Miracle-dz/UniFAR-FTUs huggingface dataset:
Pitfalls of Counterfactual Thinking in Medical Practice: Preventing Errors by Using More Functional Reference Points

Background Counterfactual thinking involves mentally simulating alternatives to reality. The current article reviews literature pertaining to the relevance counterfactual thinking has for the quality of medical decision making. Although earlier counterfactual thought research concluded that counterfactuals have important benefits for the individual, there are reasons to believe that counterfactual thinking is also associated with dysfunctional consequences. Of particular focus is whether or not medical experience, and its influence on counterfactual thinking, actually informs or improves medical practice. It is hypothesized that relatively more probable decision alternatives, followed by undesirable outcomes and counterfactual thought responses, can be abandoned for relatively less probable decision alternatives. Design and Methods Building on earlier research demonstrating that counterfactual thinking can impede memory and learning in a decision paradigm with undergraduate students, the current study examines the extent to which earlier findings can be generalized to practicing physicians (N=10). Participants were asked to complete 60 trials of a computerized Monty Hall Problem simulation. Learning by experience was operationalized as the frequency of switch-decisions. Results Although some learning was evidenced by a general increase in switch-decision frequency across block trials, the extent of learning demonstrated was not ideal, nor practical. Conclusions A simple, multiple-trial, decision paradigm demonstrated that doctors fail to learn basic decision-outcome associations through experience. An agenda for future research, which tests the functionality of reference points (other than counterfactual alternatives) for the purposes of medical decision making, is proposed. Significance for public health The quality of healthcare depends heavily on the judgments and decisions made by doctors and other medical professionals. Findings from this research indicate that doctors fail to learn basic decision-outcome associations through experience, as evidenced by the sample’s tendency to select the optimal decision strategy in only 50% of 60 trials (each of which was followed by veridical feedback). These findings suggest that professional experience is unlikely to enhance the quality of medical decision making. Thus, this research has implications for understanding how doctors’ reactions to medical outcomes shape their judgments and affect the degree to which their future treatment intentions are consistent with clinical practice guidelines. The current research is integrated with earlier research on counter-factual thinking, which appears to be a primary element inhibiting the learning of decision-outcome associations. An agenda for future research is proposed.
An Evaluation Framework Based on Gold Standard Models for Definition Question Answering

This paper presents a weak supervised evaluation framework for definition question answering (DefQA) called Solon. It automatically evaluates a set of DefQA systems using existing human definitions as gold standard models. This allows the framework to overcome known limitations of the evaluation methods in the state of the art with the advantage that it is less supervised. In addition, Solon adapts its configuration for each specific DefQA task, thus rendering a good evaluation procedure. The results obtained in our experiments show that Solon is able to detect the best systems and to score them accordingly, with state of the art performance.
Virtual Communities and the Social Dimension of Privacy

While the issue of privacy and the internet has been a popular topic among academics across the disciplines for quite some time, this study explores the unique privacy concerns of virtual community participants. Employing a broad understanding of privacy, this study points to the need to focus on the expressive and social role of privacy in regard to virtual community participants. Furthermore, this study suggests that virtual communities offer members a unique opportunity to work on their self-identity via the degree of self-expression and social interaction that is available in these groups. This finding helps to explain why users consciously bring the "private" to the public realm, despite the inherent privacy risks they face in doing so. To investigate how virtual community participants perceive privacy and how this understanding helps explain their participation in a forum where privacy is presumably jeopardized, the article describes an interview-based study of virtual community participants.
VIDIHIP - A Web Service based Integration Platform for Power Plant Control Systems

European energy supply companies typically run a conglomerate of different, geographically distributed power plants. Unfortunately, the corresponding power plant control systems are based on proprietary technology and an integration of these control systems is a very complex task. We thus conducted a project to build an integration platform for power plant control systems that is based on open standards and technologies. In this paper, we describe the Vienna District Heating Integration Platform (VIDIHIP). VIDIHIP is based on Web service technology and allows for the integration of arbitrary (heterogeneous) power plant control systems. It provides a consistent interface to access different decentralized control systems and each standard Web browser can be used as a control front-end for VIDIHIP.
The Use of Qualitative Content Analysis in Case Study Research

