Miracle-dz/UniFAR-FTUs huggingface dataset:
Slouching towards Authoritarianism? evidence from survey experiments around the 2014 Hungarian elections

We fielded a panel survey immediately before and after the 2014 Hungarian parliamentary elections to analyze how citizens respond to the process of incremental democratic decay. In embedded survey experiments we provided respondents with varying levels of information about recent reforms to Hungarian electoral rules, asking citizens about the legitimacy of the election and satisfaction with Hungarian democracy more generally. We have four main findings. First, Hungarians were widely aware of the incumbent party’s (Fidesz) electoral reforms. Second, non-Fidesz voters were significantly more pessimistic about the reforms implications for electoral fairness. Third, when non-Fidesz voters were provided with information that the opposition parties were against the reforms, their assessments of the reforms fairness became even more negative. Fourth, providing more information is not a silver bullet: the effect of information is marginal and concentrated only among those who did not already support Fidesz. No matter the information provided, voters preexisting partisan allegiances dominate how they interpret the information they receive. In short, supporters of the electoral victor believe the winners actions are legitimate and fair, while supporters of the losers see an important erosion of democracy. Our findings highlight a major difficulty for the mechanisms of a self-enforcing democracy.
The development of the small intestine of piglets - chosen aspects.

The development of the gastrointestinal tract is a very sophisticated process, which starts during prenatal life and continues after birth. Diet is the most important factor modulating structure and functions of the intestine. In the early post-natal period the major role play bioactive substances of colostrum and milk. Their properties influence the dynamics of apoptosis and mitosis and through gastrointestinal tract development they influence animal adaptation to solid feed. The weaning period is associated with dynamic intestinal microflora development, which activity also exert a significant effect on processes taking place in the intestine. Gut function during the development and the response to feed component are often evaluated on the basis of changes in the structure of intestinal mucosa and enzyme activities. The degree of intestinal maturation influence nutrient digestibility, feed efficiency and immunity, thus greatly affecting animal production.
Crowdsourcing innovation and product development: Gamification as a motivational driver

Abstract Crowdsourcing—outsourcing a job to external contributors through an open call—has become an important part of innovation process and product development. However, many crowdsourcing initiatives fail due to low engagement and participation. In the current paper, we aim to explore how companies can employ gamification—game elements and design techniques—to motivate contributions to a crowdsourcing project. Based on an exploratory case study of Threadless, a web-based apparel store, we propose implications that can assist companies in leveraging global capabilities for new product development. Threadless employs game thinking through a wide array of game elements and game mechanics including challenges, achievements, countdowns, discovery, points, reward schedules, and status. Our analysis shows that consumers are fun seekers. They will carry out activities without expecting anything in return, if they perceive it as being fun. Moreover, companies should openly acknowledge consumers’ contributions and give them recognition by using flexible reward system, statuses, and challenges. Despite the importance of extrinsic rewards, it is also essential to realize that intrinsic factors are crucial for maintaining consumers’ continued engagement and therefore we strongly recommend companies to build a strong virtual brand community around their crowdsourcing projects.
Effects of Different Solvents on the Surface Acidic Oxygen-containing Functional Groups on Xanthoceras sorbifolia Shell

This study reports the preparation of a novel biomaterial from a forestry residue - Xanthoceras sorbifolia shell (XSS) - by solvent modification. The effects of acid and base (hydrochloric acerbic, acetic acid, sodium hydroxide, ammonia water) and some organic solvents (ethanol, acetone, ethyl acetate, chloroform, petroleum ether, and n-hexane) on the surface acidic functional groups (SAFGs) on XSS were investigated. The amount of SAFGs was quantified using acid and alkali chemical titration methods, and the characteristics of virgin XSS were compared with treated ones by FT-IR spectroscopy. It was found that acid solutions can increase the concentration of SAFGs, while alkaline solutions reduce it. The XSS treated in 0.5 M HCl has the largest number of total acidic functional groups and phenolic hydroxyl groups. The shell extracted with 2 M acetic acid has the highest concentration of carboxyl. The SAFG contents were remarkably increased by treatments with ethanol and acetone, due to the outstanding enhancement of phenolic hydroxyl. These changes in the SAFGs of XSS brought about by treatments with various solutions could be a theoretical foundation for modifying this residue to create a new type of highly efficient absorbent material.
WalkNet: A Deep Learning Approach to Improving Sidewalk Quality and Accessibility

