Miracle-dz/UniFAR-FTUs huggingface dataset:
Signatures of magnetic-field effects in nonsequential double ionization manifesting as backscattering for molecules versus forward scattering for atoms

For two-electron diatomic molecules, we investigate magnetic field effects in non-sequential double ionization where recollisions prevail. We do so by formulating a three-dimensional semi-classical model that fully accounts for the Coulomb singularities and for magnetic field effects during time propagation. Using this model, we identify a prominent signature of non-dipole effects. Namely, we demonstrate that the recolliding electron back-scatters along the direction of light propagation. Hence, this electron escapes opposite to the direction of change in momentum due to the magnetic field. This is in striking contrast to strongly-driven atoms where the recolliding electron forward-scatters along the direction of light propagation. We attribute these distinct signatures to the different gate that the magnetic field creates jointly with a soft recollision in molecules compared to a hard recollision in atoms. These two different gates give rise, shortly before recollision, to different momenta and positions of the recolliding electron along the direction of light propagation. As a result, we show that the Coulomb forces from the nuclei act to back-scatter the recolliding electron in molecules and forward-scatter it in atoms along the direction of light propagation.
Delivery of Nebulised Drugs using Endotracheal Tube

The purpose of the studies was to estimate efficiency of delivering nebulised drugs into the lower respiratory tract through endotracheal tubes (ET tubes) which are commonly used in the treatment of uncooperative patients. Water solution of Disodium Cromoglycate (DSCG) was nebulised with a constant air flow (25 l/min). Experimental studies were done for eight ET tubes with varying sizes (internal diameter, length) and made of two different materials. Size distribution of aerosol leaving ET tubes was determined with the use of aerosol spectrometer. Fine Particle Fraction (FPF) and Mass Median Aerodynamic Diameter (MMAD) were calculated for the aerosol leaving each tube. Additionally, mass of the Disodium Cromoglycate deposited into each endotracheal tube was determined. ET tubes can significantly influence the parameters of delivered aerosol depending on their diameter. FPF of aerosol delivered in to the respiratory tract is lower if small endotracheal tubes are used. However, MMAD and FPF for large endotracheal tubes are almost identical with MMAD and FPF from nebuliser. The results indicate that a substantial fraction of large droplets is eliminated from the aerosol stream in long endotracheal tubes (270 mm). In this case the mass of drug delivered through ET tubes is reduced but the content of small droplets increases (high value of FPF).
Physical Relaxation Terms for Compressible Two-Phase Systems

In this note, we propose the first mathematical derivation of a macroscopic Baer-Nunziato type system for compressible two-phase flows allowing two pressure state laws depending on the different phases. By doing so, we extend the results obtained by the first author and M. Hillairet (Annales ENS 2019) to cover this important physical situation. A relaxation term in the mass fraction equation is obtained without closure assumptions contrarily to theoretical-physics literature dedicated to mixture theory, see for instance "Thermo-Fluid dynamics of Two Fluid Flows" by M. Ishii. The relaxation parameter is linked to the viscosities of the different fluids (which may be small for applications) and the relaxed quantity is linked to the laws chosen at interfaces of the two-fluid system at a mesoscale.
Handy Formulas for Binomial Moments.

Despite the relevance of the binomial distribution for probability theory and applied statistical inference, its higher-order moments are poorly understood. The existing formulas are either not general enough, or not structured or simplified enough for intended applications. This paper introduces novel formulas for binomial moments, in terms of \emph{variance} rather than success probability. The obtained formulas are arguably better structured and simpler compared to prior works. In addition, the paper presents algorithms to derive these formulas along with working implementation in the Python symbolic algebra package. The novel approach is a combinatorial argument coupled with clever algebraic simplifications which rely on symmetrization theory. As an interesting byproduct we establish \emph{asymptotically sharp estimates for central binomial moments}, improving upon partial results from prior works.
Discriminating Between Similar Nordic Languages

Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language identification for the Nordic languages, which often suffer miscategorisation by existing state-of-the-art tools. Concretely we will focus on discrimination between six Nordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokmål), Faroese and Icelandic.
Formation of Chromate Conversion Coatings on Al‐Cu‐Mg Intermetallic Compounds and Alloys

Chromium conversion coating (CCC) formation was monitored on copper-containing aluminum alloys AA2024-T3 and a specially cast Al x Cu y Mg z ingot using Raman spectroscopy of the 860 cm -1 band. The Al x Cu y Mg z ingot's different phases were compositionally analyzed with energy-dispersive spectroscopy (EDS) and Auger electron spectroscopy (AES) before and after treatments using commercial Alodine 1200S chromium solution. Profilometry and Raman intensity of the 860 cm -1 band were used to gauge CCC film thickness on AA-2024-T3 alloy. A linear relationship between CCC thickness and the integrated 860 cm -1 band was shown to exist on AA-2024 alloy. Raman intensity of the 860 cm -1 band was also integrated and spatially mapped using commercially available software. These results were verified with AES depth profiling using an Ar + ion sputter. CCC formation was found to he slower on the Al x Cu y Mg z phases with higher copper content. Similarly, in 2024-T3, CCC formation was suppressed on S-phase Al 2 CuMg and depressed Al 20 Cu 2 FeMn intermetallics of 2024 aluminum alloy. Fe(CN) 3-/4- 6 adsorption on the Al x- Cu v Mg z ingot phases, Cu, Al, Mg, and AA2024-T3 were characterized by Raman in the 2100 cm -1 range.
Cyclic orthogonal convolutions for long-range integration of features

In Convolutional Neural Networks (CNNs) information flows across a small neighbourhood of each pixel of an image, preventing long-range integration of features before reaching deep layers in the network. We propose a novel architecture that allows flexible information flow between features $z$ and locations $(x,y)$ across the entire image with a small number of layers. This architecture uses a cycle of three orthogonal convolutions, not only in $(x,y)$ coordinates, but also in $(x,z)$ and $(y,z)$ coordinates. We stack a sequence of such cycles to obtain our deep network, named CycleNet. As this only requires a permutation of the axes of a standard convolution, its performance can be directly compared to a CNN. Our model obtains competitive results at image classification on CIFAR-10 and ImageNet datasets, when compared to CNNs of similar size. We hypothesise that long-range integration favours recognition of objects by shape rather than texture, and we show that CycleNet transfers better than CNNs to stylised images. On the Pathfinder challenge, where integration of distant features is crucial, CycleNet outperforms CNNs by a large margin. We also show that even when employing a small convolutional kernel, the size of receptive fields of CycleNet reaches its maximum after one cycle, while conventional CNNs require a large number of layers.
Structure learning for extremal tree models

Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. For the important case of tree models, we develop a data‐driven methodology for learning the graphical structure. We show that sample versions of the extremal correlation and a new summary statistic, which we call the extremal variogram, can be used as weights for a minimum spanning tree to consistently recover the true underlying tree. Remarkably, this implies that extremal tree models can be learned in a completely non‐parametric fashion by using simple summary statistics and without the need to assume discrete distributions, existence of densities or parametric models for bivariate distributions.
Improving the electronic and optical properties of Carbz-PAHTDDT-based dyes through chemical modifications

To investigate geometric and electronic structure, a theoretical study is performed on the Carbz-PAHTDDT (S9) organic dye sensitizer. This dye has a reported promising efficiency when coupled with ferrocene-based electrolyte composition. The present study indicated that the long-range correction to the theoretical model in the time-dependent density functional theory is important to produce accurate absorption wavelengths. In the present study, the chemical structure of the original Carbz-PAHTDDT dye on the {\pi}-conjugated bridge is also rationally changed to produce new dyes aiming at enhancing the spectral response as a desirable property of organic dyes in DSSC application. The theoretical studies on the new dyes have shown a significant red-shifting and broadening of their absorption spectra.
Exact solutions and residual regulator dependence in functional renormalisation group flows

