nils-herrmann/clean_scirepeval huggingface dataset:
An introduction to toothpaste - its purpose, history and ingredients.

Toothpaste is a paste or gel to be used with a toothbrush to maintain and improve oral health and aesthetics. Since their introduction several thousand years ago, toothpaste formulations have evolved considerably - from suspensions of crushed egg shells or ashes to complex formulations with often more than 20 ingredients. Among these can be compounds to combat dental caries, gum disease, malodor, calculus, erosion and dentin hypersensitivity. Furthermore, toothpastes contain abrasives to clean and whiten teeth, flavors for the purpose of breath freshening and dyes for better visual appeal. Effective toothpastes are those that are formulated for maximum bioavailability of their actives. This, however, can be challenging as compromises will have to be made when several different actives are formulated in one phase. Toothpaste development is by no means complete as many challenges and especially the poor oral substantivity of most active ingredients are yet to overcome.
New Horizons for a Data-Driven Economy

ion from the underlying big data technologies is needed to enable ease of use for data scientists, and for business users. Many of the techniques required for real-time, prescriptive analytics, such as predictive modelling, optimization, and simulation, are data and compute intensive. Combined with big data these require distributed storage and parallel, or distributed computing. At the same time many of the machine learning and data mining algorithms are not straightforward to parallelize. A recent survey (Paradigm 4 2014) found that “although 49 % of the respondent data scientists could not fit their data into relational databases anymore, only 48 % have used Hadoop or Spark—and of those 76 % said they could not work effectively due to platform issues”. This is an indicator that big data computing is too complex to use without sophisticated computer science know-how. One direction of advancement is for abstractions and high-level procedures to be developed that hide the complexities of distributed computing and machine learning from data scientists. The other direction of course will be more skilled data scientists, who are literate in distributed computing, or distributed computing experts becoming more literate in data science and statistics. Advances are needed for the following technologies: • Abstraction is a common tool in computer science. Each technology at first is cumbersome. Abstraction manages complexity so that the user (e.g., 13 Big Data in the Energy and Transport Sectors 241
Deep Learning Applications in Medical Image Analysis

The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
The Conflicting Evidence of Three-dimensional Displays in Laparoscopy: A Review of Systems Old and New.

To describe studies evaluating 3 generations of three-dimensional (3D) displays over the course of 20 years.



Most previous studies have analyzed performance differences during 3D and two-dimensional (2D) laparoscopy without using appropriate controls that equated conditions in all respects except for 3D or 2D viewing.



Databases search consisted of MEDLINE and PubMed. The reference lists for all relevant articles were also reviewed for additional articles. The search strategy employed the use of keywords "3D," "Laparoscopic," "Laparoscopy," "Performance," "Education," "Learning," and "Surgery" in appropriate combinations.



Our current understanding of the performance metrics between 3D and 2D laparoscopy is mostly from the research with flawed study designs. This review has been written in a qualitative style to explain in detail how prior research has underestimated the potential benefit of 3D displays and the improvements that must be made in future experiments comparing 3D and 2D displays to better determine any advantage of using one display or the other.



Individual laparoscopic performance in 3D may be affected by a multitude of factors. It is crucial for studies to measure participant stereoscopic ability, control for system crosstalk, and use validated measures of performance.
Outcome of isolated antenatal hydronephrosis: a prospective cohort study

The purpose of this study was to report the outcome of infants with antenatal hydronephrosis. Between May 1999 and June 2006, all patients diagnosed with isolated fetal renal pelvic dilatation (RPD) were prospectively followed. The events of interest were: presence of uropathy, need for surgical intervention, RPD resolution, urinary tract infection (UTI), and hypertension. RPD was classified as mild (5–9.9 mm), moderate (10–14.9 mm) or severe (≥15 mm). A total of 192 patients was included in the analysis; 114 were assigned to the group of non-significant findings (59.4%) and 78 to the group of significant uropathy (40.6%). Of 89 patients with mild dilatation, 16 (18%) presented uropathy. Median follow-up time was 24 months. Twenty-seven patients (15%) required surgical intervention. During follow-up, UTI occurred in 27 (14%) children. Of 89 patients with mild dilatation, seven (7.8%) presented UTI during follow-up. Renal function, blood pressure, and somatic growth were within normal range at last visit. The majority of patients with mild fetal RPD have no significant findings during infancy. Nevertheless, our prospective study has shown that 18% of these patients presented uropathy and 7.8% had UTI during a medium-term follow-up time. Our findings suggested that, in contrast to patients with moderate/severe RPD, infants with mild RPD do not require invasive diagnostic procedures but need strict clinical surveillance for UTI and progression of RPD.
Every time you go away: changes in affect, behavior, and physiology associated with travel-related separations from romantic partners.

