Miracle-dz/UniFAR-FTUs huggingface dataset:
Instabilities and routes to turbulence in rotating disc boundary layers and cavities

Studied for more than a century, first in the field of geophysics, flows over rotating discs present a great diversity of complex instability behaviours that are not yet fully understood. While the primary instabilities are now well characterized experimentally, theoretically and numerically, their role in the transition mechanisms to turbulence remains an open question that still challenges the scientific community. This article brings together the main results of the literature related to the instabilities over rotating discs, but also in the connected problem of rotating cavities, and reviews the main scenarios currently assumed to describe the flow breakdown to turbulence. A particular focus is on more recent studies of generic flows in rotating cavities bounded by two coaxial rotating discs, that occur in many industrial systems, the performances of which and their improvement are linked to a better understanding of these mechanisms. This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor's seminal Philosophical Transactions paper (part 1)’.
Taylor–Couette and related flows on the centennial of Taylor’s seminal Philosophical Transactions paper: part 1

In 1923, the Philosophical Transactions published G. I. Taylor’s seminal paper on the stability of what we now call Taylor–Couette flow. In the century since the paper was published, Taylor’s ground-breaking linear stability analysis of fluid flow between two rotating cylinders has had an enormous impact on the field of fluid mechanics. The paper’s influence has extended to general rotating flows, geophysical flows and astrophysical flows, not to mention its significance in firmly establishing several foundational concepts in fluid mechanics that are now broadly accepted. This two-part issue includes review articles and research articles spanning a broad range of contemporary research areas, all rooted in Taylor’s landmark paper. This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor’s seminal Philosophical Transactions paper (part 1)’.
Solitary-like and modulated wavepackets in the Couette–Taylor flow with a radial temperature gradient

We have performed numerical and experimental studies of the flow in a large aspect ratio Couette–Taylor system with a rotating inner cylinder and a fixed radial temperature gradient. The base flow state is a superposition of an azimuthal flow induced by rotation and an axial large convective cell induced by the temperature gradient. For a relatively large temperature gradient, the rotation rate of the inner cylinder destabilizes the convective cell to give rise to travelling wave pattern through a subcritical bifurcation. This wave pattern is associated with a temperature mode and it consists of helical vortices travelling in the annulus. In a small range of the rotation rate, helical vortices have longitudinal meandering leading to the formation of kinks randomly distributed, leading to spatio-temporal disordered patterns. The flow becomes regular for a large interval of rotation rate. The friction, the momentum and the heat transfer coefficients are computed and found to be independent of the heating direction. This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor's seminal Philosophical Transactions paper (part 1)’.
Contrastive Graph Similarity Networks

Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a variety of fields, including object tracking, recommender systems, similarity search, etc. Recent methods for graph similarity learning that utilize deep learning typically share two deficiencies: (1) they leverage graph neural networks as backbones for learning graph representations but have not well captured the complex information inside data, and (2) they employ a cross-graph attention mechanism for graph similarity learning, which is computationally expensive. Taking these limitations into consideration, a method for graph similarity learning is devised in this study, namely, Contrastive Graph Similarity Network (CGSim). To enhance graph similarity learning, CGSim makes use of the complementary information of two input graphs and captures pairwise relations in a contrastive learning framework. By developing a dual contrastive learning module with a node-graph matching and a graph-graph matching mechanism, our method significantly reduces the quadratic time complexity for cross-graph interaction modeling to linear time complexity. Jointly learning in an end-to-end framework, the graph representation embedding module and the well-designed contrastive learning module can be beneficial to one another. A comprehensive series of experiments indicate that CGSim outperforms state-of-the-art baselines on six datasets and significantly reduces the computational cost, which demonstrates our CGSim model’s superiority over other baselines.
Duality for convolution on subclasses of analytic functions and weighted integral operators