We construct exact solutions to the functional renormalisation group equation of the O(N) model and the Gross–Neveu model at large N for 2 < d < 4, without specifying the form of the regulator. This allows to investigate which quantities are independent of the choice of regulator without being plagued by truncation artefacts. We find that only universal quantities, like critical exponents, and qualitative features, like the existence of a finite vacuum expectation value, are regulator-independent, whereas values of coupling constants are generically arbitrary. We also provide a general algorithm to construct a concrete operator basis for truncations in the derivative expansion and the Blaizot–Méndez–Wschebor scheme.
Suitability of aonla (Emblica officinalis) cultivars for development of osmo air dried slices

The present study was undertaken to find out most suitable cultivar of aonla (Emblica officinalis Gaertn.) for development of value added products, i.e. osmo-air dried slices from four aonla cultivars, viz. Banarasi, Chakaiya, NA7 and Desi. The osmo air dried slices were prepared using sugar syrup of 700 Brix, citric acid for 24 hrs and dried at 600C. The slices were packed in polythene pouches and stored at ambient conditions to observe the effect of storage on quality and shelf life. The prepared samples were analysed for various quality parameters. An increasing trend was observed in the acidity, where as total sugars, reducing sugars, water activity and ascorbic acid content showed a decreasing trend in all the four cultivars. The highest acidity (4.68 %) was found in Desi cultivar, whereas the highest total sugars and reducing sugar (43.42 %, 33.02 %, respectively) were recorded in Chakaiya after 135 days of storage. The acceptability of the product was evaluated in terms of colour, flavour, taste and overall acceptability. The highest mean score for colour (7.12), texture (7.59), flavour (7.68) and overall acceptability (7.43) was found in Chakaiya followed by Banarasi cultivar. Thus, both Chakaiya and Banarasi were found to be suitable cultivars for making of osmo air dried slices.
Sufficiency of a Gaussian power spectrum likelihood for accurate cosmology from upcoming weak lensing surveys

We investigate whether a Gaussian likelihood is sufficient to obtain accurate parameter constraints from a Euclid-like combined tomographic power spectrum analysis of weak lensing, galaxy clustering and their cross-correlation. Testing its performance on the full sky against the Wishart distribution, which is the exact likelihood under the assumption of Gaussian fields, we find that the Gaussian likelihood returns accurate parameter constraints. This accuracy is robust to the choices made in the likelihood analysis, including the choice of fiducial cosmology, the range of scales included, and the random noise level. We extend our results to the cut sky by evaluating the additional non-Gaussianity of the joint cut-sky likelihood in both its marginal distributions and dependence structure. We find that the cut-sky likelihood is more non-Gaussian than the full-sky likelihood, but at a level insufficient to introduce significant inaccuracy into parameter constraints obtained using the Gaussian likelihood. Our results should not be affected by the assumption of Gaussian fields, as this approximation only becomes inaccurate on small scales, which in turn corresponds to the limit in which any non-Gaussianity of the likelihood becomes negligible. We nevertheless compare against N-body weak lensing simulations and find no evidence of significant additional non-Gaussianity in the likelihood. Our results indicate that a Gaussian likelihood will be sufficient for robust parameter constraints with power spectra from Stage IV weak lensing surveys.
Bochner-Schoenberg-Eberlein-type inequality of the direct sum, ideals and quotient of Frechet algebras.

Let A and B be two commutative semisimple Frechet algebras. We first give a characterization of the multiplier algebra of the direct sum of A and B. We then prove that A \oplus B is a BSE-algebra if and only if A and B are BSE-algebras. Furthermore, for a closed ideal I of A, we study multipliers of ideals and quotient algebras of A and show that I and A/I are BSE-algebras, under certain conditions.
When is memorization of irrelevant training data necessary for high-accuracy learning?

