LLukas22/scidocs huggingface dataset:
A randomized controlled trial of a feedback method for improving empathic accuracy in psychotherapy.

OBJECTIVES
To develop and evaluate a feedback method for reducing empathic errors in psychotherapy.


DESIGN
Randomized controlled trial conducted in a university-affiliated out-patient psychiatric clinic.


METHODS
Sixteen non-psychotic patients treated for Axis I disorders by 12 psychiatry residents were randomly assigned to intervention and control conditions. In both conditions, at the end of each session, patients rated their own functioning on the Global Assessment of Functioning scale, and therapists predicted patients' ratings. Patients predicted their therapist's accuracy and therapists rated their confidence in their own predictions. In the intervention condition, therapists and patients reviewed their respective ratings from the previous session together. In the control condition, ratings were given directly to the investigator without being reviewed by either patients or therapists.


RESULTS
Therapists in the intervention condition showed greater overall accuracy than controls as well as evidence of increasing empathy later in therapy on the Barrett-Lennard empathy subscale. Patients in the control group perceived their therapists as significantly more or less accurate than was warranted according to the accuracy measure (over-/under-idealization). Therapists in the control group were more likely than those in the intervention group to overestimate their own accuracy (overconfidence). Affective responses to the instrument were positive overall and did not differ by condition.


CONCLUSION
An intervention such as the one tested in this study may be a practical and useful method for improving accuracy of understanding in a variety of training and clinical settings.
Deep Stereo: Learning to Predict New Views from the World's Imagery

Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision [22, 33], but their use in graphics problems has been limited ([23, 7] are notable recent exceptions). In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to traditional approaches, which consist of multiple complex stages of processing, each of which requires careful tuning and can fail in unexpected ways, our system is trained end-to-end. The pixels from neighboring views of a scene are presented to the network, which then directly produces the pixels of the unseen view. The benefits of our approach include generality (we only require posed image sets and can easily apply our method to different domains), and high quality results on traditionally difficult scenes. We believe this is due to the end-to-end nature of our system, which is able to plausibly generate pixels according to color, depth, and texture priors learnt automatically from the training data. We show view interpolation results on imagery from the KITTI dataset [12], from data from [1] as well as on Google Street View images. To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.
Integrating independent components and support vector machines for gender classification

Computer vision and pattern recognition systems play an important role in our lives by means of automated face detection, face and gesture recognition, and estimation of gender and age. We have developed a gender classifier with performance superior to existing gender classifiers. This paper addresses the problem of gender classification using frontal facial images. The testbed consists of 500 images (250 females and 250 males) randomly withdrawn from the FERET facial database. Independent component analysis (ICA) is used to represent each image as a feature vector in a low dimensional subspace. Different classifiers are studied in this lower dimensional subspace. Our experimental results show the best accuracy of 96% in gender classification by combining ICA and support vector machines (SVMs).
Closing the Gap How an Adaptive Behavioral Based Program on a Tablet Can Help Low Performing Children Catch Up in Math : a Randomized Placebo Controlled Study

