Miracle-dz/UniFAR-FTUs huggingface dataset:
The semantic control network mediates the relationship between symbolic numerical order processing and arithmetic performance in children

Behavioral and neuroimaging studies have recently demonstrated that symbolic numerical order processing (i.e., deciding whether numbers are in an increasing/decreasing sequence or not) may engages different cognitive mechanisms and brain regions compared to symbolic numerical magnitude processing (e.g., deciding which of two numerals is larger). Because of this behavioral dissociation, growing interest has emerged to better understand the neurocognitive mechanisms of symbolic numerical order processing and their relationship to individual differences in arithmetic performance. In the present functional imaging work we further investigated this link in a group of thirty children (7.2-10.25 years) from elementary school, who completed a symbolic numerical order verification (are the numbers going up? e.g., 1-2-3), a symbolic numerical magnitude comparison task (which is the larger number? e.g., 5-7), as well as an arithmetic fluency test outside the scanner. Behavioral results demonstrated the unique role of numerical order to predict children’s arithmetic skills and confirmed its mediating power to explain the association between numerical magnitude and arithmetic performance. Imaging results showed a significant association between numerical order and arithmetic in the intersection of the right inferior frontal gyrus and insula, as well as the posterior middle temporal gyrus. An age-dependent change in brain activity was found in the left intraparietal sulcus. These findings solidify the developmental importance of symbolic numerical order processing in children and provides new evidence that the semantic control network may mediates the relationship with arithmetic performance. Highlights Reaction times of numerical order are a unique predictor of arithmetic (73) Numerical order mediates the relationship of numerical magnitude with arithmetic (83) Brain activation of numerical order processing changes with age in the left IPS (82) The semantic control network mediates the relationship with arithmetic (79)
Learning Features that Predict Developer Responses for iOS App Store Reviews

Background: Which aspects of an iOS App Store user review motivate developers to respond? Numerous studies have been conducted to extract useful information from reviews, but limited effort has been expended to answer this question. Aims: This work aims to investigate the potential of using a machine learning algorithm and the features that can be extracted from user reviews to model developers' response behavior. Through this process, we want to uncover the learned relationship between these features and developer responses. Method: For our prediction, we run a random forest algorithm over the derived features. We then perform a feature importance analysis to understand the relative importance of each individual feature and groups thereof. Results: Through a case study of eight popular apps, we show patterns in developers' response behavior. Our results demonstrate not only that rating and review length are among the most important but also that review posted time, sentiment, and the writing style play an important role in the response prediction. Additionally, the variation in feature importance ranking implies that different app developers use different feature weights when prioritizing responses. Conclusions: Our results may provide guidance for those building review or response prioritization tools and developers wishing to prioritize their responses effectively.
The Use of Wavelets to Facilitate Finite Element Post-Processing

In modern computational mechanics, typically, but not exclusively using Finite Element techniques, solution parameters are tabulated for discrete points in the problem domain. A typical large dynamic analysis may have a complex 3D geometry modelled with a million elements, with results tabulated over a large number of time steps. From this enormous 4D data set, the stress engineer must select appropriate cross-sections in order to visualise the most significant features. The choice of such cross-sections is usually subjective, based on experience and engineering intuition. Typically, the data set will be manipulated in a high specification server, and the graphical information will be pushed down the network to engineer's local machine. However, there are three practical disadvantages: ○ the graphical computation can be time consuming - each cross-section through the model can take several minutes to load, ○ selection of views is by trial and error, and ○ interactive demands on the server interfere with its performance on the other large finite element analyses, which it is processing. For the purpose of exploring the solution domain, faster computer response is desirable. A lower fidelity model of the post-processing data may be acceptable for this purpose, and the graphical display of a model of sufficiently reduced size could be managed by the local workstation. Wavelets have been used with great success for 2D image compression, with compressions to 5% of original data still giving visually acceptable images. This leads us to expect that for 4D compression, a reduced model of 0.25% of the original size may be practical. Wavelets have the advantage over other methods of image compression in that sharp contrasts are preserved. It is, therefore expected that areas which would be of interest to the stress engineer, for example rapid changes in the stress field, high deformation rates, etc., would be preserved in high fidelity. This feature of wavelets may be further harnessed, to give a basis on which some of the most significant cross-sectional views may be automatically computed and presented to the engineer at the beginning of the post-processing stage. This paper will explore the amount of compression practically achievable, and describe the algorithms required to achieve this. The results will be shown in the form of a demonstration analysis, with both high and low fidelity results presented side by side.
Dense pose estimation of deformable objects

Statistical Deformable Models (SDMs) represent a widely used collection of computer vision techniques. SDMs serve the purpose of modeling the deformation and variability of a specific class of objects, e.g. faces, ears, hands, human poses, etc. SDMs have shown success in many areas of visual image analysis. For example, in biometrics, SDMs are commonly used for recognition, classification, detection, and alignment. During the past few years, we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models. The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large number of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. the human body, it is difficult to define a consistent set of landmarks. Even though, the SDMs construction still suffers in terms of sparse landmark annotation. With sparse annotations, the shape deformation of any deformable objects is only captured where the key points are labeled, thus nuanced shape deformation is discarded. It is merely possible to accurately annotate dense shape deforestation manually. Fortunately, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this thesis, we propose that it is possible to construct SDMs by putting objects shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We propose a framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for articulated objects. Such a system can serve as an element of cascaded architectures that jointly localize landmarks and estimate dense correspondences. Also, we show that the obtained dense correspondence can act as a source of prior knowledge that complements and extends the pure landmark-level annotations, accelerating and improving the training of pose estimation networks. The thesis contributes towards fully automatic dense correspondence estimation. Such correspondence would benefit any system that requires nuance shape deformation. We are approaching a milestone that dense correspondence and poses for deformable objects can be estimated “in-the-wild” with minimal effort on labeling. The estimated correspondence is shown to be useful for various downstream tasks for a collection of objects.
Theoretical and practical considerations when using retroelement insertions to estimate species trees in the anomaly zone Supplement