This paper aims at exploring and discussing the possibilities of applying qualitative content analysis as a (text) interpretation method in case study research. First, case study research as a research strategy within qualitative social research is briefly presented. Then, a basic introduction to (qualitative) content analysis as an interpretation method for qualitative interviews and other data material is given. Finally the use of qualitative content analysis for developing case studies is examined and evaluated. The author argues in favor of both case study research as a research strategy and qualitative content analysis as a method of examination of data material and seeks to encourage the integration of qualitative content analysis into the data analysis in case study research.
A Systematic Method for Configuring VLSI Networks of Spiking Neurons

An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e.g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.
Improving the error drift of inertial navigation based indoor location tracking

Inertial sensing based indoor localization currently requires fairly precise layout maps, to help provide constraints and landmarks that bound the error drift. In this paper, we seek to improve the accuracy of one component of inertial-based tracking, namely the estimation of an individual's stride-length, so as to reduce the cumulative drift. We show that an individual's stride-length is affected by both his/her movement speed and heading-changes in the trajectory, and present an adaptive, online stride-length estimation algorithm that learns appropriate stride-length distributions for different (speed, heading) combinations. Initial experiments conducted using our proposed approach in combination with state-of-the-art step counting and heading estimation techniques, reduce the 95th percentile of average localization error by ≈ 30%. We thus envisage that inertial tracking may become practical even with coarse-grained map information.
Can we predict device use? An investigation into mobile device use in surveys

ABSTRACT In this study, we investigate whether mobile device use in surveys can be predicted. We aim to identify possible motives for device use and build a model by drawing on theory from technology acceptance research and survey research. We then test this model with a Structural Equation Modeling approach using data of seven waves of the GESIS panel. We test whether our theoretical model fits the data by focusing on measures of fit, and by studying the standardized effects of the model. Results reveal that intention to use a particular device can predict actual use quite well. Ease of smartphone use is the most meaningful variable: if people use a smartphone for specific tasks, their intention to use a smartphone for survey completion is also more likely. In conclusion, investing in ease of use of mobile survey completion could encourage respondents to use mobile devices. This can foremost be established by building well-designed surveys for mobile devices.
Adaptive Query Processing in Data Grids

The data grid integrates wide-area autonomous data sources and provides users with a unified data query and processing infrastructure. Adapt data query and processing is required by data grids to provide better quality of services (QoS) to users and applications in spite of dynamically changing resources and environments. Existing AQP techniques can only meet partially data grid requirements. Some existing work is either addressing domain-specific or single-node query processing problems. Data grids provide new mechanisms for monitoring and discovering data and resources in a cross-domain wide area. Data query in grids can benefit from these information and provide better adaptability to the dynamic nature of the grid environment. In this work, an adaptive controller is proposed that dynamically adjusts resource shares to multiple data query requests in order to meet a specified level of service differentiation. The controller parameters are automatically tuned at runtime based on a predefined cost function and an online learning method. Simulation results show that our controller can meet given QoS differentiation targets and adapt to dynamic system resources among multiple data query processing requests while total demand from users and applications exceeds system capability.
MEASURING SPILLOVERS FROM INFORMATION TECHNOLOGY INVESTMENTS Valuing IT Opportunities

Intra-industry spillovers from information technology investments have been cited as a potentially important driver of productivity growth. Using firm-level data to measure the sizes of these spillovers, however, can be challenging because of biases caused by 1) measurement error and 2) the difficulty in separating the effects of spillovers from the effects of shared technological opportunity. In this analysis, we employ two separate approaches to develop more accurate estimates of the contributions of information technology driven spillovers to economic output. First, we use an instrumental variables approach to correct biases caused by measurement errors in IT capital, and second, we use technological variation from establishment level data to create richer models of knowledge spillover pools that are less vulnerable to the econometric problems identified above. We report estimates from both of these approaches and compare them with estimates from methods relying on conventional firm-level models of spillover. Our results suggest that existing estimates of within-industry spillovers from information technology investments may be considerably overstated. The estimates produced by our different approaches are internally consistent, and suggest that the contribution of within-industry information technology spillovers may actually be smaller than previously reported.
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification

