Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Free Linear Quadratic Control via Reduction to Expert Prediction

Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. They are appealing as they are general purpose and easy to implement; however, they also come with fewer theoretical guarantees than model-based RL. In this work, we present a new model-free algorithm for controlling linear quadratic (LQ) systems, and show that its regret scales as $O(T^{\xi+2/3})$ for any small $\xi>0$ if time horizon satisfies $T>C^{1/\xi}$ for a constant $C$. The algorithm is based on a reduction of control of Markov decision processes to an expert prediction problem. In practice, it corresponds to a variant of policy iteration with forced exploration, where the policy in each phase is greedy with respect to the average of all previous value functions. This is the first model-free algorithm for adaptive control of LQ systems that provably achieves sublinear regret and has a polynomial computation cost. Empirically, our algorithm dramatically outperforms standard policy iteration, but performs worse than a model-based approach.
True Virtue, Self-Presentation, or Both?: A Behavioral Test of Impression Management and Overclaiming

Measures of self-favoring response biases such as impression management (IM) scales or the overclaiming technique (OCT) have been developed to detect distorted self-reports. However, the validity of these approaches has been questioned. In the present study (N = 461), we further examined both IM (assessed by the respective subscale of the Balanced Inventory of Desirable Responding [BIDR]) and the OCT as measures of self-favoring response biases and their associations with honesty-humility. Specifically, we tested the 2 competing accounts of IM as a measure of true virtues or a measure of overly positive self-presentation against each other. Using 2 behavioral paradigms, we corroborated recent findings that higher IM scores are associated with higher trait honesty as well as with more honest behavior. At the same time, individuals with high IM scores also presented themselves overly favorably, as IM accounted for discrepancies between hypothetical and actual (incentivized) behavior in a dictator game. Thereby, the present results provide direct evidence for the notion that the IM subscale of the BIDR confounds trait honesty with response bias and thus measures both. In contrast, overclaiming was unrelated to trait honesty, all observed behaviors, and self-presentation, indicating that the OCT is not a valid measure of self-favoring response bias.
Biophysical Characterization of CD6—TCR/CD3 Interplay in T Cells

Activation of the T cell receptor (TCR) on the T cell through ligation with antigen-MHC complex of an antigen-presenting cell (APC) is an essential process in the activation of T cells and induction of the subsequent adaptive immune response. Upon activation, the TCR, together with its associated co-receptor CD3 complex, assembles in signaling microclusters that are transported to the center of the organizational structure at the T cell-APC interface termed the immunological synapse (IS). During IS formation, local cell surface receptors and associated intracellular molecules are reorganized, ultimately creating the typical bull's eye-shaped pattern of the IS. CD6 is a surface glycoprotein receptor, which has been previously shown to associate with CD3 and co-localize to the center of the IS in static conditions or stable T cell-APC contacts. In this study, we report the use of different experimental set-ups analyzed with microscopy techniques to study the dynamics and stability of CD6-TCR/CD3 interaction dynamics and stability during IS formation in more detail. We exploited antibody spots, created with microcontact printing, and antibody-coated beads, and could demonstrate that CD6 and the TCR/CD3 complex co-localize and are recruited into a stimulatory cluster on the cell surface of T cells. Furthermore, we demonstrate, for the first time, that CD6 forms microclusters co-localizing with TCR/CD3 microclusters during IS formation on supported lipid bilayers. These co-localizing CD6 and TCR/CD3 microclusters are both radially transported toward the center of the IS formed in T cells, in an actin polymerization-dependent manner. Overall, our findings further substantiate the role of CD6 during IS formation and provide novel insight into the dynamic properties of this CD6-TCR/CD3 complex interplay. From a methodological point of view, the biophysical approaches used to characterize these receptors are complementary and amenable for investigation of the dynamic interactions of other membrane receptors.
Clinical factors predicting blood pressure reduction after catheter-based renal denervation

