Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergence and Relevance of Criticality in Deep Learning

Deep learning has been successfully applied to various tasks, but its underlying mechanism remains unclear. Neural networks map input data to hidden states in deep layers. As deeper layers have fewer degrees of freedom, subsets of data are transformed into identical states in the deep layers. In this sense, deep learning can be considered as a hierarchical data grouping process. In this Letter, we discover that deep learning forces the size distributions of the data clusters to follow power laws with a different power exponent within each layer. In particular, we identify a critical layer where the cluster size distribution obeys a reciprocal relationship between rank and frequency, also known as Zipf's law. Deep learning ensures balanced data grouping by extracting similarities and differences between data. Furthermore, we verify that the data structure in the critical layer is most informative to reliably generate patterns of training data. Therefore, the criticality can explain the operational excellence of deep learning and provide a useful concept for probing optimal network architectures.
Efficient optical cloud removal technique for earth observation based on MOEMs device

In Earth Observation instruments, observation of scenes including bright sources leads to an important degradation of the recorded signal. We propose a new concept to remove dynamically the bright sources and then obtain a field of view with an optically enhanced Signal-to-Noise Ratio (SNR). Micro-Opto-Electro-Mechanical Systems (MOEMS) could be key components in future generation of space instruments. MOEMS-based programmable slit masks will permit the straylight control in future Earth Observation instruments. Experimental demonstration of this concept has been conducted on a dedicated bench. This successful first demonstration shows the high potential of this new concept in future spectro-imager for Earth Observation.
Dynamic Partition of Complex Networks

Finding the reduced-dimensional structure is critical to understanding complex networks. Existing approaches such as spectral clustering are applicable only when the full network is explicitly observed. In this paper, we focus on the online factorization and partition of implicit large-scale networks based on observations from an associated random walk. We propose an efficient and scalable nonconvex stochastic gradient algorithm. It is able to process dependent data dynamically generated by the underlying network and learn a low-dimensional representation for each vertex. By applying a diffusion approximation analysis, we show that the nonconvex stochastic algorithm achieves nearly optimal sample complexity. Once given the learned low-dimensional representations, we further apply clustering techniques to recover the network partition. We show that, when the associated Markov process is lumpable, one can recover the partition exactly with high probability. The proposed approach is experimented on Manhattan taxi data.
Applications of Alughte Transform for Berezin Radius Inequalities

– In functional analysis, linear operators induced by functions are frequently encountered; thesecontain Hankel operators, constitution operators, and Toeplitz operators. The symbol of the resultantoperator is another name for the inciting function. In many instances, a linear operator on a Hilbert spaceℋ results in a function on a subset of a topological space. As a result, we regularly investigate operatorsinduced by functions, and we may also investigate functions induced by operators. The Berezin sign is awonderful representation of an operator-function relationship. F. Berezin proposed the Berezin switch in[8], and it has proven to be a vital tool in operator theory given that it utilizes many essential aspects ofsignificant operators. Many mathematicians and physicists are fascinated by the Berezin symbol of anoperator defined on the functional Hilbert space. The Berezin radius inequality has been extensively studiedin this situation by a number of mathematicians. In this paper, we use the Alughte transform and thegeneralized Alughte transform to develop Berezin radius inequalities for Hilbert space operators. Weadditionally offer fresh Berezin radius inequality results. Huban et al. [15] and Başaran et al. [6] supply theBerezin radius inequality.
Herbicide Resistance: Managing Weeds in a Changing World

Over the years, several agricultural interventions and technologies have contributed immensely towards intensifying food production globally. The introduction of herbicides provided a revolutionary tool for managing the difficult task of weed control contributing significantly towards global food security and human survival. However, in recent times, the successes achieved with chemical weed control have taken a turn, threatening the very existence we have tried to protect. The side effects of conventional farming, particularly the increasing cases of herbicide resistance agricultural weeds, is quite alarming. Global calls for sustainable weed management approaches to be used in food production is mounting. This paper provides detailed information on the molecular biological background of herbicide resistant weed biotypes and highlights the alternative, non-chemical weed management methods which can be used to prevent the development and spreading of herbicide-resistant weeds.
Simulating the synchrotron emission of AGN with grid based relativistic hydrodynamics