Text in natural images contains rich semantics that is often highly relevant to objects or scene. In this paper, we focus on the problem of fully exploiting scene text for visual understanding. The main idea is combining word representations and deep visual features in a globally trainable deep convolutional neural network. First, the recognized words are obtained by a scene text reading system. Next, we combine the word embedding of the recognized words and the deep visual features into a single representation that is optimized by a convolutional neural network for fine-grained image classification. In our framework, the attention mechanism is adopted to compute the relevance between each recognized word and the given image, which further enhances the recognition performance. We have performed experiments on two datasets: con-text dataset and drink bottle dataset, which are proposed for fine-grained classification of business places and drink bottles, respectively. The experimental results consistently demonstrate that the proposed method of combining textual and visual cues significantly outperforms classification with only visual representation. Moreover, we have shown that the learned representation improves the retrieval performance on the drink bottle images by a large margin, making it potentially powerful in product search.
Development of supermedia interface for telediagnostics of breast pathology

A robotic device with haptic, tactile, and ultrasound capabilities, which can acquire and render information of breast pathology was developed. A physician interface that can examine the human breast remotely and accurately, using such a robotic device was also developed. Such a robotic device can be used to do screening or focused breast exams for patients in remote areas without convenient access to physicians. Because of in-situ ultrasound imaging, examination by the robotic device may prove to be more accurate than examination by the physicians own hand. In addition, the robotic device can also be used to train healthcare professionals in breast pathology
BRADO: scalable streaming through reconfigurable trees

Scalable live streaming delivery to large groups has been a long standing research challenge for the last fifteen years. Peer-to-peer (P2P) has emerged as the leading candidate since its capacity grow with the number of participating users [1]. The scalability of P2P schemes is quantified in two main ways. One is overhead scalability : the growth rate of forwarding tables, control messages and data relayed with respect to the number of users. The second is performance scalability : the throughput and delay achievable for arbitrary group size. In practice, these two types of scalability could be at odds, especially in a dynamic environment. On one extreme, completely unstructured peer-to-peer (P2P) systems have low overhead, but the dynamics of the system can lead to long delays or transient connectivity disruptions as the number of peers grows [2]. On the other, meshstructured networks offer low delays, but they could have high maintenance overhead in the face of user dynamics [3]. In this paper, we propose to build multiple configurable trees approach to balance between overhead scalability and performance scalability in a dynamic environment. In a a live video streaming application, there are three types of system dynamics. At the longest timescale, users join or leave the P2P service. At the medium timescale, users change what they are watching while staying in the system (which we will refer to as content switching in this paper). Finally, at the timescale of round-trip times (RTTs), there are link load dynamics such as queuing delay and packet losses. Many proposed architectures construct delivery trees either over hierarchical structures or unstructured meshes at the timescale of content switching ([4] and references therein).
PUG-SOAP and PUG-REST: web services for programmatic access to chemical information in PubChem

PubChem (http://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, developed and maintained by the US National Institutes of Health (NIH). PubChem contains more than 180 million depositor-provided chemical substance descriptions, 60 million unique chemical structures and 225 million bioactivity assay results, covering more than 9000 unique protein target sequences. As an information resource for the chemical biology research community, it routinely receives more than 1 million requests per day from an estimated more than 1 million unique users per month. Programmatic access to this vast amount of data is provided by several different systems, including the US National Center for Biotechnology Information (NCBI)'s Entrez Utilities (E-Utilities or E-Utils) and the PubChem Power User Gateway (PUG)—a common gateway interface (CGI) that exchanges data through eXtended Markup Language (XML). Further simplifying programmatic access, PubChem provides two additional general purpose web services: PUG-SOAP, which uses the simple object access protocol (SOAP) and PUG-REST, which is a Representational State Transfer (REST)-style interface. These interfaces can be harnessed in combination to access the data contained in PubChem, which is integrated with the more than thirty databases available within the NCBI Entrez system.
THE EVOLUTION OF URBAN INEQUALITY IN ETHIOPIA

