nils-herrmann/clean_scirepeval huggingface dataset:
Crowdsourced semantic annotation of scientific publications and tabular data in PDF

Significant amounts of knowledge in science and technology have so far not been published as Linked Open Data but are contained in the text and tables of legacy PDF publications. Making such information available as RDF would, for example, provide direct access to claims and facilitate surveys of related work. A lot of valuable tabular information that till now only existed in PDF documents would also finally become machine understandable. Instead of studying scientific literature or engineering patents for months, it would be possible to collect such input by simple SPARQL queries. The SemAnn approach enables collaborative annotation of text and tables in PDF documents, a format that is still the common denominator of publishing, thus maximising the potential user base. The resulting annotations in RDF format are available for querying through a SPARQL endpoint. To incentivise users with an immediate benefit for making the effort of annotation, SemAnn recommends related papers, taking into account the hierarchical context of annotations in a novel way. We evaluated the usability of SemAnn and the usefulness of its recommendations by analysing annotations resulting from tasks assigned to test users and by interviewing them. While the evaluation shows that even few annotations lead to a good recall, we also observed unexpected, serendipitous recommendations, which confirms the merit of our low-threshold annotation support for the crowd.
Porphyria in Switzerland, 15 years experience.

The porphyrias, a group of seven metabolic disorders in the haem biosynthesis, can be classified into acute and non-acute porphyrias. A common symptom of acute porphyrias is severe acute abdominal pain, whereas cutaneous photosensitivity can occur in both acute and non-acute porphyrias. All porphyrias, except for sporadic porphyria cutanea tarda (sPCT), are hereditary disorders caused by mutations in the respective genes. We present porphyria cases documented in our porphyria centre during the past 15 years.



Diagnosis was based on clinical symptoms and biochemical analyses. Mutation analysis was performed in patients/families with a confirmed hereditary porphyria.



As the porphyria specialist centre of Switzerland, we perform the specialized analyses required for the diagnosis of all types of porphyrias, and give advice to patients, physicians and other laboratories. We therefore estimated that our data cover 80-90% of all diagnosed Swiss cases. A total of 217 patients from 170 families were diagnosed including, 111 acute intermittent porphyria, 45 erythropoietic protoporphyria, 30 variegate porphyria, 21 sPCT, five congenital erythropoietic porphyria, four hereditary coproporphyria and one hepatoerythropoietic porphyria patient. Systematic monitoring of the patients would allow early detection of the potential life-threatening complications such as hepatocellular carcinoma and renal insufficiency in acute porphyrias, and liver failure in EPP. Seventy-five percent of all families underwent genetic testing. Identification of pre-symptomatic mutation carriers so that these individuals and their physicians can be consulted with safety on drug use and other preventive measures, is important in managing acute porphyrias. The unique phenomenon of founder mutations in the Swiss population is also discussed.
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder

Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages. A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embeddings). To this end, we propose a novel latent-variable generative model for semisupervised syntactic dependency parsing. As exact inference is intractable, we introduce a differentiable relaxation to obtain approximate samples and compute gradients with respect to the parser parameters. Our method (Differentiable Perturb-andParse) relies on differentiable dynamic programming over stochastically perturbed edge scores. We demonstrate effectiveness of our approach with experiments on English, French and Swedish.
Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.
RFID Antennas for Body-Area Applications: From Wearables to Implants

Radio-frequency identification (RFID) technology has recently attracted significant interest in the realm of body-area applications, including both wearables and implants. The presence of the human body in close proximity to the RFID device creates several challenges in terms of design, fabrication, and testing, while also ushering in a whole new realm of opportunities for health care and other body-area applications. With these factors in mind, this article provides a holistic and critical review of design challenges associated with body-area RFID technologies, including operation frequencies, influence of the surrounding biological tissues, antenna design and miniaturization, and conformance to international safety guidelines. Concurrently, a number of fabrication methods are discussed for realizing flexible, conformal, and robust RFID device prototypes. The article concludes by reviewing transformative RFID-based solutions for wearable and implantable applications and discussing the future opportunities and challenges raised. Notably, this is the first time that a comprehensive review has been presented in the area of RFID antennas for body-area applications, addressing challenges specific to on-/in-body RFID operation and spanning a wide range of aspects that include design, fabrication, testing, and, eventually, applications and future directions. As such, the utmost aim of this article is to be a unique point of reference for experts and nonexperts in the field.
On interestingness measures of formal concepts

