nils-herrmann/clean_scirepeval huggingface dataset:
Prediction of patient specific post operative outcomes of TAVI procedure: The impact of the positioning strategy on valve performance.
Venue: Journal of biomechanics

Prosthesis positioning in transcatheter aortic valve implantation procedures represents a crucial aspect for procedure success as demonstrated by many recent studies on this topic. Possible complications, device performance, and, consequently, also long term durability are highly affected by the adopted prosthesis placement strategy. In the present work, we develop a computational finite element model able to predict device specific and patient specific replacement procedure outcomes, which may help medical operators to plan and choose the optimal implantation strategy. We focus in particular on the effects of prosthesis implantation depth and release angle. We start from a real clinical case undergoing Corevalve self expanding device implantation. Our study confirms the crucial role of positioning in determining valve anchoring, replacement failure due to intra or para valvular regurgitation, and post operative device deformation.
TweetMT: A Parallel Microblog Corpus
Venue: LREC

We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese) all of which have an official status in the Iberian Peninsula. The corpus has been created by combining automatic collection and crowdsourcing approaches, and it is publicly available. It is intended for the development and testing of microtext machine translation systems. In this paper we describe the methodology followed to build the corpus, and present the results of the shared task in which it was tested.
Slavery scandals: Unpacking labour challenges and policy responses within the off shore fisheries sector

Over the past year, scandals around what has been labelled slave labour in the industrial fisheries sector in Thailand have revealed not only the connections between northern buyers and southern labour practices, but also the relative lack of research on fisheries labour in Asia and the global South. The slavery and trafficking framings pervading these depictions have been very useful for drawing attention to and acting on criminal activities in labour recruitment and abuse, but have limits as a basis for addressing the underlying causes of forced labour in fisheries. Insights from research on regional labour migration as well as the work of civil society organisations in Thailand suggest that broader improvements in labour relations will require changes in migration management, with a focus on addressing vulnerabilities that restrict the abilities of migrant workers to obtain better working conditions. This analysis provides the basis for assessing the potential and limits of recent programmes to improve labour relations on the oceans, including anti trafficking policies, IUU enforcement, buyer efforts to ensure that supply chains do not involve forced or illegal labour relations, and Thai government actions.
The Vel of Slavery: Tracking the Figure of the Unsovereign

This article addresses the relationship between anti racism and decolonization in the North American context. It argues that the logic of decolonization movements for indigenous sovereignty and against the settler states of Canada and the USA overlap the discursive field of contemporary post racialism in ways that circumvent the challenges and possibilities offered by black radicalism in the historic instance. After engaging recent theoretical literature on settler colonialism, it is suggested that the freedom drive that abolishes slavery unsettles both colonial and decolonial forms of sovereign determination.
Slavery By Another Name The Re Enslavement Of Black Americans From The Civil War To World War Ii

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Sex Robot Matters: Slavery, the Prostituted, and the Rights of Machines
Venue: IEEE Technology and Society Magazine

Why begin an article on sex robots, and the rights of machines, with an introduction to human slavery? Because these themes are more interlinked than one might imagine, but perhaps not for the reasons you would think. In this article, the author asks how a discussion about artificial intelligence (AI) and robots became fused with a discussion of slavery, rights, and objects (animate and inanimate) with these objects taking on the role of persons. These themes are brought together through a discussion of sex robots.
Knapsack in graph groups, HNN extensions and amalgamated products
Venue: STACS

It is shown that the knapsack problem, which was introduced by Myasnikov et al. for arbitrary finitely generated groups, can be solved in NP for graph groups. This result even holds if the group elements are represented in a compressed form by SLPs, which generalizes the classical NP completeness result of the integer knapsack problem. We also prove general transfer results: NP membership of the knapsack problem is passed on to finite extensions, HNN extensions over finite associated subgroups, and amalgamated products with finite identified subgroups.
A new QR decomposition based RLS algorithm using the split Bregman method for L1 regularized problems
Venue: Signal Process.

