National Education as a Component of Multicultural Paradigm in the Context of Worldview Confrontations in Ukrainian Society

The article deals with the need to implement the elements of national-patriotic education into a general paradigm of the modern system of higher education in Ukraine. The role of cultural dialogue and mutual understanding as a universal function of education has been discussed. The relationship between culture and education has been outlined. Much attention has been paid to the place and role of culture in shaping a student's personality in the learning process. The issue of national values in a system of global priorities has been raised. The importance of multicultural education in a multi-ethnic Ukrainian society has been emphasized. The problems of defining national identity in a fragile world have been highlighted. The results of the experimental research among students of Mykolayiv V.O.Sukhomlynsky National University in terms of multicultural expertise have been analyzed. The crucial role of the functioning of the Ukrainian language as the state language has been emphasized.

The effect of tidal force and topography on horizontal convection

We present a numerical study on how tidal force and topography influence flow dynamics, transport and mixing in horizontal convection. Our results show that local energy dissipation near topography will be enhanced when the tide is sufficiently strong. Such enhancement is related to the height of the topography and increases as the tidal frequency $\omega$ decreases. The global dissipation is found to be less sensitive to the changes in $\omega$ when the latter becomes small and asymptotically approaches a constant value. We interpret the behaviour of the dissipation as a result of the competition among the dominant forces in the system. According to which mechanism prevails, the flow state of the system can be divided into three regimes, which are the buoyancy-, tide- and drag-control regimes. We show that the mixing efficiency $\eta$ for different tidal energy and topography height can be well described by a universal function $\eta \approx \eta _{HC}/(1+\mathcal {R})$ , where $\eta _{HC}$ is the mixing efficiency in the absence of tide and $\mathcal {R}$ is the ratio between tidal and available potential energy inputs. With this, one can also determine the dominant mechanism at a certain ocean region. We further derive a power law relationship connecting the mixing coefficient and the tidal Reynolds number.

PAF Physiology in Target Organ Systems—A Deep Dive to Understand the PAF Mystery in Pathogenesis of Disease

The purpose of this literature review is to gain an overview of the role of platelet-activating factor (PAF) within each of the body systems and how it contributes to normal and pathophysiological states. The review showed that there are multiple functions of PAF that are common to several body systems; however, there is little evidence to explain why PAF has this affect across multiple systems. Interestingly, there seems to be conflicting research as to whether PAF is an overall protective or pathogenic pathway. Within this research, it was found that there are different pathways depending on the specific body system, as well as between body systems. However, one universal function reported in the literature is of PAF as a pro-inflammatory molecule. Overall, this review identified five major functions of PAF: vasoconstriction, increased inflammation, vascular remodeling, increased edema, and endothelial permeability.

The quantum solitons atomtronic interference device

We study a quantum many-body system of attracting bosons confined in a ring-shaped potential and interrupted by a weak link. With such architecture, the system defines atomtronic quantum interference devices harnessing quantum solitonic currents. We demonstrate that the system is characterized by the specific interplay between the interaction and the strength of the weak link. In particular, we find that, depending on the operating conditions, the current can be a universal function of the relative size between the strength of the impurity and interaction. The low lying many-body states are studied through a quench dynamical protocol that is the atomtronic counterpart of Rabi interferometry. With this approach, we demonstrate how our system defines a two level system of coupled solitonic currents. The current states are addressed through the analysis of the momentum distribution.

Study of Angular Distribution and KNO Scaling in the Collisions of 28Si with Emulsion Nuclei at 14.6A GeV

An attempt has been made to study the angular characteristics of heavy ion collision at high energy in the interactions of 28Si nuclei using with nuclear emulsion. The KNO scaling behavior in terms of the multiplicity distribution has been studied. A simplest universal function has been used to represent the present experimental data.

