Miracle-dz/UniFAR-FTUs huggingface dataset:
Grey wolf optimization applied to the maximum flow problem

The problem of getting the maximum flow from source to destination in networks is investigated in this paper. A proposed algorithm is presented in order to solve Maximum Flow problem by using Grey Wolf Optimization (GWO). The GWO is a recently established meta-heuristics for optimization, inspired by grey wolves (Canis Lupus). In addition; in this current research, K-means clustering algorithm is used to group each 12 vertices with each other at one cluster according to GWO constraint. This work is implemented and tested various datasets between 50 vertices and 1000 vertices. The simulation results show rapprochement between experimental and theoretical results.
Towards Sustainability: The Innovation Paths of Social Enterprise

The concepts of sustainable development and social economy are currently undergoing a process of convergence, determined by the extension of the concept of sustainability and the role of social enterprise. In a context in which the primary focus of management literature, especially of North American origin, concerns the concept and practices of social entrepreneurship, this paper explores the experiences of European social enterprises and in particular of Italian social cooperatives. Social cooperatives are experimenting with initiatives capable of responding to the emergence of new needs and changes in society in a sustainable way. Based on an analysis of the experience of various social cooperatives operating in the field of renewable energy sources, tourism, and the use of assets confiscated from the Mafia, this paper aims to highlight how social enterprise manages to create contexts of sustainable development, thereby building interorganizational and intersectoral collaborative networks aimed at the activation of new paths for local development. This represents evidence of the specific nature of the social enterprise, which is able to establih a number of coordination mechanisms. It creates new businesses with public and private, and profit and non-profit partners, with the aim of achieving a stated goal, or enters into contractual arrangements, in order to establish business partnerships and build entire local manufacturing supply chains. In this way social enterprise promotes interaction between resources of a public, market and community nature.
Using Two-layer Minimax Optimization and DEA to Determine Attribute Weights

This study aims to explore a novel method for determining attribute weights, which is a key issue in constructing and analyzing multiple-attribute decision-making (MADM) problems. To this end, a hybrid approach combining the data envelopment analysis (DEA) model without explicit inputs (DEA-WEI) and a two-layer minimax optimization scheme is developed. It is demonstrated that in this approach, the most favorable set of weights is first considered for each alternative or decision-making unit (DMU) and these weight sets are then aggregated to determine the best compromise weights for attributes, with the interests of all DMUs simultaneously considered in a fair manner. This approach is particularly suitable for situations where the preferences of decision-makers (DMs) are either unclear or difficult to acquire. Two case studies are conducted to illustrate the proposed approach and its use for determining weights for attributes in practice. The first case concerns the assessment of research strengths of 24 selected countries using certain measures, and the second concerns the analysis of the performance of 64 selected Chinese universities, where the preferences of DMs are either unknown or ambiguous, but the weights of the attributes should be assigned in a fair and unbiased manner.
Finite Element Method for Pricing Swing Options under Stochastic Volatility

This paper studies the pricing of a swing option under the stochastic volatility. A swing option is an American-style contract with multiple exercise rights. As such, it is an optimal multiple-stopping time problem. In this paper, we reduce the problem to a sequence of optimal single stopping time problems. We propose an algorithm based on the finite element method to value the contract in a Black-Scholes-Merton framework. In many real-world applications, the volatility is typically not a constant. Stochastic volatility models are commonly used for modeling dynamic changes of volatility. Here we introduce an approach to handle this added complication and present numerical results to demonstrate that the approach is accurate and efficient.
Polarization and molecular-orbital dependence of strong-field enhanced ionization

In this work, we perform the first polarization dependence study of enhanced ionization (EI) in diatomic molecules. We find that EI exists when the field polarization is parallel to the molecular axis but disappears when polarization is perpendicular. We further study EI with circular polarization and find that EI exists with circular polarization indicating that rescattering does not play a significant role for EI. Furthermore, we study, for the first time, molecular orbital effect on EI. We find that EI exists in σ -type but not π -type outmost molecular orbitals.
A finite volume scheme for solving anisotropic diffusion on ALE‐AMR grids

