nils-herrmann/clean_scirepeval huggingface dataset:
Calibrating Noise to Sensitivity in Private Data Analysis
Venue: J. Priv. Confidentiality
Year: 2016

We continue a line of research initiated in Dinur and Nissim (2003) Dwork and Nissim (2004) and Blum et al. (2005) on privacy preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function $f$ mapping databases to reals, the so called \em true answer} is the result of applying $f$ to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which $f \sum_i g(x_i) where $x_i$ denotes the $i$th row of the database and $g$ maps database rows to [0,1] We extend the study to general functions $f$ proving that privacy can be preserved by calibrating the standard deviation of the noise according to the \em sensitivity} of the function $f$ Roughly speaking, this is the amount that any single argument to $f$ can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean definition of privacy now known as differential privacy and measure of its loss. We also provide a set of tools for designing and combining differentially private algorithms, permitting the construction of complex differentially private analytical tools from simple differentially private primitives. Finally, we obtain separation results showing the increased value of interactive statistical release mechanisms over non interactive ones.
Effects of the Length of Stay on the Cost of Total Knee and Total Hip Arthroplasty from 2002 to 2013
Venue: The Journal of bone and joint surgery. American volume
Year: 2017

Utilization of total knee and hip arthroplasty has greatly increased in the past decade in the United States; these are among the most expensive procedures in patients with Medicare. Advances in surgical techniques, anesthesia, and care pathways decrease hospital length of stay. We examined how trends in hospital cost were altered by decreases in length of stay.  Procedure, demographic, and economic data were collected on 6.4 million admissions for total knee arthroplasty and 2.8 million admissions for total hip arthroplasty from 2002 to 2013 using the National (Nationwide) Inpatient Sample, a component of the Healthcare Cost and Utilization Project. Trends in mean hospital costs and their association with length of stay were estimated using inflation adjusted, survey weighted generalized linear regression models, controlling for patient demographic characteristics and comorbidity.  From 2002 to 2013, the length of stay decreased from a mean time of 4.06 to 2.97 days for total knee arthroplasty and from 4.06 to 2.75 days for total hip arthroplasty. During the same time period, the mean hospital cost for total knee arthroplasty increased from $14,988 (95% confidence interval [CI] $14,927 to $15,049) in 2002 to $22,837 (95% CI, $22,765 to $22,910) in 2013 (an overall increase of $7,849 or 52.4% The mean hospital cost for total hip arthroplasty increased from $15,792 (95% CI, $15,706 to $15,878) in 2002 to $23,650 (95% CI, $23,544 to $23,755) in 2013 (an increase of $7,858 or 49.8% If length of stay were set at the 2002 mean, the growth in cost for total knee arthroplasty would have been 70.8% instead of 52.4% as observed, and the growth in cost for total hip arthroplasty would have been 67.4% instead of 49.8% as observed.  Hospital costs for joint replacement increased from 2002 to 2013, but were attenuated by reducing inpatient length of stay. With demographic characteristics showing an upward trend in the utilization of joint arthroplasty, including a shift toward younger population groups, reduction in length of stay remains an important target for procedure level cost containment under emerging payment models.
Large scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways
Venue: Nature Genetics
Year: 2012

Through genome wide association meta analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q 0.05) Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.
Anomaly Detection in Video Using Predictive Convolutional Long Short Term Memory Networks
Venue: ArXiv
Year: 2016

Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined. We approach the problem by learning generative models that can identify anomalies in videos using limited supervision. We propose end to end trainable composite Convolutional Long Short Term Memory (Conv LSTM) networks that are able to predict the evolution of a video sequence from a small number of input frames. Regularity scores are derived from the reconstruction errors of a set of predictions with abnormal video sequences yielding lower regularity scores as they diverge further from the actual sequence over time. The models utilize a composite structure and examine the effects of conditioning in learning more meaningful representations. The best model is chosen based on the reconstruction and prediction accuracy. The Conv LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on anomaly detection datasets. Conv LSTM units are shown to be an effective tool for modeling and predicting video sequences.
THU0469 Analysis of 111 Patients with Connective Tissue Disease Associated (CTD) Pulmonary Arterial Hypertension (PAH) Among 2584 Patients with CTD
Year: 2013

