Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis Of Customer Sentiment Through Computational Study Of Product Reviews Using Machine Learning Approach

: With the increasing trend of online shopping, it is necessary for companies to know what customers think about their products through reviews and feedback in order to make improvements in the future. If there are a large number of reviews or feedback, handling these manually can become a challenging task to perform, which can be simplified by using sentiment analysis. Opinions can be widely grouped into three categories positive, negative and neutral. In this paper we have considered set of textual English reviews on product from Amazon website. Text present in the review is unstructured in nature, hence we perform pre-processing techniques. Then features are extracted from the pre-processed data using TF-IDF feature extraction method using CountVectorizer library. Sentiment analysis is done using three classification algorithms that is Support Vector Machine, Naive Bayes and Decision Tree considering Accuracy, Precision, Recall and F-Score performance parameters. Upon comparison using parameters, Naïve Bayes was found to be the better classification algorithm for sentiment analysis.
Cross-sectional maximum likelihood and bias-corrected pooled least squares estimators for dynamic panels with short T

This paper proposes new estimators for the panel autoregressive (PAR) models with short time dimensions (T) and large cross sections (N). These estimators are based on the cross-sectional regression model using the first time series observations as a regressor and the last as a dependent variable. The regressors and errors of this regression model are correlated. The first estimator is the maximum likelihood estimator (MLE) under the assumption of normal distributions. This estimator is called the cross-sectional MLE (CSMLE). The second estimator is the bias-corrected pooled least squares estimator (BCPLSE) that eliminates the asymptotic bias of PLSE by using the CSMLE. The CSMLE and BCPLSE are extended to the PAR model with endogenous time-variant and time-invariant regressors. The CSMLE and BCPLSE provide consistent estimates of the PAR coefficients for stationary, unit root and explosive PAR models, estimate the coefficients of time-invariant regressors consistently and can be computed as long as T>=2. Their finite sample properties are compared with those of some other estimators for the PAR model of order 1. The estimators of this paper are shown to perform quite well in finite samples.
The Chitin Receptor Lyk5 Is Essential for Calcium-mediated Signaling in Physcomitrella patens

Plants recognize a variety of different pathogen associated molecules in order to react effectively. In Arabidopsis thaliana and Physcomitrella patens , it is known that chitin, a component of the cell walls of fungi, causes oscillations of intracellular calcium; however, it is unknown if the chitin signaling pathway in P. patens uses the same proteins as A. thaliana . A. thaliana uses a combination of CERK1 and Lyk4 or CERK1 and Lyk5 to trigger a chitin immune response. The goal of this project was to use P. patens as a model to study the mechanism involved in the plant immune response to chitin by silencing genes related to the A. thaliana Lyk5/4 receptor. Results show that when the three P. patens Lyk5 genes are silenced there is no oscillatory calcium immune response. This result indicates that the P. patens chitin receptor likely operates similarly to the one in A. thaliana , and was confirmed quantitatively through the creation of ImageJ, R, and MATLAB programs. Results from generating the phylogenetic tree showed that the chitin receptor may have evolved from a Lyk-like gene into Lyk5 and CERK1. Further exploration on the function and evolution of the Lyk5 chitin receptor can be completed in P. patens .
Comparison of Mapping Algorithms for Deployment Onboard Robots with Uncertain Pose

Pose uncertainty is one of the main problems when applying mapping methods on-board Unmanned Aerial Vehicles (UAVs). This thesis focuses on approaches regarding Light Detection and Ranging (LiDAR) based mapping on an autonomous UAV and current state of the art. These mapping methods, namely OctoMap, UFOMap, and Voxblox, are compared in accuracy using three diﬀerent metrics, with respect to the pose uncertainty, and a knowledge of the ground truth poses of obstacles in the simulated environments. Furthermore, three potential improvements in mitigating the eﬀects of the sensor noise in order to enhance overall mapping performance are discussed, ﬁrst observing their eﬀects individually on each method
Constructing the PD Term Structure

