Miracle-dz/UniFAR-FTUs huggingface dataset:
Secondary traumatic stress (compassion fatigue): a study in allied medical sciences

This longitudinal research sought to investigate whether students (n=100) in the allied medical sciences of Physiotherapy (1997 and 1998 3rd year students) and Occupational Therapy (1998 3rd year students) are at risk of developing symptoms of compassion fatigue (which is identical to and presents in the same way as secondary traumatic stress disorder (STSD) and thus is the equivalent of PTSD). It also sought to investigate whether physiotherapy and occupational therapy students differ in the degree of risk and if a trend emerged within the groups that indicates that exposure to more traumatically injured types of patients contributes more to this risk than exposure to other patients. The study, in addition, investigated whether it is invariant that strong perceived social support reflects lower degrees of risk of compassion fatigue. The dependent variable of risk of compassion fatigue was captured by using the Compassion Fatigue Scale (CFS) developed by Figley (1995) while Joseph et al’s (1992) Crisis Support Questionnaire (CSQ) was used to measure the perceived social support variable. The psychometric properties of both the CFS and the CSQ were sound and supported the use of these instruments in the study. The statistical procedures used to analyse the data were simple descriptive statistics such as means, frequencies, standard deviatic-. i-tests, partial correlations. A number of open-ended questions were administered at r.g end of each questionnaire in order to illuminate the findings. Observations were measured, in this interrupted time series design, on seven different occasions during a 9 mouth period in 1997 and again in 1998 (physiotherapy students) and on four different occasions during 1998 (occupational therapy students) overall 18 data collections and 597 questionnaire1-' The results indicate that the students are at risk of compassion fatigue (STSD), that there is no difference in the degree of risk between physiotherapy and occupational therapy students’ post patient exposure, and that there is a trend within both the 1997 and 1998 groups which indicates that exposure to the more traumatised patient contributes to this risk to a greater degree than does exposure to other patients. Although intuitively it was hypothesised that perceived social support may ameliorate the degree of risk of compassion fatigue (STSD) no significant consistency is found between the two. Some practical recommendations are suggested which may assist in reducing this risk in future physiotherapy and occupational therapy students. Some future research suggestions are also given.
M-HEALTH MOBILE APP USABILITY TESTED WITH ENGINEERING STUDENTS

The mobile app presented in this paper allows to the end users automatically process an English or Spanish medical text entered manually or using the camera. Then the system output allows to the users navigate and discover the meaning of some concepts extracted and other semantically related with them (these techniques are known as named entity recognition and some part of entity linking). The medical text entered by the users can be a health report, a clinical case for example or some other kind of text containing medical information. The user can paste directly the text or use a photo of the text to start the process. Our idea is to use this kind of mobile apps based on the results of intelligent systems, working in the background, in the educational process of medical students, involving the students throughout an active learning activity such as a case-based. We present here the usability evaluation of this mobile app carried out with computer engineer students.
New Perspectives In Digitial Literacy Readiness

: The evolution of Information and Communication Technologies (ICT) has generated associated terms such as e-readiness, e-government, digital literacy, and digital divide. These terms are inter-related in one way or the other. Numerous services have also been created for the sake of convenience and easy access to information submission and retrieval. Digital literacy has now become an integral part of our lives and it is deeply embedded in our daily lifestyle. By the end of 2013 it was expected that broadband subscriptions would rise beyond 2 billion globally. The governments and corporates have demonstrated greater interest in taking advantage of this unique and state of the art technology to tap the potential both politically and economically. The governments are busy formulating strategies and policies and heavily investing in building ICT infrastructures in order to electronically reach out to the citizens to the last mile for better and effective governance. This will also fulfill its objective to address socio-economic issues such as education, governance, citizenship participation, employment, health, food security amongst others. The corporates on the other hand are concentrating on formulating sales and marketing strategies to achieve competitive advantage by offering their products and services to the last mile. ICT has opened up windows of opportunities for almost every sector. Efforts are on to bridge the digital and technological divide within and between countries. In this paper, a brief focus is on the digital literacy levels in developed, developing, and least developed nations.
Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)

Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of preventing and reducing substance abuse. While effective, unfortunately, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. This paper investigates the problem of optimizing the social influence related to the deviant behavior via careful construction of the intervention groups. We propose a Mixed Integer Optimization formulation that decides on the intervention groups, captures the impact of the groups on the structure of the social network, and models the impact of these changes on behavior propagation. In addition, we propose a scalable hybrid meta-heuristic algorithm that combines Mixed Integer Programming and Large Neighborhood Search to find near-optimal network partitions. Our algorithm is packaged in the form of GUIDE, an AI-based decision aid that recommends intervention groups. Being the first quantitative decision aid of this kind, GUIDE is able to assist practitioners, in particular social workers, in three key areas: (a) GUIDE proposes near-optimal solutions that are shown, via extensive simulations, to significantly improve over the traditional qualitative practices for forming intervention groups; (b) GUIDE is able to identify circumstances when an intervention will lead to deviancy training, thus saving time, money, and effort; (c) GUIDE can evaluate current strategies of group formation and discard strategies that will lead to deviancy training. In developing GUIDE, we are primarily interested in substance use interventions among homeless youth as a high risk and vulnerable population. GUIDE is developed in collaboration with Urban Peak, a homeless-youth serving organization in Denver, CO, and is under preparation for deployment.
Minipleurostomia com curativo a vácuo: uma opção minimamente invasiva a pleurostomia

A pleurostomia é uma opção frequente de tratamento para pacientes com empiema pleural fase III que não toleram decorticação pulmonar. Todavia, esse tratamento é considerado mutilante por envolver a confecção de um stoma torácico, que pode demorar anos para se fechar ou requerer nova cirurgia. Descreveu-se recentemente uma técnica minimamente invasiva que associa uso intrapleural de curativo a vácuo como opção a pleurostomia. A presente comunicação objetiva demonstrar o resultado de uma série inicial de pacientes tratados com a minipleurostomia associada ao uso de curativo a vácuo no que tange a sua efetividade e segurança.
Understanding MCMC Dynamics as Flows on the Wasserstein Space

It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel concepts, we propose a theoretical framework that recognizes a general MCMC dynamics as the fiber-gradient Hamiltonian flow on the Wasserstein space of a fiber-Riemannian Poisson manifold. The "conservation + convergence" structure of the flow gives a clear picture on the behavior of general MCMC dynamics. The framework also enables ParVI simulation of MCMC dynamics, which enriches the ParVI family with more efficient dynamics, and also adapts ParVI advantages to MCMCs. We develop two ParVI methods for a particular MCMC dynamics and demonstrate the benefits in experiments.
Tree-Sliced Approximation of Wasserstein Distances

Optimal transport ($\OT$) theory provides a useful set of tools to compare probability distributions. As a consequence, the field of $\OT$ is gaining traction and interest within the machine learning community. A few deficiencies usually associated with $\OT$ include its high computational complexity when comparing discrete measures, which is quadratic when approximating it through entropic regularization; or supercubic when solving it exactly. For some applications, the fact that $\OT$ distances are not usually negative definite also means that they cannot be used with usual Hilbertian tools. In this work, we consider a particular family of ground metrics, namely tree metrics, which yield negative definite $\OT$ metrics that can be computed in linear time. By averaging over randomly sampled tree metrics, we obtain a tree-sliced-Wasserstein distance. We illustrate that the proposed tree-sliced-Wasserstein distances compare favorably with other baselines on various benchmark datasets.
Grid Graph Reachability

The reachability problem is to determine if there exists a path from one vertex to another in a graph. Grid graphs are the class of graphs where vertices are present on the lattice points of a two-dimensional grid, and an edge can occur between a vertex and its immediate horizontal or vertical neighbor only. Asano et al. presented the first simultaneous time space bound for reachability in grid graphs by presenting an algorithm that solves the problem in polynomial time and $O(n^{1/2 + \epsilon})$ space. In 2018, the space bound was improved to $\tilde{O}(n^{1/3})$ by Ashida and Nakagawa. In this paper, we show that reachability in an $n$ vertex grid graph can be decided by an algorithm using $O(n^{1/4 + \epsilon})$ space and polynomial time simultaneously.
Counting of Teams in First-Order Team Logics

