Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular-dynamic simulation of water vapor interaction with suffering pores of the cylindrical type

Theoretical and experimental investigations of water vapor interaction with porous materials are carried out both at the macro level and at the micro level. At the macro level, the influence of the arrangement structure of individual pores on the processes of water vapor interaction with porous material as a continuous medium is studied. At the micro level, it is very interesting to investigate the dependence of the characteristics of the water vapor interaction with porous media on the geometry and dimensions of the individual pore. In this paper, a study was carried out by means of mathematical modelling of the processes of water vapor interaction with suffering pore of the cylindrical type. The calculations were performed using a model of a hybrid type combining a molecular-dynamic and a macro-diffusion approach for describing water vapor interaction with an individual pore. The processes of evolution to the state of thermodynamic equilibrium of macroscopic characteristics of the system such as temperature, density, and pressure, depending on external conditions with respect to pore, were explored. The dependence of the evolution parameters on the distribution of the diffusion coefficient in the pore, obtained as a result of molecular dynamics modelling, is examined. The relevance of these studies is due to the fact that all methods and programs used for the modelling of the moisture and heat conductivity are based on the use of transport equations in a porous material as a continuous medium with known values of the transport coefficients, which are usually obtained experimentally.
Wide bandwidth stagnation temperature measurements in vortical flows behind turbine vanes

A new fast response measurement technique for total temperature, originally developed for testing in transient facilities, has been adapted and used to make turbine vane wake flow measurements in a continuously running linear cascade. The bandwidth of the new method approaches 100 kHz, and is a considerable improvement on previous techniques. The measurements show clear evidence of vortex shedding over a range of Mach numbers. The shedding is consistent with previous schlieren and wake pressure measurements. At sub-sonic exit Mach numbers, the total temperature data shows vortex shedding at a Strouhal number of just above 0.2. This is consistent with data from other cascades. At supersonic exit conditions, the measurements show that the vortex shedding breaks down into a number of different intermittent shedding modes. This is also consistent with earlier results. The existence of strong vortex driven energy separation in the wake through the Eckert-Weise effect, in which temperatures are suppressed in the centre and discrete 'hot spots' appear towards the edges of the wake flow, is confirmed by the total temperature measurements. The new measurements are used to explain earlier results from the cascade, which were previously thought to be anomalous.
Product2Vec: Leveraging representation learning to model consumer product choice in large assortments

We propose a method, Product2Vec, based on representation learning, that can automatically learn latent product attributes that drive consumer choices, to study product-level competition when the number of products is large. We demonstrate Product2Vec’s interpretability and capability for scalable causal inference. For interpretability, first, we theoretically demonstrate that there exists a direct link between product vectors and product attributes by deriving a formal proof. Second, we use product embedding to create two metrics, complementarity and exchangeability, that allow us to distinguish between products that are complements and substitutes, respectively. For causal inference, we combine product vectors with choice models and show that we can achieve better accuracy—both in terms of model fit and unbiased price coefficients—when compared to a model based solely on observable attributes, and obtain results similar to those obtained with a more complex model that includes a fixed effect for every product. Electronic copy available at: https://ssrn.com/abstract=3519358
Generation, dynamical buildup and detection of bi- and mulipartite entangled states in cavity systems

We inspect different quantum optical setups from the viewpoint of entanglement generation and detection. As a first step we consider a planar semiconductor microcavity and optimize the Bell-type correlations and their robustness against dephasing to create strong bipartite entanglement between polariton branches, which subsequently can be transferred to the emitted photons. In a second step, in order to create multipartite entangled light, we place the microcavity in an optical resonator driven by pump pulses with a frequency comb spectrum. For this system we show how phase matching of all comb modes can be achieved and will lead to indistinguishable scattering processes causing entanglement among every mode. Finally we demonstrate the buildup of entanglement in the dissipative dynamics of emitters coupled to a single cavity photon mode driven by an external laser. From a Floquet master equation approach we find that entanglement production predominates during the first few laser oscillation periods.
educación en línea por el covid-19 performance of university students at the end of the transition from face-to-face education to online education due to COVID-19

