Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconciling nondeterministic and probabilistic choices

This thesis is written in the context of probabilistic verification. Our primary goal is to develop modeling frameworks that can be used for both specification and verification of randomized distributed algorithms. We focus on semantic aspects of such modeling frameworks; for example, we try to give precise mathematical semantics to processes definable in these frameworks and we prove basic theorems that allow us to manipulate and reason with semantic objects. Our models typically allow both nondeterministic and probabilistic choices. In order to obtain well-defined probability distributions from a specification, one must somehow aauntangle'' these two types of choices. This is usually done by means of adversaries/schedulers, which resolve all nondeterministic choices in a specification. We study mathematical properties of adversaries and try to understand how different definitions of parallel composition translate into different assumptions on the behavior of adversaries. This allows us to identify some key properties of adversaries that affect compositionality of trace-style semantics. We also try to make a connection between the notions of adversaries captured by our formal definitions and those that are actually used in distributed computing,for example, in the areas of security protocols and randomized consensus. This thesis is organized into three parts. In Part I, we work with Segala's Probabilistic Automata model and prove many technical theorems regarding adversaries and their induced probability distributions. These results are then used to extend the testing semantics proposed by Stoelinga and Vaandrager. In Part II, we introduce our own variant of Probabilistic Input/Output Automata and use that as a basis of two specialized models, both of which come with a compositional trace-style semantics. Finally, Part III presents a randomized consensus algorithm, together with a manual correctness proof and a mechanized analysis using the probabilistic model checker PRISM.
An Environment to Support Collaborative Learning by Modding

In this paper, we propose an environment to support collaborative modding, as a new way to learn a subject. Modding can be defined as the activity to modify an existing game with dedicated tools. In a constructivist approach, we base our work on the assumption that modding a learning game can help learners to acquire the concepts of the subject concerned. We also think that modding in collaborative settings can help learners both to learn the subject and to learn to collaborate. We first propose a framework to support collaborative modding activities based on four components: the game, the Game Development Kit (GDK), contextual discussions and a knowledge map. We then propose an architecture that integrates these components on a unique platform. We finally present the results of a first exploratory study that demonstrates the feasibility and the interest of this approach for learning and the need for integrating collaborative tools in a unique environment.
Investigating the reverse transmigration of neutrophils in human and murine in vitro models of inflammation

