Miracle-dz/UniFAR-FTUs huggingface dataset:
A dynamic network analysis of emergent grammar

For languages to survive as complex cultural systems, they need to be learnable. According to traditional approaches, learning is made possible by constraining the degrees of freedom in advance of experience and by the construction of complex structure during development. This article explores a third contributor to complexity: namely, the extent to which syntactic structure can be an emergent property of how simpler entities – words – interact with one another. The authors found that when naturalistic child directed speech was instantiated in a dynamic network, communities formed around words that were more densely connected with other words than they were with the rest of the network. This process is designed to mirror what we know about distributional patterns in natural language: namely, the network communities represented the syntactic hubs of semi-formulaic slot-and-frame patterns, characteristic of early speech. The network itself was blind to grammatical information and its organization reflected (a) the frequency of using a word and (b) the probabilities of transitioning from one word to another. The authors show that grammatical patterns in the input disassociate by community structure in the emergent network. These communities provide coherent hubs which could be a reliable source of syntactic information for the learner. These initial findings are presented here as proof-of-concept in the hope that other researchers will explore the possibilities and limitations of this approach on a larger scale and with more languages. The implications of a dynamic network approach are discussed for the learnability burden and the development of an adult-like grammar.
Towards UML representation for BPMN and DMN models

Unified Modeling Language (UML) is a standardised Object Management Group (OMG) notation among software engineers. There have been many attempts to design alternatives to UML. Recently, new notations such as Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) have been proposed for modelling processes and decisions. These dedicated notations provide a simpler way of capturing functional requirements in a designer-friendly fashion. Moreover, some concepts as rules cannot be directly modelled in UML. Our research considers a method of translating business models such as BPMN and DMN into a set of consistent UML models, which can be later used by business analysts and developers to understand and implement the system. As a single notation design, it allows the user to take advantage of software supporting UML modelling and consistency checking, in addition using such translation can provide additional insights into OMG business models. Furthermore, the design provided in a single notation can be easier to follow and develop. This approach supports also visualisation of rules. It is important to mention that the proposed solution does not extend any custom UML artifacts and can be used with standard UML tools.
The effect of prolonged hypotonicity on chondrocyte volume regulation and intracellular calcium signalling

Chondrocytes, present in articular cartilage, are exposed to anisotonic conditions in vivo through joint loading and articulation. Alterations in cell volume occur as a result of such changes and as optimal matrix metabolism is linked to cell volume, chondrocytes possess the capacity to rapidly re-equilibriate to their surrounding milieu by instigating the process of regulatory volume decrease (RVD). Intracellular calcium ([Ca]i) transients are proposed to act as mediators in initiating the activation of specific membrane transporters implicated in RVD (Kerrigan and Hall, 2008).
Low rank tensor decomposition for feature extraction and tensor recovery

Feature extraction and tensor recovery problems are important yet challenging, particularly for multi-dimensional data with missing values and/or noise. Low-rank tensor decomposition approaches are widely used for solving these problems. This thesis focuses on three common tensor decompositions (CP, Tucker and t-SVD) and develops a set of decomposition-based approaches. The proposed methods aim to extract low-dimensional features from complete/incomplete data and recover tensors given partial and/or grossly corrupted observations. Based on CP decomposition, semi-orthogonal multilinear principal component analysis (SO-MPCA) seeks a tensor-to-vector projection that maximizes the captured variance with the orthogonality constraint imposed in only one mode, and it further integrates the relaxed start strategy (SO-MPCA-RS) to achieve better feature extraction performance. To directly obtain the features from incomplete data, low-rank CP and Tucker decomposition with feature variance maximization (TDVM-CP and TDVM-Tucker) are proposed. TDVM methods explore the relationship among tensor samples via feature variance maximization, while estimating the missing entries via low-rank CP and Tucker approximation, leading to informative features extracted directly from partial observations. TDVM-CP extracts low-dimensional vector features viewing the weight vectors as features and TDVMTucker learns low-dimensional tensor features viewing the core tensors as features. TDVM methods can be generalized to other variants based on other tensor decompositions. On the other hand, this thesis solves the missing data problem by introducing low-rank matrix/tensor completion methods, and also contributes to
Enhancing Autonomy-Connectedness in Patients With Anxiety Disorders: A Pilot Randomized Controlled Trial

