LLukas22/scidocs huggingface dataset:
Matroids and Graphs with Few Non-Essential Elements

An essential element of a 3–connected matroid M is one for which neither the deletion nor the contraction is 3–connected. Tutte’s Wheels and Whirls Theorem proves that the only 3–connected matroids in which every element is essential are the wheels and whirls. In an earlier paper, the authors showed that a 3–connected matroid with at least one non-essential element has at least two such elements. This paper completely determines all 3–connected matroids with exactly two non-essential elements. Furthermore, it is proved that every 3–connected matroid M for which no single-element contraction is 3–connected can be constructed from a similar such matroid whose rank equals the rank in M of the set of elements e for which the deletion M\e is
Yoga Nidra as a complementary treatment of anxiety and depressive symptoms in patients with menstrual disorder

BACKGROUND
Emotional insecurity, stress, depressive or/and anxiety symptoms are common with variable severity among patients with menstrual disorder. Yogic relaxation therapy (Yoga Nidra) leads to conscious and subconscious recognition of these underlying psychological factors and helps releasing of suppressed conflicts.


OBJECTIVE
To evaluate the effect of Yoga Nidra on anxiety and depressive symptoms in patients with menstrual disorders.


MATERIALS AND METHODS
Subjects were recruited from the Department of Obstetrics and Gynecology, C.S.M. Medical University (erstwhile KGMU), Lucknow Uttar Pradesh, India. The subjects were randomly divided in to two groups: Intervention group (with yogic intervention) and control group (without yogic intervention). Assessments of all subjects were carried out by administering Hamilton anxiety scale (HAM-A) and Hamilton rating scale for depression (HAM-D) at baseline and after six months.


RESULTS
The mean age with S.D of the intervention group was 27.67 ± 7.85 years, and for control group was 26.58 ± 6.87 years (among completed intervention group nn = 65 and control group nn = 61). There was significant reduction of scores in HAM-A (P<0.003) and HAM-D (P<0.02) respectively in subjects with mild to moderate anxiety and depressive symptoms after six months of yoga therapy (Yoga Nidra) in intervention group in comparison to control group.


CONCLUSION
The patients with mild to moderate anxiety and depressive symptoms improve significantly with 'Yoga Nidra' intervention. There is no significant improvement in the patients with severe anxiety and depressive symptoms.
Antioxidant Capacities and Phenolic Levels of Different Varieties of Serbian White Wines 

The biologically active compounds in wine, especially phenolics, are responsible for reduced risk of developing chronic diseases (cardiovascular disease, cancer, diabetes, etc.), due to their antioxidant activities. We determined the contents of total phenolics (TP) and total flavonoids (TF) in selected Serbian white wines by colorimetric methods. Total antioxidant activity (TAA) of the white wines was analyzed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity assay. Medas beli had the highest content of TP, TF and TAA. The radical scavenging capacity (RSC) and total antioxidant activity (TAA) of white wines were 15.30% and 1.055 mM Trolox equivalent, respectively. Total phenolic (TP) and total flavonoid (TF) contents in white wines ranged from 238.3 to 420.6 mg gallic acid equivalent per L of wines and 42.64 to 81.32 mg catechin equivalent per L of wines, respectively. A high and significant correlation between antioxidant activity and total phenolic content was determined in wines (R2 = 0.968, p < 0.01). For the individual polyphenols determination we used a high performance liquid chromatography (HPLC)-diode array detection (DAD) technique. The majority of white wine polyphenols was represent by four hydroxycinnamic acids (HCAs).
[Validity and reliability of a clinical performance examination using standardized patients].

PURPOSE
The purpose of this study was to test the validity of a modified clinical performance examination (CPX) for preclinical students in nursing.


METHOD
70 nursing students in their second semester of the junior year at C University participated in CPX. Scenarios and checklists were developed by our research team from September to October 2005. Six stations were organized. Evaluation included physical examination of a patient with lung cancer, education on usage of a metered dosage inhaler, and lobectomy postoperative care. Students were randomly assigned to a station.


RESULT
There was a difference in the CPX scores according to stations. The agreement of scoring between trained faculty members and SPs was more than moderate (r=.647). The correlation between the CPX score and the average grade in the previous semester and between the CPX score and the average grade of a paper and pen test of the pulmonary system of adults was low (r=.276; r=.048).


CONCLUSION
Traditional CPX is generally recommended, however, modified CPX is appropriate for preclinical students in the current Korean Nursing school setting if there are additional scoring systems to balance the testing level at each station.
The imperative of the Sistrunk operation: review of 160 thyroglossal tract remnant operations.