Introduction Renal denervation (RD) can lead to a significant and sustained decrease in mean values of arterial blood pressure (BP). However, there is still a subset of patients without a significant BP drop after RD (non-responders). Aim To compare characteristics of RD responders to RD non-responders and to identify the clinical predictors of BP reduction. Material and methods Thirty-one patients with diagnosed resistant hypertension underwent RD. Three years after RD the analysis of BP reduction was performed in regard to the baseline patient characteristics. Results After 3 years’ follow-up a 10% or more reduction of systolic baseline BP was observed in 74% of patients. Ten percent or more reduction of diastolic baseline BP was observed in 71% of patients. Among responders we observed the following risk factors: hypercholesterolemia in 70%, body mass index (BMI) > 30 kg/m2 in 55%, diabetes mellitus in 35%, current smoking in 5%. Comorbidity included coronary artery disease (CAD) in 30%, cardiomyopathy in 10%, chronic obstructive pulmonary disease (COPD) in 10%, renal insufficiency in 10%, and ventricular arrhythmia in 5%. Among non-responders we observed the following risk factors: hypercholesterolemia in 38%, diabetes mellitus type 2 in 38% and BMI > 30 kg/m2 in 86%. Comorbidity included CAD in 50% and cardiomyopathy in 13% of patients. Conclusions A 10% reduction of systolic baseline BP was observed in 74% of patients 3 years after renal denervation. Clinical factors like COPD, chronic kidney disease 3a, female sex and hypercholesterolemia increase the chances of effective reduction of BP.
Neonatal Oxytocin Treatment Ameliorates Autistic-Like Behaviors and Oxytocin Deficiency in Valproic Acid-Induced Rat Model of Autism

Autism spectrum disorder (ASD) is characterized by impaired social communication and repetitive/stereotyped behaviors. The neuropeptide oxytocin (OXT) plays a critical role in regulating social behaviors in the central nervous system, as indicated in both human and animal studies. We hypothesized that central OXT deficit is one of causes of etiology of ASD, which may be responsible for the social impairments. To test our hypothesis, central OXT system was examined in valproic acid (VPA)-induced rat model of autism (VPA rat). Our results showed that adolescent VPA rats exhibited a lower level of OXT mRNA and fewer OXT-ir cells in the hypothalamus than control rats. Additionally, OXT concentration in cerebrospinal fluid (CSF) was reduced. The number of OXT-ir cells in the supraoptic nucleus (SON) of neonatal VPA rats was also lower. Autistic-like behaviors were observed in these animals as well. We found that an acute intranasal administration of exogenous OXT restored the social preference of adolescent VPA rats. Additionally, early postnatal OXT treatment had long-term effects ameliorating the social impairments and repetitive behaviors of VPA rats until adolescence. This was accompanied by an increase in OXT-ir cells. Taken together, we demonstrated there was central OXT deficiency in the VPA-induced rat model of autism, and showed evidence that early postnatal OXT treatment had a long-term therapeutic effect on the autistic-like behaviors in VPA rats.
Multi‐SKAT: General framework to test for rare‐variant association with multiple phenotypes

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait‐associated variants within genes or regions of interest. Existing multiphenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here, we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi‐SKAT). Multi‐SKAT models affect sizes of variants on the phenotypes through a kernel matrix and perform a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi‐SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed P value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome‐array data from the METabolic Syndrome In Men (METSIM) study, we show that Multi‐SKAT can improve power over single‐phenotype SKAT‐O test and existing multiple‐phenotype tests, while maintaining Type I error rate.
The impact of opt-in gamification on students' grades in a software design course

An achievement-driven methodology strives to give students more control of their learning with enough flexibility to engage them in deeper learning. We observed in the course Advanced Software Design, which uses the achievement-driven methodology, that students fail to get high grades, which may hamper deeper learning. To motivate students to pursue and get higher grades we added gamification elements to the course. To measure the success of our gamification implementation, students filled out a questionaire rating the enjoyment and motivation produced by the game. We built a statistical regression model where enjoyment and motivation explain 55% of the variation in grades. However, only the relationship between motivation and grade is significant, which implies that notivation drives the overall effect of the model. The results suggest that the more the students were motivated by the game, the higher their grades on the course (and vice versa). This implies that if gamification indeed motivates students, then it makes them go beyond what is expected.
Diagnosing Convolutional Neural Networks using Their Spectral Response

