nils-herrmann/clean_scirepeval huggingface dataset:
Designing a Bankruptcy Prediction Model Based on Account, Market and Macroeconomic Variables (Case Study: Cyprus Stock Exchange)

The development of the Cyprus Stock Exchange together with the increasing trend of investors' presence in financing activities has led to the importance of this market. In such circumstances, the first step towards a sustainable development of the Exchange is to support the investors. Risk of bankruptcy for the investee is a major challenge that an inexperienced stock investor encounters. In this study, for predicting bankruptcy, an attempt has been made to design a valid and accurate model that could act as a deterrent to improper stock selection. In most of the previous studies, non native models have been used to predict bankruptcy in companies. However, the present study has attempted to overcome the shortcomings of the earlier studies through designing an indigenous model based on the data collected from 53 non financial companies out of 103 listed companies in the Cyprus Stock Market from 2007 to 2012, using a complete set of variables affecting bankruptcy (accounting, market and macroeconomic variables) and with using the logistic regression method. The results showed that the accuracy of bankruptcy models that are based on accounting and market variables has been respectively 91.2% and 82.1% respectively. On the other hand, it was shown that there is no significant relationship between macroeconomic variables and the probability likelihood of bankruptcy.
Sugars and Dental Caries: Evidence for Setting a Recommended Threshold for Intake.
Venue: Advances in nutrition

Dental caries affects =80% of the world's population with almost a quarter of US adults having untreated caries. Dental caries is costly to health care and negatively affects well being. Dietary free sugars are the most important risk factor for dental caries. The WHO has issued guidelines that recommend intake of free sugars should provide =10% of energy intake and suggest further reductions to <5% of energy to protect dental health throughout life. These recommendations were informed by a systematic review of the evidence pertaining to amount of sugars and dental caries risk, which showed evidence of moderate quality from cohort studies that limiting free sugars to =10% of energy reduced, but did not eliminate, dental caries. Even low levels of dental caries in children are of concern because caries is a lifelong progressive and cumulative disease. The systematic review therefore explored if there were further benefits to dental health if the intake of free sugars was limited to <5% of energy. Available data were from ecologic studies and, although classified as being of low quality, showed lower dental caries when free sugar intake was <5% of energy compared with when it was >5% but =10% of energy. The WHO recommendations are intended for use by policy makers as a benchmark when assessing intake of sugars by populations and as a driving force for policy change. Multiple strategies encompassing both upstream and downstream preventive approaches are now required to translate the recommendations into policy and practice.
Superpixels: the end of pixels in OBIA. A comparison of stat of the art superpixel methods for remote sensing data

In computer vision, using superpixels or perceptually meaningful atomic regions to speed up later stage processing are becoming increasingly popular in many applications. Superpixels are used as a pre processing stage to organize an image into a low level grouping process through oversegmentation, thus simplifying the computation in later stages. However, in remote sensing domain few studies use superpixels. Even so, there is no comparison between superpixel methods and their suitability for remote sensing images. In this study, we compare four state of the art superpixel methods: Simple Linear Iterative Clustering (SLIC and SLICO) Superpixels Extracted via Energy Driven Sampling (SEEDS) and Linear Spectral Clustering (LSC) We applied them to very high resolution remote sensing data of different characteristics (extent, spatial resolution and landscape complexity) in order to see how superpixels are affected by these factors. The four algorithms were compared regarding their computational time, ability to adhere to image boundaries and the accuracy of the resulted superpixels. Furthermore, we discuss the individual strengths and weaknesses of each algorithm and draw further applications of superpixels in OBIA.
Immunology of Multiple Sclerosis.
Venue: Seminars in neurology

Multiple sclerosis (MS) is considered a prototypic autoimmune disease of the central nervous system (CNS) A complex genetic background with the HLA DR15 haplotype as the main genetic risk factor and over 100 mostly immune related minor risk alleles as well as several environmental factors contribute to the etiology of MS. With respect to pathomechanisms, autoimmune inflammation in early MS is primarily mediated by adaptive immune responses and involves autoreactive T cells, B cells, and antibodies, while the later, chronic stages of MS are characterized by a compartmentalized immune response in the CNS with activated microglia and macrophages. A host of immune cells and mediators can contribute to the autoimmune process, but CNS related factors such as localization of lesions, vulnerability of oligodendrocytes, neurons/axons, and secondary metabolic changes all play a role in the heterogeneous expression of the disease, including different pathologic lesion patterns, neuroimaging findings, disease courses, and severity and response to treatment.
Advances in cancer immunology and cancer immunotherapy.
Venue: Discovery medicine

