nils-herrmann/clean_scirepeval huggingface dataset:
Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model

Automatic skin lesion segmentation on dermoscopic images is an essential component in computer-aided diagnosis of melanoma. Recently, many fully supervised deep learning based methods have been proposed for automatic skin lesion segmentation. However, these approaches require massive pixel-wise annotation from experienced dermatologists, which is very costly and time-consuming. In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. To utilize the unlabeled data, our method encourages the consistent predictions of the network-in-training for the same input under different regularizations. Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme in our self-ensembling model. With only 300 labeled training samples, our method sets a new record on the benchmark of the International Skin Imaging Collaboration (ISIC) 2017 skin lesion segmentation challenge. Such a result clearly surpasses fully-supervised state-of-the-arts that are trained with 2000 labeled data.
International variability in 20 m shuttle run performance in children and youth: who are the fittest from a 50-country comparison? A systematic literature review with pooling of aggregate results.

To describe and compare 20 m shuttle run test (20mSRT) performance among children and youth across 50 countries; to explore broad socioeconomic indicators that correlate with 20mSRT performance in children and youth across countries and to evaluate the utility of the 20mSRT as an international population health indicator for children and youth.



A systematic review was undertaken to identify papers that explicitly reported descriptive 20mSRT (with 1-min stages) data on apparently healthy 9-17 year-olds. Descriptive data were standardised to running speed (km/h) at the last completed stage. Country-specific 20mSRT performance indices were calculated as population-weighted mean z-scores relative to all children of the same age and sex from all countries. Countries were categorised into developed and developing groups based on the Human Development Index, and a correlational analysis was performed to describe the association between country-specific performance indices and broad socioeconomic indicators using Spearman's rank correlation coefficient.



Performance indices were calculated for 50 countries using collated data on 1 142 026 children and youth aged 9-17 years. The best performing countries were from Africa and Central-Northern Europe. Countries from South America were consistently among the worst performing countries. Country-specific income inequality (Gini index) was a strong negative correlate of the performance index across all 50 countries.



The pattern of variability in the performance index broadly supports the theory of a physical activity transition and income inequality as the strongest structural determinant of health in children and youth. This simple and cost-effective assessment would be a powerful tool for international population health surveillance.
An Evolutionary Theory for the Variability Hypothesis

An evolutionary “selectivity” theory is proposed to help explain how one gender of a sexually dimorphic species might tend to evolve with greater variability than the other gender. Briefly, the theory says that if one sex is relatively selective, then more variable subpopulations of the opposite sex will tend to prevail over those with lesser variability; and conversely, if a sex is relatively non-selective, then less variable subpopulations of the opposite sex will tend to prevail over those with greater variability. This theory makes no assumptions about differences in means between the sexes, nor does it presume that one sex is selective and the other non-selective. Two mathematical models are presented: a statistical model using normally distributed fitness values; and a deterministic model using exponentially distributed fitness levels and a standard system of coupled ordinary differential equations.
Optimal vehicle dynamics control for combined longitudinal and lateral autonomous vehicle guidance

This contribution proposes a model-based predictive control approach for combined longitudinal and lateral vehicle guidance. The controller, which has been designed for an automotive collision avoidance system, aims at following a desired evasion trajectory at the handling limits. Thereby, the trajectory following problem is decomposed in a path following and a velocity trajectory tracking problem using the wheel steering angle and the longitudinal acceleration as control inputs. There are two major advantages of this approach. First, the a priori knowledge of the evasion trajectory is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the sense of an integrated vehicle dynamics control approach. Experimental results show the potential of the introduced control scheme.
Balancing plasticity/stability across brain development.

The potency of the environment to shape brain function changes dramatically across the lifespan. Neural circuits exhibit profound plasticity during early life and are later stabilized. A focus on the cellular and molecular bases of these developmental trajectories has begun to unravel mechanisms, which control the onset and closure of such critical periods. Two important concepts have emerged from the study of critical periods in the visual cortex: (1) excitatory-inhibitory circuit balance is a trigger; and (2) molecular "brakes" limit adult plasticity. The onset of the critical period is determined by the maturation of specific GABA circuits. Targeting these circuits using pharmacological or genetic approaches can trigger premature onset or induce a delay. These manipulations are so powerful that animals of identical chronological age may be at the peak, before, or past their plastic window. Thus, critical period timing per se is plastic. Conversely, one of the outcomes of normal development is to stabilize the neural networks initially sculpted by experience. Rather than being passively lost, the brain's intrinsic potential for plasticity is actively dampened. This is demonstrated by the late expression of brake-like factors, which reversibly limit excessive circuit rewiring beyond a critical period. Interestingly, many of these plasticity regulators are found in the extracellular milieu. Understanding why so many regulators exist, how they interact and, ultimately, how to lift them in noninvasive ways may hold the key to novel therapies and lifelong learning.
Characteristics of knowledge, people engaged in knowledge transfer and knowledge stickiness: evidence from Chinese R&D team