Grid based numerical hydrodynamic codes have been used to simulate the evolution of a variety of astrophysical environments, ranging from stellar winds to accretion disks in binary systems. These simulations can be used as valuable tools to correlate the theoretical models and observational data. In this study a grid based relativistic hydrodynamic simulation is created, using the freely available numerical code PLUTO ver 4.2, to study possible causes of variability in AGN. The code uses shock-capturing Godunov-type methods to solve the fluid dynamic conservation equations on a structured mesh grid. To simulate the production of a jet similar to those observed in AGN a three dimensional static grid was created containing 256 × 256 × 512 computational cells. A uniform background medium was assigned to this grid, while a nozzle was defined on the z = 0 boundary to inject a pressure matched relativistic jet, with Γ = 10 (v = 0:995c), into the medium. The simulation showed the formation of a central collimated relativistic beam along with a surrounding cocoon region. In addition, a post-processing emission modelling code is being developed, in order to determine the emission produced by the relativistic jet. The emission model utilizes the data produced by the hydrodynamic code to calculate intensity maps due to synchrotron self absorption. The emission code takes into account the effects produced by the relativistic motion of the emitting regions (excluding light travel time) which can lead to effects such as the Doppler boosting observed in blazars. Synchrotron based emission models can be applied to other astrophysical objects, such as X-ray binary systems, which means that the application of the emission code can be adapted to model a variety of astrophysical sources.
Migratory geese allow plants to disperse to cooler latitudes across the ocean

How plants can disperse in response to global change is a critical question, yet major knowledge gaps persist about long‐distance dispersal (LDD) mechanisms. We studied the potential a migratory waterfowl has for LDD of flowering plants via gut passage of seeds (endozoochory), comparing spring and autumn migration.
Traitement numérique de la singularité à l'axe pour les écoulements 3D en cavités tournantes

The present work proposes a novel method to treat the cylindrical coordinate singularity which arises when dealing with three-dimensional confined rotating flows. In this work, we have developed a method which consists in discretizing the whole diameter −R ≤ r ≤ R with an even number of radial Gauss-Lobatto nodes. In the azimuthal direction, the overlap in the discretization and especially the use of parity conditions are avoided by introducing a shift equal to Pi/2K (K the number of mesh points in that direction) for Theta > Pi in the Fourier transform. Spectral convergence of the method is illustrated on steady and unsteady analytical solutions. The ability of our numerical method to investigate complex unsteady flows is then illustrated for two rotating flow problems where other reliable experimental or numerical results are available in the literature.
Analysis of Gender Development Index to prove its correlation with the decrease of poverty line in Kediri City, Indonesia

Gender is a society’s perspective to differ men and women in term of their behavior which can be characterized through culture and religion values. This study aims to determine the close relationship between the Gender Development Index and the Poverty Line. This study used the Pearson Product Moment Correlation test tool to find the relationship be-tween the Gender Development Index data and the Poverty Line Data in Kediri City. Data were collected from statistics agency in Kediri City, East Java Province of Indonesia and analyzed using a reference study on the statistical test results. The research results showed that the Gender Development Index and the Poverty Line had a solid relationship. The findings of this study showed that the Gender Development Index has increased, indicating the opposite trend: the value of the Poverty Line in Kediri City tends to decrease significantly. For the future, it is recommended that the further researchers will conduct Gender Development Index analysis in order to prove its correlation with the decrease of the poverty line in Indonesia
Membrane Modification with Polydopamine to Improve Performances — A Mini Review

This paper provides an overview of some studies and investigations regarding the utilization of polydopamine as an additive in membrane fabrication. Polydopamine is widely used due to its high hydrophilicity and adhesive properties. The addition of polydopamine in to the membrane system mostly attributed to the improvement of membrane performance in terms of permeation, antifouling, and antibiofouling.
Universal behavior of electron g -factors in semiconductor nanostructures

We combine analytic developments and numerical tight-binding calculations to study the evolution of the electron $g$-factors in homogeneous nanostructures of III-V and II-VI semiconductors. We demonstrate that the $g$-factor can be always written as a sum of bulk and surface terms. The bulk term, the dominant one, just depends on the energy gap of the nanostructure but is otherwise isotropic and independent of size, shape, and dimensionality. At the same time, the magnetic moment density at the origin of the bulk term is anisotropic and strongly dependents on the nanostructure shape. The physical origin of these seemingly contradictory findings is explained by the relation between the spin-orbit-induced currents and the spatial derivatives of the electron envelope wave function. The tight-binding calculations show that the $g$-factor versus energy gap for spherical nanocrystals can be used as a reference curve. In quantum wells, nanoplatelets, nanorods, and nanowires, the $g$-factor along the rotational symmetry axis can be predicted from the reference curve with a good accuracy. The $g$-factors along nonsymmetric axes exhibit more important deviations due to surface contributions but the energy gap remains the main quantity determining their evolution. The importance of surface-induced anisotropies of the $g$-factors is discussed.
Homogeneously aligned liquid crystal molecules on unidirectional buckle pattern of polyurethane films