We study descriptive complexity of counting complexity classes in the range from #P to #$\cdot$NP. A corollary of Fagin's characterization of NP by existential second-order logic is that #P can be logically described as the class of functions counting satisfying assignments to free relation variables in first-order formulae. In this paper we extend this study to classes beyond #P and extensions of first-order logic with team semantics. These team-based logics are closely related to existential second-order logic and its fragments, hence our results also shed light on the complexity of counting for extensions of FO in Tarski's semantics. Our results show that the class #$\cdot$NP can be logically characterized by independence logic and existential second-order logic, whereas dependence logic and inclusion logic give rise to subclasses of #$\cdot$NP and #P , respectively. Our main technical result shows that the problem of counting satisfying assignments for monotone Boolean $\Sigma_1$-formulae is #$\cdot$NP-complete as well as complete for the function class generated by dependence logic.
Plan-Structured Deep Neural Network Models for Query Performance Prediction

Query performance prediction, the task of predicting a query's latency prior to execution, is a challenging problem in database management systems. Existing approaches rely on features and performance models engineered by human experts, but often fail to capture the complex interactions between query operators and input relations, and generally do not adapt naturally to workload characteristics and patterns in query execution plans. In this paper, we argue that deep learning can be applied to the query performance prediction problem, and we introduce a novel neural network architecture for the task: a plan-structured neural network . Our neural network architecture matches the structure of any optimizer-selected query execution plan and predict its latency with high accuracy, while eliminating the need for human-crafted input features. A number of optimizations are also proposed to reduce training overhead without sacrificing effectiveness. We evaluated our techniques on various workloads and we demonstrate that our approach can out-perform the state-of-the-art in query performance prediction.
A Holistic Approach to Evaluating Cyber Security Defensive Capabilities

Metrics and frameworks to quantifiably assess security measures have arisen from needs of three distinct research communities - statistical measures from the intrusion detection and prevention literature, evaluation of cyber exercises, e.g., red-team and capture-the-flag competitions, and economic analyses addressing cost-versus-security tradeoffs. In this paper we provide two primary contributions to the security evaluation literature - a representative survey, and a novel framework for evaluating security that is flexible, applicable to all three use cases, and readily interpretable. In our survey of the literature we identify the distinct themes from each community's evaluation procedures side by side and flesh out the drawbacks and benefits of each. The evaluation framework we propose includes comprehensively modeling the resource, labor, and attack costs in dollars incurred based on expected resource usage, accuracy metrics, and time. This framework is a more "holistic" approach in that it incorporates the accuracy and performance metrics, which dominate intrusion detection evaluation, the time to detection and impact to data and resources of an attack, favored by educational competitions' metrics, and the monetary cost of many essential security components used in financial analysis. Moreover, it is flexible enough to accommodate each use case, easily interpretable, and comprehensive in terms of costs considered. Finally, we provide two examples of the framework applied to real-world use cases. Overall, we provide a survey and a grounded, flexible framework and multiple concrete examples for evaluating security that addresses the needs of three, currently distinct communities.
The Generation and Application of Medical Image Grid Based on Differential Geometric Features

Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a difficult task due to different types of brain abnormalities. In this paper, we review the deformation method focus on the construction of diffeomorphisms, address clearly a new formation of the deformation problem for moving domains, and we apply it in natural images, face images and MRI brain images. And we use a new method to construct diffeomorphisms through a completely different approach. The idea is to control directly the Jacobian determinant and the curl vector of a transformation and use them as one CNN channel with other modalities(T1-weighted, T1-IR and T2-FLAIR) to get more accurate results of brain segmentation. More importantly, we discuss the influence of some optimization parameters to precision analysis of MRI brain segmentation by both numerical experiments and theoretical analysis. We test this method on the IBSR dataset and MRBrainS18 dataset based on VoxResNet and prove the influence of three parameters on the accuracy of MRI brain segmentation.Finally, we also compare the segmentation performance of our method in two networks, VoxResNet and 3D U-Net network. We believe the proposed method can advance the performance in brain segmentation and clinical diagnosis.
Causal Simulations for Uplift Modeling

Uplift modeling requires experimental data, preferably collected in random fashion. This places a logistical and financial burden upon any organisation aspiring such models. Once deployed, uplift models are subject to effects from concept drift. Hence, methods are being developed that are able to learn from newly gained experience, as well as handle drifting environments. As these new methods attempt to eliminate the need for experimental data, another approach to test such methods must be formulated. Therefore, we propose a method to simulate environments that offer causal relationships in their parameters.
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Adversarial Defensiveness