Convolutional Neural Networks (CNNs) are a class of artificial neural networks whose computational blocks use convolution, together with other linear and non-linear operations, to perform classification or regression. This paper explores the spectral response of CNNs and its potential use in diagnosing problems with their training. We measure the gain of CNNs trained for image classification on ImageNet and observe that the best models are also the most sensitive to perturbations of their input. Further, we perform experiments on MNIST and CIFAR-10 to find that the gain rises as the network learns and then saturates as the network converges. Moreover, we find that strong gain fluctuations can point to overfitting and learning problems caused by a poor choice of learning rate. We argue that the gain of CNNs can act as a diagnostic tool and potential replacement for the validation loss when hold-out validation data are not available.
Learning to Optimize under Non-Stationarity

We introduce algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dynamic pricing and ads allocation in a changing environment. We show how the difficulty posed by the non-stationarity can be overcome by a novel marriage between stochastic and adversarial bandits learning algorithms. Defining $d,B_T,$ and $T$ as the problem dimension, the \emph{variation budget}, and the total time horizon, respectively, our main contributions are the tuned Sliding Window UCB (\texttt{SW-UCB}) algorithm with optimal $\widetilde{O}(d^{2/3}(B_T+1)^{1/3}T^{2/3})$ dynamic regret, and the tuning free bandit-over-bandit (\texttt{BOB}) framework built on top of the \texttt{SW-UCB} algorithm with best $\widetilde{O}(d^{2/3}(B_T+1)^{1/4}T^{3/4})$ dynamic regret.
W361R mutation in GaaR, the regulator of D‐galacturonic acid‐responsive genes, leads to constitutive production of pectinases in Aspergillus niger

Polysaccharides present in plant biomass, such as pectin, are the main carbon source for filamentous fungi. Aspergillus niger naturally secretes pectinases to degrade pectin and utilize the released monomers, mainly D‐galacturonic acid. The transcriptional activator GaaR, the repressor of D‐galacturonic acid utilization GaaX, and the physiological inducer 2‐keto‐3‐deoxy‐L‐galactonate play important roles in the transcriptional regulation of D‐galacturonic acid‐responsive genes, which include the genes encoding pectinases. In this study, we described the mutations found in gaaX and gaaR that enabled constitutive (i.e., inducer‐independent) expression of pectinases by A. niger. Using promoter‐reporter strains (PpgaX‐amdS) and polygalacturonic acid plate assays, we showed that W361R mutation in GaaR results in constitutive production of pectinases. Analysis of subcellular localization of C‐terminally eGFP‐tagged GaaR/GaaRW361R revealed important differences in nuclear accumulation of N‐ versus C‐terminally eGFP‐tagged GaaR.
Applying the M2 Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies

The performance of the limited-information statistic M2 for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of M2 for specific DCMs rather than general modeling frameworks are needed. This article aims to demonstrate the usefulness of M2 in hierarchical diagnostic classification models (HDCMs). The performance of M2 in evaluating the fit of HDCMs was investigated in the presence of four types of attribute hierarchies. Two simulation studies were conducted to examine Type I error rates and statistical power of M2 under different simulation conditions, respectively. The findings suggest acceptable Type I error rates control of M2 as well as high statistical power under the conditions of a Q-matrix misspecification and the DINA model misspecification. The data of Examination for the Certificate of Proficiency in English (ECPE) were used to empirically illustrate the suitability of M2 in practice.
Mathematical Programming for Anomaly Analysis of Clafer Models

Clafer combines UML-like class- and meta-modeling with feature-oriented variability-modeling and first-order logic constraints. The considerable expressiveness of Clafer mainly stems from its built-in variability constructs, multiplicity annotations and recursive model structures which yield a potentially unbounded number of valid model instances. As a result, automated reasoning about semantic properties like model consistency (i.e., existence of valid model instances) and anomalies (e.g., false cardinality bounds) is very challenging. Recent analysis techniques are inherently incomplete as they impose an a-priori finite search space with either manually or heuristically adjusted bounds. In this paper, we present a novel approach for automated search-space restriction for a considerably rich, yet decidable fragment of the Clafer language that guarantees sound and complete detection results for a wide range of semantic anomalies. Our approach employs principles from mathematical programming by encoding Clafer models as Mixed Integer Linear Programs (MILP). Our experimental evaluation shows remarkable improvements of runtime efficiency as well as effectiveness of anomaly detection as compared to existing techniques.
Assessment of the Dimensional Application of International Safety Goals for Children in Hospitals