OBJECTIVE
Analysis of the pre- and postoperative features, long-term follow-up, and complications.


STUDY DESIGN AND METHODS
Case series with chart review of 160 thyroglossal tract remnant excisions over a 20-year period (1988-2007).


RESULTS
The mean age of diagnosis was 10.9 +/- 14.2 years with 63.8 percent male predominance. There was a prior history of thyroglossal tract remnant infections in 70 percent of patients, and 30 percent presented with cutaneous fistulas. The majority had ultrasound imaging that identified cysts mainly (66.7%) in the infrahyoid region. Preoperative fine-needle aspirations in 18 patients were benign. On pathological reevaluation, 26.5 percent had thyroid tissue inside the remnant, with one case of papillary carcinoma. After the Sistrunk operation, postoperative complications occurred in 7.5 percent including a 1.9 percent recurrence rate.


CONCLUSIONS
All age groups had similar clinical presentations and outcomes. Ultrasound is a reliable and appropriate imaging modality for most patients. Surgery must effectively incorporate the tract and cyst to allow low recurrence rates (<2%). Clinically diagnosed thyroglossal tract remnant may, in fact, be other pathologies in 10 percent of cases. Nevertheless, our recommendation is a Sistrunk procedure for all midline neck lesions suspected to be a thyroglossal tract remnant.
Evaluating software refactoring tool support

Up to 75% of the costs associated with the development of software systems occur post-deployment during maintenance and evolution. Software refactoring is a process that can significantly reduce the costs associated with software evolution. Refactoring is defined as internal modification of source code to improve system quality, without change to observable behaviour. Tool support for software refactoring attempts to further reduce evolution costs by automating manual, error-prone and tedious tasks. Although the process of refactoring is well-defined, tools supporting refactoring do not support the full process. Existing tools suffer from issues associated with the level of automation, the stages of the refactoring process supported or automated, the subset of refactorings that can be applied, and complexity of the supported refactorings. This paper presents a framework for evaluating software refactoring tool support based on the DESMET method. For the DESMET application, a functional analysis of the requirements for supporting software refactoring is used in conjunction with a case study. This evaluation was completed to assess the support provided by six Java refactoring tools and to evaluate the efficacy of using the DESMET method for evaluating refactoring tools.
A New Feature Selection Scheme Using a Data Distribution Factor for Unsupervised Nominal Data

A new efficient unsupervised feature selection method is proposed to handle nominal data without data transformation. The proposed feature selection method introduces a new data distribution factor to select appropriate clusters. The proposed method combines the compactness and separation together with a newly introduced concept of singleton item. This new feature selection method considers all features globally. It is computationally inexpensive and able to deliver very promising results. Eight datasets from the University of California Irvine (UCI) machine learning repository and a high-dimensional cDNA dataset are used in this paper. The obtained results show that the proposed method is very efficient and able to deliver very reliable results.
What does GRI‐reporting tell us about corporate sustainability?

Originality/value We critically analyze the most common framework for sustainability reporting on an empirical basis. Our qualitative study delivers insights into sustainability reporting in an industry with large impacts on global climate change and living conditions. Purpose We discuss what the business contribution to sustainable development is (or should be) and propose criteria for assessing corporate sustainability. These criteria are applied for the analysis of GRI-reports of five major cement manufacturers. This will result in a discussion if GRI-based sustainability reports really contain the information needed for judging corporate sustainability. Methodology/approach Starting from a literature review of common definitions and principals we develop main criteria of corporate sustainability and propose a set of evaluation criteria for analyzing sustainability reports. We consider definitions and principals from concepts such as Ecoefficiency, Triple-Bottom-Line, The Natural Step and stakeholder value. Using these criteria we analyze the GRI-based sustainability reports of five major cement manufacturers in order to find out to what extent the reports really address the sustainability performance of the companies. We chose the companies because of their dominant position in the building material supply chain. The building industry has multiple impacts on the environment as well as on the social system. The decisions and actions of the cement manufacturers have influence on the entire supply chain, from raw material suppliers to the end customer. Findings Our findings lead to the conclusion that the current GRI guidelines are not sufficient to make sustainability reporting for the cement industry relevant and clear. In other words, the guidelines are not sufficient for assuring that a report answers the questions of how sustainable a company is and how quickly it is approaching sustainability. Within the GRI guidelines the needs of the customers are not considered sufficiently. This points at an important area where business excellence ideas can support sustainability reporting. This could be done, for instance, by including the concept of cost of poor quality into sustainability reporting guidelines. 11th QMOD Conference. Quality Management and Organizational Development Attaining Sustainability From Organizational Excellence to Sustainable Excellence; 20-22 August; 2008 in Helsingborg; Sweden 61
Metamorphic Malware Detection Using Statistical Analysis

