Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive evolution of hybrid bacteria by horizontal gene transfer

Significance In a parallel evolution experiment, we probe lateral gene transfer between two Bacillus subtilis lineages close to the species boundary. We show that laboratory evolution by horizontal gene transfer can rapidly generate hybrid organisms with broad genomic and functional alterations. By combining genomics, transcriptomics, fitness assays, and statistical modeling, we map the selective effects underlying gene transfer. We show that transfer takes place under genome-wide positive and negative selection, generating a net fitness increase in hybrids. The evolutionary dynamics efficiently navigates this fitness landscape, finding viable paths with increasing fraction of transferred genes. Horizontal gene transfer (HGT) is an important factor in bacterial evolution that can act across species boundaries. Yet, we know little about rate and genomic targets of cross-lineage gene transfer and about its effects on the recipient organism's physiology and fitness. Here, we address these questions in a parallel evolution experiment with two Bacillus subtilis lineages of 7% sequence divergence. We observe rapid evolution of hybrid organisms: gene transfer swaps ∼12% of the core genome in just 200 generations, and 60% of core genes are replaced in at least one population. By genomics, transcriptomics, fitness assays, and statistical modeling, we show that transfer generates adaptive evolution and functional alterations in hybrids. Specifically, our experiments reveal a strong, repeatable fitness increase of evolved populations in the stationary growth phase. By genomic analysis of the transfer statistics across replicate populations, we infer that selection on HGT has a broad genetic basis: 40% of the observed transfers are adaptive. At the level of functional gene networks, we find signatures of negative, positive, and epistatic selection, consistent with hybrid incompatibilities and adaptive evolution of network functions. Our results suggest that gene transfer navigates a complex cross-lineage fitness landscape, bridging epistatic barriers along multiple high-fitness paths.
CHAOS V: Recombination Line Carbon Abundances in M 101

The CHemical Abundances Of Spirals (CHAOS) project is building a large database of Large Binocular Telescope (LBT) H ii region spectra in nearby spiral galaxies to use direct abundances to better determine the dispersion in metallicity as a function of galactic radius. Here, we present CHAOS LBT observations of C ii λ4267 emission detected in 10 H ii regions in M 101, and using a new photoionization model-based ionization correction factor, we convert these measurements into total carbon abundances. A comparison with M 101 C ii recombination line observations from the literature shows excellent agreement, and we measure a relatively steep gradient in log(C/H) of −0.37 ± 0.06 dex . The C/N observations are consistent with a constant value of log(C/N) = 0.84 with a dispersion of only 0.09 dex, which, given the different nucleosynthetic sources of C and N, is challenging to understand. We also note that when plotting N/O versus O/H, all of the H ii regions with detections of C ii λ4267 present N/O abundances at the minimum of the scatter in N/O at a given value of O/H. If the high surface brightness necessary for the detection of the faint recombination lines is interpreted as an indicator of H ii region youth, then this may point to a lack of nitrogen pollution in the youngest H ii regions. In the future, we anticipate that the CHAOS project will significantly increase the total number of C ii λ4267 measurements in extragalactic H ii regions.
Tensor-network-based machine learning of non-Markovian quantum processes

We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning algorithm. We do this by representing the process as a matrix product operator (MPO) and train it with a database of local input states at different times and the corresponding time-nonlocal output state. In particular, we analyze a qubit coupled to an environment and predict output state of the system at different time, as well as reconstruct the full system process. We show how the bond dimension of the MPO, a measure of non-Markovianity, depends on the properties of the system, of the environment and of their interaction. Hence, this study opens the way to a possible experimental investigation into the process tensor and its properties.
Modelling Strong Control Measures for Epidemic Propagation With Networks—A COVID-19 Case Study

