nils-herrmann/clean_scirepeval huggingface dataset:
Energy balance and reproduction

The physiological mechanisms that control energy balance are reciprocally linked to those that control reproduction, and together, these mechanisms optimize reproductive success under fluctuating metabolic conditions. Thus, it is difficult to understand the physiology of energy balance without understanding its link to reproductive success. The metabolic sensory stimuli, hormonal mediators and modulators, and central neuropeptides that control reproduction also influence energy balance. In general, those that increase ingestive behavior inhibit reproductive processes, with a few exceptions. Reproductive processes, including the hypothalamic-pituitary-gonadal (HPG) system and the mechanisms that control sex behavior are most proximally sensitive to the availability of oxidizable metabolic fuels. The role of hormones, such as insulin and leptin, are not understood, but there are two possible ways they might control food intake and reproduction. They either mediate the effects of energy metabolism on reproduction or they modulate the availability of metabolic fuels in the brain or periphery. This review examines the neural pathways from fuel detectors to the central effector system emphasizing the following points: first, metabolic stimuli can directly influence the effector systems independently from the hormones that bind to these central effector systems. For example, in some cases, excess energy storage in adipose tissue causes deficits in the pool of oxidizable fuels available for the reproductive system. Thus, in such cases, reproduction is inhibited despite a high body fat content and high plasma concentrations of hormones that are thought to stimulate reproductive processes. The deficit in fuels creates a primary sensory stimulus that is inhibitory to the reproductive system, despite high concentrations of hormones, such as insulin and leptin. Second, hormones might influence the central effector systems [including gonadotropin-releasing hormone (GnRH) secretion and sex behavior] indirectly by modulating the metabolic stimulus. Third, the critical neural circuitry involves extrahypothalamic sites, such as the caudal brain stem, and projections from the brain stem to the forebrain. Catecholamines, neuropeptide Y (NPY) and corticotropin-releasing hormone (CRH) are probably involved. Fourth, the metabolic stimuli and chemical messengers affect the motivation to engage in ingestive and sex behaviors instead of, or in addition to, affecting the ability to perform these behaviors. Finally, it is important to study these metabolic events and chemical messengers in a wider variety of species under natural or seminatural circumstances.
Social Network Analysis: History, Concepts, and Research

Social network analysis (SNA), in essence, is not a formal theory in social science, but rather an approach for investigating social structures, which is why SNA is often referred to as structural analysis . The most important difference between social network analysis and the traditional or classic social research approach is that the contexts of the social actor, or the relationships between actors are the first considerations of the former, while the latter focuses on individual properties. A social network is a group of collaborating, and/or competing individuals or entities that are related to each other. It may be presented as a graph, or a multi-graph; each participant in the collaboration or competition is called an actor and depicted as a node in the graph theory. Valued relations between actors are depicted as links, or ties, either directed or undirected, between the corresponding nodes. Actors can be persons, organizations, or groups – any set of related entities. As such, SNA may be used on different levels, ranging from individuals, web pages, families, small groups, to large organizations, parties, and even to nations. According to the well known SNA researcher Lin , network analysis is based on the intuitive notion that these patterns are important features of the lives of the individuals or social entities who display them; Network analysts believe that how an individual lives, or social entity depends in large part on how that they are tied into the larger web of social connections/structures. Many believe, moreover, that the success or failure of societies and organizations often depends on the patterning of their internal structure. With a history of more than 70 years, SNA as an interdisciplinary technique developed under many influences, which come from different fields such as sociology, mathematics and computer science, are becoming increasingly important across many disciplines, including sociology, economics, communication science, and psychology around the world. In the current chapter of this book, the author discusses
Seismic Retrofit for Reinforced Concrete Building Structures

Many existing structures located in seismic regions are inadequate based on current seismic design codes. In addition, a number of major earthquakes during recent years have underscored the importance of mitigation to reduce seismic risk. Seismic retrofitting of existing structures is one of the most effective methods of reducing this risk. In recent years, a significant amount of research has been devoted to the study of various strengthening techniques to enhance the seismic performance of RC structures. However, the seismic performance of the structure may not be improved by retrofitting or rehabilitation unless the engineer selects an appropriate intervention technique based on seismic evaluation of the structure. Therefore, the basic requirements of rehabilitation and investigations of various retrofit techniques should be considered before selecting retrofit schemes. In this report, the characteristics of various intervention techniques are discussed and the relationship between retrofit and structural characteristics is also described. In addition, several case study structures for which retrofit techniques have been applied are presented.
A Deep Learning Approach for Question Answering Over Knowledge Base

With the increase of the scale of the knowledge base, it’s important to answer question over knowledge base. In this paper , we will introduce a method to extract answers from Chinese knowledge base for Chinese questions. Our method uses a classifier to judge whether the relation in the triple is what the question asked, question-relation pairs are used to train the classifier. It’s difficult to identify the right relation, so we find out the focus of the question and leverage the resource of lexical paraphrase in the preprocessing of the question. And the use of lexical paraphrase also can alleviate the out of vocabulary(OOV) problem. In order to let the right answer at the top of candidate answers, we present a ranking method to rank these candidate answers. The result of the final evaluation shows that our method achieves a good result.
CONSUMERS ’ PURCHASE INTENTIONS TOWARDS NATURAL

