Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems

Realistic simulations of detailed, biophysics-based, multi-scale models often require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are typically designed to allow for high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version toward a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment which is based on the MegaMol framework and is capable of handling large amounts of simulated data. We present the results of a number of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieve good scalability on up to 768 cores.
Effects of Live and Heat-Inactivated E. coli Strains and Their Supernatants on Immune Regulation in HT-29 Cells

Probiotics are considered to have a beneficial impact on humans, but in some cases, administration of live microorganisms might be risky. In the present study, immunomodulatory effects of different Escherichia coli strains and their super-natants were examined under different inflammatory conditions with living and heat-inactivated strains. HT-29 cells were incubated with E. coli strains (S2-G1, S2-G3, S2-G4 and S2-G8) and their supernatants with or without stimulation with tumor necrosis factor alpha (TNF-α) or interleukin (IL)-1β. Quantification of IL-8 secretion and gene expression was performed by enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (PCR). IL-8 secretion by TNF-α- and IL-1β-stimulated cells was attenuated by all four live strains. In contrast, heat inactivation resulted in an elevated IL-8 expression and secretion in unstimulated cells and did not maintain the anti-inflammatory effect of live bacteria in cytokine-stimulated cells. The supernatant of the live S2-G3 led to an elevated IL-8 secretion in unstimulated and IL-1β-stimulated cells but not in TNF-α-stimulated cells. Live bacteria of all strains might induce an immunosuppressive effect after stimulation of HT-29 cells, whereas heat inactivation and the supernatant seem to induce an elevated immune response. These findings might have an impact depending on the indication and purpose of administration.
A comparative study on algorithms for influence maximization in social networks

How to disseminate information or ideas through social network connection has received much attention in recent years. The core issue is to find a seed set of initially active individuals that can maximize the influence spread. In this paper, we present a comparative study on three basic algorithms for such issue. Experimental results show that although genetic algorithm can find slightly better solution than other algorithms, it is too time-consuming to be cost-effective. Hence, our on-going work is aimed at improving the search efficiency of different bio-inspired meta-heuristic methods.
Integrality Gap of the Configuration LP for the Restricted Max-Min Fair Allocation

The max-min fair allocation problem seeks an allocation of resources to players that maximizes the minimum total value obtained by any player. Each player $p$ has a non-negative value $v_{pr}$ on resource $r$. In the restricted case, we have $v_{pr}\in \{v_r, 0\}$. That is, a resource $r$ is worth value $v_r$ for the players who desire it and value 0 for the other players. In this paper, we consider the configuration LP, a linear programming relaxation for the restricted problem. The integrality gap of the configuration LP is at least $2$. Asadpour, Feige, and Saberi proved an upper bound of $4$. We improve the upper bound to $23/6$ using the dual of the configuration LP. Since the configuration LP can be solved to any desired accuracy $\delta$ in polynomial time, our result leads to a polynomial-time algorithm which estimates the optimal value within a factor of $23/6+\delta$.
Analysis of the reasons for nurses’ confusion in relation to the concept of brain death from clinical and legal points of view

Background Nurses in intensive care units (ICU) play a key role in taking care of brain dead patients and they are often in contact with such patients given the high rate of brain deaths. Consequently, they are in a challenging and stressful condition, which may be aggravated due to the need for increased organ donation, and this may also affect the quality of care provided for these patients. Objective This review analyzes the different studies on the concept of brain death from both medical and legal points of view. The aim of this study was to identify the challenges the nurses face with the perception of the concept of brain death and its effects on the care provided for the patients suffering from brain death, as well as organ donation. Methods This Narrative review study summarizes the information obtained from relevant literature through a targeted search of library texts and electronic resources including Science Direct, PubMed, Google Scholar, Scientific Information Database (SID), IranMedex, and Magiran databases during 2000–2017 in English and Persian, on the subject of interest by keywords such as brain death, organ donation, care, legal, regulations and nursing. Finally, a total of 44 relevant articles were reviewed. Results Nurses play an important role in taking care of brain dead patients; therefore, recognizing challenges can be the first step in taking accurate care of these patients and subsequently maintaining the organs’ health for transplants. Although there are clinical and legal uncertainties regarding the concept of brain death, nurses have an important effect on taking care of brain dead patients as well as the rate of organ donation. Conclusion Nurses in intensive care units should have a clear understanding of the concept of brain death in order to be able to take care of brain dead patients by avoiding psychological effects. In this regard, it is recommended that a comprehensive educational program be designed on the dimensions of brain death. Therefore, the nurses’ awareness of brain death and their ability to clarify this concept to the patients’ relatives would have a great effect on the decrease in the challenges and the stress imposed on the nurses. Also, there would be an increase in the quality of the care given to brain dead patients and even the rate of organ donation.
Improving the accuracy of 2D-3D registration of femur bone for bone fracture reduction robot using particle swarm optimization