We show that precise knowledge of epidemic transmission parameters is not required to build an informative model of the spread of disease. We propose a detailed model of the topology of the contact network under various external control regimes and demonstrate that this is sufficient to capture the salient dynamical characteristics and to inform decisions. Contact between individuals in the community is characterised by a contact graph, the structure of that contact graph is selected to mimic community control measures. Our model of city-level transmission of an infectious agent (SEIR model) characterises spread via a (a) scale-free contact network (no control); (b) a random graph (elimination of mass gatherings); and (c) small world lattice (partial to full lockdown—“social” distancing). This model exhibits good qualitative agreement between simulation and data from the 2020 pandemic spread of a novel coronavirus. Estimates of the relevant rate parameters of the SEIR model are obtained and we demonstrate the robustness of our model predictions under uncertainty of those estimates. The social context and utility of this work is identified, contributing to a highly effective pandemic response in Western Australia.
Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms

Synthetic Aperture Radar (SAR) images contain a huge amount of information, however, the number of practical use-cases is limited due to the presence of speckle noise in them. In recent years, deep learning based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network based systems. With this paper, we propose a standard way of generating synthetic data for the training of speckle reduction algorithms and demonstrate a use-case to advance research in this domain.
Looking at Blazar Light-curve Periodicities with Gaussian Processes

Temporal analysis of blazar flux is a powerful tool to draw inferences about the emission processes and physics of these sources. In the most general case, the available light curves are irregularly sampled and influenced by gaps, and in addition are also affected by correlated noise, making their analysis complicated. Gaussian processes may offer a viable tool to assess the statistical significance of proposed periods in light curves characterized by any sampling and noise pattern. We infer the significance of the periods proposed in the literature for two well known blazars with multiple claims of possible year-long periodicity: PG 1553 + 113 and PKS 2155–304, in the high-energy and optical bands. Adding a periodic component to the modeling gives a better statistical description of the analyzed light curves. The improvement is rather solid for PG 1553 + 113, both at high energies and in the optical, while for PKS 2155–304 at high energies the improvement is not yet strong enough to allow cogent claims, and no evidence for periodicity emerged from the analysis in the optical. Modeling a light curve by means of Gaussian processes, in spite of being relatively computationally demanding, allows us to derive a wealth of information about the data under study and suggests an original analysis framework for light curves of astrophysical interest.
A simple differential geometry for complex networks

Abstract We introduce new definitions of sectional, Ricci, and scalar curvatures for networks and their higher dimensional counterparts, derived from two classical notions of curvature for curves in general metric spaces, namely, the Menger curvature and the Haantjes curvature. These curvatures are applicable to unweighted or weighted and undirected or directed networks and are more intuitive and easier to compute than other network curvatures. In particular, the proposed curvatures based on the interpretation of Haantjes definition as geodesic curvature allow us to give a network analogue of the classical local Gauss–Bonnet theorem. Furthermore, we propose even simpler and more intuitive proxies for the Haantjes curvature that allow for even faster and easier computations in large-scale networks. In addition, we also investigate the embedding properties of the proposed Ricci curvatures. Lastly, we also investigate the behavior, both on model and real-world networks, of the curvatures introduced herein with more established notions of Ricci curvature and other widely used network measures.
Preserving the Hypernym Tree of WordNet in Dense Embeddings

In this paper, we provide a novel way to generate low-dimension (dense) vector embeddings for the noun and verb synsets in WordNet, so that the hypernym-hyponym tree structure is preserved in the embeddings. We call this embedding the sense spectrum (and sense spectra for embeddings). In order to create suitable labels for the training of sense spectra, we designed a new similarity measurement for noun and verb synsets in WordNet. We call this similarity measurement the hypernym intersection similarity (HIS), since it compares the common and unique hypernyms between two synsets. Our experiments show that on the noun and verb pairs of the SimLex-999 dataset, HIS outperforms the three similarity measurements in WordNet. Moreover, to the best of our knowledge, the sense spectra is the first dense embedding system that can explicitly and completely measure the hypernym-hyponym relationship in WordNet.
Toward the Unambiguous Identification of Supermassive Binary Black Holes through Bayesian Inference

