Miracle-dz/UniFAR-FTUs huggingface dataset:
Derivation and external validation of a clinical version of the German Diabetes Risk Score (GDRS) including measures of HbA1c

Objective The German Diabetes Risk Score (GDRS) is a diabetes prediction model which only includes non-invasively measured risk factors. The aim of this study was to extend the original GDRS by hemoglobin A1c (HbA1c) and validate this clinical GDRS in the nationwide German National Health Interview and Examination Survey 1998 (GNHIES98) cohort. Research design and methods Extension of the GDRS was based on the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study with baseline assessment conducted between 1994 and 1998 (N=27 548, main age range 35–65 years). Cox regression was applied with the original GDRS and HbA1c as independent variables. The extended model was evaluated by discrimination (C-index (95% CI)), calibration (calibration plots and expected to observed (E:O) ratios (95% CI)), and reclassification (net reclassification improvement, NRI (95% CI)). For validation, data from the GNHIES98 cohort with baseline assessment conducted between 1997 and 1999 were used (N=3717, age range 18–79 years). Missing data were handled with multiple imputation. Results After 5 years of follow-up 593 incident cases of type 2 diabetes occurred in EPIC-Potsdam and 86 in the GNHIES98 cohort. In EPIC-Potsdam, the C-index for the clinical GDRS was 0.87 (0.81 to 0.92) and the overall NRI was 0.26 (0.21 to 0.30), with a stronger improvement among cases compared with non-cases (NRIcases: 0.24 (0.19 to 0.28); NRInon-cases: 0.02 (0.01 to 0.02)). Almost perfect calibration was observed with a slight tendency toward overestimation, which was also reflected by an E:O ratio of 1.07 (0.99 to 1.16). In the GNHIES98 cohort, discrimination was excellent with a C-index of 0.91 (0.88 to 0.94). After recalibration, the calibration plot showed underestimation of diabetes risk in the highest risk group, while the E:O ratio indicated overall perfect calibration (1.02 (0.83 to 1.26)). Conclusions The clinical GDRS provides the opportunity to apply the original GDRS as a first step in risk assessment, which can then be extended in clinical practice with HbA1c whenever it was measured.
Efficient Big Data Clustering

The need to support advanced analytics on Big Data is driving data scientist' interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved. Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources. In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+. By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion. Experimental results confirm the accuracy and scalability of CLUBS+.
A Multiple Instance Learning Algorithm for Color Images Classification

After a brief survey on well established methods for image classification, we focus on a recently proposed Multiple Istance Learning (MIL) method which is suitable for applications in image processing. In particular the method is based on a mixed integer nonlinear formulation of the optimization problem to be solved for MIL purposes. The algorithm is applied to a set of color images (Red, Green, Blue, RGB) with the objective of classifying the images containing some specific pattern. The results of our experimentation are reported.
Optimizer time estimation for SQL queries

Predicting the amount of time a SQL query takes to execute can help in prioritizing, optimizing and scheduling the query execution. This also helps inoptimal utilization of hardware resources. The total execution time of a query can be split into the time taken for parsing/optimizing a query and the time taken for the actual execution. In this work, we focus on solving the first part of the problem, that is predicting the optimization time of a query. Predicting optimization time can hint the optimizer not to spend too much time optimizing the query in case when parse time is much higher than execution time. Such query execution plans can be cached to speed-up execution in future. If optimization time is much lower than execution time, we can choose not to cache the plan and can make better utilization of execution plan cache. If optimization time is relatively low compared to execution time, optimizer can be hinted to spend more time in parsing to produce better optimized plan which can reduce the execution time. One method towards predicting the parse time is to use some heuristic information by looking at query text. In this work, we take advantage of machine learning techniques by designing a set of features from the SQL query text and use a neural network to predict the parse time. We have tried both regression as well as classification based approaches in this work. We report high accuracy while predicting the elapsed parse time for SQL queries.
Static Automated Program Repair for Heap Properties

