Miracle-dz/UniFAR-FTUs huggingface dataset:
It must be awful for them: Perspective and task context affects ratings for health conditions

When survey respondents rate the quality of life (QoL) associated with a health condition, they must not only evaluate the health condition itself, but must also interpret the meaning of the rating scale in order to assign a specific value. The way that respondents approach this task depends on subjective interpretations, resulting in inconsistent results across populations and tasks. In particular, patients and non-patients often give very different ratings to health conditions, a discrepancy that raises questions about the objectivity of either groups’ evaluations. In this study, we found that the perspective of the raters (i.e., their own current health relative to the health conditions they rated) influences the way they distinguish between different health states that vary in severity. Consistent with prospect theory, a mild and a severe lung disease scenario were rated quite differently by lung disease patients whose own health falls between the two scenarios, whereas healthy non-patients, whose own health was better than both scenarios, rated the two scenarios as much more similar. In addition, we found that the context of the rating task influences the way participants distinguish between mild and severe scenarios. Both patients and non-patients gave less distinct ratings to the two scenarios when each were presented in isolation than when they were presented alongside other scenarios that provided contextual information about the possible range of severity for lung disease. These results raise continuing concerns about the reliability and validity of subjective QoL ratings, as these ratings are highly sensitive to differences between respondent groups and the particulars of the rating task.
Variational Graph Auto-Encoders

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.
Connectionist language modeling for large vocabulary continuous speech recognition

This paper describes ongoing work on a new approach for language modeling for large vocabulary continuous speech recognition. Almost all state. o. f-the-art systems use statistical n-gram language models estimated on text corpora. One principle problem with such language models is the fact that many of the n-grams are never observed even in very large training corpora, and therefore it is common to back-off to a lower-order model. In this paper we propose to address this problem by carrying out the estimation task in a continuous space, enabling a smooth interpolation of the probabilities. A neural network is used to learn the projection of the words onto a continuous space and to estimate the n-gram probabilities. The connectionist language model is being evaluated on the DARPA HUB5 conversational telephone speech recognition task and preliminary results show consistent improvements in both perplexity and word error rate.
Improving DRAM Fault Characterization through Machine Learning

As high-performance computing systems continue to grow in scale and complexity, the study of faults and errors is critical to the design of future systems and mitigation schemes. Fault modes in system DRAM are a frequently-investigated key aspect of memory reliability. While current schemes require offline analysis for proper classification, current state-of-the-art mitigation techniques require accurate online prediction for optimal performance. In this work, we explore the predictive performance of an online machine learning-based approach in classifying DRAM fault modes from two leadership-class supercomputing facilities. Our results compare the predictive performance of this online approach with the current rule-based approach based on expert knowledge, finding a 12% predictive performance improvement. We also investigate the universality of our classifiers by evaluating predictive performance using training data from disparate computing systems to achieve a 7% improvement in predictive performance. Our work provides a critical analysis of this online learning technique and can benefit system designers to help inform best practices for dealing with reliability on future systems.
A neuronal gamma oscillatory signature during morphological unification in the left occipitotemporal junction

Morphology is the aspect of language concerned with the internal structure of words. In the past decades, a large body of masked priming (behavioral and neuroimaging) data has suggested that the visual word recognition system automatically decomposes any morphologically complex word into a stem and its constituent morphemes. Yet the reliance of morphology on other reading processes (e.g., orthography and semantics), as well as its underlying neuronal mechanisms are yet to be determined. In the current magnetoencephalography study, we addressed morphology from the perspective of the unification framework, that is, by applying the Hold/Release paradigm, morphological unification was simulated via the assembly of internal morphemic units into a whole word. Trials representing real words were divided into words with a transparent (true) or a nontransparent (pseudo) morphological relationship. Morphological unification of truly suffixed words was faster and more accurate and additionally enhanced induced oscillations in the narrow gamma band (60–85 Hz, 260–440 ms) in the left posterior occipitotemporal junction. This neural signature could not be explained by a mere automatic lexical processing (i.e., stem perception), but more likely it related to a semantic access step during the morphological unification process. By demonstrating the validity of unification at the morphological level, this study contributes to the vast empirical evidence on unification across other language processes. Furthermore, we point out that morphological unification relies on the retrieval of lexical semantic associations via induced gamma band oscillations in a cerebral hub region for visual word form processing. Hum Brain Mapp 35:5847–5860, 2014. © 2014 Wiley Periodicals, Inc.
The European Large-Area ISO Survey (ELAIS): the final band-merged catalogue