Autonomy-enhancing treatment (AET) is a person-centered, gender-sensitive treatment, targeting transdiagnostic personal autonomy deficits. The current study was set up as a first pilot randomized controlled trial (RCT) to investigate the preliminary efficacy of AET. Earlier small non-controlled plots showed AET to be feasible and acceptable. In the current study (Trial Code 3513), patients receiving 15-session group-based AET (N = 43) were compared with those in a waitlist control condition (N = 40). Both the intention-to-treat and completers analyses suggested a larger decrease in agoraphobic symptoms in the experimental treatment than in the waitlist condition. In both analyses, effect sizes were small. The completers analyses showed additional beneficial effects in two of three autonomy-connectedness components, as well as psychoneuroticism, anxiety, and depression, which disappeared after correcting for multiple testing. AET may alleviate agoraphobic symptoms in a patient sample with severe anxiety. Future research, including more stringent inclusion criteria and follow-up assessment, is needed to further evaluate whether AET may serve as a promising alternative or addition to existing approaches. Clinical Trial Registration: www.trialregister. nl, identifier NTR3513.
Dataset for A computationally efficient Verilog-A ReRAM model (v.2)

This is an updated version of the data set for paper of same name. (The previous version has a DOI of 10.5258/SOTON/D0082). This is a Verilog-A ReRAM model that corresponds to the behavior of a physical TiO2-based sample manufactured in Nano Group, ECS, University of Southampton. The model is ready for use and can be compiled in any electronics circuit simulator that supports Verilog-A models.A minor version change to v.2. 1 occured on 3/1/2018 (see Change Log).
“We’re Together”: An Exploration of Interracial Couples’ Perceptions of Support and Relational Therapy

Interracial couples in the United States experience disproportionate amounts of adversity throughout their relationships. Backlash for loving interracially results in denied access to supportive systems, which literature indicates occurs commonly between micro and macro systems. Using ecological systems and narrative theories, a mixed methodological study was conducted using the Fragile Families Child and Wellbeing study to explore participant’s use of relational therapy and perceptions of access support systems. Subsequently, sixty interracial couple members completed a survey expressing perceptions of relational therapy, and the influence of support systems on their usage. Results indicate interracial and same-race couples attend relational therapy at similar rates; however, interracial respondents perceive limited access to support systems. Survey results indicate several key themes that guided these interactions: silence, secrecy, stigma, positive communication, and embracing cultural differences. Suggestions for further research exploring how support systems are accessed, along with clinical implications for relational therapy are provided.
Confusion of Scale Development: Investigation of Self-Efficacy Scales

