Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Asynchronous Averaging for Community Detection

Consider the following probabilistic process on a graph G = (V,E). After initially labeling each vertex v by a real number, say randomly chosen in ±1, we repeatedly pick a random edge and replace the values of its endpoints by their average. Suppose the process is run on a graph exhibiting a community structure, such as two expanders joined by a sparse cut: is there a phase of the process, in which its state reflects the underlying community structure? Moreover, can nodes somewhat learn that structure via a simple, local procedure? The above questions arise since the expected action of the one-edge-at-a-time averaging corresponds to the repeated application of the transition matrix of a lazy walk on G, and it is known that, for certain graph classes, the resulting evolution of the state allows to uncover the underlying community structure. We answer the first question in the affirmative for a class of regular clustered graphs that includes the regular stochastic block model. Addressing the question above (in this restricted class as well) requires studying the concentration of the averaging process around its expectation. In turn, this calls for a deeper understanding of concentration properties of the product of certain random matrices around its expectation. These properties (albeit in different flavors) emerge both in the regime in which the sparsity of the cut is o(1/ log |V |) (with constant expansion within each community), and when the sparsity is constant. The analysis in the latter regime is the technically hardest part of this work, because we have to establish concentration results up to inverse polynomial errors. As for the second question, since nodes do not share a common clock, it is not immediate to translate the above results into distributed clustering protocols. To this purpose, we show that concentration holds over a long time window and most nodes are able to select a local time within this window. This results in the first asynchronous distributed algorithms that require logarithmic or polylogarithmic work per node (depending on the sparsity of the cut) and that approximately recover community structure.
Explaining Active Learning Queries

In contrast to traditional supervised machine learning that takes a set of labeled data and builds a model that best fits the given data, active learning selects instances from which it will learn. In a typical setting, active learning starts with some labeled instance, and queries an unlabeled instance that it can learn the most from. Then the queried instance is labeled by an oracle, and the learner re-trains the model and continues the learning cycle. By selecting the most informative instances, active learning attempts to find the optimal set of training data. Often, the oracle is a human annotator: for speech data, for example, an annotator can be a trained linguist. In a typical active learning setting, an annotator’s role is to provide a label to the instance that the active learner asks for. In this setting, it is difficult for the annotator to understand why the queried instance is important, and the annotator takes a passive role in a sense that he or she merely provides the label to the active learner. In this paper, I propose a technique that explains active learning queries and an expert-aided active learning procedure in which experts are more involved in the learning cycle. The technique was applied to Haverford’s Dark Reactions Project dataset, which consists of organically-templated metal oxide synthesis reactions. The explanations of queries are provided to a chemist, who was able to interpret the explanations and found it helpful identifying chemical space that is poorly understood by the model. Moreover, the expert-aided active learning showed performance commensurate with the standard active learning.
Developing e-health applications to promote a patient-centered approach to medically unexplained symptoms

Medically unexplained symptoms (MUS) are physical symptoms that cannot be (fully) explained by organic pathology despite adequate diagnostic testing. MUS are highly prevalent and range from single, self-limiting complaints to constellations of chronic and disabling symptoms. Apart from the suffering and impairments MUS impose on patients, they are very costly for society due to the associated productivity losses and burden on healthcare. Anne van Gils’ PhD research aimed to develop e-health applications, offering solutions to three obstacles in the treatment of MUS. Communication problems between patients and healthcare providers are the first obstacle. Since many healthcare providers find it difficult to manage patients with MUS, an online course (‘e-learning’) was developed to improve their knowledge, skills, and attitude. Healthcare providers found this an effective and satisfying way to learn about MUS. The second obstacle are the limited availability of and stigma surrounding psychological treatment. Even though psychotherapy is one of few evidence-based treatments for MUS, only a small proportion of patients benefits from this type of treatment. Online self-help can form a widely available and acceptable alternative. Self-help reduces physical symptoms and improves quality of life. This led to the development of Grip: an online, guided self-help intervention, offered in general practice. Grip also addresses the third obstacle: the large variation in symptom characteristics and etiological factors of MUS. Personalizing the contents of psychological treatments may thus improve their effectiveness. Grip uses algorithms, based on results of online questionnaires and diaries to personalize treatment.
Influence analysis of emotional behaviors and user relationships based on Twitter data