This paper proposes a framework for optimizing allocation of infrastructure spending on sidewalk improvement and allowing planners to focus their budgets on the areas in the most need. In this research, we identify curb ramps from Google Street View images using traditional machine learning and deep learning methods. Our convolutional neural network approach achieved an 83% accuracy and high level of precision when classifying curb cuts. We found that as the model received more data, the accuracy increased, which with the continued collection of crowdsourced labeling of curb cuts will increase the model’s classification power. We further investigated a model using the TensorFlow Object Detection API, to construct a model that could accurately isolate curbs and identify whether a curb contained a proper cut or not. Our Results suggest that a municipality could pinpoint areas for infrastructure spend automatically. We leave implementation and data layering as an area of exploration.
Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB)

Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to grasp how engagement is naturally expressed through behavior or too complex to be traced back to an overall engagement state. We carried out a longitudinal study to develop a coding system of engagement-related behavior that could tackle these issues and to create an evidence-based model of engagement to make meaning of such a coding system. Fourteen elderlies with mild to moderate dementia took part in the study. They were involved in two activities: a game-based cognitive stimulation and a robot-based free play. The coding system was developed with a mixed approach: ethographic and Laban-inspired. First, we developed two ethograms to describe the behavior of participants in the two activities in detail. Then, we used Laban Movement Analysis (LMA) to identify a common structure to the behaviors in the two ethograms and unify them in a unique coding system. The inter-rater reliability (IRR) of the coding system proved to be excellent for cognitive games (kappa = 0.78) and very good for robot play (kappa = 0.74). From the scoring of the videos, we developed an evidence-based model of engagement. This was based on the most frequent patterns of body part organization (i.e., the way body parts are connected in movement) observed during activities. Each pattern was given a meaning in terms of engagement by making reference to the literature. The model was tested using structural equation modeling (SEM). It achieved an excellent goodness of fit and all the hypothesized relations between variables were significant. We called the coding system that we developed the Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the model the Evidence-based Model of Engagement-related Behavior (EMODEB). To the best of our knowledge, the ELICSE and the EMODEB constitute the first formalization of engagement-related behavior for dementia that describes how behavior unfolds over time and what it means in terms of engagement.
On Vertex-Disjoint Paths in Regular Graphs

Let $c\in (0, 1]$ be a real number and let $n$ be a sufficiently large integer. We prove that every $n$-vertex $c n$-regular graph $G$ contains a collection of $\lfloor 1/c \rfloor$ paths whose union covers all but at most $o(n)$ vertices of $G$. The constant $\lfloor 1/c \rfloor$ is best possible when $1/c\notin \mathbb{N}$ and off by $1$ otherwise. Moreover, if in addition $G$ is bipartite, then the number of paths can be reduced to $\lfloor 1/(2c) \rfloor$, which is best possible.
Oral Health Knowledge and Attitude as Determinants of Oral Health Behavior in a Sample of Old People in Port Harcourt, Rivers State, Nigeria

Adequate oral health knowledge, attitude and behavior are very important for the improvement of patient’s oral health. The objective of the study was to assess the oral health knowledge, attitude and behavior of elderly people in Port Harcourt and determine the relationship between these variables. The research was descriptive survey and correlational in design. A total of 543 old people were selected by systematic random sampling. Data was collected with a self-developed Oral Health Assessment Questionnaire (OHAQ) designed in four sections (A-D) and analysed using SPSS version 20 (IBM SPSS Armonk, New York). Relationship between variables was established by simple regression and significance determined at 0.05 alpha level. Majority (63%) and (66%) of the pensioners in Port Harcourt Rivers State had poor knowledge of oral health and negative attitude toward oral health respectively. About 60% of the participants had poor oral health behavior. Oral health knowledge and attitude accounted for 43.56% (R 2 = 0.4356) and 22.09% ((R 2 = 0.2209) respectively of the variance in the behavior of the pensioners. There was a significant moderate and positive relationship between oral health knowledge and behavior (R = 0.66) and between oral health attitude and behavior (R = 0.47). The present study showed that oral health knowledge and attitude of the respondents significantly and moderately predicted their oral health behavior. There is therefore the need to educate this group on appropriate oral self-care and regular dental visits as a cornerstone to the promotion of oral health and prevention of oral diseases.
Development of monoclonal antibodies against axenic amastigotes of Leishmania infantum strain in Iran: implication for diagnosis of Kala-azar