In Ethiopia increasing urbanization has been accompanied by growth but not by poverty reduction. This disappointing performance in terms of poverty reduction has been accompanied by a surge in inequality, with the Gini Coefficient increasing by 10 percentage points in urban areas from 0.34 in 1995 to 0.44 in 2004. This paper seeks to shed light on the determinants of this large increase in inequality. We exploit the availability of two comparable household surveys to decompose changes in inequality based on a set of counterfactual scenarios. Our microsimulation approach builds on the methodology discussed in Bourguignon, Ferreira and Lustig (1998) and applied in Bourguignon, Ferreira and Leite (2007). Our main finding is that the increase in inequality was driven mostly by changes in returns to household characteristics, and especially by changes in the returns to education. Skills premia increased significantly while average educational levels also increased. We also find that accounting for changes in the distribution of households across urban areas significantly improves our ability to predict not only overall changes in inequality, but particularly the differential growth experienced by the top deciles. In addition, it is worth noting that the new profile of urban households, and in particular the greater likelihood of being households with younger heads, well educated, living alone or in couples with no children also contributed to increased inequality, as these types of households experienced higher growth rates in consumption per capita.
Factors in risk perception.

Risk perception is a phenomenon in search of an explanation. Several approaches are discussed in this paper. Technical risk estimates are sometimes a potent factor in accounting for perceived risk, but in many important applications it is not. Heuristics and biases, mainly availability, account for only a minor portion of risk perception, and media contents have not been clearly implicated in risk perception. The psychometric model is probably the leading contender in the field, but its explanatory value is only around 20% of the variance of raw data. Adding a factor of “unnatural risk” considerably improves the psychometric model. Cultural Theory, on the other hand, has not been able to explain more than 5–10% of the variance of perceived risk, and other value scales have similarly failed. A model is proposed in which attitude, risk sensitivity, and specific fear are used as explanatory variables; this model seems to explain well over 30–40% of the variance and is thus more promising than previous approaches. The model offers a different type of psychological explanation of risk perception, and it has many implications, e.g., a different approach to the relationship between attitude and perceived risk, as compared with the usual cognitive analysis of attitude.
The Structural Basis of Ligand Recognition by Natural Killer Cell Receptors

Natural killer cells are a group of lymphocytes which function as tightly controlled surveillance operatives which identify transformed cells through a discrete balance of activating and inhibitory receptors ultimately leading to the destruction of incongruent cells. The understanding of this finely tuned balancing act has been aided by the high-resolution structure determination of activating and inhibitory receptors both alone and in complex with their ligands. This paper collates these structural studies detailing the aspects which directly relate to the natural killer cell function and serves to inform both the specialized structural biologist reader and a more general immunology audience.
Relational Resilience: Intimate and Romantic Relationship Experiences of Women with Serious Mental Illness - Corrigendum

The present qualitative study was conducted with 20 women with serious mental illness (SMI) in order to better characterise their romantic and intimate relationship experiences. Grounded theory methodology directed the identification of intimate and romantic relationships themes for women with SMI, which included the following: function matching, pathologising problems, symptom interference, dating deal breakers, sexual foreclosure, dating deprioritised, and relational resilience strategies. Clinical implications and future research directions are discussed.
Rendering Interventionism and Non-Reductive Physicalism Compatible

In recent years, the debate on the problem of causal exclusion has seen an ‘interventionist turn’. Numerous non-reductive physicalists (e.g. Shapiro and Sober 2007) have argued that Woodward's (2003) interventionist theory of causation provides a means to empirically establish the existence of non-reducible mental-to-physical causation. By contrast, Baumgartner (2010) has presented an interventionist exclusion argument showing that interventionism is in fact incompatible with non-reductive physicalism. In response, a number of revised versions of interventionism have been suggested that are compatible with non-reductive physicalism. The first part of this paper reconstructs the definitional details of these modified interventionist theories. The second part investigates whether the modification proposed in Woodward (2011) is not only compatible with, but moreover supports non-reductive physicalism. In particular, it is examined whether that newest variant of interventionism allows for empirically resolving the problem of causal exclusion as envisaged by Shapiro, Sober and others.