Miracle-dz/UniFAR-FTUs huggingface dataset:
A Workload Manager: The Pre-assessment in Sincere Software Agent Environment

Instilling an ethical behaviour in software agent environment can influence the agent to do ethical and rational actions that brings about positive impacts towards its environment. Sincerity is one of an ethical behaviour that can be considered for instilling in software agent environment, especially for encouraging the cooperation in completing tasks within an agent community. However, a mechanism to avoid imbalance in workload between software agents should be established to avoid the sincere agent from taking many tasks from its teammates until it cannot timely complete its own task. In our study, we present three evaluators in Workload Manager: (1) permission of getting favour evaluator, (2) workload calculator and (3) the progress of a worker agent's current task. These evaluators are used to evaluate the capability of an agent before taking any task from its teammate and also to determine the right time for the agent to offer the favour to its teammate. The interaction of the Workload Manager and the agent community is presented to show how it works in sincere software agent environment.
A multi-dimensional extension of the Lightweight Temporal Compression method

Lightweight Temporal Compression (LTC) is among the lossy stream compression methods that provide the highest compression rate for the lowest CPU and memory consumption. As such, it is well suited to compress data streams in energy-constrained systems such as connected objects. The current formulation of LTC, however, is one-dimensional while data acquired in connected objects is often multi-dimensional: for instance, accelerometers and gyroscopes usually measure variables along 3 directions. In this paper, we investigate the extension of LTC to higher dimensions. First, we provide a formulation of the algorithm in an arbitrary vectorial space of dimension n. Then, we implement the algorithm for the infinity and Euclidean norms, in spaces of dimension 2D+t and 3D+t. We evaluate our implementation on 3D acceleration streams of human activities. Results show that the 3D implementation of LTC can save up to 20% in energy consumption for low-paced activities, with a memory usage of about 100 B.
Clinical management decisions in severe acute pancreatitis

W większości przypadków ostre zapalenie trzustki (OZT) ma postać łagodną i poddaje się wyleczeniu w przeciągu kilkunastu dni. Ciężkie OZT występuje u 15-25% chorych, przebiega pod postacią ciężkiej choroby ogólnoustrojowej z towarzyszącymi powikłaniami dotyczącymi innych narządów i obarczone jest wysoką śmiertelnością. Niniejsza publikacja zawiera zalecenia dotyczące postępowania u chorych z ciężkim OZT, gdy dochodzi do niewydolności wielonarządowej utrzymującej się ponad 48 godzin. Najpoważniejszym powikłaniem ogólnoustrojowym jest wstrząs septyczny w przebiegu martwicy trzustki, ropnia bądź innych procesów patologicznych będących następstwem OZT. W początkowym etapie leczenia kluczowe znaczenie ma intensywne nawadnianie dożylne chorego. Rekomenduje się żywienie dojelitowe zamiast żywienia pozajelitowego. Nie należy stosować antybiotyków profilaktycznie. Profilaktyka zakażeń grzybiczym u tych pacjentów nie jest zalecana. U wszystkich chorych z OZT oraz ze współistniejącym ostrym zapaleniem dróg żółciowych należy w ciągu 24 godzin od przyjęcia wykonać ECPW ze sfinkterotomią. U chorych stabilnych, inwazyjne usuwanie zakażonej martwicy lub bezobjawowych torbieli nie jest zalecane. Ewentualny drenaż najlepiej wykonać po 4 tygodniach, co daje czas organizmowi na wytworzenie otoczki (otorbienie) martwicy. Ponieważ OZT towarzyszy silny ból, należy na wczesnym etapie leczenia zastosować właściwe leczenie przeciwbólowe. Stosowanie silnych leków przeciwbólowych z grupy opioidów nie zwiększa ryzyka wystąpienia powikłań OZT.
Parental Alienating Behaviors: An Unacknowledged Form of Family Violence

Despite affecting millions of families around the world, parental alienation has been largely unacknowledged or denied by legal and health professionals as a form of family violence. This complex form of aggression entails a parental figure engaging in the long-term use of a variety of aggressive behaviors to harm the relationship between their child and another parental figure, and/or to hurt the other parental figure directly because of their relationship with their child. Like other forms of family violence, parental alienation has serious and negative consequences for family members, yet victims are often blamed for their experience. In order to be recognized as a form of family violence and to secure protection for victims under law and social policies, a formal review and comparison of parental alienating behaviors and outcomes to child abuse and intimate partner violence has been sorely needed. The result of this review highlights how the societal denial of parental alienation has been like the historical social and political denial or other forms of abuse in many parts of the world (e.g., child abuse a century ago). Reframing parental alienating behaviors as a form of family violence also serves as a desperate call to action for social scientists to focus more theoretical and empirical attention to this topic.
Deletion of GLX3 in Candida albicans affects temperature tolerance, biofilm formation and virulence