Objective(s): Leishmaniasis is endemic in 88 countries. Amastigote forms of Leishmania are experts at exploiting host cell processes to establish infection. Monoclonal antibodies are key reagents used in the diagnosis of infectious and non-infectious diseases. The aim of this study was to produce monoclonal antibodies against axenic amastigotes of the Leishmania infantum strain in Iran. Materials and Methods: First, standard strains were cultured and axenic amastigote antigens of L. infantum were obtained. Since then, BALB/c smice were immunized and antibody titers were determined. For hybridoma cell formation, lymphocytes isolated from spleen of immunized mice and myeloma cells were fused at a ratio of 10 to 1 in the presence of polyethylene glycol, followed by limiting dilution for the isolation of monoclones. Subsequently, antibody isotypes were determined by using the isotyping kit. The best clone was injected intraperitoneally to pristane-primed mice for large scale production of monoclonal antibodies. The specificity of antibody was determined with Western blotting. Results: Approximately 25 positive monoclones were obtained, of which four hybrids producing anti-amastigotes L. infantum monoclonal antibodies with high optical density (OD), selected and designated as 8D2 FVI6, 8D2 FVI3, 6G2 FV4 and 6G2 FV3. Results from isotype determination showed the IgG2b sub-class in 6G2FV2 and 8D2FVI6 monoclones. Conclusion: This study produced monoclonal antibody against amastigotes of Iranian strain of L. infantum for the first time. These antibodies have reactivity against Iranian strain of L. infantum and can be used in the diagnosis of Kala-azar.
Functional Astrocyte Heterogeneity and Implications for Their Role in Shaping Neurotransmission

In recent years, the role of astrocytes in shaping neuronal signaling has come to the forefront of neuroscience research. The development of genetic tools that enable targeted manipulation of astrocytes has revealed a wealth of mechanisms by which they can alter the synaptic strength and intrinsic excitability of neurons in behaviorally relevant ways. In parallel, several studies have demonstrated significant variability in the gene expression and physiology of astrocytes within and between brain regions. Thus, to form an accurate understanding of how astrocytes contribute to neuronal transmission, we must take into consideration the diversity that exists in their intrinsic properties. In this review, we will summarize recent findings on astrocyte heterogeneity and discuss the implications for their interactions with neurons and their effects on neuronal transmission.
PETglove: a new technology for portable molecular imaging

PET (Positron Emission Tomography) scanning has become a dominant force in oncology care because of its ability to identify regions of abnormal function. The current generation of PET scanners is focused on whole-body imaging, and does not address aspects that might be required by surgeons or other practitioners interested in the function of particular body parts. We are therefore developing and testing a new class of hand-operated molecular imaging scanners designed for use with physical examinations and intraoperative visualization. These devices integrate several technological advances, including (1) nanotechnology-based quantum photodetectors for high performance at low light levels, (2) continuous position tracking of the detectors so that they form a larger 'virtual detector', and (3) novel reconstruction algorithms that do not depend on a circular or ring geometry. The first incarnations of this device will be in the form of a glove with finger-mounted detectors or in a "sash" of detectors that can be draped over the patient. Potential applications include image-guided biopsy, surgical resection of tumors, assessment of inflammatory conditions, and early cancer detection. Our first prototype is in development now along with a clinical protocol for pilot testing.
On profitability of selfish mining

We review the so called selfish mining strategy in the Bitcoin network and compare its profitability to honest mining.We build a rigorous profitability model for repetition games. The time analysis of the attack has been ignored in the previous literature based on a Markov model,but is critical. Using martingale's techniques and Doob Stopping Time Theorem we compute the expected duration of attack cycles. We discover a remarkable property of the bitcoin network: no strategy is more profitable than the honest strategy before a difficulty adjustment. So selfish mining can only become profitable afterwards, thus it is an attack on the difficulty adjustment algorithm. We propose an improvement of Bitcoin protocol making it immune to selfish mining attacks. We also study miner's attraction to selfish mining pools. We calculate the expected duration time before profit for the selfish miner, a computation that is out of reach by the previous Markov models.
Learning Illuminant Estimation from Object Recognition

In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.
Novel method to automatically identify medial node correspondences between two images