The split Bregman (SB) method can solve a broad class of L1 regularized optimization problems and has been widely used for sparse signal processing in a variety of applications. To achieve lower complexity and to cope with time varying environments, we develop a new adaptive version of the SB method for finding online sparse solutions. This algorithm is derived from the recursive least squares (RLS) optimization problem, where the SB method is used to separate the regularization term from the constrained optimization. This algorithm is numerically more stable and easily amenable to multivariate implementation due to the use of a QR decomposition (QRD) structure. An efficient method is further developed for selecting the thresholding rule, which controls the sparsity level of the estimator. Moreover, the SB QRRLS algorithm is extended to a multivariate version to solve the sparse principal component analysis (SPCA) problem. Simulation results are presented to illustrate the effectiveness of the proposed algorithms in sparse system estimation and SPCA. We show that the convergence and tracking performance of the proposed algorithms compares favorably with conventional algorithms. A new adaptive split Bregman algorithm for L1 regularized problems.QR decomposition based RLS algorithm for implementation.Regularization parameter selection formulae for sparsity control.Multivariate version for sparse principal component analysis.
Tensor Networks and Hierarchical Tensors for the Solution of High Dimensional Partial Differential Equations
Venue: Found. Comput. Math.

Hierarchical tensors can be regarded as a generalisation, preserving many crucial features, of the singular value decomposition to higher order tensors. For a given tensor product space, a recursive decomposition of the set of coordinates into a dimension tree gives a hierarchy of nested subspaces and corresponding nested bases. The dimensions of these subspaces yield a notion of multilinear rank. This rank tuple, as well as quasi optimal low rank approximations by rank truncation, can be obtained by a hierarchical singular value decomposition. For fixed multilinear ranks, the storage and operation complexity of these hierarchical representations scale only linearly in the order of the tensor. As in the matrix case, the set of hierarchical tensors of a given multilinear rank is not a convex set, but forms an open smooth manifold. A number of techniques for the computation of hierarchical low rank approximations have been developed, including local optimisation techniques on Riemannian manifolds as well as truncated iteration methods, which can be applied for solving high dimensional partial differential equations. This article gives a survey of these developments. We also discuss applications to problems in uncertainty quantification, to the solution of the electronic Schrodinger equation in the strongly correlated regime, and to the computation of metastable states in molecular dynamics.
Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained environments
Venue: ETRA

We present labelled pupils in the wild (LPW) a novel dataset of 66 high quality, high speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state of the art dark pupil head mounted eye tracker. They cover people of different ethnicities and a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, and make up. We benchmark five state of the art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution and vision aids as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head mounted eye trackers.
The expression levels of cellular prion protein affect copper isotopic shifts in the organs of mice

The metabolic processes involving copper, such as cellular assimilation, electron transfer and protein formation, can partition copper isotopes in the body amongst different organs and bodily fluids. Systematic changes in copper isotopic composition in the body may assist in identifying regions where copper metabolism has been altered. The cellular prion protein (PrPC) is a copper binding protein that is highly expressed in the brain. Its misfolded isoform PrPSc is required for the development of prion diseases, fatal neurological diseases including Scrapie and Creutzfeldt Jacob's disease. As this protein is implicated in copper metabolism, we investigated how expression levels of PrPC could affect the distribution of copper isotopes in the body. We used a novel ion exchange method to isolate copper for stable copper isotope analysis of the liver, kidney, red blood cells, serum, and different regions of the brain of wild type, PrPC knockout (Prnp and mice in which the copper binding sites of the octarepeat region were mutated (His Ala, Cu del) We found the liver, kidney and brain tissue samples to be enriched in 65Cu compared to the food, but only in the liver were differences observed associated with genotype when considering the absolute d65Cu values. Characteristic isotopic shifts were identified between the serum and the liver (all genotypes, p 0.022) and between the serum and the hippocampus, cerebral cortex, and brainstem (Prnp and Cu del only, p values were 0.002, 0.053, and 0.017 respectively) We have demonstrated that altering the gene expression of a single protein, in this case PrPC, can produce systematic changes in copper isotopic distribution in transgenic mice.
Modelling of platelet fibrin clot formation in flow with a DPD PDE method
Venue: Journal of mathematical biology

