Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIEWING THE RELATIONSHIPS BETWEEN RUMINATION AND SHAME, GUILT, ENVY, AND JEALOUSY

Guilt, shame, jealousy, and envy are known as self-conscious, or self-focused emotions. These common feelings are considered “self - conscious” because the individual takes into account the real (and imagined) reactions of others to their experience of the emotion(s) and subsequently incorporates those reactions into their emotional experience. Recent research has substantially explored the relationships between these self-conscious emotions and rumination, or the negative, redundant thinking about a prior event. It would seem odd then that not a single comprehensive literature review exists on the associations between these constructs. Thus, the purpose of this paper is to define rumination and each of the four emotions, assess the statistical relationships between them, identify limitations in current orthodoxy and methodology, note clinical implications, and suggest directions for future research. Results indicate a reciprocal relationship between rumination and the self-conscious emotions, with the presence of each intensifying and facilitating the experience of the other. This cyclic relationship is strongest for shame and rumination, and jealousy and rumination. Though a mere two studies have examined envy and rumination together, a positive correlation between them exists. Results related to guilt and rumination are more ambiguous and difficult to interpret objectively. Interventions for these emotions and rumination include mindfulness meditation and self-compassion treatments among others.
Predictive Performance of Next Generation Human Physiologically Based Kinetic (PBK) Models Based on In Vitro and In Silico Input Data

The goal of the present study was to assess the predictive performance of a generic human physiologically based kinetic (PBK) model based on in vitro and in silico input data and the effect of using different input approaches for chemical parameterization on those predictions. For this purpose, a dataset was created of 38,772 C max predictions for 44 compounds by applying different combinations of in vitro and in silico approaches for chemical parameterization, and these predicted C max values were compared to reported in vivo data. Best results were achieved when the hepatic clearance was parameterized based on in vitro (i.e., hepatocytes or liver S9) measured intrinsic clearance values, the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients, and the method of Lobell and Sivarajah for calculating the fraction unbound in plasma. With these parameters, the median C max values of 34 out of the 44 compounds were predicted within 5-fold of the observed C max , and the C max values of 19 compounds were predicted within 2-fold. The median C max values of 10 compounds were more than 5-fold overestimated. Underestimations (> 5-fold) did not occur. A comparison of the current generic PBK model structure with chemical-specific PBK models available in the literature was made to identify possible kinetic processes not included in the generic PBK model that might explain the overestimations. Overall, the results provide crucial insights into the predictive performance of PBK models based on in vitro and in silico input and the influence of different input approaches on the model predictions.
Enhancing 3D Point Cloud Semantic Segmentation Using Multi-Modal Fusion With 2D Images

Deep learning algorithms can identify valid semantic information from the original 3D point cloud that can be used to create BIM models of the built environment. This is an important step in generating the digital twin of a building. Compared with traditional unimodal deep learning algorithms that directly process 3D point clouds, multimodal fusion algorithms that leverage 2D images as supplementary information for 3D scenes have greater performance advantages. It is worth noting that multimodal fusion algorithms do not exist independently but are derived from existing unimodal methods - 2D and 3D unimodal deep learning networks are chosen as the backbone networks to process the information from different modalities and fuse them at the right time. In this study, the performance of an open-source multimodal algorithm, MVPNet, is improved on 3D semantic segmentation task by using KPConv as a more robust and stronger 3D backbone. Different modules of the two networks are meaningfully combined: the 2D-3D lifting method by MVPNet aggregates selected 2D multi-view images features into 3D point cloud, and then KPConv used to fuse these features in 3D space to predict 3D semantic
Optimization of pixel size and propagation distance in X-ray phase-contrast virtual histology

X-ray phase-contrast coupled to high-spatial resolution imaging systems provides a high sensitivity for distinguishing soft tissue structures in small samples, thus being suited for X-ray virtual histology. Propagation-based phase-contrast tomography can deliver a considerable gain in signal-to-noise ratio (SNR) at small pixel sizes when it is combined to a suitable phase retrieval filter. We optimized acquisition parameters, namely the propagation distance and the pixel size, with the aim of providing adequate spatial resolution and sensitivity for virtual histology of breast surgery specimens, scanned with a phase-contrast microtomography (μCT) system employing a commercial sCMOS detector at the SYRMEP beamline of the Italian synchrotron facility Elettra (Trieste, Italy). A pathological breast tissue sample was embedded in paraffin and imaged using a polychromatic synchrotron beam at an average energy of 24 keV. The high numerical optical aperture of the imaging system enabled to adjust the pixel size to 1, 2.5 and 4 μm. The scans were acquired at five sample-to-detector distances: 4.5, 150, 250, 500 and 1000 mm. SNR was measured in an homogeneous region portion of the μCT image for each combination of pixel size and propagation distance. Experimental results were compared to a theoretical model taking into account the actual point spread function of the employed imaging system. The measured gain of SNR associated with the application of the phase-retrieval matched the predictions for large Fresnel numbers (N F > 2). For each pixel size, an optimal range of propagation distances was found. Optimal μCT reconstructions were then compared with their respective histopatological images, showing an excellent visibility of relevant structures. The optimization performed in this study will allow to select the most appropriate geometrical configurations for future acquisitions of virtual histology images of different specimens via phase-contrast microtomography.
Absence and Abnormality