Static analysis tools have demonstrated effectiveness at finding bugs in real world code. Such tools are increasingly widely adopted to improve software quality in practice. Automated Program Repair (APR) has the potential to further cut down on the cost of improving software quality. However, there is a disconnect between these effective bug-finding tools and APR. Recent advances in APR rely on test cases, making them inapplicable to newly discovered bugs or bugs difficult to test for deterministically (like memory leaks). Additionally, the quality of patches generated to satisfy a test suite is a key challenge. We address these challenges by adapting advances in practical static analysis and verification techniques to enable a new technique that finds and then accurately fixes real bugs without test cases. We present a new automated program repair technique using Separation Logic. At a high-level, our technique reasons over semantic effects of existing program fragments to fix faults related to general pointer safety properties: resource leaks, memory leaks, and null dereferences. The procedure automatically translates identified fragments into source-level patches, and verifies patch correctness with respect to reported faults. In this work we conduct the largest study of automatically fixing undiscovered bugs in real-world code to date. We demonstrate our approach by correctly fixing 55 bugs, including 11 previously undiscovered bugs, in 11 real-world projects.
Challenges in Quality Assessment of Arabic DBpedia

The development of Semantic Web technology has fueled the creation of a large amount of Linked Open Data. DBpedia is a good example of an open data repository extracted from the crowd sourced knowledge base, Wikipedia. Because of the way Wikipedia and DBpedia were created, the information available there is more vulnerable to grammatical errors, inconsistency, structures, and, data type problems. The introduction of the Arabic chapter of DBpedia created additional problems due to the nature of the language, when it comes to quality assessment issues. In this paper 1, we focus on identifying challenges in quality assessment of Arabic DBpedia, as well as analysis of existing tools and methodologies used for Linked Data quality assessments.
Modeling Customers and Products with Word Embeddings from Receipt Data

For many tasks in market research it is important to model customers and products as comparable instances. Usually, the integration of customers and products into one model is not trivial. In this paper, we will detail an approach for a combined vector space of customers and products based on word embeddings learned from receipt data. To highlight the strengths of this approach we propose four different applications: recommender systems, customer and product segmentation and purchase prediction. Experimental results on a real-world dataset with 200M order receipts for 2M customers show that our word embedding approach is promising and helps to improve the quality in these applications scenarios.
Speaker Verification based on extraction of Deep Features

In this paper we present an approach for speaker verification, based on the the extraction of deep features. More specifically, we propose a scheme that is based on a convolutional neural network. For audio representation we opt for spectrograms, i.e., images that result from the spectral content of voices. Our network is trained to extract visual features from these spectrograms. We demonstrate that our network is able to produce discriminative features for the problem at hand, and moreover, when transfer learning is used, few samples may be needed for accurate speaker verification.
Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining

This paper proposes high-dimensional data mining technique by integrating two data mining methods: Accuracy-based Learning Classifier Systems (XCS) and Random Forests (RF). Concretely the proposed system integrates RF and XCS: RF generates several numbers of decision trees, and XCS generalizes the rules converted from the decision trees. The convert manner is as follows: (1) the branch node of the decision tree becomes the attribute; (2) if the branch node does not exist, the attribute of that becomes # for XCS; and (3) One decision tree becomes one rule at least. Note that # can become any value in the attribute. From the experiments of Multiplexer problems, we derive that: (i) the good performance of the proposed system; and (ii) RF helps XCS to acquire optimal solutions as knowledge by generating appropriately generalized rules.
Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization

Motivated by applications in Game Theory, Optimization, and Generative Adversarial Networks, recent work of Daskalakis et al~\cite{DISZ17} and follow-up work of Liang and Stokes~\cite{LiangS18} have established that a variant of the widely used Gradient Descent/Ascent procedure, called "Optimistic Gradient Descent/Ascent (OGDA)", exhibits last-iterate convergence to saddle points in {\em unconstrained} convex-concave min-max optimization problems. We show that the same holds true in the more general problem of {\em constrained} min-max optimization under a variant of the no-regret Multiplicative-Weights-Update method called "Optimistic Multiplicative-Weights Update (OMWU)". This answers an open question of Syrgkanis et al~\cite{SALS15}. The proof of our result requires fundamentally different techniques from those that exist in no-regret learning literature and the aforementioned papers. We show that OMWU monotonically improves the Kullback-Leibler divergence of the current iterate to the (appropriately normalized) min-max solution until it enters a neighborhood of the solution. Inside that neighborhood we show that OMWU becomes a contracting map converging to the exact solution. We believe that our techniques will be useful in the analysis of the last iterate of other learning algorithms.
Frontotemporal Dementia-Associated N279K Tau Mutation Localizes at the Nuclear Compartment