Population-based optimization algorithms have proved themselves very effective and efficient way for solving some complex problems, particularly for problems of non-linear and non-convex nature. 2D-3D registration is one such problem where inherent non-linearity and high-dimensionality along with non-convex optimization nature makes it very difficult to achieve satisfying accuracy especially when starting point for optimization is not ideal. Because of the underlying structure of population-based optimization algorithms, especially particle swarm optimization (PSO), 2D-3D registration with PSO is expected to yield better accuracy compared to conventional gradient-based optimization algorithms. Another considerable factor is that gradient approximation error makes non-gradient methods more favorable for the case of 2D-3D registration over gradient-based methods. Our experiment on 2D-3D registration using virtual X-ray shows that PSO has better accuracy and convergence rate than gradient based approaches like Stochastic Gradient Descent (SGD), Momentum Stochastic Gradient Descent (MSGD) and Nesterov Accelerated Gradient (NAG).
DCNN-based Human-Interpretable Post-mortem Iris Recognition

With post-mortem iris recognition getting increasing attention throughout the biometric and forensic communities, no specific, cadaver-aware recognition methodologies have been proposed to date. This paper makes the first step in assessing the discriminatory capabilities of post-mortem iris images collected in multiple time points after a person's demise, by proposing a deep convolutional neural network (DCNN) classifier fine-tuned with cadaver iris images. The proposed method is able to learn these features and provide classification of post-mortem irises in a closed-set scenario, proving that even with post-mortem biological processes' onset after a person's death, features in their irises remain, and can be utilized as a biometric trait. This is also the first work (known to us) to analyze the class-activation maps produced by the DCNN-based iris classifier, and to compare them with attention maps acquired by a gaze-tracking device observing human subjects performing post-mortem iris recognition task. We show how humans perceive post-mortem irises when challenged with the task of classification, and hypothesize that the proposed DCNN-based method can offer human-intelligible decisions backed by visual explanations which may be valuable for iris examiners in a forensic/courthouse scenario.
Commentary: Glucose Self-monitoring in Non-Insulin-Treated Patients With Type 2 Diabetes in Primary Care Settings: A Randomized Trial

Citation: Brož J, Holubová A, Vlasáková M, Mužík J, Brabec M and Rahelić D (2018) Commentary: Glucose Self-monitoring in Non-Insulin-Treated Patients With Type 2 Diabetes in Primary Care Settings: A Randomized Trial. Front. Endocrinol. 9:389. doi: 10.3389/fendo.2018. 00389 Commentary: Glucose Self-monitoring in Non-Insulin-Treated Patients With Type 2 Diabetes in Primary Care Settings: A Randomized Trial
Study of the prevalence of gastroesophageal reflux symptoms and the role of each in relation to the GERD Impact Scale, based on a population of patients admitted for laparoscopic surgery compared to a control group