Formal concepts and closed itemsets proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing domain taxonomies and ontologies. Exponential explosion makes it difficult to consider the whole concept lattice arising from data, one needs to select most useful and interesting concepts. In this paper interestingness measures of concepts are considered and compared with respect to various aspects, such as efficiency of computation and applicability to noisy data and performing ranking correlation. Formal Concept Analysis intrestingess measures closed itemsets
Comparative Genomics of the Odorant-Binding and Chemosensory Protein Gene Families across the Arthropoda: Origin and Evolutionary History of the Chemosensory System

Chemoreception is a biological process essential for the survival of animals, as it allows the recognition of important volatile cues for the detection of food, egg-laying substrates, mates, or predators, among other purposes. Furthermore, its role in pheromone detection may contribute to evolutionary processes, such as reproductive isolation and speciation. This key role in several vital biological processes makes chemoreception a particularly interesting system for studying the role of natural selection in molecular adaptation. Two major gene families are involved in the perireceptor events of the chemosensory system: the odorant-binding protein (OBP) and chemosensory protein (CSP) families. Here, we have conducted an exhaustive comparative genomic analysis of these gene families in 20 Arthropoda species. We show that the evolution of the OBP and CSP gene families is highly dynamic, with a high number of gains and losses of genes, pseudogenes, and independent origins of subfamilies. Taken together, our data clearly support the birth-and-death model for the evolution of these gene families with an overall high gene turnover rate. Moreover, we show that the genome organization of the two families is significantly more clustered than expected by chance and, more important, that this pattern appears to be actively maintained across the Drosophila phylogeny. Finally, we suggest the homologous nature of the OBP and CSP gene families, dating back their most recent common ancestor after the terrestrialization of Arthropoda (380--450 Ma) and we propose a scenario for the origin and diversification of these families.
The Role of School Social Environment on Dental Caries Experience in 8- to 12-Year-Old Brazilian Children: A Multilevel Analysis.

Although children spend most of their time involved in activities related to school, few studies have focused on the association between school social environment and oral health. This cross-sectional study assessed individual and school-related social environment correlates of dental caries in Brazilian schoolchildren aged 8-12 years.



A sample of children from 20 private and public schools (n=1,211) was selected. Socio-economic data were collected from parents, and data regarding children characteristics were collected from children using a questionnaire. Dental examinations were performed to assess the presence of dental plaque: dental caries experience (DMFT≥1) and dental caries severity (mean dmf-t/DMF-T). The social school environment was assessed by a questionnaire administered to school coordinators.  Poisson regression was used to investigate the association between school social environment and dental caries prevalence and experience.



The dental caries prevalence was 32.4% (95% confidence interval: 29.7-35.2) and the mean dmf-t/DMF-T was 1.84 (standard deviation: 2.2). Multilevel models showed that the mean dmf-t/DMF-T and DMFT≥1 were associated with lower maternal schooling and higher levels of dental plaque. For contextual variables, schools offering after-hours sports activities were associated with a lower prevalence of dental caries and a lower mean of dmf-t/DMF-T, while the occurrence of violence and theft episodes was positively associated with dental caries.



