Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Social Media Advertising on Purchase Intention and Customer Loyalty in E-Commerce Systems

The emergence of new technologies has had a noteworthy impact on communication systems, leading to the importance of conducting research in this area due to the significant influence of social media. Marketing operators must implement the necessary infrastructure to identify and fulfill customers' expectations, considering the advantages and the increasing number of users in social networks. This paper presents a comprehensive framework for evaluating the purchase intention of electronic commerce systems, taking into account the impact of social media advertising and customer loyalty. The paper aims to enhance purchase intention in e-commerce through social media advertising and examine influential factors that improve online shopping performance, including social media advertising's effectiveness on customer purchase behavior and brand loyalty. The paper develops a theoretical framework of nine hypotheses to evaluate purchase intention in e-commerce systems, with a focus on the effect of social media advertising on brand loyalty and purchase intention. To validate the proposed model and test the research hypotheses, we make use of information obtained from an international company located in China. The results demonstrate the positive effect of pleasurable motivation on purchase intention, with a significance level of 3.776 and a path coefficient of 0.279. Moreover, an investigation of the positive effect of customer loyalty on the recommended advertisement confirms the hypotheses, with a significance of 32.815 and a path coefficient of 0.788.
Arbeitskreis Quantitative Steuerlehre Quantitative Research in Taxation – Discussion Papers

Empirical literature on preferences for wealth taxation almost exclusively focuses on either the emotionally loaded estate tax or rather general concepts of redistributive preferences. Yet, it remains unclear whether the exceptional opposition towards the estate tax is applicable to other instruments of net wealth taxation. This study presents, to our knowledge, the first investigation of how individuals want to tax wealth across a variety of tangible wealth tax instruments. In doing so, we particularly test for the presence of framing effects, incidence concentration and the role of wealth characteristics within the different tax configurations. For this, we conducted a factorial vignette survey experiment with over 3,200 respondents on Amazon‘s Mechanical Turk (MTurk). Each respondent was randomized into one of four burden-equivalent wealth tax instruments: an estate tax, a one-time wealth tax, a decennial wealth tax or a yearly wealth tax. Subsequently we asked each respondent to state her preferred overall lifetime tax burden for a set of hypothetical individuals. Our findings yield several interesting insights. First, we find that the exceptional opposition towards the estate tax is not applicable to other instruments of wealth taxation and is only valid for certain subgroups. In general, our empirical findings provide preferred tax rates between 12.8 to 14.9 percent of overall lifetime tax burden. Second, we document an exceptional opposition towards the mere name “estate tax” in relation to equivalent wealth tax instruments for certain subgroups. Republicans particularly reject the estate tax with a lower proposed effective tax rate of around 3.1 percentage points compared to all other wealth taxes even the perfectly congruent one-time wealth tax. Third, we uncover the influence of normative preferences for specific design features on the support for a wealth tax. Proposed effective tax rates of the estate tax and the one-time wealth tax show a significant progressivity, whereas no progressivity can be observed for both periodical taxes. The presence of children has an especially significant negative effect in one-off wealth taxes at the end of the lifetime. JEL Classification: C90, D31, D72, H2
Adaptive oscillatory neurons generate periodic bursting: analytic and numerical study of the attractor dynamics

Several experimental and numerical studies have observed that populations of oscillatory neurons can synchronize and converge to an dynamical attractor composed of periodic bursts which involve the whole network. We show here that a sufficient condition for this phenomenon to occur is the presence of an excitatory population of oscillatory neurons displaying spike-driven adaptation, which was observed for cultured pyramidal neurons from the cortex or the hippocampus. More than just synchronization – which takes place even for weak coupling – bursting occurs for stronger coupling as the adaptive neurons switch from their initial adaptive spiking to an intermittent burst-like behavior. Addition of inhibitory neurons or plastic synapses can then modulate this dynamics in many ways but is not necessary for its appearance. After discussing the origin of the bursting behavior, we provide a detailed analytic and numerical description of such dynamics, as well as the evolution of its properties depending on the neuronal and synaptic parameters. This allows us to explain the change in the neuronal dynamics and the phenomenon underlying the termination of a burst. We based our study on a mean-field model for adaptive exponential integrate-and-fire (aEIF or AdEx) neurons, thanks to which we discuss the related biological phenomena and the relevance of the explored region of parameter space. A sufficient condition for synchronous bursting in neuronal population is the existence of a population of excitatory neurons displaying spike-driven adaptation and oscillatory behavior. For strong enough coupling, the adaptive neurons not only synchronize but switch from their initial regular spiking to an emergent and intermittent burst-like behavior. Using a 2D dynamical system, we provide a detailed analytic and numerical description of such dynamics based on a simplified mean-field model. This leads to a minimal model, based on a hypothesis that can be tested experimentally, which not only reproduces the biological observations but also provides an explanation to the underlying phenomena involved in synchronous bursting. Indeed we propose possible mechanisms for both the initiation and termination of a collective burst as well as analytic results to predict the properties of the global activity and its evolution depending on neuronal and synaptic parameters.
Self-motion perception and sequential decision-making: where are we heading?

