Miracle-dz/UniFAR-FTUs huggingface dataset:
No 286 Conformity and the demand for environmental goods by

We test the hypothesis that people conform to certain social norms, i.e. that some individuals may be willing to pay a higher price premium for green products the more widespread green consumerism is in society. To investigate consumer preferences for environmentally friendly products, we conducted a choice experiment where the respondents were asked to choose among coffee products varying with respect to their share of ecological beans, share of fair trade beans, and price. Three treatments were used, differing only in the information given about the choices made by other consumers. More specifically, the respondents in the three subgroups were told that 10%, 50%, and 90% of all other consumers chose the alternative with 100% ecological beans. We find different responses to the treatments across individuals. In particular, we can only confirm our hypothesis of conformity for women, although men appear to have stronger preferences for ecological coffee than women. JEL Classification: C90; D12.
Neural excitability, Spiking and bursting

Bifurcation mechanisms involved in the generation of action potentials (spikes) by neurons are reviewed here. We show how the type of bifurcation determines the neuro-computational properties of the cells. For example, when the rest state is near a saddle-node bifurcation, the cell can fire all-or-none spikes with an arbitrary low frequency, it has a well-defined threshold manifold, and it acts as an integrator; i.e. the higher the frequency of incoming pulses, the sooner it fires. In contrast, when the rest state is near an Andronov–Hopf bifurcation, the cell fires in a certain frequency range, its spikes are not all-or-none, it does not have a well-defined threshold manifold, it can fire in response to an inhibitory pulse, and it acts as a resonator; i.e. it responds preferentially to a certain (resonant) frequency of the input. Increasing the input frequency may actually delay or terminate its firing. We also describe the phenomenon of neural bursting, and we use geometric bifurcation theory to extend the existing classification of bursters, including many new types. We discuss how the type of burster defines its neuro-computational properties, and we show that different bursters can interact, synchronize and process information differently.
Therapeutic optimism in the consent forms of phase 1 gene transfer trials: an empirical analysis

Background: “Therapeutic misconception” arises when human subjects interpret a clinical trial as aimed primarily at therapy rather than producing knowledge. Therapeutic misconceptions may be more prevalent in trials enrolling gravely ill subjects or involving novel and well publicised investigational agents. Objective: To examine the extent to which investigators express therapeutic optimism in phase 1 human gene transfer consent documents, whether highly active gene transfer researchers are more prone to expressing therapeutic optimism, and whether consent forms have grown more optimistic in their descriptions of personal benefit over the last decade. Design: Content analysis was performed on 277 consent documents to measure the number of sentences describing possibility of benefit, terminology used for experimental agents, the proportion of statements describing personal versus societal benefits, and whether investigators attempted to thwart therapeutic misconceptions. Results: Consent forms generally used therapeutic terminology to describe study agents, devoted more sentences to describing possible personal benefits than to describing benefits to society, and infrequently explained that a particular benefit was unlikely. Consent documents used by highly active gene transfer researchers tended to portray significantly greater optimism about personal benefit than less active investigators, though they were also significantly more cautious with agent terminology. Finally, therapeutic optimism expressed in consent forms has declined over the past decade. Conclusions: Consent documents used in phase 1 gene transfer trials, although increasingly attentive to possible therapeutic misconceptions, are inappropriately optimistic about direct benefits of trial participation. Such optimism is expressed more emphatically in trials involving highly active gene transfer researchers as principal investigators.
IMECE 2006-15963 Thermal Analysis for Cryosurgery of Nodular Basal Cell Carcinoma

* Corresponding author: gwang@uakron.edu ABSTRACT Basel cell carcinoma (BCC) is the most common human skin malignancy. Its incidence has increased signif icantly in Australia, Europe and North America over the past d eca e. A number of modalities are currently used for treatme nt of BCC, including cryosurgery which offers a potential for high cure rate, low cost, minimal bleeding and good cosmetic ffect. However, cryosurgery is not used frequently for BCC because no current method exists to design adequate treatme nt parameters. We present a numerical analysis on the thermal history of the target tissue during cryosurgery of a nodular BCC using liquid nitrogen (LN2) spray. The model uses Pennes equation to describe the heat transfer within the t arget tissue. A convective thermal boundary is used to describe the heat interaction between the tissue and LN 2, and the apparent heat capacity method is applied to address the tissue ph ase change process. A parametric study is conducted on the con vective heat transfer coefficient ( hs: 10 ∼10 W/m·K), cooling site area (rs/R0: 0.5∼1.0) and spray time ( t: 0∼30 sec.), with the objective to understand the thermal history during tissue fre ezing, including lethal temperature (-50 C) and cooling rate (CR). Results demonstrate that propagation of the lethal isotherm is sensitive to the convective heat transfer coefficie nt, hs, with a range of 10∼5×10 W/m·K. Increasing the cooling site area can significantly enhance cooling efficiency, produ cing dramatic increase in the amount of tissue encompass ed by the lethal isotherm. The cooling rate (CR) shows a high ly dynamic distribution during the cooling process: the highes t CR drops quickly from 140 C/sec. ( t=0.5 sec.) to 20 C/sec. ( t=5 sec.). The highest CR is initially located close to the co oling site but moves toward the inside of the tissue as treatment proceeds. The model presented herein provides a simulation to l for treatment planning of cryosurgery using LN 2 spray, in which the protocol parameters, e.g. cooling site area and spr ay time, can be determined for an optimal outcome. The quantitat ive predictions on the propagation of lethal isotherm a nd the distribution of CR should help to optimize cryosurg ery efficacy.
The social embedding of intelligence: towards producing a machine that could pass the Turing Test