This study investigated changes in day-to-day affect, behavior, and physiology (hypothalamic-pituitary-adrenocortical axis activity) associated with temporary physical separations from romantic partners (such as those brought about by work-related travel). Daily diaries and measures of salivary cortisol were collected from 42 couples over a 21-day period that was timed to coincide with a naturally occurring 4- to 7-day separation. There were significant changes from preseparation to separation and from separation to reunion in the quality of partners' interactions, their positive and negative affect, sleeping problems, subjective stress, physical symptoms, and cortisol levels. Separation and reunion effects were generally more pronounced in homebound partners, partners with high attachment anxiety, and partners who had less contact with each other during the separation. Separation and reunion effects were not moderated by relationship length, relationship satisfaction, how often couples underwent separations, or the presence of children in the home. The results are discussed with respect to the role of daily proximity and contact between partners for day-to-day affective and physiological regulation.
An enhanced intrusion detection framework for securing network layer of cloud computing

Virtualization is a key enabler of Cloud computing. Due to the numerous vulnerabilities in current implementations of virtualization, security is the major concern of Cloud computing. In this paper, we propose an enhanced security framework to detect intrusions at the virtual network layer of Cloud. It combines signature and anomaly based techniques to detect possible attacks. It uses different classifiers viz; naive bayes, decision tree, random forest, extra trees and linear discriminant analysis for an efficient and effective detection of intrusions. To detect distributed attacks at each cluster and at whole Cloud, it collects intrusion evidences from each region of Cloud and applies Dempster-Shafer theory (DST) for final decision making. We analyze the proposed security framework in terms of Cloud IDS requirements through offline simulation using different intrusion datasets.
Organic phosphorus compounds as a phosphorus source for higher plants through the activity of phosphatases produced by plant roots and microorganisms

The efficiency of phosphatases produced by clover, barley, oats and wheat was investigated in soils treated with sodium glycerophosphate, lecithin and phytin. Root exudates of aseptically grown clover were also examined for the breakdown of different organic P compounds in order to test the efficiency of plant-produced phosphatases. In general, the plants were able to use P from all the organic sources used in the study almost as efficiently as inorganic sources. Dry-matter yield, P uptake, acid and alkaline phosphatase activity and microbial population were increased in all the P treatments. Organic P enhanced alkaline phosphatase activity. Lecithin increased fungal, and phytin bacterial growth. There was no alkaline phosphatase activity in the asepticallly grown clover root exudates. Phosphatase released in aseptic culture after 4 weeks of clover growth was able to efficiently hydrolyse sodium glycerophosphate, lecithin and phytin. The amount of organic P hydrolysed in this and in the soil experiment surpassed plant uptake by a factor of 20. This suggests that the limiting factor on plant utilization of organic P is the availability of hydrolysable organic P sources.
Cyber Attacks Prediction Model Based on Bayesian Network

Cyber attacks prediction is an important part of risk management. Existing cyber attacks prediction methods did not fully consider the specific environment factors of the target network, which may make the results deviate from the true situation. In this paper, we propose a cyber attacks prediction model based on Bayesian network. We use attack graphs to represent all the vulnerabilities and possible attack paths. Then we capture the using environment factors using Bayesian network model. Cyber attacks predictions are performed on the constructed Bayesian network. Experimental analysis shows that our method gets more accurate results.
Two-Dimensional Analytical Permanent-Magnet Eddy-Current Loss Calculations in Slotless PMSM Equipped With Surface-Inset Magnets

Two-dimensional (2-D) analytical permanent-magnet (PM) eddy-current loss calculations are presented for slotless PM synchronous machines (PMSMs) with surface-inset PMs considering the current penetration effect. In this paper, the term slotless implies that either the stator is originally slotted but the slotting effects are neglected or the stator is originally slotless. The analytical magnetic field distribution is computed in polar coordinates from the 2-D subdomain method (i.e., based on formal resolution of Maxwell's equation applied in subdomain). Based on the predicted magnetic field distribution, the eddy-currents induced in the PMs are analytically obtained and the PM eddy-current losses considering eddy-current reaction field are calculated. The analytical expressions can be used for slotless PMSMs with any number of phases and any form of current and overlapping winding distribution. The effects of stator slotting are neglected and the current density distribution is modeled by equivalent current sheets located on the slot opening. To evaluate the efficacy of the proposed technique, the 2-D PM eddy-current losses for two slotless PMSMs are analytically calculated and compared with those obtained by 2-D finite-element analysis (FEA). The effects of the rotor rotational speed and the initial rotor mechanical angular position are investigated. The analytical results are in good agreement with those obtained by the 2-D FEA.
Total least squares and errors-in-variables modeling

