nils-herrmann/clean_scirepeval huggingface dataset:
I Aesthetic Surgery of the Face 6 Anatomy of the aging face

The traditional approach to assessing the face is to consider the face in thirds (upper, middle, and lower thirds). While useful, this approach limits conceptualization, as it is not based on the function of the face. From a functional perspec­ tive, the face has an anterior aspect and a lateral aspect. The anterior face is highly evolved beyond the basic survival needs, specifically, for communication and facial expression. In contrast, the lateral face predominantly covers the struc­ tures of mastication. A vertical line descending from the lateral orbital rim is the approximate division between the anterior and lateral zones of the face. Internally, a series of facial retaining ligaments are strategically located along this line to demarcate the anterior from the lateral face . The mimetic muscles of the face are located in the superficial fascia of the anterior face, mostly around the eyes and the mouth. This highly mobile area of the face is designed to allow fine movement and is prone to develop laxity with aging. In contrast, the lateral face is relatively immobile as it overlies the structures to do with mastication, the temporalis, masseter, the parotid gland and its duct, all located deep to the deep fascia. The only superficial muscle in the lateral face is the platysma in the lower third, which extends to the level of the oral commissure. Importantly, the soft tissues of the anterior face are subdi­ vided into two parts; that which overlies the skeleton and the larger part that comprises the highly specialized sphincters overlying the bony cavities. Where the soft tissues overlie the orbital and oral cavities they are modified, as there is no deep S Y N O P S I S
Dominant Factors for Online Trust

The uses of the World Wide Web on the Internet for commerce and information access continue to expand. The e-commerce business has proven to be a promising channel of choice for consumers as it is gradually transforming into a mainstream business activity. However, lack of trust has been identified as a major obstacle to the adoption of online shopping. Empirical study of online trust is constrained by the shortage of high-quality measures of general trust in the e-commence contexts. Based on theoretical or empirical studies in the literature of marketing or information system, nine factors have sound theoretical sense and support from the literature. A survey method was used for data collection in this study. A total of 172 usable questionnaires were collected from respondents. This study presents a new set of instruments for use in studying online trust of an individual. The items in the instrument were analyzed using a factors analysis. The results demonstrated reliable reliability and validity in the instrument.This study identified seven factors has a significant impact on online trust. The seven dominant factors are reputation, third-party assurance, customer service, propensity to trust, website quality, system assurance and brand. As consumers consider that doing business with online vendors involves risk and uncertainty, online business organizations need to overcome these barriers. Further, implication of the finding also provides e-commerce practitioners with guideline for effectively engender online customer trust.
Use of sodium valproate in violent and aggressive behaviors: a critical review.

Valproate was initially introduced as an antiepileptic agent in 1967, but has been used over the years to treat a variety of psychiatric disorders. Its use in the treatment of patients exhibiting aggressive and violent behaviors has been reported in the literature as far back as 1988. However, these reports are uncontrolled, which is in marked contrast to the actual wide and established use of valproate for the treatment of aggressive behaviors. The aim of this report is to critically review the available data on valproate's use in nonbipolar patients with aggressive and violent behaviors.



The MEDLINE and PsycLIT databases were searched for all reports published from 1987-1998 containing the keywords valproate, the names of all commercial preparations, aggression, and violence.



Seventeen reports with a total of 164 patients were located. Ten of these were case reports with a total of 31 patients. Three were retrospective chart reviews with 83 patients, and 3 were open-label prospective studies with a total of 34 patients. No double-blind, placebo-controlled study could be found. An overall response rate of 77.1% was calculated when response was defined as a 50% reduction of target behavior. Most frequent diagnoses recorded were dementia, organic brain syndromes, and mental retardation. The antiaggressive response usually occurred in conjunction with other psychotropic medication. The dose and plasma valproate level required for response appeared to be the same as in the treatment of seizure disorders.



While valproate's general antiaggressive effect is promising, in the absence of controlled data, conclusions are limited at this time. Specific recommendations for study design are given to obtain interpretable data for this indication.
Comparing Patent and Scientific Literature in Airborne Wind Energy

Airborne Wind Energy (AWE) is a renewable energy technology that uses wind power devices rather than traditional wind turbines that take advantage of the kinetic wind energy, and remain in the air due to aerodynamic forces. This article aims to compare the scientific literature with the patents on wind power with tethered airfoils, to obtain better insights into the literature of this area of knowledge. The method used in this study was a comparative bibliometric analysis, using the Web of Science and Derwent Innovations Index databases, and the Network Analysis Interface for Literature Review software and VosViewer. It was possible to verify the main authors, research centers and companies, countries and journals that publish on the subject; the most cited documents; the technological classes; and the networks of collaborations of this work. It was also possible to identify that researches on wind energy with tethered airfoils began their studies in the late 1970s with the first patent apparently dated from 1975 by the inventors Dai and Dai. The first scientific publication was in 1979 by authors Fletcher and Roberts, followed by Loyd in 1980. United States is the country that presented the highest number of patents and scientific papers. Both scientific papers and patents set up networks of collaboration; that is, important authors are interacting with others to establish cooperative partnerships.
Chapter 1 Self-Organizing Maps

