Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse Decision Theory

Identifying an optimal decision rule using Bayesian decision theory requires priors, likelihoods, and losses. In many medical settings, we can develop priors and likelihoods, but specifying losses can be difficult, especially when considering both patient outcomes and economic costs. If there is a widely accepted treatment strategy, then we can consider the inverse problem and find a region in the space of losses where the procedure is optimal. We call this approach inverse decision theory (IDT). We apply IDT to the standard of care for diagnosis and treatment of precancerous lesions of the cervix, and consider an alternative procedure that has been proposed. We use a Bayesian approach to estimate the probabilities associated with the diagnostic tests and make inferences about the region in loss space where these medical procedures are optimal. In particular, we find evidence supporting the current standard of care.
THE WAVE CLIMATE OF THE NORTH ATLANTIC-PAST , PRESENT AND FUTURE

The oceans are an important component of the climate system. Ocean wave height is one of the climate variables that could be affected by anthropogenic forcing. Since the design of off-shore oil platforms and other marine and coastal infrastructures, which are designed to last for several decades, is constrained by the largest wave height event anticipated during a fixed design period, changes in the extremes of wave height could have an impact on the life-span of these infrastructures, which will be in excess of impacts anticipated from the rising sea level. Ocean waves also provide an excellent proxy for trends and variability of storminess. Analysis of wave and storm variability, detection of climate changes therein, and projection of possible future wave climate, are of great importance to the operation/planning and safety of shipping, off-shore industries and coastal development, and to climate research. The purpose of this study is twofold. First, we will assess changes in the North Atlantic wave heights observed in the 40-year period from 1958 to 1997. Second, we will construct climate change scenarios of wave height in the North Atlantic. We will apply non-stationary Generalized Extreme Value (GEV) analyses to both the observed and projected wave height extremes, to assess distributional changes and their implications. The remainder of this paper is organized as follows: The datasets and methodologies used in this study are briefly described in sections 2 and 3, subsequently. The observed and projected changes are presented in sections 4 and 5, respectively. Finally, we conclude this study with a summary and discussion in section 6.
UZH@SMM4H: System Descriptions

Our team at the University of Zürich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task. We experimented with different approaches for text classification, namely traditional feature-based classifiers (Logistic Regression and Support Vector Machines), shallow neural networks, RCNNs, and CNNs. This system description paper provides details regarding the different system architectures and the achieved results.
Using Wikipedia Edits in Low Resource Grammatical Error Correction

We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5%.
Effects of a Combination of Aerobic and Resistance Training and Dietary Intervention on Body Composition , Lipid Profile , Inflammation , and Cardiorespiratory Fitness in Obese Adolescents

Albuquerque Filho NJB, Matos VAF, Felipe TR, Pinto EF, Rebouças GM, Oliveira Segundo VH, Knackfuss MI, Medeiros HJ. Effects of a Combination of Aerobic and Resistance Training and Dietary Intervention on Body Composition, Lipid Profile, Inflammation, and Cardiorespiratory Fitness in Obese Adolescents. JEPonline 2016;19(5):1-14. The aim of this study was to determine the effects of a combination of aerobic and resistance training plus dietary intervention on body composition, lipid profile, inflammation, and cardiorespiratory fitness in obese adolescents. Twenty-three obese adolescents were divided into two groups: Intervention Group (IG) that performed a combined exercise program plus nutritional intervention for 16 wks and a Control Group (CG) that did not performed any intervention. Body weight, height, body mass index, waist circumference, sum of triceps and subscapular skinfold thickness, fasting blood glucose, triglycerides, LDL-C, HDL-C, total cholesterol, hs-CRP, strength by one-maximum repetition, and VO2peak were measured. Across intervention period, effects were tested using analysis of variance for repeated measures with post
Effect of Anaerobic Digestion on Rheological Parameters and Dewaterability of Aerobic Sludges from MBR and Conventional Activated Sludge Plants

The paper investigates and compares the characteristics of sludges produced by membrane Bio-Reactor (MBR) and Conventional Activated Sludge (CAS) systems. Stability and dewaterability of full-scale MBR and CAS treatment plants are measured and compared. Obtained results show that specific methane production is higher in CAS sludge compared to MBR sludge because of the higher solid retention time of the MBR. Nonetheless MBR sludge results to be characterized by a non-negligible biometanation potential (BMP). Methane production measured during BMP tests is around 250 NmL/gVS for MBR sludge, equaling 2/3 of methane production obtained, in similar condition, for CAS sludge. Dewaterability is evaluated by Specific Resistance to Filtration (SRF), Capillary Suction Time (CST) and Time To Filter (TTF) tests. Data obtained before and after the anaerobic digestion of the sludges show that the stabilization process affects much more the resistance to filtration of CAS sludge than the resistance to filtration of MBR sludge. The same trend is observed, for the other measured parameters. This is attributed to the variation of the rheological characteristics of the two sludges during the stabilization process, and to the different initial rheological properties of them due to the different selection process of the biomass deriving from the different biomass retention methods of MBR and CAS systems.
Memory for Emotional Stimuli in Development

