Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Cognition in Preschoolers: Effects of Early Experience and Individual Differences

Social cognition is the way in which people process, remember, and use information in social contexts to explain and predict their own behavior and that of others. Children’s social cognition may be influenced by multiple factors, both external and internal to the child. In the current study, two aspects of social cognition were examined: Theory of Mind and Emotion Understanding. The aim of this study was to analyze the effects of type of early care (0–3 years of age), maternal education, parents’ country of birth, and child’s language on the social cognition of 118 Italian preschoolers. To our knowledge, the joint effect of these variables on social cognition has not previously been investigated in the literature. The measures used to collect social cognition and linguistic data were not parent- or teacher-reports, but based on direct assessment of the children through two standardized tests, the Test of Emotion Comprehension and the ToM Storybooks. Relationships among the variables showed a complex pattern. Overall, maternal education and linguistic competence showed a systematic effect on social cognition; the linguistic competence mediated the effect of maternal education. In children who had experienced centre-base care in the first 3 years of life, the effect of maternal education disappeared, supporting the protective role of centre-base care for children with less educated mothers. The children with native and foreign parents did not significantly differ on the social cognition tasks. Limits of the study, possible educational outcomes and future research lines were discussed.
Effect of education on preventive behaviors of breast cancer in female teachers of guidance schools of Zahedan city based on health belief model

Background: Breast cancer is one of the most common cancers in females. Methods of screening are the best among early detection methods. The goal of this study is effect of education on preventive behaviors of breast cancer in female teachers of guidance schools of Zahedan city based on health belief model Materials and Methods: This study was a semi-experimental, a kind of case-control research. This study was carried on 240 female teachers in guidance schools, Zahedan city, in 2011-2012 academic years with multi-stage sampling. Data collection tool was a questionnaire that was used after confirmation of validity and reliability. Data were collected with questionnaire after analysis, educational intervention with lecture, view video, group discussion, question and answer performed. Two month after intervention, secondary evaluation was performed. Collected data with SPSS software and appropriate statistical tests like: Paired t-test, independent t-test, regression analysis, Chi-square were analyzed. Results: Persons mean age in this study was 39.40(±7.4) years. In awareness item and health belief model constructs (awareness, perceived susceptibility, perceived seriousness, perceived benefits, perceived barriers, self-efficiency, behavior) and also practice, paired t-test showed significant difference among before and after education (P > 0.0001). In two groups based on Chi-square in level of education and married status, there were no significant differences. Also, regression analysis outcomes showed that perceived barriers had the most effect on behavior, and this construct could be predictor of preventive behaviors from breast cancer. Conclusions: The findings of this study could conclude that educational programs designed based on the health belief model have significant impact on improving preventive treatment of breast cancer. Given the fact that Iran has a very high incidence of breast cancer, since Iranian women's awareness level and performance specially research samples according to educational level (associate degree and higher) and employment conditions as model for today girls and future mothers in breast cancer screening before educational intervention was very low. So, due to need, increasing women's awareness in health belief model framework to promote preventive behaviors is recommended.
Conversational Contextual Cues: The Case of Personalization and History for Response Ranking

We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant's history. Our system does not rely on handwritten rules or engineered features; instead, we train deep neural networks on a large conversational dataset. In particular, we exploit the structure of Reddit comments and posts to extract 2.1 billion messages and 133 million conversations. We evaluate our models on the task of predicting the next response in a conversation, and we find that modeling both context and participants improves prediction accuracy.
Using HMM based recognizers for writer identification and verification

In this paper, we use HMM based recognizers for the identification and verification of persons based on their handwriting. For each writer, we build an individual recognizer and train it on text lines of that writer. This gives us recognizers that are experts on the handwriting of exactly one writer. In the identification or verification phase, a text line of unknown origin is presented to each of these recognizers and each one returns a transcription that includes the log-likelihood score for the considered input. These scores are sorted and the resulting ranking is used for both identification and verification. In an identification experiment in 96.56% of all cases the writer out of a set of 100 writers is correctly identified. Second, in a verification experiment using over 8,600 text lines from 120 writers an equal error rate (EER) of about 2.5% is achieved.
Exploring the effects of space and place on engagement with an interactive installation

Very little research has concurrently explored the influence of both physical space and social context (or place) on the way people engage with a public interactive display. We addressed this issue with a novel approach: studying how people engaged with the same interactive installation in ten situations with varying spatial and social properties. The main finding across these studies is that place trumps space: a conducive social context could overcome a poor physical space and encourage interaction; conversely, an inappropriate social context could inhibit interaction in spaces that might normally facilitate engagement. We discuss this finding in terms of the salience of the display within the space, the visibility of incidental interactions with the installation, the different understandings of place that people can have in the same location and the role of emergent champions and comperes in encouraging interaction.
Provis: towards an Environment for Object-oriented Visual Programming

