nils-herrmann/clean_scirepeval huggingface dataset:
Effects of leukocyte depletion filters on matrix metalloproteinase activation in an extracorporeal circulation circuit.

Matrix metalloproteinases (MMPs) are a family of endopeptidases that function to remodel tissue during both normal physiology and pathology. Research performed by the Medical University of South Carolina found an increased release of several MMP species during cardiopulmonary bypass (CPB), including the subtype MMP-9, but whether and to what degree the extracorporeal circulation circuit (ECC) induces the release of MMPs has yet to be determined. Human bank whole blood scheduled for discard was obtained and exposed to an ECC. The first set of studies (N = 8) was performed with a loop circuit using a standard arterial line filter. A leukoreduction filter was incorporated during the first 30 min of the pump run for the second set of trials; the leukoreduction filter was then bypassed and a standard arterial filter used for the remaining 60 minutes on pump (N = 8). Blood samples were drawn at four time points for analysis (baseline, 30, 60, and 90 min). Data were analyzed using repeated measures analysis of variance with between-subjects factors, and a p value of less than .1 was considered statistically significant. The MMP-9 level increased by 130.44% at 90 min on pump in the standard arterial filter group and decreased by 34.62% at 90 min on pump in the leukoreduction group. There was a significant difference between the baseline MMP-9 level and the MMP-9 concentrations at 30, 60, and 90 min for both groups (p = .0348); there was a significant difference in MMP-9 levels between the two filter groups (p = .0611). The present study found a significant increase in MMP-9 levels when blood was exposed to an ECC with a standard arterial filter. The use of a leukoreduction filter significantly reduced MMP-9 concentrations as compared to baseline levels in this study. Leukocyte depletion filtration may serve to benefit CPB patients by attenuating the inflammatory response and disrupting pathways that govern such mediators as the MMPs.
Blueberry as a Potential Radiosensitizer for Treating Cervical Cancer

Cervical cancer (CC) is a leading cause of death in women worldwide. Radiation therapy (RT) for CC is an effective alternative, but its toxicity remains challenging. Blueberry is amongst the most commonly consumed berries in the United States. We previously showed that resveratrol, a compound in red grapes, can be used as a radiosensitizer for prostate cancer. In this study, we found that the percentage of colonies, PCNA expression level and the OD value of cells from the CC cell line SiHa were all decreased in RT/Blueberry Extract (BE) group when compared to those in the RT alone group. Furthermore, TUNEL+ cells and the relative caspase-3 activity in the CC cells were increased in the RT/BE group compared to those in the RT alone group. The anti-proliferative effect of RT/BE on cancer cells correlated with downregulation of pro-proliferative molecules cyclin D and cyclin E. The pro-apoptotic effect of RT/BE correlated with upregulation of the pro-apoptotic molecule TRAIL. Thus, BE sensitizes SiHa cells to RT by inhibition of proliferation and promotion of apoptosis, suggesting that blueberry might be used as a potential radiosensitizer to treat CC.
Relation Extraction from the Web Using Distant Supervision

Extracting information from Web pages requires the ability to work at Web scale in terms of the number of documents, the number of domains and domain complexity. Recent approaches have used existing knowledge bases to learn to extract information with promising results. In this paper we propose the use of distant supervision for relation extraction from the Web. Distant supervision is a method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains, as well as extracting relations across sentence boundaries. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. Our experiments show that using a more robust entity recognition approach and expanding the scope of relation extraction results in about 8 times the number of extractions, and that strategically selecting training data can result in an error reduction of about 30%.
ScaleNet: Scale Invariant Network for Semantic Segmentation in Urban Driving Scenes

The scale difference in driving scenarios is one of the essential challenges in semantic scene segmentation. Close objects cover significantly more pixels than far objects. In this paper, we address this challenge with a scale invariant architecture. Within this architecture, we explicitly estimate the depth and adapt the pooling field size accordingly. Our model is compact and can be extended easily to other research domains. Finally, the accuracy of our approach is comparable to the state-of-the-art and superior for scale problems. We evaluate on the widely used automotive dataset Cityscapes as well as a self-recorded dataset.
International Law , International Relations and

