Miracle-dz/UniFAR-FTUs huggingface dataset:
Text-to-Text Pre-Training for Data-to-Text Tasks

We study the pre-train + fine-tune strategy for data-to-text tasks. Our experiments indicate that text-to-text pre-training in the form of T5 (Raffel et al., 2019), enables simple, end-to-end transformer based models to outperform pipelined neural architectures tailored for data-to-text generation, as well as alternatives such as BERT and GPT-2. Importantly, T5 pre-training leads to better generalization, as evidenced by large improvements on out-ofdomain test sets. We hope our work serves as a useful baseline for future research, as transfer learning becomes ever more prevalent for data-to-text tasks.
Orbital and spectral characterization of the benchmark T-type brown dwarf HD 19467B

Context. Detecting and characterizing substellar companions for which the luminosity, mass, and age can be determined independently is of utter importance to test and calibrate the evolutionary models due to uncertainties in their formation mechanisms. HD 19467 is a bright and nearby star hosting a cool brown dwarf companion detected with RV and imaging, making it a valuable object for such studies. Aims. We aim to further characterize the orbital, spectral, and physical properties of the HD 19467 system. Methods. We present new high-contrast imaging data with the SPHERE and NaCo instruments. We also analyze archival data from HARPS, NaCo, HIRES, UVES, and ASAS. We also use proper motion data of the star from Hipparcos and Gaia. Results. We refine the properties of the host star and derive an age of 8.0$^{+2.0}_{-1.0}$ Gyr based on isochrones, gyrochronology, and chemical and kinematic arguments. This estimate is slightly younger than previous estimates of ~9-11 Gyr. No orbital curvature is seen in the current imaging, RV, and astrometric data. From a joint fit of the data, we refine the orbital parameters for HD 19467B: period 398$^{+95}_{-93}$ yr, inclination 129.8$^{+8.1}_{-5.1}$ deg, eccentricity 0.56$\pm$0.09, longitude of the ascending node 134.8$\pm$4.5 deg, and argument of the periastron 64.2$^{+5.5}_{-6.3}$ deg. We assess a dynamical mass of 74$^{+12}_{-9}$ MJ. The fit with atmospheric models of the spectrophotometric data of HD 19467B indicates an atmosphere without clouds or with very thin clouds, an effective temperature of 1042$^{+77}_{-71}$ K, and a large surface gravity of 5.34$^{+0.08}_{-0.09}$ dex. The comparison to model predictions of the bolometric luminosity and dynamical mass of HD 19467B, assuming our system age estimate, indicates a better agreement with the Burrows et al. models; whereas the other evolutionary models used tend to underestimate its cooling rate.
Instance-Aware Image Colorization

Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly. Although these learning-based methods have shown impressive performance, they usually fail on the input images that contain multiple objects. The leading cause is that existing models perform learning and colorization on the entire image. In the absence of a clear figure-ground separation, these models cannot effectively locate and learn meaningful object-level semantics. In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images and uses an instance colorization network to extract object-level features. We use a similar network to extract the full-image features and apply a fusion module to full object-level and image-level features to predict the final colors. Both colorization networks and fusion modules are learned from a large-scale dataset. Experimental results show that our work outperforms existing methods on different quality metrics and achieves state-of-the-art performance on image colorization.
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images

Rain severely hampers the visibility of scene objects when images are captured through glass in heavily rainy days. We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space. We for the first time consider that, the overall visibility of objects is determined by the mixture of rain (MOR). However, existing solutions and established datasets lack full consideration of the MOR. In this work, we first formulate a new rain imaging model; by then, we enrich the popular RainCityscapes by considering raindrops, named RainCityscapes++. Furthermore, we propose a multi-branch attention generative adversarial network (termed an MBA-RainGAN) to fully remove the MOR. The experiment shows clear visual and numerical improvements of our approach over the state-of-the-arts on RainCityscapes++. The code and dataset will be available.
Escaping the Densification Plateau in Cellular Networks Through mmWave Beamforming