I claim that to pass the Turing Test over any period of extended time, it will be necessary to embed the entity into society. This chapter discusses why this is, and how it might be brought about. I start by arguing that intelligence is better characterized by tests of social interaction, especially in open-ended and extended situations. I then argue that learning is an essential component of intelligence and hence that a universal intelligence is impossible. These two arguments support the relevance of the Turing Test as a particular, but appropriate test of interactive intelligence. I look to the human case to argue that individual intelligence uses society to a considerable extent for its development. Taking a lead from the human case, I outline how a socially embedded Artificial Intelligence might be brought about in terms of four aspects: free will, emotion, empathy, and self-modeling. In each case, I try to specify what social ‘hooks’ might be required for the full ability to develop during a considerable period of in situ acculturation. The chapter ends by speculating what it might be like to live with the result.
Gesture imitation using machine learning techniques

This study is a part of an ongoing project which aims to assist in teaching Sign Language (SL) to hearing-impaired children by means of non-verbal communication and imitation-based interaction games between a humanoid robot and a child. In this paper, the problem is geared towards a robot learning to imitate basic upper torso gestures (SL signs) using different machine learning techniques. RGBD sensor (Microsoft Kinect) is employed to track the skeletal model of humans and create a training set. A novel method called Decision Based Rule is proposed. Additionally, linear regression models are compared to find which learning technique has a higher accuracy on gesture prediction. The learning technique with the highest accuracy is then used to simulate an imitation system where the Nao robot imitates these learned gestures as observed by the users.
Comparison of Responses to Double-Strand Breaks between Escherichia coli and Bacillus subtilis Reveals Different Requirements for SOS Induction

ABSTRACT DNA double-strand breaks are particularly deleterious lesions that can lead to genomic instability and cell death. We investigated the SOS response to double-strand breaks in both Escherichia coli and Bacillus subtilis. In E. coli, double-strand breaks induced by ionizing radiation resulted in SOS induction in virtually every cell. E. coli strains incapable of SOS induction were sensitive to ionizing radiation. In striking contrast, we found that in B. subtilis both ionizing radiation and a site-specific double-strand break causes induction of prophage PBSX and SOS gene expression in only a small subpopulation of cells. These results show that double-strand breaks provoke global SOS induction in E. coli but not in B. subtilis. Remarkably, RecA-GFP focus formation was nearly identical following ionizing radiation challenge in both E. coli and B. subtilis, demonstrating that formation of RecA-GFP foci occurs in response to double-strand breaks but does not require or result in SOS induction in B. subtilis. Furthermore, we found that B. subtilis cells incapable of inducing SOS had near wild-type levels of survival in response to ionizing radiation. Moreover, B. subtilis RecN contributes to maintaining low levels of SOS induction during double-strand break repair. Thus, we found that the contribution of SOS induction to double-strand break repair differs substantially between E. coli and B. subtilis.
What Does Willingness-to-Pay Reveal About Hospital Market Power in Merger Cases?

In hospital merger cases, the courts have often based geographic market areas on patient flow criteria. Given patient heterogeneity and the importance of distance to hospitals and health plan restrictions on hospital choices, Capps, Dranove, and Satterthwaite (2003) show that potential market power effects can be understated. While willingness-to-pay (WTP) measures derived from logit demand models provide an alternative approach, for antitrust purposes it is necessary to show how WTP relates to the likely price effects of mergers. This paper examines the connection between health plan prices and WTP that results from bargaining between managed care plans and hospitals. We study two merger cases in Florida and New York State to evaluate the reliability of this measure. Employing data available before a merger has occurred, we find that this method can provide reliable predictions of patients' post-merger willingness-to-pay that imply conservative predictions of post-merger prices.
National Culture And Intellectual Capital: Comparative Analysis Romania - Slovenia

