Miracle-dz/UniFAR-FTUs huggingface dataset:
The Complementary Relationships between Reading and Writing in Children with and without Writing Difficulties

This chapter deals with the relations between the processes of reading and writing, and thus the relationbetween reading andwritingdifficulties.Whereas the onset of spoken language development is assumed to happen “naturally” and with little effort for most children, the onset of written language acquisition generally happens later, and formany childrennot until they have received instruction in school. Children in different countries start school at different ages and acquire orthographies of different complexities concerning phonology, orthography, morphology and syllable structure. Therefore, different language systems will pose different challenges to writers and readers at different stages of the developmental process (Arfé, Dockrell, & Berninger, 2014). For example, in shallow orthographies spelling and decoding will bemastered earlier than in deep orthographies (e.g., Babayigit & Stainthorp, 2011), and this might to some extent affect the development of higher level processes, such as meaning-making processes in reading and writing. What seems to be relatively universal in languageswith alphabetic orthographies is however, that whereas some children learn to read and write before they start school and some even appear to crack the code more or less overnight, many do not and therefore require more explicit instruction. The period before starting to read and write is generally known as the emergent-literacy period, during which the activities of reading and writing cannot always be disentangled from each other. Research on emergent literacy is a vast research field on its own that can likely offer explanations for some of the phenomena discussed in this chapter.While keeping this inmind,wehave chosen todelimit our chapter to research dealing with the activities often described as “conventional” reading and writing, as defined by McGee & Richgels (1996):
Metric Learning across Heterogeneous Domains by Respectively Aligning Both Priors and Posteriors

In this paper, we attempts to learn a single metric across two heterogeneous do-mains where source domain is fully labeled and has many samples while targetdomain has only a few labeled samples but abundant unlabeled samples. Tothe best of our knowledge, this task is seldom touched. The proposed learningmodel has a simple underlying motivation: all the samples in both the sourceand the target domains are mapped into a common space, where both theirpriors P(sample)s and their posteriors P(label|sample)s are forced to be re-spectively aligned as much as possible. We show that the two mappings, fromboth the source domain and the target domain to the common space, can bereparameterized into a single positive semi-deﬁnite(PSD) matrix. Then we de-velop an eﬃcient Bregman Projection algorithm to optimize the PDS matrixover which a LogDet function is used to regularize. Furthermore, we also showthat this model can be easily kernelized and verify its eﬀectiveness in cross-language retrieval task and cross-domain object recognition task.
Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes

The ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process (SEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures the triggering effect of each forwarding, distinguishing itself from the reinforced Poisson process based model where all previous forwardings are simply aggregated as a single triggering effect. We validate the proposed model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that the SEHP model consistently outperforms the model based on reinforced Poisson process.
Deciphering Host Genotype-Specific Impacts on the Metabolic Fingerprint of Listeria monocytogenes by FTIR Spectroscopy

Bacterial pathogens are known for their wide range of strategies to specifically adapt to host environments and infection sites. An in-depth understanding of these adaptation mechanisms is crucial for the development of effective therapeutics and new prevention measures. In this study, we assessed the suitability of Fourier Transform Infrared (FTIR) spectroscopy for monitoring metabolic adaptations of the bacterial pathogen Listeria monocytogenes to specific host genotypes and for exploring the potential of FTIR spectroscopy to gain novel insights into the host-pathogen interaction. Three different mouse genotypes, showing different susceptibility to L. monocytogenes infections, were challenged with L. monocytogenes and re-isolated bacteria were subjected to FTIR spectroscopy. The bacteria from mice with different survival characteristics showed distinct IR spectral patterns, reflecting specific changes in the backbone conformation and the hydrogen-bonding pattern of the protein secondary structure in the bacterial cell. Coupling FTIR spectroscopy with chemometrics allowed us to link bacterial metabolic fingerprints with host infection susceptibility and to decipher longtime memory effects of the host on the bacteria. After prolonged cultivation of host-passaged bacteria under standard laboratory conditions, the host's imprint on bacterial metabolism vanished, which suggests a revertible metabolic adaptation of bacteria to host environment and loss of host environment triggered memory effects over time. In summary, our work demonstrates the potential and power of FTIR spectroscopy to be used as a fast, simple and highly discriminatory tool to investigate the mechanism of bacterial host adaptation on a macromolar and metabolic level.
The open-air trance party experience and self-image, body image, and subjective well-being