In 2019, the Covid-19 health emergency required universities around the world to face the challenge of continuing classes during lockdown. In order to do so, classes were delivered online. The results and the impact of that sudden change are still being evaluated. The objective of the work was to identify the factors that influenced the academic performance of university students, when moving from face-to-face to distance classes during the health contingency by COVID19. The methodology was descriptive and quantitative of predictive type; a questionnaire was applied to 247 students, correlations were made to know the variables with the greatest impact, a model was elaborated by the method of ordinary least squares. Results: it was found that the variables that influenced increasing the academic performance of the students were: The use of Google Classroom (0.015), videoconference (0.238), didactic resources (0.248), access to Web pages (0.238), videos (0.234), directed questions (0.228), participation in chat (0.187), digital whiteboard (0.268), achievement of objectives (0.009) and online evaluation (0.009); It was found that the messages through a learning platform have a negative influence on academic performance (-0.023). It is concluded that the academic authorities must manage the training and use of technological online learning tools (Google Classroom, videoconference, didactic resources, access to web pages, videos, chat and digital whiteboard) that allow improving the teaching process and therefore reflect higher academic performance.
Identifying galaxy mergers in observations and simulations with deep learning

Context. Mergers are an important aspect of galaxy formation and evolution. With large upcoming surveys, such as Euclid and LSST, accurate techniques that are fast and efficient are needed to identify galaxy mergers for further study. Aims. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. As one of the main difficulties of merger studies is the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. Methods. We developed a convolutional neural network architecture and trained it in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. Results. The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. Conclusions. The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalising prospects for using simulation trained networks for galaxy identification in large surveys.
Vibronic coupling in C-60 anion revisited: Derivations from photoelectron spectra and DFT calculations

The vibronic coupling constants of C$_{60}^-$ are derived from the photoelectron spectrum measured by Wang {\it et al}. [X. B. Wang, H. K. Woo, and L. S. Wang, J. Chem. Phys., {\bf 123}, 051106 (2005). ] at low temperature with high-resolutions. We find that the couplings of the Jahn-Teller modes of C$_{60}^-$ are weaker than the couplings reported by Gunnarsson {\it et al}. [O. Gunnarsson, H. Handschuh, P. S. Bechthold, B. Kessler, G. Gantef{\"{o}}r, and W. Eberhardt, Phys. Rev. Lett. , {\bf 74}, 1875 (1995). ]. The total stabilization energy after $h_g$ and $a_g$ modes is reduced with respect to the previous derivation of Gunnarsson {\it et al}. by 30 \%. The computed vibronic coupling constants using DFT with B3LYP functional agree well with the new experimental constants, so the discrepancy between theory and experiment persistent in the previous studies is basically solved.
A survey for low‐luminosity quasars at redshift z∼ 5

We present the results of a multicolour (VIZ) survey for low-luminosity (M B < - 23.5) quasars with z ∼ 5 using the 12K CCD mosaic camera on the Canada-France-Hawaii Telescope (CFHT). The survey covers 1.8 deg 2 to a limiting magnitude of m z = 22.5(Vega), about two magnitudes fainter than the SDSS (Sloan Digital Sky Survey) quasar survey. Twenty candidates were selected by their VIZ colours, and spectra for 15 of these were obtained with GMOS on the Gemini North telescope. A single quasar with z = 4.99 was recovered; the remaining candidates are all M stars. The detection of only a single quasar in the redshift range 4.8 < z < 5.2 accessible to the survey is indicative of a possible turnover in the luminosity function at faint quasar magnitudes, and a departure from the form observed at higher luminosities (in agreement with quasar lensing observations by Richards et al.). However, the derived space densities of quasars more luminous than M B (Vega) < - 23.5, of 2.96 x 10 -7 Mpc -3 is consistent at the 65 per cent confidence level with extrapolation of the quasar luminosity function as derived by Fan et al. at m i < 19.6(Vega).
Treatment of oily wastewater by electrocoagulation technology: A general review (2018-2022)

A huge amount of oily wastewater is discharged annually from several industries like petroleum and petrochemical factories. Scientists and researchers are permanently concentrated on creating conventional technologies or identifying novel treatment options for oily wastewaters, since they need to be treated before being discharged into the soil and aquatic ecosystems. Electroc­oagu­lation technology (ECT) is an electrochemical method employed to remove numerous pollutants from domestic and industrial wastewaters. This paper aims to review the recently published articles from 2018 to 2022 concerned with ECT for oily wastewater remediation. Based on the present review, it is obvious that ECT is strongly dependent on the value of electric current or voltage applied to provide the required amounts of electro-coagulants for efficient remediation, reaction time duration for the generation of electro-coagulants and pollutants elimination, and electrode configuration such as shape, type of metal, and distance between electrodes. Other operating parameters include solution pH (since some pollutants are removed based on their cationic or anionic nature), type of electrolyte which affects the electric conductivity and ohmic drop and stirring speed that may influence the contact among numerous ions throughout the EC reactor. The core findings show that the ECT is highly effective, eco-friendly, and cost-effective in eliminating organic and inorganic pollutants from oily wastewater.
Random Propagation in Complex Systems: Nonlinear Matrix Recursions and Epidemic Spread