Commitments are a persistent feature of international affairs. Disagreement over the effect of international commitments and the causes of compliance with them is equally persistent. Yet in the last decade the long-standing divide between those who believed that international rules per se shaped state behavior and those who saw such rules as epiphenomena1 or insignificant has given way to a more nuanced and complex debate. Regime theory, originally focused on the creation and persistence of regimes, increasingly emphasizes variations in regimes and in their impact on behavior. The legal quality of regime rules is one important source of regime variation. At the same time the proliferation and evolution of intema-tional legal agreements, organizations and judicial bodies in the wake of the Cold War has provided the empirical predicate and a policy imperative for heightened attention to the role of international law. Across many issue-areas, the use of law to structure world politics seems to be increasing. This phenomenon of legalization raises several questions. What factors explain the choice to create and use international law? If law is a tool or method to organize interaction, how does it work? Does the use of international law make a difference to how states or domestic actors behave? These questions are increasingly of interest to IR theorists axid policy-makers alike. The core issue is the impact of law and legal-ization on state behavior, often understood in terms of compliance. While the distinction should not be overstated, legal rules and institutions presume compliance in a way that non-legal rules and institutions do not. Law and compliance are conceptually linked because law explicitly aims to produce compliance with its rules: legal rules set the standard by which compliance is gauged Explanations of why and when states comply with international law can help account for the turn to law as a positive phenomenon, but they also provide critical policy guidance for the design of new institutions and agreements. This chapter surveys the study of compliance in both the international relations (IR) and international law (IL) literature.' In many ways, the compliance literature is a microcosm of developments in both fields, and particularly of the rapproche-For IR scholars interested in reviving the study of international law in their discipline, it was a natural step to focus first on questions of whether, when and how law 'mattered' to state behavior. For international lawyers eager to use IR theory to …
Vocal Fold Disorder Detection by applying LBP Operator on Dysphonic Speech Signal

The automatic system for voice pathology assessment is one of the active areas for researchers in the recent years due to its benefits to the clinicians and presence of a significant number of dysphonic patients around the globe. In this paper, a voice disorder detection system is developed to differentiate between a normal and pathological voice signal. The system is implemented by applying the local binary pattern (LBP) operator on Mel-weighted spectrum of a signal. The LBP is considered as one of the sophisticated techniques for the image processing. The technique also provided very good results for voice pathology detection during this study. The English voice disorder database MEEI is used to evaluate the performance of the developed system. The results of the LBP operator based system are compared with MFCC and found to be better than MFCC.  LBP operator, MFCC, Vocal fold disorders, Sustained vowel, MEEI database, disorder detection system.
51 UNDERSTANDING AND APPROACH TO TREATMENT OF SCARS AND ADHESIONS

The modern education system for manual therapy, including massage therapy, physiotherapy, osteopathy, and chiropractic works on the acceptance that manipulation of tissues for scars and adhesions is of therapeutic value. Manual therapy in its various forms is now being introduced in a growing number of integrated health clinics, and is accepted by many to be a valuable addition to allopathic care. Much of the manual therapy literature is innately conceptual, sometimes for lack of data, often using outdated concepts that may have been dispelled by modern science’s ability to accurately measure and observe cellular level mechanisms. Manual therapy education splits systems and practices into various camps, including central versus peripheral nervous system, visceral work, and connective tissue/fascia manipulation. There are various conjectures as to how mechanical forces affect these different systems of anatomy, despite almost no directly relevant science. The manipulation of fascia as a technique is relatively recent in manual therapy history, and has been separated out by various schools of thought. Patients seem to receive benefit from the treatments they receive. Interest in the mechanisms at the cellular level that govern wound healing and understanding the mechanisms of pain are critical to the practice and diagnostic reasoning of tissue manipulation for scars and adhesions.
Optimal charging strategies in lithium-ion battery

There is a strong need for advanced control methods in battery management systems, especially in the plug-in hybrid and electric vehicles sector, due to cost and safety issues of new high-power battery packs and high-energy cell design. Limitations in computational speed and available memory require the use of very simple battery models and basic control algorithms, which in turn result in suboptimal utilization of the battery. This work investigates the possible use of optimal control strategies for charging. We focus on the minimum time charging problem, where different constraints on internal battery states are considered. Based on features of the open-loop optimal charging solution, we propose a simple one-step predictive controller, which is shown to recover the time-optimal solution, while being feasible for real-time computations. We present simulation results suggesting a decrease in charging time by 50% compared to the conventional constant-current / constant-voltage method for lithium-ion batteries.
Nastaliq optical character recognition