Out of the multitude of daily events, some kinds of events are much more likely to create lasting memories. Emotion is one of the most potent factors influencing the strength and quality of memories (Hamann, 2001; LaBar & Cabeza, 2006). The memory of events associated with strong emotional responses during encoding, such as a thrilling visit to a theme park or a fall off a bicycle, are much more likely to create lasting, vivid memories than similar, neutral events (Rubin, 2005). Memory for emotional events is typically enhanced in several ways, relative to similar but non-emotional events. For example, memory for emotional events is usually more accurate, stronger, and more enduring (Christianson & Safer, 1996). The subjective quality associated with retrieval of emotional memories is also typically more vivid, detailed, and associated with higher confidence in the accuracy of the memory (Phelps, 2006). Considerable evidence indicates that emotional memories engage cognitive and neural mechanisms that are either not recruited by non-emotional memories or engaged to a much lesser extent. Given these special attributes of emotional memory, a key question that arises is whether there are important developmental differences associated with memory for emotional stimuli. We will examine this fundamental question in this chapter, focusing on the domain of episodic memory (consciously accessible memory for specific events) (Tulving & Thomson, 1973). Developmental investigations of emotional memory are important for a number of reasons. First, because many episodic memories have an emotional component, especially those that become part of our long-term autobiographical memory, the study of emotional memory is, in an important sense, inextricably intertwined with the study of episodic and autobiographical memory (Conway & Pleydell-Pearce, 2000; Nelson & Fivush, 2004). Thus, the development of emotional memory is an essential facet of the study of the development of episodic and autobiographical memory. Emotional memories and their neurobiological substrates are also frequently implicated in the development of clinical disorders (Howe, Toth, & Cicchetti, 2011; Williams et al., 2007), and potential developmental differences in emotional memory have attracted considerable attention in eyewitness testimony research (Brainerd, Reyna, & Ceci, 2008; Ceci & Bruck, 1993). These are only a few of the many reasons for the importance of this topic (see the chapter by Baker-Ward and Ornstein in this volume for more discussion).
Workflow Scheduling on the Hybrid Cloud to Maintain Data Privacy under Deadline Constraint

The development of cloud computing technology has been continuously growing since its invention and has attracted the attention of many researchers in the academia and the industry, particularly during the recent years. The majority of organizations, whether large corporate businesses or typical small companies, are moving towards adapting this cutting edge technology. The private cloud provides low cost and privacy for workflow applications execution. However, an organization’s requirements to high performance resources and high capacity storage devices encourage them to utilize public clouds. Public cloud leases information technology services in the form of small units and in larger scale compared to private cloud, but this model is potentially exposed to the risk of data and computation breach and is less secure in comparison to a pure private cloud environment. The combination of public and private clouds is known as hybrid cloud, where workflow tasks can be executed on resources residing on either public or private clouds. The objective of this paper is to present a scheduling algorithm for maintaining data privacy in workflow applications, such that the budget is minimized, while the makespan limitation imposed by the user is satisfied. A scheduling algorithm called Workflow scheduling on Hybrid Cloud to maintain Data Privacy (WHPD) is proposed and it is shown that it can perform as well as HCOC while preserving data and computation privacy.
Forecasting with the Standardized Self-Perturbed Kalman Filter

A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding the calibration of a design parameter. The standardization leads to a better tracking of the dynamics of the parameters compared to other on-line methods, especially as the level of noise increases. The proposed estimation method, coupled with dynamic model averaging and selection, is adopted to forecast S&P 500 realized volatility series with a time-varying parameters HAR model with exogenous variables.
Big-Crypto: Big Data, Blockchain and Cryptocurrency

Cryptocurrency has been a trending topic over the past decade, pooling tremendous technological power and attracting investments valued over trillions of dollars on a global scale. The cryptocurrency technology and its network have been endowed with many superior features due to its unique architecture, which also determined its worldwide efficiency, applicability and data intensive characteristics. This paper introduces and summarises the interactions between two significant concepts in the digitalized world, i.e., cryptocurrency and Big Data. Both subjects are at the forefront of technological research, and this paper focuses on their convergence and comprehensively reviews the very recent applications and developments after 2016. Accordingly, we aim to present a systematic review of the interactions between Big Data and cryptocurrency and serve as the one stop reference directory for researchers with regard to identifying research gaps and directing future explorations.
RESULTS OF INVESTIGATING THE MACROINVERTEBRATE COMMUNITY OF THE DANUBE RIVER ON THE SECTOR UPSTREAM FROM THE IRON GATE (KM 1083-1071)