One of the main purposes for which people use Twitter is to share emotions with others. Users can easily post a message as a short text when they experience emotions such as pleasure or sadness. Such tweet serves to acquire empathy from followers, and can possibly influence others' emotions. In this study, we analyze the influence of emotional behaviors to user relationships based on Twitter data using two dictionaries of emotional words. Emotion scores are calculated via keyword matching. Moreover, we design three experiments with different settings: calculate the average emotion score of a user with random sampling, calculate the average emotion score using all emotional tweets, and calculate the average emotion score using emotional tweets, excluding users of few emotional tweets. We evaluate the influence of emotional behaviors to user relationships through the Brunner-Munzel test. The result shows that a positive user is more active than a negative user in constructing user relationships in a specific condition.
Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.
THE RELATIONSHIP BETWEEN OBESITY AND CARDIOVASCULAR RISK IN POSTMENOPAUSAL WOMEN

Objective: The aim of this study was to investigate the relationship between obesity and cardiovascular risk in postmenopausal women. Methods: The study included 43 postmenopausal women who were admitted to Dicle University Medical School Family Medicine Outpatient Clinic in 2011. Body mass index (BMI), waist and hip circumferences, body fat mass and percentage, 24-hour arterial tension measurements, homocysteine (Hcy) level, vitamin B12 level, and Framingham risk score (FRS) were compared between obese (BMI>30 kg/m²) and non-obese (BMI≤30 kg/m²) patients. Results: The patients included 24 (55.8%) obese and 19 (44.2%) non-obese patients. Abdominal obesity was seen in 29 (67.4%) and truncal obesity in 33 (76.7%) participants. Family history of cardiac disease was seen in 25 (58.1%) women, most of whom were obese. Based on FRS, moderate cardiovascular risk was assessed in 21.6%, but none of the participants were at high risk. FRS was higher in patients with Hcy levels of ≥15 µmol/L. In addition, the Hcy values increased with age and there was a negative correlation between Hcy levels and vitamin B12 status (r=-0.322, p=0.035). Conclusion: The results of this study indicate that age- and gender-dependent differences must be taken into consideration for cardiovascular risk assessments and postmenopausal women should be informed about obesity and hypertension in order to improve their quality of life. Ozet Amac: Bu calismada postmenopozal kadinlarda obesite ve kardiyovaskuler risk iliskisinin incelenmesi amaclanmistir. Gerec-Yontem: calismaya Dicle Universitesi Tip Fakultesi Aile Hekimligi Poliklinigine 2011 yilinda basvuran 43 postmenopozal kadin dahil edilmistir. Beden kitle indeksi (BKI), bel ve kalca cevresi, vucut yag kitle ve yuzdesi, 24-saatlik kan basinci olcumleri, homosistein (Hcy) duzeyi, vitamin B12 duzeyi ve Framingham risk skorlamasi (FRS) obez olan (BKI>30 kg/m²) ve obez olmayan (BKI≤30 kg/m²) kadinlarda olculerek karsilastirilmistir. Bulgular: Kadinlarin 24’u (%55.8) obez ve 19’u (%44.2) non-obezdi. Abdominal obezite 29 (%67.4), trunkal obezite 33 (%76.7) hastada mevcuttu. Kardiyak hastalik aile oykusu, cogu obez olan 25 (%58.1) kadinda tespit edildi. FRS’ye gore orta duzeyde kardiyovaskuler risk %21.6 oraninda saptanirken hicbir kadin yuksek risk grubunda degildi. FRS, Hcy seviyesi ≥15 µmol/L olan kadinlarda daha yuksekti. Hcy seviyesi yasla artmaktaydi ve Hcy ile vitamin B12 arasinda negative korelasyon mevcuttu (r=-0.322, p=0.035). Sonuc: Bu calisma kardiyovaskuler risk degerlendirmesine yas ve cinsiyete bagli farkliliklarin goz onunde bulundurulmasi ve postmenopozal kadinlarin, yasam kalitelerini arttirmak icin obezite ve hipertansiyon konusunda bilgilendirilmeleri gerektigini gostermektedir. Anahtar Kelimeler: Obezite, Homosistein, Vitamin B12, Kan basinci Monitorizasyonu, Framingham Risk Skorlamasi.
Relationship of Early Vitamin D Concentrations and Gestational Diabetes Mellitus in Indian Pregnant Women

