Miracle-dz/UniFAR-FTUs huggingface dataset:
Les neurosciences au service de l’entrepreneuriat : ce que nous apprend la littérature sur le multitasking.

Summary: Research on entrepreneurship has grown over the past decade exploring entrepreneurial cognition and the analysis of the ways of thinking of entrepreneurs. The research community believes that value exists in grounding entrepreneurship in neuroscience (Mitchell et al, 2016). The daily routine of entrepreneurs consists widely in executing simultaneous tasks, process known as Multitasking. A question to be inquired is to know if there is a way of developing multitasking habits in order to improve entrepreneurial skills such as speed of information processing (Dux et al., 2009) , creativity and adaptability ( Dye, Green et Bavelier, 2009a, 2009b; Basak et al., 2008; Marmeleira et al., 2009 ; Anguera et al., 2013). Nevertheless, the direct link between the neuroscientific approach to multitasking and entrepreneurship is yet to be uncovered. That is why we conducted an extensive Literature Review on Multitasking.
HANDWRITTEN HANGUL RECOGNITION MODEL USING MULTI-LABEL CLASSIFICATION

A BSTRACT . Recently, as deep learning technology has developed, various deep learning technologies have been introduced in handwritten recognition, greatly contributing to performance improvement. The recognition accuracy of handwritten Hangeul recognition has also improved significantly, but prior research has focused on recognizing 520 Hangul characters or 2,350 Hangul characters using SERI95 data or PE92 data. In the past, most of the expressions were possible with 2,350 Hangul characters, but as globalization progresses and information and communication technology develops, there are many cases where various foreign words need to be expressed in Hangul. In this paper, we propose a model that recognizes and combines the consonants, medial vowels, and final consonants of a Korean syllable using a multi-label classification model, and achieves a high recognition accuracy of 98.38 % as a result of learning with the public data of Korean handwritten characters, PE92. In addition, this model learned only 2,350 Hangul characters, but can recognize the characters which is not included in the 2,350 Hangul characters
Invertible particle flow based Gaussian and Gaussian sum particle filters

The estimation of the state of dynamic systems using measurements is a frequently arising challenge. State estimation is used in applications like robotics, industrial manufacturing, weather forecasting, target tracking, etc. The hidden state in the dynamic systems can be estimated using the recursive Bayesian filters. These filters estimate the state by tracking the posterior distribution. These filters include two steps, prediction based on solving the equation modeling the state evolution and update of the state using the measurements. Most real-world systems have non-linear dynamic and measurement models. Additionally, both models can be affected by arbitrary noise sources, which have either Gaussian or nonGaussian characteristics. For state estimation in linear Gaussian models, Kalman filters are employed. Extended Kalman filters (EKF) are utilized for state estimation in non-linear Gaussian models. For state estimation in non-linear non-Gaussian models, particle filters are generally employed. Particle filters, however, are ineffective in high-dimensional non-linear non-Gaussian models because they experience weight degeneracy. In this thesis, we propose incorporating the invertible particle flow methods in the Gaussian particle filter (GPF) framework to generate a proposal distribution. These methods are derived under Gaussian assumptions for the flow equation. The resulting particle flow Gaussian particle filter (PFGPF) method preserves the asymptotic characteristics of Gaussian particle filters and has the potential to perform better at state estimation in high-dimensional spaces. Secondly, we construct a particle flow Gaussian sum particle filter (PFGSPF), which roughly approximates the predictive and posterior as Gaussian mixture models, using a bank of PFGPF filters. In complicated estimating issues where a simple Gaussian approximation is insufficient, this approximation is helpful. We compare the performance of the proposed filters with the existing particle flow filters and particle flow particle filters (PFPF) in high dimensional complex numerical simulations.
Generalized Riemann Functions, Their Weights and Their Evaluation on Complete Graphs

