Miracle-dz/UniFAR-FTUs huggingface dataset:
An adaptive observer for fault diagnosis in nonlinear discrete-time systems

This paper deals with the problem of fault diagnosis (FD) for a class of nonlinear systems in the presence of actuator failures. The scheme is based on a discrete-time diagnostic observer which computes an estimate of the system's state. To cope with the uncertainties and discretization errors, a discrete-time adaptive law is developed, based on a parametric model of the uncertain terms. A stability proof is developed to prove the global exponential stability of the system in the absence of faults. The effectiveness of the proposed approach is experimentally tested on a case study developed for an industrial mechanical manipulator.
Dual Approaches to the Analysis of Risk Aversion

We present a dual formulation of choice under uncertainty based on a few simple assumptions about preferences. It is shown that the additive separability restriction on preferences, key to expected-utility theory, can be dropped with little loss of analytic power for a broad class of choice problems. Dual risk premiums are characterized, and it is shown that placing various invariance restrictions on them leads naturally to generalizations of the concepts of CARA, CRRA, and LRT familiar from expected-utility theory. Each of these generalizations conforms to a notion of homotheticity.
Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodes

We investigated using poly(3,4-ethylenedioxythiophene) (PEDOT) to lower the impedance of small, gold recording electrodes with initial impedances outside of the effective recording range. Smaller electrode sites enable more densely packed arrays, increasing the number of input and output channels to and from the brain. Moreover, smaller electrode sizes promote smaller probe designs; decreasing the dimensions of the implanted probe has been demonstrated to decrease the inherent immune response, a known contributor to the failure of long-term implants. As expected, chronically implanted control electrodes were unable to record well-isolated unit activity, primarily as a result of a dramatically increased noise floor. Conversely, electrodes coated with PEDOT consistently recorded high-quality neural activity, and exhibited a much lower noise floor than controls. These results demonstrate that PEDOT coatings enable electrode designs 15 µm in diameter.
Diagnosis as an integral part of multi-agent adaptability

Agents working under real world conditions may face an environment capable of changing rapidly from one moment to the next, either through perceived faults, unexpected interactions or adversarial intrusions. The members of a multi-agent system can gracefully and efficiently handle such situations by adapting, either by evolving internal structures and behavior or repairing or isolating those external influences believed to be malfunctioning. The first step in achieving adaptability is diagnosis-being able to accurately detect and determine the cause of a fault based on its symptoms. In this paper we examine how domain independent diagnosis plays a role in multi-agent systems, including the information required to support and produce diagnoses. Particular attention is paid to coordination based diagnosis directed by a causal model. Several examples are described in the context of an Intelligent Home environment, and the issue of diagnostic sensitivity versus efficiency is addressed.
A Novel Algorithm Infomap-SA of Detecting Communities in Complex Networks

Community detection is one of the most important issues in complex networks. In this paper, integrating Infomap and Simulated Annealing (SA) algorithm, and based on the thought of optimizationof the modularity function, the authors are proposing a novel algorithm Infomap-SA for detecting community. In order to verify the accuracy and efficiency of this algorithm, the performance of this algorithm is tested on several representative real-world networks and a set of computer- generated networks by LFR-benchmark. The experimental results show that this algorithm can identify the communities accurately and efficiently, and has higher values of modularity and density and lower computable complexity than Infomap algorithm. Furthermore, the Infomap-SA is more suitable for community detection of large-scale network. Index Terms—Community detection; modularity; density; infomap-simulated annealing algorithm; simulation test
Assymetries in Bank Lending Behavior: Austria During the 1990s

This paper investigates both cross-sectional asymmetry (related to bank-specific characteristics like size and liquidity) and asymmetries overtime (potentially related to the overall state of the economy) in Austrian bank lending reaction to monetary policy. The first type of asymmetry is accounted for by including interaction terms, and the second type is captured by latent state-dependent parameters. Estimation is cast into a Bayesian framework, and the posterior inference is obtained using Markov chain Monte Carlo simulation methods. The results document a significant asymmetric effect of interest rate changes over time on bank lending. During economic recovery, lagged interest rate changes have no significant effect on lending. Where the effects are significant, liquidity emerges as the bank characteristic that determines cross-sectional asymmetry.
Monetary policy, credit constraints and international trade