Introduction: Nursing staff has an important role in understanding and apply the six international patient children safety goals to minimized hazards and errors. The Aim of the study: This study aimed to assess the dimensional application of nursing for international safety goals in hospitalized children. Research Design: A descriptive design was utilize in this study. Setting: it was conducted at Pediatric outpatient clinics in Children Hospital affiliated to Ain Shams University Hospitals and and Health Insurance Hospital in Nasr city. The subjects: The subject of this study was convenient sample composed of 100 nurses working at the previously mentioned settings regardless their experiences, level of education, position, or age. Tools of data collection: First tool: A structured Interviewing Sheet: this tool was designed by the researcher and written in simple Arabic language based on scientific literature review to gather data and knowledge about dimensional of children international safety goals in pediatric department at previously mentioned settings. Results: There was statistical significant difference between years of experience of the studied nurses and their knowledge and practices regarding to child patient safety. Conclusion: In the light of the study findings, it might be concluded that, more than half of the studied nurses had poor knowledge and unsatisfactory practices regarding the levels of the dimensional application of international safety goals for children in hospital. Meanwhile the factors contributing as obstacles for dimensional application of international safety goals for children in hospital were observed in the positive relation between the years of experiences of the studied sample and its relation with their level of knowledge and practices. Recommendation: Establishing the system of application the international safety goals for children in hospital and emphasize the important of application the international safety goals for children in hospital in nursing.
Higher-order Spectral Clustering for Heterogeneous Graphs

Higher-order connectivity patterns such as small induced sub-graphs called graphlets (network motifs) are vital to understand the important components (modules/functional units) governing the configuration and behavior of complex networks. Existing work in higher-order clustering has focused on simple homogeneous graphs with a single node/edge type. However, heterogeneous graphs consisting of nodes and edges of different types are seemingly ubiquitous in the real-world. In this work, we introduce the notion of typed-graphlet that explicitly captures the rich (typed) connectivity patterns in heterogeneous networks. Using typed-graphlets as a basis, we develop a general principled framework for higher-order clustering in heterogeneous networks. The framework provides mathematical guarantees on the optimality of the higher-order clustering obtained. The experiments demonstrate the effectiveness of the framework quantitatively for three important applications including (i) clustering, (ii) link prediction, and (iii) graph compression. In particular, the approach achieves a mean improvement of 43x over all methods and graphs for clustering while achieving a 18.7% and 20.8% improvement for link prediction and graph compression, respectively.
Finding Crash-Consistency Bugs with Bounded Black-Box Crash Testing

We present a new approach to testing file-system crash consistency: bounded black-box crash testing (B3). B3 tests the file system in a black-box manner using workloads of file-system operations. Since the space of possible workloads is infinite, B3 bounds this space based on parameters such as the number of file-system operations or which operations to include, and exhaustively generates workloads within this bounded space. Each workload is tested on the target file system by simulating power-loss crashes while the workload is being executed, and checking if the file system recovers to a correct state after each crash. B3 builds upon insights derived from our study of crash-consistency bugs reported in Linux file systems in the last five years. We observed that most reported bugs can be reproduced using small workloads of three or fewer file-system operations on a newly-created file system, and that all reported bugs result from crashes after fsync() related system calls. We build two tools, CRASHMONKEY and ACE, to demonstrate the effectiveness of this approach. Our tools are able to find 24 out of the 26 crash-consistency bugs reported in the last five years. Our tools also revealed 10 new crash-consistency bugs in widely-used, mature Linux file systems, seven of which existed in the kernel since 2014. The new bugs result in severe consequences like broken rename atomicity and loss of persisted files.
Wearable sensors for recognizing individuals undertaking daily activities

With the fast evolution in the area of processing units and sensors, wearable devices are becoming more popular among people of all ages. Recently, there has been renewed interest in exploiting the capabilities of wearable sensors for person recognition while undertaking their normal daily activities. In this paper, we focus on utilizing motion information of known daily activities gathered from wearable sensors for recognizing the person. The analysis of the results demonstrates that different fundamental classification factors have an impact on person recognition success-rates. Furthermore, the results of comparison among subjects prove that some subjects have high classification results and can be easily identifiable compared to other subjects which have high confusability rates. Lastly, a significant improvement in subject classification success rate was found for activities with little or no movement which can successfully distinguish among persons and hence producing higher classification results compared to activities with large movement.