We present the final band-merged European Large-Area ISO Survey (ELAIS) Catalogue at 6.7, 15, 90 and 175 mum, and the associated data at U, g', r', i', Z, J, H, K and 20 cm. The origin of the survey, infrared and radio observations, data-reduction and optical identifications are briefly reviewed, and a summary of the area covered and the completeness limit for each infrared band is given. A detailed discussion of the band-merging and optical association strategy is given. The total Catalogue consists of 3762 sources. 23 per cent of the 15-mum sources and 75 per cent of the 6.7-mum sources are stars. For extragalactic sources observed in three or more infrared bands, colour-colour diagrams are presented and discussed in terms of the contributing infrared populations. Spectral energy distributions (SEDs) are shown for selected sources and compared with cirrus, M82 and Arp220 starburst, and active galactic nuclei (AGN) dust torus models. Spectroscopic redshifts are tabulated, where available. For the N1 and N2 areas, the Isaac Newton Telescope ugriz Wide Field Survey permits photometric redshifts to be estimated for galaxies and quasars. These agree well with the spectroscopic redshifts, within the uncertainty of the photometric method [similar to10 per cent in (1 + z) for galaxies]. The redshift distribution is given for selected ELAIS bands and colour-redshift diagrams are discussed. There is a high proportion of ultraluminous infrared galaxies (log(10) of 1-1000 mum luminosity L-ir > 12.22) in the ELAIS Catalogue (14 per cent of 15-mum galaxies with known z), many with Arp220-like SEDs. 10 per cent of the 15-mum sources are genuine optically blank fields to r' = 24: these must have very high infrared-to-optical ratios and probably have z > 0.6, so are high-luminosity dusty starbursts or Type 2 AGN. Nine hyperluminous infrared galaxies (L-ir > 13.22) and nine extremely red objects (EROs) (r - K > 6) are found in the survey. The latter are interpreted as ultraluminous dusty infrared galaxies at z similar to 1. The large numbers of ultraluminous galaxies imply very strong evolution in the star formation rate between z = 0 and 1. There is also a surprisingly large population of luminous (L-ir > 11.5), cool (cirrus-type SEDs) galaxies, with L-ir L-opt > 0, implying A(V) > 1.
On Pending Interest Table in Named Data Networking

Internet has witnessed its paramount function transition from host-to-host communication to content dissemination. Named Data Networking (NDN) and Content-Centric Networking (CCN) emerge as a clean slate network architecture to embrace this shift. Pending Interest Table (PIT) in NDN/CCN keeps track of the Interest packets that are received but yet un-responded, which brings NDN/CCN significant features, such as communicating without the knowledge of source or destination, loop and packet loss detection, multipath routing, better security, etc. This paper presents a thorough study of PIT for the first time. Using an approximate, application-driven translation of current IP-generated trace to NDN trace, we firstly quantify the size and access frequencies of PIT. Evaluation results on a 20 Gbps gateway trace show that the corresponding PIT contains 1.5 M entries, and the lookup, insert and delete frequencies are 1.4 M/s, 0.9 M/s and 0.9 M/s, respectively. Faced with this challenging issue and to make PIT more scalable, we further propose a Name Component Encoding (NCE) solution to shrink PIT size and accelerate PIT access operations. By NCE, the memory consumption can be reduced by up to 87.44%, and the access performance significantly advanced, satisfying the access speed required by PIT. Moreover, PIT exhibits good scalability with NCE. At last, we propose to place PIT on (egress channel of) the outgoing line-cards of routers, which meets the NDN design and eliminates the cumbersome synchronization problem among multiple PITs on the line-cards.
Learning context to adapt business processes

