nils-herrmann/clean_scirepeval huggingface dataset:
The Effect of Perceiving a Calling on Pakistani Nurses' Organizational Commitment, Organizational Citizenship Behavior, and Job Stress
Venue: Journal of transcultural nursing official journal of the Transcultural Nursing Society
Year: 2018

People differ considerably in the way in which they express and experience their nursing careers. The positive effects associated with having a calling may differ substantially based on individuals' abilities to live out their callings. In a working world where many individuals have little to no choice in their type of employment and thus are unable to live out a calling even if they have one, the current study examined how perceiving a calling and living a calling interacted to predict organizational commitment, organizational citizenship behavior, and job stress with career commitment mediating the effect of the interactions on the three outcome variables. The purpose of the study is to investigate the mediating effect of career commitment between the relationships of calling and (a) nurses' attitudes (organizational commitment) (b) behaviors (organizational citizenship behavior) and (c) subjective experiences regarding work (job stress) Design. Using a descriptive exploratory design, data were collected from 332 registered nurses working in Pakistani hospitals. Descriptive analysis and hierarchical regression analysis were used for data analysis.  Living a calling moderated the effect of calling on career commitment, organizational citizenship behavior, and job stress, and career commitment fully mediated the effect of calling on organizational commitment, organizational citizenship behavior, and job stress.  Increasing the understanding of calling, living a calling, and career commitment may increase nurses' organizational commitment and organizational citizenship behavior and decrease job stress. The study provided evidence to help nursing managers and health policy makers integrate knowledge and skills related to calling into career interventions and help nurses discover their calling.
Bottom Up and Top Down Attention for Image Captioning and Visual Question Answering
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018

Top down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom up and top down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom up mechanism (based on Faster R CNN) proposes image regions, each with an associated feature vector, while the top down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state of the art for the task, achieving CIDEr SPICE BLEU 4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.
Single pulse Detection Algorithms for Real time Fast Radio Burst Searches Using GPUs
Year: 2019

The detection of non repeating or irregular events in time domain radio astronomy has gained importance over the last decade due to the discovery of fast radio bursts. Existing or upcoming radio telescopes are gathering more and more data and consequently the software, which is an important part of these telescopes, must process large data volumes at high data rates. Data has to be searched through to detect new and interesting events, often in real time. These requirements necessitate new and fast algorithms which must process data quickly and accurately. In this work we present new algorithms for single pulse detection using boxcar filters. We have quantified the signal loss introduced by single pulse detection algorithms which use boxcar filters and based on these results, we have designed two distinct "lossy" algorithms. Our lossy algorithms use an incomplete set of boxcar filters to accelerate detection at the expense of a small reduction in detected signal power. We present formulae for signal loss, descriptions of our algorithms and their parallel implementation on NVIDIA GPUs using CUDA. We also present tests of correctness, tests on artificial data and the performance achieved. Our implementation can process SKA MID like data 266$\times$ faster than real time on a NVIDIA P100 GPU and 500x faster than real time on a NVIDIA Titan V GPU with a mean signal power loss of 7% We conclude with prospects for single pulse detection for beyond SKA era, nanosecond time resolution radio astronomy.
Response to the comment by Wolf Ihlenfeldt on the paper "Indexing molecules with chemical graph identifiers"
Venue: J. Comput. Chem.
Year: 2012

The main objective of our article published  2638 was to introduce a modified and further extended version of the molecular equivalence number naming adaption of the Morgan algorithm () for the generation of a chemical graph identifier (CGI) Excluding references and conclusions, this covered six out of the eight text pages of the manuscript. As when new algorithms are presented it is advisable, whenever possible, to compare the results with those obtained with other existing established methods, a section was added to include the results from InChIKeys and Cactvs hashcodes. Thus, for the sake of clarity, Dr. Ihlenfeldt's comment on our article is exclusively about the references made on some of the Cactvs results in this final two page section. In response to the three listed problem cases
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
Year: 2020

Survival rates for colorectal cancer are higher when polyps are detected at an early stage and can be removed before they develop into malignant tumors. Automated polyp detection, which is dominated by deep learning based methods, seeks to improve early detection of polyps. However, current efforts rely heavily on the size and quality of the training datasets. The quality of these datasets often suffers from various image artifacts that affect the visibility and hence, the detection rate. In this work, we conducted a systematic analysis to gain a better understanding of how artifacts affect automated polyp detection. We look at how six different artifact classes, and their location in an image, affect the performance of a RetinaNet based polyp detection model. We found that, depending on the artifact class, they can either benefit or harm the polyp detector. For instance, bubbles are often misclassified as polyps, while specular reflections inside of a polyp region can improve detection capabilities. We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection. Our results show that such models can mitigate some of the harmful effects of artifacts, but require more work to significantly improve polyp detection capabilities.
COVID 19 Epidemic Editorial
Year: 2020