Nastaliq is a calligraphic, beautiful and more aesthetic style of writing Urdu, the national language of Pakistan, also used to read and write in India and other countries of the region.
 OCRs developed for many world languages are already under efficient use but none exist for Nastaliq -- a calligraphic adaptation of the Arabic scrip which is inherently cursive in nature.
 In Nastaliq, word and character overlapping makes optical recognition more complex.
 This paper presents the ongoing research on Nastaliq Optical Character Recognition (NOCR). In this research, we have proposed a novel segmentation-free technique for the design and implementation of a Nastaliq OCR based on cross-correlation.
Segmentation of Brain MRI Structures with Deep Machine Learning

Several studies on brain Magnetic Resonance Images (MRI) show relations between neuroanatomical abnormalities of brain structures and neurological disorders, such as Attention Defficit Hyperactivity Disorder (ADHD) and Alzheimer. These abnormalities seem to be correlated with the size and shape of these structures, and there is an active field of research trying to find accurate methods for automatic MRI segmentation. In this project, we study the automatic segmentation of structures from the Basal Ganglia and we propose a new methodology based on Stacked Sparse Autoencoders (SSAE). SSAE is a strategy that belongs to the family of Deep Machine Learning and consists on a supervised learning method based on an unsupervisely pretrained Feed-forward Neural Network. Moreover, we present two approaches based on 2D and 3D features of the images. We compare the results obtained on the different regions of interest with those achieved by other machine learning techniques such as Neural Networks and Support Vector Machines. We observed that in most cases SSAE improves those other methods. We demonstrate that the 3D features do not report better results than the 2D ones as could be thought. Furthermore, we show that SSAE provides state-of-the-art Dice Coefficient results (left, right): Caudate (90.63±1.4, 90.31±1.7), Putamen (91.03±1.4, 90.82±1.4), Pallidus (85.11±1.8, 83.47±2.2), Accumbens (74.26±4.4, 74.46±4.6).
Bidirectional Attention for SQL Generation

Generating structural query language (SQL) queries from natural language is a long-standing open problem. Answering a natural language question about a database table requires modeling complex interactions between the columns of the table and the question. In this paper, we apply the synthesizing approach to solve this problem. Based on the structure of SQL queries, we break down the model to three sub-modules and design specific deep neural networks for each of them. Taking inspiration from the similar machine reading task, we employ the bidirectional attention mechanisms and character-level embedding with convolutional neural networks (CNNs) to improve the result. Experimental evaluations show that our model achieves the state-of-the-art results in WikiSQL dataset.
Hand hygiene: a frequently missed lifesaving opportunity during patient care.

Health care-associated infections constitute one of the greatest challenges of modern medicine. Despite compelling evidence that proper hand washing can reduce the transmission of pathogens to patients and the spread of antimicrobial resistance, the adherence of health care workers to recommended hand-hygiene practices has remained unacceptably low. One of the key elements in improving hand-hygiene practice is the use of an alcohol-based hand rub instead of washing with soap and water. An alcohol-based hand rub requires less time, is microbiologically more effective, and is less irritating to skin than traditional hand washing with soap and water. Therefore, alcohol-based hand rubs should replace hand washing as the standard for hand hygiene in health care settings in all situations in which the hands are not visibly soiled. It is also important to change gloves between each patient contact and to use hand-hygiene procedures after glove removal. Reducing health care-associated infections requires that health care workers take responsibility for ensuring that hand hygiene becomes an everyday part of patient care.
Churn Intent Detection in Multilingual Chatbot Conversations and Social Media

