Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic Daily Headache in Korea: Prevalence, Clinical Characteristics, Medical Consultation and Management

Background and Purpose Chronic daily headache (CDH) is a commonly reported reason for visiting hospital neurology departments, but its prevalence, clinical characteristics, and management have not been well documented in Korea. The objective of this study was to characterize the 1-year prevalence, clinical characteristics, medical consultations, and treatment for CDH in Korea. Methods The Korean Headache Survey (KHS) is a nationwide descriptive survey of 1507 Korean adults aged between 19 and 69 years. The KHS investigated headache characteristics, sociodemographics, and headache-related disability using a structured interview. We used the KHS data for this study. Results The 1-year prevalence of CDH was 1.8% (95% confidence interval, 1.1-2.5%), and 25.7% of the subjects with CDH met the criteria for medication overuse. Two-thirds (66.7%) of CDH subjects were classified as having chronic migraine, and approximately half of the CDH subjects (48.1%) reported that their headaches either substantially or severely affected their quality of life. Less than half (40.7%) of the subjects with CDH reported having consulted a doctor for their headaches and 40.7% had not received treatment for their headaches during the previous year. Conclusions The prevalence of CDH was 1.8% and medication overuse was associated with one-quarter of CDH cases in Korea. Many subjects with CDH do not seek medical consultation and do not receive appropriate treatment for their headaches.
Blessing or curse? Revisiting security aspects of Software-Defined Networking

Software-Defined Networking (SDN) is an emerging technology, physically separating data and control planes of network devices. From a security point of view SDN has two sides. First, it enables network security functions by design, because traffic flows can be redirected or filtered based on packet content or application layer state - functionality, which to date requires additional network security devices like fire-walls, intrusion detection systems or spam filters in conventional networks. On the other hand, due to physical separation of planes, SDN possibly offers additional attack vectors compared to traditional network architectures, which may severely impact overall network availability as well as confidentiality, authenticity, integrity and consistency of network traffic and control data. In this paper, we discuss and balance security provided by SDN with security threats of SDN also in respect of traditional networks. We develop an evaluation methodology for both sides and show that from a security point of view SDN is a blessing for today's and future network design and operation.
VPR 5.0: FPGA CAD and architecture exploration tools with single-driver routing, heterogeneity and process scaling

The VPR toolset has been widely used in FPGA architecture and CAD research, but has not evolved over the past decade. This article describes and illustrates the use of a new version of the toolset that includes four new features: first, it supports a broad range of single-driver routing architectures, which have superior architectural and electrical properties over the prior multidriver approach (and which is now employed in the majority of FPGAs sold). Second, it can now model, for placement and routing a heterogeneous selection of hard logic blocks. This is a key (but not final) step toward the incluion of blocks such as memory and multipliers. Third, we provide optimized electrical models for a wide range of architectures in different process technologies, including a range of area-delay trade-offs for each single architecture. Finally, to maintain robustness and support future development the release includes a set of regression tests for the software. To illustrate the use of the new features, we explore several architectural issues: the FPGA area efficiency versus logic block granularity, the effect of single-driver routing, and a simple use of the heterogeneity to explore the impact of hard multipliers on wiring track count.
Overshadow: a virtualization-based approach to retrofitting protection in commodity operating systems

Commodity operating systems entrusted with securing sensitive data are remarkably large and complex, and consequently, frequently prone to compromise. To address this limitation, we introduce a virtual-machine-based system called Overshadow that protects the privacy and integrity of application data, even in the event of a total OScompromise. Overshadow presents an application with a normal view of its resources, but the OS with an encrypted view. This allows the operating system to carry out the complex task of managing an application's resources, without allowing it to read or modify them. Thus, Overshadow offers a last line of defense for application data. Overshadow builds on multi-shadowing, a novel mechanism that presents different views of "physical" memory, depending on the context performing the access. This primitive offers an additional dimension of protection beyond the hierarchical protection domains implemented by traditional operating systems and processor architectures. We present the design and implementation of Overshadow and show how its new protection semantics can be integrated with existing systems. Our design has been fully implemented and used to protect a wide range of unmodified legacy applications running on an unmodified Linux operating system. We evaluate the performance of our implementation, demonstrating that this approach is practical.
Evaluation of Musical Feature Extraction Tools using Perceptual Ratings.

