Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and optical studies of nanocrystalline ZnCr2O4 and CoCr2O4 spinel

Chromites ACr2O4 nanoparticles (A = Zn, Co) annealed at 800°C/8 h were successfully prepared by sol gel auto combustion (SGAC) method in the presence of nitrate ions and using citric acid as catalysis. The observed X-ray diffraction (XRD) data confirms that all the prepared samples are indexed in cubic crystal structure (space group Fd3m). The average particle size of the nanomaterial ZnCr2O4 and CoCr2O4 calculated from XRD was found in the range 19-21 nm. Four Raman active phonon modes observed around at 185, 514, 607 and 691 cm−1 as F2g(1), F2g(2), F2g(3) and A1g for ZnCr2O4 and three Raman active modes observed at 197, 514 and 685 cm−1 as F2g(1), Eg and A1g respectively, for CoCr2O4. The band gap energy of ZnCr2O4 and CoCr2O4 powder was determined by absorption spectroscopy in ultraviolet–visible range and was found to be 3.81 eV and 3.23 eV for direct band gap respectively.Chromites ACr2O4 nanoparticles (A = Zn, Co) annealed at 800°C/8 h were successfully prepared by sol gel auto combustion (SGAC) method in the presence of nitrate ions and using citric acid as catalysis. The observed X-ray diffraction (XRD) data confirms that all the prepared samples are indexed in cubic crystal structure (space group Fd3m). The average particle size of the nanomaterial ZnCr2O4 and CoCr2O4 calculated from XRD was found in the range 19-21 nm. Four Raman active phonon modes observed around at 185, 514, 607 and 691 cm−1 as F2g(1), F2g(2), F2g(3) and A1g for ZnCr2O4 and three Raman active modes observed at 197, 514 and 685 cm−1 as F2g(1), Eg and A1g respectively, for CoCr2O4. The band gap energy of ZnCr2O4 and CoCr2O4 powder was determined by absorption spectroscopy in ultraviolet–visible range and was found to be 3.81 eV and 3.23 eV for direct band gap respectively.
Heat transfer in porous medium embedded with vertical plate: Non-equilibrium approach - Part B

Heat transfer in a porous medium embedded with vertical flat plate is investigated by using thermal non-equilibrium model. Darcy model is employed to simulate the flow inside porous medium. It is assumed that the heat transfer takes place by natural convection and radiation. The vertical plate is maintained at isothermal temperature. The governing partial differential equations are converted into non-dimensional form and solved numerically using finite element method. Results are presented in terms of isotherms and streamlines for various parameters such as heat transfer coefficient parameter, thermal conductivity ratio, and radiation parameter
Use of Blood, Urine and Milk Samples in the Diagnosis of Subclinical Ketosis and Negative Energy Balance in Grazing Dairy Cows

The objective of this study was to compare and associate plasma concentrations of -hidroxi-butyrate (HB) and subclinical ketosis with blood, urinary and milk indicators in dairy cow. Seventy four Holstein-Frisian cows were used from the third week pre-partum until the eighth week post-partum. Samples of blood, urine and milk were obtained weekly to determine the plasma concentrations of HB, non-esterified fatty acids (NEFA) and glucose. Rothera test reaction, excretion of HB and pH were determined in urine samples. The concentrations of fat, protein and urea were determined on milk samples. The for subclinical ketosis were considered as: HB > 1.0, > 1.2 and >1.4 mmol/L. Negative energetic balance (NEB) presentation was considered when NEFA >300 μmol/L during pre-partum and when HB > 0.6 mmol/L during post-partum. The intensity in the reaction of Rothera test and the excretion of urinary HB were related with plasmatic concentrations of HB (r=0.57, P 0.05) with milk fat or protein content or urinary pH. In conclusion, slight and moderate reactions of the Rothera test are good indicators of NEB, while an intense test result indicates subclinical ketosis.
An identification and characterization of biodiesel fatty acid based by using dielectric sensor