Dynamic adaptation is the customization of a business process to make it applicable to a particular situation at any time of its life cycle. Adapting requires experience, and involves knowledge about various, internal and external, aspects of business. Thus, we argue for the application of adaptation rules, considering the context of a particular process instance. Furthermore, we state that a context-based adaptation environment should go beyond, and learn from decisions, as well as continuously identify new unforeseen situations (context definitions). The aim of this paper is to present a computational engine that infers the need to update situations and adaptation rules, suggesting changes to them. An application scenario is presented to discuss the usage of the proposal.
Financial impact of nosocomial infections in the intensive care units of a charitable hospital in Minas Gerais, Brazil

Objective Infections in intensive care units are often associated with a high morbidity and mortality in addition to high costs. An analysis of these aspects can assist in optimizing the allocation of relevant financial resources. Methods This retrospective study analyzed the hospital administration and quality in intensive care medical databases [Sistema de Gestão Hospitalar (SGH)] and RM Janus®. A cost analysis was performed by evaluating the medical products and materials used in direct medical care. The costs are reported in the Brazilian national currency (Real). The cost and length of stay analyses were performed for all the costs studied. The median was used to determine the costs involved. Costs were also adjusted by the patients' length of stay in the intensive care unit. Results In total, 974 individuals were analyzed, of which 51% were male, and the mean age was 57±18.24 years. There were 87 patients (8.9%) identified who had nosocomial infections associated with the intensive care unit. The median cost per admission and the length of stay for all the patients sampled were R$1.257,53 and 3 days, respectively. Compared to the patients without an infection, the patients with an infection had longer hospital stays (15 [11-25] versus 3 [2-6] days, p<0.01), increased costs per patient in the intensive care unit (median R$9.763,78 [5445.64 - 18,007.90] versus R$1.093,94 [416.14 - 2755.90], p<0.01) and increased costs per day of hospitalization in the intensive care unit (R$618,00 [407.81 - 838.69] versus R$359,00 [174.59 - 719.12], p<0.01). Conclusion Nosocomial infections associated with the intensive care unit were determinants of increased costs and longer hospital stays. However, the study design did not allow us to evaluate specific aspects of cause and effect.
RNADE: The real-valued neural autoregressive density-estimator

We introduce RNADE, a new model for joint density estimation of real-valued vectors. Our model calculates the density of a datapoint as the product of one-dimensional conditionals modeled using mixture density networks with shared parameters. RNADE learns a distributed representation of the data, while having a tractable expression for the calculation of densities. A tractable likelihood allows direct comparison with other methods and training by standard gradient-based optimizers. We compare the performance of RNADE on several datasets of heterogeneous and perceptual data, finding it outperforms mixture models in all but one case.
A Comparison of Different Neural Methods for Solving Iterative Roots

Finding iterative roots is the inverse problemof iteration. Iteration itself plays a major role in numerous theories and applications. So far it is hardly realized how many problems can be related to its counterpart. This may be due to the difficulty of the mathematics involved: There are no standard methods available for computing these fractional iterations. Previously we have shown how neural networks can be utilized to perform calculations of iterative roots by adding a weight coupling mechanism to backpropagation learning. Here we show that an easier implementationof this functionality can be achieved by a simple weight copy function. Introducing second order methods like quasi Newton learning on the other hand can significantly reduce training times and improve the reliability of the method. It also overcomes some limitations in the complexity of the problems the method can be applied to.
Supply Chain Contracting under Competition: Bilateral Bargaining vs. Stackelberg