Connective tissue disease (CTD) is a type of autoimmune disease that affects many organs and tissues. The most common manifestations in the lungs include interstitial lung disease, pulmonary arterial hypertension (PAH) pulmonary infarction, and pleuritis. Most CTD PAH patients without symptoms or with mild PAH are overlooked by doctors and therefore cannot be diagnosed or treated early. In this study, we retrospectively analysed the clinical manifestations, laboratory features and therapeutic regimens of 111 CTD PAH patients selected from 2584 CTD patients to improve our understanding of this disease.  This study aimed to estimate the incidence of pulmonary arterial hypertension (PAH) in connective tissue disease (CTD) patients, assess the relationship between the patients' clinical manifestations and the type of PAH, and discuss the effects of different diseases and treatments on prognosis.  A retrospective evaluation of 111 CTD PAH patients from 2584 CTD patients was conducted. Clinical data and experimental indexes were collected from the patients at baseline and after 1,2,3,5 years of treatment to investigate the risk factors of PAH and to assess whether different treatments or primary diseases would make a difference in the prognosis.  CTD PAH was most prevalent in SSc (18.86% and MCTD (12.00% patients compared with other CTD patients (P<0.01) Patients with mild or moderate PAH exhibited a much better response to treatment than patients with severe PAH. SSc patients had worse clinical manifestations and haemodynamic parameters than SLE patients at the baseline and after treatment. The mortality rate in CTD PAH patients was significantly lower than in CTD patients without PAH (0.85% vs 6.31% P <0.05) The 5 year survival rate for targeted PAH therapy group has become significantly higher than non targeted PAH therapy group (0.91% vs 0.71% P=0.028) Conclusions Raynaud's phenomenon, anti U1RNP( and ACA IgG( are suggestive of a higher risk of PAH in patients with CTD. The treatment outcomes in patients with severe PAH or patients with SSc and PAH were both poor. Targeted therapy can improve the 5 year survival rate of CTD PAH patients.
Self Supervised Graph Representation Learning via Global Context Prediction
Venue: ArXiv
Year: 2020

To take full advantage of fast growing unlabeled networked data, this paper introduces a novel self supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself. Inspired by human social behavior, we assume that the global context of each node is composed of all nodes in the graph since two arbitrary entities in a connected network could interact with each other via paths of varying length. Based on this, we investigate whether the global context can be a source of free and effective supervisory signals for learning useful node representations. Specifically, we randomly select pairs of nodes in a graph and train a well designed neural net to predict the contextual position of one node relative to the other. Our underlying hypothesis is that the representations learned from such within graph context would capture the global topology of the graph and finely characterize the similarity and differentiation between nodes, which is conducive to various downstream learning tasks. Extensive benchmark experiments including node classification, clustering, and link prediction demonstrate that our approach outperforms many state of the art unsupervised methods and sometimes even exceeds the performance of supervised counterparts.
Revisiting Convolutional Neural Networks for Urban Flow Analytics
Venue: ArXiv
Year: 2020

Convolutional Neural Networks (CNNs) have been widely adopted in raster based urban flow analytics by virtue of their capability in capturing nearby spatial context. By revisiting CNN based methods for different analytics tasks, we expose two common critical drawbacks in the existing uses: 1) inefficiency in learning global context, and 2) overlooking latent region functions. To tackle these challenges, in this paper we present a novel framework entitled DeepLGR that can be easily generalized to address various urban flow analytics problems. This framework consists of three major parts: 1) a local context module to learn local representations of each region; 2) a global context module to extract global contextual priors and upsample them to generate the global features; and 3) a region specific predictor based on tensor decomposition to provide customized predictions for each region, which is very parameter efficient compared to previous methods. Extensive experiments on two typical urban analytics tasks demonstrate the effectiveness, stability, and generality of our framework.
Cuenta de capitales y la regulacion de derivados del mercado de divisas: la experiencia reciente de Brasil
Year: 2015

B rasil es uno de los paises emergentes cuya moneda mostro una acentuada tendencia hacia la apreciacion hasta febrero del 2012. En este contexto, puede comprenderse la reciente instrumentacion de las regulaciones de la cuenta de capitales, llevada a cabo durante el periodo 2009 2012, las cuales fueron complementadas con otro tipo de reglamentos destinados a los llamados derivados de mercados de divisas. El presente articulo establece que solo cuando el gobierno brasileno implemento simultaneamente los tres tipos de reglamentos control de capitales, reglamentos financieros prudenciales y reglamentos para derivados del mercado de divisas) aumento la eficacia de las politicas destinadas a proteger su moneda de las presiones alcistas. Asimismo, la experiencia de Brasil indica que, a diferencia de la nueva opinion emitida por el fmi en torno a los controles de capital, no es posible establecer una jerarquia entre los instrumentos provisionales destinados a administrar los flujos de capital y las medidas prudenciales permanentes.
The Semiotics of Emoji: The Rise of Visual Language in the Age of the Internet
Year: 2016