Hassler Hallstedt, M. 2018. Closing the Gap. How an Adaptive Behavioral Based Program on a Tablet Can Help Low Performing Children Catch Up in Math: a Randomized Placebo Controlled Study. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences 150. 88 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-0200-3. Early mathematic skills have a substantial impact on later school achievement. Children with poor school achievement are at risk for adverse consequences later in life. Math competencies also have consequences for the economy at large because societies are becoming increasingly dependent on skill sets including mathematics. Proficiency with basic arithmetic, also known as math fact (i.e., 3+8, 12-3), is considered to be a critical early math skill. Intervention research in mathematics have demonstrated that math fact deficits among students with low math performance can be improved with additional targeted, nontechnological interventions (i.e., small-group tutoring). The aim of the present thesis was to investigate the effect, using a randomized placebo controlled design, of addititional adaptive, behavioral based, math training on a tablet on low performing second graders. The first study (study I), investigated if arithmetic skills could be assessed in a reliable and valid way on tablet. The examination showed that arithmetic scales could be transferred from paper-based tests to tablet with comparable psychometric properties, although not for a pictorial scale, and that separate norms are needed for tablet. Study II demonstrated that training on a tablet, for on average 19 hours across 20 weeks, improved basic arithmetic skills after training in the math conditions compared to control/ placebo conditions. The effects were medium sized at post assessment. There was a fadeout of effects at 6 months follow-up, where small effects were shown, and the effects decreased further at 12 months follow-up. Children with lower non-verbal IQ seemed to gain significantly more at followups than children with higher non-verbal IQ. The study found no additional effects of combining working memory training and math training. Study III, using a machine learning analysis, found that children demonstrating a positive response at 6 months follow-up were characterized by having completed 90 % or more of the math program at the default level, in combination with having a fairly favorable socioeconomic background. In summation, this work demonstrates how an adaptive behavioral based program on a tablet can help low performing children improve critical early math skills.
Radiofrequency ablation of medically inoperable stage IA non-small cell lung cancer: are early posttreatment PET findings predictive of treatment outcome?

OBJECTIVE
The purpose of this study was to evaluate initial experience with (18)F-FDG PET/CT after pulmonary radiofrequency ablation of stage IA non-small cell lung cancer to determine whether treatment success or residual disease can be predicted with early postablation PET.


SUBJECTS AND METHODS
Thirty patients with medically inoperable stage IA non-small cell lung cancer (12 men, 18 women; median age, 76 years; range, 60-87 years) underwent outpatient CT-guided radiofrequency ablation over a 33-month period. Mean tumor size was 2.0 cm (range, 1.3-2.9 cm). PET/CT was performed within 60 days before radiofrequency ablation (RFA), within 4 days after RFA, and 6 months after RFA. Metabolic response was categorized as complete response or partial or no response at early post-RFA PET/CT and complete response, partial response, or progressive metabolic disease at 6-month post-RFA PET/CT and was compared with the 1-year clinical event rate (death, disease progression at contrast-enhanced CT, or repeat ablation).


RESULTS
Early PET/CT images, obtained within 4 days of RFA, were evaluable for 26 patients (23 at 6 months). Patients with a complete metabolic response at early PET/CT had a 1-year event rate of 43%, whereas those with partial or no response or disease progression had a 1-year event rate of 67% (p = 0.27). Patients with a complete metabolic response at 6-month PET/CT had a 1-year event rate of 0%. Those with a partial response and those with disease progression had an overall event rate of 75% (p = 0.001).


CONCLUSION
Early post-RFA PET/CT is not necessary and 6-month post-RFA PET/CT findings correlate better with clinical outcome at 1 year.
A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models

In 2008, financial tsunami started to impair the economic development of many countries, including Taiwan. The prediction of financial crisis turns to be much more important and doubtlessly holds public attention when the world economy goes to depression. This study examined the predictive ability of the four most commonly used financial distress prediction models and thus constructed reliable failure prediction models for public industrial firms in Taiwan. Multiple discriminate analysis (MDA), logit, probit, and artificial neural networks (ANNs) methodology were employed to a dataset of matched sample of failed and non-failed Taiwan public industrial firms during 1998–2005. The final models are validated using within sample test and out-of-the-sample test, respectively. The results indicated that the probit, logit, and ANN models which used in this study achieve higher prediction accuracy and possess the ability of generalization. The probit model possesses the best and stable performance. However, if the data does not satisfy the assumptions of the statistical approach, then the ANN approach would demonstrate its advantage and achieve higher prediction accuracy. In addition, the models which used in this study achieve higher prediction accuracy and possess the ability of generalization than those of [Altman, Financial ratios—discriminant analysis and the prediction of corporate bankruptcy using capital market data, Journal of Finance 23 (4) (1968) 589–609, Ohlson, Financial ratios and the probability prediction of bankruptcy, Journal of Accounting Research 18 (1) (1980) 109–131, and Zmijewski, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research 22 (1984) 59–82]. In summary, the models used in this study can be used to assist investors, creditors, managers, auditors, and regulatory agencies in Taiwan to predict the probability of business failure. & 2009 Elsevier B.V. All rights reserved.
Prevalence of peripheral arterial disease in patients at non-high cardiovascular risk. Rationale and design of the PANDORA study

BACKGROUND
Lower extremity peripheral arterial disease (PAD) is a marker of widespread atherosclerosis. Individuals with PAD, most of whom do not show typical PAD symptoms ('asymptomatic' patients), are at increased risk of cardiovascular ischaemic events. American College of Cardiology/American Heart Association guidelines recommend that individuals with asymptomatic lower extremity PAD should be identified by measurement of ankle-brachial index (ABI). However, despite its associated risk, PAD remains under-recognised by clinicians and the general population and office-based ABI detection is still poorly-known and under-used in clinical practice. The Prevalence of peripheral Arterial disease in patients with a non-high cardiovascular disease risk, with No overt vascular Diseases nOR diAbetes mellitus (PANDORA) study has a primary aim of assessing the prevalence of lower extremity PAD through ABI measurement, in patients at non-high cardiovascular risk, with no overt cardiovascular diseases (including symptomatic PAD), or diabetes mellitus. Secondary objectives include documenting the prevalence and treatment of cardiovascular risk factors and the characteristics of both patients and physicians as possible determinants for PAD under-diagnosis.


METHODS/DESIGN
PANDORA is a non-interventional, cross-sectional, pan-European study. It includes approximately 1,000 primary care participating sites, across six European countries (Belgium, France, Greece, Italy, The Netherlands, Switzerland). Investigator and patient questionnaires will be used to collect both right and left ABI values at rest, presence of cardiovascular disease risk factors, current pharmacological treatment, and determinants for PAD under-diagnosis.


DISCUSSION
The PANDORA study will provide important data to estimate the prevalence of asymptomatic PAD in a population otherwise classified at low or intermediate risk on the basis of current risk scores in a primary care setting.


TRIAL REGISTRATION NUMBER
Clinical Trials.gov Identifier: NCT00689377.
Virage Image Search Engine: An Open Framework for Image Management

Until recently, the management of laige image databases has relied exclusively on manually entered alphanumeric annotations. Systems are beginning to emerge in both the research and commercial sectors based on "content-based" image retrieval, a technique which explicitly manages image assets by directly representing their visual attributes. The Virage Image Search Engine provides an open framework for building such systems. The Virage Engine expresses visual features as image "primitives" . Primitives can be very general (such as color, shape, or texture) or quite domain specific (face recognition, cancer cell detection, etc.) . The basic philosophy underlying this architecture is a transformation from the data-rich representation of explicit image pixels to a compact, semantic-rich representation of visually salient characteristics. In practice, the design of such primitives is non-trivial, and is driven by a number of conflicting real-world constraints (e.g. computation time vs. accuracy) . The Virage Engine provides an open framework for developers to "plug-in" primitives to solve specific image management problems. The architecture has been designed to support both static images and video in a unified paradigm. The infrastructure provided by the Virage Engine can be utilized to address high-level problems as well, such as automatic, unsupervised keyword assignment, or image classification. keywords: content-based image retrieval, image databases, visual information management, multimedia databases
Effectiveness of autofluorescence to identify suspicious oral lesions—a prospective, blinded clinical trial

Material and methods: A total of 78 patients were examined in this study. All of them suffered from suspicious oral mucosal lesions. Two different investigation methods were applied: the standard examination by white light and an examination by a novel light source of 400 nm that evoked a green light emission (>500 nm) in normal mucosa. It was proposed that malignant oral mucosal lesions show different autofluorescence characteristics than the green autofluorescence of healthy mucosa.
General Overview of ImageCLEF at the CLEF 2015 Labs