Abstract Candida albicans is a predominant cause of fungal infections in mucosal tissues as well as life‐threatening bloodstream infections in immunocompromised patients. Within the human body, C. albicans is mostly embedded in biofilms, which provides increased resistance to antifungal drugs. The glyoxalase Glx3 is an abundant proteomic component of the biofilm extracellular matrix. Here, we document phenotypic studies of a glx3&Dgr; null mutant concerning its role in biofilm formation, filamentation, antifungal drug resistance, cell wall integrity and virulence. First, consistent with its function as glyoxalase, the glx3 null mutant showed impaired growth on media containing glycerol as the carbon source and in the presence of low concentrations of hydrogen peroxide. Importantly, the glx3&Dgr; mutant showed decreased fitness at 37°C and formed less biofilm as compared to wild type and a reintegrant strain. At the permissive temperature of 28°C, the glx3&Dgr; mutant showed impaired filamentation as well as increased sensitivity to Calcofluor white, Congo red, sodium dodecyl sulfate and zymolyase, indicating subtle alterations in wall architecture even though gross quantitative compositional changes were not detected. Interestingly, and consistent with its impaired filamentation, biofilm formation and growth at 37°C, the glx3&Dgr; mutant is avirulent. Our results underline the role of Glx3 in fungal pathogenesis and the involvement of the fungal wall in this process.
Development of the Healthy Purchase Index (HPI): a scoring system to assess the nutritional quality of household food purchases

Abstract Objective To develop an index to assess the nutritional quality of household food purchases based on food expenditures only. Design A database of monthly food purchases of a convenience sample of low-income households was used to develop the Healthy Purchase Index (HPI). The HPI is the sum of two sub-scores based on expenditure shares of food categories in total household food expenditure: the purchase diversity sub-score and the purchase quality sub-score. The first was adapted from an existing diversity score. The second integrated those food categories identified as the best predictors of the nutritional quality of purchases based on associations between expenditure shares of food categories and two nutritional quality indicators: the mean adequacy ratio (MAR) and the mean excess ratio (MER). Correlation between the HPI and a score assessing adherence to French dietary guidelines (PNNS-GSmod) was performed as a first validation. Setting/Particpants Food purchases of 112 households from deprived neighbourhoods of Marseille (France), participating in the Opticourses and Jassur projects (2012–2015). Results The purchase diversity sub-score reflects the presence in food purchases of five food categories: fruits, vegetables, starches, dairy products, and meat, fish & eggs. The purchase quality sub-score is based on expenditure shares for fruit & vegetables, added fats & seasonings, sweet snacks, cheese, sugary drinks, refined grains and fish, as these were identified as predictors of the nutritional quality of purchases. The HPI was positively associated with the PNNS-GSmod (r s=0·378; P<0·001). Conclusions The HPI helps assess the healthiness of household food purchases.
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

Abstract The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.
A Greedy approximation scheme for Sparse Gaussian process regression

In their standard form Gaussian processes (GPs) provide a powerful non-parametric framework for regression and classificaton tasks. Their one limiting property is their $\mathcal{O}(N^{3})$ scaling where $N$ is the number of training data points. In this paper we present a framework for GP training with sequential selection of training data points using an intuitive selection metric. The greedy forward selection strategy is devised to target two factors - regions of high predictive uncertainty and underfit. Under this technique the complexity of GP training is reduced to $\mathcal{O}(M^{3})$ where $(M \ll N)$ if $M$ data points (out of $N$) are eventually selected. The sequential nature of the algorithm circumvents the need to invert the covariance matrix of dimension $N \times N$ and enables the use of favourable matrix inverse update identities. We outline the algorithm and sequential updates to the posterior mean and variance. We demonstrate our method on selected one dimensional functions and show that the loss in accuracy due to using a subset of data points is marginal compared to the computational gains.
Indoxyl Sulfate Promotes Arterial Thrombosis in Rat Model via Increased Levels of Complex TF/VII, PAI-1, Platelet Activation as Well as Decreased Contents of SIRT1 and SIRT3