Bu arastirmanin genel amaci, egitim ve psikolojide kullanilan oz-yeterlik olceklerinin gelistirilmesi asamasinda izlenmesi gereken olcme araci gelistirme adimlarinin ne derece karsilandigini ortaya cikarmaktir. Bu genel amac dogrultusunda giris bilgileri, kuramsal kisim, madde yazimi, gecerlik ve guvenirlik ve ikinci uygulama olmak bes bolumle ilgili incelenen her calisma icin ilgili sorulara yanit aranmistir. Calisma kapsaminda dokuman analizi yapilmistir. Elektronik ortamdan ulasilabilen 1997-2018 yillari arasinda yerli ve yabanci literaturde yer alan oz-yeterlik olcegi gelistirme calismalari incelemeye alinmistir. Arastirmaya dahil edilecek calismalar secilirken olculmesi hedeflenen oz-yeterlik kavraminin egitim ve psikoloji ile iliskili olmasina, ve olcegin tum maddelerinin ulasilabilir olmasina dikkat edilmis, bu kriterlere uymayan calismalar arastirmaya dahil edilmemistir. Oz-yeterlik olcekleri incelendiginde siklikla gorulen eksikliklerden biri psikolojik yapinin isevuruk taniminin yapilmamasi oldugu gorulmustur. Ayrica olcek gecerligi calismalarinda siklikla karsilasilan hatanin ayni veri seti uzerinden AFA ve DFA yapilmasi oldugu gorulmustur. Yapilan hatalardan en goze carpanlardan bir digerinin ise olcek-anket kavram karmasasinin yasanmis olmasidir. Kavram karmasasinin olmasi olcegi gelistirenlerin henuz olcek ve anket ayrimini bilmedikleri anlamina gelmektedir.
Does the Use of Varenicline for Smoking-Cessation Therapy Create or Increase Depression in Patients Without Existing Depressive Illness?

OBJECTIVE: The objective of this selective EBM review is to determine whether or not the use of varenicline for smoking-cessation therapy creates or increases depression in patients without existing depressive illness. STUDY DESIGN: Review of two randomized controlled trials published in 2011 and one observational cohort study published in 2009, all English language. DATA SOURCES: Two randomized, double-blind, controlled clinical trials comparing varenicline to placebo in smoking cessation, and one observational cohort study comparing varenicline use within subjects. All articles were found using PubMed and EBSCO. OUTCOMES MEASURED: Changes in depression was evaluated using the MontgomeryAsberg Depression Rating Scale (MADRS), and adverse events were recorded and classified into depression-related according to the Medical Dictionary for Regulatory Activities version 12 and, in the observational cohort study, the British Drug Safety Research Unit standards. RESULTS: Bollinger et al. and Garza et al. demonstrated a present but nonsignificant increase in depressive adverse events associated with varenicline use. Garza et al. reported a similarly small and nonsignificant worsening in MADRS score in the varenicline arm. Kasliwal et al. reported a nonsignificant change in depressive adverse events. CONCLUSIONS: Results of the three studies show that there is inconclusive evidence regarding a link between varenicline and new-onset depression in smoking cessation. None of the studies demonstrated any significant relationship between varenicline and depression or depressive adverse events, but limitations in study design prevent the results from convincingly addressing such a relationship. The results encourage further studies designed both to assess varenicline’s relationship with depression and to account for the varenicline’s higher quit rate as a possible source of depressive changes. KEY WORDS: varenicline, depression, randomized, adverse Brown, Varenicline and Depression, 1
Accommodating National Identity:New Approaches in International and Domestic Law

Introduction S. Tierney. National Identity and the Internationalisation of Constitutional Structures. From Yugoslavia to Bosnia: Accommodating National Identity in National and International Law D. McGoldrick. Canada's Two Solitudes: Constitutional and International Law in Reference re Secession of Quebec P. Oliver. The Road Back to Hell: the international response to the crisis in Kosovo S. Tierney. National Identity and the Construction of Constitutions. Scottish Devolution: Accommodating a Restless Nation C.R. Munro. Constitutional Accommodation of Ethnicity and National Identity in Nepal S.P. Subedi. National Identity and Minority Rights: the Interaction of International and Domestic Law. Accommodating an Emergent National Identity: the Roma of Central and Eastern Europe I. Pogany. The Rights of Ethnic Minorities in Pakistan: a Legal Analysis S. Ali. Ancient Peoples and New Nations in the Russian Federation: Questions of Theory and Practice B. Bowring. National Identity: the Territorial Question in International and Domestic Law. Transcending Territory: Towards an Agreed Northern Ireland B. Thompson. National Identity & the International Law of Self-Determination: the Stratification of the Western Saharan `Self' J. Castellino. Concluding Remarks J. Rehman.
Can motivation and overtraining predict burnout in professional soccer athletes in different periods of the season?