Tau is a microtubule-associated protein that plays an important role in Alzheimer’s disease and related tauopathies. Approximately one-half of all cases of Frontotemporal dementia with parkinsonism-17 (FTDP-17) are caused by mutations in the MAPT gene. The N279K mutation is one of the three mutations more prevalent in FTDP-17 cases. Several studies have demonstrated that N279K Tau mutation alters alternative splicing inducing the presence of exon 10. Tau is mainly found in the cytosol of neuronal cells although it has also been localized within the nucleus. Here we demonstrate by biochemical and immunohistochemistry studies in COS-7 cells, that the proportion of mutant N279K Tau increases compared with wild-type at the cell nucleus although cell viability is not affected. These data will provide us with a better outline of the nuclear role of tau protein offering new clues related with this tauopathie.
Glucagon‐like peptide‐1 receptor antagonism impairs basal exercise capacity and vascular adaptation to aerobic exercise training in rats

Cardiorespiratory fitness (CRF) inversely predicts cardiovascular (CV) mortality and CRF is impaired in people with type 2 diabetes (T2D). Aerobic exercise training (ET) improves CRF and is associated with decreased risk of premature death in healthy and diseased populations. Understanding the mechanisms contributing to ET adaptation may identify targets for reducing CV mortality of relevance to people with T2D. The antihyperglycemic hormone glucagon‐like peptide‐1 (GLP‐1) influences many of the same pathways as exercise and may contribute to CV adaptation to ET. We hypothesized that GLP‐1 is necessary for adaptation to ET. Twelve‐week‐old male Wistar rats were randomized (n = 8–12/group) to receive PBS or GLP‐1 receptor antagonist (exendin 9‐39 (Ex(9‐39)) via osmotic pump for 4 weeks ± ET. CRF was greater with ET (P < 0.01). Ex(9‐39) treatment blunted CRF in both sedentary and ET rats (P < 0.001). Ex(9‐39) attenuated acetylcholine‐mediated vasodilation, while this response was maintained with Ex(9‐39)+ET (P = 0.04). Aortic stiffness was greater with Ex(9‐39) (P = 0.057) and was made worse when Ex(9‐39) was combined with ET (P = 0.004). Ex vivo aortic vasoconstriction with potassium and phenylephrine was lower with Ex(9‐39) (P < 0.0001). Carotid strain improved with PBS + ET but did not change in the Ex(9‐39) rats with ET (P < 0.0001). Left ventricular mitochondrial respiration was elevated with Ex(9‐39) (P < 0.02). GLP‐1 receptor antagonism impairs CRF with and without ET, attenuates the vascular adaptation to ET, and elevates cardiac mitochondrial respiration. These data suggest that GLP‐1 is integral to the adaptive vascular response to ET.
Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.
A multi-objective optimization design framework for ensemble generation

Machine learning algorithms have found to be useful for the solution of complex engineering problems. However, due to problem's characteristics, such as class imbalance, classical methods may not be formidable. The authors believe that the application of multi-objective optimization design can improve the results of machine learning algorithms on such scenarios. Thus, this paper proposes a novel methodology for the creation of ensembles of classifiers. To do so, a multi-objective optimization design approach composed of two steps is used. The first step focus on generating a set of diverse classifiers, while the second step focus on the selection of such classifiers as ensemble members. The proposed method is tested on a real-world competition data set, using both decision trees and logistic regression classifiers. Results show that the ensembles created with such technique outperform the best ensemble members.
Familial Hypercholesterolemia: New Horizons for Diagnosis and Effective Management

Familial hypercholesterolemia (FH) is a common genetic cause of premature cardiovascular disease (CVD). The reported prevalence rates for both heterozygous FH (HeFH) and homozygous FH (HoFH) vary significantly, and this can be attributed, at least in part, to the variable diagnostic criteria used across different populations. Due to lack of consistent data, new global registries and unified guidelines are being formed, which are expected to advance current knowledge and improve the care of FH patients. This review presents a comprehensive overview of the pathophysiology, epidemiology, manifestations, and pharmacological treatment of FH, whilst summarizing the up-to-date relevant recommendations and guidelines. Ongoing research in FH seems promising and novel therapies are expected to be introduced in clinical practice in order to compliment or even substitute current treatment options, aiming for better lipid-lowering effects, fewer side effects, and improved clinical outcomes.
SUM-IML: Dynamic Scrutable User Modeling utilizing Interactive Machine Learning