We combine the credit channel of monetary policy transmission literature and the credit constraints and trade literature to examine how monetary policy affects exports through a credit channel. We identify exogenous monetary tightening events in exporting countries based on the "impossible trinity" theorem and apply them to an empirical gravity model. In a large sector bilateral trade dataset for the years 1970-2000, we find strong and robust evidence that the export-reducing effect of tight monetary policy is significantly amplified by various measures of sector financial constraints. We also show that, by reducing financial market frictions at the country level, financial development significantly alleviate the adverse effect of credit constraints. Our empirical results also have important implications for the international transmission of monetary policy.
Heterogeneous Information and Labor Market Fluctuations

I introduce dispersed information in a search and matching model of the labor market, where firms are hit by aggregate and idiosyncratic productivity shocks. The latter induce larger responses in recruiting activity than the former - because aggregate shocks have general equilibrium effects which partially offset the change in fundamentals. Informational frictions prevent firms from disentangling aggregate and idiosyncratic shocks. With Bayesian updating, firms attribute aggregate shocks largely to idiosyncratic factors, because the latter have significantly larger variances. This misattribution translates into an increased responsiveness of employment to aggregate shocks, relative to an economy with full information. I show that in a calibrated model, this channel has quantitatively significant effects and offers a potential solution to a well-known puzzle - the inability of standard search and matching models to generate sufficient volatility in labor market variables. In particular, the model with dispersed information brings the relative volatilities of employment and market tightness very close to those observed in the data.
Empirical Characterization of Optimal Contracts Under Adverse Selection : An Environmental Policy Application

Analysis of contracts under asymmetric information typically concludes by characterizing an optimal mechanism based on the principal’s beliefs regarding agent value functions and the distribution of agent types. This article demonstrates how tools from stochastic frontier analysis allow a principal to develop consistent beliefs using data that does not include agents’ private information. I employ this methodology to evaluate strategies for reducing the cost of a voluntary program that reduces cultivation on environmentally-sensitive farmland.
Analyzing the evolution of testing library usage in open source Java projects

Software development projects frequently rely on testing-related libraries to test the functionality of the software product automatically and efficiently. Many such libraries are available for Java, and developers face a hard time deciding which libraries are most appropriate for their project, or when to migrate to a competing library. We empirically analysed the usage of eight testing-related libraries in 4,532 open source Java projects hosted on GitHub. We studied how frequently specific (pairs of) libraries are used over time. We also identified if and when library usages are replaced by competing ones during a project's lifetime. We found that some libraries are considerably more popular than their competitors, while some libraries become more popular over time. We observed that many projects tend to use multiple libraries together. We also observed permanent and temporary migrations between competing libraries. These findings may pave the way for recommendation tools that allow project developers to choose the most appropriate library for their needs, and to be informed of better alternatives.
Geometric Brownian Motion with Ito's lemma approach to evaluate market fluctuations: A case study on Colombo Stock Exchange

The data forecasting in economical time series provides a significant guidance for making decisions in the financial markets today. It can be widely applied for solving economic problems with uncertainty and instability patterns. Forecasting high volatile fluctuations with instability behavioral patterns make complicated problems in stock markets around the world today. The main focus of this study is to develop a forecasting model based on Geometric Brownian Motion approach for estimating share price indices in short-term investments in the Colombo Stock Exchange (CSE), Sri Lanka. Furthermore, traditional ARIMA approach was used to compare the predictions. The results reveal that, the new proposed model is more significant for investors in making their investment decisions wisely.
Bootstrapping Multilayer Neural Networks for Portfolio Construction

Despite having become firmly established as one of the major cornerstone principles of modern finance, traditional Markowitz mean-variance analysis has, nevertheless, failed to gain widespread acceptance as a practical tool for equity management. The Markowitz optimization enigma essentially centers on the severe estimation risk associated with the input parameters, as well as the resultant financially irrelevant or even false optimal portfolios and asset allocation proposals. We therefore propose a portfolio construction method in the present study which incorporates the adoption of bootstrapping neural network architecture. In specific terms, a residual bootstrapping sample, which is derived from multilayer feedforward neural networks, is incorporated into the estimation of the expected returns and the covariance matrix, which are then, in turn, integrated into the traditional Markowitz optimization procedure. The efficacy of our proposed approach is illustrated by comparing it with traditional Markowitz mean-variance analysis, as well as the James-Stein and minimum-variance estimators, with the empirical results indicating that this novel approach significantly outperforms the benchmark models, in terms of various risk-adjusted performance measures. The evidence provided in this study suggests that this new approach has significant promise with regard to the enhancement of the investment value of Markowitz mean-variance analysis.
Cellular Physiology Cellular Physiology Cellular Physiology Cellular Physiology Cellular Physiology Micrornas in Diabetes: Tiny Players in Big Disease