49  Abstract— Typically, computer viruses and other malware are detected by searching for a string of bits found in the virus or malware. Such a string can be viewed as a " fingerprint " of the virus identified as the signature of the virus. The technique of detecting viruses using signatures is known as signature based detection. Today, virus writers often camouflage their viruses by using code obfuscation techniques in an effort to defeat signature-based detection schemes. So-called metamorphic viruses transform their code as they propagate, thus evading detection by static signature-based virus scanners, while keeping their functionality but differing in internal structure. Many dynamic analysis based detection have been proposed to detect metamorphic viruses but dynamic analysis technique have limitations like difficult to learn normal behavior, high run time overhead and high false positive rate compare to static detection technique. A similarity measure method has been successfully applied in the field of document classification problem. We want to apply similarity measures methods on static feature, API calls of executable to classify it as malware or benign. In this paper we present limitations of signature based detection for detecting metamorphic viruses. We focus on statically analyzing an executable to extract API calls and count the frequency this API calls to generate the feature set. These feature set is used to classify unknown executable as malware or benign by applying various similarity function. I. INTRODUCTION In today's age, where a majority of the transactions involving sensitive information access happen on computers and over the internet, it is absolutely imperative to treat information security as a concern of paramount importance. Computer viruses and other malware have been in existence from the very early days of the personal computer and continue to pose a threat to home and enterprise users alike. A computer virus by definition is " A program that recursively and explicitly copies a possibly evolved version of itself " [1]. A virus copies itself to a host file or system area. Once it gets control, it multiplies itself to form newer generations. A virus may carry out damaging activities on the host machine such as corrupting or erasing files, overwriting the whole hard disk, or crashing the computer. These viruses remain harmless but
Learner Awareness of Recasts in Classroom Interaction: A Case Study of an Adult EFL Student's Second Language Learning

This case study is about an adult Japanese learner’s second language learning through her teacher’s recasts in an EFL classroom in Japan. We first examined by videotaping the class how recasts were provided in the theme-based EFL classroom. Then we investigated the relationship between the student’s awarenessof recast feedback and her L2 learning. Stimulated recallinterviews were used to elicitthe student’s awarenessof the feedback, and grammaticality judgement tests of the actual sentences uttered by the teacher and students in the classroom were used for measuring learning. The findings suggest that recasting is a complex verbal behaviour influenced by the teaching environment, the interaction context, and the learner’s cognitive orientation. The effect of recasts is influenced not only by the linguistic elements (e.g. grammatical vs lexical) of the feedback,but also by paralinguisticelements and the learner’s autonomous utilisation of the learning opportunities provided by the feedback.
User Identity for WebRTC Services: A Matter of Trust

Secured interpersonal communications should come with asserted user identities and trust between the involved parties. No single trust model exists in Web Real-Time Communications services, so neither is there a single identity architecture. The authors discuss different models for provisioning user identity and their impact on user privacy.
Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data

OBJECTIVE
To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures.


METHODS
A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data.


RESULTS
Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures.


CONCLUSIONS
We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags.


SIGNIFICANCE
This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time.
Potential self-regulatory mechanisms of yoga for psychological health

Research suggesting the beneficial effects of yoga on myriad aspects of psychological health has proliferated in recent years, yet there is currently no overarching framework by which to understand yoga's potential beneficial effects. Here we provide a theoretical framework and systems-based network model of yoga that focuses on integration of top-down and bottom-up forms of self-regulation. We begin by contextualizing yoga in historical and contemporary settings, and then detail how specific components of yoga practice may affect cognitive, emotional, behavioral, and autonomic output under stress through an emphasis on interoception and bottom-up input, resulting in physical and psychological health. The model describes yoga practice as a comprehensive skillset of synergistic process tools that facilitate bidirectional feedback and integration between high- and low-level brain networks, and afferent and re-afferent input from interoceptive processes (somatosensory, viscerosensory, chemosensory). From a predictive coding perspective we propose a shift to perceptual inference for stress modulation and optimal self-regulation. We describe how the processes that sub-serve self-regulation become more automatized and efficient over time and practice, requiring less effort to initiate when necessary and terminate more rapidly when no longer needed. To support our proposed model, we present the available evidence for yoga affecting self-regulatory pathways, integrating existing constructs from behavior theory and cognitive neuroscience with emerging yoga and meditation research. This paper is intended to guide future basic and clinical research, specifically targeting areas of development in the treatment of stress-mediated psychological disorders.
Homogenization of material properties in additively manufactured structures

