nils-herrmann/clean_scirepeval huggingface dataset:
Flow measurements of a polyphenyl ether oil in an elastohydrodynamic contact.
Venue: Journal of physics. Condensed matter an Institute of Physics journal

A novel methodology, based on the use of phosphorescence imaging, is applied to determine the local through thickness velocity profile of lubricant in an elastohydrodynamic contact. The technique has spatial and temporal resolutions of 40 mm and 340 ms respectively and thus allows lubricant rheology to be investigated at conditions close to service conditions. The capability of the newly developed method is verified by examining the flow of 5P4E polyphenyl ether, a lubricant base fluid used in very high temperature applications and is well known for its high viscosity pressure coefficient. Experimental results highlight the effect of the contact pressure on the velocity profile of this fluid in lubricated contacts. At low pressures, the velocity profile of 5P4E is close to linear, characteristic of Couette flow. As the local pressure increases, its velocity profile progressively deviates from a Couette profile and shear banding is evident at high pressure.
Solar driven membrane distillation demonstration in Leupp, Arizona
Venue: Reviews on environmental health

The Navajo Nation is the largest and one of the driest Native American reservations in the US. The population in the Navajo Nation is sporadically distributed over a very large area making it extremely ineffective to connect homes to a centralized water supply system. Owing to this population distribution and the multi decadal drought prevailing in the region, over 40% of the 300,000 people living on Navajo Tribal Lands lack access to running potable water. For many people the only alternative is hauling water from filling stations, resulting in economic hardship and limited supply. A solution to this problem is a de centralized off grid water source. The University of Arizona and US Bureau of Reclamation's Solar Membrane Distillation (SMD) stand alone, pilot desalination system on the Navajo Reservation will provide an off grid source of potable water; the pilot will serve as a proximal water source, ease the financial hardships caused by the drought, and provide a model for low cost water treatment systems in arid tribal lands. Bench scale experiments and an earlier field prototype plant showed viable operation of a solar heated, membrane distillation (MD) system, but further optimization is required. The objectives of the Navajo pilot study are to i) demonstrate integration of solar collectors and membrane distillation, ii) optimize operational parameters, iii) demonstrate and monitor technology performance during extended duration operation, and iv) facilitate independent system operation by the Navajo Water Resources Department, including hand over of a comprehensive operations manual for implementation of subsequent SMD systems. The Navajo SMD system is designed as a perennial installation that includes remote communication of research data and full automation for remote, unmanned operation.
Development and Assessment of a Medication Safety Measurement Program in a Long Term Care Pharmacy.
Venue: The Consultant pharmacist the journal of the American Society of Consultant Pharmacists

Medication errors continue to be a major issue in the health care system, including in long term care facilities. While many hospitals and health systems have developed methods to identify, track, and prevent these errors, long term care facilities historically have not invested in these error prevention strategies. The objective of this study was two fold: 1) to develop a set of medication safety process measures for dispensing in a long term care pharmacy, and 2) to analyze the data from those measures to determine the relative safety of the process.  The study was conducted at In Touch Pharmaceuticals in Valparaiso, Indiana. To assess the safety of the medication use system, each step was documented using a comprehensive flowchart (process flow map) tool. Once completed and validated, the flowchart was used to complete a "failure modes and effects analysis" (FMEA) identifying ways a process may fail. Operational gaps found during FMEA were used to identify points of measurement. The research identified a set of eight measures as potential areas of failure; data were then collected on each one of these.  More than 133,000 medication doses (opportunities for errors) were included in the study during the research time frame (April 1, 2014, and ended on June 4, 2014) Overall, there was an approximate order entry error rate of 15.26% with intravenous errors at 0.37% A total of 21 errors migrated through the entire medication use system. These 21 errors in 133,000 opportunities resulted in a final check error rate of 0.015%  A comprehensive medication safety measurement program was designed and assessed. This study demonstrated the ability to detect medication errors in a long term pharmacy setting, thereby making process improvements measureable. Future, larger, multi site studies should be completed to test this measurement program.
A Back Propagation Neural Network for Identifying Multi Word Biomedical Named Entities