MicroRNAs (miRNAs) are a novel group of universally present small non-coding RNAs that have been implicated in wide ranging physiological processes and thereby are critical in the manifestation of diverse diseases. Since their discovery as developmental regulators in C.elegans, they have come a long way and are currently associated with normal and diverse pathophysiological states including Parkinson's syndrome, cardiac hypertrophy, viral infection, diabetes and several types of cancer. Of special significance is their involvement in diabetes, an area in which several emerging reports point to the fact that these small RNA species could be special and critical in this complex disease and they or their specific inhibitors may be exploited as targets for therapeutic intervention. The stable nature of these miRNAs over mRNAs is an added advantage of them being projected for the same. This review focuses on and discusses the current diabetic epidemic and the potential role(s) of these miRNAs in various physiological processes that lead to the diabetic phenotype with an objective of better understanding the emerging mechanisms of these small molecules in the development and progression of diabetes and its complications.
The effect of mental stress on psychophysiological parameters

A set-up for evaluating psychophysiological parameters during mental stress tests is described. The set-up was composed of a continuous non-invasive blood pressure monitor, skin conductance and skin temperature measurement devices and body heat-flux meter. Twelve healthy volunteers were tested using two different mental stress tests (Stroop colour-word conflict test and mathematical subtraction test). For comparison, also a physical test was employed. The results indicated that along the increase in heart rate and skin conductance, also systolic and diastolic blood pressure levels increased during all tests. Mental stress tests had weaker effect on psychophysiological parameters of the subjects as compared to physical activity. The results indicate that continuous blood pressure monitoring is a reliable physiological measurement, well correlated with mental and physical stress tests. The results also demonstrated the body heat-flux measurement could be an additional or supplemental measuring tool instead of skin temperature.
Variational Dropout Sparsifies Deep Neural Networks

We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy.
Racial and ethnic differences in albuminuria in individuals with estimated GFR greater than 60 mL/min/1.73 m(2): results from the Kidney Early Evaluation Program (KEEP).

BACKGROUND Albuminuria is an important marker for chronic kidney disease and progression to end-stage renal disease in the general population; understanding racial and ethnic differences can help inform efforts to reduce health disparities. We sought to estimate independent associations of race/ethnicity with albuminuria to determine whether observed differences were attributable to known kidney disease risk factors. METHODS This cross-sectional study included 64,161 Kidney Early Evaluation Program (KEEP) participants, 2000-2008, with estimated glomerular filtration rate > or = 60 mL/min/1.73 m(2), not on regular dialysis therapy, and without a previous kidney transplant. Albuminuria (urine albumin-creatinine ratio > or = 30 mg/g) was examined by self-reported race and ethnicity. Covariates were age, sex, educational level, body mass index, diabetes status or glucose level, hypertension status or blood pressure measurement, smoking status, health insurance status, and geographic region. RESULTS Albuminuria prevalences were 8% (n = 2,303) in whites, 11% (n = 2,310) in African Americans, 9% (n = 730) in Hispanics, 10% (n = 381) in Asians, and 15% (n = 344) in American Indians/Alaska Natives. Compared with whites, odds of albuminuria were higher for all groups after multivariate adjustment. Odds were highest for American Indians/Alaska Natives (adjusted OR, 1.93; 95% CI, 1.70-2.20), then Asians (adjusted OR, 1.42; 95% CI, 1.26-1.61), African Americans (adjusted OR, 1.38; 95% CI, 1.29-1.47), and Hispanics (adjusted OR, 1.19; 95% CI, 1.08-1.31). CONCLUSIONS In the KEEP study population, albuminuria prevalence was higher in African Americans, Hispanics, Asians, and American Indians/Alaska Natives than in non-Hispanic whites, suggesting a need for screening for early detection of kidney damage, especially in people at increased risk, in the community primary care setting.
Forecasting the Inland Empire's Economic Recovery

The Inland Empire -Riverside and San Bernardino Counties was one of the hardest hit areas in all of the United States during the Great Recession. Home prices have declined over 50%, significantly more than the 25% decline in the surrounding Los Angeles County, and housing starts have declined to over 90% from 2005. The Inland Empire has one of the highest unemployment rates in the US at 14.8%. This paper attempts to forecast the recovery for the Inland Empire. Employing univariate forecasts along with VAR(12) forecasts, focusing on housing starts and unemployment rates as the underlying variables, we find that there is little hope for a recovery over the next 3 years. The model predicts unemployment to either rise even more or, at best, remain stagnant. Housing starts are predicted to remain constant over the next three years.
Guidelines for the use of platelet transfusions