The paper is devoted to mathematical modelling of clot growth in blood flow. Great complexity of the hemostatic system dictates the need of usage of the mathematical models to understand its functioning in the normal and especially in pathological situations. In this work we investigate the interaction of blood flow, platelet aggregation and plasma coagulation. We develop a hybrid DPD PDE model where dissipative particle dynamics (DPD) is used to model plasma flow and platelets, while the regulatory network of plasma coagulation is described by a system of partial differential equations. Modelling results confirm the potency of the scenario of clot growth where at the first stage of clot formation platelets form an aggregate due to weak inter platelet connections and then due to their activation. This enables the formation of the fibrin net in the centre of the platelet aggregate where the flow velocity is significantly reduced. The fibrin net reinforces the clot and allows its further growth. When the clot becomes sufficiently large, it stops growing due to the narrowed vessel and the increase of flow shear rate at the surface of the clot. Its outer part is detached by the flow revealing the inner part covered by fibrin. This fibrin cap does not allow new platelets to attach at the high shear rate, and the clot stops growing. Dependence of the final clot size on wall shear rate and on other parameters is studied.
Effect of dilution in an inter turbine Flameless combustor

Alternatives to combustion in aircraft engines are not expected to become feasible in the decades to come. As the aviation traffic increases and regulations become more stringent, reduction in pollutant emissions are needed. The Flameless Combustion (FC) regime has been one of the promising candidates to achieve lower emissions in gas turbine engines. This combustion regime is characterized by welldistributed reactions, with low peak temperatures, resulting in lower emissions and acoustic oscillations. However, the attainment of the FC regime is not straight forward considering the conditions and requirements of gas turbines. Most of the previous combustor design attempts failed to provide broad operational range, high combustion efficiency, or were difficult to integrate in an engine. Along with a novel aircraft concept, the European project AHEAD (Advanced Hybrid Engines for Aircraft Development) resulted in the conceptual design of a gas turbine engine with two sequential combustion chambers1. As the aircraft concept allows the use of cryogenic fuels, the first combustion chamber was designed to operate with hydrogen or natural gas. The second is the inter turbine combustor herein studied, which would operate under the FC regime burning conventional fuels.
PDF text classification to leverage information extraction from publication reports
Venue: J. Biomed. Informatics

Data extraction from original study reports is a time consuming, error prone process in systematic review development. Information extraction (IE) systems have the potential to assist humans in the extraction task, however majority of IE systems were not designed to work on Portable Document Format (PDF) document, an important and common extraction source for systematic review. In a PDF document, narrative content is often mixed with publication metadata or semi structured text, which add challenges to the underlining natural language processing algorithm. Our goal is to categorize PDF texts for strategic use by IE systems.  We used an open source tool to extract raw texts from a PDF document and developed a text classification algorithm that follows a multi pass sieve framework to automatically classify PDF text snippets (for brevity, texts) into TITLE,  To validate the algorithm, we developed a gold standard of PDF reports that were included in the development of previous systematic reviews by the Cochrane Collaboration. In a two step procedure, we evaluated (1) classification performance, and compared it with machine learning classifier, and (2) the effects of the algorithm on an IE system that extracts clinical outcome mentions.  The multi pass sieve algorithm achieved an accuracy of 92.6% which was 9.7% (p<0.001) higher than the best performing machine learning classifier that used a logistic regression algorithm. F measure improvements were observed in the classification of TITLE +15.6% ABSTRACT +54.2% BODYTEXT +3.7% SEMISTRUCTURE +34% and MEDADATA +14.2% In addition, use of the algorithm to filter semi structured texts and publication metadata improved performance of the outcome extraction system (F measure +4.1% p=0.002) It also reduced of number of sentences to be processed by 44.9% (p<0.001) which corresponds to a processing time reduction of 50% (p=0.005)  The rule based multi pass sieve framework can be used effectively in categorizing texts extracted from PDF documents. Text classification is an important prerequisite step to leverage information extraction from PDF documents.
Metadata Extraction and Management in Data LakesWith GEMMS
Venue: Complex Syst. Informatics Model. Q.