Abstract In this article, we investigate a class of analytic functions defined on the unit open disc U = { z : ∣ z ∣ < 1 } {\mathcal{U}}=\left\{z:| z| \lt 1\right\} , such that for every f ∈ P α ( β , γ ) f\in {{\mathcal{P}}}_{\alpha }\left(\beta ,\gamma ) , α > 0 \alpha \gt 0 , 0 ≤ β ≤ 1 0\le \beta \le 1 , 0 < γ ≤ 1 0\lt \gamma \le 1 , and ∣ z ∣ < 1 | z| \lt 1 , the inequality Re f ′ ( z ) + 1 − γ α γ z f ″ ( z ) − β 1 − β > 0 {\rm{Re}}\left\{\frac{f^{\prime} \left(z)+\frac{1-\gamma }{\alpha \gamma }z{f}^{^{\prime\prime} }\left(z)-\beta }{1-\beta }\right\}\gt 0 holds. We find conditions on the numbers α , β \alpha ,\beta , and γ \gamma such that P α ( β , γ ) ⊆ S P ( λ ) {{\mathcal{P}}}_{\alpha }\left(\beta ,\gamma )\subseteq SP\left(\lambda ) , for λ ∈ ( − π 2 , π 2 ) \lambda \in \left(-\frac{\pi }{2},\frac{\pi }{2}) , where S P ( λ ) SP\left(\lambda ) denotes the set of all λ \lambda -spirallike functions. We also make use of Ruscheweyh’s duality theory to derive conditions on the numbers α , β , γ \alpha ,\beta ,\gamma and the real-valued function φ \varphi so that the integral operator V φ ( f ) {V}_{\varphi }(f) maps the set P α ( β , γ ) {{\mathcal{P}}}_{\alpha }\left(\beta ,\gamma ) into the set S P ( λ ) SP\left(\lambda ) , provided φ \varphi is non-negative normalized function ( ∫ 0 1 φ ( t ) d t = 1 ) \left({\int }_{0}^{1}\varphi \left(t){\rm{d}}t=1) and V φ ( f ) ( z ) = ∫ 0 1 φ ( t ) f ( t z ) t d t . {V}_{\varphi }(f)\left(z)=\underset{0}{\overset{1}{\int }}\varphi \left(t)\frac{f\left(tz)}{t}{\rm{d}}t.
Concentration inequalities, large and moderate deviations for self-normalized empirical processes

We consider the supremum w n of self-normalized empirical processes indexed by unbounded classes of functions F. Such variables are of interest in various statistical applications, for example, the likelihood ratio tests of contamination. Using the Herbst method, we prove an exponential concentration inequality for w n under a second moment assumption on the envelope function of F. This inequality is applied to obtain moderate deviations for w n . We also provide large deviations results for some unbounded parametric classes F.
Calculation and Measurement of Depth Dose Distributions in Bricks

The dose accumulated in bricks exposed to gamma radiation can be measured as a function of depth using luminescence methods. The dependence of dose on depth has the potential of providing information on the energy as well as on the angular distribution of the incident radiation, which could give indications on the configuration of the radiation sources. A prerequisite for such an analysis is a comprehensive knowledge on the dependence of dose on depth for different source energies and for specific source configurations. Depth dose distributions in brick walls have been calculated by Monte Carlo simulations for a source distribution on a wall, for a source distribution on the ground and for a parallel photon beam, for source energies ranging from 140 keV to 1600 keV. It is shown that depth dose distributions depend substantially on source configuration and energy. Depth dose distributions measured in ceramic material irradiated in the laboratory and in a brick from a contaminated area are compared with results of Monte Carlo calculations.
CRITICAL PERSISTENT CURRENTS IN HARD SUPERCONDUCTORS

Persistent currents induced in hard superconductors (HSC) were investigated by measuring the resulting magnetic effects. The tubular HSC examined were Nb powder, Nb/sub 3/Sn, and 3Nb- Zr samples of uniform wall thickness and length large compared to the diameter. Experimental evidence is presented that in HSC the Lorentz force plays a crucial role in determining the critical current density and the critical persistent currents in decay with a measurable rate. (H.D.R.)
Quantifying the impact of different copulas in a generalized CreditRisk+ framework An empirical study

Abstract Without any doubt, credit risk is one of the most important risk types in the classical banking industry. Consequently, banks are required by supervisory audits to allocate economic capital to cover unexpected future credit losses. Typically, the amount of economical capital is determined with a credit portfolio model, e.g. using the popular CreditRisk+ framework (1997) or one of its recent generalizations (e.g. [8] or [15]). Relying on specific distributional assumptions, the credit loss distribution of the CreditRisk+ class can be determined analytically and in real time. With respect to the current regulatory requirements (see, e.g. [4, p. 9-16] or [2]), banks are also required to quantify how sensitive their models (and the resulting risk figures) are if fundamental assumptions are modified. Against this background, we focus on the impact of different dependence structures (between the counterparties of the bank’s portfolio) within a (generalized) CreditRisk+ framework which can be represented in terms of copulas. Concretely, we present some results on the unknown (implicit) copula of generalized CreditRisk+ models and quantify the effect of the choice of the copula (between economic sectors) on the risk figures for a hypothetical loan portfolio and a variety of parametric copulas.
Growth of nominally undoped p-type ZnO on Si by pulsed-laser deposition