Supermassive binary black holes at subparsec orbital separations have yet to be discovered, with the possible exception of blazar OJ 287. In parallel to the global hunt for nanohertz gravitational waves from supermassive binaries using pulsar timing arrays, there has been a growing sample of candidates reported from electromagnetic surveys, particularly searches for periodic variations in the optical light curves of quasars. However, the periodicity search is prone to false positives from quasar red noise and quasiperiodic oscillations from the accretion disk of a single supermassive black hole, especially when the data span fewer than a few signal cycles. We present a Bayesian method for the detection of quasar (quasi)periodicity in the presence of red noise. We apply this method to the binary candidate PG 1302−102 and show that (a) there is very strong support (Bayes factor >106) for quasiperiodicity and (b) the data slightly favor a quasiperiodic oscillation over a sinusoidal signal, which we interpret as modest evidence against the binary black hole hypothesis. We also find that the prevalent damped random walk red-noise model is disfavored with more than 99.9% credibility. Finally, we outline future work that may enable the unambiguous identification of supermassive binary black holes.
The Category of Node-and-Choice Extensive-Form Games

This paper develops the category $\mathbf{NCG}$. Its objects are node-and-choice games, which include essentially all extensive-form games. Its morphisms allow arbitrary transformations of a game's nodes, choices, and players, as well as monotonic transformations of the utility functions of the game's players. Among the morphisms are subgame inclusions. Several characterizations and numerous properties of the isomorphisms are derived. For example, it is shown that isomorphisms preserve the game-theoretic concepts of no-absentmindedness, perfect-information, and (pure-strategy) Nash-equilibrium. Finally, full subcategories are defined for choice-sequence games and choice-set games, and relationships among these two subcategories and $\mathbf{NCG}$ itself are expressed and derived via isomorphic inclusions and equivalences.
Assessment of supervised machine learning for atmospheric retrieval of exoplanets

Atmospheric retrieval of exoplanets from spectroscopic observations requires an extensive exploration of a highly degenerate and high-dimensional parameter space to accurately constrain atmospheric parameters. Retrieval methods commonly conduct Bayesian parameter estimation and statistical inference using sampling algorithms such as Markov chain Monte Carlo or Nested Sampling. Recently several attempts have been made to use machine learning algorithms either to complement or to replace fully Bayesian methods. While much progress has been made, these approaches are still at times unable to accurately reproduce results from contemporary Bayesian retrievals. The goal of this work is to investigate the efficacy of machine learning for atmospheric retrieval. As a case study, we use the Random Forest supervised machine learning algorithm which has been applied previously with some success for atmospheric retrieval of the hot Jupiter WASP-12b using its near-infrared transmission spectrum. We reproduce previous results using the same approach and the same semi-analytic models, and subsequently extend this method to develop a new algorithm that results in a closer match to a fully Bayesian retrieval. We combine this new method with a fully numerical atmospheric model and demonstrate excellent agreement with a Bayesian retrieval of the transmission spectrum of another hot Jupiter, HD 209458b. Despite this success, and achieving high computational efficiency, we still find that the machine learning approach is computationally prohibitive for high-dimensional parameter spaces that are routinely explored with Bayesian retrievals with modest computational resources. We discuss the trade-offs and potential avenues for the future.
Human-Machine Collaboration for Democratizing Data Science

Everybody wants to analyse their data, but only few posses the data science expertise to do this. Motivated by this observation, we introduce a novel framework and system VisualSynth for human-machine collaboration in data science. Its aim is to democratize data science by allowing users to interact with standard spreadsheet software in order to perform and automate various data analysis tasks ranging from data wrangling, data selection, clustering, constraint learning, predictive modeling and auto-completion. VisualSynth relies on the user providing colored sketches, i.e., coloring parts of the spreadsheet, to partially specify data science tasks, which are then determined and executed using artificial intelligence techniques.
Prediction of separation and transition on a low-pressure turbine blade using a RANS grid