After decades of setbacks, cancer immunology is living its Golden Age. Recent advances in cancer immunology have provided new therapeutic approaches to treat cancer. The objective clinical response observed in patients treated with antibodies that block the immune checkpoints, cytotoxic T lymphocyte associated protein 4 (CTLA 4) and programmed cell death protein 1 (PD 1)/programmed cell death 1 ligand 1 (PD L1) pathways, has led to their FDA approval for the treatment of melanoma in 2011 and in 2014, respectively. The anti PD 1 antibody nivolumab has received the FDA approval in March 2015 for squamous lung cancer treatment. In addition, antibodies targeting PD 1 or PD L1 have demonstrated their efficacy and safety in additional tumors, including non small cell lung carcinoma (NSCLC) renal cell carcinoma (RCC) bladder cancer, and Hodgkin's lymphoma. Almost at the same time, the field of adoptive cell transfer has exploded. The chimeric antigen receptor (CAR) T technology has provided strong evidence of efficacy in the treatment of B cell malignancies, and different T cell based treatments are currently under investigation for different types of tumors. In this review we will discuss the latest advances in cancer immunology and immunotherapy as well as new treatments now under development in the clinic and potential strategies that have shown promising results in preclinical models.
Learning Deep Representation for Imbalanced Classification
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Data in vision domain often exhibit highly skewed class distribution, i.e. most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re sampling or cost sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both intercluster and inter class margins. This tighter constraint effectively reduces the class imbalance inherent in the local data neighborhood. We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple header hinge loss. The representation learned by our approach, when combined with a simple k nearest neighbor (kNN) algorithm, shows significant improvements over existing methods on both high and low level vision classification tasks that exhibit imbalanced class distribution.
Visual narrative methodology in educational research with babies: triadic play in babies' room

This paper examines an experimental technique that uses visual narrative methodology and dialogue commentary to create an effective research methodology for a pilot project studying babies and toddlers in long day care centres and family contexts. Researchers from different cultural backgrounds using video technology, formed the team of chief investigators. One video clip was chosen to make independent descriptions, comments and interpretations of what was noticed. Later, initial visual narrative descriptions were shared and extended after reading one another's responses. This process created a dialogue commentary that enabled data overview, interpretative analyses and synthesis supported by snapshot moments taken from video clip. One aim of the project was to visually capture the cultural worlds and transitory relationships of babies and toddlers. Researchers showed the selected video clip separately to babies' room educator, centre director, and parents recording their responses. Using visual narrative methodology, dialogue commentary, and a shared cultural historical theoretical framework, revealed useful contradictions that raised social and cultural questions such as: How do educators recognize cultural worlds and transitory relationships of babies and toddlers? How are transitory moments related to pedagogically by educators? Integrating researchers' personal, cultural and affective responses, affords new critical cultural perspectives. This paper draws on screen capture snapshot moments from one video clip, taken from babies' pilot project data. These offer small windows into methodological approaches used to research the cultural world and transitory moments of three infants and their educator, located in the babies' room of an Australian long day care (LDC) site.
Analgesia, nil or placebo to babies, in trials that test new analgesic treatments for procedural pain
Venue: Acta paediatrica

This review assessed how often neonates in control groups experienced unnecessary pain during clinical trials involving procedural pain. We retrieved 45 studies in the 30 months up to June 2015 and found that in 29 (64% the control babies received either placebos or no treatment. Placebos were used in 15/25 (60% studies involving heel pricks and in 6/8 (75% involving venepuncture.
Optical multiple image encryption based on the chaotic structured phase masks under the illumination of a vortex beam in the gyrator domain.
Venue: Optics express