Recent work in deep learning has shown that the artificial neural networks are vulnerable to adversarial attacks, where a very small perturbation of the inputs can drastically alter the classification result. In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,. e.g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions. The traditional back propagation method cannot train the artificial neural networks with aforementioned brain-like learning mechanisms. Numerical results show that various artificial neural networks trained by the new method can significantly improve the defensiveness against the adversarial attacks. Code is available: \url{this https URL} .
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI

Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data. In this paper, we tackle the problem of designing a sampling mask for an arbitrary reconstruction method and a limited acquisition budget. Namely, we look for an optimal probability distribution from which a mask with a fixed cardinality is drawn. We demonstrate that this problem admits a compactly supported solution, which leads to a deterministic optimal sampling mask. We then propose a stochastic greedy algorithm that (i) provides an approximate solution to this problem, and (ii) resolves the scaling issues of [1], [2]. We validate its performance on in vivo dynamic MRI with retrospective undersampling, showing that our method preserves the performance of [1], [2] while reducing the computational burden by a factor close to 200. Our implementation is available at https://github.com/t-sanchez/stochasticGreedyMRI.
Investigating the normal and tangential peeling behaviour of gecko spatulae using a coupled adhesion-friction model

ABSTRACT The present work investigates the normal and tangential peeling behaviour of a gecko spatula using a coupled adhesion-friction model. The objective is to explain the strong attachment and easy detachment behaviour of the spatulae as well as to understand the influence of various parameters on the pull-off forces. Using nonlinear finite element computations, it is shown that during tangentially-constrained peeling the partial sliding of the spatula pad near the peeling front stretches the spatula, thus increasing the strain energy and leading to high pull-off forces. The model is used to investigate the influence of various parameters on the pull-off forces – such as the peeling angle, spatula shaft angle, strip thickness, and material stiffness. The model shows that increasing the spatula pad thickness beyond a certain level does not lead to a significant increase in the attachment forces. Further, the easy detachment behaviour of geckos is studied under tangentially-free peeling conditions. It is found that the spatulae readily detach from the substrate by changing their shaft angle and eventually peel vertically like a tape. Since the present computational model is not limited by geometrical, kinematical, and material restrictions, it can be employed to analyse similar biological adhesive systems.
Temozolomide Induced Hypermutation in Glioma: Evolutionary Mechanisms and Therapeutic Opportunities

Glioma are the most common type of malignant brain tumor, with glioblastoma (GBM) representing the most common and most lethal type of glioma. Surgical resection followed by radiotherapy and chemotherapy using the alkylating agent Temozolomide (TMZ) remain the mainstay of treatment for glioma. While this multimodal regimen is sufficient to temporarily eliminate the bulk of the tumor mass, recurrence is inevitable and often poses major challenges for clinical management due to treatment resistance and failure to respond to targeted therapies. Improved tumor profiling capacity has enabled characterization of the genomic landscape of gliomas with the overarching goal to identify clinically relevant subtypes and inform treatment decisions. Increased tumor mutational load has been shown to correlate with higher levels of neoantigens and is indicative of the potential to induce a durable response to immunotherapy. Following treatment with TMZ, a subset of glioma has been identified to recur with increased tumor mutational load. These hypermutant recurrent glioma represent a subtype of recurrence with unique molecular vulnerabilities. In this review, we will elaborate on the current knowledge regarding the evolution of hypermutation in gliomas and the potential therapeutic opportunities that arise with TMZ-induced hypermutation in gliomas.
White Blood Cell Detection and Segmentation from Fluorescent Images with an Improved Algorithm using K-means Clustering and Morphological Operators

Cell detection is the most basic and essential step for the analysis of cells. There are enormous types of blood disorders that can be identified by analyzing blood cells. There are several approaches used for this purpose. However, every method has their pros and cons. Improvement of segmentation of cells can increase the performance of cell classification and cell counting in later stages. The main concern of our paper is to segment the white blood cells from fluorescent images using K-means Clustering and Morphological Operators. We detect the cluster with WBC and refine the result depending on the presence of nucleus in the segmented cells. Non-WBCs are the cells without a nucleus and smaller in size. Presence of nucleus in a cell can be an indicator of WBC. We segment nucleus in cells and calculate the average area of the nucleus. We then refine the segmentation result based on the presence and size of the nucleus. Our analysis on WBCs demonstrates a comparison with ground truth values. We achieved our result with sensitivity of 96.4932% and precision of 9S.3584%. Our algorithm and analysis of results outperform state of the art method in several aspects.