We propose a new method to detect when users express the intent to leave a service, also known as churn. While previous work focuses solely on social media, we show that this intent can be detected in chatbot conversations. As companies increasingly rely on chatbots, they need an overview of potentially churny users. To this end, we crowdsource and publish a dataset of churn intent expressions in chatbot interactions in German and English. We show that classifiers trained on social media data can detect the same intent in the context of chatbots. We introduce a classification architecture that outperforms existing work on churn intent detection in social media. Moreover, we show that, using bilingual word embeddings, a system trained on combined English and German data outperforms monolingual approaches. As the only existing dataset is in English, we crowdsource and publish a novel dataset of German tweets. We thus underline the universal aspect of the problem, as examples of churn intent in English help us identify churn in German tweets and chatbot conversations.
A big data architecture for managing oceans of data and maritime applications

Data in the maritime domain is growing at an unprecedented rate, e.g., terabytes of oceanographic data are collected every month, and petabytes of data are already publicly available. Big data from heterogeneous sources such as sensors, buoys, vessels, and satellites could potentially fuel a large number of interesting applications for environmental protection, security, fault prediction, shipping routes optimization, and energy production. However, because of several challenges related to big data and the high heterogeneity of the data sources, such applications are still underdeveloped and fragmented. In this paper, we analyze challenges and requirements related to big maritime data applications and propose a scalable data management solution. A big data architecture meeting these requirements is described, and examples of its implementation in concrete scenarios are provided. The related data value chain and use cases in the context of a European project, BigDataOcean, are also described.
A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection

Existing algorithms for joint clustering and feature selection can be categorized as either global or local approaches. Global methods select a single cluster-independent subset of features, whereas local methods select cluster-specific subsets of features. In this paper, we present a unified probabilistic model that can perform both global and local feature selection for clustering. Our approach is based on a hierarchical beta-Bernoulli prior combined with a Dirichlet process mixture model. We obtain global or local feature selection by adjusting the variance of the beta prior. We provide a variational inference algorithm for our model. In addition to simultaneously learning the clusters and features, this Bayesian formulation allows us to learn both the number of clusters and the number of features to retain. Experiments on synthetic and real data show that our unified model can find global and local features and cluster data as well as competing methods of each type.
Gender identity and lexical variation in social media

We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female/male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus-based work has also had little to say about individuals whose linguistic styles defy population-level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections, and that in general, social network homophily is correlated with the use of same-gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms.
EVALUATION OF MOLLUSC AS SENSITIVE INDICATIOR OF HEAVY METAL POLLUTION IN AQUATIC SYSTEM : A REVIEW

A The potential ecological effects of rising levels of heavy metals concentrations in the environment are of great concern due to their highly bioaccumulative nature, persistent behavior and higher toxicity. These chemicals biomagnify in the food chain and impose various toxic effects in aquatic organisms. Molluscs reflect the higher degree of environmental contamination by heavy metals and are the most useful bioindicator tools. Several studies and research work have been cited to establish and evaluate the relationship between metal contents of water column, sediment fractions, suspended matter and mollusc tissue concentrations. The metals body burden in molluscs may reflect the concentrations of metals in surrounding water and sediment, and may thus be an indication of quality of the surrounding environment. The objectives of this work are to gather more information on the use of different species of molluscs as cosmopolitan bioindicators for heavy metal pollution in aquatic
Mining Subjective Knowledge from Customer Reviews: A Specific Case of Irony Detection

The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered as ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony. In particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents whose effect triggers a chain reaction on people. The findings were assessed employing three classifiers. The results show interesting hints regarding the patterns and, especially, regarding the implications for sentiment analysis.
ARBITRAGE PRICING WITH STOCHASTIC VOLATILITY

We address the problem of pricing contingent claims in the presence of stochastic volatility. Former works claim that, as volatility itself is not a traded asset, no riskless hedge can be established, so equilibrium arguments have to be invoked and risk premia specified. We show that if instead of trying to find the prices of standard options we take these prices as exogenous, we can derive arbitrage prices of more complicated claims indexed on the Spot (and possibly on the volatility itself). This paper is an expansion of a former version presented at the AFFI conference in Paris, June 1992. I am grateful to Nicole El Karoui, Darrell Duffie, John Hull, and my colleagues from SORT (Swaps and Options Research Team) at Paribas for enriching conversations. All errors are indeed mine. Arbitrage Pricing with Stochastic Volatility 2