nils-herrmann/clean_scirepeval huggingface dataset:
Supernumerary and supplemental teeth: case report.

This is to report the case of a ten year old child affected by a numeric dental anomaly showing the pathologic condition characterised by the simultaneous presence of supernumerary and supplemental teeth. The anomaly was analysed to plan the best surgical and orthodontic treatments.



Dental history, clinical and instrumental examinations were made to perform a correct orthodontic examination and diagnosis. A young patient was affected by numeric dental anomaly in the upper jaw. We observed a high number of teeth, specifically two normally formed supplemental lateral permanent incisors and an unerupted mesiodens placed between the upper central incisors. Firstly, the supplemental lateral teeth were extracted. This surgical therapy and the application of a space maintainer were made to permit the eruption of the permanent canines. Then the mesiodens also underwent surgical treatment (i.e. extraction). Eventually, physiologic eruption of permanent teeth was allowed by the planned surgical-orthodontic treatment.



Aim of the surgical-orthodontic treatment was extraction of the unerupted supernumerary teeth to obtain the physiologic eruption of the permanent ones. Orthodontic treatment is important to solve malocclusions and maintaining the space for the eruption of permanent teeth.



Aesthetics and function are two important parameters in modern dentistry. All clinicians should try to make a correct and rational diagnosis for both simple and complex dental pathologies. Particularly in young children, invasive and surgical disinclusive techniques can be substituted by interceptive orthodontic treatments.
Feature-Based Classification of Networks

Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural building blocks . These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. At a finer scale of classification within each such class, networks describing more similar systems tend to have more similar features. This occurs presumably because networks representing similar purposes or constructions would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible to classify these networks into categories based on their features at various structural levels. Here we describe and demonstrate a new, hybrid approach that combines manual selection of features of potential interest with existing automated classification methods. In particular, selecting well-known and well-studied features that have been used throughout social network analysis and network science [2, 3] and then classifying with methods such as random forests [4] that are of special utility in the presence of feature collinearity, we find that we achieve higher accuracy, in shorter computation time, with greater interpretability of the network classification results. Past work in the area of network classification has primarily focused on distinguishing networks from different categories using two different broad classes of approaches. In the first approach , network classification is carried out by examining certain specific structural features and investigating whether networks belonging to the same category are similar across one or more dimensions as defined by these features [5, 6, 7, 8]. In other words, in this approach the investigator manually chooses the structural characteristics of interest and more or less manually (informally) determines the regions of the feature space that correspond to different classes. These methods are scalable to large networks and yield results that are easily interpreted in terms of the characteristics of interest, but in practice they tend to lead to suboptimal classification accuracy. In the second approach, network classification is done by using very flexible machine learning classi-fiers that, when presented with a network as an input, classify its category or class as an output To somewhat oversimplify, the first approach relies on manual feature specification followed by manual selection of a classification system, whereas the second approach is its opposite, relying on automated feature detection followed by automated classification. While …
Organization of Multi-Agent Systems: An Overview

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.
Sentence Encoding with Tree-constrained Relation Networks

The meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variants of relation networks (RNs) which represent a set of objects (for us, words forming a sentence) in terms of representations of pairs of objects. We propose two extensions to the basic RN model for natural language. First, building on the intuition that not all word pairs are equally informative about the meaning of a sentence, we use constraints based on both supervised and unsupervised dependency syntax to control which relations influence the representation. Second, since higher-order relations are poorly captured by a sum of pairwise relations, we use a recurrent extension of RNs to propagate information so as to form representations of higher order relations. Experiments on sentence classification, sentence pair classification, and machine translation reveal that, while basic RNs are only modestly effective for sentence representation, recurrent RNs with latent syntax are a reliably powerful representational device.
Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics

