the ground-ings of the deriving template rules. Through extensive experiments over large benchmark datasets on KGC task, our al-gorithm demonstrates consistent and substantial performance improvements over all of the state-of-the-art baselines.
Asymmetric spatial distribution of subsolar metallicity stars in the Milky Way nuclear star cluster

We present stellar metallicity measurements of more than 600 late-type stars in the central 10 pc of the Galactic Centre. Together with our previously published KMOS data, this data set allows us to investigate, for the first time, spatial variations of the nuclear star cluster’s metallicity distribution. Using the integral-field spectrograph KMOS (VLT), we observed almost half of the area enclosed by the nuclear star cluster’s effective radius. We extract spectra at medium spectral resolution and apply full spectral fitting utilizing the PHOENIX library of synthetic stellar spectra. The stellar metallicities range from [M/H] = −1.25 dex to [M/H] > +0.3 dex, with most of the stars having supersolar metallicity. We are able to measure an anisotropy of the stellar metallicity distribution. In the Galactic north, the portion of subsolar metallicity stars with [M/H] < 0.0 dex is more than twice as high as in the Galactic south. One possible explanation for different fractions of subsolar metallicity stars in different parts of the cluster is a recent merger event. We propose to test this hypothesis with high-resolution spectroscopy and by combining the metallicity information with kinematic data.
Perceptual Image Super-Resolution with Progressive Adversarial Network

Single Image Super-Resolution (SISR) aims to improve resolution of small-size low-quality image from a single one. With popularity of consumer electronics in our daily life, this topic has become more and more attractive. In this paper, we argue that the curse of dimensionality is the underlying reason of limiting the performance of state-of-the-art algorithms. To address this issue, we propose Progressive Adversarial Network (PAN) that is capable of coping with this difficulty for domain-specific image super-resolution. The key principle of PAN is that we do not apply any distance-based reconstruction errors as the loss to be optimized, thus free from the restriction of the curse of dimensionality. To maintain faithful reconstruction precision, we resort to U-Net and progressive growing of neural architecture. The low-level features in encoder can be transferred into decoder to enhance textural details with U-Net. Progressive growing enhances image resolution gradually, thereby preserving precision of recovered image. Moreover, to obtain high-fidelity outputs, we leverage the framework of the powerful StyleGAN to perform adversarial learning. Without the curse of dimensionality, our model can super-resolve large-size images with remarkable photo-realistic details and few distortions. Extensive experiments demonstrate the superiority of our algorithm over state-of-the-arts both quantitatively and qualitatively.
An Evaluation of Change Point Detection Algorithms

Change point detection is an important part of time series analysis, as the presence of a change point indicates an abrupt and significant change in the data generating process. While many algorithms for change point detection exist, little attention has been paid to evaluating their performance on real-world time series. Algorithms are typically evaluated on simulated data and a small number of commonly-used series with unreliable ground truth. Clearly this does not provide sufficient insight into the comparative performance of these algorithms. Therefore, instead of developing yet another change point detection method, we consider it vastly more important to properly evaluate existing algorithms on real-world data. To achieve this, we present the first data set specifically designed for the evaluation of change point detection algorithms, consisting of 37 time series from various domains. Each time series was annotated by five expert human annotators to provide ground truth on the presence and location of change points. We analyze the consistency of the human annotators, and describe evaluation metrics that can be used to measure algorithm performance in the presence of multiple ground truth annotations. Subsequently, we present a benchmark study where 14 existing algorithms are evaluated on each of the time series in the data set. This study shows that binary segmentation (Scott and Knott, 1974) and Bayesian online change point detection (Adams and MacKay, 2007) are among the best performing methods. Our aim is that this data set will serve as a proving ground in the development of novel change point detection algorithms.
On the Role of Software Architecture in DevOps Transformation: An Industrial Case Study

Development and Operations (DevOps), a particular type of Continuous Software Engineering, has become a popular Software System Engineering paradigm. Software architecture is critical in succeeding with DevOps. However, there is little evidence-based knowledge of how software systems are architected in the industry to enable and support DevOps. Since architectural decisions, along with their rationales and implications, are very important in the architecting process, we performed an industrial case study that has empirically identified and synthesized the key architectural decisions considered essential to DevOps transformation by two software development teams. Our study also reveals that apart from the chosen architecture style, DevOps works best with modular architectures. In addition, we found that the performance of the studied teams can improve in DevOps if operations specialists are added to the teams to perform the operations tasks that require advanced expertise. Finally, investment in testing is inevitable for the teams if they want to release software changes faster.
Existence and uniqueness of the Kronecker covariance MLE

