nils-herrmann/clean_scirepeval huggingface dataset:
In vivo accuracy of multispectral magnetic resonance imaging for identifying lipid-rich necrotic cores and intraplaque hemorrhage in advanced human carotid plaques.

High-resolution MRI has been shown to be capable of identifying plaque constituents, such as the necrotic core and intraplaque hemorrhage, in human carotid atherosclerosis. The purpose of this study was to evaluate differential contrast-weighted images, specifically a multispectral MR technique, to improve the accuracy of identifying the lipid-rich necrotic core and acute intraplaque hemorrhage in vivo.



Eighteen patients scheduled for carotid endarterectomy underwent a preoperative carotid MRI examination in a 1.5-T GE Signa scanner using a protocol that generated 4 contrast weightings (T1, T2, proton density, and 3D time of flight). MR images of the vessel wall were examined for the presence of a lipid-rich necrotic core and/or intraplaque hemorrhage. Ninety cross sections were compared with matched histological sections of the excised specimen in a double-blinded fashion. Overall accuracy (95% CI) of multispectral MRI was 87% (80% to 94%), sensitivity was 85% (78% to 92%), and specificity was 92% (86% to 98%). There was good agreement between MRI and histological findings, with a value of kappa=0.69 (0.53 to 0.85).



Multispectral MRI can identify the lipid-rich necrotic core in human carotid atherosclerosis in vivo with high sensitivity and specificity. This MRI technique provides a noninvasive tool to study the pathogenesis and natural history of carotid atherosclerosis. Furthermore, it will permit a direct assessment of the effect of pharmacological therapy, such as aggressive lipid lowering, on plaque lipid composition.
Effective Arabic Stemmer Based Hybrid Approach for Arabic Text Categorization

Text pre-processing of Arabic Language is a challenge and crucial stage in Text Categorization (TC) particularly and Text Mining (TM) generally. Stemming algorithms can be employed in Arabic text preprocessing to reduces words to their stems/or root. Arabic stemming algorithms can be ranked, according to three category, as root-based approach (ex. Khoja); stem-based approach (ex. Larkey); and statistical approach (ex. N-Garm). However, no stemming of this language is perfect: The existing stemmers have a small efficiency. In this paper, in order to improve the accuracy of stemming and therefore the accuracy of our proposed TC system, an efficient hybrid method is proposed for stemming Arabic text. The effectiveness of the aforementioned four methods was evaluated and compared in term of the F-measure of the Naïve Bayesian classifier and the Support Vector Machine classifier used in our TC system. The proposed stemming algorithm was found to supersede the other stemming ones: The obtained results illustrate that using the proposed stemmer enhances greatly the performance of Arabic Text Categorization.
Penile Enlargement Surgery: Is It Feasible?

Penile size is a major concern among men all over world. Men throughout history and still today, feel the need to enlarge their penis in order to improve their self-esteem and sexual performance. There are a variety of social, cultural, and psychological aspects regarding the size of men genitals, resulting such that, men often feel the need to enlarge their penis. “Bigger is better” is still a relevant belief in our days and based on the “phallic identity” – the tendency of males to seek their personality in their penis. This trend is supported by the numerous and still increasing number of penile enlargement procedures performed in the past years and today, generally in men with normal size penises. This condition is called “the locker room syndrome” – men concerned about their flaccid penile size even though in most cases their penile length and girth are normal. however, the surgical procedures available for changing penile appearance remains highly controversial mainly due to high complication rates and low satisfactory surgical outcomes.
AI-based playtesting of contemporary board games

Ticket to Ride is a popular contemporary board game for two to four players, featuring a number of expansions with additional maps and tweaks to the core game mechanics. In this paper, four different game-playing agents that embody different playing styles are defined and used to analyze Ticket to Ride. Different playing styles are shown to be effective depending on the map and rule variation, and also depending on how many players play the game. The performance profiles of the different agents can be used to characterize maps and identify the most similar maps in the space of playstyles. Further analysis of the automatically played games reveal which cities on the map are most desirable, and that the relative attractiveness of cities is remarkably consistent across numbers of players. Finally, the automated analysis also reveals two classes of failures states, where the agents find states which are not covered by the game rules; this is akin to finding bugs in the rules. We see the analysis performed here as a possible template for AI-based playtesting of contemporary board games.
Broadband push-pull power amplifier design at microwave frequencies

