nils-herrmann/clean_scirepeval huggingface dataset:
Time Series Analysis Using Fractal Theory and Online Ensemble Classifiers with Application to Stock Portfolio Optimization

Neural Network method is a technique that is heavily researched and used in applications within the engineering field for various purposes ranging from process control to biomedical applications. The success of Neural Networks (NN) in engineering applications, e.g. object tracking and face recognition has motivated its application to the finance industry. In the financial industry, time series data is used to model economic variables. As a result, finance researchers, portfolio managers and stockbrokers have taken interest in applying NN to model non-linear problems they face in their practice. NN facilitates the approach of predicting stocks due to its ability to accurately and intuitively learn complex patterns and characterizes these patterns as simple equations. In this research, a methodology that uses fractal theory and NN framework to model the stock market behavior is proposed and developed. The time series analysis is carried out using the proposed approach with application to modelling the Dow Jones Average Index’s future directional movement. A methodology to establish self-similarity of time series and long memory effects that result in classifying the time series signal as persistent, random or non-persistent using the rescaled range analysis technique is developed. A linear regression technique is used for the estimation of the required parameters and an incremental online NN algorithm is implemented to predict the directional movement of the stock. An iterative fractal analysis technique is used to select the required signal intervals using the approximated parameters. The selected data is later combined to form a signal of interest and then pass it to the ensemble of classifiers. The classifiers are modelled using a neural network based algorithm. The performance of the final algorithm is measured based on accuracy of predicting the direction of movement and also on the algorithm’s
Septoplasty and turbinate surgery.

When a patient requesting rhinoplasty also complains of nasal obstruction, it is critical to accurately diagnose the cause. The author reviews diagnostic procedures, including anterior rhinoscopy, nasal endoscopy, and coronal-sinus computed tomography scan. He discusses technical aspects of septoplasty and turbinate surgery, which address common causes of nasal obstruction, emphasizing traditional and endoscopic septoplasty, septoplasty techniques to address the caudal septum, and a graduated stepwise approach to the inferior turbinates.
Economic Globalization and Transnational Terrorism

The effect of economic globalization on the number of transnational terrorist incidents within countries is analyzed statistically, using a sample of 112 countries from 1975 to 1997. Results show that trade, foreign direct investment (FDI), and portfolio investment have no direct positive effect on transnational terrorist incidents within countries and that economic developments of a country and its top trading partners reduce the number of terrorist incidents inside the country. To the extent that trade and FDI promote economic development, they have an indirect negative effect on transnational terrorism.
Genetic and other diseases in the pottery of Tumaco-La Tolita culture in Colombia-Ecuador.

The people of Tumaco-La Tolita culture inhabited the borders of present-day Colombia and Ecuador. Already extinct by the time of the Spaniards arrival, they left a huge collection of pottery artifacts depicting everyday life; among these, disease representations were frequently crafted. In this article, we present the results of the personal examination of the largest collections of Tumaco-La Tolita pottery in Colombia and Ecuador; cases of Down syndrome, achondroplasia, mucopolysaccharidosis I H, mucopolysaccharidosis IV, a tumor of the face and a benign tumor in an old woman were found. We believe these to be among the earliest artistic representations of disease.
Chapter 2 Normal Pubertal Physiology in Females

J.E. Dietrich (ed.),  A Comprehensive Guide for Clinicians, DOI 10.1007/978-1-4939-0912-  The development of a female child into an adult woman is a complex process. Puberty, and the hormones that fuel the physical and psychological changes which are its hallmarks, is generally viewed as a rough and often unpredictable storm that must be weathered by the surrounding adults. The more we learn, however, about the intricate interplay between the endocrine regulators and the endorgan responses to this hormonal symphony, puberty seems less like chaos, and more of an incredible metamorphosis that leads to reproductive capacity and psychosocial maturation. Physically, female puberty is marked by accelerated growth and the development of secondary sexual characteristics. Secondary sexual characteristics are those that distinguish two different sexes in a species, but are not directly part of the reproductive system. Analogies from the animal kingdom include manes in male lions and the elaborate tails of male peacocks. The visible/external sequence of events is generally: breast budding (thelarche), onset of pubic hair (pubarche), maximal growth velocity, menarche, development of axillary hair, attainment of the adult breast type, adult pubic hair pattern. Underlying these external developments is the endocrine axis orchestrating the increase in gonadal steroid production (gonadarche), the increase in adrenal androgen production (adrenarche) and the associated changes in the reproductive tract that allow fertility. Meanwhile, the brain is rapidly adapting to the new hormonal milieu. The extent of variation in this scenario is enormous. On average, the process from accelerated growth and breast budding to menarche is approximately 4.5 years with a range from 1.5 to 6 years. There are differences in timing and expression of maturation based on ethnicity, geography, and genetics. Being familiar with the spectrum that encompasses normal development is Chapter 2 Normal Pubertal Physiology in Females
Hybrid Seven-Level Converter Based on T-Type Converter and H-Bridge Cascaded Under SPWM and SVM