Biomedical Named Entity Recognition (BNER) is the task of classifying biomedical instances such as genes, proteins, diseases, chemical compounds and others. Several approaches have been proposed for BNER specifically supervised machine learning techniques. Most of these techniques demonstrated reasonable performance. However, there is still a gap that lies on the multi word BNEs such as the chemical compound 'Tri acetyl glucal galactono lactone' in which the classifier could not recognize these instances due to the complex characters used to separate the words. Complex characters could be punctuation (e.g. *_^ or numeric characters. Therefore, this paper aims to propose a Back propagation Neural Network (BPNN) for identifying BNEs. BPNN has the ability to encode the characters which facilitate the identification of multi word BNEs. For this purpose, this study proposed multiple features for the encoding task including digits, special characters, affixes and capitalization. Experiments have been conducted using two benchmark datasets including SCAI and GENIA. SCAI is a corpus that contains chemical compounds, whereas GENIA is a corpus that contains multiple biomedical instances such as genes, proteins, DNA and RNA. Using 80% training and 20% testing, BPNN has shown 90% f measure for the SCAI corpus and 82% f measure for the GENIA corpus. Such results emphasize an enhancement of f measure when compared to other related work. This implies that BPNN is effective in classifying BNEs.
A Study on the Satisfaction and Stress Factors of Clinical Practice for Dental Hygiene Students

Correspondence to: Jung Hee KimDepartment of Dental Hygiene, Youngdong University, 310 Daehakro, Youngdong eup, Youngdong gun, Chungbuk 29131, KoreaTel: +82 43 740 1495,  E mail: open0811@yd.ac.krCopyright (c) 2016 by the Korean Society of Dental Hygiene Science ) which permits unrestricted non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Adversarial Manipulation of Deep Representations
Venue: ICLR

We show that the representation of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations to the original image. Previous methods for generating adversarial images focused on image perturbations designed to produce erroneous class labels, while we concentrate on the internal layers of DNN representations. In this way our new class of adversarial images differs qualitatively from others. While the adversary is perceptually similar to one image, its internal representation appears remarkably similar to a different image, one from a different class, bearing little if any apparent similarity to the input; they appear generic and consistent with the space of natural images. This phenomenon raises questions about DNN representations, as well as the properties of natural images themselves.
A Focused Dynamic Attention Model for Visual Question Answering
Venue: ArXiv

Visual Question and Answering (VQA) problems are attracting increasing interest from multiple research disciplines. Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers. Regarding visual content modeling, most of existing VQA methods adopt the strategy of extracting global features from the image or video, which inevitably fails in capturing fine grained information such as spatial configuration of multiple objects. Extracting features from auto generated regions as some region based image recognition methods do cannot essentially address this problem and may introduce some overwhelming irrelevant features with the question. In this work, we propose a novel Focused Dynamic Attention (FDA) model to provide better aligned image content representation with proposed questions. Being aware of the key words in the question, FDA employs off the shelf object detector to identify important regions and fuse the information from the regions and global features via an LSTM unit. Such question driven representations are then combined with question representation and fed into a reasoning unit for generating the answers. Extensive evaluation on a large scale benchmark dataset, VQA, clearly demonstrate the superior performance of FDA over well established baselines.
POWER IN POWERLESSNESS: THE STRANGE JOURNEY AND CAREER OF REGINA SALOMEA PILSZTYNOWA

"My parents married me at a young age to doctor Jacob Halpir, who took me that very year from the Nowogrodek Voivodeship in Lithuania to Istanbul, where we moved among fine people, and because my husband was a very capable medical doctor, and by God's grace, we attained some money."1 So begins the richly detailed and so far virtually unknown memoir of Regina Salomea Pilsztynowa, a Polish female physician whose life spans eighteenth century Eastern and Southern Europe from the Ottoman Empire and the Balkans to Poland and Russia.2 The account of her travels and adventures offers us an intimate and exciting glimpse of a period and a society that is at once distant, and yet still resonates to our contemporary experiences. A stranger in a foreign land, a female Catholic Pole in a Muslim male dominated society, Salomea managed to successfully carve out for herself a viable niche, an early "room of her own." Contrary to every modem expectation, she became a highly sought after and well respected practitioner of the medical arts, especially ophthalmology and, as a result, became the official court physician to Turkish pashas and Hungarian princes, and indeed, even to the Sultan Mustafa III (1757 1773) himself. Called "Poland's first female doctor" by the very few historians who know of her, her story offers us a window on eighteenth century social, professional, and gender interaction between Christians, Jews, and Muslims. It is also a richly woven tapestry of everyday life, in which are recorded fascinating vignettes of love, travel, romance, popular superstitions, war, black magic, serial killers, and bandits and that's only in the very first chapter alone. Her memoir forms an invaluable repository of personal accounts, as well as historical, ethnographic, religious, and geographical
Centennial scale variations in diatom productivity off Peru over the last 3000 years