We study how dense multi-antenna millimeter wave (mmWave) cellular network performance scales in terms of the base station (BS) spatial density <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>, by studying the signal-to-interference-plus-noise ratio (SINR) and the area spectral efficiency (ASE). If the number of antennas at each BS scales at least linearly with <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>, which increases the number of possible beam configurations and their main-lobe gain, and decreases their side-lobe gain, we prove that the SINR approaches a finite random variable that is independent of <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> and the ASE scales at least linearly with <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>. In contrast, if the number of antennas scales sub-linearly with <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>, then the SINR decays to zero and the ASE saturates to a constant. Thus, by moving to higher carrier frequencies with successively smaller antennas, and exploiting the correspondingly increased directionality, cellular operators can in principle avoid the densification plateau (or collapse) in cellular networks and instead continue to harvest linear sum throughput gains through BS densification.
Toxic Pseudo-nitzschia multistriata (Bacillariophyceae) from the Gulf of Naples: morphology, toxin analysis and phylogenetic relationships with other Pseudo-nitzschia species

The genus Pseudo-nitzschia includes several species capable of producing domoic acid, the causative agent of Amnesic Shellfish Poisoning. Some of these species have been recorded frequently in the Gulf of Naples. For one of the species, P. multistriata, which has been recurrently found in our sampling area since 1995, this is the first report for European waters. Here we provide further details on the fine structure of this species. Pseudo-nitzschia multistriata was the only one found to produce domoic acid among all the Pseudo-nitzschia species from the Gulf of Naples, and this finding raises the number of potentially toxic species in this genus to nine. Phylogenetic relationships among several Pseudo-nitzschia species were assessed using the hypervariable domains (D1–D3) of the large subunit (LSU) rDNA. The match between the phylogeny obtained and important taxonomic characters used in this genus are discussed. Results show that P. multistriata clusters with wider species lacking a central larger interspace in the raphe. Close genetic relationships were determined between P. fraudulenta and P. subfraudulenta, and between P. pungens and P. multiseries. Genetic differences among these pairs of species are comparable to those among isolates of P. pseudodelicatissima from the Gulf of Naples, indicating high intraspecific genetic diversity of Pseudo-nitzschia species in the relatively conserved LSU region. This could explain the problematic results obtained when testing a match between species-specific Pseudo-nitzschia LSU probes and our sequences.
Catalogue of central stars of planetary nebulae: Expanded edition

Planetary nebulae represent a potential late stage of stellar evolution, however the central stars (CSPNe) are relatively faint and therefore pertinent information is merely available for <20% of the Galactic sample. Consequently, the literature was surveyed to construct a new catalogue of 620 CSPNe featuring important spectral classifications and information. The catalogue supersedes the existing iteration by 25%, and includes physical parameters such as luminosity, surface gravity, temperature, magnitude estimates, and references for published spectra. The marked statistical improvement enabled the following pertinent conclusions to be determined: the H-rich/H-poor ratio is 2:1, there is a deficiency of CSPNe with types [WC 5-6], and nearly 80% of binary central stars belong to the H-rich group. The last finding suggests that evolutionary scenarios leading to the formation of binary central stars interfere with the conditions required for the formation of H-poor CSPN. Approximately 50% of the sample with derived values of log L, log Teff, and log g, exhibit masses and ages consistent with single stellar evolutionary models. The implication is that single stars are indeed able to form planetary nebulae. Moreover, it is shown that H-poor CSPNe are formed by higher mass progenitors. The catalogue is available through the Vizier database.
DNA scission inhibition, antioxidant, and antiproliferative activities of water chestnut (Trapa natans) extracted in different solvents

This investigation deals with the study of polyphenolic, antioxidant, and antiproliferative activities of water chestnut extracted in different solvents. The antioxidant activities of the extract in different solvents were the maximum in methanol, followed by ethanol and aqueous solution and were found to be 39.8%, 34.1% and 32.6% from DPPH assay, and 45.64%, 35.08% and 30.52% from lipid peroxidation assay, respectively, whereas the reducing power inhibition assay showed the maximum antioxidant potential in the aqueous extract of the sample, followed by methanolic and ethanolic extracts. The polyphenolic content of the water chestnut was found to be more in methanol than in aqueous and ethanolic extracts. The water chestnut extract also showed the antiproliferative potential when tested against the human colon cancer cell line (Colo-205), human ductal breast epithelial tumor cell line (T47D), and human breast adenocarcinoma cell line (MCF7). It also showed some protection from DNA damage induced by hydroxyl radicals in calf thymus DNA.
Single Image Super-Resolution Via Residual Neuron Attention Networks