The increasing availability of digital music has created a demand for organizing and retrieving the music. Thus, a new multi-disciplinary research area called music information retrieval, MIR, has emerged. An important part of the content-based field of the research area is to extract musical features, such as tempo or modality, directly from the content, i.e. the audio.This thesis is an evaluation of available musical feature extraction tools. The evaluation is done by using the extracted musical features as predictors for perceptual ratings in correlation and regression analyses. The 11 perceptual ratings were gathered from a listening test. 22 musical audio features were extracted from the same stimuli, using different systems for musical feature extraction. These were chosen to predict some of the perceptual ratings based on findings in literature.High inter-subject reliability in the listening test implied a high agreement among the subjects, indicating that 20 subjects were enough. Fairly low inter-correlations between the ratings indicated that they were rated independently from each other. Six out of seven perceptual ratings with a priori selected predictors correlated moderately with their corresponding predictor (r>0.6). The results from the stepwise regression analyses were also moderate, where the amount of variance explained by the predictors ranged from 29-76%, indicating that there is room for improvements for developing new feature extraction algorithms.
PSG hybrid approach to automatic corpus annotation

This paper describes and evaluates a hybrid non-probabilistic parsing method for the grammatical annotation of large corpora and the live analysis of teaching sentences, employing a layered scheme of lexiconand contextbased Constraint Grammars on the one hand, and Phrase Structure Grammars or syntactic bracketing algorithms on the other. The method has been fully implemented by the author for Danish and Portuguese, and to a certain degree, Spanish. Add-on-modules were also produced for existing English and French taggers. On running newspaper text, overall correctness rates (F-scores) for the two most mature systems approach 99% for word class (PoS) and 95-96% for syntactic function tags at the shallow CG-level. Though propagating CG-errors into structural errors, subsequent constituent tree analysis adds under 1% of new attachment errors on manually revised CG-input. All modules in combination, without revision, generate 50-75% structurally ”legal” syntactic trees.
Test-Quality Optimization for Variable  $n$ -Detections of Transition Faults

Aggressive technology scaling in modern chips resulted in complicated faulty timing behaviors, which necessitate undesirable long development cycle and high test volumes to ensure product quality. To reduce the test time, cost-effective and timing-efficient test selection algorithms are used to choose optimal test inputs from a large-volume test set. In this paper, we define an approximate longest sensitized path (ALSP) metric to derive the longest sensitized path for all transition faults (TFs) from the detectability of TFs with very low computational complexity. With the ALSP metric, a general public utilities-based parallel test selection method is proposed to choose a small test set with high delay test quality from the timing-unaware n-detection test set. Our results demonstrate the comparison with a commercial automatic test pattern generation tool and a previous timing-aware test selection method targeting small delay defects, and confirm that our test selection algorithm can achieve better delay test coverage and higher n -detection fault coverage with steeper fault coverage curves of ordered patterns, for the same pattern count.
Overnight Luteinizing and Follicle Stimulating Hormone Profiles During GnRHa Treatment in Short Girls Born Small for Gestational Age

BACKGROUND Since puberty starting at a height less than 140 cm might reduce adult height, postponement of puberty was studied in short pubertal girls born SGA. Data on overnight LH and FSH profiles during GnRHa treatment are very limited. AIMS To evaluate whether 3 months of GnRHa treatment results in sufficient suppression of pubertal LH and FSH profile patterns. To evaluate whether girls show sufficient pubertal suppression according to a consensus-based peak LH cut-off level of 3 IU/l during a GnRH agonist test. PARTICIPANTS Twenty-one short pubertal girls born SGA. INTERVENTION After baseline LH and FSH profiles, children received leuprorelide acetate depots of 3.75 mg subcutaneously, every 4 weeks. RESULTS At baseline, amplitude and frequency of LH and FSH pulsatility were higher in girls with breast stage 3, compared to girls with breast stage 2. After 3 months of GnRHa treatment, all girls showed clinical arrest of puberty and their LH and FSH levels during overnight profiles had significantly decreased to prepubertal levels. In contrast, peak LH during the GnRH agonist test indicated insufficient pubertal suppression in 33% of girls. No differences in LH and FSH profiles were found between girls with a peak LH above or below 3 IU/l. CONCLUSION After 3 months of GnRHa treatment, central puberty was adequately suppressed in all girls, as shown by the prepubertal LH and FSH profiles. The GnRH agonist falsely indicated insufficient pubertal suppression in 33% of these girls.
Advanced Sports Medicine Concepts and Controversies

