nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning Convolutional Neural Network

This study presents the preliminary results for the use of Convolutional Neural Network 6 (CNN) in the stability analysis of geosystems. The long goal is to understand the image data7 driven deep learning in geosystems to explore the use of image data for the safety analysis of 8 infrastructure. For this goal, the core concept enabling the recent breakthroughs in computer vision, 9 CNN, which helped machines surpass humans in visual classification capacity, was used to process 10 the image data for the stability analysis of geosystems. The concept used in the famous Dogs vs. 11 Cats Kaggle challenge (www.kaggle.com) in which machine learning algorithms were used to 12 classify whether an image contains a dog or a cat, was employed. We will use CNN to analyze 13 images for a retaining wall to tell whether the wall is a "cat" (safe) or a "dog" (failed) For 14 quantitative analysis, 10000 2D images for retaining walls were generated using stochastic 15 methods and analyzed using a traditional limit analysis method, i.e. Bishop Simplified Method, 16 for labeling. 5/6 of these labeled image data were used as input to train CNNs for supervised 17 learning. The trained nets will be tested against another independent set of data (2000 images) 18 generated in the same way as the training data. The results indicated that the success rate can reach 19 97.2% 20
Delisting of liver transplant candidates with chronic hepatitis C after viral eradication: A European study.
Venue: Journal of hepatology

All oral direct acting antivirals (DAA) have been shown to improve the liver function of patients with decompensated cirrhosis but it is presently unknown whether this clinical improvement may lead to the delisting of some patients. The aim of this study was to assess if and which patients can be first inactivated due to clinically improvement and subsequently delisted in a real life setting.  103 consecutive listed patients without hepatocellular carcinoma were treated with different DAA combinations in 11 European centres between February 2014 and February 2015.  The cumulative incidence of inactivated and delisted patients by competing risk analysis was 15.5% and 0% at 24weeks, 27.6% and 10.3% at 48weeks, 33.3% and 19.2% at 60weeks. The 34 patients who were inactivated showed a median improvement of 3.4 points for MELD (delta MELD, p<0.0001) and 2 points for Child Pugh (CP) (delta CP, p<0.0001) Three variables emerged from the most parsimonious multivariate competing risk model as predictors of inactivation for clinical improvement, namely, baseline MELD classes (MELD 16 20: HR=0.120; p=0.0005, MELD >20:HR=0.042; p<0.0001) delta MELD (HR=1.349; p<0.0001) and delta albumin (HR=0.307; p=0.0069) both assessed after 12weeks of DAA therapy.  This study showed that all oral DAAs were able to reverse liver dysfunction and favoured the inactivation and delisting of about one patient out of three and one patient out of five in 60weeks, respectively. Patients with lower MELD scores had higher chances to be delisted. The longer term benefits of therapy need to be ascertained.  The excellent efficacy and safety profile of the new drugs against Hepatitis C virus, "direct acting antivirals" or DAAs, have made antiviral therapy possible also for patients with advanced liver disease and for those on the waiting list for liver transplantation (LT) This study shows for the first time that the DAAs may lead to a remarkable clinical improvement allowing the delisting of one patient out of 5.
Notes on "An Anonymous Multi server Authenticated Key Agreement Scheme Based on Trust Computing Using Smart Card and Biometrics"
Venue: Int. J. Netw. Secur.

Nowadays, multi server remote user authentication schemes have been studied extensively in the literature. Recently, Chuang and Chen proposed a multi server authentication scheme based on trust computing using smart cards and biometrics. Their scheme is more efficient and can achieve more security requirements than other related schemes. However, we found that Chuang and Chen's scheme can disclose private information shared between a legal user and an authorized server to another server. Moreover, loss of smart card attacks can be amounted and user anonymity cannot be achieved.
Joint Low Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification
Venue: IEEE Transactions on Image Processing

Recovering low rank and sparse subspaces jointly for enhanced robust representation and classification is discussed. Technically, we first propose a transductive low rank and sparse principal feature coding (LSPFC) formulation that decomposes given data into a component part that encodes low rank sparse principal features and a noise fitting error part. To well handle the outside data, we then present an inductive LSPFC (I LSPFC) I LSPFC incorporates embedded low rank and sparse principal features by a projection into one problem for direct minimization, so that the projection can effectively map both inside and outside data into the underlying subspaces to learn more powerful and informative features for representation. To ensure that the learned features by I LSPFC are optimal for classification, we further combine the classification error with the feature coding error to form a unified model, discriminative LSPFC (D LSPFC) to boost performance. The model of D LSPFC seamlessly integrates feature coding and discriminative classification, so the representation and classification powers can be enhanced. The proposed approaches are more general, and several recent existing low rank or sparse coding algorithms can be embedded into our problems as special cases. Visual and numerical results demonstrate the effectiveness of our methods for representation and classification.
Joint Low Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification.
Venue: IEEE transactions on image processing a publication of the IEEE Signal Processing Society