The trance-dancing culture in Israel is expressed by dance parties held in open, rather than closed, spaces. This research wished to investigate the effect of open-air dancing on self-image, body image, and subjective well-being. This is a combined quantitative and qualitative research study, with 72 participants, 25 males and 47 females, ages 18–30. The results indicate a positive correlation between the degree to which young people prefer open-air parties and the level of their body image, but no positive connection was found between self-image and participation in open-air parties. Young people who greatly prefer participating in open-air parties report a higher level of subjective well-being.
Serotonin targets inhibitory synapses to induce modulation of network functions

The cellular effects of serotonin (5-HT), a neuromodulator with widespread influences in the central nervous system, have been investigated. Despite detailed knowledge about the molecular biology of cellular signalling, it is not possible to anticipate the responses of neuronal networks to a global action of 5-HT. Heterogeneous expression of various subtypes of serotonin receptors (5-HTR) in a variety of neurons differently equipped with cell-specific transmitter receptors and ion channel assemblies can provoke diverse cellular reactions resulting in various forms of network adjustment and, hence, motor behaviour. Using the respiratory network as a model for reciprocal synaptic inhibition, we demonstrate that 5-HT1AR modulation primarily affects inhibition through glycinergic synapses. Potentiation of glycinergic inhibition of both excitatory and inhibitory neurons induces a functional reorganization of the network leading to a characteristic change of motor output. The changes in network operation are robust and help to overcome opiate-induced respiratory depression. Hence, 5-HT1AR activation stabilizes the rhythmicity of breathing during opiate medication of pain.
Frontostriatal Mechanisms in Instruction-Based Learning as a Hallmark of Flexible Goal-Directed Behavior

The present review intends to provide a neuroscientific perspective on the flexible (here: almost instantaneous) adoption of novel goal-directed behaviors. The overarching goal is to sketch the emerging framework for examining instruction-based learning and how this can be related to more established research approaches to instrumental learning and goal-directed action. We particularly focus on the contribution of frontal and striatal brain regions drawing on studies in both, animals and humans, but with an emphasize put on human neuroimaging studies. In section one, we review and integrate a selection of previous studies that are suited to generally delineate the neural underpinnings of goal-directed action as opposed to more stimulus-based (i.e., habitual) action. Building on that the second section focuses more directly on the flexibility to rapidly implement novel behavioral rules as a hallmark of goal-directed action with a special emphasis on instructed rules. In essence, the current neuroscientific evidence suggests that the prefrontal cortex and associative striatum are able to selectively and transiently code the currently relevant relationship between stimuli, actions, and the effects of these actions in both, instruction-based learning as well as in trial-and-error learning. The premotor cortex in turn seems to form more durable associations between stimuli and actions or stimuli, actions and effects (but not incentive values) thus representing the available action possibilities. Together, the central message of the present review is that instruction-based learning should be understood as a prime example of goal-directed action, necessitating a closer interlacing with basic mechanisms of goal-directed action on a more general level.
Inverse proximity effect in superconductors near ferromagnetic material

We study the electronic density of states in a mesoscopic superconductor near a transparent interface with a ferromagnetic metal. In our tunnel spectroscopy experiment, a substantial density of states is observed at sub-gap energies close to a ferromagnet. We compare our data with detailed calculations based on the Usadel equation, where the effect of the ferromagnet is treated as an effective boundary condition. We achieve an excellent agreement with theory when non-ideal quality of the interface is taken into account.
Rotation Invariant Spatial Pyramid Matching for Image Classification

This paper proposes a new Spatial Pyramid representation approach for image classification. Unlike the conventional Spatial Pyramid, the proposed method is invariant to rotation changes in the images. This method works by partitioning an image into concentric rectangles and organizing them into a pyramid. Each pyramidal region is then represented using a histogram of visual words. Our experimental results show that our proposed method significantly outperforms the conventional method.
Characterization of the script using adjacent local binary patterns

The paper proposed an algorithm for the identification of the script by adjacent local binary patterns (ALBP). In the first phase, each letter in the text is modeled with the so-called script type, which is based on its status in the baseline area. Then, the feature extraction is made with the adjacent local binary pattern (ALBP). According to ALBP, the distinctive features of the script are set and stored for further analysis. Because of the difference in script characteristics, the analysis shows significant diversity between different scripts. Hence, it represents the key point for decision-making process of script identification. The proposed method is tested on the example of old Slavic printed documents, which contain Latin and Glagolitic script. The results of experiments are encouraging.
Investigating the links between resilience, perceived HRM practices, and retirement intentions