To navigate and guide adaptive behaviour in a dynamic environment, animals must accurately estimate their own motion relative to the external world. This is a fundamentally multisensory process involving integration of visual, vestibular and kinesthetic inputs. Ideal observer models, paired with careful neurophysiological investigation, helped to reveal how visual and vestibular signals are combined to support perception of linear self-motion direction, or heading. Recent work has extended these findings by emphasizing the dimension of time, both with regard to stimulus dynamics and the trade-off between speed and accuracy. Both time and certainty—i.e. the degree of confidence in a multisensory decision—are essential to the ecological goals of the system: terminating a decision process is necessary for timely action, and predicting one's accuracy is critical for making multiple decisions in a sequence, as in navigation. Here, we summarize a leading model for multisensory decision-making, then show how the model can be extended to study confidence in heading discrimination. Lastly, we preview ongoing efforts to bridge self-motion perception and navigation per se, including closed-loop virtual reality and active self-motion. The design of unconstrained, ethologically inspired tasks, accompanied by large-scale neural recordings, raise promise for a deeper understanding of spatial perception and decision-making in the behaving animal. This article is part of the theme issue ‘Decision and control processes in multisensory perception’.
Metacognitive awareness in the sound-induced flash illusion

Hundreds (if not thousands) of multisensory studies provide evidence that the human brain can integrate temporally and spatially discrepant stimuli from distinct modalities into a singular event. This process of multisensory integration is usually portrayed in the scientific literature as contributing to our integrated, coherent perceptual reality. However, missing from this account is an answer to a simple question: how do confidence judgements compare between multisensory information that is integrated across multiple sources, and multisensory information that comes from a single, congruent source in the environment? In this paper, we use the sound-induced flash illusion to investigate if confidence judgements are similar across multisensory conditions when the numbers of auditory and visual events are the same, and the numbers of auditory and visual events are different. Results showed that congruent audiovisual stimuli produced higher confidence than incongruent audiovisual stimuli, even when the perceptual report was matched across the two conditions. Integrating these behavioural findings with recent neuroimaging and theoretical work, we discuss the role that prefrontal cortex may play in metacognition, multisensory causal inference and sensory source monitoring in general. This article is part of the theme issue ‘Decision and control processes in multisensory perception’.
Sparse interpolation and rational approximation

Sparse interpolation or exponential analysis, is widely used and in quite diﬀerent applications and areas of science and engineering. Therefore researchers are often not aware of similar studies going on in another ﬁeld. The current text is written as a concise tutorial, from an approximation theorist point of view. In Section 2 we summarize the mathematics involved in exponential analysis: structured matrices, generalized eigenvalue problems, singular value decomposition. The section is written with the numerical computation of the sparse interpolant in mind. In Section 3 we outline several connections of sparse interpolation with other mostly non-numeric subjects: computer algebra, number theory, linear recurrences. Some problems are only solved using exact arithmetic. In Section 4 we connect sparse interpolation to rational approximation theory. One of the major hurdles in sparse interpolation is still the correct detection of the number of components in the model. Here we show how to reliably obtain the number of terms in a numeric and noisy environment. The new insight allows to improve on existing state-of-the-art algorithms.
Management of COVID-19-Associated Guillain-Barré Syndrome in a Full-Term Pregnant Woman: A Case Report

Abstract Guillain-Barré syndrome (GBS) can occur after viral infections. Its occurrence after COVID-19 infection in the peripartum period is a very rare co-occurrence. Therefore, there are no guidelines for the management of these patients. We report the case of a 32-year-old pregnant woman who developed COVID-19-associated GBS with aspiration pneumonia, motor weakness, and ascending paralysis at 39 weeks of gestation. Preoperative plasmatic exchange (plasmapheresis) and oxygen support were very effective and allowed for a rapid recovery within five days. Because of foetal distress during labor, the patient had a caesarean section under spinal anaesthesia with no maternal complications or adverse foetal outcomes.
Two Approaches Towards the Flavour Puzzle: Dynamical Minimal Flavour Violation and Warped Extra Dimensions