A broadband, high efficiency push-pull power amplifier is presented between 0.5 GHz and 1.5 GHz. Coaxial cable transmission line baluns are utilised to transform the impedance environment of the transistors down to 25 Ω, greatly simplifying the matching, whilst still providing a 50 Ω environment to interface with other components. Using packaged GaN HEMT transistors, typical output powers of 45 dBm and efficiencies of 44% to 75% have been measured across a 3:1 bandwidth. The small signal input match is less than −10 dB and small signal gain is greater than 10 dB across the entire band.
Mapping brain maturation and cognitive development during adolescence

Non-invasive mapping of brain structure and function with magnetic resonance imaging (MRI) has opened up unprecedented opportunities for studying the neural substrates underlying cognitive development. There is an emerging consensus of a continuous increase throughout adolescence in the volume of white matter, both global and local. There is less agreement on the meaning of asynchronous age-related decreases in the volume of grey matter in different cortical regions; these might equally represent loss ("pruning") or gain (intra-cortical myelination) of tissue. Functional MRI studies have so far focused mostly on executive functions, such as working memory and behavioural inhibition, with very few addressing questions regarding the maturation of social cognition. Future directions for research in this area are discussed in the context of processing biological motion and matching perceptions and actions.
The chi-square test for independence

In one of the most frequent empirical scenarios in applied linguistics, a researcher's empirical results can be summarized in a two-dimensional table, in which − the rows list the levels of a nominal/categorical variable; − the columns list the levels of another nominal/categorical variable; − the cells in the table defined by these row and column levels provide the frequencies with which combinations of row and column levels were observed in some data set. An example of data from a study of disfluencies in speech is shown in Table 1, which shows the parts of speech of 335 words following three types of disfluencies. Both the part of speech and the disfluency markers represent categorical variables. Noun Verb Conjunction Totals uh 30 70 90 190 uhm 50 20 40 110 silence 20 5 10 35 Totals 100 95 140 335 Table 1 shows that 30 uh's were followed by a noun, 20 uhm's were followed by a verb, etc. One question a researcher may be interested in exploring is whether there is a correlation between the kind of disfluency produced – the variable in the rows – and the part of speech of the word following the disfluency – the variable in the columns. An exploratory glance at the data suggests that uh mostly precedes conjunctions while silences most precede nouns, but an actual statistical test is required to determine (i) whether the distribution of the parts of speech after the disfluencies is in fact significantly different from chance and (ii) what preferences and dispreferences this data set reflects. The most frequent statistical test to analyze two-dimensional frequency tables such as Table 1 is the chi-square test for independence [A] The chi-square test for independence
Character-Level Question Answering with Attention

We show that a character-level encoderdecoder framework can be successfully applied to question answering with a structured knowledge base. We use our model for singlerelation question answering and demonstrate the effectiveness of our approach on the SimpleQuestions dataset (Bordes et al., 2015), where we improve state-of-the-art accuracy from 63.9% to 70.9%, without use of ensembles. Importantly, our character-level model has 16x fewer parameters than an equivalent word-level model, can be learned with significantly less data compared to previous work, which relies on data augmentation, and is robust to new entities in testing.
Learning to Generalize: Meta-Learning for Domain Generalization

Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. Domain Generalization (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. We propose a novel meta-learning method for domain generalization. Rather than designing a specific model that is robust to domain shift as in most previous DG work, we propose a model agnostic training procedure for DG. Our algorithm simulates train/test domain shift during training by synthesizing virtual testing domains within each mini-batch. The meta-optimization objective requires that steps to improve training domain performance should also improve testing domain performance. This meta-learning procedure trains models with good generalization ability to novel domains. We evaluate our method and achieve state of the art results on a recent cross-domain image classification benchmark, as well demonstrating its potential on two classic reinforcement learning tasks.
Understanding Chinese Consumers ’ Intention to Purchase Sustainable Fashion Products : The Moderating Role of Face-Saving Orientation

In a culture where collectivism is pervasive such as China, social norms can be one of the most powerful tools to influence consumers’ behavior. Individuals are driven to meet social expectations and fulfill social roles in collectivist cultures. Therefore, this study was designed to investigate how Chinese consumers’ concern with saving face affects sustainable fashion product purchase intention and how it also moderates consumers’ commitment to sustainable fashion. An empirical data set of 469 undergraduate students in Beijing and Shanghai was used to test our hypotheses. Results confirmed that face-saving is an important motivation for Chinese consumers’ purchase of sustainable fashion items, and it also attenuated the effect of general product value while enhancing the effect of products’ green value in predicting purchasing trends. The findings contribute to the knowledge of sustainable consumption in Confucian culture, and thus their managerial implications were also discussed.
Gibbs Sampling Methods for Stick-Breaking Priors