Ο στόχος της παρούσας διδακτορικής διατριβής ήταν η μελέτη της στρατολόγησης και της αντίστροφης μετανάστευσης ουδετερόφιλων, με χρήση in vitro μοντέλων φλεγμονής από κύτταρα ποντικού και ανθρώπου. Για το σκοπό αυτό αναπτύχθηκαν in vitro μοντέλα φλεγμονής, χρησιμοποιώντας αρχικά μια αθανατοποιημένη κυτταρική σειρά μικροαγγειακών ενδοθηλιακών κυττάρων ποντικού (MCEC-1) και στη συνέχεια πρωτογενή ενδοθηλιακά κύτταρα (mEC) απομονωμένα από την καρδιά και τον πνεύμονα ποντικών. Ανακαλύψαμε ότι τα MCEC-1 μπορούσαν να στρατολογήσουν ποντικίσια ουδετερόφιλα απουσία διέγερσης με κυτταροκίνες, αν και η αποτελεσματική μετανάστευση απαιτούσε ένα τέτοιο ερέθισμα. Τα πρωτογενή κύτταρα mEC απαιτούσαν διέγερση με κυτταροκίνες για να προσελκύσουν ουδετερόφιλα. Σε αντίθεση με τα ανθρώπινα ενδοθηλιακά κύτταρα (EC), τα mEC έδειξαν ελαττωμένη ευαισθησία σε διέγερση με TNF-α, συγκριτικά με την IL-1β, η οποία μπορούσε να επάγει αρκετά αποτελεσματικά την προσκόλληση και τη μετανάστευση των ουδετερόφιλων. Χρησιμοποιώντας IL-1β για την επαγωγή οξείας φλεγμονής στα in vitro μοντέλα, συλλέξαμε ποντικίσια ουδετερόφιλα που είχαν πραγματοποιήσει αντίστροφη μετανάστευση (mRPMN) και αξιολογήθηκε ο φαινότυπος και η παρατεταμένη επιβίωσή τους. Στη συνέχεια, με τη χρήση στρατηγικών μεταφοράς κυττάρων ερευνήσαμε την τύχη των mRPMN in vivo. Παράλληλα, εξετάστηκε η επίδραση της διατμητικής τάσης και του μονοξειδίου του αζώτου στη ρύθμιση της διαδικασίας της αντίστροφης μετανάστευσης των ουδετερόφιλων με τη χρήση ενός in vitro μοντέλου φλεγμονής από ανθρώπινα κύτταρα. Ένα σημαντικό μέρος αυτής της εργασίας περιλάμβανε τη μελέτη των ανθρώπινων ουδετερόφιλων που είχαν πραγματοποιήσει αντίστροφη μετανάστευση (RPMN), σε πρωτεομικό επίπεδο με μεθοδολογία ηλεκτροφόρησης δύο διαστάσεων για τον εντοπισμό αλλαγών στα ουδετερόφιλα που σχετίζονται με τη διαδικασία αντίστροφης μετανάστευσης. Τα αποτελέσματα κατέδειξαν ότι τα ποντικίσια mRPMN είχαν παρόμοιο φαινότυπο με τα ανθρώπινα RPMN όσον αφορά συγκεκριμένους επιφανειακούς δείκτες, ενώ έδειξαν επίσης παρατεταμένη επιβίωση. Ωστόσο, τα προκαταρκτικά μας δεδομένα, όσον αφορά την in vivo κατανομή τους, δεν έδωσαν σαφή ένδειξη σχετικά με την τελική τους τύχη μετά τη μεταφορά τους σε ποντίκια-αποδέκτες. Οι μελέτες στο in vitro μοντέλο φλεγμονής από ανθρώπινα κύτταρα έδειξαν ότι η παρουσία διατμητικής τάσης και το μονοξείδιο του αζώτου καθυστέρησαν, αλλά δεν ανέστειλαν, τη διαδικασία της αντίστροφης μετανάστευσης των ουδετερόφιλων. Τέλος, η πρωτεομική ανάλυση αποκάλυψε τη διαφορική έκφραση ενός αριθμού μεταβολικών, κυτταροσκελετικών και ρυθμιστικών πρωτεϊνών στα ανθρώπινα RPMN, αν και η λειτουργική σημασία αυτών των αλλαγών δεν έχει ακόμη διερευνηθεί.
Water Stable Isotopes in Ecohydrological Field Research: Comparison Between In Situ and Destructive Monitoring Methods to Determine Soil Water Isotopic Signatures

Ecohydrological isotope based field research is often constrained by a lack of temporally explicit soil water data, usually related to the choice of destructive sampling in the field and subsequent analysis in the laboratory. New techniques based on gas permeable membranes allow to sample soil water vapor in situ and infer soil liquid water isotopic signatures. Here, a membrane-based in situ soil water vapor sampling method was tested at a grassland site in Freiburg, Germany. It was further compared with two commonly used destructive sampling approaches for determination of soil liquid water isotopic signatures: cryogenic vacuum extraction and centrifugation. All methods were tested under semi-controlled field conditions, conducting an experiment with dry-wet cycling and two isotopically different labeling irrigation waters. We found mean absolute differences between cryogenic vacuum extraction and in situ vapor measurements of 0.3–14.2‰ (δ18O) and 0.4–152.2‰ (δ2H) for soil liquid water. The smallest differences were found under natural abundance conditions of 2H and 18O, the strongest differences were observed after irrigation with labeled waters. Labeling strongly increased the isotopic variation in soil water: Mean soil water isotopic signatures derived by cryogenic vacuum extraction were -11.6 ± 10.9‰ (δ18O) and +61.9 ± 266.3‰ (δ2H). The in situ soil water vapor method showed isotopic signatures of -12.5 ± 9.4‰ (δ18O) and +169.3 ± 261.5‰ (δ2H). Centrifugation was unsuccessful for soil samples due to low water recovery rates. It is therefore not recommended. Our study highlights that the in situ soil water vapor method captures the temporal dynamics in the isotopic signature of soil water well while the destructive approach also includes the natural lateral isotopic heterogeneity. The different advantages and limitations of the three methods regarding setup, handling and costs are discussed. The choice of method should not only consider prevailing environmental conditions but the experimental design and goal. We see a very promising tool in the in situ soil water vapor method, capturing both temporal developments and spatial variability of soil water processes.
High Psychiatric Morbidity and Comorbidity Among Female Prisoners in Hunan, China

