Miracle-dz/UniFAR-FTUs huggingface dataset:
Colonization Efficacy of the Endophytic Insect-Pathogenic Fungus, Beauveria bassiana, Across the Plant Kingdom: A Meta-Analysis

Abstract The insect-pathogenic fungus Beauveria bassiana (Bb) colonizes several plant species as an endophyte. However, the diversity of plants colonized and the extent of colonization by this fungus have not been summarized comprehensively across all plant species. To fill this knowledge gap, a meta-analysis of published studies (years 2002–2018) on the percentage of B. bassiana plant colonization across the plant kingdom was conducted. We collected 232 published papers from which 1,051 studies (individual treatments) were extracted and analyzed with Comprehensive Meta-Analysis, Version 3 (CMA) software. Factors (n = 26) influencing plant colonization by B. bassiana were identified. Across all studies, the mean endophytic colonization of B. bassiana was 29% in different plant taxa. Plant colonization by B. bassiana was highest for isolates collected from insects. Most studies applied B. bassiana at 1 × 108 conidia/ml; however, plant colonization was greatest with an application rate of 1 × 1012 conidia/ml. Among all plant hosts, colonization percentage was highest in faba bean, plants in the family Brassicaceae, angiosperms (eudicots, annuals), plants classified with a forb/herb/vine growth habit, and plants with fibrous or tap root systems. As an inoculant, B. bassiana was most frequently applied to the whole plant after true leaves had developed. The foliar spray was the most studied inoculation method, but the application of solid grain substrate to foliage gave higher plant colonization. Studies conducted in controlled environments resulted in higher endophytic colonization with B. bassiana than field studies. Endophytic B. bassiana presence was confirmed primarily with microscopy, rather than molecular methods. High heterogeneity (I 2 = 97%) across studies was identified with large variability in endophytic B. bassiana colonization across a diversity of plant species. These findings increase our understanding and knowledge of the endophytic lifestyle of B. bassiana, which will facilitate the development of novel, sustainable, and eco-friendly disease management strategies with B. bassiana.
The small and the beautiful: how the star formation law affects galactic disc structure

We investigate the influence of different analytical parameterizations and fit functions for the local star formation rate in AMR simulations of an isolated disk galaxy with the Nyx code. Such parameterizations express the star formation efficiency as function of the local turbulent Mach number and virial parameter. By employing the method of adaptively refined large eddy simulations, we are able to evaluate these physical parameters from the numerically unresolved turbulent energy associated with the grid scale. We consider both single and multi free-fall variants of star formation laws proposed by Padoan & Nordlund, Hennebelle & Chabrier, and Krumholz & McKee, summarised and tested recently with numerical simulations by Federrath & Klessen. We find that the global star formation rate and the relation between the local star formation rate and the gas column density is reproduced in agreement with observational constraints by all multi free-fall models of star formation. Some models with obsolete calibration or a single free-fall time scale, however, result in an overly clumpy disk that does not resemble the structure of observed spirals.
Three lectures on noncommutative field theories

Classical and quantum aspects of noncommutative field theories are discussed. In particular, noncommutative solitons and instantons are constructed and also d=2,3 noncommutative fermion and bosonic (Wess-Zumino-Witten and Chern-Simons)theories are analyzed.
On the spatial power spectrum of the E x B gradient drift instability in ionospheric plasma clouds. Memorandum report

It is shown that the two dimensional power law spatial power spectra of magnetic field line integrated Pedersen conductivity perturbations in the plane perpendicular to the magnetic field recently computed from numerical simulation studies of ionospheric plasma clouds are consistent with conservation laws implied by the fundamental plasma fluid equations describing the E X B gradient drift instability in these ionospheric plasma clouds.
Particle Physics and Dark Energy: Beyond Classical Dynamics

In this work, quantum corrections to classical equations of motion are investigated for dynamical models of dark energy featuring a time-evolving quintessence scalar field. Employing effective quantum field theory, the robustness of tracker quintessence potentials against quantum corrections as well as their impact on cosmological observables are discussed. Furthermore, it is demonstrated that a rolling quintessence field can also play an important role for baryogenesis in the early universe. The macroscopic time-evolution of scalar quantum fields can be described from first principles within nonequilibrium quantum field theory based on Kadanoff-Baym equations derived from the 2PI effective action. A framework for the nonperturbative renormalization of Kadanoff-Baym equations is provided. Renormalized Kadanoff-Baym equations are proposed and their finiteness is shown for a special case.
A Comparison of Laboratory Experiments and Numerical Simulations of Steady Baroclinic Waves Produced