Emoji have gone from being virtually unknown to being a central topic in internet communication. What is behind the rise and rise of these winky faces, clinking glasses and smiling poos? Given the sheer variety of verbal communication on the internet and English's still controversial role as lingua mundi for the web, these icons have emerged as a compensatory universal language. The Semiotics of Emoji looks at what is officially the world's fastest growing form of communication. Emoji, the colourful symbols and glyphs that represent everything from frowning disapproval to red faced shame, are fast becoming embedded into digital communication. Controlled by a centralized body and regulated across the web, emoji seems to be a language: but is it? The rapid adoption of emoji in such a short span of time makes it a rich study in exploring the functions of language. Professor Marcel Danesi, an internationally known expert in semiotics, branding and communication, answers the pertinent questions. Are emoji making us dumber? Can they ultimately replace language? Will people grow up emoji literate as well as digitally native? Can there be such a thing as a Universal Visual Language? Read this book for the answers.
Principled Frameworks for Evaluating Ethics in NLP Systems
Venue: WNLP@ACL
Year: 2019

We critique recent work on ethics in natural language processing. Those discussions have focused on data collection, experimental design, and interventions in modeling. But we argue that we ought to first understand the frameworks of ethics that are being used to evaluate the fairness and justice of algorithmic systems. Here, we begin that discussion by outlining deontological and consequentialist ethics, and make predictions on the research agenda prioritized by each.
A comparative in situ 195Pt electrochemical NMR investigation of PtRu nanoparticles supported on diverse carbon nanomaterials.
Venue: Faraday discussions
Year: 2008

This paper reports a detailed in situ 195Pt electrochemical nuclear magnetic resonance (EC NMR) study of PtRu nanoparticles (NPs) that had a nominal atomic ratio of Pt Ru 1 1 and were supported on carbon nanocoils and carbon black (Vulcan XC 72) respectively. The particle sizes of the two samples were determined by X ray diffraction using the Sherrer equation: 3.6 nm for the former and 3.2 nm for the latter, which were further corroborated by transmission electron microscope measurements. By taking advantage of a unique correlation between the spectral frequency of the 195Pt NMR resonance and the radial atomic position in a particle, qualitatively and spatially resolved local Pt atomic fractions in the particles were deduced by using a Ruderman Kittel Kasuya Yosida (RKKY) J coupling based method as a function of different electrode potentials. The results indicated that both samples had Pt enriched cores and Pt deprived surfaces and, most importantly, the local Pt concentration varied as the electrochemical environment changed. The spatially resolved Fermi level local densities of states (E(f) LDOS) which are a measure of the electronic frontier orbitals in metals, were deduced across the NMR spectrum and correlated with the EC activity in methanol electro oxidation. The results were also compared to those obtained previously from Pt/Ru NPs supported respectively on carbon and graphite nanofibers.
Evaluation on Non Point Source Pollution of Soil and Water Loss and Its Economic Loss of China
Year: 2008

It conducts an overall and systematic evaluation of non point source pollution load caused by soil erosion in China(excluding Taiwan,Hongkong and Macao) of year 2000 after revising output coefficient model by introducing rainfall affecting coefficients,i.e.COD 13.664 6 million tons,TP 197 700 tons and TN 2.741 6 million tons.Its economic loss is about 30.443 72 billion Yuan calculated by the method of cost of replacement.The outcomes of economic loss calculation reflect the status of non point source pollution of China soil and water conservation to a certain degree and provide decision basis of preliminary quantification for soil and water conservation work and water pollution prevention and control of river basin.
Dispersal limitation and thermodynamic constraints govern spatial structure of permafrost microbial communities.
Venue: FEMS microbiology ecology
Year: 2018