This paper presents an overview of the ImageCLEF 2015 evaluation campaign, an event that was organized as part of the CLEF labs 2015. ImageCLEF is an ongoing initiative that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to databases of images in various usage scenarios and domains. In 2015, the 13th edition of ImageCLEF, four main tasks were proposed: 1) automatic concept annotation, localization and sentence description generation for general images; 2) identification, multi-label classification and separation of compound figures from biomedical literature; 3) clustering of x-rays from all over the body; and 4) prediction of missing radiological annotations in reports of liver CT images. The x-ray task was the only fully novel task this year, although the other three tasks introduced modifications to keep up relevancy of the proposed challenges. The participation was considerably positive in this edition of the lab, receiving almost twice the number of submitted working notes papers as compared to previous years.
Measuring technology self efficacy: reliability and construct validity of a modified computer self efficacy scale in a clinical rehabilitation setting.

PURPOSE
To describe a modification of the computer self efficacy scale for use in clinical settings and to report on the modified scale's reliability and construct validity.


METHODS
The computer self efficacy scale was modified to make it applicable for clinical settings (for use with older people or people with disabilities using everyday technologies). The modified scale was piloted, then tested with patients in an Australian inpatient rehabilitation setting (n = 88) to determine the internal consistency using Cronbach's alpha coefficient. Construct validity was assessed by correlation of the scale with age and technology use. Factor analysis using principal components analysis was undertaken to identify important constructs within the scale.


RESULTS
The modified computer self efficacy scale demonstrated high internal consistency with a standardised alpha coefficient of 0.94. Two constructs within the scale were apparent; using the technology alone, and using the technology with the support of others. Scores on the scale were correlated with age and frequency of use of some technologies thereby supporting construct validity.


CONCLUSIONS
The modified computer self efficacy scale has demonstrated reliability and construct validity for measuring the self efficacy of older people or people with disabilities when using everyday technologies. This tool has the potential to assist clinicians in identifying older patients who may be more open to using new technologies to maintain independence.
Plasma concentrations and dosing of 2 long-acting injectable formulations of aripiprazole

Maintaining therapeutic plasma concentrations of an antipsychotic agent is essential in preventing relapse of symptoms in schizophrenia. Long-acting injectable (LAI) formulations provide extended exposure to antipsychotic therapy and have been useful in addressing treatment nonadherence. Aripiprazole is an atypical antipsychotic used in the treatment of schizophrenia and is available in 2 chemically different (aripiprazole monohydrate and aripiprazole lauroxil [AL]) and pharmaceutically different LAI formulations (aripiprazole once-monthly 400 mg [AOM 400] and AL). The pharmaceutical difference is that AL, unlike AOM 400, is a prodrug that requires additional metabolic steps to form the active drug aripiprazole. We present data demonstrating that aripiprazole plasma concentrations are similar for AOM 400 and the 882 mg dose of AL when administered once every 4 weeks and that both provide similar therapeutic plasma concentrations of aripiprazole when compared with therapeutic oral doses.
Offline Policy-search in Bayesian Reinforcement Learning