The purpose of this scientific endeavour is to identify the possible relationships between the dimensions of national culture and intellectual capital. As such, we have directed our research towards making an analysis at the level of EU countries, on six dimensions of the national culture based on Geert Hofstede’s approach, as well as towards the analysis of the performance of the three dimensions of intellectual capital, namely: structural, human and relational capital. We also aimed at conducting a comparative analysis of the national culture and intellectual capital between Romania and Slovenia. To meet the purpose of the paper we used the following research techniques: investigating secondary data, content analysis, as well as statistical methods for data analysis and interpretation of results. For our research resources we also considered chief models and methodologies of national culture and intellectual capital, publications and reports of international organisations. The main results of the research highlight the influence of national culture on intellectual capital. We demonstrated that some dimensions of national culture, such as Individualism versus collectivism and Indulgence versus restraint positively influence intellectual capital, while other dimensions, namely Distance versus power and Uncertainty avoidance negatively influence intellectual capital.
Blind multichannel identification via frequency selection

In a recent study on blind multichannel identification, we presented a fast Fourier transform based algorithm, called the BI-FFT algorithm. The BI-FFT algorithm is capable of dealing with channel outputs of a very short length, which is of practical importance in communication applications where wireless channels involve high mobility. However, when the output sample size is relatively large, the BI-FFT algorithm becomes cumbersome and computationally costly. To address this issue, we develop a frequency selection approach in this paper. The proposed method strategically chooses those frequency bins that significantly contribute to the error dynamics in the channel estimation process. As a result, the row dimension of a key matrix in the identification equation is considerably reduced, effectively decreasing the computational cost. With a comparable performance, the new method is much more efficient in computation than the original BI-FFT algorithm in case of long data sequences.
Genetic algorithms in software and in hardware-a performance analysis of workstation and custom computing machine implementations

The paper analyzes the performance differences found between the hardware and software versions of a genetic algorithm used to solve the travelling salesman problem. The hardware implementation requires 4 FPGA's on a Splash 2 board and runs at 11 MHz. The software implementation was written in C++ and executed on a 125 MHz HP PA-RISC workstation. The software run time was more than four times that of the hardware (up to 50 times as many cycles). The paper analyses the contribution made to this performance difference by the following hardware features: hard-wired control, custom address generation logic, memory hierarchy efficiency, and both fine- and course-grained parallelism. The results indicate that the major contributor to the hardware performance advantage is fine-grained parallelism-RTL-level parallelism due to operator pipelining. This alone accounts for as much as a 38X cycle-count reduction over the software in one section of the algorithm. The next major contributors include hard-wired control and custom address generation which account for as much as a 3X speedup in other sections of the algorithm. Finally, memory hierarchy inefficiencies in the software (cache misses and paging) and coarse-grained parallelism in the hardware are each shown to have lesser effect on the performance difference between the implementations.
Gene Expression Network Analysis of ETV1 Reveals KCTD10 as a Novel Prognostic Biomarker in Gastrointestinal Stromal Tumor

Background Prognostic biomarkers are required for risk stratification therapy in the patients with gastrointestinal stromal tumor (GIST). In this study, we aimed to identify prognostic biomarkers in GIST. We assessed the prognostic value of E twenty-six variant 1 (ETV1), a recently identified transcription factor unique to GIST. We also examined the clinical utility and functions of its downstream gene, potassium channel tetramerization domain containing protein 10 (KCTD10). Methods The levels of ETV1 and KCTD10 were evaluated immunohistochemically in 112 patients with GIST treated at two hospitals. The functional properties of KCTD10 were examined by gene silencing assay in cultured GIST cells. Results Immunohistochemistry revealed that ETV1 expression in GIST had no prognostic significance. In contrast, the disease-free survival rate was 88.5% in patients with KCTD10-positive tumors and 55.8% in those with KCTD10-negative tumors (p <0.0001). KCTD10 was an independent prognostic factor (p <0.05). In the low-risk classification group, KCTD10 was significantly associated with favorable prognosis (p = 0.0008). Gene silencing of KCTD10 increased cell proliferation and invasion, suggesting that KCTD10 has a tumor-suppressive function. Conclusions The GIST-specific transcription factor ETV1 may have no prognostic potential, whereas its downstream gene KCTD10 is associated with a favorable prognosis. Our study indicated the novel prognostic utility of KCTD10 in GIST, and suggested its tumor-suppressive effects on GIST cells. Further validation studies of KCTD10 for clinical applications, and functional verification of KCTD10 for better understanding of molecular basis of malignant phenotypes are worth challenging in GIST.
Joint Delay and Doppler Estimation for Passive Sensing With Direct-Path Interference

