nils-herrmann/clean_scirepeval huggingface dataset:
A Fast Iterative Method for Solving the Eikonal Equation on Triangulated Surfaces

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton-Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker,  (2008), pp. 2512-2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.
Implementation of RSA 2048-bit and AES 256-bit with digital signature for secure electronic health record application

This research addresses the implementation of encryption and digital signature technique for electronic health record to prevent cybercrime problem such as robbery, modification and unauthorized access. In this research, RSA 2048-bit algorithm, AES 256-bit and SHA 256 will be implemented in Java programming. Secure Electronic Health Record Information (SEHR) application design is intended to combine given services, such as confidentiality, integrity, authentication, and non-repudiation. Cryptography is used to ensure the file records and electronic documents for detailed information on the medical past, present and future forecasts that have been given only for the patients. The document will be encrypted using an encryption algorithm based on NIST Standard. In the application, there are two schemes, namely the protection and verification scheme. This research uses black-box testing and white-box testing to test the software input, output, and code without testing the process and design that occurs in the system. We demonstrated the implementation of cryptography in Secure Electronic Health Record Information (SEHR). The implementation of encryption and digital signature in this research can prevent archive thievery which is shown on implementation and is proven on the test.
Towards a 99% efficient three-phase buck-type PFC rectifier for 400 V DC distribution systems

In telecom applications, the vision for a total power conversion efficiency from the mains to the output of PoL converters of 95% demands for an optimization of every conversion step, i.e. the PFC rectifier front-end should show an outstanding efficiency in the range of 99%. For recently discussed 400 V DC distribution bus voltages a buck-type PFC rectifier is a logical solution. In this paper, an efficiency-optimized, nearly 99% efficient, 5 kW three-phase buck-type PFC rectifier with 400 V output is presented. Methods for calculating losses of all components are described, and are used to optimize the converter design for efficiency at full load. Special attention is paid to semiconductor losses, which are shown to be dominant, with the parasitic device capacitance losses being a significant component. A prototype of the proposed rectifier is constructed which verifies the accuracy of the models used for loss calculation and optimization.
Integrating the 3+1 SysML view model with safety engineering

System safety is the property of the system that characterizes its ability to prevent from hazards, which may lead to accidents or losses. Traditionally, system developers are not familiar with system safety analysis processes which are performed by safety engineers. One reason for this is the gap that exists between the traditional development processes, methodologies, notations and tools and the ones used in safety engineering. This gap makes the development of safety aware systems a very complicated task. Several approaches based on UML have been proposed to address this gap. In this paper, an approach to integrate safety engineering with a SysML based development process that is expressed in the form of the V-model, is presented. Preliminary hazard analysis is adopted and applied to a SysML based requirements specification of the mechatronic system that exploits essential use cases. A case study from the railway domain is used to illustrate the proposed approach.
A systematic literature review of nurse shortage and the intention to leave.

To present the findings of a literature review regarding nurses' intention to leave their employment or the profession.



The nursing shortage is a problem that is being experienced worldwide. It is a problem that, left unresolved, could have a serious impact on the provision of quality health care. Understanding the reasons why nurses leave their employment or the profession is imperative if efforts to increase retention are to be successful.



Electronic databases were systematically searched to identify English research reports about nurses' intention to leave their employment or the profession. Key results concerning the issue were extracted and synthesized.



The diversified measurement instruments, samples and levels of intention to leave caused difficulties in the attempt to compare or synthesize findings. The factors influencing nurses' intention to leave were identified and categorized into organizational and individual factors.



The reasons that trigger nurses' intention to leave are complex and are influenced by organizational and individual factors. Further studies should be conducted to investigate how external factors such as job opportunities correlate with nurses' intention to leave.