An Empirical Investigation Into Deep and Shallow Rule Learning

Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest case, concept learning, this is a disjunctive normal form (DNF) description of the positive class. While it is clear that this is sufficient from a logical point of view because every logical expression can be reduced to an equivalent DNF expression, it could nevertheless be the case that more structured representations, which form deep theories by forming intermediate concepts, could be easier to learn, in very much the same way as deep neural networks are able to outperform shallow networks, even though the latter are also universal function approximators. However, there are several non-trivial obstacles that need to be overcome before a sufficiently powerful deep rule learning algorithm could be developed and be compared to the state-of-the-art in inductive rule learning. In this paper, we therefore take a different approach: we empirically compare deep and shallow rule sets that have been optimized with a uniform general mini-batch based optimization algorithm. In our experiments on both artificial and real-world benchmark data, deep rule networks outperformed their shallow counterparts, which we take as an indication that it is worth-while to devote more efforts to learning deep rule structures from data.

What can astrocytes compute?

I demonstrate theoretically that calcium waves in astrocytes can compute anything neurons can. A foundational result in neural computation was proving the firing rate model of neurons defines a universal function approximator. In this work I show a similar proof extends to a model of calcium waves in astrocytes, which I confirm in a series of computer simulations. I argue the major limit in astrocyte computation is not their ability to find approximate solutions, but their computational complexity. I suggest some initial experiments that might be used to confirm these predictions.

Simulating diffusion in the conditions of vapor-liquid phase transition by the molecular dynamics method

The molecular dynamics calculations of diffusion coefficients in binary Lennard-Jones systems have been carried out. The parameters of Lennard-Jones potentials correspond to argon and krypton atoms. The universal dependence of the reduced diffusion coefficient of krypton atoms on density for the homogeneous systems of low and middle densities is found. The deviations of the diffusion coefficients from the universal function are observed for the systems in the vapor – liquid phase transition region. The simulations have shown that almost all krypton atoms have situated inside the liquid phase of argon. Special numerical experiments have shown that the nanodroplets of argon are formed as a result of homogeneous nucleation and then the krypton atoms are captured by these droplets. This phenomenon decreases the diffusion coefficient of krypton atoms greatly.

An improved data-free surrogate model for solving partial differential equations using deep neural networks

Partial differential equations (PDEs) are ubiquitous in natural science and engineering problems. Traditional discrete methods for solving PDEs are usually time-consuming and labor-intensive due to the need for tedious mesh generation and numerical iterations. Recently, deep neural networks have shown new promise in cost-effective surrogate modeling because of their universal function approximation abilities. In this paper, we borrow the idea from physics-informed neural networks (PINNs) and propose an improved data-free surrogate model, DFS-Net. Specifically, we devise an attention-based neural structure containing a weighting mechanism to alleviate the problem of unstable or inaccurate predictions by PINNs. The proposed DFS-Net takes expanded spatial and temporal coordinates as the input and directly outputs the observables (quantities of interest). It approximates the PDE solution by minimizing the weighted residuals of the governing equations and data-fit terms, where no simulation or measured data are needed. The experimental results demonstrate that DFS-Net offers a good trade-off between accuracy and efficiency. It outperforms the widely used surrogate models in terms of prediction performance on different numerical benchmarks, including the Helmholtz, Klein–Gordon, and Navier–Stokes equations.

Learning Aggregation Functions

Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as sum or maximum. However, recent results showed that universal function representation by sum- (or max-) decomposition requires either highly discontinuous (and thus poorly learnable) mappings, or a latent dimension equal to the maximum number of elements in the set. To mitigate this problem, we introduce LAF (Learning Aggregation Function), a learnable aggregator for sets of arbitrary cardinality. LAF can approximate several extensively used aggregators (such as average, sum, maximum) as well as more complex functions (e.g. variance and skewness). We report experiments on semi-synthetic and real data showing that LAF outperforms state-of-the-art sum- (max-) decomposition architectures such as DeepSets and library-based architectures like Principal Neighborhood Aggregation, and can be effectively combined with attention-based architectures.