The main purpose of this special issue is to present an overview of the progress of a modeling technique which is known as total least squares (TLS) in computational mathematics and engineering, and as errors-in-variables (EIV) modeling or orthogonal regression in the statistical community. The TLS method is one of several linear parameter estimation techniques that has been devised to compensate for data errors. The basic motivation is the following: let a set of multidimensional data points (vectors) be given. How can one obtain a linear model that explains these data? The idea is to modify all data points in such a way that some norm of the modification is minimized subject to the constraint that the modified vectors satisfy a linear relation. Although the name “TLS” appeared in the literature only 27 years (Golub and Van Loan, 1980) ago, this method of fitting is certainly not new and has a long history in the statistical literature, where the method is known as “orthogonal regression”, “EIV regression” or “measurement error (ME) modeling”. The univariate line fitting problem was already discussed since 1877 (Adcock, 1877). More recently, the TLS approach to fitting has also stimulated interests outside statistics. One of the main reasons for its popularity is the availability of efficient and numerically robust algorithms in which the singular value decomposition (SVD) plays a prominent role (Golub and Van Loan, 1980). Another reason is the fact that TLS is an application oriented procedure. It is suited for situations in which all data are corrupted by noise, which is almost always the case in engineering applications ( Van Huffel et al., 2007). In this sense, TLS and EIV modeling are a powerful extension of classical least squares and ordinary regression, which corresponds only to a partial modification of the data. The problem of linear parameter estimation arises in a broad class of scientific disciplines such as signal processing, automatic control, system theory and in general engineering, statistics, physics, economics, biology, medicine, etc. It starts from a model described by a linear equation:
Anterior cruciate ligament injury and ankle dorsiflexion

The aim was to study whether the degree of ankle dorsiflexion differs between subjects with an anterior cruciate ligament (ACL) injury and uninjured controls. Another aim was to study ankle dorsiflexion between the injured and the uninjured leg and in addition between women and men with an ACL injury. Sixty subjects (ACL injury, n = 30 and controls, n = 30) were enroled consecutively at two physical therapy settings. Ankle dorsiflexion was measured with a goniometer in a standardized way in a weight-bearing lunge position. Repeated-measures ANOVA revealed a significant difference (p < 0.001) in ankle dorsiflexion between subjects with an ACL injury (mean 41.1° SD 5.7) and those without (mean 46.6° SD 5.3). No difference in ankle dorsiflexion was found between the injured leg and the uninjured or between women and men with ACL injury. The present findings suggest lower degree of ankle dorsiflexion in subjects with an ACL injury than in uninjured controls. A functional test measuring ankle dorsiflexion with a goniometer may be one way of identifying individuals at increased risk of ACL injury. Comparative study,
Insights into Electrochemical Oxidation of NaO2 in Na-O2 Batteries via Rotating Ring Disk and Spectroscopic Measurements.

O2 reduction in aprotic Na-O2 batteries results in the formation of NaO2, which can be oxidized at small overpotentials (<200 mV) on charge. In this study, we investigated the NaO2 oxidation mechanism using rotating ring disk electrode (RRDE) measurements of Na-O2 reaction products and by tracking the morphological evolution of the NaO2 discharge product at different states of charge using scanning electron microscopy (SEM). The results show that negligible soluble species are formed during NaO2 oxidation, and that the oxidation occurs predominantly via charge transfer at the interface between NaO2 and carbon electrode fibers rather than uniformly from all NaO2 surfaces. X-ray absorption near edge structure (XANES), and X-ray photoelectron spectroscopy (XPS) measurements show that the band gap of NaO2 is smaller than that of Li2O2 formed in Li-O2 batteries, in which charging overpotentials are much higher (∼1000 mV). These results emphasize the importance of discharge product electronic structure for rationalizing metal-air battery mechanisms and performance.
Quantitative Analysis of Intrusion Detection Systems : Snort and Suricata