The fatty acids composition is identified by a gas chromatography mass spectrometer (GC-MS), then it is characterized in saturated and unsaturated components. This paper investigates biodiesel fatty acid by using dielectric constant measurements and focuses on dielectric sensor based on biodiesel chemical properties characterization. The objectives of this paper are identification fatty acids and determination of correlation dielectric properties and biodiesel fatty acid characterization. The proposed method is dielectric constant by using capacitance sensor are applied to determine the response dielectric sensor from the fatty acid composition. Sixteen fatty acid methyl esters were identified and two characterizations the amount of saturated and unsaturated fatty ester fractions. The model parameter was determined by regression analysis for estimating the relationships among fatty acid content and dielectric properties. The results show that measurements of electrical properties, successfully used for the characterization of fatty acids. The dielectric constant of biodiesel was found increasing as the saturated decreases. This relationship becomes calibration for the assessment of the quality of biodiesel based on the dielectric sensor. The model reveals that the fatty acid composition affects the value of the biodiesel dielectric and show that dielectric sensor potentially to handle for characterization of biodiesel fatty acid content.
Explicitly solvable systems of two autonomous first-order Ordinary Differential Equations with homogeneous quadratic right-hand sides

After tersely reviewing the various meanings that can be given to the property of a system of nonlinear ODEs to be solvable, we identify a special case of the system of two first-order ODEs with homogeneous quadratic right-hand sides which is explicitly solvable. It is identified by 2 explicit algebraic constraints on the 6 a priori arbitrary parameters that characterize this system. Simple extensions of this model to cases with nonhomogeneous quadratic right-hand sides are also identified, including isochronous cases
On dust evolution in planet-forming discs in binary systems – I. Theoretical and numerical modelling: radial drift is faster in binary discs

Many stars are in binaries or higher-order multiple stellar systems. Although in recent years a large number of binaries have been proven to host exoplanets, how planet formation proceeds in multiple stellar systems has not been studied much yet from the theoretical standpoint. In this paper we focus on the evolution of the dust grains in planet-forming discs in binaries. We take into account the dynamics of gas and dust in discs around each component of a binary system under the hypothesis that the evolution of the circumprimary and the circumsecondary discs is independent. It is known from previous studies that the secular evolution of the gas in binary discs is hastened due to the tidal interactions with their hosting stars. Here we prove that binarity affects dust dynamics too, possibly in a more dramatic way than the gas. In particular, the presence of a stellar companion significantly reduces the amount of solids retained in binary discs because of a faster, more efficient radial drift, ultimately shortening their lifetime. We prove that how rapidly discs disperse depends both on the binary separation, with discs in wider binaries living longer, and on the disc viscosity. Although the less-viscous discs lose high amounts of solids in the earliest stages of their evolution, they are dissipated slowly, while those with higher viscosities show an opposite behaviour. The faster radial migration of dust in binary discs has a striking impact on planet formation, which seems to be inhibited in this hostile environment, unless other disc substructures halt radial drift further in. We conclude that if planetesimal formation were viable in binary discs, this process would take place on very short time scales.
Non-planar degenerations and related fundamental groups

In this paper we establish a preliminary study of non-planar degenerations. We study two primary cases, the tetrahedron and the double tetrahedron. We calculate the fundamental groups and signatures of the Galois covers of surfaces that degenerate to these two objects. We find that the fundamental group of the Galois cover related to the tetrahedron is trivial, while the one related to the double tetrahedron is Z2.
Stability of the multidimensional wave equation in port-Hamiltonian modelling

We investigate the stability of the wave equation with spatial dependent coefficients on a bounded multidimensional domain. The system is stabilized via a scattering passive feedback law. We formulate the wave equation in a port-Hamiltonian fashion and show that the system is semi-uniformly stable, which is a stability concept between exponential stability and strong stability. Hence, this also implies strong stability of the system. In particular, classical solutions are uniformly stable. This will be achieved by showing that the spectrum of the port-Hamiltonian operator is contained in the left half plane C− and the port-Hamiltonian operator generates a contraction semigroup. Moreover, we show that the spectrum consists of eigenvalues only and the port-Hamiltonian operator has a compact resolvent.
Adversarial Robustness Guarantees for Gaussian Processes

Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications. Such scenarios demand that GP decisions are not only accurate, but also robust to perturbations. In this paper we present a framework to analyse adversarial robustness of GPs, defined as invariance of the model's decision to bounded perturbations. Given a compact subset of the input space $T\subseteq \mathbb{R}^d$, a point $x^*$ and a GP, we provide provable guarantees of adversarial robustness of the GP by computing lower and upper bounds on its prediction range in $T$. We develop a branch-and-bound scheme to refine the bounds and show, for any $\epsilon>0$, that our algorithm is guaranteed to converge to values $\epsilon$-close to the actual values in finitely many iterations. The algorithm is anytime and can handle both regression and classification tasks, with analytical formulation for most kernels used in practice. We evaluate our methods on a collection of synthetic and standard benchmark datasets, including SPAM, MNIST and FashionMNIST. We study the effect of approximate inference techniques on robustness and demonstrate how our method can be used for interpretability. Our empirical results suggest that the adversarial robustness of GPs increases with accurate posterior estimation.
Random graphs with node and block eﬀects: models, goodness-of-ﬁt tests, and applications to biological networks

: Many popular models from the networks literature can be viewed through a common lens. We describe it here and call the class of models log-linear ERGMs. It includes degree-based models, stochastic blockmodels, and combinations of these. Given the interest in combined node and block eﬀects in network formation mechanisms, we introduce a general directed relative of the degree-corrected stochastic blockmodel: an exponential family model we call p 1 -SBM. It is a generalization of several well-known variants of the blockmodel. We study the problem of testing model ﬁt for the log-linear ERGM class. The model ﬁtting approach we take, through the use of quick estimation algorithms borrowed from the contingency table literature and eﬀective sampling methods rooted in graph theory and algebraic statistics, results in an exact test whose p -value can be approximated eﬃciently in networks of moderate sizes. We showcase the performance of the method on two data sets from biology: the connectome of C. elegans and the interactome of Arabidopsis thaliana . These two networks, a neuronal network and a protein-protein interaction network, have been popular examples in the network science literature, but a model-based approach to studying them has been missing thus far.
Image Composition Assessment with Saliency-augmented Multi-pattern Pooling

Image composition assessment is crucial in aesthetic assessment, which aims to assess the overall composition quality of a given image. However, to the best of our knowledge, there is neither dataset nor method specifically proposed for this task. In this paper, we contribute the first composition assessment dataset CADB with composition scores for each image provided by multiple professional raters. Besides, we propose a composition assessment network SAMP-Net with a novel Saliency-Augmented Multi-pattern Pooling (SAMP) module, which analyses visual layout from the perspectives of multiple composition patterns. We also leverage composition-relevant attributes to further boost the performance, and extend Earth Mover's Distance (EMD) loss to weighted EMD loss to eliminate the content bias. The experimental results show that our SAMP-Net can perform more favorably than previous aesthetic assessment approaches.
Partial thermalisation of a two-state system coupled to a finite quantum bath

The eigenstate thermalisation hypothesis (ETH) is a statistical characterisation of eigen-energies, eigenstates and matrix elements of local operators in thermalising quantum systems. We develop an ETH-like ansatz of a partially thermalising system composed of a spin- 12 coupled to a ﬁnite quantum bath. The spin-bath coupling is suﬃciently weak that ETH does not apply, but suﬃciently strong that perturbation theory fails. We calculate (i) the distribution of ﬁdelity susceptibilities, which takes a broadly distributed form, (ii) the distribution of spin eigenstate entropies, which takes a bi-modal form, (iii) inﬁnite time memory of spin observables, (iv) the distribution of matrix elements of local operators on the bath, which is non-Gaussian, and (v) the intermediate entropic enhancement of the bath, which interpolates smoothly between zero and the ETH value of log 2 . The enhancement is a consequence of rare many-body resonances, and is asymptotically larger than the typical eigenstate entanglement entropy. We verify these results numerically and discuss their connections to the many-body localisation transition.
Generalized Kähler-Ricci flow on toric Fano varieties