Developing transparent predictive analytics has attracted significant research attention recently. There have been multiple theories on how to model learning transparency but none of them aims to understand the internal and often complicated modeling processes. In this paper we adopt a contemporary philosophical concept called "constructivism", which is a theory regarding how human learns. We hypothesize that a critical aspect of transparent machine learning is to "reveal" model construction with two key process: (1) the assimilation process where we enhance our existing learning models and (2) the accommodation process where we create new learning models. With this intuition we propose a new learning paradigm, constructivism learning, using a Bayesian nonparametric model to dynamically handle the creation of new learning tasks. Our empirical study on both synthetic and real data sets demonstrate that the new learning algorithm is capable of delivering higher quality models (as compared to base lines and state-of-the-art) and at the same time increasing the transparency of the learning process.
When Gaussian Process Meets Big Data: A Review of Scalable GPs

The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process (GP), a well-known non-parametric and interpretable Bayesian model, which suffers from cubic complexity to training size. To improve the scalability while retaining the desirable prediction quality, a variety of scalable GPs have been presented. But they have not yet been comprehensively reviewed and discussed in a unifying way in order to be well understood by both academia and industry. To this end, this paper devotes to reviewing state-of-theart scalable GPs involving two main categories: global approximations which distillate the entire data and local approximations which divide the data for subspace learning. Particularly, for global approximations, we mainly focus on sparse approximations comprising prior approximations which modify the prior but perform exact inference, and posterior approximations which retain exact prior but perform approximate inference; for local approximations, we highlight the mixture/product of experts that conducts model averaging from multiple local experts to boost predictions. To present a complete review, recent advances for improving the scalability and model capability of scalable GPs are reviewed. Finally, the extensions and open issues regarding the implementation of scalable GPs in various scenarios are reviewed and discussed to inspire novel ideas for future research avenues.
GTmoPass: two-factor authentication on public displays using gaze-touch passwords and personal mobile devices

As public displays continue to deliver increasingly private and personalized content, there is a need to ensure that only the legitimate users can access private information in sensitive contexts. While public displays can adopt similar authentication concepts like those used on public terminals (e.g., ATMs), authentication in public is subject to a number of risks. Namely, adversaries can uncover a user's password through (1) shoulder surfing, (2) thermal attacks, or (3) smudge attacks. To address this problem we propose GTmoPass, an authentication architecture that enables Multi-factor user authentication on public displays. The first factor is a knowledge-factor: we employ a shoulder-surfing resilient multimodal scheme that combines gaze and touch input for password entry. The second factor is a possession-factor: users utilize their personal mobile devices, on which they enter the password. Credentials are securely transmitted to a server via Bluetooth beacons. We describe the implementation of GTmoPass and report on an evaluation of its usability and security, which shows that although authentication using GTmoPass is slightly slower than traditional methods, it protects against the three aforementioned threats.
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification

State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.


Availability and Implementation
TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation .



Supplementary data are available at Bioinformatics online.
Analysis of weighted networks.

The connections in many networks are not merely binary entities, either present or not, but have associated weights that record their strengths relative to one another. Recent studies of networks have, by and large, steered clear of such weighted networks, which are often perceived as being harder to analyze than their unweighted counterparts. Here we point out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighted graphs to weighted ones as well. We give a number of examples of the method, including an algorithm for detecting community structure in weighted networks and a simple proof of the maximum-flow-minimum-cut theorem.
StreamMap: Smooth Dynamic Visualization of High-Density Streaming Points

Interactive visualization of streaming points for real-time scatterplots and linear blending of correlation patterns is increasingly becoming the dominant mode of visual analytics for both big data and streaming data from active sensors and broadcasting media. To better visualize and interact with inter-stream patterns, it is generally necessary to smooth out gaps or distortions in the streaming data. Previous approaches either animate the points directly or present a sampled static heat-map. We propose a new approach, called StreamMap, to smoothly blend high-density streaming points and create a visual flow that emphasizes the density pattern distributions. In essence, we present three new contributions for the visualization of high-density streaming points. The first contribution is a density-based method called super kernel density estimation that aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robust density morphing algorithm that generates several smooth intermediate frames for a given pair of frames. The third contribution is a trend representation design that can help convey the flow directions of the streaming points. The experimental results on three datasets demonstrate the effectiveness of StreamMap when dynamic visualization and visual analysis of trend patterns on streaming points are required.
PSYCHOLOGICAL RESILIENCE IN OLYMPIC CHAMPIONS 3 A Grounded Theory of Psychological Resilience in Olympic Champions