Background: A high prevalence of vitamin D deficiency exists in pregnant Indian women (~90%). Increasing evidence suggests that vitamin D could play a pivotal role in maintaining normal glucose homeostasis. We aimed to determine the association between maternal vitamin D concentrations in early pregnancy and the risk of gestational diabetes mellitus (GDM). Methods: A prospective observational study was conducted on healthy pregnant women (n = 392) attending routine antenatal care at St. John's Medical College Hospital, Bangalore recruited at ~12 weeks of gestation. At baseline, details on socio-economic status, obstetric history, dietary intakes, and anthropometry were collected. Venous plasma total vitamin D concentration was assessed using tandem liquid chromatography mass spectrophotometry (LC-MS/MS). Oral glucose tolerance test (OGTT) at recruitment, followed by glucose tolerance test (GTT) at mid-pregnancy was conducted. GDM was diagnosed and confirmed using the International Association of Diabetes and Pregnancy Study Groups (IADPSG) classification. Univariate and adjusted logistic regression models were used to evaluate the associations between total vitamin D concentrations at enrollment with GDM. Results: Of the cohort, 10.2% were diagnosed as GDM. Women with GDM were older (26 vs. 24 years) and heavier (51.6 vs. 51.2 kg) compared to the rest. A higher prevalence of GDM was observed among women with 1st trimester plasma total vitamin D in the lowest quartile (≤23.6 nmol/L) compared to the subjects in the other three quartiles (16.1 vs. 8.6%, p = 0.033). Adjusted multivariable regression analysis showed that women in the lowest quartile of plasma total vitamin D had twice the odds of GDM compared to women belonging to the remaining quartiles [OR = 2.32 (95%CI: 1.10, 4.91), p = 0.028]. Conclusions: Low plasma total vitamin D concentrations in early pregnancy may be associated with a higher risk of GDM.
UČINKOVITOST I PODNOŠLJIVOST KEMOTERAPIJE PO TC PROTOKOLU VEĆE GUSTOĆE U PRVOLINIJSKOM LIJEČENJU KARCINOMA JAJNIKA

Goals: Our goal was to investigate efficacy of TC dose dense chemotherapy on Croatian population of ovarian carcinoma patients in everyday clinical practice at University Hospital of Split. Methods: A retrospective cohort study was used on consecutive laboratory and clinical samples of 74 patients treated with the conventional 3-weekly TC protocol (2008-2011) and on 70 patients treated with TC dose dense protocol (2012-2016). Only those patients who recieved at least four chemotherapy cycles were included. The primary endpoint of this study was overall survival (OS). Secondary endpoints were progression free survival (PFS) and toxicity. Results: After adjustment for pre-planned clinical and sociodemographic factors, patients treated with dose dense protocol showed a significantly lower hazard for dying from any cause, than patients treated with conventional protocol (HR=0.50; 95% CI 0.25-0.97; P=0.040). Median OS, at 60 months follow-up had not been reached in the dose-dense group, while in the standard treatment group was 48 months (95% CI 33-62). Unadjusted PFS was significantly longer in the dose dense group (HR=0.58; 95% CI 0.38-0.88; P=0.011), but not after the adjustment (P=0.096). Generally, the level of toxicity was similar in both groups of patients. The need for blood transfusions andusage of filgrastim was significantly higher in the TC dose dense group (p=0,010 i p<0,001). The incidence of neutropenia and thrombocytopenia grade 3 or 4 were not significanly different in both regimens. Conclusions: Our retrospective study has shown the superior efficacy and comparable toxicity of dose dense chemotherapy regimen over the conventional regimen in treatment of ovarian cancer on Caucasian population at a single-institution.
Medical Cannabis Treatment in Patients with Trigeminal Neuralgia (P5.10-020)