With the ongoing implementation of the IFRS 9 impairment framework increasing attention is paid to a consistent determination of expected credit lifetime losses. A clear distinction between point-in-time (PIT) and through-the-cycle (TTC) credit default probabilities is well known to be crucial for the construction of suitable PD term structures. However, due to typically insufficient data availability and quality the most widely spread approach to the PD term structure construction, i.e. the Exponentiation Approach, still relies on the exponentiation of observed one-year migration matrices irrespective of whether these are derived in a PIT- or TTC-context. In this paper the authors discuss the source of the conceptual inadequacy of this frequently used approach. Addressing these shortcomings, a new and conceptually consistent approach to deriving the genuine PD term structure is introduced and the fixation of its underlying model parameters is benchmarked on synthetic migration time series. This new method, which will be called Consistent Approach, allows in particular to estimate the dependence of the genuine PD term structure on the macroeconomic starting condition, rendering it applicable to both regulatory and internal stress testing. Finally, both the Exponentiation Approach and the Consistent Approach outlined in this paper are applied to actual migration data provided by GCD, the Global Credit Data consortium, allowing to observe the quantitative impact on PD term structure curves and thus on lifetime expected loss estimates. A profound impact on the term structure is found when comparing the market-standard Exponentiation Approach and the newly developed Consistent Approach.
SIRA: Relightable Avatars from a Single Image Supplemental Document

(1) where l(ωi) is the incident light from direction ωi, the functions f, f s are the diffuse and specular components of the BRDF respectively, and the scalar ks ∈ [0, 1] controls their relative weight. The integral is computed over the hemisphere Ω = {ωi : ωi · n > 0}. We express all the terms in the rendering equation using spherical gaussians, which allows us to compute the integral in a closed form. A spherical gaussian (SG) is a spherical function of the form
Investigating an indirect aviation effect on mid-latitude cirrus clouds – linking lidar-derived optical properties to in situ measurements

Abstract. Aviation has a large impact on the Earth's atmosphere and climate by various processes. Line-shaped contrails and contrail cirrus clouds lead to changes in the natural cirrus cloud cover and have a major contribution to the effective radiative forcing from aviation. In addition, aviation-induced aerosols might also change the microphysical properties and optical properties of naturally formed cirrus clouds. Latter aerosol–cloud interactions show large differences in the resulting effective radiative forcing, and our understanding on how aviation-induced aerosols affect cirrus cloud properties is still poor. Up to now, observations of this aviation-induced aerosol effect have been rare. In this study, we use combined airborne lidar and in situ ice cloud measurements to investigate differences in the microphysical and optical properties of naturally formed cirrus clouds, which formed in regions that are highly affected by aviation-induced aerosol emissions and, of those, which formed in regions rather unaffected by aviation. Urbanek et al. (2018) showed that those cirrus clouds, which are more affected by aviation-induced soot emission, are characterized by larger values of the particle linear depolarization ratio (PLDR). In this follow-on study we relate collocated lidar measurements performed aboard HALO during the ML-CIRRUS mission of the particle linear depolarization ratio with in situ cloud probe measurements of the number concentration and effective diameter of the ice particles. In situ measurements for both cloud types (high-PLDR-mode – aviation-affected – and low-PLDR-mode – pristine – cirrus) can be reliably compared in a temperature range between 210 and 215 K. Within this temperature range we find that high-PLDR-mode cirrus clouds tend to show larger effective ice particle diameters with a median value of 61.4 compared to 50.7 µm for low-PLDR-mode pristine cirrus clouds. Larger effective ice particles in aviation-influenced (high-PLDR-mode) cirrus are connected to lower ice particle number concentration with a median value of 0.05 compared to 0.11 cm−3 (low-PLDR-mode), which evolved in more pristine regions with only little impact from aviation. We suspect that a suppression of homogeneous ice formation by the heterogeneously freezing soot aerosol particles included in the areas affected by air traffic is the cause of the reduced ice crystal concentrations.
Modelling sequential branching dynamics with a multivariate branching Gaussian process