In the context of High Energy Density Physics and more precisely in the field of laser plasma interaction, Lagrangian schemes are commonly used. The lack of robustness due to strong grid deformations requires the regularization of the mesh through the use of Arbitrary Lagrangian Eulerian methods. Theses methods usually add some diffusion and a loss of precision is observed. We propose to use Adaptive Mesh Refinement (AMR) techniques to reduce this loss of accuracy. This work focuses on the resolution of the anisotropic diffusion operator on Arbitrary Lagrangian Eulerian‐AMR grids.
Methods of information systems protection

The paper considers the possibility of using a mosaic composed of many miniature images to hide the fact of text information transmission. The results of the study of optimal mosaic construction when using steganographic methods of hiding information are presented. The use of the proposed algorithms implies the use of сryptographically secure pseudorandom number generator with dynamically changing parameters for concealing information. The generators are used to determine the location of information bits in a certain mini-images, as well as in a specific pixels and color channels. Using the proposed algorithm, no more than twenty-five percent of the color channels are changed, provided that one bit per color is used. This paper is supplemented with illustrations and examples of how the algorithm works.
INTERACTION AND CYTOTOXICITY OF COMPOUNDS WITH HUMAN CELL LINES

The interactions of compounds on human cell lines are either influenced by the composition of substances present in plant material or alteration of constituents by solvent fractionation. These substances or constituents have an influence on the percentage cytotoxicity readings of compounds in human cell culture. Understanding and correlating the relationship between cytotoxicity and other parameters, such as cell death inducing mechanisms, will assist pharmaceutical chemists to synthesize compounds that can target particular ailments with greater efficiency. This will also allow scientists to understand the interaction of compounds with different cell types for different compound fractions.
MINIATURIZE NEGATIVE INDEX METAMATERIAL STRUCTURE LOADED FILTENNA

In this article, a negative index metamaterial (NIM) superstrate is designed and cooperated with the filtenna to produce miniaturize communication front end for gain enhancement without any substantial increase in the profile of the whole structure. A finite array Double H Split Ring (DHSR) of 11× 9 unit cells has been designed on a dielectric substrate to form the NIM metamaterial superstrate. The proposed superstrates and filtenna have an overall dimension of 0. 67λo×0. 54λo×1. 19λo at 10.16 GHz with 10.6 dB total broadside gain in simulation and 9.8 dB in measurement at 10.22 GHz (λo = 30 mm). This miniaturized communication front end which consists of a filter, an antenna and a gain enhancer affords smaller size with the overall volume of 0. 43λo in the context of using a metamaterial superstrate for gain enhancement reported in the earlier literature.
Comment on Article by Pratola

I’d like to offer my congratulations to Pratola for engaging in timely study on a highimpact topic, namely the efficient exploration of the space of reasonable partitionbased representations of the input–output relationships in data. Tree-based partitioning schemes for regression and classification have proliferated in machine learning, spatial statistics, and computer experiments. However, the Bayesian approach has long been limited by expensive Markov chain Monte Carlo (MCMC) and poor mixing therein.
New optimization techniques for point feature and general curve feature based SLAM

This doctoral thesis deals with the feature based Simultaneous Localization and Mapping (SLAM) problem. SLAM as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the robot. In feature based SLAM, the robot relies on its ability to extract useful navigation information from the data returned by its sensors. The robot typically starts at an unknown location without priori knowledge of feature locations. From relative observations of features and relative pose measurements, estimates of entire robot trajectory and feature locations can be derived. Thus, the solution to SLAM problem enables an autonomous vehicle navigates in a unknown environment autonomously. The advantage of eliminating the need for artificial infrastructures or a priori topological knowledge of the environment makes SLAM problem one of the hot research topics in the robotics literature. Solution to the SLAM problem would be of inestimable value in a range of applications such as exploration, surveillance, transportation, mining etc. The critical problems for feature based SLAM implementations are as follows: 1) Because SLAM problems are high dimensional, nonlinear and non-convex, when solving SLAM problems, robust optimization techniques are required. 2) When the environment is complex and unstructured, appropriate parametrization method is required to represent environments with minimum information loss. 3) As robot navigates in the environment, the information acquired by the onboard sensor increases. It is essential to develop computationally tractable SLAM algorithms especially for general curve features. This thesis presents the following contributions to feature based SLAM. First, a convex optimization based approach for point feature SLAM problems is developed. Using the proposed method, a unique solution can be obtained without any initial state estimates. It will be shown that, the unique SDP solution obtained from the proposed method is very close to the true solution to the SLAM problem. Second, a general curve feature based SLAM formulation is presented. Instead of scattered points, in this formulation, the environment is represented by a number of continuous curves. Using the new formulation, all the available information from the sensor is utilized in the optimization process. Third, method for converting curve feature to point feature is presented. Using the conversion method, the curve feature SLAM problem can be transferred to point feature SLAM problem and can be solved by the convex optimization based approach.
Goal-Driven Measurement Framework for Software Innovation Processes