1  Although it is well-established that the ability to manage stress is a prerequisite of 2 sporting excellence, the construct of psychological resilience has yet to be systematically 3 examined in athletic performers. The study reported here sought to explore and explain the 4 relationship between psychological resilience and optimal sport performance. 5  Twelve Olympic champions (8 men and 4 women) from a range of sports 6 were interviewed regarding their experiences of withstanding pressure during their sporting 7 careers. A grounded theory approach was employed throughout the data collection and 8 analysis, and interview transcripts were analyzed using open, axial and selective coding. 9 Methodological rigor was established by incorporating various verification strategies into the 10 research process, and the resultant grounded theory was also judged using the quality criteria of 11 fit, work, relevance, and modifiability.  Results indicate that numerous psychological factors (relating to a 13 positive personality, motivation, confidence, focus, and perceived social support) protect the 14 world’s best athletes from the potential negative effect of stressors by influencing their 15 challenge appraisal and meta-cognitions. These processes promote facilitative responses that 16 precede optimal sport performance. The emergent theory provides sport psychologists, coaches 17 and national sport organizations with an understanding of the role of resilience in athletes’ lives 18 and the attainment of optimal sport performance.
Current neuroimaging techniques in Alzheimer's disease and applications in animal models.

With Alzheimer's disease (AD) quickly becoming the most costly disease to society, and with no disease-modifying treatment currently, prevention and early detection have become key points in AD research. Important features within this research focus on understanding disease pathology, as well as finding biomarkers that can act as early indicators and trackers of disease progression or potential treatment. With the advances in neuroimaging technology and the development of new imaging techniques, the search for cheap, noninvasive, sensitive biomarkers becomes more accessible. Modern neuroimaging techniques are able to cover most aspects of disease pathology, including visualization of senile plaques and neurofibrillary tangles, cortical atrophy, neuronal loss, vascular damage, and changes in brain biochemistry. These methods can provide complementary information, resulting in an overall picture of AD. Additionally, applying neuroimaging to animal models of AD could bring about greater understanding in disease etiology and experimental treatments whilst remaining in vivo. In this review, we present the current neuroimaging techniques used in AD research in both their human and animal applications, and discuss how this fits in to the overall goal of understanding AD.
The Category Theoretic Understanding of Universal Algebra: Lawvere Theories and Monads

Lawvere theories and monads have been the two main category theoretic formulations of universal algebra, Lawvere theories arising in 1963 and the connection with monads being established a few years later. Monads, although mathematically the less direct and less malleable formulation, rapidly gained precedence. A generation later, the definition of monad began to appear extensively in theoretical computer science in order to model computational effects, without reference to universal algebra. But since then, the relevance of universal algebra to computational effects has been recognised, leading to renewed prominence of the notion of Lawvere theory, now in a computational setting. This development has formed a major part of Gordon Plotkin’s mature work, and we study its history here, in particular asking why Lawvere theories were eclipsed by monads in the 1960’s, and how the renewed interest in them in a computer science setting might develop in future.
Cryptanalysis of the SIMON Family of Block Ciphers