Background High prevalence of mental disorders has been found among female prisoners in Western countries, however, little is known about the epidemiology of mental disorders in such populations in China. This study aims to investigate psychiatric morbidity and comorbidity among sentenced prisoners in a female prison in China. Methods A cluster sample of 2,703 female adult prisoners from Hunan Provincial Female Prison were interviewed with the Mini International Neuropsychiatric Interview, a semi-structured Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) mental disorder diagnostic tool. The rates of psychotic disorders, affective disorders, anxiety disorders, and substance use disorders were reported. Results Nearly 2/3 (66.2%, N=1,790) of the sample fulfilled the criteria for at least one lifetime DSM-IV disorder 36.5% had major depression, 22.2% had post-traumatic stress disorder (PTSD), and 16.5% had drug use disorder. Drug use disorders were the major comorbid disorders. 60.8% of people with alcohol use disorder and 37.0% of those with psychotic disorders also had a drug use disorder. More than one-quarter (26.1%) of the population met criteria for a current diagnosis of any mental disorder, of which major depression was the most common (14.7%), followed by PTSD (6.4%) and psychotic disorder (1.8%). Conclusion The high levels of psychiatric morbidity and comorbidity in a representative sample of female prisoners in China indicate unmet needs that require identification and therapeutic intervention in prisons.
Anti-microbial immune profiles in obstructive lung diseases

Obstructive lung diseases are major causes of morbidity and mortality globally. The most common, chronic obstructive pulmonary disorder (COPD), is caused by smoke inhalation, whilst bronchiectasis (BR) is often caused by lung infection, and cystic fibrosis (CF) is due to the defective passage of fluids through lung epithelial cells. Whilst it is now recognised that the healthy lung harbours a microbial flora, obstructive lung diseases are characterised by periodic or persistent heavy bacterial colonization that can be determined using microbiological cultures of patient sputum as well as by novel genomic analysis, the most common species identified being Pseudomonas aeruginosa and Haemophilus influenzae. Adaptive immune responses generated against such organisms, in the form of antigen-specific antibodies and T cells, may be protective against infection but may also contribute to the inflammatory disease mechanism such as through IL-17-mediated neutrophil recruitment. This thesis asks whether such immune responses can serve as biomarkers of microbial colonization and of disease; whether levels of antibodies and T cells indicate lung infection and exacerbations frequency. However, high levels may simply indicate exposure to microbes. Thus, the aim of this study was to measure antibody and T cell responses against a range of lung-colonizing microbes in patients with BR (n=119), COPD (n=58), CF (n=30), asthma (n=14), and in healthy controls (n=28). Patients were clinically characterised in terms of exacerbations, lung function, sputum microbiology and underlying disease. Enzyme linked immunosorbent assays (ELISAs) were set up to measure specific antibodies in serum, whilst T cell responses in peripheral blood mononuclear cells (PBMCs) were measured by specific Enzyme-linked ImmunoSpot assay (ELIspot), flow cytometry and multiplex cytokine ELISA. Typical microbial colonization for the given disease was seen in the sputum-producing patients. The results showed that in BR, specific IgG responses against P. aeruginosa increased according to episodes of colonization, whilst T cell responses in the form of antigen-specific IFNY showed the opposite effect, suggesting T cell dysregulation. As well as IFNY, T cells were shown to secrete IL-17 and IL-22 in response to microbial antigens and to possess activation and homing receptors. Antibody responses were also further characterised for cross-reactivity and Ig isotype, confirming specificity and isotype switching. In CF patients that harboured P. aeruginosa, high anti-pseudomonas IgG titres were detected, and T cell IFNY responses were similar to healthy controls and were associated with greater disease stability and FEV1. In conclusion, immune responses were successfully characterised in patients with obstructive lung diseases, and specific antibody and T cells showed some associations with colonization and clinical disease, respectively, depending upon the disease and the microbe.
A Review on Various Techniques to Resolve Multiclass Imbalance Problem