We report on the growth of nominally undoped p-type ZnO films on Si(111) substrates by pulsed-laser deposition. Hall effect measurements show that the undoped ZnO films change from n-type to p-type material when the oxygen pressure changes from 6×10−5to3×10−4Torr during growth. Ti∕Au contacts produce ohmic behavior to n-type ZnO (∼1017cm−3), but leaky Schottky behavior to p-type ZnO (∼1018cm−3). Electrical and photoluminescence results indicate that native defects, such as oxygen and zinc vacancies, could play an important role in determining the conductivity of these nominally undoped ZnO films.
Computing stereoscopic disparity with binocular cortical simple and complex cells

The analysis of the depth coordinates of objects in a visual scene is of vital importance for animals and in technological applications. In 2D camera projections of a 3D visual scene depth information is initially lost but can be recovered when using two cameras in a stereoscopic setup. The projection of an object located at a finite distance from the camera is laterally displaced in the left camera image compared to the right camera image. This displacement, called disparity, can be used to retrieve the object's depth coordinate. Sanger (1988) proposed that the phase relation between two spatial band-pass filter responses (Gabor filters) could be used to measure local disparity. Simple cells in the visual cortex have receptive fields which can be described as Gabor filters. Most of them are driven binocularly and tuned to respond most strongly to stimuli at a certain preferred distance from the fixation plane. Cortical complex cells receive input from simple cells. As a consequence also complex cells implicitly encode visual depth. Here we formalize the computational procedure which could underlie the extraction of depth information from complex cell responses and solve it analytically for two different stimulus situations. The theory predicts that a strong discrepancy should exist between the actual and the perceived depth of sine-wave luminance modulated ("grating") stimuli. If the spatial frequency of the grating is increased it should appear to move closer to the observer.
Jet stripping of molten metallic coatings

The thickness of the coating on a strip produced by modern hot‐dip galvanizing lines is controlled with air jets which generate a ‘‘barrier’’ to the molten coating material as it is drawn from its bath by the emerging strip. It is shown that an improved understanding of the process and more accurate predictions may be obtained by modifying the existing theory to include the jet‐induced surface shear acting in conjunction with the jet stagnation pressure gradient effect.
Spectroscopic evidence for rapid blob ejection in Wolf-Rayet stars

The five bright Cygnus WN stars in the unblended He II 5411A emission line has been monitored at high spectral resolution and high S/N during four or five nights. Typical data are presented here for the two single WN6 stars HD 191765 and HD 192163. The spectra reveal narrow emission bumps superposed on a broad smooth background wind profile with FWHM of about 2-10 A and height 10 percent or less in continuum units for HD 191765. The bumps always appear to accelerate outward along with the general wind, on a time scale of hours. 20 references.
BLIND ADAPTATION OF DECISION FEEDBACK EQUALIZERS: GROSS CONVERGENCE PROPERTIES*

SUMMARY An analysis of the stochastic dynamics of the blind adaptation of decision feedback equalizers is presented. The analysis accounts for the presence of decision errors which, under feedback, are propagated. A number of blind algorithms are presented and a theory is developed to explain gross convergence properties observed through simulations. The possibility of and mechanism behind undesirable local minima are highlighted and a detailed case study is given. The potential capture by local minima shows the importance of good initialization. These results superficially resemble those obtained for blind adaptation applied to linear equalizers.
Skew product maps related to finitely generated rational semigroups

We consider skew-product maps related to dynamics of semigroups generated by rational maps on the Riemann sphere. The entropy of these maps will be given and we will see there exists the unique maximal entropy measure. We will also show the uniqueness of the self-similar measure. We will estimate the Hausdorff dimension of the Julia sets of semigroups.
Rough‐and‐ready assessment of the degree and importance of smoothing in functional estimation

In nonparametric estimation of functionals of a distribution, it may or may not be desirable, or indeed necessary, to introduce a degree of smoothing into this estimation. In this article, I describe a method for assessing, with just a little thought about the functional of interest, (i) whether smoothing is likely to prove worthwhile, and (ii) if so, roughly how much smoothing is appropriate (in order‐of‐magnitude terms). This rule‐of‐thumb is not guaranteed to be accurate nor does it give a complete answer to the smoothing problem. However, I have found it very useful over a number of years; many examples of its use, and limitations, are given.
Quantum Mechanics Helps in Learning for More Intelligent Robots

A learning algorithm based on the state superposition principle is presented. The theoretical analysis shows that the needed fundamental transformations to realize this algorithm are the same as those needed in the Grover algorithm and are within current state-of-the-art technology. The simulated experiment shows that the quantum learning algorithm can help robots to learn faster and to become more intelligent.
A Method to Obtain the Position of Space Object Based on Monocular Vision and Laser Ring