This dissertation studies long-term behavior of random Riccati recursions and mathematical epidemic model. Riccati recursions are derived from Kalman filtering. The error covariance matrix of Kalman filtering satisfies Riccati recursions. Convergence condition of time-invariant Riccati recursions are well-studied by researchers. We focus on time-varying case, and assume that regressor matrix is random and identical and independently distributed according to given distribution whose probability distribution function is continuous, supported on whole space, and decaying faster than any polynomial. We study the geometric convergence of the probability distribution. We also study the global dynamics of the epidemic spread over complex networks for various models. For instance, in the discrete-time Markov chain model, each node is either healthy or infected at any given time. In this setting, the number of the state increases exponentially as the size of the network increases. The Markov chain has a unique stationary distribution where all the nodes are healthy with probability 1. Since the probability distribution of Markov chain defined on finite state converges to the stationary distribution, this Markov chain model concludes that epidemic disease dies out after long enough time. To analyze the Markov chain model, we study nonlinear epidemic model whose state at any given time is the vector obtained from the marginal probability of infection of each node in the network at that time. Convergence to the origin in the epidemic map implies the extinction of epidemics. The nonlinear model is upper-bounded by linearizing the model at the origin. As a result, the origin is the globally stable unique fixed point of the nonlinear model if the linear upper bound is stable. The nonlinear model has a second fixed point when the linear upper bound is unstable. We work on stability analysis of the second fixed point for both discrete-time and continuous-time models. Returning back to the Markov chain model, we claim that the stability of linear upper bound for nonlinear model is strongly related with the extinction time of the Markov chain. We show that stable linear upper bound is sufficient condition of fast extinction and the probability of survival is bounded by nonlinear epidemic map.
Spatially resolved spectroscopy on superconducting proximity nanostructures

We investigated the local density of states (LDOS) of a normal metal (N) in good electrical contact with a superconductor (S) as a function of the distance $x$ to the NS interface. The sample consists of a pattern of alternate stripes of Au and Nb made by UV-lithography. We used a low temperature scanning tunneling microscope to record simultaneously $dI/dV(V,x)$ curves and the topographic profile $z(x)$. Nearby the NS interface, all the spectra show a dip near the Fermi energy but depending on the geometry, different behaviours can be distinguished. First, when the characteristic size of the normal metal $L$ is much larger than the coherence length $\xi_N \simeq \sqrt{\hbar D_N/ 2\Delta}$, the spectral extension of the dip decreases from $\Delta$ at the NS interface to zero at distances $x\gg\xi_N$. Second, when $L$ is comparable to $\xi_N$ the apparent gap in the LDOS is space-independent and related to the Thouless energy.
Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction

We present a novel offline-online method to mitigate the computational burden of the characterization of posterior random variables in statistical learning. In the offline phase, the proposed method learns the joint law of the parameter random variables and the observable random variables in the tensor-train (TT) format. In the online phase, the resulting order-preserving conditional transport can characterize the posterior random variables given newly observed data in real time. Compared with the state-of-the-art normalizing flow techniques, the proposed method relies on function approximation and is equipped with a thorough performance analysis. The function approximation perspective also allows us to further extend the capability of transport maps in challenging problems with high-dimensional observations and high-dimensional parameters. On the one hand, we present novel heuristics to reorder and/or reparametrize the variables to enhance the approximation power of TT. On the other hand, we integrate the TT-based transport maps and the parameter reordering/reparametrization into layered compositions to further improve the performance of the resulting transport maps. We demonstrate the efficiency of the proposed method on various statistical learning tasks in ordinary differential equations (ODEs) and partial differential equations (PDEs).
Anisotropically inflating universes