Recovering low rank and sparse subspaces jointly for enhanced robust representation and classification is discussed. Technically, we first propose a transductive low rank and sparse principal feature coding (LSPFC) formulation that decomposes given data into a component part that encodes low rank sparse principal features and a noise fitting error part. To well handle the outside data, we then present an inductive LSPFC (I LSPFC) I LSPFC incorporates embedded low rank and sparse principal features by a projection into one problem for direct minimization, so that the projection can effectively map both inside and outside data into the underlying subspaces to learn more powerful and informative features for representation. To ensure that the learned features by I LSPFC are optimal for classification, we further combine the classification error with the feature coding error to form a unified model, discriminative LSPFC (D LSPFC) to boost performance. The model of D LSPFC seamlessly integrates feature coding and discriminative classification, so the representation and classification powers can be enhanced. The proposed approaches are more general, and several recent existing low rank or sparse coding algorithms can be embedded into our problems as special cases. Visual and numerical results demonstrate the effectiveness of our methods for representation and classification.
Implementing oxygen control in chip based cell and tissue culture systems.
Venue: Lab on a chip

Oxygen is essential in the energy metabolism of cells, as well as being an important regulatory parameter influencing cell differentiation and function. Interest in precise oxygen control for in vitro cultures of tissues and cells continues to grow, especially with the emergence of the organ on a chip and the desire to emulate in vivo conditions. This was recently discussed in this journal in a Critical Review by Brennan et al. (Lab Chip (2014)  Microfluidics can be used to introduce flow to facilitate nutrient supply to and waste removal from in vitro culture systems. Well defined oxygen gradients can also be established. However, cells can quickly alter the oxygen balance in their vicinity. In this Tutorial Review, we expand on the Brennan paper to focus on the implementation of oxygen analysis in these systems to achieve continuous monitoring. Both electrochemical and optical approaches for the integration of oxygen monitoring in microfluidic tissue and cell culture systems will be discussed. Differences in oxygen requirements from one organ to the next are a challenging problem, as oxygen delivery is limited by its uptake into medium. Hence, we discuss the factors determining oxygen concentrations in solutions and consider the possible use of artificial oxygen carriers to increase dissolved oxygen concentrations. The selection of device material for applications requiring precise oxygen control is discussed in detail, focusing on oxygen permeability. Lastly, a variety of devices is presented, showing the diversity of approaches that can be employed to control and monitor oxygen concentrations in in vitro experiments.
Sample Enrichment for Bioanalytical Assessment of Disinfected Drinking Water: Concentrating the Polar, the Volatiles, and the Unknowns.
Venue: Environmental science technology

Enrichment methods used in sample preparation for the bioanalytical assessment of disinfected drinking water result in the loss of volatile and hydrophilic disinfection byproducts (DBPs) and hence likely tend to underestimate biological effects. We developed and evaluated methods that are compatible with bioassays, for extracting nonvolatile and volatile DBPs from chlorinated and chloraminated drinking water to minimize the loss of analytes. For nonvolatile DBPs, solid phase extraction (SPE) with TELOS ENV as solid phase performed superior compared to ten other sorbents. SPE yielded >70% recovery of nonpurgeable adsorbable organic halogens (AOX) For volatile DBPs, cryogenic vacuum distillation performed unsatisfactorily. Purge and cold trap with crushed ice serving as condensation nuclei achieved recoveries of 50 100% for trihalomethanes and haloacetonitriles and approximately 60 90% for purged AOX from tap water. We compared the purgeable versus the nonpurgeable fraction by combining purge and trap extraction with SPE. The purgeable DBP fraction enriched with the purge and trap method exerted a lower oxidative stress response in mammalian cells than the nonpurgeable DBPs enriched with SPE after purging, while contributions of both fractions to bacterial cytotoxicity was more variable. 37 quantified DBPs explained almost the entire AOX in the purge and trap extracts, but <16% in the SPE extracts demonstrating that the nonpurgeable fraction is dominated by unknown DBPs.
Sabiston Spencer surgery of the chest