This paper presents a hybrid seven-level converter based on T-type converter and H-bridge cascaded suitable for low-voltage and high-power-density applications. The operation principles and conduction paths are analyzed comprehensively. Voltage balance control of the floating capacitors is the key point of this paper. Mathematical expressions of capacitor voltage balance conditions are deduced based on sinusoidal pulse width modulation (SPWM). The results show that floating capacitor voltages are only balanced when the modulation index is below 0.82 under SPWM. In order to enlarge the modulation index ensuring the capacitor voltage balance, space vector modulation (SVM) is also applied to regulate floating capacitor voltages taking advantage of redundant switching vectors. Meanwhile, in SVM, there are no additional control requirements for the capacitor voltage. Taken both SPWM and SVM into consideration, the limit to the range of operation for this seven-level converter is presented, which is a function of the modulation index and the power factor. The merit of dc voltage utilization improvement in this topology is also analyzed based on simulation results. Simulations and experimental results under different modulation index and RL load are presented to verify the modulation strategies illustrated in this paper.
Deep Supervised Hashing for Multi-Label and Large-Scale Image Retrieval

One of the most challenging tasks in large-scale multi-label image retrieval is to map images into binary codes while preserving multilevel semantic similarity. Recently, several deep supervised hashing methods have been proposed to learn hash functions that preserve multilevel semantic similarity with deep convolutional neural networks. However, these triplet label based methods try to preserve the ranking order of images according to their similarity degrees to the queries while not putting direct constraints on the distance between the codes of very similar images. Besides, the current evaluation criteria are not able to measure the performance of existing hashing methods on preserving fine-grained multilevel semantic similarity. To tackle these issues, we propose a novel Deep Multilevel Semantic Similarity Preserving Hashing (DMSSPH) method to learn compact similarity-preserving binary codes for the huge body of multi-label image data with deep convolutional neural networks. In our approach, we make the best of the supervised information in the form of pairwise labels to maximize the discriminability of output binary codes. Extensive evaluations conducted on several benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art supervised and unsupervised hashing methods at the accuracies of top returned images, especially for shorter binary codes. Meanwhile, the proposed method shows better performance on preserving fine-grained multilevel semantic similarity according to the results under the Jaccard coefficient based evaluation criteria we propose.
Combined therapy (ultrasound and interferential current) in patients with fibromyalgia: once or twice in a week?

Combined Therapy (CT) composed of ultrasound and Interferential Therapy has been reported as a cost-effective, local analgesic intervention on tender points in Fibromyalgia (FM). This study aims to investigate the difference between CT applied once a week and twice a week in patients with FM.



Fifty patients with the diagnosis of FM were randomized into two groups (G1 = once a week treatment and G2 = twice a week treatment) with each group containing 25 patients. All eighteen tender points were assessed and treated with CT during each session, over a three-month time period. Interferential Therapy was modulated at 4,000 Hz of current carrier, 100 Hz of amplitude modulated frequency and at a bearable sensorial threshold of intensity. Pulsed ultrasound of 1 MHz at 20% of 2.5 W/cm² was used. For evaluation, the Visual Analogue Scale, Fibromyalgia Impact Questionnaire, Post Sleep Inventory and the tender point count were utilized, and the examiner was blinded to the group assignments.



G1 and G2 showed a significant improvement in Visual Analogue Scale (p < 0.0001 and p < 0.0005, respectively), Tender Points (p < 0.005 and p < 0.001, respectively), Fibromyalgia Impact Questionnaire and Post Sleep Inventory (p < 0.005 and p < 0.05, respectively). However, there was no significant difference between the two groups in all performed analyses.



There is no advantage in increasing the number of sessions of combined therapy in terms of reducing generalized pain, quality of life and sleep quality for patients with FM.
A perspective on cancer cell metastasis.