Objective: The purpose of this study is to assess medical cannabis’ (MC) efficacy, reported adverse effects (AE), and dosages used in the treatment of patients with trigeminal neuralgia (TN). Background: Few treatments currently exist for the treatment of TN, and almost half of patients become refractory to those medications with limited and often invasive recommendations for symptom control. A growing amount of evidence suggests that MC may be effective in alleviating neuropathic pain, making MC a potentially valuable treatment option for TN. Design/Methods: A retrospective chart review of patients with TN that were treated with MC through the New York State Medical Marijuana Program was conducted in Buffalo, New York. Results: Of 42 patients (32=female 10=male) included in the study, 81% reported improvement in their TN symptoms. AE were reported in 40%, and two discontinued because of AE. The most common side effects were fatigue, somnolence, nausea, and dizziness. Of patients who reported ≥50% improvement in TN symptoms, 69% used one product and 50% used a 1:1 ratio of tetrahydrocannabinol to cannabidiol. Of patients reporting opioid use at the beginning of MC treatment, 50% were able to reduce their opioid consumption on MC. Conclusions: This study found that MC is well tolerated in the treatment of TN, with the 81% of patients reporting improvement and 50% of patients reducing opioid consumption with MC. The most common efficacious dosage for these patients is a 1:1 ratio of THC to CBD. These results suggest that MC is a useful part of a comprehensive pain management plan for patients with TN, but future randomized placebo controlled trials are needed. Disclosure: Dr. Mechtler has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Teva, Promius, Allergan, Avanir, andAmgen. Dr. Mechtler has received research support from DENT Family Foundation. Dr. Hart has received research support from Dent Family Foundation . Dr. Bargnes has nothing to disclose. Dr. Saikali has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Allergan, Amgen, Promius, Supernus, Teva, Assertio, Avanir, Cefaly, Egalet, Gammacore, and Pernix.
Detecção de anomalias em telecomunicações através de um sistema baseado em conhecimento que utiliza consulta por similaridade, DWT e RDR como ferramentas de apoio

Telephony fraud is a world phenomenon. Current estimates show losses of from 15 to 55 billion dollars a year in the telephony industry, which runs businesses around 1.5 trillion dollars [42]. Fraud detection is a critical point for these companies. Fraud risks force companies to make a great e ort to analyze clients' calls tra c. This paper proposes a Knowledge System (KS) that performs similarity search in time series with the purpose of detecting anomalies, in advance, in the clients' call tra c. Since there is a great amount of data (call) to be analyzed, the system uses Haar Wavelets as a dimensionality reduction technique to improve the e ciency of the searches. For the purpose of indicating the actions that should be taken as a result of similarity searches, a knowledge system based on production systems and on case based reasoning system is utilized. This hybrid system is called Ripple Down Rules (RDR). The system proposed represents a technically e cient and innovating alternative for detecting anomalies in telephony systems.
Cardiac evaluation of patients with chronic obstructive pulmonary disease using echocardiography