Absences pose a dilemma for theories of causation. Allowing them to be causes seems to make theories too permissive (Lewis, 2000). Banning them from being causes seems to make theories too restrictive (Schaffer, 2000, 2004). An increas-ingly popular approach to this dilemma is to acknowledge that norms can affect which absences count as causes (e.g., Thomson, 2003; McGrath, 2005; Henne et al., 2017; Willemsen, 2018). In this article, I distinguish between two inﬂu-ential implementations of such ‘abnormality’ approaches and argue that so-called ‘double-prevention mechanisms’ provide counterexamples against both.
A New Method for Measuring Infrared Band Strengths in H2O Ices: First Results for OCS, H2S, and SO2

Infrared (IR) band strengths are needed to extract accurate molecular abundances from astronomical observations of interstellar and solar system ices. However, laboratory measurements of such intensities often have required multiple assumptions about ice composition and thickness. Here we describe a method that circumvents most of the common assumptions and uncertainties in IR band-strength determinations. We have applied the method to measure IR band strengths of OCS, H2S, and SO2 in the absence and presence of H2O ice at 10 K, the first measurements of their type. Our results show for the first time that the presence of H2O makes little difference in IR intensities for these three sulfur-containing molecules’ strongest IR features. The immediate application will be to laboratory studies of low-temperature chemistry of interstellar and cometary ices.
Institutional Corporate Bond Pricing

We estimate an equilibrium demand-based corporate bond pricing model linking institutional holdings to bond characteristics. Our estimates show heterogeneity in demand elasticities across institutions, with elastic mutual funds demanding liquidity, akin to reaching for yield, and inelastic insurance companies. Moreover, we document stark diﬀerences in preferences for maturity, credit risk, and liquidity across institutions. In counterfactuals, we evaluate the pricing implications of credit quality migration, mutual fund fragility, monetary policy tightening, and a tapering of the Fed’s corporate credit facility. Our model predicts substantial disruptions in bond prices through shifts in institutional demand and identiﬁes the composition of institutional demand as an important state variable for corporate bond pricing.
Satisfaction level of Postnatal Mothers about Antenatal Care in Jumla, Nepal

Background: Client satisfaction is essential for improvement of quality of antenatal care and to increase service utilization. Therefore, this article aimed to assess the level of satisfaction of postnatal mothers About antenatal care services in Chandannath Municipality of Jumla, Nepal. Methods: The descriptive cross sectional study design was conducted to assess the level of satisfaction of postnatal mothers about antenatal care services in Chandannath Municipality of Jumla, Nepal. The study population was postnatal mothers within one year of child birth who came in maternal and child health (MCH) clinic for the immunization of their baby. Total 367 postnatal mothers within one year of childbirth were recruited by adopting simple random sampling technique, and face to face interview was done by using Likert scale to collect the data. Data analysis was done by using descriptive statistics. Results : Regarding the satisfaction on antenatal care received by postnatal mothers on different aspects, majority were satisfied with the information received regarding prenatal test and procedures (68.1%), and antenatal guidance/counseling on preparation for child birth (61.3%). Similarly, more than half were satisfied with time given to answer their questions (57.5%). About approachability, less than half (45.8%) were dissatisfied because providers were hurried during antenatal visit. Concerning the availability; majority (68.9%) were satisfied because they got in touch with care providers. Likewise, majority of mothers were satisfied with respect (67.0%) and support (64.0%) from providers. Regarding the satisfaction level on antenatal care received by postnatal mother, majority of them (71.4%) were satisfied and 28.6% were dissatisfied from antenatal care they received. Conclusion : The facility based study showed that the majority of postnatal mothers were satisfied and one third were not satisfied from antenatal care. So community based study should be conducted to find out the actual satisfaction level.
Model-based UAV mission planning for photogrammetric capture of existing buildings