By a Riemann function we mean a function f : Zn → Z such that f(d) is equals 0 for d1 + · · ·+ dn sufficiently small, and equals d1 + · · ·+ dn +C for a constant, C, for d1 + · · · + dn sufficiently large. By adding 1 to the Baker-Norine rank function of a graph, one gets an equivalent Riemann function, and similarly for related rank functions. To each Riemann function we associate a related function W : Zn → Z via Möbius inversion that we call the weight of the Riemann function. We give evidence that the weight seems to organize the structure of a Riemann function in a simpler way: first, a Riemann function f satisfies a Riemann-Roch formula iff its weight satisfies a simpler symmetry condition. Second, we will calculate the weight of the Baker-Norine rank for certain graphs and show that the weight function is quite simple to describe; we do this for graphs on two vertices and for complete graphs. For complete graphs, we build on the work of Cori and Le Borgne who gave a linear time method to compute the Baker-Norine rank of the complete graph. The associated weight function has a simple formula and is extremely sparse (i.e., mostly zero). Our computation of the weight function leads to a new linear time algorithm to compute the Baker-Norine rank, via a new formula likely related to one of Cori and Le Borgne, but seemingly simpler for general d ∈ Zn, namely rBN,Kn(d) = −1+ ∣∣∣∣{i = 0, . ,deg(d) ∣∣∣ n−2 ∑ j=1 ( (dj−dn−1+i) mod n ) 6 deg(d)−i }∣∣∣∣. However, the formula of Cori and Le Borgne, which requires d ∈ Zn to be a sorted parking function, is easier to evaluate for such d. ∗Research supported in part by an NSERC grant. †Research supported in part by an NSERC grant. the electronic journal of combinatorics 30(2) (2023), #P2.33 https://doi.org/10.37236/11281 Our study of weight functions leads to a natural generalization of Riemann functions, with many of the same properties exhibited by Riemann functions. Mathematics Subject Classifications: 05C90
Simulink Model of Solar Operated EZ-Source Inverter

: Z-Source inverters are used mainly for buck-boost energy conversion with the help of passive elements. In Z-source inverter topology, LC impedance network is used to couples the source and the inverter to achieve voltage boost and inversion. Embedded EZ-Source inverter and Z-Source inverter can produce the same gain. Input to the Embedded EZ-Source inverter can be obtained from solar cell. The ripple content in the output voltage of solar cell is filtered and pure DC is given to the three-phase inverter which converts DC in to three-phase balanced AC. The output of the Embedded EZ Source inverter is used to control the harmonics present in the load. The limitations of the conventional Z-source inverter can be overcome by using Embedded Z-source inverters. It can produce smoother current and increase voltage level across the Z-source and it has low harmonic distortions (THD). Simulations are carried out using MATLAB-SIMULINK. Hardware implementation and Microcontroller programming can be done in the lab.
Accurate and Scalable Rate Limiter for RDMA NICs

Rate limiter is required by RDMA NIC (RNIC) to enforce the rate limits calculated by congestion control. RNIC expects the rate limiter to be accurate and scalable: to precisely shape the traffic for numerous flows with minimized resource consumption, thereby mitigating the incasts and congestions and improving the network performance. Previous works, however, fail to meet the performance requirements of RNIC while achieving accuracy and scalability. In this paper, we present Tassel, an accurate and scalable rate limiter for RNICs, including the algorithm and architecture design. Tassel first extends the classical WF2Q + algorithm to support rate limiting in the context of the RNIC scenario. Then Tassel designs a high-precision and resource-friendly rate limiter and integrates it into classical RNIC architecture. Preliminary simulation results show that Tassel precisely enforces the rate limits ranging from 100 Kbps to 100 Gbps among 1 K concurrent flows while the resource consumption is limited.
Facial Emotion Recognition System Using Machine Learning

: FER system is growing attention in the research field. The face emotions are rely upon facial expressions and its classification. Emotions play crucial role in silent communication which involves gestures, postures, expression, etc. There are eight universal facial emotions i.e. happy, sad, anger, fear, surprise, disgust, neutral, contempt [1]. The emotion detection of face image is highly used in the field of machine learning and artificial intelligence. Many researches are in progress to get the human sentiments on facial emotions. In the field of robotics the classification of facial emotion play a vital role in improving human-machine interaction [2]. In this paper, we are detecting facial emotions based on real time as well as static images. In this paper, we have used Japanese female face expressions database (JAFFE), Extended Cohn-Kanade (CK+) Database that contains many images with 640 x 480 pixels. In this paper we are using Haar feature classifier for face detection and feature extraction purposes and it is further preprocessed for facial landmarks. These facial landmarks with the dataset is trained using SVM algorithm and then classify them on the basis of eight universal emotions.
Influence of the Solar Cycle on Turbulence Properties and Cosmic-Ray Diffusion