It is important for modern day software organizations to remain competitive in the market. Being innovative is important for the survival of the organizations. The nature of innovation processes in place in different organizations varies. It is important for organizations to measure and continuously improve the innovation processes. In this paper, we present a goaldriven measurement framework for Software Innovation (SI) processes. We also present a roadmap for the implementation of the framework.
Analysis of latent class models in economic research

Summary: Latent variable models are used and applied in many areas of the social and behavioral sciences. The increasing availability of computer packages for fitting such models makes latent variable models popular, known and applied in many scientific areas. Latent variable models have a very wide range of applications, especially in the presence of repeated observations, longitudinal data, and multilevel data. The basic model postulates an underlying categorical latent variable; within any category of the latent variable the manifest or observed categorical variables are assumed independent of one another (the axiom of conditional independence). The observed relationships between the manifest variables are thus assumed to result from the underlying classification of the data produced by the categorical latent variable. In this paper we present the theoretical and methodological aspects of latent variable models, as well as their application in R software in the field of economic research.
Sur quelques équations aux différences mêlées

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The union-split algorithm and cluster-based anonymization of social networks

Knowledge discovery on social network data can uncover latent social trends and produce valuable findings that benefit the welfare of the general public. A growing amount of research finds that social networks play a surprisingly powerful role in people's behaviors. Before the social network data can be released for research purposes, the data needs to be anonymized to prevent potential re-identification attacks. Most of the existing anonymization approaches were developed for relational data, and cannot be used to handle social network data directly. In this paper, we model social networks as undirected graphs and formally define privacy models, attack models for the anonymization problem, in particular an i-hop degree-based anonymization problem, i.e., the adversary's prior knowledge includes the target's degree and the degrees of neighbors within i hops from the target. We present two new and efficient clustering methods for undirected graphs: bounded t-means clustering and union-split clustering algorithms that group similar graph nodes into clusters with a minimum size constraint. These clustering algorithms are contributions beyond the specific social network problems studied and can be used to cluster general data types besides graph vertices. We also develop a simple-yet-effective inter-cluster matching method for anonymizing social networks by strategically adding and removing edges based on nodes' social roles. We carry out a series of experiments to evaluate the graph utilities of the anonymized social networks produced by our algorithms.
An Exploration of Locality, Conservation Laws, and Spin

Conservation rules are central to our understanding of the physical world, they place restrictions on how particles can move and dictate what can occur during an interaction. However, it is often taken for granted how a conservation law is implemented. For example, “conservation of momentum” is the condition that the sum of incoming and outgoing momenta equals zero. In particular, we place a constraint on the momenta by means of a linear function. The assumption of a linear conservation rule is intimately linked to both the geometry of momentum space and locality of the corresponding interaction. In this thesis we investigate the link between locality and conservation rules in a variety of settings. Part 1 is principally concerned with scalar particles. We begin by constructing the interaction vertex for an arbitrary scattering process in a generic spacetime, showing that curvature is not sufficient to induce a non-local interaction. Along the way we develop a notion of covariant Fourier transform which is used to translate between spacetime and momentum space in the presence of a non-trivial geometry. We also explore the effect on quantum fields of explicitly imposing non-locality via the “Relative Locality” framework. It is found that the fields depend, implicitly, on a fixed point in momentum space with fields based at different points related by a non-local transformation. On the other hand, all non-local behavior in the action can be concentrated in the interaction term. In the second part of this thesis we generalize the analysis of Part 1 to particles with internal structure, specifically spin. Of particular interest was the possibility that the presence of internal degrees of freedom could provide a sufficient modification of the vertex factor to allow for non-local interactions. Utilizing the coadjoint orbit method we develop a classical model of the relativistic spinning particle called the “Dual Phase Space” model (DPS) which allows for a coherent analysis of the vertex factor. We find that in addition to locality in the standard spacetime variable, interactions are “local” in a second “dual” spacetime variable. Inspired by this overt “duality” we show that DPS can be reformulated as a bilocal model. Specifically, DPS can be realized as the relativistic extension of a mechanical system consisting of two particles coupled by a rigid rod with fixed angular momentum about the center of mass. Interpreted in this way the model is easily quantized and yields the correct values for the spin quantum numbers. Next we consider a spinorial parameterization of DPS which is entirely first class and reveals several insights into how spin affects the dynamics of a relativistic particle. In particular, we find that the spin motion acts as a Lorentz contraction on the four-velocity
Towards non‐invasive in vivo characterization of the pathophysiological state and mechanical wall strength of the individual human AAA wall based on 4D ultrasound measurements