There are different modalities of exercise testing that can provide valuable information to physicians about patient and athlete fitness and cardiopulmonary status. Cardiopulmonary exercise testing (CPX) is a form of exercise testing that measures ventilatory and gas exchange, heart rate, electrocardiogram, and blood pressures to provide detailed information on the cardiovascular, pulmonary, and muscular systems. This testing allows an accurate quantification of functional capacity/measure of exercise tolerance, diagnosis of cardiopulmonary disease, disease-progression monitoring or response to intervention, and the prescription of exercise and training. CPX directly measures inhaled and exhaled ventilator gases to determine the maximal oxygen uptake, which reflects the body’s maximal use of oxygen and defines the limits of the cardiopulmonary system. CPX is the ideal modality to evaluate causes of exertional fatigue and dyspnea, especially in complex cases in which the etiology could be cardiac, pulmonary, or deconditioning. Exercise tolerance has become an important outcome measure in patients with chronic obstructive pulmonary disease and congestive heart failure, as well as other chronic diseases, and is a wellrecognized predictor of mortality. Older athletes or those with underlying medical conditions could benefit from exercise testing for risk stratification and clearance to participate, as well as to help set their training zones and determine their functional limitations.
Elaboration of an oviductin by the oviductal epithelium in relation to embryo development as visualized by immunocytochemistry

The hamster oviduct secretes a high molecular weight antigen that belongs to the family of glycoproteins known as oviductins. In the present study, using immuno‐electron microscopy, we examined the location of this hamster oviductin‐1 (Hm Ov‐1) in hamster oviductal oocytes and early embryos up to the blastocyst stage. The immunoreactive pattern of Hm Ov‐1 changes markedly during the embryo development. In oviductal oocytes prior to fertilization, Hm Ov‐1 was associated exclusively with the zona pellucida. Following fertilization, immunolabeling was detected in the perivitelline space and over the plasma membrane of 2‐cell, 4‐cell, and 8‐cell embryos as well as young blastocysts. The change of the immunoreactive pattern was accompanied by the formation of an abundant number of coated pits, endocytic vesicles, multivesicular bodies, and lysosomal‐like structures which were strongly labeled by gold particles. These immunogold‐labeled cytoplasmic organelles characteristic of the endosomal‐lysosomal apparatus were particularly evident in 2‐cell, 4‐cell, and 8‐cell embryos and showed a decrease in number in the blastocysts. The close resemblence between the labeled flocculent material detected in the perivitelline space and that found in the zona matrix of early embryos and blastocysts suggested that the Hm Ov‐1‐associated electron‐dense, flocculent material in the perivitelline space originated from the zona pellucida and was later endocytosed by the blastomeres through coated pits and endocytic vesicles. The detection of Hm Ov‐1 in numerous multivesicular bodies and lysosomal structures indicated that the oviductin is eventually degraded. Although the exact functional role of Hm Ov‐1 is not known, the presence of a copious amount of Hm Ov‐1 in early hamster embryos may be ascribed to a special relationship between this particular oviductin and embryo development. © 1995 Wiley‐Liss, Inc.
YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.
Enhancement of micro-channels within the human mastoid bone based on local structure tensor analysis

Numerous micro-channels have recently been discovered in the human temporal bone by x-ray micro-CT-scanning. After a preliminary study suggesting that these micro-channels form a separate blood supply for the mucosa of the mastoid air cells, a structural analysis of the micro-channels using a local structure tensor was carried out. Despite the high-resolution of the micro-CT scan, presence of noise within the air cells along with missing information in some micro-channels suggested the need of image enhancement. This paper proposes an adaptive enhancement of the micro-channels based on a local structure analysis while minimizing the impact of noise on the overall data. Comparison with an anisotropic diffusion PDE based scheme was also performed.
Evolution strategy classification utilizing meta features and domain-specific statistical a priori models for fully-automated and entire segmentation of medical datasets in 3D radiology