In this thesis we investigate two approaches towards the flavour puzzle. Incorporating the concept of minimal flavour violation, we promote the Yukawa entries to dynamical spurion fields in an effective field theory. Their vacuum expectation values sequentially break the maximal flavour symmetry of the Standard Model and generate the hierarchy in the quark masses and mixings. Integrating out the heavy Yukawa Higgs modes leads to flavour-changing neutral currents (FCNCs). While we interpret the Goldstone bosons of the local non-abelian part of the flavour symmetry as the longitudinal gauge boson modes, we use the ones of the global abelian subgroup to resolve the strong CP problem. In Randall-Sundrum models the 5-dimensional quark mass matrices render the effective 4-dimensional Yukawa matrices hierarchical by a different quark localisation in the extra dimension. We carefully work out the flavour and electroweak sector of a custodially protected model, where we focus on the tree-level FCNCs induced by non-universal fermion-gauge boson couplings.
Scaling between cell cycle duration and wing growth is regulated by Fat-Dachsous signaling in Drosophila

The atypical cadherins Fat and Dachsous (Ds) signal through the Hippo pathway to regulate growth of numerous organs, including the Drosophila wing. Here, we find that Ds-Fat signaling tunes a unique feature of cell proliferation found to control the rate of wing growth. The duration of the cell cycle increases in direct proportion to the size of the wing, leading to linear rather than exponential growth. Ds-Fat signaling enhances the rate at which the cell cycle lengthens with wing size, thus diminishing the linear rate of wing growth. We show that this results in a complex but stereotyped relative scaling of wing growth with body growth in Drosophila. Finally, we examine the dynamics of Fat and Ds protein distribution in the wing, observing graded distributions that change during growth. However, the significance of these dynamics is unclear since perturbations in expression have negligible impact on wing growth.
Human kinesin-5 KIF11 drives the helical motion of anti-parallel and parallel microtubules around each other

During mitosis, motor proteins and microtubule-associated protein organize the spindle apparatus by cross-linking and sliding microtubules. Kinesin-5 plays a vital role in spindle formation and maintenance, potentially inducing twist in the spindle fibers. The off-axis power stroke of kinesin-5 could generate this twist, but its implications in microtubule organization remain unclear. Here, we investigate 3D microtubule-microtubule sliding mediated by the human kinesin-5, KIF11, and found that the motor caused right-handed rotation of anti-parallel microtubules around each other. The effective sidestepping probability of KIF11 increased with reduced ATP concentration, indicating that forward and sideways stepping of the motor are not strictly coupled. Further, the microtubule-microtubule distance (motor extension) during sliding decreased with increasing sliding velocity. Intriguingly, parallel microtubules cross-linked by KIF11 orbit without forward motion, with nearly full extension. Altering the length of the neck linker increased the forward velocity and pitch of microtubules in anti-parallel overlaps. Taken together, we suggest that helical motion and orbiting of microtubules, driven by KIF11, enable flexible and context-dependent filament organization, as well as torque regulation within the mitotic spindle.
Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation

Abstract. Doppler beam sharpening (DBS) is a critical technology for airborne radar ground mapping in forward-squint region. In conventional DBS technology, the narrow-band Doppler filter groups formed by fast Fourier transform (FFT) method suffer from low spectral resolution and high side lobe levels. The iterative adaptive approach (IAA), based on the weighted least squares (WLS), is applied to the DBS imaging applications, forming narrower Doppler filter groups than the FFT with lower side lobe levels. Regrettably, the IAA is iterative, and requires matrix multiplication and inverse operation when forming the covariance matrix, its inverse and traversing the WLS estimate for each sampling point, resulting in a notably high computational complexity for cubic time. We propose a fast IAA (FIAA)-based super-resolution DBS imaging method, taking advantage of the rich matrix structures of the classical narrow-band filtering. First, we formulate the covariance matrix via the FFT instead of the conventional matrix multiplication operation, based on the typical Fourier structure of the steering matrix. Then, by exploiting the Gohberg–Semencul representation, the inverse of the Toeplitz covariance matrix is computed by the celebrated Levinson–Durbin (LD) and Toeplitz-vector algorithm. Finally, the FFT and fast Toeplitz-vector algorithm are further used to traverse the WLS estimates based on the data-dependent trigonometric polynomials. The method uses the Hermitian feature of the echo autocorrelation matrix R to achieve its fast solution and uses the Toeplitz structure of R to realize its fast inversion. The proposed method enjoys a lower computational complexity without performance loss compared with the conventional IAA-based super-resolution DBS imaging method. The results based on simulations and measured data verify the imaging performance and the operational efficiency.
Varifocal liquid lens actuated by laser-induced thermal Marangoni forces

This paper proposes a simple and easily implemented configuration of a varifocal liquid lens actuated by laser-induced thermal Marangoni forces. The lens consists of a sessile droplet of low-volatile liquid placed on a transparent solid surface. The focal length tunability is achieved by changing a local curvature of the droplet surface due to the thermocapillary displacement of liquid from the core part of the droplet to its edge caused by heating with a laser beam. Depending on the power of the laser beam, the droplet can operate as a varifocal convergent or varifocal divergent lens. In the convergent mode, the curvature of the droplet surface varies from an initial positive value to a near zero value (flat surface), whereas in the divergent mode, the curvature changes from a positive value to a negative value due to the formation of a thermocapillary dimple in the droplet.
Physical Roles of Interstellar-origin Pickup Ions at Heliospheric Termination Shock. II. Impact of the Front Nonstationary on the Energy Partition and Particle Velocity Distribution