The school social environment has an influence on dental caries in children. The results suggest that strategies focused on the promotion of healthier environments should be stimulated to reduce inequalities in dental caries.
The Normalized Difference Vegetation Index ( NDVI ) : unforeseen successes in animal ecology

This review highlights the latest developments associated with the use of the Normalized Difference Vegetation Index (NDVI) in ecology. Over the last decade, the NDVI has proven extremely useful in predicting herbivore and non-herbivore distribution, abundance and life history traits in space and time. Due to the continuous nature of NDVI since mid-1981, the relative importance of different temporal and spatial lags on population performance can be assessed, widening our understanding of population dynamics. Previously thought to be most useful in temperate environments, the utility of this satellite-derived index has been demonstrated even in sparsely vegetated areas. Climate models can be used to reconstruct historical patterns in vegetation dynamics in addition to anticipating the effects of future environmental change on biodiversity. NDVI has thus been established as a crucial tool for assessing past and future population and biodiversity consequences of change in climate, vegetation phenology and primary productivity.
Active $du/dt$—New Output-Filtering Approach for Inverter-Fed Electric Drives

Active du/dt is a new output-filtering method to mitigate motor overvoltages. The inverter pulse pattern edges are broken down into narrower pulses, which control the filter LC circuit. This results in an output voltage that does not have to exhibit the overshoot typically seen in common LC circuits in output-filtering applications. Furthermore, the shape of the output-voltage edge has properties well suited for output-filtering applications. An appropriate filter rise time is selected according to the motor-cable length to eliminate the motor overvoltage. The basis of the active du/dt method is discussed in brief. Considerations on the application of the active du/dt filtering in electric drives are presented together with simulations and experimental data to verify the potential of the method.
Cloud Federation

This paper suggests a definition of the term Cloud Federation, a concept of service aggregation characterized by interoperability features, which addresses the economic problems of vendor lock-in and provider integration. Furthermore, it approaches challenges like performance and disaster-recovery through methods such as co-location and geographic distribution. The concept of Cloud Federation enables further reduction of costs due to partial outsourcing to more cost-efficient regions, may satisfy security requirements through techniques like fragmentation and provides new prospects in terms of legal aspects. Based on this concept, we discuss a reference architecture that enables new service models by horizontal and vertical integration. The definition along with the reference architecture serves as a common vocabulary for discussions and suggests a template for creating value-added software solutions.
Generative models for access control policies: applications to role mining over logs with attribution

We consider a fundamentally new approach to role and policy mining: finding RBAC models which reflect the observed usage of entitlements and the attributes of users. Such policies are interpretable, i.e., there is a natural explanation of why a role is assigned to a user and are conservative from a security standpoint since they are based on actual usage. Further, such "generative" models provide many other benefits including reconciliation with policies based on entitlements, detection of provisioning errors, as well as the detection of anomalous behavior. Our contributions include defining the fundamental problem as extensions of the well-known role mining problem, as well as providing several new algorithms based on generative machine learning models. Our algorithms find models which are causally associated with actual usage of entitlements and any arbitrary combination of user attributes when such information is available. This is the most natural process to provision roles, thus addressing a key usability issue with existing role mining algorithms.
 We have evaluated our approach on a large number of real life data sets, and our algorithms produce good role decompositions as measured by metrics such as coverage, stability, and generality We compare our algorithms with traditional role mining algorithms by equating usage with entitlement. Results show that our algorithms improve on existing approaches including exact mining, approximate mining, and probabilistic algorithms; the results are more temporally stable than exact mining approaches, and are faster than probabilistic algorithms while removing artificial constraints such as the number of roles assigned to each user. Most importantly, we believe that these roles more accurately capture what users actually do, the essence of a role, which is not captured by traditional methods.
Roller: a novel approach to Web information extraction

The research regarding Web information extraction focuses on learning rules to extract some selected information from Web documents. Many proposals are ad hoc and cannot benefit from the advances in machine learning; furthermore, they are likely to fade away as the Web evolves, and their intrinsic assumptions are not satisfied. Some authors have explored transforming Web documents into relational data and then using techniques that got inspiration from inductive logic programming. In theory, such proposals should be easier to adapt as the Web evolves because they build on catalogues of features that can be adapted without changing the proposals themselves. Unfortunately, they are difficult to scale as the number of documents or features increases. In the general field of machine learning, there are propositio-relational proposals that attempt to provide effective and efficient means to learn from relational data using propositional techniques, but they have seldom been explored regarding Web information extraction. In this article, we present a new proposal called Roller: it relies on a search procedure that uses a dynamic flattening technique to explore the context of the nodes that provide the information to be extracted; it is configured with an open catalogue of features, so that it can adapt to the evolution of the Web; it also requires a base learner and a rule scorer, which helps it benefit from the continuous advances in machine learning. Our experiments confirm that it outperforms other state-of-the-art proposals in terms of effectiveness and that it is very competitive in terms of efficiency; we have also confirmed that our conclusions are solid from a statistical point of view.
Detecting anomalous behavior of PLC using semi-supervised machine learning