Qualitative and quantitative comparisons aremade between laboratory measurements and numerical solutions of steady rotating annulus flows. The governing equations of the fluid with the Boussinesq approximation are solved numerically by the finite difference method on nonuniform grids. The comparisons as to some characteristics of baroclinic waves such as rates of radial heat transport and drift periods of waves, which are measured by reducing probe effects to a negligibleextent, demonstrate the reliability of the numerical model. Numerical results of internal structures ofsteady baroclinic waves are presented on the basis of this model verification and compared with experimental results. Comparisons reveal that not only probe drag but also heat leakage modifies flow fields to a considerable extent. Analyses made by using data obtained by simulations indicate that (1) the inward heat transport by the deviatoric) motion overcomes the outward heat transport by the indirect circulation and plays an essential role in the heat transport to the inner cylinder; (2) the deviatoric motion as well as the mean meridional circulation transports angular momentum to the axis of the relative angular momentum jet; and (3) the conversion rate from deviatoric kinetic energy to zonal kinetic energy is positive at lower rotation rates but it becomes negative at higher rotation rates.
The impact of website quality on online compulsive buying behavior: evidence from online shopping organizations

PurposeThe influence of website quality on online compulsive buying behavior (OCBB) in the context of online shopping based on the usage of a credit card (UCC) and online impulsive buying behavior (OIBB) was investigated in this study.Design/methodology/approachThe authors used a research model to examine the relationships between the study components as per the prescription. For this investigation, the authors used an online survey form to obtain primary data from 350 respondents on social media. A covariance-based structural equation modeling approach was used to evaluate the structural research model and data.FindingsThe findings reveal that the quality of online shopping websites positively affects consumers' UCC and OIBB, and these in turn positively influence their OCBB.Practical implicationsThe study emphasized impacting elements on consumer behavior and gave advice for future research based on the results. Using several dimensions of website quality, this study bridges the knowledge gap between UCC, OIBB and OCBB.Originality/valueBased on UCC and OIBB, the authors developed a new model to investigate the link between website quality and OCBB. To the best of the authors' knowledge, it is the first experimental result that assesses the impact of website quality on OCBB.
COLLIDING DECIMETER DUST

Collisional evolution is a key process in planetesimal formation and decimeter bodies play a key role in the different models. However, the outcome of collisions between two dusty decimeter bodies has never been studied experimentally. Therefore, we carried out microgravity collision experiments in the Bremen drop tower. The agglomerates consist of quartz with irregularly shaped micrometer-sized grains and the mean volume filling factor is 0.437 ± 0.004. The aggregates are cylindrical with 12 cm in height and 12 cm in diameter, and typical masses are 1.5 kg. These are the largest and most massive dust aggregates studied in collisions to date. We observed rebound and fragmentation but no sticking in the velocity range between 0.8 and 25.7 cm s−1. The critical fragmentation velocity for split up of an aggregate is 16.2 ± 0.4 cm s−1. At lower velocities the aggregates bounce off each other. In this velocity range, the coefficient of restitution decreases with increasing collision velocity from 0.8 to 0.3. While the aggregates are very weak, the critical specific kinetic energy for fragmentation Qμ = 1 is a factor of six larger than expected. Collisions of large bodies in protoplanetary disks are supposed to be much faster and the generation of smaller fragments is likely. In planetary rings, collision velocities are of the order of a few cm s−1 and are thereby in the same range investigated in these experiments. The coefficient of restitution of dust agglomerates and regolith-covered ice particles, which are common in planetary rings, are similar.
Unified Models of Dark Energy and Dark Matter

Faculdade de Ciencias Departmento de Fisica e Astronomia Unified Models of Dark Energy and Dark Matter by Vasco Ferreira In this work we give a succinct review of the main ideas behind modern cosmology and introduce the concept of models that represent dark energy and dark matter by the same underlying fluid. The background evolution and general considerations of the generalized Chaplygin gas (GCG) are presented and the parameters of the model constrained using Supernovae Ia (SNIa) observations. Next, we deal with the evolution of cosmological perturbations of the GCG and find that the matter power spectrum of the model is inconsistent with the observations, unless we constrain the value of the parameter α close to zero. The importance of including baryons to account for the formation of structures is also discussed. By analysing the clustering properties of the GCG, we motivate the idea that it is essential to take in account the effects of nonlinearities when considering unified dark energy models. Finally, we study a model that introduces a parameter to characterize the level of small non-linear clustering in these scenarios, using the GCG as a representative model. We conclude that the model is consistent for all values of α, with the most recent SNIa observations, and avoids the late time oscillations of the matter power spectrum, providing that the level of non-linear clustering is sufficiently high.
Workshop on Squeezed States and Uncertainty Relations