Patients suffering from chronic kidney disease (CKD) are at a 20-fold higher risk of dying due to cardiovascular diseases (CVDs), primarily thrombosis following vascular injury. CKD is connected with retention of uremic toxins, especially indoxyl sulfate (IS), which are currently considered as a non-classical CKD-specific risk factor for CVDs. The present study aimed to examine the effect of chronic exposure to IS on the hemostatic system and arterial thrombosis in a model without greater interferences from the uremic milieu consisting of additional uremic toxins. Forty-eight male Wistar Crl:WI (cmdb) rats were divided into three groups: one control group and two experimental groups, which were exposed to 100 or 200 mg/kg of b.w. /day of IS in drinking water for a period of 28 days. The control group received water without IS. At the end of the experiment, the induction of arterial thrombosis was performed. We investigated the impact of IS on thrombosis incidence, kinetics and strength of clot formation, platelet activity, aortic contents of sirtuin (SIRT) 1 and sirtuin 3 (SIRT3), hemostatic system, cardiorespiratory parameters, biochemistry of plasma and urine as well as histology of the thrombus, kidney, and liver. Obtained data revealed that chronic exposure to IS promotes arterial thrombosis via increased levels of complex tissue factor/factor VII, plasminogen activator inhibitor-1 (PAI-1), platelet activation, as well as decreased aortic levels of SIRT1 and SIRT3. Therefore, we hypothesize that IS enhances primary hemostasis leading to augmented formation of platelet plug with increased amounts of fibrin and affects secondary hemostasis through the influence on plasma coagulation and fibrinolysis factors, which results in the increased kinetics and strength of clot formation. The findings described may contribute to a better understanding of the mechanisms leading to increased thrombotic events in patients with CKD with elevated levels of IS.
Learning based biological image analysis

The fate of contemporary scientific research in biology and medicine is bound to the advancements in computational methods. The unprecedented data explosion in microscopy and the crescent interest of life scientists in studying more complex and more subtle interactions stimulate the research for innovative computational solutions on challenging real world applications. Extensions and novel formulations of generic and flexible methods based on learning/inference are necessary to cope with the large variety of the produced data and to avoid continuous reimplementation and heavy parameter tuning. This thesis exploits cutting edge machine learning methods based on structured probabilistic models and weakly supervised learning to provide four novel solutions in the areas of large-scale microscopic imaging and multiple objects tracking. Chapter 2 introduces a weakly supervised learning framework to tackle the problem of detecting defect images while mining massive microscopic imagery databases. This thesis demonstrates accurate prediction with low user annotation effort. Chapter 3 presents a learning approach for counting overlapping objects in images based on local structured predictors. This problem has numerous applications in high throughput microscopy screening such as cells counting for drug toxicity assays. Chapter 4 develops a deterministic graphical model to impose temporal consistency in objects counts when dealing with a video sequence. This Chapter shows that global (temporal and spatial) structural inference consistently improves over local (only spatial) predictions. The method developed in Chapter 4 is used in a novel downstream tracking algorithm which is introduced in Chapter 5. This Chapter tackles, for the first time, the difficult problem of tracking heavily overlapping, translucent and indistinguishable objects. The mutual occlusion event handling of such objects is formulated as a novel structured inference problem based on the minimization of a convex multi-commodity flow energy. The optimal weights of the energy terms are learned with partial user supervision using structured learning with latent variables.To support behavioral biologists, we apply this method to the problem of tracking a community of interacting Drosophila larvae.
Attributions of Blame among Victims of Child Sexual Abuse: Findings from a Community Sample

ABSTRACT According to the attribution theory, negative outcomes of child sexual abuse (CSA) are thought to vary depending on whether CSA victims attribute the abuse to internal or external factors, respectively, self-blame and perpetrator-blame. Therefore, the purpose of this study was to identify abuse characteristics and attitudes that influence blame attributions among CSA victims from a community sample. Data from respondents with a history of CSA (N = 1,496) have been used in predicting blame attributions; perpetrator-blame, self-blame, or both. Results from a multinomial logistic regression analysis showed that attitudes toward gender roles had a significant effect on blame: victims were more likely to blame themselves when they endorsed more conservative gender attitudes than victims with more liberal attitudes. Implications for this finding are discussed.
Cuticle deposition improves the biosecurity of eggs through the laying cycle and can be measured on hatching eggs without compromising embryonic development