The purpose of this paper is to determine which variables influence consumers’ intentions towards purchasing natural cosmetics. Several variables are included in the regression analysis such as age, gender, consumers’ purchase tendency towards organic food, consumers’ new natural cosmetics brands and consumers’ tendency towards health consciousness. The data was collected through an online survey questionnaire using the purposive sample of 204 consumers from the Dubrovnik-Neretva County in March and April of 2015. Various statistical analyses were used such as binary logistic regression and correlation analysis. Binary logistic regression results show that gender, consumers’ purchase tendency towards organic food and consumers’ purchase tendency towards new natural cosmetics brands have an influence on consumer purchase intentions. However, consumers’ tendency towards health consciousness has no influence on consumers’ intentions towards purchasing natural cosmetics. Results of the correlation analysis indicate that there is a strong positive correlation between purchase intentions towards natural cosmetics and consumer references of natural cosmetics. The findings may be useful to online retailers, as well as marketers and practitioners to recognize and better understand the new trends that occur in the industry of natural cosmetics.
End-to-End Audiovisual Speech Recognition

Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition. However, research on end-to-end audiovisual models is very limited. In this work, we present an end-to-end audiovisual model based on residual networks and Bidirectional Gated Recurrent Units (BGRUs). To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the image pixels and audio waveforms and performs within-context word recognition on a large publicly available dataset (LRW). The model consists of two streams, one for each modality, which extract features directly from mouth regions and raw waveforms. The temporal dynamics in each stream/modality are modeled by a 2-layer BGRU and the fusion of multiple streams/modalities takes place via another 2-layer BGRU. A slight improvement in the classification rate over an end-to-end audio-only and MFCC-based model is reported in clean audio conditions and low levels of noise. In presence of high levels of noise, the end-to-end audiovisual model significantly outperforms both audio-only models.
BlockSci: Design and applications of a blockchain analysis platform

Analysis of blockchain data is useful for both scienti c research and commercial applications. We present BlockSci, an open-source software platform for blockchain analysis. BlockSci is versatile in its support for di erent blockchains and analysis tasks. It incorporates an in-memory, analytical (rather than transactional) database, making it several hundred times faster than existing tools. We describe BlockSci’s design and present four analyses that illustrate its capabilities. This is a working paper that accompanies the rst public release of BlockSci, available at github.com/citp/BlockSci. We seek input from the community to further develop the software and explore other potential applications.
Point cloud databases

We introduce the concept of the point cloud database, a new kind of database system aimed primarily towards scientific applications. Many scientific observations, experiments, feature extraction algorithms and large-scale simulations produce enormous amounts of data that are better represented as sparse (but often highly-clustered) points in a k-dimensional (k ≲ 10) metric space than on a multi-dimensional grid. Dimensionality reduction techniques, such as principal components, are also widely-used to project high dimensional data into similarly low dimensional spaces. Analysis techniques developed to work on multi-dimensional data points are usually implemented as in-memory algorithms and need to be modified to work in distributed cluster environments and on large amounts of disk-resident data. We conclude that the relational model, with certain additions, is appropriate for point clouds, but point cloud databases must also provide unique set of spatial search and proximity join operators, indexing schemes, and query language constructs that make them a distinct class of database systems.
FRAUD DETECTION IN MOBILE TELECOMMUNICATION

Fraud has been very common in our society, and it affects private enterprises as well as public entities. However, in recent years, the development of new technologies has also provided criminals more sophisticated way to commit fraud and it therefore requires more advanced techniques to detect and prevent such events. The types of fraud in Telecommunication industry includes: Subscription Fraud, Clip on Fraud, Call Forwarding, Cloning Fraud, Roaming Fraud, and Calling Card. Thus, detection and prevention of these frauds is one of the main objectives of the telecommunication industry. In this research, we developed a model that detects fraud in Telecommunication sector in which a random rough subspace based neural network ensemble method was employed in the development of the model to detect subscription fraud in mobile telecoms. This study therefore presents the development of patterns that illustrate the customers’ subscription's behaviour focusing on the identification of non-payment events. This information interrelated with other features produces the rules that lead to the predictions as earlier as possible to prevent the revenue loss for the company by deployment of the appropriate actions.
Sentiment Analysis Using Common-Sense and Context Information

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information.  based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.
Dynamic and Kinematic Models and Control for Differential Drive Mobile Robots

The two-wheel differential drive mobile robots, are one of the simplest and most used structures in mobile robotics applications, it consists of a chassis with two fixed and in-line with each other electric motors. This paper presents new models for differential drive mobile robots and some considerations regarding design, modeling and control solutions. The presented models are to be used to help in facing the two top challenges in developing mechatronic mobile robots system; early identifying system level problems and ensuring that all design requirements are met, as well as, to simplify and accelerate Mechatronics mobile robots design process, including proper selection, analysis, integration and verification of the overall system and sub-systems performance throughout the development process.
Powder-based additive manufacturing – a review of types of defects , generation mechanisms , detection , property evaluation and metrology