Machine learning? is a powerful tool in modeling historic user data and converting it into a computational user model. However, it is nearly impossible for a machine learning process to have access to all data pertaining to a user. This leads to potential gaps in the user model produced. Thus, involving the user in overseeing and controlling the modelling process may be considered as a worthwhile goal. This form of user model control is often referred to as scrutability in the personalization research domain. The approach proposed in this research is to combine the benefits of machine learning driven user modelling and scrutable user control in the modelling process. This PhD is at its midpoint and is currently focused on building the model while taking into consideration how user feedback may be incorporated. This feedback is solicited iteratively, enabling the ML process to retrain with the benefit of the user input. The early results of the experimental work show promising prediction accuracy results.
Multi-method Evaluation in Scientific Paper Recommender Systems

Recommendation techniques in scientific paper recommender systems (SPRS) have been generally evaluated in an offline setting, without much user involvement. Nonetheless, user relevance of recommended papers is equally important as system relevance. In this paper, we present a scientific paper recommender system (SPRS) prototype which was subject to both offline and user evaluations. The lessons learnt from the evaluation studies are described. In addition, the challenges and open questions for multi-method evaluation in SPRS are presented.
The Kannan-Lov\'asz-Simonovits Conjecture.

The Kannan-Lovasz-Simonovits conjecture says that the Cheeger constant of any logconcave density is achieved to within a universal, dimension-independent constant factor by a hyperplane-induced subset. Here we survey the origin and consequences of the conjecture (in geometry, probability, information theory and algorithms) as well as recent progress resulting in the current best bounds. The conjecture has lead to several techniques of general interest.
Topic Diffusion Discovery Based on Sparseness-Constrained Non-Negative Matrix Factorization

Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. Various scholarly search websites, citation recommendation engines, and research databases have been created to simplify the text search tasks. However, it is still difficult for researchers to be able to identify potential research topics without doing intensive reviews on a tremendous number of articles published by journals, conferences, meetings, and workshops. In this paper, we consider a novel topic diffusion discovery technique that incorporates sparseness-constrained Non-negative Matrix Factorization with generalized Jensen-Shannon divergence to help understand term-topic evolutions and identify topic diffusions. Our experimental result shows that this approach can extract more prominent topics from large article databases, visualize relationships between terms of interest and abstract topics, and further help researchers understand whether given terms/topics have been widely explored or whether new topics are emerging from literature.
Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification

For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissue and to preprocess the resulting data for a set of machine learning algorithms. Our study confirms the established mechanisms of human skeletal muscle aging, including dysregulation of cytosolic Ca2+ homeostasis, PPAR signaling and neurotransmitter recycling along with IGFR and PI3K-Akt-mTOR signaling. Applying several supervised machine learning techniques, including neural networks, we built a panel of tissue-specific biomarkers of aging. Our predictive model achieved 0.91 Pearson correlation with respect to the actual age values of the muscle tissue samples, and a mean absolute error of 6.19 years on the test set. The performance of models was also evaluated on gene expression samples of the skeletal muscles from the Gene expression Genotype-Tissue Expression (GTEx) project. The best model achieved the accuracy of 0.80 with respect to the actual age bin prediction on the external validation set. Furthermore, we demonstrated that aging biomarkers can be used to identify new molecular targets for tissue-specific anti-aging therapies.
SCALABLE CONVOLUTIONAL DICTIONARY LEARNING WITH CONSTRAINED RECURRENT SPARSE AUTO-ENCODERS

Given a convolutional dictionary underlying a set of observed signals, can a carefully designed auto-encoder recover the dictionary in the presence of noise? We introduce an auto-encoder architecture, termed constrained recurrent sparse auto-encoder (CRsAE), that answers this question in the affirmative. Given an input signal and an approximate dictionary, the encoder finds a sparse approximation using FISTA. The decoder reconstructs the signal by applying the dictionary to the output of the encoder. The encoder and decoder in CRsAE parallel the sparse-coding and dictionary update steps in optimization-based alternating-minimization schemes for dictionary learning. As such, the parameters of the encoder and decoder are not independent, a constraint which we enforce for the first time. We derive the back-propagation algorithm for CRsAE. CRsAE is a framework for blind source separation that, only knowing the number of sources (dictionary elements), and assuming sparsely-many can overlap, is able to separate them. We demonstrate its utility in the context of spike sorting, a source separation problem in computational neuroscience. We demonstrate the ability of CRsAE to recover the underlying dictionary and characterize its sensitivity as a function of SNR.