The Branching Gaussian Process (BGP) model is a modification of the Overlapping Mixture of Gaussian Processes (OMGP) where latent functions branch in time. The BGP model was introduced as a method to model bifurcations in single-cell gene expression data and order genes by inferring their branching time parameter. A limitation of the current BGP model is that the assignment of observations to latent functions is inferred independently for each output dimension (gene). This leads to inconsistent assignments across outputs and reduces the accuracy of branching time inference. Here, we propose a multivariate branching Gaussian process (MBGP) model to perform joint branch assignment inference across multiple output dimensions. This ensures that branch assignments are consistent and leverages more data for branching time inference. Model inference is more challenging than for the original BGP or OMGP models because assignment labels can switch from trunk to branch lineages as branching times change during inference. To scale up inference to large datasets we use sparse variational Bayesian inference. We examine the effectiveness of our approach on synthetic data and a single-cell RNA-Seq dataset from mouse haematopoietic stem cells (HSCs). Our approach ensures assignment consistency by design and achieves improved accuracy in branching time inference and assignment accuracy.
Privacy-Preserving Detection of COVID-19 in X-Ray Images

Chest X-rays enable a fast and safe diagnosis of COVID-19 in patients. Applying Machine Learning (ML) methods can support medical professionals by classifying large numbers of images. However, the amount of data needed for training such classifiers poses problems due to clinical data privacy regulations, which present strict limitations on data sharing between hospitals. Specifically, the models resulting from ML are vulnerable to attacks and can compromise data integrity by leaking details about their training data. Privacy-Preserving ML (PPML) offers methods to create private models that satisfy Differential Privacy (DP), enabling the development of medical applications while maintaining patient privacy. This work aims at investigating the privacy-preserving detection of COVID-19 in X-ray images. The PPML training methods DP-SGD and PATE are matched against non-private training. The private models should mitigate the data leakage threats posed by Membership Inference Attacks (MIAs). However, the inclusion of DP showed no improvements in MIA defense on the COVID-19 detection task. Instead, the non-private models presented the same repelling properties as the private models. Thus, if only the defense against MIAs is of concern, the non-private approach achieving 97.6% classification accuracy is the best choice. Private DP-SGD training for ε = 1 is a more sensible alternative when a theoretical privacy guarantee is needed. The best DP-SGD model reaches 74.1% accuracy. Even though the accuracyprivacy trade-off of 23.5% is significant, the private model performs 0.3% better than related work and keeps much tighter privacy guarantees. Ultimately, the conflicting findings from the additional experiments on the MNIST database, where DP significantly increased MIA defense, indicate that PPML (or DP-SGD) is heavily task-dependent. Thus, research on one dataset might not carry over to others. Lucas Lange 3737104 I
Changes in Mediterranean flood processes and seasonality