We study the generalized Kähler-Ricci flow with initial data of symplectic type, and show that this condition is preserved. In the case of a Fano background with toric symmetry, we establish global existence of the normalized flow. We derive an extension of Perelman’s entropy functional to this setting, which yields convergence of nonsingular solutions at infinity. Furthermore, we derive an extension of Mabuchi’s K-energy to this setting, which yields weak convergence of the flow.
The quantification of Simpson’s paradox and other contributions to contingency table theory

The analysis of contingency tables is a powerful statistical tool used in experiments with categorical variables. This study improves parts of the theory underlying the use of contingency tables. Specifically, the linkage disequilibrium parameter as a measure of two-way interactions applied to three-way tables makes it possible to quantify Simpson’s paradox by a simple formula. With tests on three-way interactions, there is only one that determines whether the partial interactions of all variables agree or whether there is at least one variable whose partial interactions disagree. To date, there has been no test available that determines whether the partial interactions of a certain variable agree or disagree, and the presented work closes this gap. This work reveals the relation of the multiplicative and the additive measure of a three-way interaction. Another contribution addresses the question of which cells in a contingency table are fixed when the first- and second-order marginal totals are given. The proposed procedure not only detects fixed zero counts but also fixed positive counts. This impacts the determination of the degrees of freedom. Furthermore, limitations of methods that simulate contingency tables with given pairwise associations are addressed.
Initial Perturbation of the Mean Curvature Flow for closed limit shrinker

This is a contribution to the program of dynamical approach to mean curvature flow initiated by Colding and Minicozzi. In this paper, we prove two main theorems. The first one is local in nature and the second one is global. In this first result, we pursue the stream of ideas of [CM3] and get a slight refinement of their results. We apply the invariant manifold theory from hyperbolic dynamics to study the dynamics close to a closed shrinker that is not a sphere. In the second theorem, we show that if a hypersurface under the rescaled mean curvature flow converges to a closed shrinker that is not a sphere, then a generic perturbation on initial data would make the flow leave a small neighborhood of the shrinker and never come back.
Concentration inequalities for two-sample rank processes with application to bipartite ranking

The ROC curve is the gold standard for measuring the performance of a test/scoring statistic regarding its capacity to discriminate between two statistical populations in a wide variety of applications, ranging from anomaly detection in signal processing to information retrieval, through medical diagnosis. Most practical performance measures used in scor-ing/ranking applications such as the AUC, the local AUC, the p -norm push, the DCG and others, can be viewed as summaries of the ROC curve. In this paper, the fact that most of these empirical criteria can be expressed as two-sample linear rank statistics is highlighted and concentration inequalities for collections of such random variables, referred to as two-sample rank processes here, are proved, when indexed by VC classes of scoring functions. Based on these nonasymptotic bounds, the generalization capacity of empirical maximizers of a wide class of ranking performance criteria is next investigated from a theoretical perspective. It is also supported by empirical evidence through convincing numerical experiments.
The AGNIFS survey: distribution and excitation of the hot molecular and ionized gas in the inner kpc of nearby AGN hosts

We use the Gemini NIFS instrument to map the H2 2. 1218μm and Brγ flux distributions in the inner 0.04–2 kpc of a sample of 36 nearby active galaxies (0.001 . z . 0.056) at spatial resolutions from 4 to 250 pc. We find extended emission in 34 galaxies. In ∼55% of them, the emission in both lines is most extended along the galaxy major axis, while in the other 45% the extent follows a distinct orientation. The emission of H2 is less concentrated than that of Brγ, presenting a radius that contains half of the flux 60% greater, on average. The H2 emission is driven by thermal processes – X-ray heating and shocks – at most locations for all galaxies, where 0.4 < H2/Brγ < 6. For regions where H2/Brγ > 6 (seen in 40% of the galaxies), shocks are the main H2 excitation mechanism, while in regions with H2/Brγ < 0.4 (25% of the sample) the H2 emission is produced by fluorescence. The only difference we found between type 1 and type 2 AGN was in the nuclear emission-line equivalent widths, that are smaller in type 1 than in type 2 due to a larger contribution to the continuum from the hot dusty torus in the former. The gas masses in the inner 125 pc radius are in the range 10 − 10 M for the hot H2 and 10 − 10 M for the ionised gas and would be enough to power the AGN in our sample for 10 − 10 yr at their current accretion rates.
Dissecting the Quadruple Binary Hyad vA 351 Masses for Three M Dwarfs and a White Dwarf