The review provides insight that can be useful in designing and implementing strategies to maintain a sustainable workforce in nursing.
Standardization versus adaptation of international marketing strategy : an integrative assessment of the empirical research

Despite 40 years of debate on international marketing strategy standardization vs adaptation, extant empirical research is too fragmented to yield clear insights. Based on an integrative analysis of 36 studies centering around strategy standardization/adaptation, its antecedents, and performance outcomes, this stream of research was found to be characterized by non-significant, contradictory, and, to some extent, confusing findings attributable to inappropriate conceptualizations, inadequate research designs, and weak analytical techniques. The central conclusion that stems from this analysis is that the decision whether to standardize or adapt the marketing strategy to achieve superior business performance will largely depend on the set of circumstances that a firm is confronted by within a particular foreign market at a specific period of time.
Trajectory Generation and Tracking Control for Double-Steering Tractor–Trailer Mobile Robots With On-Axle Hitching

A multisteering tractor-trailer mobile robot (MSTTMR) is a kind of complex multibody system, which consists of a tractor and a chain of steerable trailers. If both the tractor and the trailers can track an identical geometric path, the overall width of the system is only equal to that of the tractor or the trailer. Thus, the robot can perform transport tasks in a narrow space. The MSTTMR with one trailer is referred to as the double-steering tractor-trailer mobile robot (DSTTMR), since the system has two steering inputs. In this paper, trajectory generation and tracking control for the DSTTMR with on-axle hitching are addressed. We first propose an approach to generate the desired full-state trajectory from a desired geometric path given in the Cartesian plane, in order that the desired state trajectories of the tractor and trailer can form the same desired path. Then, a trajectory tracking controller is designed based on the backstepping technique to drive the system's states to converge to their desired trajectories. As a result, the tractor and the trailer can track the desired geometric path simultaneously. Simulation and experimental results are presented to show the effectiveness of the proposed approach.
Prognostics and Health Management of Industrial Equipment

Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL).
SEARCH ENGINE OPTIMIZATION USING DATA MINING APPROACH

Search Engine Optimization (SEO) is the procedure used to improve the visibility of the results searched for on a free search engine for a website or a web page. The optimization targets different types of items such as images, videos, academic articles, etc. The SEO can also be defined as the process affecting the visibility of a website or a webpage in search engines. This paper aims to improve the search time of search engines to the greatest extent using the K-Means Algorithm which does the Clustering of the database. Upon conducting experiments, it is found that the algorithm produces better results without the clustering. As the size of data increases, the time used in searching is affected as search time increases to a great extent and the search process becomes slower. The paper decided to use the Message Passing Application Programming Interface(MPAPI) technique with the K-Means to solve the delay problem during search. Using this technique, search takes place in more one cluster in parallel. Each thread can connect to its own case; connection takes place through messages not State transfer between Threads. We then compare the results of the normal search results (Sphider) and the results that come out of using K-Means and MPAPI.
Association with litter size of new polymorphisms on ESR1 and ESR2 genes in a Chinese-European pig line

The objective of this study was to search for polymorphisms in the coding region of the estrogen receptors 1 and 2 (ESR1 and ESR2 )and to analyze the effects of these variants and the well known intronic ESR1 Pvu II polymorphism on litter size in a Chinese-European pig line. We identified five silent single nucleotide polymorphisms (SNP) in the ESR1 cDNA: c.669T > C (exon 3), c.1227C > T (exon 5), c.1452C > T (exon 7), c.1665T > C and c.1755A > G (exon 8). One pair of these SNP (c.1665T > C and c.1755A > G) co-segregated in the analyzed line, and the SNP c.669T > C showed the same segregation pattern as the Pvu II polymorphism. These polymorphisms were tested in this study, although the c.1452C > T SNP within exon 7 was not analyzed due to its low informativeness. In the ESR2 cDNA, one missense SNP was found within exon 5, which caused an amino acid substitution in the coded protein: "c.949G > A (p.Val317Met)" and was tested on sow litter size. Information on 1622 litter records from 408 genotyped sows was analyzed to determine whether these SNP influenced the total number of piglets born (TNB) or the number of born alive (NBA). The polymorphisms ESR1:
A robotic device using gecko-inspired adhesives can grasp and manipulate large objects in microgravity

Grasping and manipulating uncooperative objects in space is an emerging challenge for robotic systems. Many traditional robotic grasping techniques used on Earth are infeasible in space. Vacuum grippers require an atmosphere, sticky attachments fail in the harsh environment of space, and handlike opposed grippers are not suited for large, smooth space debris. We present a robotic gripper that can gently grasp, manipulate, and release both flat and curved uncooperative objects as large as a meter in diameter while in microgravity. This is enabled by (i) space-qualified gecko-inspired dry adhesives that are selectively turned on and off by the application of shear forces, (ii) a load-sharing system that scales small patches of these adhesives to large areas, and (iii) a nonlinear passive wrist that is stiff during manipulation yet compliant when overloaded. We also introduce and experimentally verify a model for determining the force and moment limits of such an adhesive system. Tests in microgravity show that robotic grippers based on dry adhesion are a viable option for eliminating space debris in low Earth orbit and for enhancing missions in space.
Designing resilient system-of-systems networks