ABSTRACT The cuticle is part of the egg's natural defense and it can be improved by genetic selection. Prior to adoption of this measurement in breeding programs, questions that need to be addressed are whether improved cuticle deposition will result in a reduced risk of eggs becoming contaminated and whether selection for this trait will have any unintended consequences on the incubation process. Bacterial penetration experiments were carried out using eggs from a pedigree line of broiler breeders (BB) and Rhode Island Red (RIR) layers. Within the natural variation in cuticle deposition in each line, a good cuticle was shown to reduce an egg's susceptibility to penetration by Escherichia coli (BB, P = 0.023) and Salmonella typhimurium (RIR, P < 0.001). Deglycosylation of cuticle proteins had little effect on their antimicrobial activity. The effect of bird age on cuticle deposition was also examined. Shell color decreased with age as anticipated; however, we found no evidence that cuticle deposition decreases with age, at least up to 50 wk. A thicker cuticle could affect the water vapor conductance (WPC) of hatching eggs. The WPC of eggs was, therefore, measured on eggs selected from the top and tail of the cuticle distribution, this time in a Lohmann Selected Leghorn (LSL) pedigree line. Broiler breeder eggs were also tested. No evidence of a relationship between cuticle deposition and WPC was found for LSL or BB eggs. Cuticle deposition measurements require eggs to be stained. Here, we show that this has no adverse effect on embryo development at d 12 of incubation. Thus, we conclude that cuticle deposition is important in preventing bacterial penetration of eggs in genetically divergent breeds of chicken and that the measurement can be practically incorporated into breeding programs. This will contribute to improving the biosecurity of eggs by reducing vertical and horizontal transmission of potentially zoonotic and pathogenic organisms from parent to offspring.
Aplicação da Teoria de Múltiplos Níveis para a Modelagem de Processos Intensivos em Conhecimento(Applying Multi Level Theory for Modeling Knowledge-intensive Processes)

A Knowledge Intensive Process (KiP) is a composition of activities whose execution contributes to satisfy an objective and whose flow of control between the activities at the instance level presents a high degree of variability. KiP modeling has been the focus of several recent efforts in BPM theory and practice but, despite several recent advances, it is still a challenge. Among the existing difficulties, this work deals with the difficulty of distinguishing between elements in diferente instantiation levels, which in scenarios of KiP becomes critical due to its complexity. This work proposes a systematization for the application of the Multi Level Theory (MLT) on top of KiPO (Knowledge-intensive Process Ontology), for modeling KiPs. The results are illustrated in a scenario of a KiP for Data Management. Resumo. Um processo intensivo em conhecimento (PiC) é uma composição de atividades cuja execução contribui para satisfazer um objetivo e cujo fluxo de controle entre as atividades, no nível de instância, apresenta alto grau de variabilidade. A modelagem de PiC tem sido foco de diversos esforços recentes na teoria e prática em BPM mas, apesar de vários avanços recentes, ainda é um desafio. Dentre as dificuldades existentes, este trabalho trata da dificuldade da distinção entre elementos de nível de instanciação distintos, que em cenários de PiC torna-se crítica em função da sua complexidade. É proposta uma sistematização para a aplicação da teoria de múltiplos níveis (MLT) na ontologia KiPO (Ontologia para Processos Intensivos em Conhecimento) para modelagem de PiC, cujos resultados são ilustrados no cenário do processo de Gerenciamento de Dados.
Moving Target Indication Using Deep Convolutional Neural Network

Existing moving target indication (MTI) methods are limited either by the sufficient and homogeneous secondary data (SD) condition or by the high signal-to-clutter-ratio (SCR) requirement. In practice, such constraints are hard to be satisfied. In this paper, we propose a novel deep convolutional neural network (CNN)-based method for the MTI (CNN-MTI) to overcome the limitations of these methods. In the proposed method, the CNN architecture is deliberately designed, and the training data set is greatly augmented to ensure the CNN be deep and well-trained; in addition, the SD only act as the background interference during the augmentation of the training data set. These procedures ensure that the effective features of different classes of moving targets can be extracted, so that good MTI performance can be achieved without the requirement of the sufficient and homogeneous SD or the high SCR condition. Simulation results from the synthetic data and the experimental data demonstrate the validity and the robustness of the CNN-MTI with limited SD support in non-homogeneous and low SCR environment.
Pulse-Distortion Analysis for Millimeter-Wave Time-Domain Material Identification

Analyzing the frequency dependent dielectric properties can be used to identify and distinguish materials in biomedical instruments. In the millimeter-wave range, the dielectric properties of biological tissues are mainly determined by their water content. For instance, it has been shown, that cancer cells have a lower water content than healthy skin cells, resulting in a significant dielectric contrast between them. In this work, we analyze the pulse distortion observed in measurements of the millimeter-wave reflection of water and porcine skin. We propose to use the distortion caused by the frequency dependent reflection coefficient at the interface of a material as a feature for identification. Using a simplified model for the reflection coefficient of the material under test, we simulate the distortion caused by the frequency dispersion. In simulation as well as in measurement, we observe different startup transients depending on the dispersion properties of a material under test. The startup behavior can therefore be used as feature, to directly identify dispersive materials in the time domain.
Generating stochastic plans for a programmable parts feeder

A programmable parts feeder, a mechanism that can be reprogrammed to handle differently shaped parts, is discussed. The authors present a planning algorithm that accepts an n-sided polygonal part as input and, in time O(n/sup 2/), generates a program (plan) for the feeder that maximizes expected feedrate. They have implemented the planner and verified some of the resulting plans in the laboratory. This work illustrates a stochastic framework for manipulation planning. <<ETX>>