We extend results first announced by Franz et al., that identified vA 351 = H346 in the Hyades as a multiple star system containing a white dwarf. With Hubble Space Telescope Fine Guidance Sensor fringe tracking and scanning, and more recent speckle observations, all spanning 20.7 years, we establish a parallax, relative orbit, and mass fraction for two components, with a period, and total mass 2.1 . With ground-based radial velocities from the McDonald Observatory Otto Struve 2.1 m Telescope Sandiford Spectrograph, and Center for Astrophysics Digital Speedometers, spanning 37 years, we find that component B consists of BC, two M dwarf stars orbiting with a very short period ( days), having a mass ratio / = 0.95. We confirm that the total mass of the system can only be reconciled with the distance and component photometry by including a fainter, higher-mass component. The quadruple system consists of three M dwarfs (A, B, C) and one white dwarf (D). We determine individual M dwarf masses = 0.53 ± 0.10 , = 0.43 ± 0.04 , and = 0.41 ± 0.04 . The white dwarf mass, 0.54 ± 0.04 , comes from cooling models, an assumed Hyades age of 670 Myr, and consistency with all previous and derived astrometric, photometric, and radial velocity results. Velocities from Hα and He i emission lines confirm the BC period derived from absorption lines, with similar (He i) and higher (Hα) velocity amplitudes. We ascribe the larger Hα amplitude to emission from a region each component shadows from the other, depending on the line of sight.
Interpreting A Pre-trained Model Is A Key For Model Architecture Optimization: A Case Study On Wav2Vec 2.0

A deep Transformer model with good evaluation score does not mean each subnetwork (a.k.a transformer block) learns reasonable representation. Diagnosing abnormal representation and avoiding it can contribute to achieving a better evaluation score. We propose an innovative perspective for analyzing attention patterns: summarize block-level patterns and assume abnormal patterns contribute negative influence. We leverage Wav2Vec 2.0 as a research target and analyze a pre-trained model's pattern. All experiments leverage Librispeech-100-clean as training data. Through avoiding diagnosed abnormal ones, our custom Wav2Vec 2.0 outperforms the original version about 4.8% absolute word error rate (WER) on test-clean with viterbi decoding. Our version is still 0.9% better when decoding with a 4-gram language model. Moreover, we identify that avoiding abnormal patterns is the main contributor for performance boosting.
On-device Federated Learning with Flower

Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud. Despite the algorithmic advancements in FL, the support for on-device training of FL algorithms on edge devices remains poor. In this paper, we present an exploration of on-device FL on various smartphones and embedded devices using the Flower framework. We also evaluate the system costs of on-device FL and discuss how this quantification could be used to design more efficient FL algorithms.
Finite-Sample Analysis for Two Time-scale Non-linear TDC with General Smooth Function Approximation

Temporal-difference learning with gradient correction (TDC) is a two time-scale algorithm for policy evaluation in reinforcement learning. This algorithm was initially proposed with linear function approximation, and was later extended to the one with general smooth function approximation. The asymptotic convergence for the on-policy setting with general smooth function approximation was established in [Bhatnagar et al., 2009], however, the ﬁnite-sample analysis remains unsolved due to challenges in the non-linear and two-time-scale update structure, non-convex objective function and the time-varying projection onto a tangent plane. In this paper, we develop novel techniques to explicitly characterize the ﬁnite-sample error bound for the general off-policy setting with i.i. d. or Markovian samples, and show that it converges as fast as O (1 / √ T ) (up to a factor of O (log T ) ). Our approach can be applied to a wide range of value-based reinforcement learning algorithms with general smooth function approximation.