LLukas22/scidocs huggingface dataset:
From dictatorship to a reluctant democracy: stroke therapists talking about self-management

PURPOSE
Self-management is being increasingly promoted within chronic conditions including stroke. Concerns have been raised regarding professional ownership of some programmes, yet little is known of the professional's experience. This paper aims to present the views of trained therapists about the utility of a specific self-management approach in stroke rehabilitation.


METHOD
Eleven stroke therapists trained in the self-management approach participated in semi-structured interviews. These were audio recorded, transcribed verbatim and analysed thematically.


RESULTS
Two overriding themes emerged. The first was the sense that in normal practice therapists act as "benign dictators", committed to help their patients, but most comfortable when they, the professional, are in control. Following the adoption of the self-management approach therapists challenged themselves to empower stroke survivors to take control of their own recovery. However, therapists had to confront many internal and external challenges in this transition of power resulting in the promotion of a somewhat "reluctant democracy".


CONCLUSIONS
This study illustrates that stroke therapists desire a more participatory approach to rehabilitation. However, obstacles challenged the successful delivery of this goal. If self-management is an appropriate model to develop in post stroke pathways, then serious consideration must be given to how and if these obstacles can be overcome.


IMPLICATIONS FOR REHABILITATION
Stroke therapists perceive that self-management is appropriate for encouraging ownership of rehabilitation post stroke. Numerous obstacles were identified as challenging the implementation of self-management post stroke. These included: professional models, practices and expectations; institutional demands and perceived wishes of stroke survivors. For self-management to be effectively implemented by stroke therapists, these obstacles must be considered and overcome. This should be as part of an integrated therapy service, rather than as an add-on.
How to Train Triplet Networks with 100K Identities?

Training triplet networks with large-scale data is challenging in face recognition. Due to the number of possible triplets explodes with the number of samples, previous studies adopt the online hard negative mining(OHNM) to handle it. However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found. To solve the problem, in this paper, we propose training triplet networks with subspace learning, which splits the space of all identities into subspaces consisting of only similar identities. Combined with the batch OHNM, hard triplets can be found much easier. Experiments on the large-scale MS-Celeb-1M challenge with 100 K identities demonstrate that the proposed method can largely improve the performance. In addition, to deal with heavy noise and large-scale retrieval, we also make some efforts on robust noise removing and efficient image retrieval, which are used jointly with the subspace learning to obtain the state-of-the-art performance on the MS-Celeb-1M competition (without external data in Challenge1).
Hysteresis and phase transitions for 1 D and 3 D models in shape memory alloys

By means of the Ginzburg-Landau theory of phase transitions, we study a non-isothermal model to characterize the austenite-martensite transition in shape memory alloys. In the first part of this paper, the onedimensional model proposed in [3] is modified by varying the expression of the free energy. In this way, the description of the phenomenon of hysteresis, typical of these materials, is improved and the related stressstrain curves are recovered. Then, a generalization of this model to the three dimensional case is proposed and its consistency with the principles of thermodynamics is proved. Unlike other three dimensional models, the transition is characterized by a scalar valued order parameter φ and the Ginzburg-Landau equation, ruling the evolution of φ, allows us to prove a maximum principle, ensuring the boundedness of φ itself.
The role of translators in children's literature : invisible storytellers

List of Figures Series Editor's Foreword Acknowledgments Preface Introduction Part One 1: Didactic Translation: Religious Texts, Courtesy Books, Schoolbooks and Political Persuasion 2: Popular Fiction in Translation: The Child as Consumer of Romances and Fables in the Medieval and Early Modern Periods 3: Tales of the Marvellous 1690-1760: The Arabian Nights and the French Fairy Tale 4: Imagination, Reason and Mapping the World 1750-1820 5: Religious Stories and the Artful Fairy Tale in the Nineteenth Century 6: The Translating Woman: Assertive Professional or Invisible Storyteller 7: Summary of Part One: Translation Practices and the Child Audience Part Two Introduction 8: Into the Twentieth Century: Classics, the Folk Tale and Internationalism 1870-1940 9: Rewarding Translation for Children: Landmark Translations from 1940 and the Batchelder and Marsh Awards 10: Retranslation in the Twentieth And Twenty-First Centuries: For Children or Adults? 11: Translators' Voices 12: From Anonymity to Global Marketing: The Role of Translators in Children's Literature Notes Bibliography Index
Forecasting models of emergency department crowding.

OBJECTIVES
The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison.


METHODS
From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well.


RESULTS
The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days.