At the scene of an emergency, such as an accident or a natural disaster, first responders require an actual and rapid analysis of the environment with all its possible dangers. Gaining insight into the operating environment will help first responders effectively prevent radical damages and save potential victims. With outdoor Unmanned Aerial Vehicles (UAVs), first responders receive scanned information of disaster zones fast and accurately. That obtains a precise representation of the situation for emergency response work. An essential requirement for the usability of the drone scans is the preliminary calculation of an optimal path. A drone path aims to generate good scans for a better overview of the scene considering parameters such as a reasonable distance and overlap. A distance between the drone and the examined build environment ensures scans accuracy and collisions avoidance. The overlap helps the 3D reconstruction of the object to be scanned. This bachelor thesis focuses on developing a module that generates a route allowing the scanning of buildings. Software for automated path planning is developed by collecting parameters describing what conditions an optimal path must fulfill and inspecting existing path planning strategies for outdoor UAVs. The generated tool produces a path, represented as several waypoints, that allows an optimal point cloud acquisition depending on the given camera and the criteria defined with the objectives of the thesis. The thesis is part of the EU-funded INTREPID project for helping the first responders. This thesis focuses on implementing a path planning module for a given outdoor drone and a Building Information Modeling (BIM) input. The project goal is the drone’s ability to scan the outside of the building from above and side. The scans serve the create a quality 3D reconstruction. The generated path, represented by a number of waypoints and their position defined by x, y, and z coordinates, is required to fulfill some objectives defined based on comprehensive literature analysis. The selection of parameters is one of the major topics to be investigated in this thesis. That is due to ensuring a good scanning with a high-quality 3D reconstruction. One requirement for safe flight execution is distance parametrization. The background is that selecting a reasonable distance helps collisions avoidance or drone damages during the flight while considering scans efficiency. A certain percentage of overlap, for instance, supports a detailed and accurate 3D reconstruction of the object to be scanned. Further requirements are generated based on state-of-the-art, conducted as the first step of this thesis. The method is implemented in python and tested with the BIM model on different build environments.
Microbial Populations and Activities in the Rhizoplane of Rock-Weathering Desert Plants. I. Root Colonization and Weathering of Igneous Rocks

Abstmct: Dense layers of bacteria and fungi in the rhizoplane of three species of cactus (Pachycereus pringlei, Stenocereus thurberi, Opuntia cholla) and a wild fig tree (Ficus palmeri) growing in rocks devoid of soil were revealed by bright-field and fluores-cence microscopy and field emission scanning electron microscopy. These desert plants are responsible for rock weathering in an ancient lava flow at La Purisima-San lsidro and in sedimentary rock in the Sierra de La Paz, both in Baja California Sur, Mexico. The dominant bacterial groups colonizing the rhizoplane were fluorescent pseudomonads and bacilli. Seven of these bacterial species were identified by the 165 rRNA molecular method. Unidentified fungal and adimomycete species were also present. Some of the root-colonizing microorganisms fixed in vitro N,, produced volatile and non-volatile organic acids that subsequently reduced the pH of the rock medium in which the bacteria grew, and significantly dissolved insoluble phosphates, extrusive igneous rock, marble, and limestone. The bacteria were able to release significant amounts of useful minerals, such as P. K, Mg, Mn, Fe, Cu, and Zn from the rocks and were therrno-tol-erant, halo-tolerant, and drought-tolerant. The microbial com-munity survived in the rhizoplane of cacti during the annual 10-month dry season. This study indicates that rhizoplane bacteria on cacti roots in rock may be involved in chemical weathering in hot, subtropical deserts.
Correlates of Physical Activity in Black, Hispanic, and White Correlates of Physical Activity in Black, Hispanic, and White Middle School Girls Middle School Girls

Background: A need exists to better understand multilevel influences on physical activity among diverse samples of girls. This study examined correlates of moderate-to-vigorous physical activity (MVPA) among adolescent girls from different racial/ethnic backgrounds. Methods: 1,180 6th grade girls (24.5% black, 15.7% Hispanic, and 59.8% white) completed a supervised self-administered questionnaire that measured hypothesized correlates of PA. MVPA data were collected for 6 days using the ActiGraph accelerometer. Hierarchical regression analysis was used to examine correlates of PA in each racial/ethnic group. Results: Hispanic girls (n = 185) engaged in 21.7 minutes of MVPA per day, black girls (n = 289) engaged in 19.5 minutes of MVPA per day, and white girls (n = 706) engaged in 22.8 minutes of MVPA per day. Perceived transportation barriers (+; P = .010) were significantly and positively related to MVPA for Hispanic girls. For black girls, Body Mass Index (BMI) (–; P = .005) and social support from friends (+; P = .006) were significant correlates of MVPA. For white girls, BMI (–; P < .001), barriers (–; P = .012), social support from friends (+; P = .010), participation in school sports (+; P = .009), and community sports (+; P = .025) were significant correlates of MVPA. Explained variance ranged from 30% to 35%. Conclusions: Correlates of MVPA varied by racial/ ethnic groups. Effective interventions in ethnically diverse populations may require culturally tailored strategies.
A Study of the Applications of Convolutional Neural Networks

convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.
CHORE: Supplementary material