A topographic map is a two-dimensional, nonlinear approxim at on of a potentially high-dimensional data manifold, which makes i t an appealing instrument for visualizing and exploring high-dimensional data. The Self-Organizing Map (SOM) is the most widely used algorithm, and it has led to thou sands of applications in very diverse areas. In this chapter, we will introdu ce the SOM algorithm, discuss its properties and applications, and also discuss s ome of its extensions and new types of topographic map formation, such as the ones that can be used for processing categorical data, time series and tree structured d ata.
The smallest grammar problem

This paper addresses the smallest grammar problem: What is the smallest context-free grammar that generates exactly one given string /spl sigma/? This is a natural question about a fundamental object connected to many fields such as data compression, Kolmogorov complexity, pattern identification, and addition chains. Due to the problem's inherent complexity, our objective is to find an approximation algorithm which finds a small grammar for the input string. We focus attention on the approximation ratio of the algorithm (and implicitly, the worst case behavior) to establish provable performance guarantees and to address shortcomings in the classical measure of redundancy in the literature. Our first results are concern the hardness of approximating the smallest grammar problem. Most notably, we show that every efficient algorithm for the smallest grammar problem has approximation ratio at least 8569/8568 unless P=NP. We then bound approximation ratios for several of the best known grammar-based compression algorithms, including LZ78, B ISECTION, SEQUENTIAL, LONGEST MATCH, GREEDY, and RE-PAIR. Among these, the best upper bound we show is O(n/sup 1/2/). We finish by presenting two novel algorithms with exponentially better ratios of O(log/sup 3/n) and O(log(n/m/sup */)), where m/sup */ is the size of the smallest grammar for that input. The latter algorithm highlights a connection between grammar-based compression and LZ77.
Social Media Use During Disasters

This study provides insights that can inform disaster communication management, policymaking, and theory building through a nationally representative field experiment (N = 2,015 U.S. adults) grounded in media richness theory, information and communication technologies (ICTs) succession theory, and the social-mediated crisis communication (SMCC) model. Key findings include the following: (1) Significant main effects of disaster information source were detected on how likely participants were to seek further disaster information from TV, local government websites, and federal government websites; (2) regardless of information form and source, participants reported strongest intentions to immediately communicate about the disaster predominately via offline interpersonal forms rather than through online organizational and personal forms; and (3) regardless of information source, participants reported strong intentions to evacuate if instructed to do so by the government. These findings call for developing crisis communication theory that is more focused on how publics communicate with each other rather than with organizations about disasters and predict a wider variety of crisis communication outcomes.
SPARK: Adapting Keyword Query to Semantic Search

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named ‘SPARK’ has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al.  showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network. In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227 &#xd7; 227) than previous generative models, and does so for all 1000 ImageNet categories. In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models Plug and Play Generative Networks. PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable condition network C that tells the generator what to draw. We demonstrate the generation of images conditioned on a class (when C is an ImageNet or MIT Places classification network) and also conditioned on a caption (when C is an image captioning network). Our method also improves the state of the art of Multifaceted Feature Visualization [40], which generates the set of synthetic inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting. While image models are used in this paper, the approach is modality-agnostic and can be applied to many types of data.
No Reciprocity in "Liking" Photos: Analyzing Like Activities in Instagram

In social media, people often press a "Like" button to indicate their shared interest in a particular content or to acknowledge the user who posted the content. Such activities form relationships and networks among people, raising interesting questions about their unique characteristics and implications. However, little research has investigated such Likes as a main study focus. To address this lack of understanding, based on a theoretical framework, we present an analysis of the structural, influential, and contextual aspects of Like activities from the test datasets of 20 million users and their 2 billion Like activities in Instagram. Our study results first highlight that Like activities and networks increase exponentially, and are formed and developed by one's friends and many random users. Second, we observe that five other essential Instagram elements influence the number of Likes to different extents, but following others will not necessarily increase the number of Likes that one receives. Third, we explore the relationship between LDA-based topics and Likes, characterize two user groups-specialists and generalists-and show that specialists tend to receive more Likes and promote themselves more than generalists. We finally discuss theoretical and practical implications and future research directions.
In Vitro – In Vivo Correlation : Importance of Dissolution in IVIVC

Correlations between in vitro and in vivo data (IVIVC) are often used during pharmaceutical development in order to reduce development time and optimize the formulation. A good correlation is a tool for predicting in vivo results based on in vitro data. IVIVC allows dosage form optimization with the fewest possible trials in man,fixes dissolution acceptance criteria,and can be used as a surrogate for further bioequivalence studies; it is also recommended by regulatory authorities (1–5). Many studies reported in the late ’70s and early ’80s established the basic concept of IVIVC (6). Various definitions of in vitro–in vivo correlation have been proposed by the International Pharmaceutical Federation (FIP), the USP working group (7),and regulatory authorities such as the FDA or EMEA (2–5). The FDA (2) defines IVIVC as “a predictive mathematical model describing the relationship between an in vitro property of an extended release dosage form (usually the rate or extent of drug dissolution or release) and a relevant in vivo response,e.g.,plasma drug concentration or amount of drug absorbed.” As stressed in this definition, IVIVC is more an in vitro–in vivo relationship than a strict correlation. It should be kept in mind that a relationship does not imply a causality link between the in vitro data, in our case,and the in vivo data. Bases on the type of data used to establish the relationship, three main levels are defined by the FDA:
Plant cell cultures for the production of recombinant proteins