Purpose Human resource management (HRM) scholars’ interest in older employees’ resilience has only recently started to emerge. Little is known about how resilience and perceived HRM are linked to different retirement intentions. Drawing on the conservation of resources and social exchange theories, the purpose of this paper is to investigate the links between perceived HRM practices, resilience and retirement intentions. Additionally, the paper examines the possible mediating role of resilience in the relationship between perceived HRM practices and retirement intentions. Design/methodology/approach In 2016, a cross-sectional study was conducted among older (50+) nursing professionals working in a Finnish university hospital. Statistical methods, including mean comparisons and linear and logistic regression analyses, were used to analyze the data. Findings The results indicated that resilience partly mediated the relationship between perceived HRM practices and early retirement intentions, and fully mediated the association between perceived HRM practices and intentions to continue working after retirement age. Originality/value This study produces new knowledge regarding the links between resilience, perceived High involvement work practices and retirement intentions.
Combining Dictionary- and Corpus-Based Concept Extraction

Concept extraction is an increasingly popular topic in deep text analysis. Concepts are individual content elements. Their extraction offers thus an overview of the content of the material from which they were extracted. In the case of domain-specific material, concept extraction boils down to term identification. The most straightforward strategy for term identification is a look up in existing terminological resources. In recent research, this strategy has a poor reputation because it is prone to scaling limitations due to neologisms, lexical variation, synonymy, etc., which make the terminology to be submitted to a constant change. For this reason, many works developed statistical techniques to extract concepts. But the existence of a crowdsourced resource such as Wikipedia is changing the landscape. We present a hybrid approach that combines state-of-the-art statistical techniques with the use of the large scale term acquisition tool BabelFy to perform concept extraction. The combination of both allows us to boost the performance, compared to approaches that use these techniques separately.
AN ONTOLOGICAL MODEL FOR REPRESENTING SEMANTIC LEXICONS: AN APPLICATION ON TIME NOUNS IN THE HOLY QURAN

ABSTRACT Although Arabic is the language of over two hundred million speakers, little has been achieved in regards to computational Arabic resources, especially lexicons. Most of what has been developed was originally tailored for Roman languages, and is not necessarily satisfactory for the Arabic community. In this research, we propose a computational model for representing Arabic lexicons using ontologies. Ontologies are knowledge representation structures which form the central building block of the Semantic Web. The model is based on the field theory of semantics from the linguistics domain, and the data which drives the design of the model is obtained from the most accurate text that presents superiority and perfection of the Arabic language, the Holy Quran. Creating such lexicons will be invaluable in a number of Arabic applications. This paper presents the design and implementation of the proposed ontological model. Results of its application on “Time nouns” vocabulary of the Holy Quran are presented.
Decision tree and instance-based learning for label ranking

The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.
Simultaneous Solution of the RNA Folding, Alignment and Protosequence Problems

The alignment of finite sequences, the inference of ribonucleic acid secondary structures (folding), and the reconstruction of ancestral sequences on a phylogenetic tree, are three problems which have dynamic programming solutions, which we formulate in a common mathematical framework. Combining the objective functions for alignment (parsimony, or minimal mutations) and folding (free energy), we present an algorithm which solves all three problems simultaneously for a set of N sequences of length n in time proportional to $n^{3N} $ and storage $n^{2N} $. Incorporating a “cutting corners” constraint against biologically unlikely alignments reduces these requirements so that they are proportional to $n^{3} $ and $n^{2} $, respectively, for fixed N.
Which is the best dual-port SRAM in 45-nm process technology? — 8T, 10T single end, and 10T differential —

This paper compares readout powers and operating frequencies among dual-port SRAMs: an 8T SRAM, 10T single-end SRAM, and 10T differential SRAM. The conventional 8T SRAM has the least transistor count, and is the most area efficient. However, the readout power becomes large and the cycle time increases due to peripheral circuits. The 10T single-end SRAM is our proposed SRAM, in which a dedicated inverter and transmission gate are appended as a single-end read port. The readout power of the 10T single-end SRAM is reduced by 75% and the operating frequency is increased by 95%, over the 8T SRAM. On the other hand the 10T differential SRAM can operate fastest, because its small differential voltage of 50 mV achieves the high-speed operation. In terms of the power efficiency, however, the sense amplifier and precharge circuits lead to the power overhead. As a result, the 10T single-end SRAM always consumes lowest readout power compared to the 8T and the 10T differential SRAM.
Impact of Work Function Fluctuations on Threshold Voltage Variability in a Nanoscale FinFETs