A novel multiple image encryption scheme using the nonlinear iterative phase retrieval algorithm in the gyrator transform domain under the illumination of an optical vortex beam is proposed. In order to increase the randomness, the chaotic structured phase mask based on the logistic map, Fresnel zone plate and radial Hilbert mask is proposed. With the help of two chaotic phase masks, each plain image is encoded into two phase only masks that are considered as the private keys by using the iterative phase retrieval process in the gyrator domain. Then, the second keys of all plain images are modulated into the ciphertext, which has the stationary white noise distribution. Due to the use of the chaotic structured phase masks, the problem of axis alignment in the optical setup can easily be solved. Two private keys are directly relative to the plain images, which makes that the scheme has high resistance against various potential attacks. Moreover, the use of the vortex beam that can integrates more system parameters as the additional keys into one phase mask can improve the security level of the cryptosystem, which makes the key space enlarged widely. Simulation results are given to verify the feasibility and robustness of the proposed encryption scheme.
Fuzzy VIKOR approach for selection of big data analyst in procurement management

Big data and predictive analysis have been hailed as the fourth paradigm of science. Big data and analytics are critical to the future of business sustainability. The demand for data scientists is increasing with the dynamic nature of businesses, thus making it indispensable to manage big data, derive meaningful results and interpret management decisions.  The purpose of this study was to provide a brief conceptual review of big data and analytics and further illustrate the use of a multicriteria decision making technique in selecting the right skilled candidate for big data and analytics in procurement management.  It is important for firms to select and recruit the right data analyst, both in terms of skills sets and scope of analysis. The nature of such a problem is complex and multicriteria decision making, which deals with both qualitative and quantitative factors. In the current study, an application of the Fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method was used to solve the big data analyst selection problem.  From this study, it was identified that Technical knowledge (C1) Intellectual curiosity (C4) and Business acumen (C5) are the strongest influential criteria and must be present in the candidate for the big data and analytics job.  Fuzzy VIKOR is the perfect technique in this kind of multiple criteria decisionmaking problematic scenario. This study will assist human resource managers and procurement managers in selecting the right workforce for big data analytics.
End to End Relation Extraction using LSTMs on Sequences and Tree Structures
Venue: ACL

We present a novel end to end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional tree structured LSTM RNNs on bidirectional sequential LSTM RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the state of the art feature based model on end to end relation extraction, achieving 12.1% and 5.7% relative error reductions in F1 score on ACE2005 and ACE2004, respectively. We also show that our LSTM RNN based model compares favorably to the state of the art CNN based model (in F1 score) on nominal relation classification (SemEval 2010 Task 8) Finally, we present an extensive ablation analysis of several model components.
Towards a meaningful assessment of marine ecological impacts in life cycle assessment (LCA)
Venue: Environment international

Human demands on marine resources and space are currently unprecedented and concerns are rising over observed declines in marine biodiversity. A quantitative understanding of the impact of industrial activities on the marine environment is thus essential. Life cycle assessment (LCA) is a widely applied method for quantifying the environmental impact of products and processes. LCA was originally developed to assess the impacts of land based industries on mainly terrestrial and freshwater ecosystems. As such, impact indicators for major drivers of marine biodiversity loss are currently lacking. We review quantitative approaches for cause effect assessment of seven major drivers of marine biodiversity loss: climate change, ocean acidification, eutrophication induced hypoxia, seabed damage, overexploitation of biotic resources, invasive species and marine plastic debris. Our review shows that impact indicators can be developed for all identified drivers, albeit at different levels of coverage of cause effect pathways and variable levels of uncertainty and spatial coverage. Modeling approaches to predict the spatial distribution and intensity of human driven interventions in the marine environment are relatively well established and can be employed to develop spatially explicit LCA fate factors. Modeling approaches to quantify the effects of these interventions on marine biodiversity are less well developed. We highlight specific research challenges to facilitate a coherent incorporation of marine biodiversity loss in LCA, thereby making LCA a more comprehensive and robust environmental impact assessment tool. Research challenges of particular importance include i) incorporation of the non linear behavior of global circulation models (GCMs) within an LCA framework and ii) improving spatial differentiation, especially the representation of coastal regions in GCMs and ocean carbon cycle models.
Ecological and Pharmacological Activities of Antarctic Marine Natural Products.
Venue: Planta medica