A rich and  exible class of random probability measures, which we call stick-breaking priors, can be constructed using a sequence of independent beta random variables. Examples of random measures that have this characterization include the Dirichlet process, its two-parameter extension, the two-parameter Poisson–Dirichlet process,  nite dimensional Dirichlet priors, and beta two-parameter processes. The rich nature of stick-breaking priors offers Bayesians a useful class of priors for nonparametri c problems, while the similar construction used in each prior can be exploited to develop a general computational procedure for  tting them. In this article we present two general types of Gibbs samplers that can be used to  t posteriors of Bayesian hierarchical models based on stick-breaking priors. The  rst type of Gibbs sampler, referred to as a Pólya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling method currently employed for Dirichlet process computing. This method applies to stick-breaking priors with a known Pólya urn characterization, that is, priors with an explicit and simple prediction rule. Our second method, the blocked Gibbs sampler, is based on an entirely different approach that works by directly sampling values from the posterior of the random measure. The blocked Gibbs sampler can be viewed as a more general approach because it works without requiring an explicit prediction rule. We  nd that the blocked Gibbs avoids some of the limitations seen with the Pólya urn approach and should be simpler for nonexperts to use.
Biodegradable Orthopedic Magnesium-Calcium (MgCa) Alloys, Processing, and Corrosion Performance

Magnesium-Calcium (Mg-Ca) alloy has received considerable attention as an emerging biodegradable implant material in orthopedic fixation applications. The biodegradable Mg-Ca alloys avoid stress shielding and secondary surgery inherent with permanent metallic implant materials. They also provide sufficient mechanical strength in load carrying applications as opposed to biopolymers. However, the key issue facing a biodegradable Mg-Ca implant is the fast corrosion in the human body environment. The ability to adjust degradation rate of Mg-Ca alloys is critical for the successful development of biodegradable orthopedic implants. This paper focuses on the functions and requirements of bone implants and critical issues of current implant biomaterials. Microstructures and mechanical properties of Mg-Ca alloys, and the unique properties of novel magnesium-calcium implant materials have been reviewed. Various manufacturing techniques to process Mg-Ca based alloys have been analyzed regarding their impacts on implant performance. Corrosion performance of Mg-Ca alloys processed by different manufacturing techniques was compared. In addition, the societal and economical impacts of developing biodegradable orthopedic implants have been emphasized.
Variational Neural Machine Translation

Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoderdecoder model that can be trained end-to-end. Different from the vanilla encoder-decoder model that generates target translations from hidden representations of source sentences alone, the variational model introduces a continuous latent variable to explicitly model underlying semantics of source sentences and to guide the generation of target translations. In order to perform efficient posterior inference and large-scale training, we build a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on both Chinese-English and EnglishGerman translation tasks show that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machine translation baselines.
Addressing Noise in Multidialectal Word Embeddings

Word embeddings are crucial to many natural language processing tasks. The quality of embeddings relies on large nonnoisy corpora. Arabic dialects lack large corpora and are noisy, being linguistically disparate with no standardized spelling. We make three contributions to address this noise. First, we describe simple but effective adaptations to word embedding tools to maximize the informative content leveraged in each training sentence. Second, we analyze methods for representing disparate dialects in one embedding space, either by mapping individual dialects into a shared space or learning a joint model of all dialects. Finally, we evaluate via dictionary induction, showing that two metrics not typically reported in the task enable us to analyze our contributions’ effects on low and high frequency words. In addition to boosting performance between 2-53%, we specifically improve on noisy, low frequency forms without compromising accuracy on high frequency forms.
The Strength Model of Self-Control