The networked nature of many system-of-systems (SoS) requires consideration of resilience in their design and analysis. We present an approach for the design space exploration of such resilient SoS networks. We model candidate network designs using complex network methods, and generate and analyze a network design space with Design of Experiments. Results show an interaction between adaptation strategy and threat type, indicating that the best adaptation strongly depends on the threat faced. However, no interaction is seen with initial topology, suggesting that random initial topologies provide more resilience than scale-free ones regardless of the threat or adaptation strategy.
Regional differences in synaptogenesis in human cerebral cortex.

The formation of synaptic contacts in human cerebral cortex was compared in two cortical regions: auditory cortex (Heschl's gyrus) and prefrontal cortex (middle frontal gyrus). Synapse formation in both cortical regions begins in the fetus, before conceptual age 27 weeks. Synaptic density increases more rapidly in auditory cortex, where the maximum is reached near postnatal age 3 months. Maximum synaptic density in middle frontal gyrus is not reached until after age 15 months. Synaptogenesis occurs concurrently with dendritic and axonal growth and with myelination of the subcortical white matter. A phase of net synapse elimination occurs late in childhood, earlier in auditory cortex, where it has ended by age 12 years, than in prefrontal cortex, where it extends to midadolescence. Synaptogenesis and synapse elimination in humans appear to be heterochronous in different cortical regions and, in that respect, appears to differ from the rhesus monkey, where they are concurrent. In other respects, including overproduction of synaptic contacts in infancy, persistence of high levels of synaptic density to late childhood or adolescence, the absolute values of maximum and adult synaptic density, and layer specific differences, findings in the human resemble those in rhesus monkeys.
Multiscale Shear-Lag Analysis of Stiffness Enhancement in Polymer-Graphene Nanocomposites.

Graphene and other two-dimensional (2D) materials are of emerging interest as functional fillers in polymer-matrix composites. In this study, we present a multiscale atomistic-to-continuum approach for modeling interfacial stress transfer in graphene-high-density polyethylene (HDPE) nanocomposites. Via detailed characterization of atomic-level stress profiles in submicron graphene fillers, we develop a modified shear-lag model for short fillers. A key feature of our approach lies in the correct accounting of stress concentration at the ends of fillers that exhibits a power-law dependence on filler ("flaw") size, determined explicitly from atomistic simulations, without any ad hoc modeling assumptions. In addition to two parameters that quantify the end stress concentration, only one additional shear-lag parameter is required to quantify the atomic-level stress profiles in graphene fillers. This three-parameter model is found to be reliable for fillers with dimensions as small as ∼10 nm. Our model predicts accurately the elastic response of aligned graphene-HDPE composites and provides appropriate upper bounds for the elastic moduli of nanocomposites with more realistic randomly distributed and oriented fillers. This study provides a systematic approach for developing hierarchical multiscale models of 2D material-based nanocomposites and is of particular relevance for short fillers, which are, currently, typical of solution-processed 2D materials.
A Fuzzy Model With Online Incremental SVM and Margin-Selective Gradient Descent Learning for Classification Problems

This paper proposes a new incremental learning approach to endow a Takagi-Sugeno-type fuzzy classification model with high generalization ability. The proposed fuzzy model is learned through incremental support vector machine (SVM) and margin-selected gradient descent learning and is called FM3. In this learning approach, training samples are fed incrementally one-by-one instead of all in one batch. The FM3 evolves from an empty rule set. A one-pass clustering algorithm is used to determine the number of rules and initial fuzzy sets in the rule antecedent part. An online incremental linear SVM is proposed to tune the rule consequent parameters to endow the FM3 with high generalization ability. The use of incremental instead of batch SVM enables the FM3 to handle online training problems with only one new sample available at a time. For antecedent parameter learning, a margin-selected gradient descent algorithm is proposed to prevent overtraining. Simulation results and comparisons with SVMs and fuzzy classifiers with different learning algorithms demonstrate the advantage of the FM3.
Text Document Categorization by Term Association