Industrial Control System (ICS) is used to monitor and control critical infrastructures. Programmable logic controllers (PLCs) are major components of ICS, which are used to form automation system. It is important to protect PLCs from any attacks and undesired incidents. However, it is not easy to apply traditional tools and techniques to PLCs for security protection and forensics because of its unique architectures. Semi-supervised machine learning algorithm, One-class Support Vector Machine (OCSVM), has been applied successfully to many anomaly detection problems. This paper proposes a novel methodology to detect anomalous events of PLC by using OCSVM. The methodology was applied to a simulated traffic light control system to illustrate its effectiveness and accuracy. Our results show that high accuracy of identification of anomalous PLC operations is obtained which can help investigators to perform PLC forensics efficiently and effectively.
Pole-zero tracking frequency compensation for low dropout regulator

Most of the Low Dropout Regulators (LDRs) have limited operation range of load current due to their stability problem. This paper proposes a new frequency compensation scheme for LDR to optimize the regulator performance over a wide load current range. By introducing a tracking zero to cancel out the regulator output pole, the frequency response of the feedback loop becomes load current independent. The open-loop DC gain is boosted up by a low frequency dominant pole, which increases the regulator accuracy. To demonstrate the feasibility of the proposed scheme, a LDR utilizing the new frequency compensation scheme is designed and fabricated using TSMC 0.3511~1 digital CMOS process. Simulation results show that with output current from 0 pA to 100 mA the bandwidth variation is only 2.3 times and the minimum DC gain is 72 dB. Measurement of the dynamic response matches well with simulation.
Collaborative Filtering: A Machine Learning Perspective

Collaborative Filtering: A Machine Learning Perspective Benjamin Marlin Master of Science Graduate Department of Computer Science University of Toronto 2004 Collaborative filtering was initially proposed as a framework for filtering information based on the preferences of users, and has since been refined in many different ways. This thesis is a comprehensive study of rating-based, pure, non-sequential collaborative filtering. We analyze existing methods for the task of rating prediction from a machine learning perspective. We show that many existing methods proposed for this task are simple applications or modifications of one or more standard machine learning methods for classification, regression, clustering, dimensionality reduction, and density estimation. We introduce new prediction methods in all of these classes. We introduce a new experimental procedure for testing stronger forms of generalization than has been used previously. We implement a total of nine prediction methods, and conduct large scale prediction accuracy experiments. We show interesting new results on the relative performance of these methods.
Theory-based Bayesian models of inductive learning and reasoning

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.
Learning Stochastic Feedforward Neural Networks

Multilayer perceptrons (MLPs) or neural networks are popular models used for nonlinear regression and classification tasks. As regressors, MLPs model the conditional distribution of the predictor variables Y given the input variables X . However, this predictive distribution is assumed to be unimodal (e.g. Gaussian). For tasks involving structured prediction, the conditional distribution should be multi-modal, resulting in one-to-many mappings. By using stochastic hidden variables rather than deterministic ones, Sigmoid Belief Nets (SBNs) can induce a rich multimodal distribution in the output space. However, previously proposed learning algorithms for SBNs are not efficient and unsuitable for modeling real-valued data. In this paper, we propose a stochastic feedforward network with hidden layers composed of both deterministic and stochastic variables. A new Generalized EM training procedure using importance sampling allows us to efficiently learn complicated conditional distributions. Our model achieves superior performance on synthetic and facial expressions datasets compared to conditional Restricted Boltzmann Machines and Mixture Density Networks. In addition, the latent features of our model improves classification and can learn to generate colorful textures of objects.