Powder-based additive manufacturing (AM) technologies have been evaluated for use in different fields of application (aerospace, medical, etc.). Ideally, AM parts should be at least equivalent, or preferably better quality than conventionally produced parts. Manufacturing defects and their effects on the quality and performance of AM parts are a currently a major concern. It is essential to understand the defect types, their generation mechanisms, and the detection methodologies for mechanical properties evaluation and quality control. We consider the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capability of existing characterisation methodologies for powder based AM parts in this work. Methods of in-situ non-destructive evaluation and the influence of defects on mechanical properties and design considerations are also reviewed. Together, these provide a framework to understand the relevant machine and material parameters, optimise the process and production, and select appropriate characterisation methods.
Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing

Large-scale classification is an increasingly critical Big Data problem. So far, however, very little has been published on how this is done in practice. In this paper we describe Chimera, our solution to classify tens of millions of products into 5000+ product types at WalmartLabs. We show that at this scale, many conventional assumptions regarding learning and crowdsourcing break down, and that existing solutions cease to work. We describe how Chimera employs a combination of learning, rules (created by in-house analysts), and crowdsourcing to achieve accurate, continuously improving, and cost-effective classification. We discuss a set of lessons learned for other similar Big Data systems. In particular, we argue that at large scales crowdsourcing is critical, but must be used in combination with learning, rules, and in-house analysts. We also argue that using rules (in conjunction with learning) is a must, and that more research attention should be paid to helping analysts create and manage (tens of thousands of) rules more effectively.
The cellular and molecular basis of hyperthermia.

In oncology, the term 'hyperthermia' refers to the treatment of malignant diseases by administering heat in various ways. Hyperthermia is usually applied as an adjunct to an already established treatment modality (especially radiotherapy and chemotherapy), where tumor temperatures in the range of 40-43 degrees C are aspired. In several clinical phase-III trials, an improvement of both local control and survival rates have been demonstrated by adding local/regional hyperthermia to radiotherapy in patients with locally advanced or recurrent superficial and pelvic tumors. In addition, interstitial hyperthermia, hyperthermic chemoperfusion, and whole-body hyperthermia (WBH) are under clinical investigation, and some positive comparative trials have already been completed. In parallel to clinical research, several aspects of heat action have been examined in numerous pre-clinical studies since the 1970s. However, an unequivocal identification of the mechanisms leading to favorable clinical results of hyperthermia have not yet been identified for various reasons. This manuscript deals with discussions concerning the direct cytotoxic effect of heat, heat-induced alterations of the tumor microenvironment, synergism of heat in conjunction with radiation and drugs, as well as, the presumed cellular effects of hyperthermia including the expression of heat-shock proteins (HSP), induction and regulation of apoptosis, signal transduction, and modulation of drug resistance by hyperthermia.
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

DeepNeural Networks (DNNs) are becoming an important tool inmodern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning.
The biomechanics of running.

This review article summarizes the current literature regarding the analysis of running gait. It is compared to walking and sprinting. The current state of knowledge is presented as it fits in the context of the history of analysis of movement. The characteristics of the gait cycle and its relationship to potential and kinetic energy interactions are reviewed. The timing of electromyographic activity is provided. Kinematic and kinetic data (including center of pressure measurements, raw force plate data, joint moments, and joint powers) and the impact of changes in velocity on these findings is presented. The status of shoewear literature, alterations in movement strategies, the role of biarticular muscles, and the springlike function of tendons are addressed. This type of information can provide insight into injury mechanisms and training strategies.
Name usage pattern in the synonym ambiguity problem in bibliographic data

Individuals often appear with multiple names when considering large bibliographic datasets, giving rise to the synonym ambiguity problem. Although most related works focus on resolving name ambiguities, this work focus on classifying and characterizing multiple name usage patterns—the root cause for such ambiguity. By considering real examples bibliographic datasets, we identify and classify patterns of multiple name usage by individuals, which can be interpreted as name change, rare name usage, and name co-appearance. In particular, we propose a methodology to classify name usage patterns through a supervised classification task and show that different classes are robust (across datasets) and exhibit significantly different properties. We show that the collaboration network structure emerging around nodes corresponding to ambiguous names from different name usage patterns have strikingly different characteristics, such as their common neighborhood and degree evolution. We believe such differences in network structure and in name usage patterns can be leveraged to design more efficient name disambiguation algorithms that target the synonym problem.
Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking

Methods that learn representations of graph nodes play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss – an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as (point) vectors in a lower-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, in contrast to previous approaches we propose a completely unsupervised method that is also able to handle inductive learning scenarios and is applicable to different types of graphs (plain, attributed, directed, undirected). By leveraging both the topological network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering between the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks.