LLukas22/scidocs huggingface dataset:
Parametric frailty and shared frailty survival models

Frailty models are the survival data analog to regression models, which account for heterogeneity and random effects. A frailty is a latent multiplicative effect on the hazard function and is assumed to have unit mean and variance θ, which is estimated along with the other model parameters. A frailty model is an heterogeneity model where the frailties are assumed to be individualor spell-specific. A shared frailty model is a random effects model where the frailties are common (or shared) among groups of individuals or spells and are randomly distributed across groups. Parametric frailty models were made available in Stata with the release of Stata 7, while parametric shared frailty models were made available in a recent series of updates. This article serves as a primer to those fitting parametric frailty models in Stata via the streg command. Frailty models are compared to shared frailty models, and both are shown to be equivalent in certain situations. The user-specified form of the distribution of the frailties (whether gamma or inverse Gaussian) is shown to subtly affect the interpretation of the results. Methods for obtaining predictions that are either conditional or unconditional on the frailty are discussed. An example that analyzes the time to recurrence of infection after catheter insertion in kidney patients is studied.
A Randomized Controlled Trial for the Effectiveness of Aromatherapy in Decreasing Salivary Gland Damage following Radioactive Iodine Therapy for Differentiated Thyroid Cancer

Objective. The aim of this study was to investigate effects of aromatherapy in decreasing salivary gland damage for patients undergoing radioactive iodine (RAI) therapy with differentiated thyroid cancer (DTC). Materials and Methods. The subjects were 71 patients with DTC. They were divided into aromatherapy group (group A, n = 35) and a control group (group B, n = 36). We blended 1.0 mL of lemon and 0.5 mL of ginger essential oils. The patients in the inhalation aromatherapy group inhaled this blend oil and those in the control group inhaled distilled water as placebo for 10 min during admission. We statistically compared salivary gland function before and after treatment between groups A and B. Results. In comparison with group B, the rate of change of the accumulation rate was significantly higher in the parotid glands and submandibular glands of group A (P < 0.05). In comparison with group B, a significant increase in rate of secretion change before and after treatment was noted in the bilateral parotid glands in group A (P < 0.05). Conclusion. Because an amelioration of salivary gland function was observed in the present study, our results suggest the efficacy of aromatherapy in the prevention of treatment-related salivary gland disorder. This trial is registered with UMIN Clinical Trial Registry: UMIN000013968.
Effectiveness of two intensive treatment methods for smoking cessation and relapse prevention in patients with coronary heart disease: study protocol and baseline description

BackgroundThere is no more effective intervention for secondary prevention of coronary heart disease than smoking cessation. Yet, evidence about the (cost-)effectiveness of smoking cessation treatment methods for cardiac inpatients that also suit nursing practice is scarce. This protocol describes the design of a study on the (cost-)effectiveness of two intensive smoking cessation interventions for hospitalised cardiac patients as well as first results on the inclusion rates and the characteristics of the study population.Methods/designAn experimental study design is used in eight cardiac wards of hospitals throughout the Netherlands to assess the (cost-)effectiveness of two intensive smoking cessation counselling methods both combined with nicotine replacement therapy. Randomization is conducted at the ward level (cross-over). Baseline and follow-up measurements after six and 12 months are obtained. Upon admission to the cardiac ward, nurses assess patients’ smoking behaviour, ensure a quit advice and subsequently refer patients for either telephone counselling or face-to-face counselling. The counselling interventions have a comparable structure and content but differ in provider and delivery method, and in duration. Both counselling interventions are compared with a control group receiving no additional treatment beyond the usual care. Between December 2009 and June 2011, 245 cardiac patients who smoked prior to hospitalisation were included in the usual care group, 223 in the telephone counselling group and 157 in the face-to-face counselling group. Patients are predominantly male and have a mean age of 57 years. Acute coronary syndrome is the most frequently reported admission diagnosis. The ultimate goal of the study is to assess the effects of the interventions on smoking abstinence and their cost-effectiveness. Telephone counselling is expected to be more (cost-)effective in highly motivated patients and patients with high SES, whereas face-to-face counselling is expected to be more (cost-)effective in less motivated patients and patients with low SES.DiscussionThis study examines two intensive smoking cessation interventions for cardiac patients using a multi-centre trial with eight cardiac wards. Although not all eligible patients could be included and the distribution of patients is skewed in the different groups, the results will be able to provide valuable insight into effects and costs of counselling interventions varying in delivery mode and intensity, also concerning subgroups.Trial registrationDutch Trial Register NTR2144
Edge-Coloring Partial k-Trees

Ž Many combinatorial problems can be efficiently solved for partial k-trees graphs . of treewidth bounded by k . The edge-coloring problem is one of the well-known combinatorial problems for which no efficient algorithms were previously known, except a polynomial-time algorithm of very high complexity. This paper gives a linear-time sequential algorithm and an optimal parallel algorithm which find an edge-coloring of a given partial k-tree with the minimum number of colors for fixed k. Q 1996 Academic Press, Inc.
Election manipulation: the average case

We review recent research on quantitative versions of the Gibbard-Satterthwaite theorem, which analyze the average-case manipulability of elections. The main message of these results is that computational hardness cannot hide manipulations completely. We conclude with open problems.
A Concept Map Extractor Tool for Teaching and Learning

This work presents a tool to help concept maps’ design process. This tool was developed to organize contents from thematic modules of a course, applying concept maps techniques. In this tool, teachers can build their own concept maps based in textual reference documents. Teachers also can use a visual editor to design maps and linking learning objects to concepts. Students can access learning objects and make their own notes in their own concept maps improving teaching and learning process. The concept map extractor is an algorithm based in text mining techniques for term extraction. This algorithm extracts relevant terms that can be considered concepts or links, making agile the concept map building process.
Harnessing the wisdom of the inner crowd

Ever since Galton's classic demonstration of the wisdom of crowds in estimating the weight of a slaughtered ox, scholars of the mind and the public alike have been fascinated by the counterintuitive accuracy achieved by simply averaging a number of people's estimates. Surprisingly, individuals can, to some extent, harness the wisdom of crowds within the confines of their own mind by averaging self-generated, nonredundant estimates.
Experimental design and optimization

The aim with this tutorial is to give a simple and easily understandable introduction to experimental design and optimization. The screening methods described in the paper are factorial and fractional factorial designs. Identification of significant variables are performed by normal distribution plots as well as by confidence intervals. Refinements of the models are also discussed. For optimization, the simplex method, central composite designs and the Doehlert design are discussed. The paper also gives an introduction to mixture designs. The paper contains 14 hands-on examples and if anyone needs the answers on these it is just to contact the authors. q 1998 Elsevier Science B.V. All rights reserved.
The theory of planned behavior: a review of its applications to health-related behaviors.

PURPOSE
To review applications of Ajzen's theory of planned behavior in the domain of health and to verify the efficiency of the theory to explain and predict health-related behaviors.


METHODS
Most material has been drawn from Current Contents (Social and Behavioral Sciences and Clinical Medicine) from 1985 to date, together with all peer-reviewed articles cited in the publications thus identified.


FINDINGS
The results indicated that the theory performs very well for the explanation of intention; an averaged R2 of .41 was observed. Attitude toward the action and perceived behavioral control were most often the significant variables responsible for this explained variation in intention. The prediction of behavior yielded an averaged R2 of .34. Intention remained the most important predictor, but in half of the studies reviewed perceived behavioral control significantly added to the prediction.


CONCLUSIONS
The efficiency of the model seems to be quite good for explaining intention, perceived behavioral control being as important as attitude across health-related behavior categories. The efficiency of the theory, however, varies between health-related behavior categories.
Endovascular cooling and endothelial activation in hemorrhagic stroke patients.

BACKGROUND
Little is known about endothelial activation under the influence of endovascular temperature management. This analysis was designed to measure the endothelial markers Angiopoietin-1 (Ang-1) and -2 (Ang-2) in endovascularly based prophylactic normothermia versus conventional temperature management.


METHODS
In this randomized controlled trial patients with spontaneous subarachnoid or spontaneous intracerebral hemorrhage were prospectively enrolled and randomized in two treatment arms: (a) prophylactic normothermia group with target core temperature 36.5°C using endovascular cooling, (b) active control group with conventional stepwise predefined fever management using antipyretic medication and surface cooling. Blood samples were obtained on days 1, 4, and 7. In a substudy Ang-1 and -2 were measured in 63 patients for whom samples on consecutive days were available.


RESULTS
The median total fever burden during the course of treatment was 0.0°C and 5.9°C h in the endovascular and the conventional group, respectively (P < 0.0001). Angiopoietin serum levels did not yield a statistical difference when comparing the two treatment arms. Ang-1 was significantly lowered, whereas Ang-2 levels were significantly elevated on day 4 compared to baseline levels irrespective of group allocation (P < 0.0001). The application of non-steroidal anti-inflammatory drugs (NSAIDs) was associated with significantly increased Ang-1 (P < 0.05) and lower Ang-2 levels on day 7 (P < 0.05).