We analyze contracting behaviors in a two-tier supply chain system consisting of competing manufacturers and competing retailers. We contrast the contracting outcome of a Stackelberg game, in which the manufacturers offer take-it-or-leave-it contracts to the retailers, with that of a bargaining game, in which the firms bilaterally negotiate contract terms via a process of alternating offers. The manufacturers in the Stackelberg game possess a Stackelberg-leader advantage in that the retailers are not entitled to make counteroffers. Our analysis suggests that whether this advantage would benefit the manufacturers depends on the contractual form. With simple contracts such as wholesale-price contracts, which generally do not allow one party to fully extract the trade surplus, the Stackelberg game replicates the boundary case of the bargaining game with the manufacturers possessing all bargaining power. In contrast, with sophisticated contracts such as two-part tariffs, which enable full surplus extraction, the two games lead to distinct outcomes. We further show that the game structure being Stackelberg or bargaining critically affects firms’ preferences over contract types and thus their equilibrium contract choices. These observations suggest that the Stackelberg game may not be a sufficient device to predict contracting behaviors in reality where bargaining is commonly observed.
Advances in Geosynthetics Materials and Applications for Soil Reinforcement and Environmental Protection Works

Geosynthetics have become well established construction materials for geotechnical and environmental applications in most parts of the world. Because they constitute manufactured materials, new products and applications are developed on a routine basis to provide solutions to routine and critical problems alike. Results from recent research and from monitoring of instrumented structures throughout the years have led to new design methods for different applications of geosynthetics. Because of the significant breath of geosynthetic applications, this paper focuses on recent advances on geosynthetics products, applications and design methodologies for reinforced soil and environmental protection works.
Automatic segmentation of the lung fields in portable chest radiographs based on Bézier interpolation of salient control points

Portable chest radiographs are performed on the most critically-ill patients, who need highly skilled patient care and the best diagnostic tools. However, such radiographs are usually of low quality, mainly due to misaligned body positioning during their acquisition, and subject to a higher misinterpretation rate than the ones obtained with a non-portable X-ray device. This paper presents a pioneering computational approach that copes with the segmentation of the lung fields in portable chest radiographs. The proposed methodology involves detection of salient points on the anatomic structures around the lung fields by subsequent application of simple intensity and edge feature extraction techniques. The salient points detected are interpolated using Bezier curves intuitively approximating the boundaries of the lung fields. Unlike current methodologies, the proposed one does not exclude the overlapping region of the heart from the lung fields, where abnormalities can also be present. The results illustrate the robustness of the proposed methodology on a set of real portable radiographs of patients with bacterial pulmonary infections. A qualitative comparison with a state of the art approach based on graph cuts validates its effectiveness.
The Influence of Social Support on the Physical and Mental Health of the Rural Elderly

Based on the data from "China Health and Retirement Longitudinal Study"in 2013, using factor analysis and Logistic regression analysis,this paper investigates the influence of social support on the physical and mental health of the rural elderly in China. It founds that receiving financial support,daily care from adult children has positive effect on the physical and mental health of the elderly. The informal social support from non-core relatives and friends of the elder also play a beneficial role in "main-effect model"while the formal support make a "buffer model" effect. The formal social support produces crowding out effect and crowding in effect on informal social support at the same time. On account of the importance to rural elderly's physical and mental health,formal social support does not show obvious crowding out effect,this implies that the formal social support does not fundamentally change the endowment patterns in rural family,so the power of formal support needs to be strengthened.
The Demand for Audit in Private Firms: Recent Large-Sample Evidence from the UK

Abstract Although theory suggests that companies would rationally select into audit even if it were not a legal requirement, many countries impose mandatory audits. This is arguably due to an audit having elements of a public good, which may result in not enough audits being purchased without regulatory intervention. The mandatory nature of public company audit has created problems for researchers wishing to investigate the demand for voluntary audit. Recent events in the UK, however, have provided such an environment. In the UK, private companies must publicly file financial statements and, until recently, they had also to be audited. However, this requirement has now been relaxed for many private companies. We are therefore able to examine the determinants of voluntary audit in a large sample of companies for which we have financial statement data. We analyse a sample of 6274 recently exempt companies, following them for three years post-exemption. We use agency theory and prior evidence to generate our hypotheses and examine them using a more comprehensive set of explanatory variables than has previously been available in the literature. Our results indicate that companies are more likely to purchase voluntary audits if they have greater agency costs, are riskier, wish to raise capital, purchase non-audit services from their auditor, and exhibited greater demand for audit assurance in the mandatory audit regime. We also document a trend away from audit over time. Overall, our results strongly support the idea that companies choose to be audited when it is in their interests to do so.