Recently, the U.S National Security Agency has published the specifications of two families of lightweight block ciphers, SIMON and SPECK, on ePrint . The ciphers are developed with optimization towards both hardware and software in mind. While the specification paper discusses design requirements and performance of the presented lightweight ciphers thoroughly, no security assessment is given. This paper is a move towards filling that cryptanalysis gap for the SIMON family of ciphers. We present a series of observations on the presented construction that, in some cases, yield attacks, while in other cases may provide basis of further analysis by the cryptographic community. Specifically, we obtain attacks using classicalas well as truncated differentials. In the former case, we show how the smallest version of SIMON, Simon32/64, exhibits a strong differential effect.
The spectral sensitivity of the human short-wavelength sensitive cones derived from thresholds and color matches

We used two methods to estimate short-wave (S) cone spectral sensitivity. Firstly, we measured S-cone thresholds centrally and peripherally in five trichromats, and in three blue-cone monochromats, who lack functioning middle-wave (M) and long-wave (L) cones. Secondly, we analyzed standard color-matching data. Both methods yielded equivalent results, on the basis of which we propose new S-cone spectral sensitivity functions. At short and middle-wavelengths, our measurements are consistent with the color matching data of Stiles and Burch (1955, Optica Acta, 2, 168-181; 1959, Optica Acta, 6, 1-26), and other psychophysically measured functions, such as pi 3 (Stiles, 1953, Coloquio sobre problemas opticos de la vision, 1, 65-103). At longer wavelengths, S-cone sensitivity has previously been over-estimated.
Unified Cloud Computing Adoption Framework

Cloud computing adoption has represented a big challenge for all kinds of companies all over the world. The challenge involves such questions as where to start, which provider should the company choose or whether it is even worthwhile. With a constantly changing economic environment, businesses must assess current technologies and offerings to remain competitive. The possibility of migrating a company's services and infrastructure to the cloud may seem attractive. However, without proper guidance, the results may not be as expected, leading a loss of time and money. As each company has its own needs and requirements, industry-focused frameworks have been proposed (e.g. for educational or governmental institutions). Although these frameworks are useful, they are not applicable to every business. Hence, a generic, widely-applicable and implementable cloud computing adoption framework is proposed in this paper. It takes the best outcomes of previous studies, best-practice suggestions, as well as authors' additions, and sums them up into a more robust, unified framework. The framework consists of 6 detailed phases carrying the user from knowing the company's current state to successfully migrating the data, services, and infrastructure to the cloud. These steps are intended to help IT directors and other decision-makers to reduce risks and maximize benefits throughout the cloud computing adoption process. Data security risks are not discussed in this paper as other authors have already sufficiently studied them. This framework was developed from a business perspective.
Attention selection, distractor suppression and N2pc

N2pc is generally interpreted as the electrocortical correlate of the distractor-suppression mechanisms through which attention selection takes place in humans. Here, we present data that challenge this common N2pc interpretation. In Experiment 1, multiple distractors induced greater N2pc amplitudes even when they facilitated target identification, despite the suppression account of the N2pc predicted the contrary; in Experiment 2, spatial proximity between target and distractors did not affect the N2pc amplitude, despite resulting in more interference in response times; in Experiment 3, heterogeneous distractors delayed response times but did not elicit a greater N2pc relative to homogeneous distractors again in contrast with what would have predicted the suppression hypothesis. These results do not support the notion that the N2pc unequivocally mirrors distractor-suppression processes. We propose that the N2pc indexes mechanisms involved in identifying and localizing relevant stimuli in the scene through enhancement of their features and not suppression of distractors.
Evolution of Subsumption Using Genetic Programming

1 .  The repetitive application of seemingly simple rules can lead to complex overall emergent behavior. Emergent functionality means that overall functionality is not achieved in the conventional tightly coupled, centrally controlled way, but, instead, indirectly by the interaction of relatively primitive components with the world and among themselves [Steels 1991]. Emergent functionality is one of the main themes of research in artificial life [Langton 1989]. In this paper, we use the genetic programming paradigm to evolve a computer program that exhibits emergent behavior and enables an autonomous mobile robot to follow the walls of an irregularly shaped room. The evolutionary process is driven only by the fitness of the programs in solving the problem.