The present work cites results of investigating aquatic macroinvertebrates of the Danube River on the sector upstream from the Iron Gate (KM 1083-1071). The investigated part is interesting from the hydrobiological standpoint above all due to differences of faunal composition in relation to higher sections that could be expected in view of dif- ferences in overall characteristics of the river. A rich macroinvertebrate community (84 taxa) was observed. The diver- sity of taxa is primarily a result of habitat diversity within the given stretch. Oligochaeta and Mollusca were the princi- pal components.
Big Data Analysis with Apache Spark

Manipulating big data distributed over a cluster is one of the big challenges which most of the current big data oriented companies face. This is evident by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework which caters to provide solution for big data management. This paper, present a discussion on how technically Apache Spark help us in Big Data Analysis and Management. The paper aims to provide the conclusion stating apache Spark is more beneficial by almost 50 percent while working on big data. As when data size was increased to 5*106 the time taken was drastically reduced by around 50 percent compared to when queried Cassandra without Spark. Cassandra is used as Data Source for conducting our experiment. For this, a experiment is conducted comparing spark with normal Cassandra DataSet or ResultSet. Gradually increased the number of records in Cassandra table and time taken to fetch the records from Cassandra using Spark and traditional Java ResultSet was compared. For the initial stages, when data size was less than 10 percent, Spark showed almost an average response time which was almost equal to the time taken without the use of Spark. As the data size exceeded beyond 10 percent of
Joint Patch-Group Based Sparse Representation for Image Inpainting

Sparse representation has achieved great successes in various machine learning and image processing tasks. For image processing, typical patch-based sparse representation (PSR) models usually tend to generate undesirable visual artifacts, while group-based sparse representation (GSR) models produce over-smooth phenomena. In this paper, we propose a new sparse representation model, termed joint patch-group based sparse representation (JPG-SR). Compared with existing sparse representation models, the proposed JPG-SR provides a powerful mechanism to integrate the local sparsity and nonlocal self-similarity of images. We then apply the proposed JPG-SR model to a low-level vision problem, namely, image inpainting. To make the proposed scheme tractable and robust, an iterative algorithm based on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed JPG-SR model. Experimental results demonstrate that the proposed model is efficient and outperforms several state-of-the-art methods in both objective and perceptual quality.
Document de travail ASSESSING FUTURE SUSTAINABILITY OF FRENCH PUBLIC FINANCES

This paper contributes to the debate on the French public finances’ consolidation by investigating the long-term sustainability of France’s fiscal position. We trace the historical trends of government’s tax receipts and expenditures. We find that while the level of public expenditure in France is larger than in the rest of the Euro Area (mostly because of public wages and social benefits), its trend is comparable to its neighbours. Net lending is also under control, thanks to the high levels of taxation, so that we see no real risk of future unsustainability. However, the French tax system is unfair, is not sufficiently progressive, and is too complex. The paper then proceeds to assess the future of France’s public finances on the basis of the current debate on the Euro Area fiscal rules. We report two analyses – theoretical and empirical – that project the inflation rate and output gap paths for the next twenty years. We finally assess fiscal rules on this ground. The ‘fiscal compact’ fares rather poorly compared to the alternative rules that we assess.
Chemical Abundances of the Damped Lyα Systems at z > 1.5

We present chemical abundance measurements for 19 damped Lyα systems observed with the high-resolution echelle spectrograph (HIRES) on the 10 m W.M. Keck Telescope. We perform a detailed analysis of every system, deriving ionic column densities for all unblended metal-line transitions. Our principal goal is to investigate the abundance patterns of the damped systems and thereby determine the underlying physical processes that dominate their chemical evolution. We place particular emphasis on gauging the relative importance of two complementary effects often invoked to explain the damped Lyα abundances (1) nucleosynthetic enrichment from Type II supernovae and (2) an interstellar medium-like (ISM-like) dust-depletion pattern. Similar to the principal results of Lu et al., our observations lend support both for dust depletion and Type II supernova (SN) enrichment. Specifically, the observed overabundance of Zn/Fe and underabundance of Ni/Fe relative to solar abundances suggest significant dust depletion within the damped Lyα systems. Meanwhile, the relative abundances of Al, Si, and Cr versus Fe are consistent with both dust depletion and Type II supernova enrichment. Our measurements of Ti/Fe and the Mn/Fe measurements from Lu et al., however, cannot be explained by dust depletion and indicate an underlying Type II SN pattern. Finally, the observed values of [S/Fe] are inconsistent with the combined effects of dust depletion and the nucleosynthetic yields expected for Type II supernovae. This last result emphasizes the need for another physical process to explain the damped Lyα abundance patterns. We also examine the metallicity of the damped Lyα systems both with respect to Zn/H and Fe/H. Our results confirm previous surveys by Pettini and collaborators, i.e., [⟨ Zn/H ⟩] = -1.15±0.15 dex. In contrast with other damped Lyα surveys at z > 1.5, we do not formally observe an evolution of metallicity with redshift, although we stress that this result is based on the statistics from a small sample of high-z damped systems.
A Monte Carlo approach to estimating the spectrum of large positive semidefinite matrices.