COVID 19 is a novel form of coronavirus which has spread from its initial identification in Wuhan, China, and has been declared a pandemic by the World Health Organisation (WHO)  Coronaviruses are a large family of viruses that cause illnesses ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERSCoV) and Severe Acute Respiratory Syndrome (SARS CoV) Coronaviruses are zoonotic, meaning they are transmitted between animals and people. Several known coronaviruses are circulating in animals that have not yet infected humans  but it appears that COVID 19 has crossed species from bats to snakes  and pangolins then to humans, initially via the live animal 'wet markets' of Wuhan
Midostaurin a cost effective option for AML in Canada
Venue: PharmacoEconomics Outcomes News
Year: 2018

Midostaurin "represents a new cost effective option" when combined with standard of care in patients with newly diagnosed FLT3 mutation positive acute myeloid leukaemia (AML) in Canada, according to the results of a study* presented at the 23rd Annual Meeting of ISPOR. The researchers developed a partition survival model to compare the costs and outcomes of midostaurin standard of care, followed by midostaurin maintenance, versus standard of care alone over a 15 year time horizon. Input data were obtained from the phase III, RATIFY trial and other published data sources. Direct medical costs were included, and costs and benefits were discounted at a rate of 1.5% per annum. Overall, midostaurin was associated with incremental life year (LY) and QALY gains, compared with standard of care alone (0.72 and 0.85, respectively) Over the 15 year time horizon, midostaurin resulted in incremental costs of $Can79 147 per LY and $66 937 per QALY versus standard of care alone.
Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine
Year: 2012

This paper presents a laboratory study of multi class classification problem for multiple indoor air contaminants which belongs to a completely linear inseparable case. Six kinds of indoor air contaminations (formaldehyde, benzene, toluene, carbon monoxide, ammonia and nitrogen dioxide) were recognized as indicators of air quality in this project. The effectiveness of the proposed HSVM model has been rigorously evaluated on the experimental E nose data sets. In addition, we have also compared it with existing five methods including Euclidean distance to centroids (EDC) simplified fuzzy ARTMAP network (SFAM) multilayer perceptron neural network (MLP) based on back propagation learning rule, individual FLDA and single SVM. Experimental results have demonstrated that the HSVM model outperforms other classifiers in general. Also, HSVM classifier preliminarily shows its superiority in solution to discrimination in various electronic nose applications.
N2O and CH4 Emissions, and NO3 Leaching on a Crop Yield Basis from a Subtropical Rain fed Wheat Maize Rotation in Response to Different Types of Nitrogen Fertilizer
Venue: Ecosystems
Year: 2013

Guaranteeing high crop yields while reducing environmental impacts of nitrogen fertilizer use due to associated losses of N2O emissions and nitrate (NO3 leaching is a key challenge in the context of sustainable intensification of crop production. However, few field data sets are available that explore the effect of different forms of N management on yields as well as on N losses in the form of N2O or NO3 Here we report on a large scale field lysimeter (8 x 4 m2) experiment, which was designed to determine soil CH4 and N2O emissions, NO3 leaching losses and crop yields from a subtropical rain fed wheat maize rotation in the Sichuan Basin, one of the most intensively used agricultural regions in China. One control and three different fertilizer treatments with the same total rate of N application (280 kg N ha 1 y 1) were included: NF: control (no fertilizer) NPK: synthetic N fertilizer; OMNPK: synthetic N fertilizer plus pig manure; RSDNPK: synthetic N fertilizer plus crop residues. As compared to the standard NPK treatment, annual NO3 leaching losses for OMNPK and RSDNPK treatments were decreased by 36 and 22% respectively (P 0.05) Similarly, crop yield scaled NO3 leaching for NPK treatment was higher than those for either OMNPK or RSDNPK treatments (P 0.05) Direct N2O emissions for RSDNPK treatment were decreased as compared with NPK and OMNPK treatments (P 0.05) Furthermore, the yield scaled GWP (global warming potential) was lower for the treatments where either pig manure or crop residues were incorporated as compared to the standard NPK treatment (P 0.05) Our study indicates that it is possible to reduce the negative environmental impact of NO3 leaching and N2O emissions without compromising crop productivity. Yield scaled NO3 leaching, similar to the yield scaled GWP, represents another valuable integrated metric to address the dual goals of reducing nitrogen pollution and maintaining crop grain yield for a given agricultural system.
Privacy preserving attribute keyword based data publish subscribe service on cloud platforms
Venue: Inf. Sci.
Year: 2017