Understanding drivers of permafrost microbial community composition is critical for understanding permafrost microbiology and predicting ecosystem responses to thaw. We hypothesize that permafrost communities are shaped by physical constraints imposed by prolonged freezing, and exhibit spatial distributions that reflect dispersal limitation and selective pressures associated with these physical constraints. To test this, we characterized patterns of environmental variation and microbial community composition in permafrost across an Alaskan boreal forest landscape. We used null modeling to estimate the importance of selective and neutral assembly processes on community composition, and identified environmental factors influencing ecological selection through regression and structural equation modeling (SEM) Proportionally, the strongest process influencing community composition was dispersal limitation (0.36) exceeding the influence of homogenous selection (0.21) variable selection (0.16) and homogenizing dispersal (0.05) Fe(II) content was the most important factor explaining variable selection, and was significantly associated with total selection by univariate regression (R2 0.14, P 0.003) SEM supported a model in which Fe(II) content mediated influences of the Gibbs free energy of the organic matter pool and organic acid concentration on total selection. These findings suggest that the dominant processes shaping microbial communities in permafrost result from the stability of the permafrost environment, which imposes dispersal and thermodynamic constraints.
Versatile genius: A case study intersecting math, science, art, and California's national parks
Year: 2017

The advantages of a polymathic approach are remarkable: it cross pollinates ideas, encourages creativity, defines new methodologies, and solves complex problems. Interdisciplinarity also presents challenges. In this case study, which intersects math, science, and art, we describe how a project can benefit from a respect for the rigors, focus, and coherence of multiple disciplines. Although most interdisciplinary projects are dominated by a particular field (in point of view, level of expertise, and disciplinary significance) this does not preclude a project's author maker from complying with the standards of the component disciplines. The author maker of the project described in this case study is a professional artist who holds a decidedly mathematical point of view. The art, which is created with a high degree of disciplinary proficiency, predominates; and although elementary, the math is no less legitimate.
Consumer Behaviour and Insurance Claim Fraud in Malaysia
Year: 2018

This review paper aims to determine the factors influencing the attitudes of consumers towards insurance claim fraud. In this research, data review was conducted on 60 articles associated with this study's objectives according to experts' views and researchers' experience. It was shown in this research's primary finding that economic issues, moral hazard, and perceived fairness are the elements which will result in insurance claim fraud. This has validated the influence posed by consumer behaviour on insurance claim fraud. However, the estimated insurance claim fraud data provided by Bank Negara Malaysia (BNM) rather than the actual data, along with the obstacles of acquiring data from consumers were the limitations in this research.  Insurance Claim Fraud, Consumer Behaviour, Insurance
Priority actions for the non communicable disease crisis
Venue: The Lancet
Year: 2011

566 www.thelancet.com Vol 378 August 13, 2011 of proven cost eff ective and aff ordable inter ventions to address them. We advocate the strengthening of health systems, and in particular primary health services for long term patientcentred care; such a focus will benefi t patients with mental illnesses as well as people with other chronic disorders. We agree with Nicholas Freudenberg that there is a need to confront industries that profi t from unhealthy products and contribute to the rising NCD burden. We support the full range of fi scal, market, regulatory, legislative, and educational measures to reduce the NCD burden. The history of tobacco control reveals that inter action with such industries cannot simply be viewed as "partnerships" Dialogue with industry excluding the tobacco industry should be based on a shared vision of public health goals and independent monitoring of progress and commitments.
Optimizing Extreme Learning Machine via Generalized Hebbian Learning and Intrinsic Plasticity Learning
Venue: Neural Processing Letters
Year: 2018

Traditional extreme learning machine (ELM) has random weights between input layer and hidden layer, this kind of random feature mapping brings non discriminative feature space and unstable classification accuracy, which greatly limits the performance of the ELM networks. Therefore, to get the well pleasing input weights, two biologically inspired, unsupervised learning methods were introduced to optimize the traditional ELM networks, namely the generalized hebbian algorithm (GHA) and intrinsic plasticity learning (IPL) The GHA is able to extract the principal components of the input data of arbitrary size, while the IPL tunes the probability density of the neuron's output towards a desired distribution such as exponential distribution or weber distribution, thereby maximizing the networks information transmission. With the incorporation of the GHA and IPL approach, the optimized ELM networks generates a discriminative feature space and preserves much more characteristic of the input data, accordingly, achieving a better task performance. Based on the above two unsupervised methods, a simple, yet effective hierarchical feature mapping extreme learning machine (HFMELM) is further proposed. With almost no information loss in the layer wise feature mapping process, the HFMELM is able to learn the high level representation of the input data. To evaluate the effectiveness of the proposed methods, extensive experiments on several datasets are presented, the results show that the proposed methods significantly outperform the traditional ELM networks.