Introduction Gastroesophageal reflux disease (GERD), demonstrated to impair quality of life (QoL), appears to show significant variation in its prevalence. Estimation of the prevalence is difficult. When defined as at least weekly heartburn and/or acid regurgitation, the prevalence reported in Asia is 2.5–27.6%, in Europe 23.7% and in the US 28.8%. Aim The study evaluates the prevalence of GERD symptoms in the assessment of the GERD Impact Scale (GERD-IS) in two age groups of patients. Material and methods Evaluation of the prevalence of GERD symptoms with the GERD Impact Scale survey in two groups of patients: younger and older. A total of 2,649 surveys were rated. Statistical analysis was performed using the data analysis software system Statistica version 10.0 and Microsoft Excel. Results According to this study the symptoms of GERD included in GERD-IS in northern Poland vary from 0.9–2.4% as daily sensations to 18.9–40.5% occurring sometimes. Individuals with hiatal hernia (HH) were significantly older than those without HH. HH was significantly more frequent in males than females. Conclusions Estimation of the prevalence of GERD is difficult, because the medications are widely available and people use them without any consultation. They do not recognize the symptoms as a disease whose treatment can also be surgical. Our analysis shows that the prevalence of symptoms of GERD in northern Poland is as high as 5%. Hence further investigation should be performed and people’s awareness should be raised.
Towards Practical Program Repair with On-demand Candidate Generation

Effective program repair techniques, which modify faulty programs to fix them with respect to given test suites, can substantially reduce the cost of manual debugging. A common repair approach is to iteratively first generate candidate programs with possible bug fixes and then validate them against the given tests until a candidate that passes all the tests is found. While this approach is conceptually simple, due to the potentially high number of candidates that need to first be generated and then be compiled and tested, existing repair techniques that embody this approach have relatively low effectiveness, especially for faults at a fine granularity. To tackle this limitation, we introduce a novel repair technique, SketchFix, which generates candidate fixes on demand (as needed) during the test execution. Instead of iteratively re-compiling and re-executing each actual candidate program, SketchFix translates faulty programs to sketches, i.e., partial programs with "holes", and compiles each sketch once which may represent thousands of concrete candidates. With the insight that the space of candidates can be reduced substantially by utilizing the runtime behaviors of the tests, SketchFix lazily initializes the candidates of the sketches while validating them against the test execution. We experimentally evaluate SketchFix on the Defects4J benchmark and the experimental results show that SketchFix works particularly well in repairing bugs with expression manipulation at the AST node-level granularity compared to other program repair techniques. Specifically, SketchFix correctly fixes 19 out of 357 defects in 23 minutes on average using the default setting. In addition, SketchFix finds the first repair with 1.6% of re-compilations (#compiled sketches/#candidates) and 3.0% of re-executions out of all repair candidates.
Regulating Collaborative Learning in SQL-Tutor

In recent years, there has been a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools to improve student learning. The aim of this project is to provide adaptive support for student collaboration. My work will focus on designing effective interventions to enhance peer-to-peer collaboration in the context of an Intelligent Tutoring System (ITS), as well as to promote socially-shared regulation of learning (SSRL). In online collaborative learning environments, it can be challenging to engage students. A lack of genuine interaction, social identity, background of students and user empowerment may have negative effects on the learning process. This research will focus on virtualization of the online collaboration, such that the system would help students collaborate as well as support self-regulation and group regulation to achieve higher learning gains.
A Semi-supervised regressor based on model trees

Plenty of Machine Learning (ML) approaches have been applied all these years over a great variety of tasks, leading to several kind of algorithms that face efficiently specific problems. However, their inability to generalize well enough over data that stem from more generic sources or other specialized occasions that are not connected with their initial field of interest, still remains an open issue. Thus, the interest of data scientists has been shifted towards ensemble learners, which try to leverage the predictive behavior of the contained algorithms, offering improved learning performance and robustness over wide spectrum of posed implications. Thus, an ensemble semi-supervised regressor that operates under the presence of a few examples is presented, covering the applications under which shortage of labeled data is usually met, offering at the same time well enough predictive behavior without spending much computational time, since the fast enough algorithm of M5 plays a crucial role.
Orbits of monomials and factorization into products of linear forms