The Peruvian coastal upwelling is one of the most productive systems in the global ocean, with important impacts on the carbon cycle. Primary productivity there displays strong variations at the interannual to decadal timescales. However, down core investigations rarely reach sufficient temporal resolution to assess the response of productivity to climatic variations at these timescales beyond the instrumental and historical periods. We here analyzed diatom assemblages, sea surface temperatures (SSTs) and nitrogen and organic carbon contents on a laminated sediment core from the Peruvian continental shelf to trace variations in regional productivity over the last 3000 years. Our record provides evidence for different climatic and oceanic conditions with more humid and less productive conditions older than 2500 cal. yr BP and drier and more productive conditions younger than 2500 cal. yr BP. The last 2500 years also present much stronger centennial scale variability with the occurrence of six intervals with higher total diatom abundances and stronger percentages in upwelling related diatom species, representative of intensified productivity, congruent to lower percentages in benthic diatoms, indicative of reduced rainfall. These six periods were synchronous to intervals of enhanced Walker circulation, suggesting a strong imprint of the Pacific zonal circulation on productivity variations off Peru. Our record also demonstrates that SSTs did not vary in phase with productivity, arguing against the idea of regional SSTs controlled by the upwelling intensity, but were rather in agreement to SST records off southern Chile, suggesting that Peruvian SST variations were largely controlled by oceanic currents at southern high latitudes.
Imaging and image based fluid transport modeling at the pore scale in geological materials a practical introduction to the current state of the art

Fluid flow and mass transport in geological materials are crucial in diverse Earth science applications. To fully understand the behavior of geological materials in this context, the pore scale properties of these materials have to be investigated and related to effective material properties. Imaging techniques are becoming ever more valuable tools to characterize the microstructure (especially in three dimensions) while numerical models to calculate transport properties based on experimental images of the microstructure are quickly maturing. The results of image based modeling studies depend crucially on both the employed model and the quality of the pore space image on which the model runs. Given the technicality and the cross disciplinary nature of this matter, this review aims to provide a practical and accessible introduction to both the experimental and numerical state of the art, intended for students and researchers with backgrounds in experimental geo sciences or computational sciences alike.
ClioPatria: A SWI Prolog infrastructure for the Semantic Web
Venue: Semantic Web

ClioPatria is a comprehensive semantic web development framework based on SWI Prolog. SWI Prolog provides an efficient C based main memory RDF store that is designed to cooperate naturally and efficiently with Prolog, realizing a flexible RDF based environment for rule based programming. ClioPatria extends this core with a SPARQL and LOD server, an extensible web frontend to manage the server, browse the data, query the data using SPARQL and Prolog and a Git based plugin manager. The ability to query RDF using Prolog provides query composition and smooth integration with application logic. ClioPatria is primarily positioned as a prototyping platform for exploring novel ways of reasoning with RDF data. It has been used in several research projects in order to perform tasks such as data integration and enrichment and semantic search.
Novel RF interference rejection technique using a four port diplexer
Venue: 2016 46th European Microwave Conference (EuMC)

A novel RF interference rejection technique using four port network is presented in this paper by using two diplexers combined together. This technique offers the signal isolation of 68.46 dB between transmitter and receiver module, which is the best figure ever reported. The four port network exploits both high and low Q factor filters for the cost reduction. The design tolerance with phase deviation between 180o and 183o of four port network was investigated and the novel concept still has signal isolation (S32) of better than 65.47 dB, which is still superior compared to the existing diplexer. Finally, RF interference rejection technique can be used in wireless communication systems whereas small size, low losses and low complexity are required.
An introduction to digital media research methods: how to research and the implications of new media data

Digital methods have previously been described as 'a term that seeks to capture a recent development in Internet related research, summarized as approaches to the web as data set' Using this as a starting point, this paper positions digital media methods as a methodological approach that incorporates internet based data, while also including other communicative and social media platforms such as Instagram, Vine, Twitter, giffy, Periscope, and Facebook amongst others. Digital media methods also extends to database research, data generated by sensors, drones and autonomous automobiles. Contemporary research engaging digital media methods is built upon the 'computational turn' where 'computational approaches is increasingly reflected across a number of disciplines, including the arts, humanities and social sciences, which use technologies to shift the critical ground of their concepts and theories' As media and communication scholars, our 'research is increasingly being mediated through digital technology. affecting both the epistemologies and ontologies that underlie a research program' This paper highlights three significant points of departure for digital media methods in the media and communication discipline: the increasing need for typologies and ontologies in social media research; the significance of mapping public issues; and the difficulties researchers face as text based communication shifts to visually oriented platforms.
Application of digital media art in exhibit design