CONCLUSIONS
Endovascular long-term temperature management did not alter Ang-1 and -2 levels compared to the control group indicating that the endovascular cooling technique itself does not lead to additional endothelial impairment. However, application of NSAIDs led to lower Ang-2 serum concentrations in the endovascular group.
Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach

We propose a generalized framework for influence maximization in large-scale, time evolving networks. Many real-life influence graphs such as social networks, telephone networks, and IP traffic data exhibit dynamic characteristics, e.g., the underlying structure and communication patterns evolve with time. Correspondingly, we develop a dynamic framework for the influence maximization problem, where we perform effective local updates to quickly adjust the top-k influencers, as the structure and communication patterns in the network change. We design a novel NFamily method (N=1, 2, 3, . . .) based on the maximum influence arborescence (MIA) propagation model with approximation guarantee of (1− 1/e). We then develop heuristic algorithms by extending theN-Family approach to other information propagation models (e.g., independent cascade, linear threshold) and influence maximization algorithms (e.g., CELF, reverse reachable sketch). Based on a detailed empirical analysis over several real-world, dynamic, and large-scale networks, we find that our proposed solution, NFamily improves the updating time of the top-k influencers by 1 ∼ 2 orders ofmagnitude, compared to state-of-the-art algorithms, while ensuring similar memory usage and influence spreads. ACM Reference Format: Vijaya Krishna Yalavarthi and Arijit Khan. 2018. Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach. In Proceedings of X . ACM, New York, NY, USA, Article 4, 14 pages. https://doi.org/10.1145/
Social Comparison, Social Presence, and Enjoyment in the Acceptance of Social Shopping Websites

With businesses seeking to seize the momentum of social media and social networking, technology-enabled social commerce has emerged to combine the power of online social networking with shopping. This study examines an emerging area in E-commerce, i.e., social commerce. Extending the online consumer behavior typology, this study categorizes online consumer behaviors into three types: transactional, informational, and social. While traditional E-commerce supports the transactional and informational aspects of online shopping, social commerce fulfils the social aspects of shopping, and potentially enhances the informational aspect as well. This research examines the online shopper as a prospective user of an emerging social commerce platform, the social shopping website, which are sites designed specifically to support social interactions while online consumers shop. The study augments the Technology Acceptance Model with constructs that enhance the specificity of the model to the social shopping application of social commerce. The model was empirically tested and supported. The results provide empirical evidence to support the importance of distinguishing the social aspect of shopping from the information and transactional aspects, as well as the potential advantage to using technology to promote social interactions on E-commerce sites. Implications and future research are discussed.
Bayesian Value-at-Risk and expected shortfall forecasting via the asymmetric Laplace distribution

A parametric approach to estimating and forecasting Value-at-Risk (VaR) and expected shortfall (ES) for a heteroscedastic financial return series is proposed. The well-known GJR–GARCH form models the volatility process, capturing the leverage effect. To capture potential skewness and heavy tails, the model assumes an asymmetric Laplace form as the conditional distribution of the series. Furthermore, dynamics in higher moments are modeled by allowing the shape parameter in this distribution to be time-varying. Estimation is via an adaptive Markov chain Monte Carlo (MCMC) sampling scheme, employing the Metropolis–Hastings (MH) algorithm with a mixture of Gaussian proposal distributions. A simulation study highlights accurate estimation and improved inference compared to a single-Gaussian-proposal MH method. The model is illustrated by applying it to four international stock market indices and two exchange rates, generating onestep-ahead forecasts of VaR and ES. Standard and non-standard tests are applied to these forecasts, and the finding is that the proposed model performs favourably compared to some popular competitors: in particular it is the only conservative model of risk over the period studied, which includes the recent global financial crisis. © 2010 Elsevier B.V. All rights reserved.
Sensorless operation of permanent magnet motor using direct voltage sensing circuit

In this paper, position-sensorless operation of the permanent magnet motor is introduced. To estimate the rotor position and speed, a new closed-loop flux observer is proposed for wide speed operation. To extend the operation range of the proposed algorithm, a direct voltage sensing circuit is proposed that enables correct disturbance voltage rejection and stable operation of the proposed flux observer at extremely low speed. Experimental results of position-sensorless torque control are shown in order to verify the feasibility of the new algorithm using the proposed voltage sensing circuit.
A fast abstract syntax tree interpreter for R

Dynamic languages have been gaining popularity to the point that their performance is starting to matter. The effort required to develop a production-quality, high-performance runtime is, however, staggering and the expertise required to do so is often out of reach of the community maintaining a particular language. Many domain specific languages remain stuck with naive implementations, as they are easy to write and simple to maintain for domain scientists. In this paper, we try to see how far one can push a naive implementation while remaining portable and not requiring expertise in compilers and runtime systems. We choose the R language, a dynamic language used in statistics, as the target of our experiment and adopt the simplest possible implementation strategy, one based on evaluation of abstract syntax trees. We build our interpreter on top of a Java virtual machine and use only facilities available to all Java programmers. We compare our results to other implementations of R.
Learning multi-agent state space representations