Characteristics of knowledge, people engaged in knowledge transfer, and knowledge stickiness: evidence from Chinese R & D team Huang Huan, Ma Yongyuan, Zhang Sheng, Dou Qinchao, Article information: To cite this document: Huang Huan, Ma Yongyuan, Zhang Sheng, Dou Qinchao, "Characteristics of knowledge, people engaged in knowledge transfer, and knowledge stickiness: evidence from Chinese R & D team", Journal of Knowledge Management, https:// doi.org/10.1108/JKM-02-2017-0054 Permanent link to this document:
Lexicon Infused Phrase Embeddings for Named Entity Resolution

Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embeddings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for CoNLL 2003—significantly better than any previous system trained on public data, and matching a system employing massive private industrial query-log data.
Self-efficacy I. Sources of Self-efficacy Beliefs Ii. Efficacy-mediated Processes Iii. Adaptive Benefits of Optimistic Self-beliefs of Efficacy Iv. Development and Exercise of Self-efficacy over the Lifespan I. Sources of Self-efficacy Ii. Efficacy-activated Processes

Glossary Affective Processes: Processes regulating emotional states and elicitation of emotional reactions.  Thinking processes involved in the acquisition, organization and use of information.  Activation to action. Level of motivation is reflected in choice of courses of action, and in the intensity and persistence of effort. Perceived Self-Efficacy: People's beliefs about their capabilities to produce effects.  Exercise of influence over one's own motivation, thought processes, emotional states and patterns of behavior. Perceived self-efficacy is defined as people's beliefs about their capabilities to produce designated levels of performance that exercise influence over events that affect their lives. Self-efficacy beliefs determine how people feel, think, motivate themselves and behave. Such beliefs produce these diverse effects through four major processes. They include cognitive, motivational, affective and selection processes. A strong sense of efficacy enhances human accomplishment and personal well-being in many ways. People with high assurance in their capabilities approach difficult tasks as challenges to be mastered rather than as threats to be avoided. Such an efficacious outlook fosters intrinsic interest and deep engrossment in activities. They set themselves challenging goals and maintain strong commitment to them. They heighten and sustain their efforts in the face of failure. They quickly recover their sense of efficacy after failures or setbacks. They attribute failure to insufficient effort or deficient knowledge and skills which are acquirable. They approach threatening situations with assurance that they can exercise control over them. Such an efficacious outlook produces personal accomplishments, reduces stress and lowers vulnerability to depression. 2 In contrast, people who doubt their capabilities shy away from difficult tasks which they view as personal threats. They have low aspirations and weak commitment to the goals they choose to pursue. When faced with difficult tasks, they dwell on their personal deficiencies, on the obstacles they will encounter, and all kinds of adverse outcomes rather than concentrate on how to perform successfully. They slacken their efforts and give up quickly in the face of difficulties. They are slow to recover their sense of efficacy following failure or setbacks. Because they view insufficient performance as deficient aptitude it does not require much failure for them to lose faith in their capabilities. They fall easy victim to stress and depression.  So here is the crux of the matter: How do we raise children with serious, chronic illnesses to have strong self-efficacy? It can literally be a matter of …
FEEDSTOCK PRETREATMENT STRATEGIES FOR PRODUCING ETHANOL FROM WOOD , BARK , AND FOREST RESIDUES

Energy and environmental issues are among the major concerns facing the global community today. Transportation fuel represents a large proportion of energy consumption, not only in the US, but also worldwide. As fossil fuel is being depleted, new substitutes are needed to provide energy. Ethanol, which has been produced mainly from the fermentation of corn starch in the US, has been regarded as one of the main liquid transportation fuels that can take the place of fossil fuel. However, limitations in the supply of starch are creating a need for different substrates. Forest biomass is believed to be one of the most abundant sources of sugars, although much research has been reported on herbaceous grass, agricultural residue, and municipal waste. The use of biomass sugars entails pretreatment to disrupt the lignin-carbohydrate complex and expose carbohydrates to enzymes. This paper reviews pretreatment technologies from the perspective of their potential use with wood, bark, and forest residues. Acetic acid catalysis is suggested for the first time to be used in steam explosion pretreatment. Its pretreatment economics, as well as that for ammonia fiber explosion pretreatment, is estimated. This analysis suggests that both are promising techniques worthy of further exploration or optimization for commercialization.
Point convolutional neural networks by extension operators

This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds. The framework consists of two operators: extension and restriction, mapping point cloud functions to volumetric functions and vise-versa. A point cloud convolution is defined by pull-back of the Euclidean volumetric convolution via an extension-restriction mechanism.
 The point cloud convolution is computationally efficient, invariant to the order of points in the point cloud, robust to different samplings and varying densities, and translation invariant, that is the same convolution kernel is used at all points. PCNN generalizes image CNNs and allows readily adapting their architectures to the point cloud setting.
 Evaluation of PCNN on three central point cloud learning benchmarks convincingly outperform competing point cloud learning methods, and the vast majority of methods working with more informative shape representations such as surfaces and/or normals.
Inductive matrix completion for predicting gene–disease associations

Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive.



Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has <15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature.


AVAILABILITY
Source code and datasets can be downloaded from http://bigdata.ices.utexas.edu/project/gene-disease.
Group-wise Deep Co-saliency Detection

In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output. The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single-image individual feature representation, and model this in a collaborative learning framework. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.
Classifying Relations by Ranking with Convolutional Neural Networks

Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.
One-Class Training for Masquerade Detection

We extend prior research on masquerade detection using UNIX commands issued by users as the audit source. Previous studies using multi-class training requires gathering data from multiple users to train specific profiles of self and non-self for each user. Oneclass training uses data representative of only one user. We apply one-class Naïve Bayes using both the multivariate Bernoulli model and the Multinomial model, and the one-class SVM algorithm. The result shows that oneclass training for this task works as well as multi-class training, with the great practical advantages of collecting much less data and more efficient training. One-class SVM using binary features performs best among the oneclass training algorithms.
Support vector machines and Word2vec for text classification with semantic features

With the rapid expansion of new available information presented to us online on a daily basis, text classification becomes imperative in order to classify and maintain it. Word2vec offers a unique perspective to the text mining community. By converting words and phrases into a vector representation, word2vec takes an entirely new approach on text classification. Based on the assumption that word2vec brings extra semantic features that helps in text classification, our work demonstrates the effectiveness of word2vec by showing that tf-idf and word2vec combined can outperform tf-idf because word2vec provides complementary features (e.g. semantics that tf-idf can't capture) to tf-idf. Our results show that the combination of word2vec weighted by tf-idf and tf-idf does not outperform tf-idf consistently. It is consistent enough to say the combination of the two can outperform either individually.
A review of drought indices

Drought is a stochastic natural hazard that is instigated by intense and persistent shortage of precipitation. Following an initial meteorological phenomenon, subsequent impacts are realized on agriculture and hydrology. Among the natural hazards, droughts possess certain unique features; in addition to delayed effects, droughts vary by multiple dynamic dimensions including severity and duration, which in addition to causing a pervasive and subjective network of impacts makes them difficult to characterize. In order manage drought, drought characterization is essential enabling both retrospective analyses (e.g., severity versus impacts analysis) and prospective planning (e.g., risk assessment). The adaptation of a simplified method by drought indices has facilitated drought characterization for various users and entities. More than 100 drought indices have so far been proposed, some of which are operationally used to characterize drought using gridded maps at regional and national levels. These indices correspond to different types of drought, including meteorological, agricultural, and hydrological drought. By quantifying severity levels and declaring drought’s start and end, drought indices currently aid in a variety of operations including drought early warning and monitoring and contingency planning. Given their variety and ongoing development, it is crucial to provide a comprehensive overview of available drought indices that highlights their difference and examines the trend in their development. This paper reviews 74 operational and proposed drought indices and describes research directions.
qp : A Tool for Generating 3 D Models of Ancient Greek Pottery

The development of content based retrieval mechanisms is a very active research area. Present studies are mainly focused on automating the information extraction and indexing processes. Usually for the development and evaluation of such mechanisms there is always a need for a ground-truth database. In this paper we present a software tool named qp that is able to semi-automatically produce a collection of random 3D vessels, with morphological characteristics similar to those found in ancient Greek pottery, a ceramic group exhibited worldwide with great impact to scholars as well as general public. A 3D vessel collection has been produced by qp and can be used as a test bed dataset for the development of shape-based 3D descriptors applicable to pottery. Additionally, qp can be considered as a 3D vessel modelling software tool which can be used by people not related to computer graphics technology and particularly to 3D modelling.
SCIENTIFIC AND ARTISTIC PRINCIPLES OF TOOTH SHADE SELECTION : A REVIEW

1 Assistant Professor, Department of Prosthodontics, Ibn-e-Siena Hospital and Research Institute, Multan Medical and Dental College, Multan, Pakistan 2 Assistant Professor, Department of Prosthodontics, College of Dentistry, King Saud University, Riyadh, Saudi Arabia 3 Head Department of Prosthodontics, Armed Forces Institute of Dentistry, Rawalpindi, Pakistan For Correspondence: Dr Salman Ahmad, House No 10, Street No 2, Gulshan Sakhi Sultan Colony, Surej Miani Road, Multan, Pakistan.