Metastasis causes most cancer deaths, yet this process remains one of the most enigmatic aspects of the disease. Building on new mechanistic insights emerging from recent research, we offer our perspective on the metastatic process and reflect on possible paths of future exploration. We suggest that metastasis can be portrayed as a two-phase process: The first phase involves the physical translocation of a cancer cell to a distant organ, whereas the second encompasses the ability of the cancer cell to develop into a metastatic lesion at that distant site. Although much remains to be learned about the second phase, we feel that an understanding of the first phase is now within sight, due in part to a better understanding of how cancer cell behavior can be modified by a cell-biological program called the epithelial-to-mesenchymal transition.
Detecting Student Engagement: Human Versus Machine

Engagement is complex and multifaceted, but crucial to learning. Computerized learning environments can provide a superior learning experience for students by automatically detecting student engagement (and, thus also disengagement) and adapting to it. This paper describes results from several previous studies that utilized facial features to automatically detect student engagement, and proposes new methods to expand and improve results. Videos of students will be annotated by third-party observers as mind wandering (disengaged) or not mind wandering (engaged). Automatic detectors will also be trained to classify the same videos based on students' facial features, and compared to the machine predictions. These detectors will then be improved by engineering features to capture facial expressions noted by observers and more heavily weighting training instances that were exceptionally-well classified by observers. Finally, implications of previous results and proposed work are discussed.
Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation

Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and downsampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper network architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters. The code is available at https://github.com/shahsohil/sunets.
Greedy Deep Dictionary Learning

—In this work we propose a new deep learning tool – deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion – one layer at a time. This requires solving a simple (shallow) dictionary learning problem; the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state-of-the-art supervised dictionary learning tools like discriminative K-SVD and label consistent K-SVD. Our method yields better results than all.
Energy drinks and their component modulate attention, memory, and antioxidant defences in rats.

This study aimed to evaluate the effects of the subchronic consumption of energy drinks and their constituents (caffeine and taurine) in male Wistar rats using behavioural and oxidative measures.



Energy drinks (ED 5, 7.5, and 10 mL/kg) or their constituents, caffeine (3.2 mg/kg) and taurine (40 mg/kg), either separately or in combination, were administered orally to animals for 28 days. Attention was measured though the ox-maze apparatus and the object recognition memory test. Following behavioural analyses, markers of oxidative stress, including SOD, CAT, GPx, thiol content, and free radicals, were measured in the prefrontal cortex, hippocampus, and striatum.



The latency time to find the first reward was lower in animals that received caffeine, taurine, or a combination of both (P = 0.003; ANOVA/Bonferroni). In addition, these animals took less time to complete the ox-maze task (P = 0.0001; ANOVA/Bonferroni), and had better short-term memory (P < 0.01, Kruskal-Wallis). The ED 10 group showed improvement in the attention task, but did not differ on other measures. In addition, there was an imbalance in enzymatic markers of oxidative stress in the prefrontal cortex, the hippocampus, and the striatum. In the group that received both caffeine and taurine, there was a significant increase in the production of free radicals in the prefrontal cortex and in the hippocampus (P < 0.0001; ANOVA/Bonferroni).



Exposure to a combination of caffeine and taurine improved memory and attention, and led to an imbalance in the antioxidant defence system. These results differed from those of the group that was exposed to the energy drink. This might be related to other components contained in the energy drink, such as vitamins and minerals, which may have altered the ability of caffeine and taurine to modulate memory and attention.
Power consumption in wireless sensor networks

In wireless sensor networks (WSNs), long lifetime requirement of different applications and limited energy storage capability of sensor nodes has led us to find out new horizons for reducing power consumption upon nodes. To increase sensor node's lifetime, circuit and protocols have to be energy efficient so that they can make a priori reactions by estimating and predicting energy consumption. The goal of this study is to present and discuss several strategies such as power-aware protocols, cross-layer optimization, and harvesting technologies used to alleviate power consumption constraint in WSNs.
The relationships among social comparisons, body surveillance, and body dissatisfaction in the natural environment.

We examined the relationships among social comparisons (i.e., body, eating, and exercise), body surveillance, and body dissatisfaction in the natural environment. Participants were 232 college women who completed a daily diary protocol for 2 weeks, responding to online surveys 3 times per day. When the contemporaneous relationships among these variables were examined in a single model, results indicated that comparing one's body, eating, or exercise to others or engaging in body surveillance was associated with elevated body dissatisfaction in the same short-term assessment period. Additionally, individuals with high trait-like engagement in body comparisons or body surveillance experienced higher levels of body dissatisfaction. Trait-like eating and exercise comparison tendencies did not predict unique variance in body dissatisfaction. When examined prospectively in a single model, trait-like body comparison and body surveillance remained predictors of body dissatisfaction, but the only more state-like behavior predictive of body dissatisfaction at the next assessment was eating comparison. Results provide support for the notion that naturalistic body dissatisfaction is predicted by both state- and trait-like characteristics. In particular, social comparisons (i.e., body, eating, and exercise) and body surveillance may function as proximal triggers for contemporaneous body dissatisfaction, with eating comparisons emerging as an especially important predictor of body dissatisfaction over time. Regarding trait-like predictors, general tendencies to engage in body comparisons and body surveillance may be more potent distal predictors of body dissatisfaction than general eating or exercise comparison tendencies.
The Medical Imaging Interaction Toolkit: challenges and advances