Abdominal aortic aneurysms (AAA) are a degenerative disease of the aorta with high associated mortality. Currently, there is a lack of in vivo information, which allows to characterise the individual aneurysmal wall with regard to wall strength or rupture risk. We have used 4D ultrasound strain imaging to compute the spatially resolved distribution of the in‐plane strain tensor and of the local distensibility in three patient groups (young, < 40 y. o.; elderly, > 60 y. o.; AAA). Mean and local maximum deformation and distensibility as well as indices for the local variations of the strain and the distensibility distribution were determined. Mean distensibility is significantly decreasing from young (3.83 [2.83, 5.87] 10−3 mmHg−1) to elderly (0.67 [0.39, 0.87] 10−3 mmHg−1) and AAA (0.27 [0.20, 0.54] 10−3 mmHg−1). Both indices characterising the heterogeneous elastic behaviour are significantly increasing from young through elderly to AAA. Linear dependency of some of the distensibility distribution indices on age and aortic diameter was observed in the young and elderly group. In contrast, none of the indices depended on age or maximum diameter in the AAA group. We hypothesize that the heterogeneous distensibility distribution is indicative of microstructural changes in the aortic and aneurysmal wall and might contribute to a more complete and informative pathophysiological characterisation of the AAA wall, either with respect to wall strength or rupture risk. To confirm this hypothesis, further studies will be necessary in which the correlation between the proposed indices and individual wall strength and rupture risk is tested.
Particle filter for nonlinear systems with multi-sensor asynchronous random delays

: This paper is concerned with the recursive ﬁ ltering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle ﬁ lter is developed with the ability to fuse multi-sensor information in the case of known delay probability. In addition, an online delay probability estimation module is introduced in the particle ﬁ ltering framework, which leads to another new ﬁ lter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting ﬁ lter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed ﬁ lters.
Direct Visual-Inertial Odometry using Epipolar Constraints for Land Vehicles

Autonomously operating vehicles are being developed to take over human supervision in applications such as search and rescue, surveillance, exploration and scientific data collection. For a vehicle to operate autonomously, it is important for it to predict its location with respect to its surrounding in order to make decisions about its next movement. Simultaneous Localization and Mapping (SLAM) is a technique that utilizes information from multiple sensors to not only estimate the vehicle’s location but also simultaneously build a map of the environment. Substantial research efforts are being devoted to make pose predictions using fewer sensors. Currently, laser scanners, which are expensive, have been used as a primary sensor for environment perception as they measure obstacle distance with good accuracy and generate a point-cloud map of the surrounding. Recently, researchers have used the method of triangulation to generate similar point-cloud maps using only cameras, which are relatively inexpensive. However, point-clouds generated from cameras have an unobservable scale factor. To get an estimate of scale, measurements from an additional sensor such as another camera (stereo configuration), laser scanners, wheel encoders, GPS or IMU, can be used. Wheel encoders are known to suffer from inaccuracies and drifts, using laser scanners is not cost effective, and GPS measurements come with high uncertainty. Therefore, stereo-camera and camera-IMU methods have been topics of constant development for the last decade. A stereo-camera pair is typically used with a graphics processing unit (GPU) to generate a dense environment reconstruction. The scale is estimated from the pre-calculated base-line (distance between camera centers) measurement. However, when the environment features are far away, the base-line becomes negligible to be effectively used for triangulation and the stereo-configuration reduces to monocular. Moreover, when the environment is texture-less, information from visual measurements only cannot be used. An IMU provides metric measurements but suffers from significant drifts. Hence, in a camera-IMU configuration, an IMU typically is used only for short-durations, i.e. in-between two camera frames. This is desirable as it not only helps to estimate the global scale, but also to give a pose estimate during temporary camera failure. Due to these reasons, a camera-