Modern machine learning models are complex and frequently encode surprising amounts of information about individual inputs. In extreme cases, complex models appear to memorize entire input examples, including seemingly irrelevant information (social security numbers from text, for example). In this paper, we aim to understand whether this sort of memorization is necessary for accurate learning. We describe natural prediction problems in which every sufficiently accurate training algorithm must encode, in the prediction model, essentially all the information about a large subset of its training examples. This remains true even when the examples are high-dimensional and have entropy much higher than the sample size, and even when most of that information is ultimately irrelevant to the task at hand. Further, our results do not depend on the training algorithm or the class of models used for learning. Our problems are simple and fairly natural variants of the next-symbol prediction and the cluster labeling tasks. These tasks can be seen as abstractions of text- and image-related prediction problems. To establish our results, we reduce from a family of one-way communication problems for which we prove new information complexity lower bounds.
Impact of Parental Involvement towards School Education between Boys and Girls

Children are the building blocks of the nation and home is the first and most important school for the child, will ever have. Parental involvement has been recognized as the most important factor to influence the childhood and adulthood. Any enrichment programme, for maximizing the potentials of all the children has to have active parent-child interaction. The role of parents in bringing up their children is quite important both in terms of their devotion with respect to time spent with them, money and other consideration. Parents transmit the culture and custom of society to the incoming generation. Parent involvement implies how the parents involve themselves in developing overall personality of the child. It may be described as the allocation of resources to the child’s school behavior. Parents’ involvement in education either in school or at home, will create a partnership between school and parents. The resulting partnership between parents and school will develop effective communication from home to school and school to home. The main objective of the present study was to compare the parent knowledge, attitude and involvement towards school education between boys and girls children. Data was collected with the help of standardized tool ‘Parent Involvement Scale’ by Dr. Rita Chopra and Surbala Sahoo. Sample size was 100 boys and girls of IVth and Vth Standards, will be selected from Agra city. Analysis of data was done by percentile technique. Findings of the study indicate that involvement of parents towards boy is more than girls. Although they support both of them but in some areas like tours from schools, outdoor group study, financial matter etc, they still ignore girls.
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization

This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources. Recently, deep neural networks have led to promising results for this task, yet they require a large amount of labeled data for training. Recording and labeling such datasets is very costly, especially because data needs to be diverse enough to cover different acoustic conditions. In this paper, we leverage acoustic simulators to inexpensively generate labeled training samples. However, models trained on synthetic data tend to perform poorly with real-world recordings due to the domain mismatch. For this, we explore two domain adaptation methods using adversarial learning for sound source localization which use labeled synthetic data and unlabeled real data. We propose a novel ensemble approach that combines discriminators applied at different feature levels of the localization model. Experiments show that our ensemble discrimination method significantly improves the localization performance without requiring any label from the real data.
Selfish Creation of Social Networks

Understanding real-world networks is a core research endeavor within the last two decades. Network Creation Games are a promising approach for this from a game-theoretic perspective. In these games, selfish agents corresponding to nodes in a network strategically decide which links to form to optimize their centrality. Many versions have been introduced and analyzed, but none of them fits to modeling the evolution of social networks. In real-world social networks connections are often established by recommendations from common acquaintances or by a chain of such recommendations. Thus establishing and maintaining a contact with a friend of a friend is easier than connecting to complete strangers. This explains the high clustering, i.e., the abundance of triangles, in real-world social networks. We propose and analyze a network creation model inspired by real-world social networks. In our model edges are formed via bilateral consent of both endpoints and the cost for establishing and maintaining an edge is proportional to the distance of the endpoints before establishing the connection. We provide results for generic cost functions which essentially only must be convex functions in the distance of the endpoints without the respective edge. For this broad class of cost functions we provide many structural properties of equilibrium networks and prove (almost) tight bounds on the diameter, the Price of Anarchy and the Price of Stability. Moreover, as a proof-of-concept we show via experiments that the created equilibrium networks of our model indeed closely mimic real-world social networks. We observe degree distributions that seem to follow a power-law, high clustering, and low diameters. This can be seen as a promising first step towards game-theoretic network creation models that predict networks featuring all core real-world properties.
Complete 1H and 13C NMR Spectral Assignment of Cabraleadiol, a Dammarane Triterpene from Dysoxylum malabaricum Bedd