This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose a multi-level approach which builds a progressively more advanced representation of the learning problem. The idea is that agents start with a minimal single agent state space representation, which is expanded only when necessary. In cases where agents detect conflicts, they automatically expand their state to explicitly take into account the other agents. These conflict situations are then analyzed in an attempt to find an abstract representation which generalises over the problem states. Our system allows agents to learn effective policies, while avoiding the exponential state space growth typical in multi-agent environments. Furthermore, the method we introduce to generalise over conflict states allows knowledge to be transferred to unseen and possibly more complex situations. Our research departs from previous efforts in this area of multi-agent learning because our agents combine state space generalisation with an agentcentric point of view. The algorithms that we introduce can be used in robotic systems to automatically reduce the sensor information to what is essential to solve the problem at hand. This is a must when dealing with multiple agents, since learning in such environments is a cumbersome task due to the massive amount of information, much of which may be irrelevant. In our experiments we demonstrate a simulation of such environments using various gridworlds.
Effects of motivational interviewing-based adherence therapy for schizophrenia spectrum disorders: a randomized controlled trial

BACKGROUND
Non-adherence to antipsychotic medication is commonly found in schizophrenia and other psychotic disorders, thus forming a major obstacle to long-term maintenance treatment and contributing to high relapse rates. With limited evidence on the success of interventions in enhancing medication adherence, this controlled trial was designed to test and evaluate the effectiveness of an adherence therapy (AT) for outpatients with schizophrenia spectrum disorders, based on a motivational interviewing approach over a six-month follow-up period.


METHODS
A single-blind, randomized controlled trial with a repeated-measures, two parallel groups design was conducted in a random sample of 114 participants with schizophrenia spectrum disorders in one community psychiatric nursing service. After pre-test, the participants were randomly assigned to either an eight-session course of AT plus usual care or usual psychiatric care (n = 57 per group). The main outcomes, including medication adherence, symptom severity, insight into treatment, hospitalization rate, and functioning, were measured at baseline and immediately and six months post-intervention.


RESULTS
A total of 110 participants completed this trial and thus the attrition rate was 3.5%. Results of repeated-measures analysis of variance followed by Helmert's contrasts test indicated that the AT participants reported significantly greater improvements in their insight into illness and/or treatment, psychosocial functioning, symptom severity, number of re-hospitalizations, and medication adherence (F = 5.01 to 7.45, P = 0.007 to 0.030) over six months follow-up, when compared with usual care.


CONCLUSIONS
Motivational interviewing-based AT for people with schizophrenia can be effective to reduce symptom severity and re-hospitalizations, and improve medication adherence, functioning, and insight into illness and/or treatment over a medium term (six months) period of follow-up. Further study on the effects of AT in people with psychotic disorders in terms of diverse sociodemographic and illness characteristics, and a longer term (for example, over 12 months) follow-up period is recommended.


TRIAL REGISTRATION
The trial was registered at Clinicaltrials.gov (identifier: NCT01780116) on 6 July 2014.
Comment on ``An Evolutionary Picture for Quantum Physics'' by Rudolf Haag

Widespread unjustified views on the role of the observer, the individuality of quantum processes, the relation between decoherence and irreversibility, Bell's quest for `beables', the direction of time, and the concept of experience are revealed, whereby a better understanding is achieved of what the evolutionary picture is all about and what is regarded as missing in already existing realistic pictures.
Combination of Preferred Illuminance and Preferred Color Temperature for Work

We have been conducting research on an Intelligent Lighting System which is able to provides required illuminance for each worker. In the Intelligent Lighting System, it is possible to provide illuminance and color temperature in an arbitrary location by incorporating chroma sensor into the system. By simply setting a target illuminance and target color temperature, the worker can create the optimal illuminance and color temperature environment for work. However, when setting the target illuminance and target color temperature, selecting the illuminance as well as a color temperature suitable for that illuminance may be difficult for users because it may be unclear which color temperature should be set for the selected illuminance. Thus, in this research we conduct experiments related to illuminance and color temperature, and investigate the combination of preferred illuminance and preferred color temperature for work. By applying the experiment results to the Intelligent Lighting System and simplifying the operations of setting the target illuminance and target color temperature, we aim to improve the level of comfort for worker in the office equipped with LED lighting.