Chronic obstructive pulmonary disease (COPD) is a significant cause of death. Cardiovascular disease is a significant cause of morbidity and mortality in COPD. We used echocardiography to evaluate cardiac function in patients with COPD and correlated echocardiographic findings with COPD severity. We performed a prospective cross-sectional study on 60 patients with stable COPD who presented to the Abbasia Chest Hospital during the period from November 2016 till August 2017. Spirometry was performed for all participants using American and European Thoracic Society (2005) recommendations. They were classified according to GOLD guidelines (2017) and evaluated by two-dimensional Doppler echocardiography according to American and European Association of Echocardiography ASE recommendations. Echocardiographic examination of left ventricular functions revealed no cases of left ventricular systolic dysfunction, but left ventricular diastolic dysfunction was found in ~25%. Right ventricle dilatation was found in ~18% of the patients. Tricuspid regurge was seen in ~75%, with variable grades from mild to severe. Pulmonary hypertension (PH) was found in ~40% of the patients. It was more prevalent in patients with severe and very severe disease. Correlation between echocardiographic findings and severity of COPD revealed significant positive correlation only with right ventricle size, tricuspid regurgitation, and PH. Left ventricular diastolic dysfunction appears to be frequent in patients with COPD, but it is not related to the disease severity. Abnormal right heart changes could be expected. Presence of PH has a linear relationship with COPD severity.
Charge-Recycling-Based Redundant Write Prevention Technique for Low-Power SOT-MRAM

In spin–orbit torque magnetic random access memory (SOT-MRAM), as write energy is much larger than read energy, writing data to memory only when the new data is different from the stored data can lead to considerable write energy savings. In this paper, we propose three low-power techniques that can significantly reduce write energy in the read–compare–write process. In the proposed approaches, redundant charge in read operation has been efficiently reused to generate a negative voltage for write assistance, which leads to short write time. A selective precharging technique is also proposed to minimize the voltage swings between read and write operations. In addition, asymmetric write current due to source degeneration of write transistor can be resolved with the write voltage suppression scheme. Our circuit simulations with 65-nm CMOS technology show that when the stored data and new data are the same, up to 61% of write energy savings have been achieved compared with the conventional 2T-1MTJ cell. When the proposed SOT-MRAM is used as L3 caches of X86 processor, the gem5 simulations also show that average 48.2% of write energy savings can be achieved in various workloads of SPEC2006.
Comparison of first-line eradication therapy protocols for Helicobacter pylori in regions with high clarithromycin resistance

Aim: First-line eradication therapy protocols for Helicobacter pylori infection and their success rates still prove to be matter of interest for researchers. The aim of this study was to examine retrospectively eradication therapy protocols used in patients infected with H. pylori in our region with high resistance to clarithromycin, compare success rates and determine the factors affecting success rates. Material and Methods: Eradication therapies for dyspeptic patients who were found to be Helicobacter pylori positive as revealed by upper gastrointestinal endoscopy and biopsy results and success rates attained in the microscopic examination of stool in the 4th week after the therapy were analyzed. Group 1 (legacy triple therapy): clarithromycin 500 mg film-coated tablet 2x1, lansoprazole 30 mg capsule 2x1, amoxicillin 1000 mg tablet 2x1, 14-day therapy period; Group 2 (bismuth-free quadruple therapy): clarithromycin 500 mg film tablet 2x1, rabeprazole 20 mg tablet 2*1, amoxicillin 1000 mg tablet 2x1, metronidazole 500mg 2x1 tablet, 14-day therapy period; Group 3 (bismuthal quadruple therapy): bismuth subsalicylate 262 mg tablet 2x2, metronidazole 500 mg tablet 3x1, tetracycline 500 mg capsule 3x1, pantoprazole 40mg tablet 2x1, 10-day therapy period. Results: Data of 168 patients were analyzed. The patients were divided into Group 1 (classical therapy) with 80 patients, Group 2 (bismuth-free quadruple therapy) with 46 patients and Group 3 (bismuthal quadruple therapy) with 42 patients. Eradication success rates were as follows: Group 1 (80%), Group 2 (80.4%) and Group 3 (83.3%). Conclusion: Antibiotic resistance is the sole reason for the low success rate in eradication therapy for Helicobacter pylori. In regions with high clarithromycin resistance bismuth-free quadruple therapy can be employed as an alternative. In regions with metronidazole resistance in addition to clarithromycin resistance bismuthal therapy protocols can be employed.