This paper is devoted to the factorization of multivariate polynomials into products of linear forms, a problem which has applications to differential algebra, to the resolution of systems of polynomial equations and to Waring decomposition (i.e., decomposition in sums of d-th powers of linear forms; this problem is also known as symmetric tensor decomposition). We provide three black box algorithms for this problem. Our main contribution is an algorithm motivated by the application to Waring decomposition. This algorithm reduces the corresponding factorization problem to simultaenous matrix diagonalization, a standard task in linear algebra. The algorithm relies on ideas from invariant theory, and more specifically on Lie algebras. Our second algorithm reconstructs a factorization from several bi-variate projections. Our third algorithm reconstructs it from the determination of the zero set of the input polynomial, which is a union of hyperplanes.
Open Research

Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a “top” (e.g. a shirt) and a “bottom” (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain.
Privacy-Adversarial User Representations in Recommender Systems

Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks have been previously studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. We briefly discuss further applications of this method towards the generation of deeper and more insightful recommendations.
Hospital Information Systems: The status and approaches in selected countries of the Middle East

Background and aim Since hospital information systems (HISs) are among the most important information systems in the health sector, the present study aimed to shed light upon the situation of HISs in selected countries of the Persian Gulf region to reflect national strategies and activities in the case of HISs. Methods In this descriptive cross-sectional study, the information about HISs status of the selected countries of the Persian Gulf region were obtained through approaches including review of national and international resources. Then, according to aspects included in HIS status, content analysis was performed. Information obtained from content analysis after several stages of reading, was categorized and then findings, presented. Results The findings revealed that the surveyed countries have widely used HISs since the 1980s. HIS use and development vary to a certain extent. Since the middle of the first decade of the 21st century, Ministry of Health officials of the studied countries have considered the importance of HIS’s integration and creating Electronic Health Record where, Oman, Bahrain and UAE are among the leading countries. Conclusion Since the emergence of HISs in healthcare centers, these countries have employed various approaches. In any case, in order to avoid wasting important resources, including financial and human ones, the use of successful experiences of other countries along with the formulation of a comprehensive plan is highly recommended while considering all HIS aspects, including national and international standards.
Explanations that are Intrinsic to Recommendations

Explanations can give credibility to recommendations and help users to make better choices. In current recommender systems, explanation is a step that comes after recommendation. In this paper, we describe an approach that turns recommender systems on their head. In our approach, which we call Recommendation-by-Explanation (hr-by-e ), the system constructs a reason, or explanation, for recommending each candidate item; then it recommends those candidate items that have the best explanations. By unifying recommendation and explanation, r-by-e finds relevant recommendations with explanations that have a high degree of fidelity. We present the results of an offline experiment using a movie recommendation dataset. We show that r-by-e achieves higher precision than a comparable recommender, while both produce recommendations with roughly equal levels of diversity and serendipity. We also present the results of deploying a web-based system through which we have conducted two user trials. In one trial, we evaluate recommendation quality. Participants in this trial found r-by-e's recommendations to be more diverse, serendipitous and relevant than those of the competitor system. In another trial, we evaluate explanation quality. We used a re-rating task: users rated recommendations initially in the case where they were given only the explanation and not the identity of the movie, and then re-rated in the case where they were given information about the recommended movie. We found a stronger correlation between the pairs of ratings in the case of r-by-e. This suggests that r-by-e's explanations allow users to make more accurate judgments about the quality of recommended items.
TwistIn

Smart devices contain sensitive information that has to be guarded against unauthorized access through authentication. Existing authentication methods become obsolete as they are designed either for logging-in one device at a time or are ineffective in a multi-user multi-device environment. This paper presents TwistIn, a simple gesture that takes a smartwatch as an authentication token for fast access and control of other smart devices. Our mechanism is particularly useful for devices such as smartphones, smart glasses, and small IoT objects. To log in a device, one simply need to pick it up and twist it a few times. Then, by co-analyzing the motion data from the device and the watch, our method can extend the user authentication on the watch to the device. This is a simple and tangible interaction that takes only one to two seconds to perform. Furthermore, to account for user variation in wrist bending, we decompose wrist and forearm rotations via an optimization to improve the method accuracy. We implemented TwistIn, collected thousands of gesture samples, and conducted various experiments to evaluate our prototype system and show that it achieved over 95% detection accuracy.