The employment of modern machine learning algorithms marks a huge advance towards automated and generalized segmentation in medical image analysis. Entire radiological datasets are classified, leading to a meaningful morphological interpretation, clearly distinguishing pathologies. After standard pre-processing, e.g. smoothing the input image data, the entire volume is partitioned into a large number of sub-regions utilizing watershed transform. These fragments are atomic and fused together building contiguous structures representing organs and typical morphology. This fusion is driven by similarity of regions. The relevant similarity measures respond to statistical a-priori models, derived from training datasets. In this work, the applicability of evolution strategy as classifier for a generic image segmentation approach is evaluated. Furthermore, it is analyzed if accuracy and robustness of the segmentation are improved by incorporation of meta features evaluated on the entire classification solution besides local features evaluated for the pre-fragmented regions to classify. The proposed generic strategy has a high potential in new segmentation domains, relying only on a small set of reference segmentations, as evaluated for different imaging modalities and diagnostic domains, such as brain MRI or abdominal CT. Comparison with results from other machine learning approaches, e.g. neural networks or genetic programming, proves that the newly developed evolution strategy is highly applicable for this classification domain and can best incorporate meta features for evaluation of solution fitness.
Commodity price retrieval system in Bangla: an IVR based application

Present paper describes the overall architecture, design and evaluation process of an Interactive Voice Response based Commodity Price Retrieval System for semiliterate or illiterate farmers. Here users just need to dial a 24x7 helpline number to get market wise latest price information on recognition of district and commodity names in Bangla language. Telephonic speech data has been collected automatically from all geographical regions of West Bengal to cover major dialectal variations. Sustained efforts have been given in real-time speech data collection, transcription, system design, evaluation and field trial analysis. Additionally to meet users' expectations in field conditions, distinctive error recovery methods like Signal Analysis and Decision, Confidence Measure and Polling, Complementary Information, Runtime model generation etc. are incorporated to confirm performance enhancement in final trial. This system is providing excellent value-addition to the already existing AGMARKNET website managed by ministry of agriculture, India; as no computers, internet, or even reading/writing skills are required for this application.
Describing the Reading Motivation of Four Second-Grade Students with Varying Abilities.

Describing the Reading Motivation of Four Second-Grade Students with Varying Abilities Kathy White Department of Teacher Education Master of Arts The purpose of this study was to describe the differences and similarities among four second-grade students with different abilities and different motivations in the development of their reading attitudes and motivations. A multi-case study design was used to describe the literacy profiles of the participants. Data relating to participants’ attitudes, motivations, and values for reading were collected for six weeks from conversational interviews, student observations, parent interviews, student histories, and interviews with previous teachers. Four major results were found in the areas of attitudes, motivations, and values. First, the results for attitudes showed high-ability students were confident readers who were motivated to take a leadership role in collaborating about reading. Students with low abilities had poorer reading self-efficacy, blamed others for not being able to read, thought they were unlucky in reading, and lacked the confidence needed to collaborate with others about their reading. Second, results for assessing motivation were unpredictable and varied from student to student. High-and-low ability students were motivated by both extrinsic and intrinsic motivation. A third result showed participants in the study whose families were more involved in supporting reading at home, regardless of the participants’ reading abilities, had high self-esteem, had future goals for learning, and were more motivated to read than the student from the home without achievement related values. Fourth, reading motivation scales may not be accurate with early elementary students. Qualitative methods are a more accurate source of information about young children’s motivations to read. Hoping to read, another aspect of motivation, is discussed.
Complex amplitude estimation in the known steering matrix and generalized waveform case

We consider a special growth-curve (SGC) model with a known steering matrix and generalized waveform in the presence of unknown interference and noise. Several estimators of the complex amplitude based on this model are derived, including the methods of approximate maximum likelihood (AML), minimum variance distortionless response (MVDR), and amplitude and phase estimation (APES). We analyze the statistical properties of these estimators and show that in the presence of temporally white but spatially correlated noise and interference, AML is asymptotically statistically efficient for a large snapshot number while MVDR and APES are asymptotically equivalent but not statistically efficient. Via several numerical examples, we also show that when the noise and interference are both spatially and temporally correlated, the APES estimator can achieve better estimation accuracy and exhibit greater robustness than the other methods.