The solar cycle dependence of various turbulence quantities and cosmic-ray (CR) diffusion coefficients is investigated by using OMNI 1 minute resolution data over 22 years. We employ Elsässer variables z± to calculate the magnetic field turbulence energy and correlation lengths for both the inwardly and outwardly directed interplanetary magnetic field (IMF). We present the temporal evolution of both large-scale solar wind (SW) plasma variables and small-scale magnetic fluctuations. Based on these observed quantities, we study the influence of solar activity on CR parallel and perpendicular diffusion using quasi-linear theory and nonlinear guiding center theory, respectively. We also evaluate the radial evolution of the CR diffusion coefficients by using the boundary conditions for different solar activity levels. We find that in the ecliptic plane at 1 au (1), the large-scale SW temperature T, velocity Vsw, Alfvén speed VA, and IMF magnitude B0 are positively related to solar activity; (2) the fluctuating magnetic energy density , residual energy ED, and corresponding correlation functions all have an obvious solar cycle dependence. The residual energy ED is always negative, which indicates that the energy in magnetic fluctuations is larger than the energy in kinetic fluctuations, especially at solar maximum; (3) the correlation length λ for magnetic fluctuations does not show significant solar cycle variation; (4) the temporally varying shear source of turbulence, which is most important in the inner heliosphere, depends on the solar cycle; (5) small-scale fluctuations may not depend on the direction of the background magnetic field; and (6) high levels of SW fluctuations will increase CR perpendicular diffusion and decrease CR parallel diffusion, but this trend can be masked if the background IMF changes in concert with turbulence in response to solar activity. These results provide quantitative inputs for both turbulence transport models and CR diffusion models, and also provide valuable insight into the long-term modulation of CRs in the heliosphere.
A Nonparametric Graphical Model for Functional Data With Application to Brain Networks Based on fMRI

ABSTRACT We introduce a nonparametric graphical model whose observations on vertices are functions. Many modern applications, such as electroencephalogram and functional magnetic resonance imaging (fMRI), produce data are of this type. The model is based on additive conditional independence (ACI), a statistical relation that captures the spirit of conditional independence without resorting to multi-dimensional kernels. The random functions are assumed to reside in a Hilbert space. No distributional assumption is imposed on the random functions: instead, their statistical relations are characterized nonparametrically by a second Hilbert space, which is a reproducing kernel Hilbert space whose kernel is determined by the inner product of the first Hilbert space. A precision operator is then constructed based on the second space, which characterizes ACI, and hence also the graph. The resulting estimator is relatively easy to compute, requiring no iterative optimization or inversion of large matrices. We establish the consistency and the convergence rate of the estimator. Through simulation studies we demonstrate that the estimator performs better than the functional Gaussian graphical model when the relations among vertices are nonlinear or heteroscedastic. The method is applied to an fMRI dataset to construct brain networks for patients with attention-deficit/hyperactivity disorder. Supplementary materials for this article are available online
When couples fight about money, what do they fight about?

Conflicts about money and finances can be destructive for both the quality and longevity of relationships. This paper reports on a descriptive analysis of the contents of financial conflicts in two samples. Study 1 examined severe financial conflicts in social media posts (N = 1014) from reddit (r/relationships). Eight themes were identified via thematic analysis: “unfair relative contributions” “who pays for joint expenses”, “job and income”, “exceptional expenses”, “terms of financial arrangements”, “discrepant financial values”, “one-sided financial decisions”, and “perceived irresponsibility”. Study 2 examined reports of more mundane financial disagreements recalled by married individuals (N = 481). Seven themes were identified via thematic analysis: “relative contributions”, “job and income”, “different values”, “exceptional expenses”, “mundane expenses”, “money management”, and “perceived irresponsibility”. In both samples, themes could be ordered along the dimensions of “concerns about fairness” and “concerns about responsibility”. The association of relationship outcomes (perceived partner responsiveness, couple satisfaction) with each theme and demographic predictors (income, relationship length, shared finances) were explored. Independent t-tests suggested that participants who recalled disagreements fitting the themes at the extreme ends of the two dimensions (“unfair relative contributions” and “perceived irresponsibility”) reported worse relationship outcomes. In contrast, participants recalling disagreements fitting the theme of “mundane expenses” reported better relationship outcomes.
Temporal and consumer driven cluster analysis for identification of FDI attacks in smart grid

The performance and stability of the electric network are improved through smart grid technologies. The smart grid strongly rely on digital communication technologies creates security issues that must be addressed to distribute power efficiently and safely. For billing, load balancing, and energy management, the customer‐side smart meter regularly transmits the consumption reading to the system operator. However, dishonest customers created cyberattacks by entering fictitious readings of their electrical consumption in order to steal electricity for financial benefit. The distributed monitoring meter‐based network concept is utilized in this paper to closely monitor the losses. A two‐stage clustering‐based technique has also been suggested for the purpose of detecting theft. The approach can be detect the customer ID having abnormality in consumption pattern. To verify the effectiveness of the suggested strategy, numerical experiments on two datasets of smart meters have been conducted and four different types of attack patterns have been constructed. Simulation results show that the proposed scheme is very effective for detecting various types of attack patterns. In addition, the paper helps to predict how much power and financial loss the company will suffer as a result of multiple attacks.
Evaluation of stochastic policies for factored Markov decision processes