CONCLUSIONS
Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours.
Beyond the Doubting of a Shadow A Reply to Commentaries on Shadows of the Mind

1. Bernard J. Baars: Can physics provide a theory of consciousness? 2. David J. Chalmers: Minds, machines, and mathematics 3. Solomon Feferman: Penrose's Gödelian argument 4. Stanley A. Klein: Is quantum mechanics relevant to understanding consciousness? 5. Tim Maudlin: Between the motion and the act.... 6. John McCarthy: Awareness and understanding in computer programs 7. Daryl McCullough: Can humans escape Gödel? 8. Drew McDermott: [STAR] Penrose is wrong 9. Hans Moravec: Roger Penrose's gravitonic brains
Recognition of multiple overlapping activities using compositional CNN-LSTM model

This paper introduces a new task, a recognition of multiple overlapping activities in the context of activity recognition. We propose a compositional CNN+LSTM algorithm. The experimental results show on the artificial dataset that it improved the accuracy from 27% to 43%.
Predictors of response and remission in the acute treatment of first-episode schizophrenia patients — Is it all about early response?

BACKGROUND
To evaluate the predictive validity of early response compared to other well-known predictor variables in acutely ill first-episode patients.


METHODS
112 patients were treated with a mean dosage of 4.14 mg (±1.70) haloperidol and 112 patients with a mean dosage of 4.17 mg (±1.55) risperidone for a mean inpatient treatment duration of 42.92 days (±16.85) within a double-blind, randomized controlled trial. Early response was defined as a ≥ 30% improvement in the PANSS total score by week 2, response as a ≥ 50% reduction in the PANSS total score from admission to discharge and remission according to the consensus criteria. Univariate tests and logistic regression models were applied to identify significant predictors of response and remission.


RESULTS
52% of the patients were responders and 59% remitters at discharge. Non-remitters at discharge were hindered from becoming remitters mainly by the presence of negative symptoms. Univariate tests revealed several significant differences between responders/non-responders and remitters/non-remitters such as age, severity of baseline psychopathology as well as the frequency of early response. Both early response (p<0.0001) and a higher PANSS positive subscore at admission (p=0.0002) were identified as significant predictors of response at discharge, whereas a shorter duration of untreated psychosis (p=0.0167), a lower PANSS general psychopathology subscore (p<0.0001), and early treatment response (p=0.0002) were identified as significant predictors of remission.


CONCLUSION
Together with the finding that early response is a significant predictor of response and remission, the relevance and predictive validity of negative and depressive symptoms for outcome is also highlighted.
AT-MAC: Adaptive MAC-Frame Payload Tuning for Reliable Communication in Wireless Body Area Networks

In wireless sensor networks, adaptive tuning of Medium Access Control (MAC) parameters is necessary in order to assure the QoS requirements. In this paper, we propose an adaptive MAC-frame payload tuning mechanism for wireless body area networks (WBANs) to maximize the probability of successful packet delivery or reliability of the associated sensor nodes based on real-time situation. The enabling algorithm, Adaptively Tuned MAC (AT-MAC), has been proposed to tune the MAC-frame payload of a WBAN sensor node, which is compliant with the IEEE 802.15.4 protocol. AT-MAC prioritizes sensor nodes based on the seriousness of the health parameters that are being measured by the respective sensor nodes. Further, we consider a Markov chain-based analytical approach that acknowledges the slotted CSMA/CA backoff mechanism with retry limits, as described in the IEEE 802.15.4 protocol. We derive expressions for reliability, power consumption, and throughput, which are the key metrics to evaluate the network performance of the proposed protocol, and analyze the impact of MAC parameters on them. Finally, results indicate that the low rate and low power IEEE 802.15.4 can be used effectively in case of WBANs if the payload is tuned properly through the proposed algorithm. The proposed AT-MAC algorithm yields around 70 percent increase in reliability of a critical node in a WBAN.
The Brief Obsessive–Compulsive Scale (BOCS): A self-report scale for OCD and obsessive–compulsive related disorders

BACKGROUND
The Brief Obsessive Compulsive Scale (BOCS), derived from the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and the children's version (CY-BOCS), is a short self-report tool used to aid in the assessment of obsessive-compulsive symptoms and diagnosis of obsessive-compulsive disorder (OCD). It is widely used throughout child, adolescent and adult psychiatry settings in Sweden but has not been validated up to date.


AIM
The aim of the current study was to examine the psychometric properties of the BOCS amongst a psychiatric outpatient population.


METHOD
The BOCS consists of a 15-item Symptom Checklist including three items (hoarding, dysmorphophobia and self-harm) related to the DSM-5 category "Obsessive-compulsive related disorders", accompanied by a single six-item Severity Scale for obsessions and compulsions combined. It encompasses the revisions made in the Y-BOCS-II severity scale by including obsessive-compulsive free intervals, extent of avoidance and excluding the resistance item. 402 adult psychiatric outpatients with OCD, attention-deficit/hyperactivity disorder, autism spectrum disorder and other psychiatric disorders completed the BOCS.