Any modern organization that is serious about security deploys a network intrusion detection system (NIDS) to monitor network traffic for signs of malicious activity. The most widely deployed NIDS system is Snort, an open source system originally released in 1998. Snort is a single threaded system that uses a set of clear text rules to instruct a base engine how to react when particular traffic patterns are detected. In 2009, the US Department of Homeland Security and a consortium of private companies provided substantial grant funding to a newly created organization known as the Open Information Security Foundation (OISF), to build a multi-threaded alternative to Snort, called Suricata. Despite many similarities between Snort and Suricata, the OISF stated it was essential to replace the older single-threaded Snort engine with a multi-threaded system that could deliver higher performance and better scalability.  Snort developers argued that Suricata’s multi-threaded architecture would actually slow the detection process. Given these competing claims, an objective head-to-head comparison of the performance of Snort and Suricata is needed. In this paper, we present a comprehensive quantitative comparison of the two systems. We have developed a rigorous testing framework that examines the performance of both systems as we scale system resources. Our results show that a single instance of Suricata is able to deliver substantially higher performance than a corresponding single instance of Snort, but has problems scaling with a higher number of cores. We find that while Suricata needs tuning for a higher number of cores, it is still able to surpass Snort even at 1 core where we would have expected Snort to shine.
Interactive Chinese Character Learning System though Pictograph Evolution

This paper proposes an Interactive Chinese Character Learning System (ICCLS) based on pictorial evolution as an edutainment concept in computer-based learning of language. The advantage of the language origination itself is taken as a learning platform due to the complexity in Chinese language as compared to other types of languages. Users especially children enjoy more by utilize this learning system because they are able to memories the Chinese Character easily and understand more of the origin of the Chinese character under pleasurable learning environment, compares to traditional approach which children need to rote learning Chinese Character under un-pleasurable environment. Skeletonization is used as the representation of Chinese character and object with an animated pictograph evolution to facilitate the learning of the language. Shortest skeleton path matching technique is employed for fast and accurate matching in our implementation. User is required to either write a word or draw a simple 2D object in the input panel and the matched word and object will be displayed as well as the pictograph evolution to instill learning. The target of computer-based learning system is for pre-school children between 4 to 6 years old to learn Chinese characters in a flexible and entertaining manner besides utilizing visual and mind mapping strategy as learning methodology.
Management of Relapsed/Refractory Acute Myeloid Leukemia in the Elderly: Current Strategies and Developments.

Elderly patients with acute myeloid leukemia (AML) who are refractory to or relapse following frontline treatment constitute a poor-risk group with a poor long-term outcome. Host-related factors and unfavorable disease-related features contribute to early treatment failures following frontline therapy, thus making attainment of remission and long-term survival with salvage therapy particularly challenging for elderly patients. Currently, no optimal salvage strategy exists for responding patients, and allogeneic hematopoietic stem cell transplant is the only curative option in this setting; however, the vast majority of elderly patients are not candidates for this procedure due to poor functional status secondary to age and age-related comorbidities. Furthermore, the lack of effective salvage programs available for elderly patients with recurrent AML underscores the need for therapies that consistently yield durable remissions or durable control of their disease. The purpose of this review was to highlight the currently available strategies, as well as future strategies under development, for treating older patients with recurrent AML.
Calibration of the Local Volatility in a Generalized Black-Scholes Model Using Tikhonov Regularization

Following an approach introduced by Lagnado and Osher (1997), we study Tikhonov regularization applied to an inverse problem important in mathematical finance, that of calibrating, in a generalized Black–Scholes model, a local volatility function from observed vanilla option prices. We first establish W 1,2 p estimates for the Black–Scholes and Dupire equations with measurable ingredients. Applying general results available in the theory of Tikhonov regularization for ill-posed nonlinear inverse problems, we then prove the stability of this approach, its convergence towards a minimum norm solution of the calibration problem (which we assume to exist), and discuss convergence rates issues.
An Overview of Convolutional Neural Network Architectures for Deep Learning

Since AlexNet was developed and applied to the ImageNet classi cation competition in 2012 , the quantity of research on convolutional networks for deep learning applications has increased remarkably. In 2015, the top 5 classi cation error was reduced to 3.57%, with Microsoft's Residual Network . The previous top 5 classi cation error was 6.67%, achieved by GoogLeNet . In recent years, new arti cial neural network architectures have been developed which improve upon previous architectures. Speci cally, these are the inception modules in GoogLeNet, and residual networks, in Microsoft's ResNet . Here we will examine convolutional neural networks (convnets) for image recognition, and then provide an explanation for their architecture. The role of various convnet hyperparameters will be examined. The question of how to correctly size a neural network, in terms of the number of layers, and layer size, for example, will be considered. An example for determining GPU memory required for training a de ned network architecture is presented. The training method of backpropagation will be discussed in the context of past and recent developments which have improved training e ectiveness. Other techniques and considerations related to network training, such as choosing an activation function, and proper weight initialization, are discussed brie y. Finally, recent developments in convnet architecture are reviewed.