Flow past a high-lift low-pressure turbine (LPT) blade in a cascade could be quite complex as phenomena like separation and transition are often involved. For a highly loadedT106A blade at a high incidence and relatively low Reynolds number(25, 000 < Re < 1, 00, 000), separation-induced transition is observed on the suction side of the blade, making it a challenging problem for model-based simulations. In this work, computations for this flow are carried out using RANS and hybrid LES/RANS approaches. The RANS simulations are performed with six popular low- Re turbulence models. While turbulence models by themselves fail to predict any separation on the T106A blade, the four-equation Langtry-Menter transition model predicts a short separation bubble. The characteristic of this bubble, however, is very different from what is observed in experiments and DNS, and therefore transition is not accurately predicted. An embedded hybrid LES/RANS approach, Limited numerical scales(LNS), with an automatic switch to LES in sufficiently resolved grids, is then used for predictions on the sameRANS grid. With the statistical turbulence on fine grids, LES-like behavior of LNS results in an unphysical drop in Reynolds stresses as the turbulent fluctuations are not appropriately represented on the resolved scale. Therefore, the LNS results are very similar to those obtained with turbulence models. However, when synthetic turbulence with correct statistical characteristics is used to stimulate the large eddies in the embedded LES zone, LNS is able to predict separation and recovers a solution very close to DNS and experimental results.
Some Applications of Lie Groups in Theory of Technical Progress

In recent decades, we have known some interesting applications of Lie theory in the theory of technological progress. Firstly, we will discuss some results of R. Saito in \cite{rS1980} and \cite{rS1981} about the application modeling of Lie groups in the theory of technical progress. Next, we will describe the result on Romanian economy of G. Zaman and Z. Goschin in \cite{ZG2010}. Finally, by using Sato's results and applying the method of G. Zaman and Z. Goschin, we give an estimation of the GDP function of Viet Nam for the 1995-2018 period and give several important observations about the impact of technical progress on economic growth of Viet Nam.
Improving spectroscopic lithium abundances

Context. Accurate spectroscopic lithium abundances are essential in addressing a variety of open questions, such as the origin of a uniform lithium content in the atmospheres of metal-poor stars (Spite plateau) or the existence of a correlation between the properties of extrasolar planetary systems and the lithium abundance in the atmosphere of their host stars. Aims. We have developed a tool that allows the user to improve the accuracy of standard lithium abundance determinations based on 1D model atmospheres and the assumption of local thermodynamic equilibrium (LTE) by applying corrections that account for hydrodynamic (3D) and non-LTE (NLTE) effects in FGK stars of different metallicity. Methods. Based on a grid of CO5BOLD 3D models and associated 1D hydrostatic atmospheres, we computed three libraries of synthetic spectra of the lithium λ 670.8 nm line for a wide range of lithium abundances, accounting for detailed line formation in 3D NLTE, 1D NLTE, and 1D LTE, respectively. The resulting curves-of-growth were then used to derive 3D NLTE and 1D NLTE lithium abundance corrections. Results. For all metallicities, the largest corrections are found at the coolest effective temperature, Teff = 5000 K. They are mostly positive, up to + 0.2 dex, for the weakest lines (lithium abundance A(Li)1DLTE = 1.0), whereas they become more negative towards lower metallicities, where they can reach − 0.4 dex for the strongest lines (A(Li)1DLTE = 3.0) at [Fe/H] = − 2.0. We demonstrate that 3D and NLTE effects are small for metal-poor stars on the Spite plateau, leading to errors of at most ± 0.05 dex if ignored. We present analytical functions evaluating the 3D NLTE and 1D NLTE corrections as a function of Teff [5000…6500 K], log g [3.5…4.5], and LTE lithium abundance A(Li) [1.0…3.0] for a fixed grid of metallicities [Fe∕H] [ − 3.0…0.0]. In addition, we also provide analytical fitting functions for directly converting a given lithium abundance into an equivalent width, or vice versa, a given equivalent width (EW) into a lithium abundance. For convenience, a Python script is made available that evaluates all fitting functions for given Teff, log g, [Fe∕H], and A(Li) or EW. Conclusions. By means of the fitting functions developed in this work, the results of complex 3D and NLTE calculations are made readily accessible and quickly applicable to large samples of stars across a wide range of metallicities. Improving the accuracy of spectroscopic lithium abundance determinations will contribute to a better understanding of the open questions related to the lithium content in metal-poor and solar-like stellar atmospheres.
Assessing and Visualizing Simultaneous Simulation Error