In matrix-valued datasets the sampled matrices often exhibit correlations among both their rows and their columns. A useful and parsimonious model of such dependence is the matrix normal model, in which the covariances among the elements of a random matrix are parameterized in terms of the Kronecker product of two covariance matrices, one representing row covariances and one representing column covariance. An appealing feature of such a matrix normal model is that the Kronecker covariance structure allows for standard likelihood inference even when only a very small number of data matrices is available. For instance, in some cases a likelihood ratio test of dependence may be performed with a sample size of one. However, more generally the sample size required to ensure boundedness of the matrix normal likelihood or the existence of a unique maximizer depends in a complicated way on the matrix dimensions. This motivates the study of how large a sample size is needed to ensure that maximum likelihood estimators exist, and exist uniquely with probability one. Our main result gives precise sample size thresholds in the paradigm where the number of rows and the number of columns of the data matrices differ by at most a factor of two. Our proof uses invariance properties that allow us to consider data matrices in canonical form, as obtained from the Kronecker canonical form for matrix pencils.
A Link between Ram Pressure Stripping and Active Galactic Nuclei

The dense environment of a galaxy cluster can radically transform the content of in-falling galaxies. Recent observations have found a significant population of active galactic nuclei (AGN) within “jellyfish galaxies,” galaxies with trailing tails of gas and stars that indicate significant ram pressure stripping. The relationship between AGN and ram pressure stripping is not well understood. In this Letter, we investigate the connection between AGN activity and ram pressure in a fully cosmological setting for the first time using the RomulusC simulation, one of the highest resolution simulations of a galaxy cluster to date. For lower mass galaxies (with stellar masses M* ≲ 109.5 M⊙) both star formation and black hole accretion are suppressed by ram pressure before they reach pericenter, whereas for more massive galaxies accretion onto the black hole is enhanced during pericentric passage. Our analysis also indicates that as long as the galaxy retains gas, AGN with higher Eddington ratios are more likely to be found in galaxies experiencing higher ram pressure. We conclude that prior to quenching star formation, ram pressure triggers enhanced accretion onto the black hole, which in our model then produces heating and outflows due to AGN feedback. These simulations suggest that AGN feedback may in turn serve to aid in the quenching of star formation in tandem with ram pressure.
Efficient acceleration of cylindrical jets: effects of radiative cooling and tangled magnetic field

Diverging supersonic flows are accelerating, as in the case of a de Laval nozzle, and the same concept has been applied for acceleration of magnetohydrodynamic flows in the universe. Here, we study the dynamics of "non-diverging" cylindrical supersonic flows and show that they can be accelerated by effects of radiative cooling and the tangled magnetic field. In addition to radiative cooling of the jet materials (cooling effect), conversion of the ordered magnetic field into the turbulent one (conversion effect) and dissipation of the turbulent magnetic field (dissipation effect) are formulated according to our study on pulsar wind nebulae. Although each of the cooling and conversion effects is an ineffective acceleration process, the terminal velocity of magnetized cylindrical jets attains about half of the maximum possible value when the cooling, conversion and dissipation effects work simultaneously. The radiation efficiency is also about half of the total luminosity of the jet in the case of maximal acceleration. The concept for flow acceleration by the non-ideal MHD effects may be useful for studying relativistic jets in active galactic nuclei, in which the region near the jet axis is expected to be cylindrical and kink unstable.
A Non-Standard Finite Difference Scheme for MHD Boundary Layer Fluid Flow

This paper deals with a non-standard finite difference scheme defined on a quasi-uniform mesh for approximate solutions of the MHD boundary layer flow of an incompressible fluid past a flat plate for a wide range of the magnetic parameter. The obtained numerical results are compared with those available in the literature. We show how to improve the obtained numerical results via a mesh refinement and a Richardson extrapolation.
Communication-Efficient Distributed Deep Learning: A Comprehensive Survey

Distributed deep learning becomes very common to reduce the overall training time by exploiting multiple computing devices (e.g., GPUs/TPUs) as the size of deep models and data sets increases. However, data communication between computing devices could be a potential bottleneck to limit the system scalability. How to address the communication problem in distributed deep learning is becoming a hot research topic recently. In this paper, we provide a comprehensive survey of the communication-efficient distributed training algorithms in both system-level and algorithmic-level optimizations. In the system-level, we demystify the system design and implementation to reduce the communication cost. In algorithmic-level, we compare different algorithms with theoretical convergence bounds and communication complexity. Specifically, we first propose the taxonomy of data-parallel distributed training algorithms, which contains four main dimensions: communication synchronization, system architectures, compression techniques, and parallelism of communication and computing. Then we discuss the studies in addressing the problems of the four dimensions to compare the communication cost. We further compare the convergence rates of different algorithms, which enable us to know how fast the algorithms can converge to the solution in terms of iterations. According to the system-level communication cost analysis and theoretical convergence speed comparison, we provide the readers to understand what algorithms are more efficient under specific distributed environments and extrapolate potential directions for further optimizations.