The method presented in this paper aims at furnishing a series of numerical values to approximate the matrix eigenvalues. The method is based on a statistical model that involves the first three central moments of the eigenvalue distribution, and it relies on the solution of a nonlinear system of equations that implements the moment-matching method and on a subsequent procedure of Monte Carlo simulations. The method is only applicable to real positive semidefinite matrices (PSD), and it is especially useful when other techniques lead to computational problems, e.g., when the matrices become too large to be processed or the required storage space sets heavy limits to the computational process.
PAH dimerization as the first step to soot particle inception

Particle inception, or the transition from the gas-phase to solid-phase, is a complex process that influences the growth of combustion-emitted particles, especially their size distribution and morphology. Unfortunately, although it is widely believed that nanoparticles form via polycyclic aromatic hydrocarbons (PAHs), little is known about the exact molecular mechanisms involved in the process. In this paper, we investigate the effects of molecular structure on the tendency of PAHs to dimerize. We measured the thermodynamic stability of aliphatic-substituted PAHs by calculating the free energy surfaces of monomer-monomer interactions. Specifically, we varied the geometry and chain lengths of these PAHs. Free energy was chosen as the best measure of thermodynamic stability because it takes into account stabilization due to both potential and entropic effects. The results show that the stability of aliphatic-substituted PAH dimers increases with the length and saturation of the aliphatic chains, resulting in lower free energy minima than their unsaturated analogs. The results can be used to expand the current soot models to include various species for the particle inception process.
Scanning magnetic tunnel junction microscope for high-resolution imaging of remanent magnetization fields

Scanning magnetic microscopy is a new methodology for mapping magnetic fields with high spatial resolution and field sensitivity. An important goal has been to develop high-performance instruments that do not require cryogenic technology due to its high cost, complexity, and limitation on sensor-to-sample distance. Here we report the development of a low-cost scanning magnetic microscope based on commercial room-temperature magnetic tunnel junction (MTJ) sensors that typically achieves spatial resolution better than 7 µm. By comparing different bias and detection schemes, optimal performance was obtained when biasing the MTJ sensor with a modulated current at 1.0 kHz in a Wheatstone bridge configuration while using a lock-in amplifier in conjunction with a low-noise custom-made preamplifier. A precision horizontal (x–y) scanning stage comprising two coupled nanopositioners controls the position of the sample and a linear actuator adjusts the sensor-to-sample distance. We obtained magnetic field sensitivities better than 150 nT/Hz1/2 between 0.1 and 10 Hz, which is a critical frequency range for scanning magnetic microscopy. This corresponds to a magnetic moment sensitivity of 10–14 A m2, a factor of 100 better than achievable with typical commercial superconducting moment magnetometers. It also represents an improvement in sensitivity by a factor between 10 and 30 compared to similar scanning MTJ microscopes based on conventional bias-detection schemes. To demonstrate the capabilities of the instrument, two polished thin sections of representative geological samples were scanned along with a synthetic sample containing magnetic microparticles. The instrument is usable for a diversity of applications that require mapping of samples at room temperature to preserve magnetic properties or viability, including paleomagnetism and rock magnetism, nondestructive evaluation of materials, and biological assays.
Development and in vitro- in vivo evaluation of Nitazoxanide sustained release tablets

Objective: The objective of the present investigation was to develop a sustained release (SR) tablet formulation of Nitazoxanide.Methods: Nitazoxanide Tablets were prepared by employing wet granulation methods and hydrophilic polymers used as drug release retardants. The dissolution data also evaluated for the drug release mechanism and kinetics. The optimized formulation was subjected for in vivo studies in rabbits.Results: Based on data obtained from the in vitro drug release studies 5%w/w of Methocel K100M was selected as a release retardant. The drug release followed first order kinetics and fickian diffusion.Conclusion: Present investigation indicates that the developed formulation was able to sustain the drug release.