Additive manufacturing transforms material into three-dimensional parts incrementally, layer by layer or path by path. Subject to the build direction and machine resolution, an additively manufactured part deviates from its design model in terms of both geometry and mechanical performance. In particular, the material inside the fabricated part often exhibits spatially varying material distribution (heterogeneity) and direction dependent behavior (anisotropy), indicating that the design model is no longer a suitable surrogate to consistently estimate the mechanical performance of the printed component. We propose a new two-stage approach tomodeling and estimating effective elastic properties of parts fabricated by fused deposition modeling (FDM) process. First, we construct an implicit representation of an effective mesoscale geometry–material model of the printed structure that captures the details of the particular process and published material information. This representation of mesoscale geometry and material of the printed structure is then homogenized at macro scale through a solution of an integral equation formulated using Green’s function. We show that the integral equation can be converted into a system of linear equations that is symmetric and positive definite and can be solved efficiently using conjugate gradient method and Fourier transform. The computed homogenized properties are validated by both finite element method and experiment results. The proposed two-stage approach can be used to estimate other effective material properties in a variety of additive manufacturing processes, whenever a similar effective mesoscale geometry–material model can be constructed. © 2016 Elsevier Ltd. All rights reserved.
Episodic antiretroviral therapy increases HIV transmission risk compared with continuous therapy: results of a randomized controlled trial.

OBJECTIVE
To compare the HIV transmission risk among patients randomized to episodic versus continuous antiretroviral therapy.


DESIGN
This was a substudy of the Strategies of Management of Antiretroviral Therapy study, in which patients were randomized to continuous versus CD4-guided episodic antiretroviral therapy. Participants were surveyed about sexual activity and needle sharing and had laboratory testing for gonorrhea, chlamydia, and syphilis.


RESULTS
A total of 883 patients were enrolled in this study, the mean age of the patients was 45 years, 25% were women, and 78% were on antiretroviral therapy. At baseline, 136 participants (15.4%) had high-risk behavior (vaginal or anal sex without a condom, needle sharing, or incident bacterial sexually transmitted infection). After randomization, the proportion of participants reporting high-risk behavior was stable and did not differ by randomized arm (P = 0.39). Among participants off therapy at baseline, high-risk behavior was less common 4 months after randomization among those who were randomized to start antiretroviral therapy (P = 0.03). HIV transmission risk (high-risk behavior while HIV RNA level >1500 copies/mL) with partners perceived to be HIV uninfected was higher in the episodic therapy arm (P = 0.02).


CONCLUSIONS
Patients on episodic antiretroviral therapy did not decrease high-risk behavior, and because HIV RNA levels were higher, this strategy may result in increased HIV transmission.
ABNORMALITY DETECTION IN LUMBAR DISCS FROM CLINICAL MR IMAG ES WITH A PROBABILISTIC MODEL

Purpose: Detection of abnormal discs from clinical T2weighted MR scans. This aids the radiologist as well as subsequent CAD methods in focusing only on abnormal discs for further diagnosis. Furthermore, it gives a degree of con fidence about the abnormality of the intervertebral discs th at helps the radiologist in making his decision. Material and Methods: We propose a probabilistic model for detection of abnormality of intervertebral discs . We use three features to label abnormal discs that includes appearance, location, and context. We model the abnormal disc appearance with a Gaussian model, the location with a 2D Gaussian model, and the context with a Gaussian model for the distance between abnormal discs. We use clinical T2-weighted MR volume for each case and inference on the middle slide of each volume. These MR scans are specific for the lumbar area. The ground truth is provided by our collaborating radiologist. Results: We achieve over 91% abnormality detection accuracy in a cross-validation experiment with 80 clinical cases. The experiment runs ten rounds, in every round 30 cases are randomly left out for testing and the rest are used for training. Conclusion: We achieve high accuracy for detection of abnormal discs using our proposed model that incorporates disc appearance, location, and context. We show that our proposed model is extensible for subsequent diagnosis task specific to each intervertebral disc abnormality such as des iccation, stenosis, and herniation.
Self-Belief Change Despite the Desire to Remain the Same

University of Minnesota Ph.D. dissertation. March 2009. Major: Psychology. Advisors: Alexander J. Rothman, Marti Hope Gonzales. 1 computer file (PDF); vi, 124 pages.