The impact of the nonstationarity of the heliospheric termination shock in the presence of pickup ions (PUIs) on the energy partition between different plasma components is analyzed self-consistently by using a one-dimensional particle-in-cell simulation code. Solar wind ions (SWIs) and PUIs are introduced as Maxwellian and shell distributions, respectively. For a fixed time, (a) with a percentage of 25% PUIs, a large part of the downstream thermal pressure is carried by reflected PUIs, in agreement with previous hybrid simulations; (b) the total downstream distribution includes three main components: (i) a low-energy component dominated by directly transmitted (DT) SWIs, (ii) a high-energy component dominated by reflected PUIs, and (iii) an intermediate-energy component dominated by reflected SWIs and DT PUIs. Moreover, results show that the front nonstationarity (self-reformation) persists even in presence of 25% PUIs, and has some impacts on both SWIs and PUIs: (a) the rate of reflected ions suffers some time fluctuations for both SWIs and PUIs; (b) the relative percentage of downstream thermal pressure transfered to PUIs and SWIs also suffers some time fluctuations, but depends on the relative distance from the front; (c) the three components within the total downstream heliosheath distribution persist in time, but the contribution of the ion subpopulations to the low- and intermediate-energy components are redistributed by the front nonstationarity. Our results allow clarifying the respective roles of SWIs and PUIs as a viable production source of energetic neutral atoms and are compared with previous results.
Nonparametric and Semi-parametric Survival methods for Assessing days Open and Conception rates of Lactating cows

Evaluation of conception rates in dairy livestock requires specific statistical methods because of the followings: (i) some cows are still alive after the follow up period and their fate is not known, which means that records of these animals should be censored, (ii) the risk factors connected with fertility are not perform linearly and may vary with time, (iii) not all lactation records are normally distributed. Therefore, this study was undertaken to assess factors influencing days open (DO) and conception rates of dairy cattle, using nonparametric and semi-parametric survival functions. Parity, season of calving, milk yield levels (MYL), age at calving (AAC), days to first estrus (DFE), days to first insemination (DFI), number of inseminations per conception (NIPC) were significantly associated with days open for a number of 1980 lactation cows, of which 12.73 % were censored. The overall median of DO was 123 days (95 % CI = 119.55-126.44). For the first five parities studied, medians of DO were 111, 121, 128, 123 and 152 days, respectively. The Kaplan-Meier survivor functions and log-rank tests denoted highly significant differences (p < 0.001) among parities, seasons of calving and levels of milk yield in terms of time to conception. Proportions of pregnant cows were estimated at every postpartum time point alongside their cumulative probabilities. The overall percentages of pregnant cows were 0.5, 34.7, 67.2, 92.07 and 99.34 % at days 50, 100, 150, 200 and 250 postpartum. Cox PH models reveled significant contribution of all studied factors as hazards for conception. The hazard ratios (varied from 0.632 to 1.393) were used to quantify the exact differences between all categories. The present study has provided important survival functions that were used to identify and quantify the economic risk factors associated with days open length and conception rates of dairy cattle in Egypt.
Multivariate Long-Memory Processes and Nonparametric Density Estimation, with Applications to Ridge Detection

In this thesis we consider nonparametric estimation of the probability density function and its derivatives for multivariate linear long-memory processes. The results are applied to establish simultaneous confidence regions for density ridges. After a short motivation the theoretical background for analyzing multivariate long-memory processes is provided. Density ridges are introduced as generalizations of local maxima. Their role in topological statistics is explained briefly. In order to estimate the ridge of a probability density function, asymptotic results for kernel density derivative estimators are derived. The main tool is a multivariate extension of the uniform reduction principle for linear long-memory processes. The asymptotic distribution of the empirical process is derived and weak convergence to a degenerate zero mean Gaussian process is shown. This leads to asymptotic results for multivariate kernel density estimators and their derivatives. Under long-memory assumptions the asymptotic behaviour is quite different for small and large bandwidths: For small bandwidths, the results are the same as for i.i. d. observations. For large bandwidths, we obtain weak convergence to a degenerate limiting process. Implications for bandwidth selection are discussed. The detection of ridge points involves checking conditions on the gradient and the eigenvectors and eigenvalues of the Hessian matrix of the underlying probability density function. Since the functional limit theorem carries over to the eigenvalues and eigenvectors of the estimated Hessian matrix, a combination of these results implies weak convergence of the estimated ridge. This allows for a fairly simple construction of asymptotic confidence regions. Numerical examples illustrate the results.