RESULTS
Principal component factor analysis produced five subscales titled "Symmetry", "Forbidden thoughts", "Contamination", "Magical thoughts" and "Dysmorphic thoughts". The OCD group scored higher than the other diagnostic groups in all subscales (P < 0.001). Sensitivities, specificities and internal consistency for both the Symptom Checklist and the Severity Scale emerged high (Symptom Checklist: sensitivity = 85%, specificities = 62-70% Cronbach's α = 0.81; Severity Scale: sensitivity = 72%, specificities = 75-84%, Cronbach's α = 0.94).


CONCLUSIONS
The BOCS has the ability to discriminate OCD from other non-OCD related psychiatric disorders. The current study provides strong support for the utility of the BOCS in the assessment of obsessive-compulsive symptoms in clinical psychiatry.
Runtime Verification and Enforcement for Android Applications with RV-Droid

RV-Droid is an implemented framework dedicated to runtime verification (RV) and runtime enforcement (RE) of Android applications. RV-Droid consists of an Android application that interacts closely with a cloud. Running RV-Droid on their devices, users can select targeted Android applications from Google Play (or a dedicated repository) and a property. The cloud hosts thirdparty RV tools that are used to synthesize AspectJ aspects from the property. According to the chosen RV tool and the specification, some appropriate monitoring code, the original application and the instrumentation aspect are woven together. Weaving can occur either on the user’s device or in the dedicated cloud. The woven application is then retrieved and executed on the user’s device and the property is runtime verified. RV-Droid is generic and currently works with two existing runtime verification frameworks for (pure) Java programs: with JavaMOP and (partially) with RuleR. RV-Droid does not require any modification to the Android kernel and targeted applications can be retrieved off-the-shelf. We carried out several experiments that demonstrated the effectiveness of RV-Droid on monitoring (security) properties.
Contour-aware network for semantic segmentation via adaptive depth

Semantic segmentation has been widely investigated for its important role in computer vision. However, some challenges still exist. The first challenge is how to perceive semantic regions with various attributes, which can result in unbalanced distribution of training samples. Another challenge is accurate semantic boundary determination. In this paper, a contour-aware network for semantic segmentation via adaptive depth is proposed which particularly exploits the power of adaptive-depth neural network and contouraware neural network on pixel-level semantic segmentation. Specifically, an adaptive-depth model, which can adaptively determine the feedback and forward procedure of neural network, is constructed. Moreover, a contour-aware neural network is respectively built to enhance the coherence and the localization accuracy of semantic regions. By formulating the contour information and coarse semantic segmentation results in a unified manner, global inference is proposed to obtain the final segmentation results. Three contributions are claimed: (1) semantic segmentation via adaptive depth neural network; (2) contouraware neural network for semantic segmentation; and (3) global inference for final decision. Experiments on three popular datasets are conducted and experimental results have verified the superiority of the proposed method compared with the state-of-the-art methods. © 2018 Elsevier B.V. All rights reserved.
A Capability-Based Framework for Open Innovation: Complementing Absorptive Capacity

We merge research into knowledge management, absorptive capacity, and dynamic capabilities to arrive at an integrative perspective, which considers knowledge exploration, retention, and exploitation inside and outside a firm’s boundaries. By complementing the concept of absorptive capacity, we advance towards a capability-based framework for open innovation processes. We identify the following six ‘knowledge capacities’ as a firm’s critical capabilities of managing internal and external knowledge in open innovation processes: inventive, absorptive, transformative, connective, innovative, and desorptive capacity. ‘Knowledge management capacity’ is a dynamic capability, which reconfigures and realigns the knowledge capacities. It refers to a firm’s ability to successfully manage its knowledge base over time. The concept may be regarded as a framework for open innovation, as a complement to absorptive capacity, and as a move towards understanding dynamic capabilities for managing knowledge. On this basis, it contributes to explaining interfirm heterogeneity in knowledge and alliance strategies, organizational boundaries, and innovation
Prior and contextual emotion of words in sentential context

A set of words labeled with their prior emotion is an obvious place to start on the automatic discovery of the emotion of a sentence, but it is clear that context must also be considered. It may be that no simple function of the labels on the individual words captures the overall emotion of the sentence; words are interrelated and they mutually influence their affect-related interpretation. It happens quite often that a word which invokes emotion appears in a neutral sentence, or that a sentence with no emotional word carries an emotion. This could also happen among different emotion classes. The goal of this work is to distinguish automatically between prior and contextual emotion, with a focus on exploring features important in this task. We present a set of features which enable us to take the contextual emotion of a word and the syntactic structure of the sentence into account to put sentences into emotion classes. The evaluation includes assessing the performance of different feature sets across multiple classification methods. We show the features and a promising learning method which significantly outperforms two reasonable baselines. We group our features by the similarity of their nature. That is why another facet of our evaluation is to consider each group of the features separately and investigate how well they contribute to the result. The experiments show that all features contribute to the result, but it is the combination of all the features that gives the best performance. © 2013 Elsevier Ltd. All rights reserved.
How Can Childbirth Care for the Rural Poor Be Improved? A Contribution from Spatial Modelling in Rural Tanzania