Abstract. Floods are a major natural hazard in the Mediterranean region, causing deaths and extensive damages. Recent studies have shown that intense rainfall events are becoming more extreme in this region but, paradoxically, without leading to an increase in the severity of floods. Consequently, it is important to understand how flood events are changing to explain this absence of trends in flood magnitude despite increased rainfall extremes. A database of 98 stations in southern France with an average record of 50 years of daily river discharge data between 1959 and 2021 was considered, together with a high-resolution reanalysis product providing precipitation and simulated soil moisture and a classification of weather patterns associated with rainfall events over France. Flood events, corresponding to an average occurrence of 1 event per year (5317 events in total), were extracted and classified into excess-rainfall, short-rainfall, and long-rainfall event types. Several flood event characteristics have been also analyzed: flood event durations, base flow contribution to floods, runoff coefficient, total and maximum event rainfall, and antecedent soil moisture. The evolution through time of these flood event characteristics and seasonality was analyzed. Results indicated that, in most basins, floods tend to occur earlier during the year, the mean flood date being, on average, advanced by 1 month between 1959–1990 and 1991–2021. This seasonal shift could be attributed to the increased frequency of southern-circulation weather types during spring and summer. An increase in total and extreme-event precipitation has been observed, associated with a decrease of antecedent soil moisture before rainfall events. The majority of flood events are associated with excess rainfall on saturated soils, but their relative proportion is decreasing over time, notably in spring, with a concurrent increased frequency of short rain floods. For most basins there is a positive correlation between antecedent soil moisture and flood event runoff coefficients that is remaining stable over time, with dryer soils producing less runoff and a lower contribution of base flow to floods. In a context of increasing aridity, this relationship is the likely cause of the absence of trends in flood magnitudes observed in this region and the change of event types. These changes in flood characteristics are quite homogeneous over the domain studied, suggesting that they are rather linked to the evolution of the regional climate than to catchment characteristics. Consequently, this study shows that even in the absence of trends, flood properties may change over time, and these changes need to be accounted for when analyzing the long-term evolution of flood hazards.
Exploring Socioeconomic Factors of Waste Generation in Addis Ababa, Ethiopia

- This study was conducted to examine socioeconomic factors that affect Muncipal Solid Waste (MSW) generation level in Addis Ababa city administration. In this paper, the researcher used both descriptive and econometric analysis. The study used a fixed effect regression model for econometric analysis. The study's empirical result indicates that population density has a positive and statistically significant effect on municipal solid waste generation. In contrast, secondary school enrollment and environmental protection clubs in school have a negative and statistically significant impact on municipal solid waste generation. This suggests that the composition of these waste types may remain relatively stable over time. Consequently, this study emphasizes the need for the city administration to enhance its urban planning, management, and implementation capacity. The city administration should also encourage the formation of Environmental Protection Clubs in schools and other community groups because it can effectively reduce waste generation. Education and awareness programs should also be strengthened to improve solid waste management practices.
On Bagchi–Pal urn models and related Pólya–Friedman ones

We revisit the main features of the Bagchi–Pal urn process as a skewed urn scheme with persistence effects, sometimes emphasizing the specificities of its Pólya–Friedman symmetric version. The Bagchi–Pal scheme is an urn model with two types of balls and iterative replacement of balls obeying positive balance and tenability conditions. Interest is in the long-run behavior of the urn composition, population gap and proportion processes. Transient and limit laws are investigated, aiming at finding regions of Gaussian and non-Gaussian behavior. Emphasis is on the well-known dynamical recurrence/transience phase transition arising as the persistence parameter crosses the critical value 1/2, with limit laws keeping track of the initial composition of the urn. A Pólya–Friedman process, closely related to the symmetric Bagchi–Pal urn scheme, is also revisited. It also has a tuning persistence parameter extrapolating the Pólya and Friedman extreme urn processes. Making use of a diffusion approximation of the Pólya–Friedman gap process, first hitting time tail distributions appearing in a role reversal problem are characterized. Consequences on similar questions for the skewed Bagchi–Pal urn scheme are drawn.
Digital ecosystem and computer engineering

The present research paper regards the importance of the discipline of computer engineering for creating an ecosystem of transport and logistics services. Special attention is paid to modern knowledge control systems and, in particular, testing when assessing the level of knowledge of students. The variants of tasks prepared by the authors for the Moodle distance learning system are given. Modern systems for processing test results based on the theory of parametrization of Birnbauer-Rasch tests (Item Response Theory) are discussed. The results of testing of students in the discipline “Computer Engineering” at PSUPS are discussed.
Grain growth of natural and synthetic ice at 0ºC