These guidelines are based on many years experience in the use of platelet transfusions by the authors, on searching the literature in MedLine and other databases using appropriate keywords, and a review of the existing guidelines published by expert groups [British Committee for Standards in Haematology (BCSH), 1992; Schiffer et al, 2001], including a previous guideline on platelet transfusions (BCSH, 1992) and the recommendations of Consensus Conferences (National Institutes of Health Consensus Conference, 1987; Consensus Conference on Platelet Transfusion, 1998). The section on indications for platelet transfusions was drafted in collaboration with the Haemato-Oncology Task Force of the BCSH. The authors were selected to encompass a wide range of interests in platelet transfusion therapy, including clinical and laboratory haematology, paediatrics, surgery, anaesthetics and critical care, and nursing.
Framing, aligning, paradoxing, abstracting, and directing: how design mood boards work

This paper builds upon the earlier work of Gaver on design workbooks by taking another design method and making a case for using it in HCI and interaction design. In this paper I discuss design mood boards, which consist of a collection of visually stimulating images and related materials. I present the results of an empirical study of how experienced designers from different disciplines (i.e., fashion, textile and industrial design) use mood boards as part of their work. The results suggest that mood boards can play five main roles in the early stages of the design process: framing, aligning, paradoxing, abstracting, and directing. I also reflect on design practice by providing concrete examples of mood boards and the resulting prototypes for an interaction design project. These examples are used to ground the discussion on the five roles found in the study.
Comparison of Neural Networks and Gravity Models in Trip Distribution

Abstract: Transportation engineers are commonly faced with the question of how to extract information from expensive and scarce field data. Modeling the distribution of trips between zones is complex and dependent on the quality and availability of field data. This research explores the performance of neural networks in trip distribution modeling and compares the results with commonly used doubly constrained gravity models. The approach differs from other research in several respects; the study is based on both synthetic data, varying in complexity, as well as real‐world data. Furthermore, neural networks and gravity models are calibrated using different percentages of hold out data. Extensive statistical analyses are conducted to obtain necessary sample sizes for significant results. The results show that neural networks outperform gravity models when data are scarce in both synthesized as well as real‐world cases. Sample size for statistically significant results is forty times lower for neural networks.
Learning outside the classroom

Learning Outside the Classroom outlines theory and practice that will enable and encourage teachers to systematically and progressively incorporate meaningful outdoor learning opportunities into their daily teaching activities in a wide variety of environments and with diverse populations of pupils. This is the first textbook based around the curriculum for prospective and practising primary and secondary teachers and other outdoor educators. The principles and examples presented are intended to be adapted by teachers to suit the needs of their students in ways that draw upon content offered by the local landscape and its natural and built heritage. Although the focus of this book is ‘the real world’ beyond the classroom, it is also about good teaching — wherever it takes place. While there are chapters on practical issues such as risk-management and supervising groups outdoors, the chapters on curriculum, sustainability, curiosity, responsibility, and educational communities will serve as a valuable guide for anyone interested in applying educational theory to practice.
Converging New Public Management Reforms and Diverging Accounting Practices in Flemish Local Governments

This paper aims at presenting a comparative study of the diverging development of accounting reforms in Flemish local governments in terms of accounting from a technical point of view. On the one hand, the objectives and the framework of current governmental accounting reforms aiming at improving New Public Management are currently converging. On the other hand, a conceptual examination reveals that the prescribed accounting practices are widely diverging and apparently this is also the case for the practical implementation of the reformed accounting systems. By way of conclusion, a number of possible reasons for this unsuccessful proliferation are presented.
Tacit Collusion with Quantity‐Matching Punishments in a Homogeneous Market

Tacit collusion with quantity‐matching punishments in a homogeneous market is investigated. The findings are the following: (i) tacit collusion can arise with quantity‐matching punishments in a homogeneous market, (ii) the monopoly quantity cannot be supported by quantity‐matching punishment strategy, and (iii) compared with Nash‐reversion punishments, collusion is no easier to sustain, but not necessarily harder. The results are compared with those in Lu and Wright (International Journal of Industrial Organization, Vol. 28 (2010), pp. 298–306) with price‐matching punishments. A linear demand example is provided to illustrate the theory.