Many modern forms of segmentation and registration require manual input, making them tedious and time-consuming processes. There have been some successes with automating these methods, but these tend to be unreliable due to inherent variations in anatomical shapes and image quality. It is toward this goal that we have developed methods of identifying correspondences in two images between medial nodes; image features related to anatomical structures. Medial based image features are used because they have proven robust against image noise and shape variation, and provide rotationally invariant properties of dimensionality and scale, while preserving orientation information independently. We have introduced several novel metrics for comparing the medial and geometric relationships between medial nodes and different cliques of medial nodes (a clique is a set of multiple medial nodes). These metrics overcome problems introduced by symmetry between cliques and provide increasing discriminability with the size of the clique. In this paper, we demonstrate medial-based correspondences and validate their specificity with standard Receiver Operator Characteristic (ROC) analysis. It is believed that our method of locating corresponding medial features may be useful for automatically locating anatomical structures or generating landmarks for registration.
Abscisic Acid Related Metabolites in Sweet Cherry Buds (Prunus avium L.)

As our climate changes, plant mechanisms involved for dormancy release become increasingly important for commercial orchards. It is generally believed that abscisic acid (ABA) is a key hormone that responds to various environmental stresses which affects bud dormancy. For this reason, a multi-year study was initiated to obtain data on plant metabolites during winter rest and ontogenetic development in sweet cherry buds (Prunus avium L.). In this paper, we report on metabolites involved in ABA synthesis and catabolism and its effect on bud dormancy in the years 2014/15-2016/17. In previous work, the timings of the different phases of para-, endo-, ecodormancy and ontogenetic development for cherry flower buds of the cultivar ‘Summit’ were determined, based on classical climate chamber experiments and changes in the bud’s water content. Based on these time phases, we focused now on the different aspects of the ABA-metabolism. The results show that there is a continual synthesis of ABA about 5 weeks before leaf fall, and a degradation of ABA during ecodormancy and bud development until the phenological stage ‘open cluster’. This is confirmed by relating the ABA content to that of the total precursor carotenoids, neoxanthin and violaxanthin. The tentative monitoring of individual intermediate metabolites revealed that dihydroxyphaseic acid is the most abundant catabolite of ABA and ABA glucosyl ester is in terms of mass intensity, the most abundant ABA metabolite observed in this study. The results suggest that the direct route for ABA biosynthesis from farnesyl pyrophosphate may also be relevant in cherry flower buds.
Augmented reality system for CT-guided interventions: system description and initial phantom trials

We are developing an augmented reality (AR) image guidance system, in which information derived from medical images is overlaid onto a video view of the patient. The interventionalist wears a head-mounted display (HMD) that presents him with the augmented stereo view. The HMD is custom fitted with two miniature color video cameras that capture the stereo view of the scene. A third video camera, operating in the near IR, is also attached to the HMD and is used for head tracking. The system achieves real-time performance of 30 frames per second. The graphics appears firmly anchored in the scne, without any noticeable swimming or jitter or time lag. For the application of CT-guided interventions, we extended our original prototype system to include tracking of a biopsy needle to which we attached a set of optical markers. The AR visualization provides very intuitive guidance for planning and placement of the needle and reduces radiation to patient and radiologist. We used an interventional abdominal phantom with simulated liver lesions to perform an inital set of experiments. The users were consistently able to locate the target lesion with the first needle pass. These results provide encouragement to move the system towards clinical trials.
Improved global convergence probability using multiple independent optimizations

For some problems global optimization algorithms may have a significant probability of not converging to the global optimum or require an extremely large number of function evaluations to reach it. For such problems, the probability of finding the global optimum may be improved by performing multiple independent short searches rather than using the entire available budget of function evaluations on a single long search. The main difficulty in adopting such a strategy is to decide how many searches to carry out for a given function evaluation budget. The basic premise of this paper is that different searches may have substantially different outcomes, but they all start with rapid initial improvement of the objective function followed by much slower progress later on. Furthermore, we assume that the number of function evaluations to the end of the initial stage of rapid progress does not change drastically from one search to another for a given problem and algorithmic setting. Therefore we propose that the number of function evaluations required for this rapid‐progress stage be estimated with one or two runs, and then the same number of function evaluations be allocated to all subsequent searches. We show that these assumptions work well for the particle swarm optimization algorithm applied to a set of difficult analytical test problems with known global solutions. For these problems we show that the proposed strategy can substantially improve the probability of obtaining the global optimum for a given budget of function evaluations. We also test a Bayesian criterion for estimating the probability of having reached the global optimum at the end of the series of searches and find that it can provide a conservative estimate for most problems. Finally, we demonstrate the approach on a particularly challenging engineering design problem constructed so as to have at least 32 widely separated local optima. Copyright © 2006 John Wiley & Sons, Ltd.