ABSTRACT We present the fabrication of an anisotropic structure on a polyurethane (PU) film for aligning liquid crystal (LC) molecules with ion beam (IB) irradiation at an incident angle. The obtained anisotropic structure assembles the LC cells in a layer that aligns LC molecules uniformly. Polarised optical microscopy images of an LC cell fabricated with IB-irradiated PU were captured to confirm the LC alignment state and compared with those fabricated with non-treated PU. To analyse the effects of the IB irradiation, X-ray photoelectron spectroscopy and field-emission scanning electron microscopy were used to investigate the chemical and morphological modifications, respectively. IB irradiation modifies the chemical structure of PU, which indicates that a new skin layer is formed on the PU films. This skin layer generates an in-plane compressive strain, thereby creating buckles on the PU films. Simultaneously, the physical collision of the reactive Ar+ ions during IB irradiation induces a directional strain on the surface, thereby forming a unidirectional structure of buckles along the direction of IB irradiation. The PU film annealed at 200°C showed the high average transmittance of 88.9%, which is appropriate as an alignment layer. Graphical Abstract
Aspects of F-Theory and M-Theory

Non-perturbative phenomena have received much attention in string theory in the last years. M-Theory and F-Theory are the two main frameworks in which it is possible to explore such phenomena. This thesis focuses on aspects of both theories. In the first part of this thesis we study F-Theory compactifications with additional abelian gauge symmetries. This was motivated by problems affecting usual F-Theory compactifications and 4-dimensional Grand Unified Theories such as the presence of proton decay operators, which could in principle be resolved with additional abelian symmetries. In the F-Theory context, this translated into the novel analysis of elliptic fibrations with additional (two, in particular) rational sections. A systematic study of the possible degenerations of such elliptic fibrations through the application of Tate’s algorithm was carried out and provided new insight into the phenomenology of F-Theory models with additional U(1) factors. The second part of this thesis consists of the study of some aspects of membranes in M-Theory. D-branes in string theory are well understood thanks to a perturbative definition via open strings. On the contrary, membranes and fivebranes in M-Theory lack such a description and their effective theories are not as well understood. In particular the theory on parallel M5-branes, the so-called (2,0) theory, was studied in some detail. Following a number of results and dualities in lower dimensional field theories obtained in the last years starting from the (2,0) theory, the latter was compactified on a 2-dimensional sphere to obtain a 4-dimensional sigma model into the moduli space of monopoles. A supergravity background was turned on in order to preserve supersymmetry and an intermediate reduction to 5-dimensional N = 2 Super-Yang-Mills theory was used by considering the two-sphere as a circle fibration over an interval. Insight into the theory on parallel M5-branes was also gained by relating it to the better known dynamics on coincident M2-branes. This followed a recent proposal for the realization of the (2,0) algebra on a non-abelian tensor multiplet through the use of 3-algebras. In this thesis we generalize this proposal and find an algebraic structure which describes two parallel M5-branes or two parallel M2-branes depending on whether a particular abelian three-form is turned on.
Detecting heart diseases through machine learning

Heart diseases are ranked the first cause of death in the world. Australia has the highest incidence of heart disease. Approximately 125 lives every single day that’s one life every 12 minutes. Heart disease describes a range of conditions that affect the heart or blood vessels and can affect anyone at any age. Also, a major concern, heart disease could cause a heart attack or stroke. Some symptoms may include chest pain, shortness of death, dizziness, fatigue, or nausea. Other serious symptoms, such as diabetes and high cholesterol, may lead to heart attacks. A healthy lifestyle, quitting smoking, and exercising are small steps to avoid heart disease. Heart diseases are easier to treat when detected early. In this paper, an effective heart disease framework is proposed. Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and Radial Basic Function (RBF) techniques for classification are used. Moreover, Feature selection is performed to minimize the features to have better accuracy. Info Gain Attribute Eval – Ranker algorithm is used for feature selection. In addition, classification techniques and feature selection algorithms are applied to the LIAC heart stat log dataset which depends on the heart diseases dataset. The result’s effectiveness is described by accuracy, precision, recall, and ROC Curve.
Ultra-Miniature and Circularly Polarized Ferrite Patch Antenna