The paper presents the Provis (\Programming visually") tool for object-oriented visual programming and some of the ideas behind it. Provis is based on object diagrams from Rumbaugh's \Object Modeling Technique" (OMT) 18] and on dataaow diagrams (DFDs) enhanced with control ows, logical input and output connectives, as well as object icons. Object diagrams specify class structures, while enhanced DFDs specify class interfaces and method implementations. The tool generates | and eventually will also parse | C++ source code, and it has been designed to provide some degree of freedom with respect to source-code language. Work on Provis has been motivated by several concurring trends in the software engineering eld, including the need to eliminate phase gaps in software development work, the availability of powerful , high-level target platform software, the gradual merging of design and implementation tasks, as well as the increasing interest for object-oriented programming (OOP) and visual programming (VP) in general. In particular, Provis distinguishes itself from the existing plethora of similar visual programming environments through its application of diagramming techniques already used in the earlier stages of software development. In this way, Provis aims at integrating object-oriented programming more closely with analysis and design work.
Probabilistic and Bottle-Neck Features for LVCSR of Meetings

In recent years, probabilistic features became an integral part of state-of-the-are LVCSR systems. In this work, we are exploring the possibility of obtaining the features directly from neural net without the necessity of converting output probabilities to features suitable for subsequent GMM-HMM system. We experimented with 5-layer MLP with bottle-neck in the middle layer. After training such a neural net, we used outputs of the bottle-neck as features for GMM-HMM recognition system. The benefits are twofold: first, improvement was gained when these features are used instead of the probabilistic features, second, the size of the system was reduced, as only part of the neural net is used. The experiments were performed on meetings recognition task defined in MST RT'05 evaluation.
Learning and Design of Principal Curves

Principal curves have been defined as "self-consistent" smooth curves which pass through the "middle" of a d-dimensional probability distribution or data cloud. They give a summary of the data and also serve as an efficient feature extraction tool. We take a new approach by defining principal curves as continuous curves of a given length which minimize the expected squared distance between the curve and points of the space randomly chosen according to a given distribution. The new definition makes it possible to theoretically analyze principal curve learning from training data and it also leads to a new practical construction. Our theoretical learning scheme chooses a curve from a class of polygonal lines with k segments and with a given total length to minimize the average squared distance over n training points drawn independently. Convergence properties of this learning scheme are analyzed and a practical version of this theoretical algorithm is implemented. In each iteration of the algorithm, a new vertex is added to the polygonal line and the positions of the vertices are updated so that they minimize a penalized squared distance criterion. Simulation results demonstrate that the new algorithm compares favorably with previous methods, both in terms of performance and computational complexity, and is more robust to varying data models.
Evaluation of txt2MEDLINE and development of short messaging service-optimized, clinical practice guidelines in Botswana.

OBJECTIVE Currently clinicians in sub-Saharan Africa have limited access to the Internet, whereas mobile phone access and use is extensive. The University of Pennsylvania in collaboration with the National Library of Medicine launched txt2MEDLINE, a short messaging service (SMS) query of PubMed/MEDLINE, and SMS-optimized clinical guidelines in Botswana. The objective of this project was to establish and evaluate the utility of these tools for clinicians in Botswana. MATERIALS AND METHODS A local server was established at the University of Botswana that allowed clinicians to send queries and receive results via local (in-country) SMS text messaging on any type of cellular phone. The queries sent via txt2MEDLINE were returned as abbreviated "the bottom line" summaries of abstracts. The 2007 Botswana Treatment Guide was converted into a format that can be queried by SMS. Various types of healthcare workers were recruited to use and evaluate these services. RESULTS Seventy-six healthcare workers attended training sessions for these services. In the preusage survey, most said they would use the services daily or weekly. During a 4-week trial period, use of these services dropped off dramatically. Participant feedback was collected and indicated that improvements in ease of use would increase the usage. CONCLUSIONS This pilot project enables clinicians to query and receive PubMed abstract summaries and country-specific clinical guidelines using mobile phones. Feedback offers insight on how to improve this technology so that it can be adopted for long-term use. With further adjustments, these resources may provide an effective working model for other countries where limited Internet access impedes upon patient care.
Stability Analysis of Laminated Soft Core Sandwich Plates Using Higher Order Zig-Zag Plate Theory

C0 finite element model based on higher order zig-zag plate theory is used to study the stability analysis of laminated sandwich plates. The in-plane displacement field is obtained by superposing a global cubically varying displacement field on a zig-zag linearly varying displacement field with different slope in each layer. The transverse displacement assumes to have a quadratic variation within the core and constant in the faces. The conditions regarding transverse shear stress at layer interfaces and top and bottom are satisfied. Numerical examples covering different features of laminated sandwich plates are presented to illustrate the accuracy of the model.
Semi-supervised Regression with Order Preferences