We are interested in the resolution of general Markov decision processes (MDP) with factored state and action spaces, called FA-FMDP [4]. We are in particular interested in reward functions which constrain the system to be in an admissible domain, defined by several conditions, the longest time as possible. There are few algorithms able to resolve FA-FMDPs with both a reasonable complexity and a reasonable quality of approximation. Some recent algorithms described in [9], can be used with affine algebraic decision diagrams (AADDs), which are more suited to multiplicative rewards than algebraic decision diagrams (ADDs). The drawback of these algorithms is that they are designed for binary state and action variables, and do not scale to variables with more than two modalities. Most of the other existing methods for solving MDPs with large state and action spaces make the assumption of an additive reward, like approximate linear programming [3] or mean field approaches [10]. As an alternative, we propose to consider multiplicative rewards as an interesting way of modelling objectives defined as admissible domains, and because this trick, as we will see, allows to find methods of approximate policy evaluation. Recently, several decision problems have been resolved with methods of inference in graphical models, an idea which has been recently called planning as inference [2]. We may cite for example [11] which proposes an EM algorithm for solving (non factored) MDPs or [6] which proposes a belief propagation algorithm for solving influence diagrams. Our idea is to follow this trend and propose an (approximate) policy iteration type algorithm [8] for solving FAFMDPs with multiplicative rewards. This algorithm iterates over two steps : an evaluation step and an optimization step. In this communication, we focus on the approximate evaluation step of stochastic policies for FA-FMDPs with multiplicative rewards. The method we propose is based on the computation of normalization constants of factor graphs with increasing sizes by existing variational or belief propagation methods which are applicable on large size graphs.
A Simple and Reproducible Stereomicroscopic Method to Assess Fungal Biofilms: Application to Antifungal Susceptibility Testing

Candida albicans, a well-known opportunistic pathogen, is a major cause of human fungal infections. Biofilm formation is considered an important pathogenesis factor. Biofilms are less sensitive to antibiotics and immune responses, allowing them to colonize and persist in host niches. Biofilm screening is important in the identification of anti-biofilm drugs. However, developing nations, with limited financial resources, often do not have access to advanced scientific equipment. Here, we describe an in vitro, protocol using common materials and simple equipment to evaluate static microbial biofilms.
Bayesian Integrated Data Analysis of Fast-Ion Measurements by Velocity-Space Tomography

Abstract Bayesian integrated data analysis combines measurements from different diagnostics to jointly measure plasma parameters of interest such as temperatures, densities, and drift velocities. Integrated data analysis of fast-ion measurements has long been hampered by the complexity of the strongly non-Maxwellian fast-ion distribution functions. This has recently been overcome by velocity-space tomography. In this method two-dimensional images of the velocity distribution functions consisting of a few hundreds or thousands of pixels are reconstructed using the available fast-ion measurements. Here we present an overview and current status of this emerging technique at the ASDEX Upgrade tokamak and the JET toamak based on fast-ion D-alpha spectroscopy, collective Thomson scattering, gamma-ray and neutron emission spectrometry, and neutral particle analyzers. We discuss Tikhonov regularization within the Bayesian framework. The implementation for different types of diagnostics as well as the uncertainties are discussed, and we highlight the importance of integrated data analysis of all available detectors.
Effects of COVID-19 Vaccines on the Menstrual Cycle: A Cross-Sectional Study

: Background: Prior clinical studies that sought to investigate the safety and efficacy of COVID-19 vaccines did not list menstrual cycle changes as a side-effect. However, following reported cases of menstrual cycle disturbances after vaccination, this study sought to examine the link between COVID-19 vaccination and post-vaccine menstrual cycle abnormalities in pre-and post-menopausal women. Methods: A cross-sectional research design approach using online surveys were employed to investigate the link between vaccination and changes in the menstrual cycle. The participants consisted of a cohort of 657 pre-and post-menopausal women, with the majority drawn from the reproductive age group (25-44 years). The inclusion criteria were that participants must have received any type of COVID-19 vaccine, not be pregnant and those that did not have a negative diagnosis in any gynecologic condition. Of the eligible sample size, only 344 participants met the inclusion criteria. The sociodemographic and menstrual cycle data were collected from an online survey. Data was analyzed using descriptive, inferential chi-square tests, logistic regression, and correlation. Results: The results partially confirmed the findings from prior studies that COVID-19 vaccination is associated with significant changes in the women’s menstrual cycle flow and menstrual period length even after controlling for age, Body Mass Index, and ethnicity. Other menstrual cycle disturbances such as missed periods, cycle regularity, and spotting/vaginal bleeding were noted to be less significant. However, the extent of menstrual cycle changes was less severe and decreased after the second dose of vaccination. It was found that 11.1% and 37.5% of post-menopausal women reported menstrual symptoms after the first and second dose cycles, respectively. Conclusion: The study concludes that although COVID-19 vaccines tend to adversely affect women’s menstrual cycle, these changes are short-lived. The findings have important implications in enhancing the success of COVID-19 vaccination programs by reducing cases of vaccine hesitancy among reproductive-age women.