Three dammarane triterpenoids, cabraleone (20S,24S‐eopxy‐25‐hydroxydammaran‐3‐one), cabraleadiol (20S,24S‐epoxydammarane‐3α,25‐diol) and shoreic acid (20S,24S‐epoxy‐25‐hydroxy‐3,4‐secodammar‐4(28)‐en‐3‐oicacid), were isolated for the first time from the stem bark of Dysoxylum malabaricum Bedd. (Meliaceae). The complete 1H and 13C NMR spectral assignment of one of these compounds, namely cabraleadiol, and its 3‐O‐acetyl derivative were achieved by 2D NMR spectroscopy and NOE difference spectroscopy. The C‐20 and C‐24configurations in these compounds are discussed on the basis of the present NMR spectroscopic data.
Short communication First rainfall data from the KZN Drakensberg escarpment edge (2002 and 2003)

Rainfall measured on the KwaZulu-Natal Drakensberg escarpment, the first from above 2 800m a.s. l. , are presented from two locations. Total rainfall at the top of Sani Pass (2 850 m a.s.l.) in the southern Drakensberg was 742 mm in 2002, while the January months of 2002 and 2003 averaged 109 mm. Rainfall on Sentinel Peak (3 165 m a.s.l.) in the northern KZN Drakensberg during 2003 totalled 765 mm and 145 mm was measured in January 2003. Recorded rainfall was marginally lower than, but within 6% of, rainfall recorded at adjacent lower altitude Drakensberg stations over the same period. The number of rain days increased marginally with altitude and the data suggest that even though the amount of rainfall on the escarpment is similar to that at lower altitude, the frequency of rainfall events is higher on the escarpment. Although 2002 and 2003 were dryer than normal years in the region, comparisons between these data and prior estimations, where rainfall was expected to range between 1 500 and 2 000 mm/a, shows that totals for the summit of the escarpment could have been over-estimated in the past. Measurement of rainfall is ongoing. Water SA Vol.31 (3) 2005: pp.399-402
Laser-Induced Europium(III) Luminescence and NMR Spectroscopic Characterization of Macrocyclic Diaza Crown Ether Complexes Containing Carboxylate Ligating Groups

The Eu 3+ and Y 3+ complexes of 1,10-diaza-4,7,13,16-tetraoxacyclooctadecane-N,N'-diacetic acid 1,7-diaza-4,10,13- trioxacyclopentadecane-N,N'-diacetic acid, and the open-chain analogue ethylene glycol bis (β-aminoethyl ether)-N,- N,N',N'-tetraacetic acid have been characterized in solution by using Eu 3+ laser-induced luminescence and 1 H and 13 C NMR spectroscopy. All of these ligands form 1:1 complexes with Eu 1+  in solution with the luminescence lifetimes in H 2 O and D 2 O providing the number of coordinated water molecules
Self‐diffusion in fluids in microporous solids

Enskog’s kinetic theory of tracer diffusion has been generalized recently to strongly inhomogeneous fluids and is here applied to self‐diffusion in fluids in slit pores, i.e., fluids confined between parallel, flat, solid walls. In pores narrower than ten molecular diameters, the diffusivity deviates significantly from its value in bulk phase at the same temperature and chemical potential. Because of the confining pore walls, the fluid tends to form layers. The pore diffusivity oscillates as a function of pore width, a local minimum occurring when the packing of a given number of fluid layers is favored and a local maximum in the region of transition between these favored packing widths. The theory predicts fluid density distributions in good agreement with computer simulations of a similar fluid. The predicted diffusivities are in good qualitative agreement with computer simulations, although quantitatively the predicted oscillations are sharper than those observed in the computer simulations. This work .