When the cylindrical laser shines on the target object, a spot can be obtained, which the edge is a closed curve, marked as C1. The imaging of the curve C1 on the image surface of CCD is a closed curve C2 too. Coordinate system is established to describe the position relationship among camera, image and light source, and to analyze the principle for monocular vision and laser ring to get the information about the object depth. In order to solve the problem and make the above principle clear, the key is to work out the expression for the curve C2 on the image surface of CCD. In order to calculate the closed curve C2 expression, the curve C2 will firstly be divided into two parts, the upper curve and the lower one. According to least-square polynomial, discrete points on the curves of two parts are drawn out, constraints are established and the curve equations are fitted. Then, to verify practicality of this method, a virtual model scene will be created, through which relevant data describing edge of virtual CCD image and that of a virtual spot when the virtual light source alights on the virtual object will be obtained. At last, closed curve equation will be fitted in accordance with data describing edge of virtual image; the position of space object will be fixed by making use of light source equation and closed curve equation; and a contrast will be made between the calculated value and data of the spot edge to prove whether a method to obtain the position of space objects based on monocular vision and laser ring is feasible.
Quasi-local mass for small surfaces

The authors calculate Penrose's quasi-local mass for a small sphere by expanding in powers of the affine parameter along the generators of a light cone. They then check the result and extend it to small surfaces of more general shape by techniques involving more direct use of spinor calculus. In a non-vacuum spacetime, the mass inside a small sphere is obtained from a 4-momentum Pa which is the product of the volume enclosed by the surface and the 4-vector Tabtb, where ta is a unit timelike vector orthogonal to a spanning 3-surface. In the vacuum case, the mass vanishes at the fifth order in the diameter of the surface, both for small spheres and for small surfaces of more general shape.
Mapping meaning onto form: A corpus-based contrastive study of nominal modification in English and Spanish

This paper is a corpus-based contrastive study of one particular semantic area — the modification of nouns — and the grammatical structures available in English and Spanish to actualize this particular meaning within the scope of the NP. Two large monolingual reference corpora, one in each language, have been used to extract the empirical data on which to base the analysis. A functional approach has been followed, starting from perceived similarities in meaning and trying to reveal the subtle links between particular semantic functions and the structures most commonly employed by native speakers of the two languages to convey those semantic functions. The working procedure will be described in detail, from the data selection process, to the description, juxtaposition and final contrast. This paper presents part of the contrastive results found in a larger study (Ramon 2003), and shows the appropriateness of the methodology employed in order to obtain contrastive data useful in applied areas such as foreign language teaching, and, especially, in the field of translation.
An intrinsically interpretable neural network architecture for sequence to function learning

Motivation Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called tiSFM (totally interpretable sequence to function model). tiSFM improves upon the performance of standard multi-layer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multi-layer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. Results We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM’s model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. Availability and implementation The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python. Contact atb44@pitt.edu
An analysis of a scheme proposed for electronic voting systems

Voting mechanisms allow the expression of the elections by a democratic approach. Any voting scheme must ensure, preferably in an efficient way, a series of safety measures such as confidentiality, integrity and anonymity. Since the 1980s, the concept of electronic voting became more and more of interest, being an advantageous or even necessary alternative for the organization of secure elections. In this paper, we give an overview for the e-voting mechanisms together with the security features they must fulfill. Then we focus on the blind signature paradigm, specifically on the Pairing Free Identity-Based Blind Signature Scheme with Message Recovery (PF-IDBS-MR) [1]. Our goal is to give a better understanding on the PF-IDBS-MR scheme by offering an adaptation on the standard voting protocol’s phases. More important, we analyze if the general security requirements and the recommendations proposed by the Council of Europe are met by the scheme.
It Runs and it Hides: A Function-Hiding Construction for Private-Key Multi-Input Functional Encryption

Functional Encryption (FE) is a modern cryptographic technique that allows users to learn only a specific function of the encrypted data and nothing else about its actual content. While the first notions of security in FE revolved around the privacy of the encrypted data, more recent approaches also consider the privacy of the computed function. While in the public key setting, only a limited level of function-privacy can be achieved, in the private-key setting privacy potential is significantly larger. However, this potential is still limited by the lack of rich function families. For this work, we started by identifying the limitations of the current state-of-the-art approaches which, in its turn, allowed us to consider a new threat model for FE schemes. To the best of our knowledge, we here present the first attempt to quantify the leakage during the execution of an FE scheme. By leveraging the functionality offered by Trusted Execution Environments, we propose a construction that given any message-private functional encryption scheme yields a function-private one. Finally, we argue in favour of our construction’s applicability on constrained devices by showing that it has low storage and computation costs.