In this supplementary, we discuss in more detail about our implementations and show more qualitative examples to highlight the importance of explicit contact modelling and object pose prediction. We show more qualitative results on BEHAVE [2], NTU-RGBD [5] as well as images in the wild [4] and discuss limitations of our method in the end.
RProbSup: CALCULATING THE PROBABILITY OF SUPERIORITY

Researchers are encouraged to report statistics in their studies, including measures of effect size and confidence intervals (CIs). The probability of superiority ( A ) has many appealing characteristics, such as being robust to parametric assumptions, easy to understand, and generalizable. While A was originally created to compare scores from two discrete groups, extensions of the statistic have been created to allow researchers to use A for correlated samples, as well as for more than two conditions. Former research on methods for creating confidence intervals for A suggests the use of bootstrap methods, which can be used for each variation of calculating A . The use of bootstrapping methods for calculating CIs and the various extensions of A have been brought together to create a package, RProbSup , which allows users to easily calculate different variations of A , its standard error (SE), and CI. This paper illustrates how to use RProbSup and highlights the benefits of using these statistical methods.
The spatial scope of agglomeration economies in Brazil

This paper provides evidences about the location and colocation patterns of the new ﬁrms of manufacturing and the spatial scope of agglomeration economies in Brazil. Using unique geocoded data for all Brazilian manufacturing activities and distance-based measures, we ﬁnd that there are patterns of colocalization between entrants and existing establishments and that these patterns occurs mainly at short distances. Furthermore, exploring the spatial scope of the agglomeration economies, focusing on the localization eﬀects (own-industry employment), we ﬁnd that these eﬀects, particularly to industries colocalized at short distances, attenuate rapidly with distance (around 5 km). These results are robust to the inclusion of a comprehensive set of controls variables for observable and unobservable local characteristics and the use of instrumental variables to address endogeneity concerns.
WIP: On Robustness of Lane Detection Models to Physical-World Adversarial Attacks

Abstract —Deep Neural Network (DNN)-based lane detection is widely utilized in autonomous driving technologies. At the same time, recent studies demonstrate that adversarial attacks on lane detection can cause serious consequences on particular production-grade autonomous driving systems. However, the generality of the attacks, especially their effectiveness against other state-of-the-art lane detection approaches, has not been well studied. In this work, we report our progress on conducting the first large-scale empirical study to evaluate the robustness of 4 major types of lane detection methods under 3 types of physical-world adversarial attacks in end-to-end driving scenarios. We find that each lane detection method has different security characteristics, and in particular, some models are highly vulnerable to certain types of attack. Surprisingly, but probably not coincidentally, popular production lane centering systems properly select the lane detection approach which shows higher resistance to such attacks. In the near future, more and more automakers will include autonomous driving features in their products. We hope that our research will help as many automakers as possible to recognize the risks in choosing lane detection algorithms.
Promoting Motivation For Employee Performance: A Case Of Nigeria Public Sector

The aim of promoting employee motivation is to enhance the level of commitment, increase the level of display of skills, and intelligence of the employees for competitive performances. Therefore motivation is an indispensable arsenal that can be used to unleash the best out of employees of an organization. Based on this, the study was carried out to critically analyze the effect of promoting motivation on the performance level of employees in the Nigerian public sector. Having linked the research work to several theories, the research work was anchored on the two broad theories of process theory and content theory of motivation. The essence of this anchor was because there are peculiarities between the postulations of the theories and the assertion of this study. The research work was given quantitative analysis with the help of data got through primary sources. The primary source of data was via questionnaire instrumentation. The data obtained from these respondents were analyzed descriptively and sub-divided into two sections. The results of the findings revealed that the financial rewards in the Nigerian public sector do not significantly motivate or improve employee performances; it was also revealed that satisfaction of physical needs does not significantly lead to improved performance and lastly it was revealed that public relation does significantly have an impact
A clebsch method for free-surface vortical flow simulation

We propose a novel Clebsch method to simulate the free-surface vortical flow. At the center of our approach lies a level-set method enhanced by a wave-function correction scheme and a wave-function extrapolation algorithm to tackle the Clebsch method's numerical instabilities near a dynamic interface. By combining the Clebsch wave function's expressiveness in representing vortical structures and the level-set function's ability on tracking interfacial dynamics, we can model complex vortex-interface interaction problems that exhibit rich free-surface flow details on a Cartesian grid. We showcase the efficacy of our approach by simulating a wide range of new free-surface flow phenomena that were impractical for previous methods, including horseshoe vortex, sink vortex, bubble rings, and free-surface wake vortices.