INTRODUCTION
Maternal and perinatal mortality remain a challenge in resource-limited countries, particularly among the rural poor. To save lives at birth health facility delivery is recommended. However, increasing coverage of institutional deliveries may not translate into mortality reduction if shortage of qualified staff and lack of enabling working conditions affect quality of services. In Tanzania childbirth care is available in all facilities; yet maternal and newborn mortality are high. The study aimed to assess in a high facility density rural context whether a health system organization with fewer delivery sites is feasible in terms of population access.


METHODS
Data on health facilities' location, staffing and delivery caseload were examined in Ludewa and Iringa Districts, Southern Tanzania. Geospatial raster and network analysis were performed to estimate access to obstetric services in walking time. The present geographical accessibility was compared to a theoretical scenario with a 40% reduction of delivery sites.


RESULTS
About half of first-line health facilities had insufficient staff to offer full-time obstetric services (45.7% in Iringa and 78.8% in Ludewa District). Yearly delivery caseload at first-line health facilities was low, with less than 100 deliveries in 48/70 and 43/52 facilities in Iringa and Ludewa District respectively. Wide geographical overlaps of facility catchment areas were observed. In Iringa 54% of the population was within 1-hour walking distance from the nearest facility and 87.8% within 2 hours, in Ludewa, the percentages were 39.9% and 82.3%. With a 40% reduction of delivery sites, approximately 80% of population will still be within 2 hours' walking time.


CONCLUSIONS
Our findings from spatial modelling in a high facility density context indicate that reducing delivery sites by 40% will decrease population access within 2 hours by 7%. Focused efforts on fewer delivery sites might assist strengthening delivery services in resource-limited settings.
Concentration profiles in the underlying alloy during the oxidation of iron-chromium alloys

Abstract Compositional changes in Fe-27.4, 37.4 and 59.5% Cr alloys during the formation of Cr 2 O 3 -rich scales have been measured by electron probe microanalysis and calculated by solution of the appropriate transport equations. Agreement between corresponding results is within the limits of experimental error. Chromium concentration profiles have also been calculated for Fe-14.0 and Fe-18.0% Cr by a similar method, allowance being made for the formation of γ-phase at the surface of the alloy. The chromium concentration at the alloy/oxide interface never falls below the limiting value for thermodynamic stability of a Cr 2 O 3 scale. Chemical transformation of the Cr 2 O 3 scales from within is therefore not possible and the failure of such scales on Fe-14.0 and Fe-18.0% Cr is attributed to mechanical causes.
Westward extension of the Snaefellsnes Volcanic Zone of Iceland

On the basis of new geophysical data it is postulated that volcanic features similar to those occurring in the east-west trending Snaefellsnes volcanic zone of Iceland extend at least 100 km to the west of the Snaefellsnes peninsula.
Lung transplantation for advanced cystic lung disease due to nonamyloid kappa light chain deposits.

RATIONALE
Cystic lung light chain deposition disease (LCDD) is a severe and rare form of nonamyloid kappa light chain deposits localized in the lung, potentially leading to end-stage respiratory insufficiency.


OBJECTIVES
To assess the outcome after lung transplantation (LT) in this setting with particular attention to disease recurrence.


METHODS
We conducted a retrospective multicenter study of seven patients who underwent LT for cystic lung LCDD in France between September 1992 and June 2012 in five centers.


MEASUREMENTS AND MAIN RESULTS
In total, five females and two males (mean age, 39.1 ± 5.3 yr) underwent one single LT or seven double LT (one retransplantation). Before LT, the patients showed a constant obstructive ventilatory pattern with low carbon monoxide diffusing capacity and resting hypoxemia. Lung computed tomography revealed widespread cysts with occasional micronodulations. No extrapulmonary disease or plasma cell neoplasm was detected. The serum-free kappa/lambda light chain ratio was increased in three cases. The median follow-up after LT was 56 months (range, 1-110 mo). Kaplan-Meier survival was 85.7, 85.7, and 64.3% at 1, 3, and 5 years, respectively. Three patients died from multiorgan failure (n = 1), chronic rejection (n = 1), and breast cancer (n = 1) at 23 days, 56 months, and 96 months, respectively. At the end of follow-up, no patients showed recurrence on imaging or histopathology.


CONCLUSIONS
This small case series confirms that cystic lung LCDD is a severe disease limited to the lung, affecting mostly young females. LT appears to be a good therapeutic option allowing for satisfactory long-term survival. We found no evidence of recurrence of the disease after LT.