Deep Convolutional Neural Networks (DCNNs) have achieved impressive performance in Single Image Super-Resolution (SISR). To further improve the performance, existing CNN-based methods generally focus on designing deeper architecture of the network. However, we argue blindly increasing network’s depth is not the most sensible way. In this paper, we propose a novel end-to-end Residual Neuron Attention Networks (RNAN) for more efficient and effective SISR. Structurally, our RNAN is a sequential integration of the well-designed Global Context-enhanced Residual Groups (GCRGs), which extracts super-resolved features from coarse to fine. Our GCRG is designed with two novelties. Firstly, the Residual Neuron Attention (RNA) mechanism is proposed in each block of GCRG to reveal the relevance of neurons for better feature representation. Furthermore, the Global Context (GC) block is embedded into RNAN at the end of each GCRG for effectively modeling the global contextual information. Experiments results demonstrate that our RNAN achieves the comparable results with state-of-the-art methods in terms of both quantitative metrics and visual quality, however, with simplified network architecture.
Brane-worlds and the Calabi–Yau complex structure moduli

In this paper we extend previous work on the relation between the complex structure moduli of the underlying Calabi–Yau manifold in five dimensional supergravity with the time evolution of an embedded 3-brane. We numerically solve the fields’ equations for such a construction and focus on dust and radiation filled branes; with the possible application of modeling the Universe as a brane-world. It is shown that in both cases the time evolution of the moduli causally connects to the expansion of the brane-world. We also find that in most cases considered there is an early short period of rapid accelerative expansion, indicating an inflationary epoch. We report on these results; leaving analysis of the underlying causes for future work.
The velocity structure of Cygnus OB2

The kinematic structure of the Cygnus OB2 association is investigated. No evidence of expansion or contraction is found at any scale within the region. Stars that are within ∼0.5 pc of one another are found to have more similar velocities than would be expected by random chance, and so it is concluded that velocity substructure exists on these scales. At larger scales velocity substructure is not found. We suggest that bound substructures exist on scales of ∼0.5 pc, despite the region as a whole being unbound. We further suggest that any velocity substructure that existed on scales > 0.5 pc has been erased. The results of this study are then compared to those of other kinematic studies of Cygnus OB2.
Coswara - A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis

The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respiratory sounds, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19.
Dynamical Evolution of Cosmic Supermassive Binary Black Holes and Their Gravitational-wave Radiation

We investigate the evolution of supermassive binary black holes (BBHs) in galaxies with realistic property distributions and the gravitational-wave (GW) radiation from the cosmic population of these BBHs. We incorporate a comprehensive treatment of the dynamical interactions of the BBHs with their environments by including the effects of galaxy triaxial shapes and inner stellar distributions, and generate a large number of BBH evolution tracks. By combining these BBH evolution tracks, galaxy mass functions, galaxy merger rates, and supermassive black hole–host galaxy relations into our model, we obtain statistical distributions of surviving BBHs, BBH coalescence rates, the strength of their GW radiation, and the stochastic GW background (GWB) contributed by the cosmic BBH population. About ∼1%–3% (or ) of supermassive BHs at nearby galactic centers are expected to be binaries with mass ratio (or ). The characteristic strain amplitude of the GWB at frequency is estimated to be , and the upper bound of its results obtained with the different BH–host galaxy relations can be up to , which await testing by future experiments (e.g., the Square Kilometer Array, FAST, Next-Generation Very Large Array). The turnover frequency of the GWB spectrum is at . The uncertainties on the above estimates and prospects for detecting individual sources are also discussed. The application of the cosmic BBH population to the Laser Interferometer Space Antenna (LISA) band provides a lower limit to the detection rate of BBHs by LISA, .
The Semi-Hierarchical Dirichlet Process and Its Application to Clustering Homogeneous Distributions

Assessing homogeneity of distributions is an old problem that has received considerable attention, especially in the nonparametric Bayesian literature. To this effect, we propose the semi-hierarchical Dirichlet process, a novel hierarchical prior that extends the hierarchical Dirichlet process of Teh et al. (2006) and that avoids the degeneracy issues of nested processes recently described by Camerlenghi et al. (2019a). We go beyond the simple yes/no answer to the homogeneity question and embed the proposed prior in a random partition model; this procedure allows us to give a more comprehensive response to the above question and in fact find groups of populations that are internally homogeneous when I greater or equal than 2 such populations are considered. We study theoretical properties of the semi-hierarchical Dirichlet process and of the Bayes factor for the homogeneity test when I = 2. Extensive simulation studies and applications to educational data are also discussed.
Graphical continuous Lyapunov models