In this paper, interactive experience design and virtual reality technology and its application in modern display. The research discusses the concept of modern display design new displacement phenomenon. Digital media arts background modern display design, features, performance methods and basic types. The development of modern display design has a role in fueling, enabling a better understanding of how digital art is rendered in modern display design. Because of analogy, exhibition design and other modern art design art have a variety of related, so it has a multi dimensional, multi faceted character. Modern display design features from its own natural properties. Its function, purpose, means and methods are all constitute an important factor in modern display design. Exhibition design has also created modern special laws.
MicroTalk: Using Argumentation to Improve Crowdsourcing Accuracy
Venue: HCOMP

Crowd workers are human and thus sometimes make mistakes. In order to ensure the highest quality output, requesters often issue redundant jobs with gold test questions and sophisticated aggregation mechanisms based on expectation maximization (EM) While these methods yield accurate results in many cases, they fail on extremely difficult problems with local minima, such as situations where the majority of workers get the answer wrong. Indeed, this has caused some researchers to conclude that on some tasks crowdsourcing can never achieve high accuracies, no matter how many workers are involved. This paper presents a new quality control workflow, called MicroTalk, that requires some workers to Justify their reasoning and asks others to Reconsider their decisions after reading counter arguments from workers with opposing views. Experiments on a challenging NLP annotation task with workers from Amazon Mechanical Turk show that (1) argumentation improves the accuracy of individual workers by 20% (2) restricting consideration to workers with complex explanations improves accuracy even more, and (3) our complete MicroTalk aggregation workflow produces much higher accuracy than simpler voting approaches for a range of budgets.
Training LDCRF model on unsegmented sequences using connectionist temporal classification
Venue: 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)

Many machine learning problems such as speech recognition, gesture recognition, and handwriting recognition are concerned with simultaneous segmentation and labeling of sequence data. Latent dynamic conditional random field (LDCRF) is a well known discriminative method that has been successfully used for this task. However, LDCRF can only be trained with pre segmented data sequences in which the label of each frame is available apriori. In the realm of neural networks, the invention of connectionist temporal classification (CTC) made it possible to train recurrent neural networks on unsegmented sequences with great success. In this paper, we use CTC to train an LDCRF model on unsegmented sequences. Experimental results on two gesture recognition tasks show that the proposed method outperforms LDCRFs, hidden Markov models, and conditional random fields.
Training LDCRF model on unsegmented sequences using connectionist temporal classification

Many machine learning problems such as speech recognition, gesture recognition, and handwriting recognition are concerned with simultaneous segmentation and labeling of sequence data. Latent dynamic conditional random field (LDCRF) is a well known discriminative method that has been successfully used for this task. However, LDCRF can only be trained with pre segmented data sequences in which the label of each frame is available. In the realm of neural networks, the invention of Connectionist Temporal Classification (CTC) made it possible to train Recurrent Neural Networks on unsegmented sequences with great success. In this paper, we use CTC to train LDCRF model on unsegmented sequences. Experimental results on gesture recognition tasks show that our proposed method can outperform LDCRF, Hidden Markov Models, and Conditional Random Fields.
Is the Bifactor Model a Better Model or Is It Just Better at Modeling Implausible Responses? Application of Iteratively Reweighted Least Squares to the Rosenberg Self Esteem Scale
Venue: Multivariate behavioral research

Although the structure of the Rosenberg Self Esteem Scale (RSES) has been exhaustively evaluated, questions regarding dimensionality and direction of wording effects continue to be debated. To shed new light on these issues, we ask (a) for what percentage of individuals is a unidimensional model adequate, (b) what additional percentage of individuals can be modeled with multidimensional specifications, and (c) what percentage of individuals respond so inconsistently that they cannot be well modeled? To estimate these percentages, we applied iteratively reweighted least squares (IRLS) to examine the structure of the RSES in a large, publicly available data set. A distance measure, ds, reflecting a distance between a response pattern and an estimated model, was used for case weighting. We found that a bifactor model provided the best overall model fit, with one general factor and two wording related group factors. However, on the basis of dr values, a distance measure based on individual residuals, we concluded that approximately 86% of cases were adequately modeled through a unidimensional structure, and only an additional 3% required a bifactor model. Roughly 11% of cases were judged as "unmodelable" due to their significant residuals in all models considered. Finally, analysis of ds revealed that some, but not all, of the superior fit of the bifactor model is owed to that model's ability to better accommodate implausible and possibly invalid response patterns, and not necessarily because it better accounts for the effects of direction of wording.