A goodtext classifieris a classifierthat efficiently categorizeslarge setsof text documentsin a reasonabletime frameand with an acceptableaccuracy, and that provides classificationrules that are humanreadablefor possible fine-tuning. If the training of the classifier is also quick, this couldbecomein someapplicationdomainsa goodasset for the classifier . Many techniquesand algorithmsfor automatictext categorizationhavebeendevised.According to publishedliterature, somearemoreaccuratethanothers, andsomeprovidemore interpretableclassificationmodels thanothers. However, nonecancombineall thebeneficial propertiesenumer atedabove. In this paper, we presenta novel approach for automatictext categorizationthat borrowsfrom market basket analysistechniquesusingassociation rule miningin thedata-miningfield. We focuson two major problems:(1) findingthebesttermassociationrules in a textual databaseby generating and pruning; and (2) using the rules to build a text classifier . Our text categorization methodprovesto beefficientandeffective, andexperimentsonwell-knowncollectionsshowthattheclassifier performswell. In addition,trainingaswell asclassification arebothfastandthegeneratedrulesarehumanreadable.
GENERATIVE ADVERSARIAL NETWORK BASED ACOUSTIC SCENE TRAINING SET AUGMENTATION AND SELECTION USING SVM HYPERPLANE

Although it is typically expected that using a large amount of labeled training data would lead to improve performance in deep learning, it is generally difficult to obtain such DataBase (DB). In competitions such as the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge Task 1, participants are constrained to use a relatively small DB as a rule, which is similar to the aforementioned issue. To improve Acoustic Scene Classification (ASC) performance without employing additional DB, this paper proposes to use Generative Adversarial Networks (GAN) based method for generating additional training DB. Since it is not clear whether every sample generated by GAN would have equal impact in classification performance, this paper proposes to use Support Vector Machine (SVM) hyper plane for each class as reference for selecting samples, which have class discriminative information. Based on the crossvalidated experiments on development DB, the usage of the generated features could improve ASC performance.
Learning Generative End-to-end Dialog Systems with Knowledge

Dialog systems are intelligent agents that can converse with human in natural language and facilitate human. Traditional dialog systems follow a modular approach and often have trouble expanding to new or more complex domains, which hinder the development of more powerful future dialog systems. This dissertation targets at an ambitious goal: to create domainagnostic learning algorithms and dialog models that can continuously learn to converse in any new domains and only requires a small amount of new data for the adaption. Achieving this goal first requires powerful statistic models that are expressive enough to model the natural language and decision-making process in dialogs of many domains; and second requires a learning framework enabling models to share knowledge from previous experience so it can learn to converse in new domains with limited data. End-to-end (E2E) generative dialog models based on encoder-decoder neural networks are strong candidates for the first requirement. The basic idea is to use an encoder network to map a dialog context into a learned distributed representation and then use a decoder network to generate the next system response. These models are not restricted to hand-crafted intermediate states and can in principle generalize to novel system responses that are not observed in the training. However, it is far from trivial to build a full-fledged dialog system using encoder-decoder models. Thus in the first stage of this thesis, we develop a set of novel neural network architectures that offer key properties that are required by dialog modeling. Experiments prove that the resulting system can interact with both users and symbolic knowledge bases, model complex dialog policy and reason over long discourse history. We tackle the second requirement by proposing a novel learning with knowledge (LWK) framework that can adapt the proposed system to new domains with minimum data. Two types of knowledge are studied: 1) domain knowledge from human experts 2) models' knowledge from learning related domains. To incorporate these knowledge, a domain description that can compactly encode domain knowledge is first proposed. Then we develop novel domain-aware models and training algorithms to teach the system learn from data in related domains and generalize to unseen ones. Experiments show the proposed framework is able to achieve strong performance in new domains with limited, even zero, in-domain training data. In conclusion, this dissertation shows that by combing specialized encoderdecoder models with the proposed LWK framework, E2E generative dialog models can be readily applied in complex dialog applications and can be easily expanded to new domains with extremely limited resources, which we believe is an important step towards future general-purpose conversational agents that are more natural and intelligent.  2017 DRAFT November 21, 2017 DRAFT