— In classification algorithm data is distributed in two or more classes. Large difference in number of instances causes skewness in data. This skewness in data also referred as data imbalance which causes difficulties in processing dataset and affects the accuracy of data classification. The data containing different number of instances with respect to class labels is called as imbalanced dataset. The major issue is the classification of minority class samples. There are various techniques proposed to deal with imbalance class problem without affecting the classification accuracy of majority class. This work aims to study various imbalance data handling techniques and its classification accuracy.
Maintaining Caring Relationships in Spouses Affected by Alzheimer’s Disease

The purpose of this study was to uncover ways that spouses living with Alzheimer’s disease (AD) sustain engagement in their relationships. Coming to understand caring marital relationships when a partner is diagnosed with AD will help nurses to provide more effective support. Fifteen couples participated. Thirty conversations were analyzed with qualitative methods. Three overall themes of spousal caring were identified: engaging with compassion, patiently reaching out, and trusting in the existence of deep attachment. A central theme emerged: relating with compassion by reaching out and trusting in the existence of deep attachment strengthened by many years of marriage. Nurses have the opportunity to support caregivers struggling to find ways to connect with loved ones with AD. Understanding conversation patterns will help nurses provide support for caregivers affected by challenges of AD.
Practical Geospatial Analysis of Open and Public-Use Data

As a geospatial data scientist, you might want to analyze publicly available data sets from several sources, including kaggle.com, data.gov, and other academic or scientiﬁc organizations. Analyzing open and public-use data sets such as these poses many challenges, including disparate data formats (ESRI shapeﬁle, NetCDF, GeoJSON, and so on), disparate geographic references, and the use of street address data instead of actual spatial units. This paper presents a variety of real-world examples of public-use geospatial data to show you how to transform the data into a prespeciﬁed geographic reference, import the data into SAS ® Viya ® via a Python interface, and select suitable subsets that can be convenient for data analysis. These subsets are then analyzed by using SAS/STAT ® procedures KRIGE2D, SPP, and GLIMMIX. The paper also demonstrates how to map the estimates from these procedures by using the Base SAS ® mapping procedure SGMAP invoked from Python. Examples in the paper show you how to use Python as a common programming environment to perform data management functions by using the SAS Scripting Wrapper for Analytics Transfer (SWAT) interface to SAS Viya and how to perform statistical analysis by submitting procedure calls via the SASPy interface to SAS software.
State of the Art on: Deep Image Denoising

Recent technological and methodological advances have allowed the employment of deep learning techniques, in particular deep artificial neural networks, in a large variety of fields. One of the fields that most is benefiting from the introduction of deep learning is image processing and computer vision, which mainly exploits convolutional neural networks (CNNs) for addressing visual understanding problems. For instance, the use of CNNs for image classification and object detection has led to outstanding results. In the last years, deep learning models have been successfully employed also for the tasks of image restoration. Starting from a corrupted image (e.g. noisy, blurred), the goal of image restoration is to recover the original image (i.e. the clean image). Depending on the type of corruption, image restoration tasks can be divided into deblurring, super-resolution, denoising, inpainting, text removal and many others. In particular, the main focus of our research work is image denoising, which is perhaps the most widely addressed problem in the literature. The major journals and conferences regarding deep learning for image denoising, sorted for h5-index, are the following:
Genetic Variation in Virulence among Chalkbrood Strains Infecting Honeybees

Ascosphaera apis causes chalkbrood in honeybees, a chronic disease that reduces the number of viable offspring in the nest. Although lethal for larvae, the disease normally has relatively low virulence at the colony level. A recent study showed that there is genetic variation for host susceptibility, but whether Ascosphaera apis strains differ in virulence is unknown. We exploited a recently modified in vitro rearing technique to infect honeybee larvae from three colonies with naturally mated queens under strictly controlled laboratory conditions, using four strains from two distinct A. apis clades. We found that both strain and colony of larval origin affected mortality rates. The strains from one clade caused 12–14% mortality while those from the other clade induced 71–92% mortality. Larvae from one colony showed significantly higher susceptibility to chalkbrood infection than larvae from the other two colonies, confirming the existence of genetic variation in susceptibility across colonies. Our results are consistent with antagonistic coevolution between a specialized fungal pathogen and its host, and suggest that beekeeping industries would benefit from more systematic monitoring of this chronic stress factor of their colonies.
Emotion metaphor recognition based on synergetic analysis and multi-source knowledge space

: Emotional metaphor is not only a cognitive phenomenon, but also a special language use phenomenon. It is one of the central problems in artificial intelligence, especially in natural language processing. At present, the research methods of emotional metaphor analysis have reached the platform stage, and it is unable to use the deep semantic knowledge. To solve this problem, a multi-scale semantic vector space is proposed to meet the needs of emotional metaphor recognition, and a synergetic analysis based emotional metaphor recognition model is proposed. Furthermore, several key problems of the model are studied, including prototype vector reconstruction, order parameter reconstruction, and evolution equation reconstruction. The experimental results in the emotional metaphor corpus show that the model proposed in this paper has a greater advantage than the general model, and the F1 value of the general metaphor is as high as 82.92. It is clear that this study has a certain reference value for emotional metaphor calculation and other natural language processing tasks.
Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain.

BACKGROUND This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time. AIMS Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by Estep using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject's age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter. METHODS The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean ± SD = 59.32±16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2. RESULTS We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer's disease (AD) identification utilizing support vector machine. CONCLUSION In this study, st-SSM based adult brain shape feature extraction and classification techniques are introduced to classify between normal and AD subject as an application.
Predicting interest to use mobile-device telerehabilitation (mRehab) by baby-boomers with stroke

Context and purpose: Demand for stroke rehabilitation services are reaching unprecedented levels due to an overall population aging, driven by the aging of the baby-boomer generation. Delivery of rehabilitation via mobile-device technologies may provide advantages towards meeting the increasing demands on the rehabilitation system by providing individuals with rehabilitation services in their homes and communities. The aim of this paper is to gain an understanding of the interest of current baby-boomers with stroke to use mobile-device technology to receive rehabilitation services such as education, assessments and exercise programs (mRehab). Methods: People living in the community with stroke born between 1946 and 1964 (i.e., baby-boomer generation) who participated in a larger telerehabilitation survey were included in this study. Regression modeling was used to evaluate personal, health/disability and technological predictors of interest to use mobile-devices for telerehabilitation. Results and significance: Fifty people with stroke, mean age 62.7 (4.4) years, 58% male, 54.2% with moderate or moderately severe disability were included; 86% had access to a mobile phone or tablet. Regression analysis resulted in statistically significant personal (education, β = 0.29 [95% CI = 0.05 to 1.11], population of residence, β = 0.30 [95% CI = 0.07 to 0.69]), health (comorbid conditions, β = 0.30 [95% CI = 0.02 to 0.20]) technology (ownership, β = 0.26 [95% CI = 0.01 to 0.86] and attitude towards telerehabilitation, β = 0.25 [95% CI = 0.01 to 0.79]) predictors of interest to use mobile-devices for telerehabilitation (R 2 = 33.1%). This study identifies personal, health and technological factors which predict interest of baby-boomers with stroke with ongoing and complex health needs to use mRehab. Health professionals can use this information as they integrate mRehab into their practice and inform future development of mRehab solutions.
RealNav: Exploring natural user interfaces for locomotion in video games

We present a reality-based locomotion study directly applicable to video game interfaces; specifically, locomotion control of the quarterback in American football. Focusing on American football drives requirements and ecologically grounds the interface tasks of: running down the field, maneuvering in a small area, and evasive gestures such as spinning, jumping, and the “juke. ”