We show that in theories of gravity that add quadratic curvature invariants to the Einstein-Hilbert action there exist expanding vacuum cosmologies with positive cosmological constant which do not approach the de Sitter universe. Exact solutions are found which inflate anisotropically. This behavior is driven by the Ricci curvature invariant and has no counterpart in the general-relativistic limit. These examples show that the cosmic no-hair theorem does not hold in these higher-order extensions of general relativity and raises new questions about the ubiquity of inflation in the very early universe and the thermodynamics of gravitational fields.
Critical behavior of SU(3) lattice gauge theory with 12 light flavors

It is expected that when the number of light flavors of gauge theories is increased near or beyond some critical value, new and interesting behavior occurs. We discuss the qualitative properties of the RG flows for a local $SU(3)$ theory with $N_f$ light fundamental flavors for $N_f$ near 12. We discuss the realization of the chiral symmetry and remind that the Wigner mode seems an unlikely alternative. We propose to use a linear sigma model to describe the light $\sigma$ masses found in recent lattice calculations for $N_f$=8 and 12. Progress made after the conference are reported in arXiv:1709.09264, where it is claimed that the breaking of the axial $U(1)_A$ is a key ingredient to get a small $\sigma$ mass. For SU(3) lattice gauge theory with 12 flavors of unimproved staggered fermions, the scaling of the imaginary part of the zeros of the partition function in the complex coupling plane is consistent with a first order phase transition for small values of the mass. Zech Gelzer and Diego Floor are investigating the scaling near the endpoint of the line of first order phase transition in the mass-coupling plane where a light and weakly interacting scalar is expected. We briefly discuss recent calculations of the second-order R\'enyi entanglement entropy and estimations of the central charge using the Tensor Renormalization Group method for the two-dimensional O(2) model and the possibility to extend these calculations to higher dimensions.
Mutually Unbiased Bases in Low Dimensions

The density matrix of a qudit may be reconstructed with optimal efficiency if the expectation values of a specific set of observables are known. The required observables only exist if it is possible to identify d mutually unbiased (MU) complex (d×d) Hadamard matrices, defining a complete set of d+1 MU bases. This thesis is an exploration of sets of r≤d+1 MU bases in low dimensions. We derive all inequivalent sets of MU bases in dimensions two to five confirming that in these dimensions, the complete sets of (d+1) MU bases are unique. In dimension six, we prescribe a first Hadamard matrix and construct all others mutually unbiased to it, using algebraic computations performed by a computer program. We repeat this calculation many times, sampling all known complex Hadamard matrices, and never find more than two that are mutually unbiased. We also study subsets of a complete set of MU bases by considering sets of pure states which satisfy the desired properties. We use this concept to provide the strongest numerical evidence so far that no seven MU bases exist in dimension six. In the final part of the thesis, we introduce a new quantum key distribution protocol that uses d-level quantum systems to encode an alphabet with c letters. It has the property that the error rate introduced by an intercept-and-resend attack is higher than the BB84 or six-state protocols when the legitimate parties use a complete set of MU bases.
Recounting special Lagrangian cycles in twistor families of K3 surfaces (or: How I learned to stop worrying and count BPS states)

We consider asymptotics of certain BPS state counts in M-theory compactified on a K3 surface. Our investigation is parallel to (and was inspired by) recent work in the mathematics literature by Filip, who studied the asymptotic count of special Lagrangian fibrations of a marked K3 surface, with fibers of volume at most $V_*$, in a generic twistor family of K3 surfaces. We provide an alternate proof of Filip's results by adapting tools that Douglas and collaborators have used to count flux vacua and attractor black holes. We similarly relate BPS state counts in 4d ${\cal N}=2$ supersymmetric gauge theories to certain counting problems in billiard dynamics and provide a simple proof of an old result in this field.
Scaling of magnetic fluctuations near a quantum phase transition

We use inelastic neutron scattering to measure the magnetic fluctuations in a single crystal of the heavy fermion alloy CeCu_5. 9Au_0. 1 close to the antiferromagnetic quantum critical point. The energy and temperature-dependent spectra obey (E/T) scaling at Q near (1,0,0). The neutron data and earlier bulk susceptibility are consistent with the form 1/X ~ f(Q)+(-iE+bT)^a, with an anomalous exponent a=0.8. We confirm the earlier observation of quasi-low dimensionality and show how both the magnetic fluctuations and the thermodynamics can be understood in terms of a quantum Lifshitz point.