We consider the problem of joint delay-Doppler estimation of a moving target in a passive radar that employs a non-cooperative illuminator of opportunity (IO) for target illumination, a reference channel (RC) steered to the IO to obtain a reference signal, and a surveillance channel (SC) for target monitoring. We consider a practically motivated scenario, where the RC receives a noise-contaminated copy of the IO signal and the SC observation is polluted by a direct-path interference that is usually neglected by prior studies. We develop a data model without discretizing the parameter space, which may lead to a straddle loss, by treating both the delay and Doppler as continuous parameters. We propose an expectation-maximization based estimator, as well as a modified cross-correlation (MCC) estimator that is a computationally simpler solution resulting from an approximation of the former. In addition, we derive the Cramér-Rao lower bound for the estimation problem. Simulation results are presented to illustrate the performance of the proposed estimators and the widely used CC estimator.
On a Multiobjective Training Algorithm for RBF Networks Using Particle Swarm Optimization

This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machine complexity. The training is done in three steps: i) a conventional minimization of the training error, ii) multiobjective least-squares optimization for the linear parameters and, iii) particle swarm optimization for the nonlinear parameters. Some results are presented and they show the effectiveness of the proposed approach.
The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments

Propensity score methods are increasingly being used to estimate causal treatment effects in observational studies. In medical and epidemiological studies, outcomes are frequently time‐to‐event in nature. Propensity‐score methods are often applied incorrectly when estimating the effect of treatment on time‐to‐event outcomes. This article describes how two different propensity score methods (matching and inverse probability of treatment weighting) can be used to estimate the measures of effect that are frequently reported in randomized controlled trials: (i) marginal survival curves, which describe survival in the population if all subjects were treated or if all subjects were untreated; and (ii) marginal hazard ratios. The use of these propensity score methods allows one to replicate the measures of effect that are commonly reported in randomized controlled trials with time‐to‐event outcomes: both absolute and relative reductions in the probability of an event occurring can be determined. We also provide guidance on variable selection for the propensity score model, highlight methods for assessing the balance of baseline covariates between treated and untreated subjects, and describe the implementation of a sensitivity analysis to assess the effect of unmeasured confounding variables on the estimated treatment effect when outcomes are time‐to‐event in nature. The methods in the paper are illustrated by estimating the effect of discharge statin prescribing on the risk of death in a sample of patients hospitalized with acute myocardial infarction. In this tutorial article, we describe and illustrate all the steps necessary to conduct a comprehensive analysis of the effect of treatment on time‐to‐event outcomes. © 2013 The authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
SNPer: An R Library for Quantitative Variant Analysis on Single Nucleotide Polymorphisms among Influenza Virus Populations

Influenza virus (IFV) can evolve rapidly leading to genetic drifts and shifts resulting in human and animal influenza epidemics and pandemics. The genetic shift that gave rise to the 2009 influenza A/H1N1 pandemic originated from a triple gene reassortment of avian, swine and human IFVs. More minor genetic alterations in genetic drift can lead to influenza drug resistance such as the H274Y mutation associated with oseltamivir resistance. Hence, a rapid tool to detect IFV mutations and the potential emergence of new virulent strains can better prepare us for seasonal influenza outbreaks as well as potential pandemics. Furthermore, identification of specific mutations by closely examining single nucleotide polymorphisms (SNPs) in IFV sequences is essential to classify potential genetic markers associated with potentially dangerous IFV phenotypes. In this study, we developed a novel R library called “SNPer” to analyze quantitative variants in SNPs among IFV subpopulations. The computational SNPer program was applied to three different subpopulations of published IFV genomic information. SNPer queried SNPs data and grouped the SNPs into (1) universal SNPs, (2) likely common SNPs, and (3) unique SNPs. SNPer outperformed manual visualization in terms of time and labor. SNPer took only three seconds with no errors in SNP comparison events compared with 40 hours with errors using manual visualization. The SNPer tool can accelerate the capacity to capture new and potentially dangerous IFV strains to mitigate future influenza outbreaks.
Exposing Strategic Assets to Create New Competencies: The Case of Technological Acquisition in the Waste Management Industry in Europe and North America

This paper presents a model that complements the research stream of transaction cost economies with the dynamic capabilities approach. The paper shows that, even though technological alliances involving specific assets deployed in emerging industries are exposed to high transaction costs, they possess attributes that make them attractive. First, they facilitate the creation of tacit competencies, and second, they reduce the uncertainty arising from technological innovation and regulatory changes. The model is empirically tested in the hazardous waste management industry by using primary data collected through the use of questionnaires. The method links governance structure choices to managers' perceptions of the uncertainty surrounding the acquisition of technology. Copyright 1999 by Oxford University Press.