In todays advanced nanoscale regime, use of metal gate stacks have a different impact on the intrinsic parameter variability. The metal gates have a natural granular structure, where work function of each grain depends upon its orientation. Circuit and device designing are limited by the threshold voltage variability. In this study, threshold voltage variability induced by work function fluctuations of metal for different grain sizes (10, 7 and 5 nm) in a 14 nm gate length FinFETs device is analyzed using 3-D drift-diffusion device simulator. It is observed that with a reduction in grain size threshold voltage variability reduces linearly. It is seen that the different Fin shapes have approximately ~6% shift in the threshold voltage where as there is approximately ~16% improvement in the standard deviation of the threshold voltage. Further, on reducing the average grain size from 10 nm down to 5 nm results in an approximately ~45% reduction in variability induced by work function.
Coronary artery stents: influence of adaptive statistical iterative reconstruction on image quality using 64-HDCT.

OBJECTIVE The assessment of coronary stents with present-generation 64-detector row computed tomography (HDCT) scanners is limited by image noise and blooming artefacts. We evaluated the performance of adaptive statistical iterative reconstruction (ASIR) for noise reduction in coronary stent imaging with HDCT. METHODS AND RESULTS In 50 stents of 28 patients (mean age 64 ± 10 years) undergoing coronary CT angiography (CCTA) on an HDCT scanner the mean in-stent luminal diameter, stent length, image quality, in-stent contrast attenuation, and image noise were assessed. Studies were reconstructed using filtered back projection (FBP) and ASIR-FBP composites. ASIR resulted in reduced image noise vs. FBP (P < 0.0001). Two readers graded the CCTA stent image quality on a 4-point Likert scale and determined the proportion of interpretable stent segments. The best image quality for all clinical images was obtained with 40 and 60% ASIR with significantly larger luminal area visualization compared with FBP (+42.1 ± 5.4% with 100% ASIR vs. FBP alone; P < 0.0001) while the stent length was decreased (-4.7 ± 0.9%, <P = 0.002) and volume measurements were unaffected. CONCLUSION Reconstruction of CCTA from HDCT using 40 and 60% ASIR incrementally improves intra-stent luminal area, diameter visualization, and image quality compared with FBP reconstruction.
The splitting theorem for orbifolds

In this paper we wish to examine a generalization of the splitting theorem of Cheeger–Gromoll [CG] to Riemannian orbifolds. Roughly speaking, a Riemannian orbifold is a metric space locally modelled on quotients of Riemannian manifolds by finite groups of isometries. The term orbifold was coined by W. Thurston [T] sometime around the year 1976–77. The term is meant to suggest the orbit space of a group action on a manifold. A similar concept was introduced by I. Satake in 1956, where he used the term V– manifold (See [S1]). The “V” was meant to suggest a cone–like singularity. Since then, orbifold has become the preferred terminology. Recall that if M is a complete connected n–dimensional Riemannian manifold with nonnegative Ricci curvature that contains a line, then the Cheeger– Gromoll Splitting Theorem [CG] states that that M is isometric to N × R. Recall that a line is a unit speed geodesic γ : R → M such that for any s, t ∈ R, d(γ(s), γ(t)) = |s− t|.
Cohesion and leadership in individual sports: a social network analysis of participation in recreational running groups

ABSTRACT Sixty percent of recreational runners in the United States use running groups to participate in the sport. Within these groups, cohesion, or the process of working together to achieve goals and objectives, can develop. The purpose of this study was to investigate the levels of cohesion occurring among recreational runners using running groups to train for a running event. This study used social network analysis to assess task cohesion (achieving collective goals) and social cohesion (developing relationships among group members) in two running groups. The groups exhibited cohesion; task cohesion exceeded social cohesion. Informal leaders emerged alongside formal leaders in both groups. Running group organizers can use these findings to increase task cohesion and shared leadership in their groups. The results add to the few studies examining cohesion in individual sports and extend the application of social network analysis in sport research.