The use of whole plants for the synthesis of recombinant proteins has received a great deal of attention recently because of advantages in economy, scalability and safety compared with traditional microbial and mammalian production systems. However, production systems that use whole plants lack several of the intrinsic benefits of cultured cells, including the precise control over growth conditions, batch-to-batch product consistency, a high level of containment and the ability to produce recombinant proteins in compliance with good manufacturing practice. Plant cell cultures combine the merits of whole-plant systems with those of microbial and animal cell cultures, and already have an established track record for the production of valuable therapeutic secondary metabolites. Although no recombinant proteins have yet been produced commercially using plant cell cultures, there have been many proof-of-principle studies and several companies are investigating the commercial feasibility of such production systems.
Interpretability in sequence tagging models for Named Entity Recognition

The field of Explainable Artificial Intelligence has taken steps towards increasing transparency in the decision-making process of machine learning models for classification tasks. Understanding the reasons behind the predictions of models increases our trust in them and lowers the risks of using them. In an effort to extend this to other tasks apart from classification, this thesis explores the interpretability aspect for sequence tagging models for the task of Named Entity Recognition (NER). This work proposes two approaches for adapting LIME, an interpretation method for classification, to sequence tagging and NER. The first approach is a direct adaptation of LIME to the task, while the second includes adaptations following the idea that entities are conceived as a group of words and we would like one explanation for the whole entity. Given the challenges in the evaluation of the interpretation method, this work proposes an extensive evaluation from different angles. It includes a quantitative analysis using the AOPC metric; a qualitative analysis that studies the explanations at instance and dataset levels as well as the semantic structure of the embeddings and the explanations; and a human evaluation to validate the model's behaviour. The evaluation has discovered patterns and characteristics to take into account when explaining NER models.
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews

Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discriminate only between positive and negative sentiment, turning the task into a standard binary classification problem. We compare several machine learning approaches to this problem, and combine them to achieve a new state of the art. We show how to use for this task the standard generative language models, which are slightly complementary to the state of the art techniques. We achieve strong results on a well-known dataset of IMDB movie reviews. Our results are easily reproducible, as we publish also the code needed to repeat the experiments. This should simplify further advance of the state of the art, as other researchers can combine their techniques with ours with little effort.
The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions

Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SELU nonlinearities “solve” the exploding gradient problem, we show that this is not the case in general and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice. We explain why exploding gradients occur and highlight the collapsing domain problem, which can arise in architectures that avoid exploding gradients. ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks. We show this is a direct consequence of the Pythagorean equation. By noticing that any neural network is a residual network, we devise the residual trick, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.
Inflammation, fracture and bone repair.

The reconstitution of lost bone is a subject that is germane to many orthopedic conditions including fractures and non-unions, infection, inflammatory arthritis, osteoporosis, osteonecrosis, metabolic bone disease, tumors, and periprosthetic particle-associated osteolysis. In this regard, the processes of acute and chronic inflammation play an integral role. Acute inflammation is initiated by endogenous or exogenous adverse stimuli, and can become chronic in nature if not resolved by normal homeostatic mechanisms. Dysregulated inflammation leads to increased bone resorption and suppressed bone formation. Crosstalk among inflammatory cells (polymorphonuclear leukocytes and cells of the monocyte-macrophage-osteoclast lineage) and cells related to bone healing (cells of the mesenchymal stem cell-osteoblast lineage and vascular lineage) is essential to the formation, repair and remodeling of bone. In this review, the authors provide a comprehensive summary of the literature related to inflammation and bone repair. Special emphasis is placed on the underlying cellular and molecular mechanisms, and potential interventions that can favorably modulate the outcome of clinical conditions that involve bone repair.
Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation

Human pose estimation using deep neural networks aims to map input images with large variations into multiple body keypoints, which must satisfy a set of geometric constraints and interdependence imposed by the human body model. This is a very challenging nonlinear manifold learning process in a very high dimensional feature space. We believe that the deep neural network, which is inherently an algebraic computation system, is not the most efficient way to capture highly sophisticated human knowledge, for example those highly coupled geometric characteristics and interdependence between keypoints in human poses. In this work, we propose to explore how external knowledge can be effectively represented and injected into the deep neural networks to guide its training process using learned projections that impose proper prior. Specifically, we use the stacked hourglass design and inception-resnet module to construct a fractal network to regress human pose images into heatmaps with no explicit graphical modeling. We encode external knowledge with visual features, which are able to characterize the constraints of human body models and evaluate the fitness of intermediate network output. We then inject these external features into the neural network using a projection matrix learned using an auxiliary cost function. The effectiveness of the proposed inception-resnet module and the benefit in guided learning with knowledge projection is evaluated on two widely used human pose estimation benchmarks. Our approach achieves state-of-the-art performance on both datasets.