Are motivation and overtraining reliable predictors of burnout in male professional soccer athletes over the course of the season? To answer this question, 32 Brazilian athletes from the same team (age: 24.16 ± 4.58 years) participated in this study and completed the following questionnaires during different periods of the season (n = 1 baseline, n = 2 preparatory, and n = 5 competitive): the Sport Motivation Scale (SMS I), the Recovery-Stress Questionnaire for Athletes (RESTQ-Sport 76), and the Athlete Burnout Questionnaire (ABQ). The results indicated positive and negative as well as moderate and strong associations among the dimensions of motivation, overtraining and burnout. Regression analysis was used to estimate three models during the season (baseline/preparatory, baseline/competitive, and preparatory/competitive). Model 1 showed that the amotivation dimension predicts emotional and physical exhaustion (SEE = 0.39; p = 0.001; = 0.38) and total burnout (SEE = 0.25; p = 0.001; = 0.32). Model 2 indicated that amotivation predicts emotional and physical exhaustion (SEE = 0.32; p = 0.002; = 0.24) and total burnout (SEE = 0.40; p = 0.001; = 0.35). Model 3 evidenced that amotivation and sport-specific stress predict emotional and physical exhaustion (SEE = 0.19; p = 0.001; = 0.73) and total burnout (SEE = 0.27; p = 0.001; = 0.72). In conclusion, it was verified that the dimensions of amotivation and sport-specific stress are effective markers to predict burnout in the preparatory and competitive periods.
The Model and Training Algorithm of Compact Drone Autonomous Visual Navigation System

Trainable visual navigation systems based on deep learning demonstrate potential for robustness of onboard camera parameters and challenging environment. However, a deep model requires substantial computational resources and large labelled training sets for successful training. Implementation of the autonomous navigation and training-based fast adaptation to the new environment for a compact drone is a complicated task. The article describes an original model and training algorithms adapted to the limited volume of labelled training set and constrained computational resource. This model consists of a convolutional neural network for visual feature extraction, extreme-learning machine for estimating the position displacement and boosted information-extreme classifier for obstacle prediction. To perform unsupervised training of the convolution filters with a growing sparse-coding neural gas algorithm, supervised learning algorithms to construct the decision rules with simulated annealing search algorithm used for finetuning are proposed. The use of complex criterion for parameter optimization of the feature extractor model is considered. The resulting approach performs better trajectory reconstruction than the well-known ORB-SLAM. In particular, for sequence 7 from the KITTI dataset, the translation error is reduced by nearly 65.6% under the frame rate 10 frame per second. Besides, testing on the independent TUM sequence shot outdoors produces a translation error not exceeding 6% and a rotation error not exceeding 3.68 degrees per 100 m. Testing was carried out on the Raspberry Pi 3+ single-board computer.
Dynamic voice directivity in room acoustic auralizations

The use of room acoustic auralizations has increased due to the improving computing power available and the quality of numerical modelling software. In such auralizations, it is often possible to prescribe the directivity of an acoustic source in order to better represent the way in which a given acoustic source excites the room. However, such directivities are static, being defined according to source excitation as a function of frequency for the numerical simulation. While sources such as pianos vary little over the course of playing, it is known that voice directivity varies, sometimes considerably, due to both phoneme dependent radiation patterns [1] linked to changes in mouth geometry and dynamic orientation.
Cross Disease Network Analysis