This article describes the development, the design and the measurement of a circularly polarized ultra-miniature antenna. The modeled device takes advantage of the magnetic properties of ferrite materials to achieve the desired specifications. Indeed, the antenna is operating at a frequency where the effective permeability is greater than one and the permeability has a negative real part, i.e., in the zone between the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mu _{eff}$ </tex-math></inline-formula> resonances of the ferrite. The dimensions of the designed antenna are about <inline-formula> <tex-math notation="LaTeX">$\mathrm {} (\mathrm {\lambda }_{0}/23.6)\times \mathrm { } (\mathrm {\lambda }_{0}/20.7)\times \mathrm { } (\mathrm {\lambda }_{0}/29.4)$ </tex-math></inline-formula> at 2.42 GHz and a good circular polarization is achieved at the central operating frequency of the device, with a boresight axial ratio (AR) of 1.6 dB. This article is based on the modal study of a ferrite cavity which will be presented and detailed in a first step. The physical properties of the antenna and related material (dimensions, ferrite internal magnetic field, the saturation magnetization, etc.) will be deduced from this modal study and from the resolution of the wave equation inside a cylindrical ferrite cavity. All the antenna performances in terms of impedance matching, axial-ratio, and radiation pattern are reported and commented. This concept is finally validated by the measurement of a prototype.
Comparison of 2D and 3D meso‐model of concrete under high strain loading rate

In this paper, 2D and 3D meso‐models of concrete are developed numerically using different shapes of aggregate. For 2D model, circular and random polygon aggregate shapes are considered while for 3D model, spherical and convex hull aggregate shapes are adopted. Five different spatial distributions are employed in both circular and polygon shapes to get Circle1–Circle5 and Poly1–Poly5 geometrical model. Similarly, for 3D meso‐model, Spherical1–Spherical5 and one Convex hull geometrical models are developed. In all the numerical models, three different phases of concrete are considered, that is aggregate, mortar, and interfacial transition zone. Explicit analysis of the developed models has been performed using LS‐DYNA. All the developed models are verified by comparing the obtained results of stress–strain variations with the existing literature. The behavior of concrete is studied for both the loading conditions, that is quasi‐static and high strain loading rate. Added to this, the effect of spatial distribution of aggregates is also evaluated. Finally, the dynamic impact factors (DIF) are evaluated and compared for all the developed numerical models. Finally, cubic polynomial equations for DIF, for different shape aggregate 3D models, are obtained using regression analysis.
THE ROLE OF THE HRM NETWORKS IN THE ATTRACTION, RETENTION AND DEVELOPMENT OF TALENTS OF ESTONIAN ORGANIZATIONS

Most global talent surveys mutually emphasize the labour market’s general and painful talent shortage. No country or industry is protected against talent migration and drought. Although some countries or regions have understood the vast role of talent attraction and retention in the region’s success, most governments need to pay more attention to this issue. Employees and HRs feel alone in this continuous war for valuable employees. The labour market parties increasingly see the trend where only mutual efforts in attracting talent can lead to success in this global competition. The talent management functions have exceeded the companies’ borders lines, and regional/sectoral collaboration is the only possibility to attract and keep talent in the region and its industries. This article aims to conceptualize the strategic activities and principles of the HR networks in supporting the member companies’ talent attraction and retention ability based on the Estonian experience. Together with the HR networks and with the help of the theory and other cases, the authors bring out the main principles and actions on how the HR networks can support organizations in the continuous fight for talent. The originality of the article is that talent management from the point of view of the network and networking opens a new perspective for talent attraction and development – for different levels of crossborder organizational collaboration and research opportunities.
Jeux différentiels à information imparfaite

This work copes with differential games with asymmetric information. First of all, we deal with differential games with incomplete information on both sides. We prove that the Mertens-Zamir operator appears in the discrete approximation of the value. We also build some epsilon-optimal strategy for the uninformed player in the game with lack of information on one side with no dynamics. We then characterize Nash equilibrium payoffs in nonzero sum differential games using random non anticipative strategies as feasible and individually rational as in the "folk theorem". We prove that the set of Nash equilibrium payoffs using these strategies is exactly the same as the set of publicly correlated equilibrium payoffs. Finally, we study a special case of game with imperfect observation, where one player is blind. We prove the existence of the value, characterized as the unique viscosity solution of some Hamilton-Jacobi equation on the Wasserstein space.
Learning Causal Graphs with Latent Variables

Causality is one of the central notions that allows us to decompose reality in meaningful components, construct cohesive stories (explanations) about complex aspects of this reality, and then understand the world around us and make principled decisions in intricate situations. The challenge of learning cause-and-effect relationships from non-experimental data was studied in different settings in the literature of causal structural learning, which includes delineating the boundary conditions such that causal relations can be learned from passive data (Pearl, 2000; SGS, 2001). In this paper, we consider the problem of learning causal structures in the presence of latent variables aided by the use of interventions. It is known that a naive approach to this problem would require O(n2) interventions for a causal graph with n observed variables, which due to costs, technical, or ethical considerations, is rarely feasible to perform in practice. We propose an efficient randomized algorithm to try to ameliorate this problem that can learn the causal graph (including the existence and location of latent variables) using O(log2 n) interventions, for graphs with a constant degree. We further propose an efficient deterministic variant of this procedure that is useful for different classes of graphs, including common cases such as bipartite, time-series, and relational type of systems.