Faculty of Applied Sciences Department of Electrical Engineering and Computer Science Doctor of Computer Science Offline Policy-search in Bayesian Reinforcement Learning by Michael CASTRONOVO This thesis presents research contributions in the study field of Bayesian Reinforcement Learning — a subfield of Reinforcement Learning where, even though the dynamics of the system are unknown, the existence of some prior knowledge is assumed in the form of a distribution over Markov decision processes. In this thesis, two algorithms are presented: OPPS (Offline Priorbased Policy Search) and ANN-BRL (Artificial Neural Networks for Bayesian Reinforcement Learning), whose philosophy consists to analyse and exploit the knowledge available beforehand prior to interacting with the system(s), and which differ by the nature of the model they make use of. The former makes use of formula-based agents introduced by Maes et al. in (Maes, Wehenkel, and Ernst, 2012), while the latter relies on Artificial Neural Networks built via SAMME (Stagewise Additive Modelling using a Multi-class Exponential loss function) — an AdaBoost algorithm developed by Zhu et al. in (Zhu et al., 2009). Moreover, we also describe a comprehensive benchmark which has been created to compare Bayesian Reinforcement Learning algorithms. In real life applications, the choice of the best agent to fulfil a given task depends not only on their performances, but also on the computation times required to deploy them. This benchmark has been designed to identify the best algorithms by taking both criteria into account, and resulted in the development of an open-source library: BBRL (Benchmarking tools for Bayesian Reinforcement Learning)1. 1https://github.com/mcastron/BBRL/wiki
Revascularization Technique for the Treatment of External Inflammatory Root Resorption: A Report of 3 Cases.

The current external inflammatory root resorption treatment protocol, which uses calcium hydroxide dressing, usually comprises multiple and long-term applications. In addition to the need for multiple appointments for calcium hydroxide replacement, the long-term maintenance of this compound in the root canal weakens dental structures. A modification of this therapy would be advisable. In this clinical investigation, 3 patients with external inflammatory root resorption were submitted to revascularization therapy protocol usually used in teeth with necrotic pulp and open apices. The teeth were treated with revascularization therapy protocol, which consisted of disinfecting the root canal system with triantibiotic paste, filling it with blood clot, and sealing of the root canal with mineral trioxide aggregate and bonded resin restoration. During the follow-up, the pathologic process was arrested with tissue repair in pre-existing radiolucent areas. Reduced mobility was observed in the treated teeth. The 3 cases were followed up for 30, 18, and 15 months, respectively. All teeth remained asymptomatic and retained function and physiological mobility. The therapy used in the revascularization procedure was efficient in the treatment of external inflammatory root resorption, reducing the number of appointments and increasing patient compliance.
Transcutaneous carbon dioxide pressure monitoring in a specialized weaning unit.

OBJECTIVE
To evaluate transcutaneously measured P(CO(2)) (P(tcCO(2))) values during ventilator weaning and during bronchoscopies on ventilated patients, and to compare P(tcCO(2)) values to P(aCO(2)) values from arterial blood analysis and end-tidal P(CO(2)) (P(ETCO(2))) values from capnography.


METHODS
In our specialized weaning unit we measured P(tcCO(2)) in tracheostomized patients with prolonged weaning failure during daytime spontaneous breathing trials (SBTs) (23 measurement sessions in 15 patients), during their first nights off the ventilator (12 measurement sessions in 12 patients), during bronchoscopy while ventilated (80 measurement sessions in 21 patients), simultaneous with arterial blood draw for blood gas analysis (48 measurements in 38 patients), and simultaneous with P(ETCO(2)) measurements (39 measurements in 31 patients).


RESULTS
There were often large changes (> 10 mm Hg) in P(tcCO(2)) during daytime SBTs (23%) and the initial overnight off-the-ventilator periods (42%), which influenced the decisions of whether to continue the SBT. P(tcCO(2)) often rose during bronchoscopy (mean +/- SD increase of 10.7 +/- 5.8 mm Hg), which influenced the physician to change the ventilator settings 44% of the time. P(aCO(2)) closely matched P(tcCO(2)) (mean +/- SD difference of 0.5 +/- 4.1 mm Hg). There was a greater difference between P(aCO(2)) and P(ETCO(2)) (3.7 +/- 7.7 mm Hg during prolonged exhalation, and 6.8 +/- 7.2 mm Hg during tidal breathing).


CONCLUSIONS
Monitoring P(tcCO(2)) is very helpful in assessing and managing patients undergoing SBTs, during the first night off the ventilator, and during bronchoscopy on ventilated patients. P(tcCO(2)) more closely matches P(aCO(2)) than does P(ETCO(2)).