Diseases are caused by the deregulation of cellular networks, leading to faulty physiological functions. Different diseases can have common deregulated molecular pathways, particularly if they partially share phenotypes. These commonalities are frequently represented as an overlap of disease-related cellular subnetworks. The discovery of the overlaps and thus genes simultaneously associated with multiple diseases can shed light on the molecular pathomechanisms and provide new polyvalent therapeutic targets.This work builds upon a previously developed double specific-betweenness (S2B) network-based method, to prioritize proteins with a higher probability of being simultaneously associated with two phenotypically similar diseases. This method was designed to use undirected networks of protein physical interactions.The present work aims to expand the S2B method, enabling the analysis of networks with directed interactions, such as signaling and transcriptional regulatory cellular networks, providing new regulatory information and contributing to richer mechanistic hypothesis to explain the common physiological deficiencies. The new extended version of the method was tested with artificial disease modules, which enabled to test the S2B method ability to predict the overlap between the disease-related subnetworks. As in the undirected version of S2B, this novel version was applied to a pair of motor neuron diseases - Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) - demonstrating once again its potential to bring new insights into the common pathological mechanisms and particularly, its ability to retrieve novel disease genes associated with the disturbance of regulatory mechanisms involved in motor neuron degeneration.
DEVELOPMENT AND USABILITY OF A GAMIFIED MOBILE APP TO HELP CHILDREN MANAGE STRESS: AN EVALUATION STUDY

Children generally have difficulty managing stress. As a result, stress may escalate to anxiety. Informal-learning, stress prevention interventions that are easily accessible on mobile devices could be one way for children to learn how to manage stress before it reaches the levels of anxiety. There are less than a handful of stress management apps targeting children presently available and these do not combine gamification techniques with behavior change theory. This study describes the design and development of a gamified interactive storytelling mobile app to teach young children how to manage stress through relaxation exercises. It evaluates the app’s usability using learning analytics data and the SUS usability scale. The gamified app called Kids’ Stress Relief received a satisfactory usability score (73.55) and was well accepted by a sample of 71 children (5-12 years old). It may have the potential to support children in learning how to perform stress relief techniques as a stand-alone application. Instructional and design implications, of interest to developers of psychology-based apps, are drawn.
The Influence of music experience and body movement prior to ideational fluency.

The purpose of this study was to examine how shifting attention from the experience of music listening or body movement can work to overcome functional fixedness, i.e., to make people improve on ideational fluency the ability to combine knowledge objects, or fragments of these, to form new concepts compared to a traditional workplace meeting. The basic assumption was that music and body movement influence emotions to make people improve on ideational fluency compared to traditional meetings. The result, presented in this report, indicated that music listening suppressed functional fixedness to greater extent than a formal meeting, which in turn was a better choice compared to physical activity.
“The edge of harm and help”: ethical considerations in the care of transgender youth with complex family situations

Health-care providers frequently face clinical ethical dilemmas when working with transgender youth who require hormone therapy but lack parental support for this intervention. Through semi-structured interviews and grounded theory analysis, we explored ethical and clinical decision-making processes of health-care providers, as well as the health care experiences of trans youth with family discordance. We analyzed responses in relation to North American bioethics principles, best interests standard, and the harm principle, exploring issues of autonomy, evidence, and anti-trans bias. We propose an ethically acceptable clinical approach termed parallel process hormone therapy initiation to address the needs of transgender youth with complex family situations.
Studying the Robustness of the Triadic Trust Design with Mechanical Turk Subjects

This paper uses subjects recruited from an online employment exchange to study the robustness of the triadic trust design with a different subject pool. In running our experiments we tried to take advantage of the cost reducing features of the micro-employment culture found on Amazon’s Mechanical Turk. We find that first mover trust is robust to the change in subject pool, but second mover reciprocity was not.
Child abuse and neglect : diagnosis, treatment and evidence

Carole Jenny's "Child Abuse and Neglect: Diagnosis, Treatment and Evidence" focuses attention on the clinical evidence of child abuse to help you correctly diagnose and treat such cases in your own practice. This unique, well-illustrated clinical reference provides new insights into the presentation and differential diagnosis of physical abuse, a look at shaken baby syndrome, sex offenders and abuse in religious organizations, information on the biomechanics of injury, and more. Great for general review, as well as clinical reference, it's also ideal for those taking the American Board of Pediatrics' new subspecialty board exam in Child Abuse Pediatrics.