The Medical Imaging Interaction Toolkit (MITK) has been available as open-source software for almost 10 years now. In this period the requirements of software systems in the medical image processing domain have become increasingly complex. The aim of this paper is to show how MITK evolved into a software system that is able to cover all steps of a clinical workflow including data retrieval, image analysis, diagnosis, treatment planning, intervention support, and treatment control.    MITK provides modularization and extensibility on different levels. In addition to the original toolkit, a module system, micro services for small, system-wide features, a service-oriented architecture based on the Open Services Gateway initiative (OSGi) standard, and an extensible and configurable application framework allow MITK to be used, extended and deployed as needed. A refined software process was implemented to deliver high-quality software, ease the fulfillment of regulatory requirements, and enable teamwork in mixed-competence teams.    MITK has been applied by a worldwide community and integrated into a variety of solutions, either at the toolkit level or as an application framework with custom extensions. The MITK Workbench has been released as a highly extensible and customizable end-user application. Optional support for tool tracking, image-guided therapy, diffusion imaging as well as various external packages (e.g. CTK, DCMTK, OpenCV, SOFA, Python) is available. MITK has also been used in several FDA/CE-certified applications, which demonstrates the high-quality software and rigorous development process.    MITK provides a versatile platform with a high degree of modularization and interoperability and is well suited to meet the challenging tasks of today’s and tomorrow’s clinically motivated research.
Iris recognition and the challenge of homeland and border control security in UAE

This article discusses the implementation of iris recognition in improving the security of border control systems in the United Arab Emirates. The article explains the significance of the implemented solution and the advantages the government has gained to-date. The UAE deployment of iris recognition technology is currently the largest in the world, both in terms of number of Iris records enrolled (more than 840,751) and number of iris comparisons performed daily 6,225,761,155 (6.2 billion) in ‘all-against-all’ search mode.
Entity Retrieval

Generalizing recent attention to retrieving entities and not just documents, we introduce two entity retrieval tasks: list completion and entity ranking. For each task, we propose and evaluate several algorithms. One of the core challenges is to overcome the very limited amount of information that serves as input—to address this challenge we explore different representations of list descriptions and/or example entities, where entities are represented not just by a textual description but also by the description of related entities. For evaluation purposes we make use of the lists and categories available in Wikipedia. Experimental results show that cluster-based contexts improve retrieval results for both tasks.
Soft-core Processor Design Soft-core Processor Design

2004 Recent advancements in Field Programmable Gate Array (FPGA) technology have resulted in FPGA devices that support the implementation of a complete computer system on a single FPGA chip. A soft-core processor is a central component of such a system. A soft-core processor is a microprocessor defined in software, which can be synthesized in programmable hardware, such as FPGAs. The Nios soft-core processor from Altera Corporation is studied and a Verilog implementation of the Nios soft-core processor has been developed, called UT Nios. The UT Nios is described, its performance dependence on various architectural parameters is investigated and then compared to the original implementation from Altera. Experiments show that the performance of different types of applications varies significantly depending on the architectural parameters. The performance comparison shows that UT Nios achieves performance comparable to the original implementation. Finally, the design methodology, experiences from the design process and issues encountered are discussed. iii Acknowledgments
Basic Notions of Information Structure

This article takes stock of the basic notions of Information Structure (IS). It first provides a general characterization of IS — following Chafe (1976) — within a communicative model of Common Ground (CG), which distinguishes between CG content and CG management. IS is concerned with those features of language that concern the local CG. Second, this paper defines and discusses the notions of Focus (as indicating alternatives) and its various uses, Givenness (as indicating that a denotation is already present in the CG), and Topic (as specifying what a statement is about). It also proposes a new notion, Delimitation, which comprises contrastive topics and frame setters, and indicates that the current conversational move does not entirely satisfy the local communicative needs. It also points out that rhetorical structuring partly belongs to IS.