Abstract Monte Carlo experiments produce samples to estimate features such as means and quantiles of a given distribution. However, simultaneous estimation of means and quantiles has received little attention. In this setting, we establish a multivariate central limit theorem for any finite combination of sample means and quantiles under the assumption of a strongly mixing process, which includes the standard Monte Carlo and Markov chain Monte Carlo settings. We build on this to provide a fast algorithm for constructing hyperrectangular confidence regions having the desired simultaneous coverage probability and a convenient marginal interpretation. The methods are incorporated into standard ways of visualizing the results of Monte Carlo experiments enabling the practitioner to more easily assess the reliability of the results. We demonstrate the utility of this approach in various Monte Carlo settings including simulation studies based on independent and identically distributed samples and Bayesian analyses using Markov chain Monte Carlo sampling. Supplementary materials for this article are available online.
TESS unveils the phase curve of WASP-33b

We present the detection and characterization of the full-orbit phase curve and secondary eclipse of the ultra-hot Jupiter WASP-33b at optical wavelengths, along with the pulsation spectrum of the host star. We analyzed data collected by the Transiting Exoplanet Survey Satellite (TESS) in sector 18. WASP-33b belongs to a very short list of highly irradiated exoplanets that were discovered from the ground and were later visited by TESS. The host star of WASP-33b is of δ Scuti-type and shows nonradial pulsations in the millimagnitude regime, with periods comparable to the period of the primary transit. These completely deform the photometric light curve, which hinders our interpretations. By carrying out a detailed determination of the pulsation spectrum of the host star, we find 29 pulsation frequencies with a signal-to-noise ratio higher than 4. After cleaning the light curve from the stellar pulsations, we confidently report a secondary eclipse depth of 305.8 ± 35.5 parts-per-million (ppm), along with an amplitude of the phase curve of 100.4 ± 13.1 ppm and a corresponding westward offset between the region of maximum brightness and the substellar point of 28.7 ± 7.1 degrees, making WASP-33b one of the few planets with such an offset found so far. Our derived Bond albedo, AB = 0.369 ± 0.050, and heat recirculation efficiency, ɛ = 0.189 ± 0.014, confirm again that he behavior of WASP-33b is similar to that of other hot Jupiters, despite the high irradiation received from its host star. By connecting the amplitude of the phase curve to the primary transit and depths of the secondary eclipse, we determine that the day- and nightside brightness temperatures of WASP-33b are 3014 ± 60 K and 1605 ± 45 K, respectively. From the detection of photometric variations due to gravitational interactions, we estimate a planet mass of MP = 2.81 ± 0.53 MJ. Based on analyzing the stellar pulsations in the frame of the planetary orbit, we find no signals of star-planet interactions.
IJSRD || National Conference on Technological Advancement and Automatization in Engineering || January 2016

In recent time’s Wireless Sensor Networks (WSN) have grown enormously and become progressively attractive in wide variety of applications because of their low cost, low power, small in size and self-organizing behavior in harsh environments. Clustering is the process where sensing area is divided in groups to balance the energy level of sensor nodes known as clusters. It is used to prolong the lifetime of the wireless sensor networks. An Optimal Clustering technique can reduce the energy consumption in WSN and the lifetime of the network. In this paper we present the study of different clustering based energy efficient routing protocols in WSN and compared them on their parameters such as Cluster Head selection, data aggregation, scalability, complexity and energy efficiency and it is found that improved TEEN ensures creditability of nodes which leads to better lifetime of network.