Through seven successful editions, "Sabiston and Spencer's Surgery of the Chest" has set the standard in cardiothoracic surgery references. Now, the new 8th Edition, edited by Frank Sellke, MD, Pedro J. del Nido, MD, and Scott Swanson, MD, carries on this tradition with updated coverage of today's essential clinical knowledge from leaders worldwide. Guidance divided into three major sections Adult Cardiac Surgery, Pediatric Cardiac Surgery, and Thoracic Surgery lets you quickly find what you need, while new and revised chapters reflect all of the important changes within this rapidly evolving specialty. Expert Consult functionality new to this edition enables you to access the complete contents of the 2 volume set from anyplace with an Internet connection for convenient consultation where and when you need it. This is an ideal source for mastering all of the most important current knowledge and techniques in cardiac and thoracic surgery whether for specialty board review or day to day practice.
Seeing like a market

Socio Economic Review, 2017,  Fourcade 1, and Kieran Healy 2, University of California, Berkeley and 2 Duke University *Correspondence: fourcade@berkeley.edu; kjhealy@soc.duke.edu Abstract What do markets see when they look at people? Information dragnets increasingly yield huge quantities of individual level data, which are analyzed to sort and slot people into categories of taste, riskiness or worth. These tools deepen the reach of the market and define new strategies of profit making. We present a new theoretical framework for understanding their development. We argue that (a) modern organ izations follow an institutional data imperative to collect as much data as possible; (b) as a result of the analysis and use of this data, individuals accrue a form of capital flowing from their positions as measured by various digital scoring and ranking methods; and (c) the facticity of these scoring methods makes them organizational devices with potentially stratifying effects. They offer firms new opportunities to structure and price offerings to consumers. For individuals, they create classification situations that identify shared life chances in product and service markets. We dis cuss the implications of these processes and argue that they tend toward a new economy of moral judgment, where outcomes are experienced as morally deserved positions based on prior good actions and good tastes, as measured and classified by this new infrastructure of data collection and analysis.  classification, big data, technology, markets, institutions, morality JEL classification: A13, O33 Across institutional domains, tracking and measurement is expanding and becoming ever more fine grained (Limn, 2012; Gillespie et al. 2014; Pasquale, 2015) We see it in everyday consumption, in housing and credit markets, in health, employment, education (Cottom, 2016) social relations, including intimate ones (Levy, 2015) legal services, and even into political life (Ziewitz, 2016) and the private sphere (Neff and Nafus, 2016) Sociologists studying the state, technology and the market have sought to describe and understand these trends in different ways. This article proposes a framework to analytically unify their con cerns, and to grasp the implications of contemporary technological developments for proc esses of inequality and stratification.
High resolution WRF simulations of Hurricane Irene: Sensitivity to aerosols and choice of microphysical schemes

Recent studies have pointed to the possible sensitivity of hurricanes to aerosols via aerosol effects on microphysical and thermodynamic processes in clouds. Hurricane Irene, occurring in August 2011, is an excellent case study for investigating aerosol effects on tropical cyclone (TC) structure and intensity: it moved northward along the eastern coast of the United States, and weakened much faster than was predicted by the National Hurricane Center. Moreover, the minimum pressure in Irene occurred, atypically, about 40 h later than the time of maximum wind speed. In this study, we simulate Hurricane Irene with 1 km grid spacing using Spectral Bin Microphysics (SBM) and various bulk microphysical schemes in WRF. Simulations with SBM showed that aerosols penetrating the eyewall of Irene from the Saharan Air Layer (SAL) led to an intensification of convection at Irene's eyewall and to a deepening of the hurricane. When Irene moved along the eastern coast of the United States, continental aerosols led to an intensification of convection at Irene's periphery, which interfered with the re forming of the inner eyewall and to Irene weakening. Sensitivity tests using different "bulk" microphysics schemes indicated a large dispersion of simulated minimum pressure and maximum wind between different simulations. This showed that the simulated hurricane intensity was very sensitive to microphysical processes. Moreover, in consequence, forecast hurricane intensity was highly dependent on the choice of microphysical scheme. New bulk parameterization schemes simulated the tropical storm intensity of Irene reasonably well. Most bulk schemes that used saturation adjustment indicate the weak sensitivity to aerosols that prevents them from precisely predicting the time evolution of TC intensity and structure.
Combining Discrete and Neural Features for Sequence Labeling
Venue: CICLing