The proceedings from the workshop are presented, and the focus was on the application of squeezed states. There are many who say that the potential for industrial applications is enormous, as the history of the conventional laser suggests. All those who worked so hard to produce squeezed states of light are continuing their efforts to construct more efficient squeezed-state lasers. Quite naturally, they are looking for new experiments using these lasers. The physical basis of squeezed states is the uncertainty relation in Fock space, which is also the basis for the creation and annihilation of particles in quantum field theory. Indeed, squeezed states provide a unique opportunity for field theoreticians to develop a measurement theory for quantum field theory.
Hydrolight 4.0 Users Guide

Abstract : HYDROLIGHT 4.0 is a radiative transfer numerical model that computes spectral radiance distributions and related quantities for water bodies. Input to the model consists of user-supplied information about the absorbing and scattering properties of the water body, the sky radiance incident onto the water surface, the wind speed, and the bottom of the water column. The water absorbing and scattering properties can vary with depth and wavelength. The model includes internal sources and inelastic scattering. Output from the model includes the radiance distribution within and leaving the water body as a function of depth, direction, and wavelength as well as various irradiances, reflectances, and diffuse attenuation functions. This report is a self-contained guide to running the HYDROLIGHT 4.0 model, which includes a Graphical User Interface for personal computers with Microsoft Windows 95/98/NT, Microsoft Excel spreadsheet analysis of output, automatic file management, and easy input of users data.
How to Cope with an Abusive Leader? Examinations of Subordinates’ Affective Reactions, CWB-O and Turnover Intentions

When subordinates experience abusive supervision, they often respond with “fight” (e.g., organizationally-directed counterproductive work behavior: CWB-O) or “flight” reactions (e.g., turnover intentions). Drawing on cognitive appraisal theory, we propose that negative (NA) and positive affect (PA) explain these distinct responses and that coworker emotional support differentially moderates the relationships of affective states with CWB-O and turnover intention. That is, we expect that emotional support exacerbates the mediating effect of NA, whereas it weakens the mediating effect of PA. We tested the hypothesized model in two time-lagged studies, one among subordinates (N = 162), and the other among supervisor-subordinate dyads (N = 255 dyads). Results confirmed that the relation between abusive supervision and CWB-O was mediated by NA, and that the relation between abusive supervision and turnover intentions was mediated by PA. Coworker emotional support strengthened the mediating effect of NA, but it did not attenuate the mediating effect of PA. These results only hold for CWB-O, not for supervisor-rated CWB-O. Explanations, implications as well as limitations and ideas for future research are discussed.
Denoising Self-Attentive Sequential Recommendation

Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self-attention networks to exploit pairwise item-item interactions within the sequence. However, real-world item sequences are often noisy, which is particularly true for implicit feedback. For example, a large portion of clicks do not align well with user preferences, and many products end up with negative reviews or being returned. As such, the current user action only depends on a subset of items, not on the entire sequences. Many existing Transformer-based models use full attention distributions, which inevitably assign certain credits to irrelevant items. This may lead to sub-optimal performance if Transformers are not regularized properly. Here we propose the Rec-denoiser model for better training of self-attentive recommender systems. In Rec-denoiser, we aim to adaptively prune noisy items that are unrelated to the next item prediction. To achieve this, we simply attach each self-attention layer with a trainable binary mask to prune noisy attentions, resulting in sparse and clean attention distributions. This largely purifies item-item dependencies and provides better model interpretability. In addition, the self-attention network is typically not Lipschitz continuous and is vulnerable to small perturbations. Jacobian regularization is further applied to the Transformer blocks to improve the robustness of Transformers for noisy sequences. Our Rec-denoiser is a general plugin that is compatible to many Transformers. Quantitative results on real-world datasets show that our Rec-denoiser outperforms the state-of-the-art baselines.
Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems

Calls to action to implement evaluation of fairness and bias into industry systems are increasing at a rapid rate. The research community has attempted to meet these demands by producing ethical principles and guidelines for AI, but few of these documents provide guidance on how to implement these principles in real world settings. Without readily available standardized and practice-tested approaches for evaluating fairness in recommendation systems, industry practitioners, who are often not experts, may easily run into challenges or implement metrics that are potentially poorly suited to their specific applications. When evaluating recommendations, practitioners are well aware they should evaluate their systems for unintended algorithmic harms, but the most important, and unanswered question, is how? In this talk, we will present practical challenges we encountered in addressing algorithmic responsibility in recommendation systems, which also present research opportunities for the RecSys community. This talk will focus on the steps that need to happen before bias mitigation can even begin.
Fair Ranking Metrics