In addition to volume and velocity, Big data is also characterized by its variety. Variety in structure and semantics requires new integration approaches which can resolve the integration challenges also for large volumes of data. Data lakes should reduce the upfront integration costs and provide a more flexible way for data integration and analysis, as source data is loaded in its original structure to the data lake repository. Some syntactic transformation might be applied to enable access to the data in one common repository; however, a deep semantic integration is done only after the initial loading of the data into the data lake. Thereby, data is easily made available and can be restructured, aggregated, and transformed as required by later applications. Metadata management is a crucial component in a data lake, as the source data needs to be described by metadata to capture its semantics. We developed a Generic and Extensible Metadata Management System for data lakes (called GEMMS) that aims at the automatic extraction of metadata from a wide variety of data sources. Furthermore, the metadata is managed in an extensible metamodel that distinguishes structural and semantical metadata. The use case applied for evaluation is from the life science domain where the data is often stored only in files which hinders data access and efficient querying. The GEMMS framework has been proven to be useful in this domain. Especially, the extensibility and flexibility of the framework are important, as data and metadata structures in scientific experiments cannot be defined a priori
Dental Undergraduate Views of Objective Structured Clinical Examinations (OSCEs) A Literature Review
Venue: Dentistry journal

Structured Clinical Examinations (OSCEs) are widely used in dental undergraduate assessment, often determining progression or graduation. Student evaluation of this assessment process is important, and this includes identifying the views of the student. The aim of this paper is to present a review of the current literature regarding dental student perceptions of OSCEs. A search of the PubMed database covering the period 1975 to 2015 identified 121 possible papers from which only six were suitable for review. The remaining papers were excluded due to them not reporting on dental undergraduate views. Students perceived the OSCE to be a valid assessment in three studies, but not in one. The educational benefit of an OSCE is well supported by these studies. OSCEs can induce high levels of anxiety compared to other forms of assessments, but this did not affect student performance. The majority of students would chose to have a similar format of assessment again, showing support for OSCEs. Further research using larger cohorts of students could be undertaken in order to support these finding which would give added evidence for the continuing use of OSCEs as a valid method of both dental undergraduate education and assessment.
Effect of Macroeconomic Variables on Stock Market

The paper aims to study the effect of macroeconomic factors on the performance of the Indian Stock market Index (BSE: 500) using monthly and yearly data over the time frame of June 2003 to June 2016. The macroeconomic variables are; Inflation, exchange rate, Oil Prices, Gold Prices, Aggregated monsoon. The study also found the correlation between different macroeconomic variables using which they were fitted in the VAR regression model. The long term relation of the Indian stock market was studied with these macroeconomic parameters. The paper also looks at which variables are more significantly related to the Stock market in the long run the domestic macroeconomic variables or global. One of the most significant macroeconomic factor is Gold. Gold prices have an inverse relationship with the price of the dollar. Given the importance of Gold in the global economy, we have analyzed the close co relation between the gold prices and the stock market prices and the factors that have created such relations between gold and stock market.
Development of an E learning System for the Endoscopic Diagnosis of Early Gastric Cancer: An International Multicenter Randomized Controlled Trial
Venue: EBioMedicine

In many countries, gastric cancer is not diagnosed until an advanced stage. An Internet based e learning system to improve the ability of endoscopists to diagnose gastric cancer at an early stage was developed and was evaluated for its effectiveness.  The study was designed as a randomized controlled trial. After receiving a pre test, participants were randomly allocated to either an e learning or non e learning group. Only those in the e learning group gained access to the e learning system. Two months after the pre test, both groups received a post test. The primary endpoint was the difference between the two groups regarding the rate of improvement of their test results. Findings 515 endoscopists from 35 countries were assessed for eligibility, and 332 were enrolled in the study, with 166 allocated to each group. Of these, 151 participants in the e learning group and 144 in the non e learning group were included in the analysis. The mean improvement rate (standard deviation) in the e learning and non e learning groups was 1*24 (0*26) and 1*00 (0*16) respectively (P 0*001)  This global study clearly demonstrated the efficacy of an e learning system to expand knowledge and provide invaluable experience regarding the endoscopic detection of early gastric cancer (R000012039)