Neural network models have recently received heated research attention in the natural language processing community. Compared with traditional models with discrete features, neural models have two main advantages. First, they take low dimensional, real valued embedding vectors as inputs, which can be trained over large raw data, thereby addressing the issue of feature sparsity in discrete models. Second, deep neural networks can be used to automatically combine input features, and including non local features that capture semantic patterns that cannot be expressed using discrete indicator features. As a result, neural network models have achieved competitive accuracies compared with the best discrete models for a range of NLP tasks. On the other hand, manual feature templates have been carefully investigated for most NLP tasks over decades and typically cover the most useful indicator pattern for solving the problems. Such information can be complementary the features automatically induced from neural networks, and therefore combining discrete and neural features can potentially lead to better accuracy compared with models that leverage discrete or neural features only. In this paper, we systematically investigate the effect of discrete and neural feature combination for a range of fundamental NLP tasks based on sequence labeling, including word segmentation, POS tagging and named entity recognition for Chinese and English, respectively. Our results on standard benchmarks show that state of the art neural models can give accuracies comparable to the best discrete models in the literature for most tasks and combing discrete and neural features unanimously yield better results.
Oxford Handbook of Genitourinary Medicine, HIV, and Sexual Health

This comprehensive yet concise handbook provides an essential, evidence based guide to sexual health, genitourinary medicine (GUM) and HIV medicine, in an easy to use format. It is aimed at speciality trainees in GUM and HIV, nurses and GPs, and is a valuable reference for specialists within the field of sexual health and HIV. This fully updated 3rd edition covers a wide range of key topics from medico legal aspects of practice, with new sections on commissioning arrangements in sexual health and HIV care, public health initiatives, and sociocultural issues, including female genital mutilation, child sexual exploitation, gender dysphoria, and sexuality. It includes chapters on contraception and dermatology, and additional chapters on viral hepatitis and pre exposure prophylaxis for HIV, all written and edited by specialists within the field. Existing chapters have all been updated in line with current national guidance
Explainable Restricted Boltzmann Machines for Collaborative Filtering
Venue: ArXiv

Most accurate recommender systems are black box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap between accuracy and transparency or explainability has generated an interest in automated explanation generation methods. Restricted Boltzmann Machines (RBM) are accurate models for CF that also lack interpretability. In this paper, we focus on RBM based collaborative filtering recommendations, and further assume the absence of any additional data source, such as item content or user attributes. We thus propose a new Explainable RBM technique that computes the top n recommendation list from items that are explainable. Experimental results show that our method is effective in generating accurate and explainable recommendations.
The Role of Culture, Historicity, and Human Agency in the Evolution of the State: A Case Against Cultural Fatalism

I examine how the nature of the state and its relationship to its people are determined and evolve. I bring together differing contributions of traditional institutionalism to further an emerging evolutionary institutionalist discourse concerning the role played by culture and historicity, on one hand, and by individual actions and awareness, on the other, in shaping and reshaping the nature of the state. Such discourse is significant in understanding that, although inherited culture plays an important role in influencing the character of the state in a given society, the future of the state is not fully determined by its history. In equal measure, it depends on the volition of individuals who purposefully amend the state's institutions through rearranging power distribution. Using Russia's authoritarian state as a case in point, I demonstrate how this emerging evolutionary institutionalist discourse can circumvent ideological misuse of the institutionalist paradigm in non democratic societies.
Planck intermediate results XXX. The angular power spectrum of polarized dust emission at intermediate and high Galactic latitudes

The polarized thermal emission from diffuse Galactic dust is the main foreground present in measurements of the polarization of the cosmic microwave background (CMB) at frequencies above 100 GHz. In this paper we exploit the uniqueness of the Planck HFI polarization data from 100 to 353 GHz to measure the polarized dust angular power spectra C_l^(EE) and C_l^(BB) over the multipole range 40 <l< 600 well away from the Galactic plane. These measurements will bring new insights into interstellar dust physics and allow a precise determination of the level of contamination for CMB polarization experiments. Despite the non Gaussian and anisotropic nature of Galactic dust, we show that general statistical properties of the emission can be characterized accurately over large fractions of the sky using angular power spectra. The polarization power spectra of the dust are well described by power laws in multipole, C_l l_a, with exponents a^(EE,BB) 2.42 0.02. The amplitudes of the polarization power spectra vary with the average brightness in a way similar to the intensity power spectra. The frequency dependence of the dust polarization spectra is consistent with modified blackbody emission with b_d 1.59 and T_d 19.6 K down to the lowest Planck HFI frequencies. We find a systematic difference between the amplitudes of the Galactic B and E modes, C_l^(BB)/C_l^(EE) 0.5. We verify that these general properties are preserved towards high Galactic latitudes with low dust column densities. We show that even in the faintest dust emitting regions there are no "clean" windows in the sky where primordial CMB B mode polarization measurements could be made without subtraction of foreground emission. Finally, we investigate the level of dust polarization in the specific field recently targeted by the BICEP2 experiment. Extrapolation of the Planck 353 GHz data to 150 GHz gives a dust power