Grain growth can modify the microstructure of natural ice, including the grain size and crystallographic preferred orientation (CPO). To understand better grain-growth processes and kinetics, we compared microstructural data from synthetic and natural ice samples that were annealed at ice-solidus temperature (0ºC) to successfully long durations. The synthetic ice has a 15 homogeneous initial microstructure, which is characterised by polygonal grains, little intragranular distortion and bubble content, and a near-random CPO. The natural ice samples were sub-sampled from ice cores acquired from the Priestley Glacier, Antarctica; they have a heterogeneous microstructure, which is characterised by a considerable number of air bubbles, widespread intragranular distortion, and a preferred crystallographic alignment. During annealing, the average grain size of natural ice barely changes, whilst the average grain size of synthetic ice gradually increases. This observation suggests grain 20 growth in natural ice can be much slower than synthetic ice; the grain-growth law derived from synthetic ice data cannot be directly applied to estimate the grain-size evolution in natural ice. The microstructure of natural ice characterised by many bubbles pinning at grain boundaries. Previous studies suggest bubble pinning reduces the driving force of grain boundary migration, and it should be directly linked to an inhibition of grain growth observed in natural ice. As annealing progresses, the number density (number per unit area) of bubbles on natural-ice
Robust weather-adaptive postprocessing using MOS random forests

Physical numerical weather prediction models have biases and miscalibrations that can depend on the weather situation, which makes it difﬁcult to postprocess them effectively using the traditional model output statistics (MOS) framework based on parametric regression models. Consequently, much recent work has focused on using ﬂexible machine learning methods that are able to take additional weather-related predictors into account during postprocessing, beyond the forecast of the variable of interest only. Some of these methods have achieved impressive results, but they typically require signiﬁcantly 5 more training data than traditional MOS and are less straightforward to implement and interpret. We propose MOS random forests, a new postprocessing method that avoids these problems by fusing traditional MOS with a powerful machine learning method called random forests to estimate "weather-adapted" MOS coefﬁcients from a set of predictors. Since the assumed parametric base model contains valuable prior knowledge, much smaller training data sizes are required to obtain skillful forecasts and model results are easy to interpret. MOS random forests are straightforward to imple-10 ment and typically work well, even with no or very little hyperparameter tuning. For the difﬁcult task of postprocessing daily precipitation sums in complex terrain, they outperform reference machine learning methods at most of the stations considered. Additionally, the method is highly robust to changes in data size and works well even when less than a hundred observations are available for training.
BIPEDAL WALKING CONSTRAINED CONTROLLER

This thesis presents a method to control a quasi-static bipedal walking based on quadratic programming and differential inequalities using geometric primitives. The center of mass is allowed to move anywhere inside the support polygon during the walking cycle, as opposed to classic methods, which usually rely on tracking a desired trajectory for the zero moment point. The constraints keep the robot balance, the pelvis above a minimum height, and prevent the violation of joint limits during the complete walking cycle. Simulation results using the legs of the Poppy humanoid robot show that the trajectories of the closed-loop system converge to the desired center of mass position during the double support phase and the swing foot’s trajectories converge to the desired pose during the single support phase while all constraints are obeyed.
Taboo and Otherwise: Epistemic Life Histories of Knowledge Resources

Taboo shapes the way that people talk about im-portant topics like sex and reproduction. How does taboo shape the way that Wikipedia contributors create and sustain articles on taboo topics? This work presents a mixed-methods series of comparative case studies of taboo and non-taboo articles over a range of years. Drawing from the histories of “Clitoris” (taboo), “Cell Membrane” (non-taboo), “Menstruation” (taboo)
Fluctuations of Eigenvalues and Concentration Inequalities for Patterned Random Matrices

In this thesis, we first review some basic concepts in random matrix theory and then we show how these concepts motivate and help solve two independent problems. The first result is a Gaussian central limit theorem for eigenvalue statistics of inhomogeneous matrix models which interpolate between Wigner matrices and certain patterned random matrices. The second result is a non-asymptotic concentration inequality for the operator norm of random matrices with sub-Gaussian entries.