Self-control is a central function of the self and an important key to success in life. The exertion of self-control appears to depend on a limited resource. Just as a muscle gets tired from exertion, acts of self-control cause short-term impairments (ego depletion) in subsequent self-control, even on unrelated tasks. Research has supported the strength model in the domains of eating, drinking, spending, sexuality, intelligent thought, making choices, and interpersonal behavior. Motivational or framing factors can temporarily block the deleterious effects of being in a state of ego depletion. Blood glucose is an important component of the energy. self-control; ego depletion; willpower; impulse; strength Every day, people resist impulses to go back to sleep, to eat fattening or forbidden foods, to say or do hurtful things to their relationship partners, to play instead of work, to engage in inappropriate sexual or violent acts, and to do countless other sorts of problematic behaviors—that is, ones that might feel good immediately or be easy but that carry long-term costs or violate the rules and guidelines of proper behavior. What enables the human animal to follow rules and norms prescribed by society and to resist doing what it selfishly wants? Self-control refers to the capacity for altering one’s own responses, especially to bring them into line with standards such as ideals, values, morals, and social expectations, and to support the pursuit of long-term goals. Many writers use the terms selfcontrol and self-regulation interchangeably, but those whomake a distinction typically consider self-control to be the deliberate, conscious, effortful subset of self-regulation. In contrast, homeostatic processes such as maintaining a constant body temperature may be called self-regulation but not self-control. Self-control enables a person to restrain or override one response, thereby making a different response possible. Self-control has attracted increasing attention from psychologists for two main reasons. At the theoretical level, self-control holds important keys to understanding the nature and functions of the self. Meanwhile, the practical applications of self-control have attracted study in many contexts. Inadequate self-control has been linked to behavioral and impulse-control problems, including overeating, alcohol and drug abuse, crime and violence, overspending, sexually impulsive behavior, unwanted pregnancy, and smoking (e.g., Baumeister, Heatherton, & Tice, 1994; Gottfredson & Hirschi, 1990; Tangney, Baumeister, & Boone, 2004; Vohs & Faber, 2007). It may also be linked to emotional problems, school underachievement, lack of persistence, various failures at task performance, relationship problems and dissolution, and more.
A Processing Model for Free Word Order Languages

Like many verb-final languages, German displays considerable word-order freedom: there is no syntactic constraint on the ordering of the nominal arguments of a verb, as long as the verb remains in final position. This effect is referred to as “scrambling”, and is interpreted in transformational frameworks as leftward movement of the arguments. Furthermore, arguments from an embedded clause may move out of their clause; this effect is referred to as “long-distance scrambling”. While scrambling has recently received considerable attention in the syntactic literature, the status of long-distance scrambling has only rarely been addressed. The reason for this is the problematic status of the data: not only is long-distance scrambling highly dependent on pragmatic context, it also is strongly subject to degradation due to processing constraints. As in the case of center-embedding, it is not immediately clear whether to assume that observed unacceptability of highly complex sentences is due to grammatical restrictions, or whether we should assume that the competence grammar does not place any restrictions on scrambling (and that, therefore, all such sentences are in fact grammatical), and the unacceptability of some (or most) of the grammatically possible word orders is due to processing limitations. In this paper, we will argue for the second view by presenting a processing model for German. Comments University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-95-13. This technical report is available at ScholarlyCommons:
A Novel and Fast MPPT Method Suitable for Both Fast Changing and Partially Shaded Conditions

In this study, a novel and fast maximum power point tracking (MPPT) algorithm for a photovoltaic generation system is proposed. The main idea is to remove the random number in the voltage calculation equation of the conventional cuckoo search method. The advantages of the proposed method include 1) fast maximum power point (MPP) tracking speed and high MPP tracking accuracy under both uniform insolation and partially shaded conditions (PSCs), 2) simple MPPT structure, and 3) consistent solution can be achieved with only three particles and only one parameter is required to be tuned. In order to validate the effectiveness and correctness of the proposed method, both simulation and experiments are carried out on a 300 W prototyping circuit. According to the experimental results, the proposed MPPT method can improve the tracking time by 46.42% and 11.76% comparing to conventional perturb and observe (P&O) and variable-step P&O technique, respectively. In addition, the proposed method can successfully track the global MPP in 249 of 252 different PSC patterns. The tracking accuracies under both uniform irradiance and PSC conditions are all higher than 99.8%.
Music training for the development of auditory skills

The effects of music training in relation to brain plasticity have caused excitement, evident from the popularity of books on this topic among scientists and the general public. Neuroscience research has shown that music training leads to changes throughout the auditory system that prime musicians for listening challenges beyond music processing. This effect of music training suggests that, akin to physical exercise and its impact on body fitness, music is a resource that tones the brain for auditory fitness. Therefore, the role of music in shaping individual development deserves consideration.
Diagnosis Of Heart Disease Using Datamining Algorithm

The diagnosis of heart disease is a significant and tedious task in medicine. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not “mined” to determine concealed information for effective decision making by healthcare practitioners. The term Heart disease encompasses the diverse diseases that affect the heart. Cardiomyopathy and Cardiovascular disease are some categories of heart diseases. The reduction of blood and oxygen supply to the heart leads to heart disease. In this paper the data classification is based on supervised machine learning algorithms which result in accuracy, time taken to build the algorithm. Tanagra tool is used to classify the data and the data is evaluated using 10-fold cross validation and the results are compared.