Following a discussion on the general form of regularization for semi-supervised learning, we propose a semi-supervised regression algorithm. It is based on the assumption that we have certain order preferences on unlabeled data (e.g., point x1 has a larger target value than x2). Semi-supervised learning consists of enforcing the order preferences as regularization in a risk minimization framework. The optimization problem can be effectively solved by a linear program. Experiments show that the proposed semi-supervised regression outperforms standard regression. 1 Semi-supervised learning as regularization on unlabeled data Semi-supervised learning works when its assumption on unlabeled data, often expressed as regularization, fits the reality of the problem domain. In this paper we first generalize the regularization formulation of some common semi-supervised learning approaches, namely manifold regularization, semi-supervised support vector machines, and multi-view learning [1, 2, 3]. Regularization for each individual approach is not new. However these approaches have been studied largely in isolation. Our general form serves as a bridge to connect them, and to inspire novel semi-supervised approaches. As an example of the latter, we propose a novel algorithm for semi-supervised regression. The proposed regression algorithm is able to incorporate domain knowledge about the relative order of target values on unlabeled points. It thus differs from, and complements, existing semi-supervised regression methods, which do not use such domain knowledge but require multiple views [4, 5]. Let us review the three common semi-supervised learning methods. Manifold regularization [6, 7] generalizes several graph-based semi-supervised learning methods. Let l be the number of labeled points, u the number of unlabeled points. Graph-based semi-supervised learning requires a weighted, undirected graph, characterized by an (l + u) × (l + u) weight matrix W defined on labeled and unlabeled data. It is assumed that from the features of two points xi, xj (e.g., by computing their Euclidean distance), domain experts can assign a non-negative weight wij . A large wij implies a preference for f(xi), f(xj) to be similar. Therefore subgraphs with large weights tend to have the same label. This is sometimes called the cluster assumption. Let K be a kernel and H the corresponding Reproducing Kernel Hilbert Space (RKHS). Let y be the labels, which can be categories for classification or real numbers for regression. Manifold regularization seeks a prediction function f ∈ H, such that f is the solution to
Macro- and Microstructure of the Superior Cervical Ganglion in Dogs, Cats and Horses during Maturation

The superior cervical ganglion (SCG) provides sympathetic input to the head and neck, its relation with mandible, submandibular glands, eyes (second and third order control) and pineal gland being demonstrated in laboratory animals. In addition, the SCG’s role in some neuropathies can be clearly seen in Horner’s syndrome. In spite of several studies published involving rats and mice, there is little morphological descriptive and comparative data of SCG from large mammals. Thus, we investigated the SCG’s macro- and microstructural organization in medium (dogs and cats) and large animals (horses) during a very specific period of the post-natal development, namely maturation (from young to adults). The SCG of dogs, cats and horses were spindle shaped and located deeply into the bifurcation of the common carotid artery, close to the distal vagus ganglion and more related to the internal carotid artery in dogs and horses, and to the occipital artery in cats. As to macromorphometrical data, that is ganglion length, there was a 23.6% increase from young to adult dogs, a 1.8% increase from young to adult cats and finally a 34% increase from young to adult horses. Histologically, the SCG’s microstructure was quite similar between young and adult animals and among the 3 species. The SCG was divided into distinct compartments (ganglion units) by capsular septa of connective tissue. Inside each ganglion unit the most prominent cellular elements were ganglion neurons, glial cells and small intensely fluorescent cells, comprising the ganglion’s morphological triad. Given this morphological arrangement, that is a summation of all ganglion units, SCG from dogs, cats and horses are better characterized as a ganglion complex rather than following the classical ganglion concept. During maturation (from young to adults) there was a 32.7% increase in the SCG’s connective capsule in dogs, a 25.8% increase in cats and a 33.2% increase in horses. There was an age-related increase in the neuronal profile size in the SCG from young to adult animals, that is a 1.6-fold, 1.9-fold and 1.6-fold increase in dogs, cats and horses, respectively. On the other hand, there was an age-related decrease in the nuclear profile size of SCG neurons from young to adult animals (0.9-fold, 0.7-fold and 0.8-fold in dogs, cats and horses, respectively). Ganglion connective capsule is composed of 2 or 3 layers of collagen fibres in juxtaposition and, as observed in light microscopy and independently of the animal’s age, ganglion neurons were organised in ganglionic units containing the same morphological triad seen in light microscopy.
Effect of right ventricular pacing lead site on left ventricular function in patients with high-grade atrioventricular block: results of the Protect-Pace study.