Information access systems such as search engines and recommender systems often display results in a sorted ranked list based on their relevance. Fairness of these ranked list has received attention as an important evaluation criteria along with traditional metrics such as utility or accuracy. Fairness broadly involves both provider and consumer side fairness at both group and individual levels. Several fair ranking metrics have been proposed to measure group fairness for providers based on various “sensitive attributes”. These metrics differ in their fairness goal, assumptions, and implementations. Although there are several fair ranking metrics to measure group fairness, multiple open challenges still exist in this area to consider. In my thesis, I work on the area of fair ranking metrics for provider-side group fairness. I am interested in understanding the fairness concepts and practical applications of these metrics to identify their strength and limitations to aid the researchers and practitioner by pointing out the gaps. Moreover, I will contribute to this research area by focusing on some of the limitations like considering different browsing models and bias in relevance information.
Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences

Previous studies on exploration have shown that users can be nudged to explore further away from their current preferences. However, these effects were shown in a single session study, while it often takes time to explore new tastes and develop new preferences. In this work, we present a longitudinal study on users’ exploration behavior and behavior change over time after they have used a music genre exploration tool for four sessions in six weeks. We test two relevant nudges to help them explore more: the starting point (the personalization of the default initial playlist) and the visualization of users’ previous position(s). Our results show that the personalization level of the default initial playlist in the first session influences the preferred personalization level users set in the second session but fades away in later sessions as users start exploring in different directions. Visualization of users’ previous positions did not anchor users to stay closer to the initial defaults. Over time, users perceived the playlist to be more personalized to their tastes and helpful to explore the genre. Perceived helpfulness increased more when users explored further away from their current preferences. Apart from differences in self-reported measures, we also find some objective evidence for preference change in users’ top tracks from their Spotify profile, that over the period of 6 weeks moved somewhat closer to the genre that users selected to explore with the tool.
Don’t recommend the obvious: estimate probability ratios

Sequential recommender systems are becoming widespread in the online retail and streaming industry. These systems are often trained to predict the next item given a sequence of a user’s recent actions, and standard evaluation metrics reward systems that can identify the most probable items that might appear next. However, some recent papers instead evaluate recommendation systems with popularity-sampled metrics, which measure how well the model can find a user’s next item when hidden amongst generally-popular items. We argue that these popularity-sampled metrics are more appropriate for recommender systems, because the most probable items for a user often include generally-popular items. If the probability that a customer will watch Toy Story is not much more probable than for the average customer, then the movie isn’t especially relevant for them and we should not recommend it. This paper shows that optimizing popularity-sampled metrics is closely related to estimating point-wise mutual information (PMI). We propose and compare two techniques to fit PMI directly, which both improve popularity-sampled metrics for state-of-the-art recommender systems. The improvements are large compared to differences between recently-proposed model architectures.
Atmospheric turbulence, meteorological modeling and aerodynamics

Preface Atmospheric Turbulence: Prediction, Measurement, and Effects. Climatology of the Arctic Planetary Boundary Layer Nonequilibrium Thermodynamic Theory Of The Atmospheric Turbulence Generalized Scale Invariance of Edge Plasma Turbulence Turbulence, Turbulent Mixing and Diffusion in Shallow-Water Estuaries Turbulence Scalar Transfer Modeling in Reacting Flows Large-Eddy Simulation of Free Shear and Wall-Bounded Turbulent Flows The research turbulence method with using sensitivity? of the solution quality for the k- analysis of flow properties to model coefficients Passive Air Sampler for the Determination of Atmospheric Nitrogen Dioxide Using Flat Porous Polyethylene Membrane as Turbulence Limiting Diffuser Artificial Intelligence Technique for Modelling and Forecasting of Meteorological Data: A Survey A Semi-Analytic Model of Fog Effects on Vision New Trends on Phenological Modelling Time Dependent Shape Optimization Using Adjoint Variable Method for Reducing Drag Numerical and Experimental Investigations of Fluid Dynamics of High speed Flows Aerodynamic Research and Development of Vertical-Axis Wind Turbines with Rotary Blades Grand Computational Challenges for Prediction of the Turbulent Wind Flow and Contaminant Transport and Dispersion in the Complex Urban Environment The Determination of Aerodynamic Forces on Sails -- Challenges and Status On parameterising inclined stable boundary layers Ambient Air Temperature Interpolation in Inhomogeneous Regions Peat Moisture in Relation to Meteorological Factors: Monitoring, Modelling, and Implications for the Application of the Canadian Forest Fire Weather Index System Index.