The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process. This parametrization can be interpreted as a new graphical model class, and we show how the model class behaves under marginalization and introduce a method for structure learning via $\ell_1$-penalized loss minimization. Our proposed method is demonstrated to outperform alternative structure learning algorithms in a simulation study, and we illustrate its application for protein phosphorylation network reconstruction.
Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging

The ability to take into account the characteristics - also called features - of observations is essential in Natural Language Processing (NLP) problems. Hidden Markov Chain (HMC) model associated with classic Forward-Backward probabilities cannot handle arbitrary features like prefixes or suffixes of any size, except with an independence condition. For twenty years, this default has encouraged the development of other sequential models, starting with the Maximum Entropy Markov Model (MEMM), which elegantly integrates arbitrary features. More generally, it led to neglect HMC for NLP. In this paper, we show that the problem is not due to HMC itself, but to the way its restoration algorithms are computed. We present a new way of computing HMC based restorations using original Entropic Forward and Entropic Backward (EFB) probabilities. Our method allows taking into account features in the HMC framework in the same way as in the MEMM framework. We illustrate the efficiency of HMC using EFB in Part-Of-Speech Tagging, showing its superiority over MEMM based restoration. We also specify, as a perspective, how HMCs with EFB might appear as an alternative to Recurrent Neural Networks to treat sequential data with a deep architecture.
Information scrambling at finite temperature in local quantum systems

Quantum information scrambling provides diagnostics about how information spreads when an isolated quantum system evolves in time. Here, the authors study the temperature dependence of scrambling in locally gapped systems. Using tensor networks, they numerically evaluate out-of-time-ordered commutators in a large, strongly interacting spin chain as a function of temperature. They find that scrambling crucially depends on the ordering of operators around the thermal circle, exposing subtleties in information propagation at finite temperature. For a regulated operator ordering, the butterfly velocity decreases with lowering the temperature, as seen in the figure. The low temperature behavior is derived from a field-theoretical analysis.
Response Theory: A Trajectory-Based Approach

We collect recent results on deriving useful response relations also for non-equilibrium systems. The approach is based on dynamical ensembles, determined by an action on trajectory space. (Anti)Symmetry under time-reversal separates two complementary contributions in the response, one entropic the other frenetic. Under time-reversal invariance of the unperturbed reference process, only the entropic term is present in the response, giving the standard fluctuation–dissipation relations in equilibrium. For non-equilibrium reference ensembles, the frenetic term contributes essentially and is responsible for new phenomena. We discuss modifications in the Sutherland-Einstein relation, the occurence of negative differential mobilities and the saturation of response. We also indicate how the Einstein relation between noise and friction gets violated for probes coupled to a non-equilibrium environment. We end with some discussion on the situation for quantum phenomena, but the bulk of the text concerns classical mesoscopic (open) systems. The choice of many simple examples is trying to make the notes pedagogical, to introduce an important area of research in non-equilibrium statistical mechanics.
Infinite-dimensional gradient-based descent for alpha-divergence minimisation

This paper introduces the $(\alpha, \Gamma)$-descent, an iterative algorithm which operates on measures and performs $\alpha$-divergence minimisation in a Bayesian framework. This gradient-based procedure extends the commonly-used variational approximation by adding a prior on the variational parameters in the form of a measure. We prove that for a rich family of functions $\Gamma$, this algorithm leads at each step to a systematic decrease in the $\alpha$-divergence. Our framework recovers the Entropic Mirror Descent (MD) algorithm with improved $O(1/N)$ convergence results and provides an alternative to the Entropic MD that we call the Power descent and for which we prove convergence to an optimum. Moreover, the $(\alpha, \Gamma)$-descent allows to optimise the mixture weights of any given mixture model without any information on the underlying distribution of the variational parameters. This renders our method compatible with many choices of parameters updates and applicable to a wide range of Machine Learning tasks. We demonstrate empirically on both toy and real-world examples the benefit of using the Power descent and going beyond the Entropic MD framework, which fails as the dimension grows.