AIM Chronic right ventricle (RV) apical (RVA) pacing is standard treatment for an atrioventricular (AV) block but may be deleterious to left ventricle (LV) systolic function. Previous clinical studies of non-apical pacing have produced conflicting results. The aim of this randomized, prospective, international, multicentre trial was to compare change in LV ejection fraction (LVEF) between right ventricular apical and high septal (RVHS) pacing over a 2-year study period. METHODS AND RESULTS We randomized 240 patients (age 74 ± 11 years, 67% male) with a high-grade AV block requiring >90% ventricular pacing and preserved baseline LVEF >50%, to receive pacing at the RVA (n = 120) or RVHS (n = 120). At 2 years, LVEF decreased in both the RVA (57 ± 9 to 55 ± 9%, P = 0.047) and the RVHS groups (56 ± 10 to 54 ± 10%, P = 0.0003). However, there was no significant difference in intra-patient change in LVEF between confirmed RVA (n = 85) and RVHS (n = 83) lead position (P = 0.43). There were no significant differences in heart failure hospitalization, mortality, the burden of atrial fibrillation, or plasma brain natriutetic peptide levels between the two groups. A significantly greater time was required to place the lead in the RVHS position (70 ± 25 vs. 56 ± 24 min, P < 0.0001) with longer fluoroscopy times (11 ± 7 vs. 5 ± 4 min, P < 0.0001). CONCLUSION In patients with a high-grade AV block and preserved LV function requiring a high percentage of ventricular pacing, RVHS pacing does not provide a protective effect on left ventricular function over RVA pacing in the first 2 years. PROTECT-PACE ClinicalTrials.gov number NCT00461734.
Maximizing Cooperative Diversity Energy Gain for Wireless Networks

We are concerned with optimally grouping active mobile users in a two-user-based cooperative diversity system to maximize the cooperative diversity energy gain in a radio cell. The optimization problem is formulated as a non-bipartite weighted-matching problem in a static network setting. The weighted-matching problem can be solved using maximum weighted (MW) matching algorithm in polynomial time O(n3). To reduce the implementation and computational complexity, we develop a Worst-Link-First (WLF) matching algorithm, which gives the user with the worse channel condition and the higher energy consumption rate a higher priority to choose its partner. The computational complexity of the proposed WLF algorithm is O(n) while the achieved average energy gain is only slightly lower than that of the optimal maximum weighted- matching algorithm and similar to that of the 1/2-approximation Greedy matching algorithm (with computational complexity of O(n2 log n)) for a static-user network. We further investigate the optimal matching problem in mobile networks. By intelligently applying user mobility information in the matching algorithm, high cooperative diversity energy gain with moderate overhead is possible. In mobile networks, the proposed WLF matching algorithm, being less complex than the MW and the Greedy matching algorithms, yields performance characteristics close to those of the MW matching algorithm and better than the Greedy matching algorithm.
Analysis of the 16S–23S rRNA Gene Internal Transcribed Spacer Region in Klebsiella Species

ABSTRACT The 16S-23S rRNA gene internal transcribed spacer (ITS) regions of Klebsiella spp. , including Klebsiella pneumoniae subsp. pneumoniae, Klebsiella pneumoniae subsp. ozaenae, Klebsiella pneumoniae subsp. rhinoscleromatis, Klebsiella oxytoca, Klebsiella planticola, Klebsiella terrigena, and Klebsiella ornithinolytica, were characterized, and the feasibility of using ITS sequences to discriminate Klebsiella species and subspecies was explored. A total of 336 ITS sequences from 21 representative strains and 11 clinical isolates of Klebsiella were sequenced and analyzed. Three distinct ITS types—ITSnone (without tRNA genes), ITSglu [with a tRNAGlu (UUC) gene], and ITSile+ala [with tRNAIle (GAU) and tRNAAla (UGC) genes]—were detected in all species except for K. pneumoniae subsp. rhinoscleromatis, which has only ITSglu and ITSile+ala. The presence of ITSnone in Enterobacteriaceae had never been reported before. Both the length and the sequence of each ITS type are highly conserved within the species, with identity levels from 0.961 to 1.000 for ITSnone, from 0.967 to 1.000 for ITSglu, and from 0.968 to 1.000 for ITSile+ala. Interspecies sequence identities range from 0.775 to 0.989 for ITSnone, from 0.798 to 0.997 for ITSglu, and from 0.712 to 0.985 for ITSile+ala. Regions with significant interspecies variations but low intraspecies polymorphisms were identified; these may be targeted in the design of probes for the identification of Klebsiella to the species level. Phylogenetic analysis based on ITS regions reveals the relationships among Klebsiella species similarly to that based on 16S rRNA genes.