Data publish subscribe service is an effective approach to selectively share and selectively receive data. Towards the huge amount of data generated in our daily life, cloud systems, with economical but powerful storage and computing resources, are inevitably becoming the most appropriate platform for data publication and subscription. However, cloud server may also curious about both the published data and the interests of the subscribers. In this paper, we propose a privacy preserving Attribute Keyword based data Publish Subscribe (AKPS) scheme for cloud platforms. Specifically, in order to protect the privacy of the published data against the cloud server and other none subscribers, we employ the attribute based encryption with decryption outsourcing to encrypt the published data, such that the publishers can control the data access by themselves and the major decryption overhead can be shift from the subscribers devices to the cloud server. To protect the subscribers interests, we propose a new searchable encryption to enable the subscribers to selectively receive interested data. Different from existing symmetric searchable encryption methods, the AKPS can support multiple publishers and multiple subscribers, while none of two publishers/subscribers share the same secret keys. Moreover, the publishers cannot act as the subscribers, and vice versa. To avoid bypassing access/subscription policy checking procedure, the AKPS smartly ties both access policy and subscription policy by two secrets. One secret is used to bundle the ciphertext and the tags together, while the other secret is used to bundle the subscription trapdoor and the pre decryption key together. The security proof and performance evaluation show that the proposed AKPS scheme is provable secure in random oracle model and efficient in practice.
Multiple Criteria ABC Analysis with FCM Clustering
Year: 2013

The number of stock keeping units (SKUs) possessed by organizations can easily reach quite a few. An inventory management policy for each individual SKU is not economical to design. ABC analysis is one of the conventionally used approaches to classify SKUs. In the classical method, the SKUs are ranked with respect to the descending order of the annual dollar usage, which is the product of unit price and annual demand. The few of the SKUs that have the highest annual dollar usage are in group A and should be taken into account mostly; the SKUs with the least annual dollar usage are in group C and should be taken into account least; the remaining SKUs are in group B. In this study, we proposed fuzzy c means (FCM) clustering to a multicriteria ABC analysis problem to help managers to make better decision under fuzzy circumstancse. The obtained results show that the FCM is a quite simple and an easily adaptable method to inventory management.
PU Net: Point Cloud Upsampling Network
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018

Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data driven point cloud upsampling technique. The key idea is to learn multi level features per point and expand the point set via a multi branch convolution unit implicitly in feature space. The expanded feature is then split to a multitude of features, which are then reconstructed to an upsampled point set. Our network is applied at a patch level, with a joint loss function that encourages the upsampled points to remain on the underlying surface with a uniform distribution. We conduct various experiments using synthesis and scan data to evaluate our method and demonstrate its superiority over some baseline methods and an optimization based method. Results show that our upsampled points have better uniformity and are located closer to the underlying surfaces.
Distributed model predictive control for vessel train formations of cooperative multi vessel systems
Year: 2018

Recently, the cooperative control of multiple vessels has been gaining increasing attention because of the potential robustness, reliability and efficiency of multi agent systems. In this paper, we propose the concept of Cooperative Multi Vessel Systems (CMVSs) consisting of multiple coordinated autonomous vessels. We in particular focus on the so called Vessel Train Formation (VTF) problem. The VTF problem considers not only cooperative collision avoidance, but also grouping of vessels. An MPC based approach is proposed for addressing the VTF problem. A centralized and a distributed formulation based on the Alternating Direction of Multipliers Method (ADMM) are investigated. The distributed formulation adopts a single layer serial iterative architecture, which gains the benefits of reduced communication requirements and robustness against failures. The impacts of information updating sequences and responsibility parameters are discussed. We furthermore analyze the scalability of the proposed method. Simulation experiments of a CMVS navigating from different terminals in the Port of Rotterdam to inland waterways are carried out to illustrate the effectiveness of our method. The proposed method successfully steers the vessels from different origins to form a vessel train. Due to the effective communication, vessels can timely respond to the velocity changes that others make. After the formation is formed, the distances between vessels become constant. The results show the potential to use CMVSs for inland shipping with enhanced safety.
Patterns of Rare and Abundant Marine Microbial Eukaryotes
Venue: Current Biology
Year: 2014

Biological communities are normally composed of a few abundant and many rare species. This pattern is particularly prominent in microbial communities, in which most constituent taxa are usually extremely rare. Although abundant and rare subcommunities may present intrinsic characteristics that could be crucial for understanding community dynamics and ecosystem functioning, microbiologists normally do not differentiate between them. Here, we investigate abundant and rare subcommunities of marine microbial eukaryotes, a crucial group of organisms that remains among the least explored biodiversity components of the biosphere. We surveyed surface waters of six separate coastal locations in Europe, independently considering the picoplankton, nanoplankton, and microplankton/mesoplankton organismal size fractions.  Deep Illumina sequencing of the 18S rRNA indicated that the abundant regional community was mostly structured by organismal size fraction, whereas the rare regional community was mainly structured by geographic origin. However, some abundant and rare taxa presented similar biogeography, pointing to spatiotemporal structure in the rare microeukaryote biosphere. Abundant and rare subcommunities presented regular proportions across samples, indicating similar species abundance distributions despite taxonomic compositional variation. Several taxa were abundant in one location and rare in other locations, suggesting large oscillations in abundance. The substantial amount of metabolically active lineages found in the rare biosphere suggests that this subcommunity constitutes a diversity reservoir that can respond rapidly to environmental change.  We propose that marine planktonic microeukaryote assemblages incorporate dynamic and metabolically active abundant and rare subcommunities, with contrasting structuring patterns but fairly regular proportions, across space and time.
Banklaves: Concept for a Trustworthy Decentralized Payment Service for Bitcoin
Venue: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
Year: 2019

We explore challenges of and present a concept for a decentralized payment service which is based on trusted execution environments. The system guarantees that users can always cash out their funds without depending on the cooperation of other network members, hence minimizing the trust required in other network members. We present an overview of the system, motivate key components for a secure architecture and provide a communication protocol. We prove that the payment service users can cash out their funds at any time without any dependence on other network members.
A new ophthalmic formulation containing antiseptics and dexpanthenol: In vitro antimicrobial activity and effects on corneal and conjunctival epithelial cells.
Venue: Experimental eye research
Year: 2020

Antibiotic resistance is increasing even in ocular pathogens, therefore the interest towards antiseptics in Ophthalmology is growing. The aim of this study was to analyze the in vitro antimicrobial efficacy and the in vitro effects of an ophthalmic formulation containing hexamidine diisethionate 0.05% polyhexamethylene biguanide (PHMB) 0.0001% disodium edetate (EDTA) 0.01% dexpanthenol 5% and polyvinyl alcohol 1.25% (Keratosept, Bruschettini, Genova, Italy) on cultured human corneal and conjunctival cells. The in vitro antimicrobial activity was tested on Staphylococcus aureus, Methicillin Resistant Staphylococcus aureus (MRSA) Pseudomonas aeruginosa, Streptococcus pneumoniae, Streptococcus pyogenes and Streptococcus mitis. For each microbial strain 10 mL of a 0.5 MacFarland standardized bacterial inoculum were incubated at 25 degC with 100 mL of ophthalmic solution for up to 6 h. After different periods of time, samples were inoculated on blood agar with 5% sheep blood. Moreover, a 0.5 MacFarland bacterial inoculum was seeded in triplicate on Mueller Hinton Agar or on Mueller Hinton Fastidious Agar; then a cellulose disc soaked with 50 mL of ophthalmic solution was applied on the surface of agar and plates were incubated for 18 h at 37 degC, in order to evaluate the inhibition of bacterial growth around the disc. Human corneal and conjunctival epithelial cells in vitro were incubated for 5, 10 and 15 min with Keratosept or its components. The cytotoxicity was assessed through the release of cytoplasmic enzyme lactate dehydrogenase (LDH) into the medium immediately after exposure to the drugs; the 3 (4,5 dimethylthiazol 2 yl) 2,5 diphenyltetrazolium bromide (MTT) assay was performed to evaluate the metabolic cell activity. Our results show that Keratosept ophthalmic solution gave an average logarithmic (log) reduction of bacterial load of 2.14 0.35 within 6 h of exposure (p value 0.05 versus control saline solution) On agar plates, all microbial strains, excluding P. Aeruginosa, showed an inhibition zone of growth around the Keratosept soaked discs. Keratosept and its components after 5 and 10 min did not show any cytotoxic effect on cultured corneal and conjunctival cells, and only after 15 min a significant reduction of cell viability and an increase of cytotoxicity compared to control (vehicle) was seen; dexpanthenol 5% and polyvinyl alcohol accelerated the wounding of corneal cells in vitro. In conclusion, Keratosept showed good antimicrobial activity on the tested strains; the ophthalmic solution and its components were safe and non toxic for the corneal and conjunctival epithelial cells for 5 and 10 min at the concentrations analyzed, and dexpanthenol 5% and polyvinyl alcohol promoted the wounding of corneal cells.