Antarctic benthic communities are regulated by abundant interactions of different types among organisms, such as predation, competition, etc. Predators are usually sea stars, with omnivorous habits, as well as other invertebrates. Against this strong predation pressure, many organisms have developed all sorts of defensive strategies, including chemical defenses. Natural products are thus quite common in Antarctic organisms with an important ecological and pharmacological potential. In this paper, the chemical defenses of the Antarctic organisms studied during the ECOQUIM and ACTIQUIM projects, as well as their pharmacological potential, are reviewed. For the ecological defenses, predation against the sea star Odontaster validus is analyzed and evaluated along depth gradients as well as considering the lifestyle of the organisms. For the pharmacological activity, the anticancer, anti inflammatory, and antibacterial activities tested are evaluated here. Very often, only crude extracts or fractions have been tested so far, and therefore, the natural products responsible for such activities remain yet to be identified. Even if the sampling efforts are not uniform along depth, most ecologically active organisms are found between 200 and 500 m depth. Also, from the samples studied, about four times more sessile organisms possess chemical defenses against the sea star than the vagile ones; these represent 50 of sessile organisms and 35 of the vagile ones, out of the total tested, being active. Pharmacological activity has not been tested uniformly in all groups, but the results show that relevant activity is found in different phyla, especially in Porifera, Cnidaria, Bryozoa, and Tunicata, but also in others. No relationship between depth and pharmacological activity can be established with the samples tested so far. More studies are needed in order to better understand the ecological relationships among Antarctic invertebrates mediated by natural products and to fully explore their pharmacological potential.
Feature tailored spectroscopic analysis of the SNR Puppis A in X rays

We introduce a distinct method to perform spatially resolved spectral analysis of astronomical sources with highly structured X ray emission. The method measures the surface brightness of neighbouring pixels to adaptively size and shape each region, thus the spectra from the bright and faint filamentary structures evident in the broadband images can be extracted. As a test case, we present the spectral analysis of the complete X ray emitting plasma in the supernova remnant Puppis A observed with XMM Newton and Chandra. Given the angular size of Puppis A, many pointings with different observational configurations have to be combined, presenting a challenge to any method of spatially resolved spectroscopy. From the fit of a plane parallel shocked plasma model we find that temperature, absorption column, ionization time scale, emission measure and elemental abundances of O, Ne, Mg, Si, S and Fe, are smoothly distributed in the remnant. Some regions with overabundances of O Ne Mg, previously characterized as ejecta material, were automatically selected by our method, proving the excellent response of the technique. This method is an advantageous tool for the exploitation of archival X ray data.
Physics driven Spatiotemporal Regularization for High dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem
Venue: Scientific reports

This paper presents a novel physics driven spatiotemporal regularization (STRE) method for high dimensional predictive modeling in complex healthcare systems. This model not only captures the physics based interrelationship between time varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two sphere geometry and a realistic torso heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero order, Tikhonov first order and L1 first order regularization methods.
La comprension lectora en estudiantes de educacion primaria en Peru

El articulo presenta los principales resultados de una investigacion que estudio el desarrollo de la comprension lectora en estudiantes de Educacion Primaria. La muestra es intencionada de dos docentes y doce estudiantes, mediante la entrevista semiestructurada, guia de observacion y prueba de comprension lectora. Las docentes trabajan de manera empirica, trabajan con modelos tradicionales que inciden en bajos niveles de comprension lectora de los estudiantes. Los resultados evidencian que las estrategias empleadas por los docentes no favorecen la comprension lectora de los estudiantes, no siguen una secuencia de actividades planificadas y no ponen en practica actividades para el antes, durante y el despues de la lectura. Metodologicamente, la investigacion considero el paradigma interpretativo, con enfoque cualitativo por ser flexible y emergente con caracter educacional. Como contribucion a la solucion de las dificultades identificadas se propone una estrategia didactica con fundamentos pedagogicos y curriculares desde la perspectiva cognitiva, comunicativa e interactiva que permita dirigir el proceso de ensenanza y aprendizaje de la compresion lectora de una manera creativa e innovadora.
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs) We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch patch' context between image regions, and 'patch background' context. For learning from the patch patch context, we formulate Conditional Random Fields (CRFs) with CNN based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch background context, we show that a network design with traditional multi scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state of the art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL Context, and SIFT flow. In particular, we achieve an intersection overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.