1 Supplementary Methods 2 1.1 Commands for testing whether model species trees are in anomaly zone . 2 1.1.1 26-taxa model species tree . 2 1.1.2 4-ingroup-taxa-AZ model species tree . 3 1.1.3 5-ingroup-taxa-AZ model species tree . 3 1.1. 4 Palaeognathae-GT-AZ model species tree . 4 1.2 Commands for simulating data sets . 4 1.3 Commands for estimating species trees . 4 1.3. 1 Parsimony Methods for 0/1 character states . 4 1.3. 2 SDPquartets . 5 1.3. 3 ASTRAL BP . 5 1.3. 4 ASTRID BP . 5 1.3. 5 MDC BP . 5
Analysis of Gaia radial-velocity standards: stability and new substellar companion candidates

Our main aim is to test the non-variability of the radial velocity (RV) of a sample of 2351 standard stars used for wavelength calibration of the RVS instrument onboard Gaia. In this paper, we present the spectroscopic analysis of these stars with the determination of their physical parameters by matching observed and synthetic spectra. We estimate the offset between different instruments after determining the shift between measured and archived RVs since the instrument pipelines use various numerical masks. Through the confirmation of the stability of the target RVs, we find 68 stars with a long-term variation having an acceleration which exceeds $10 \, \rm {m\, s^{-1}yr^{-1}}$. This suggests a barycentric reflex motion caused by a companion. As activity phenomena may be the source of periodic and trend-like RV variations in stars with putative planetary companions, we analysed various activity indicators in order to check their correlations to the RV changes. Among the trend stars, 18 have a trend model scatter greater than $100 \, \rm {m\, s^{-1}}$ over a time span from 10 to 12 years. We also confirm that six stars with known substellar companions have a total model scatter, 3σ, exceeding the threshold set by Gaia, that is, $300 \, \rm {m\, s^{-1}}$. In addition, TYC8963-01543-1 an SB2 star has data scatter $\sigma = 176.6\, \rm {m\, s^{-1}}$. Four more other stars are revealed to be variable after combining data from different instruments. Despite the presence of low-amplitude changes, a very large fraction of our sample (98.8 per cent) appears suitable as RV calibrators for Gaia RVS.
Processus de branchement, génétique des populations et généalogies aléatoires

Nous cherchons a construire une genetique des populations branchantes, basee notamment sur les processus de branchement a espace d'etats continu, dits CB-processus. Nous modifions d'abord les arbres branchants afin d'en obtenir des versions stationnaires, de deux facons : en introduisant des interactions de type competitif entre les individus, de maniere a reguler la taille de la population ("processus de branchement logistique"); en appliquant divers conditionnements, au sens des h-processus de Doob : conditionnement du processus de Levy associe a rester dans un intervalle, conditionnement a la non-extinction des CB-processus (Q-processus), mais aussi des CB-processus avec interactions, sous leur forme diffusion. Nous etudions la probabilite de fixation d'un mutant, question qui conditionne l'evolution de la diversite. En utilisant la theorie des diffusions, nous proposons un cadre unifie permettant de comparer deux modeles classiques et le modele de branchement logistique. Puis nous munissons les individus d'un trait quantitatif soumis a des mutations, et nous suivons, par une approche micro--macro, l'evolution du trait resident ("diffusion canonique de la dynamique adaptative"). Nous etudions la genealogie associee aux CB-processus avec immigration, dont un cas particulier est le Q-processus cite plus haut. Nous construisons des arbres branchants (dont la largeur n'est pas markovienne), dits \emph{arbres de ramification}, sur lesquels se voient directement les deux types de genealogies associees aux CB-processus, qui ont ete decouvertes par J.-F. Le Gall et ses collaborateurs. Nous donnons egalement une demonstration de la representation de Lamperti des CB-processus comme processus de Levy changes de temps. Nous decrivons de facon retrospective la structure genealogique des CB-processus, puis celle des arbres de ramification, comme le fait une des approches phares de la genetique des populations moderne, dite theorie de la coalescence. Des collaborations dans divers domaines de la biologie des populations sont egalement exposees : genetique des populations classique, ecologie des invasions, biologie de la conservation.
Noise‐induced systematic errors in ratio imaging: serious artefacts and correction with multi‐resolution denoising

Ratio imaging is playing an increasingly important role in modern cell biology. Combined with ratiometric dyes or fluorescence resonance energy transfer (FRET) biosensors, the approach allows the detection of conformational changes and molecular interactions in living cells. However, the approach is conducted increasingly under limited signal‐to‐noise ratio (SNR), where noise from multiple images can easily accumulate and lead to substantial uncertainty in ratio values. This study demonstrates that a far more serious concern is systematic errors that generate artificially high ratio values at low SNR. Thus, uneven SNR alone may lead to significant variations in ratios among different regions of a cell. Although correct average ratios may be obtained by applying conventional noise reduction filters, such as a Gaussian filter before calculating the ratio, these filters have a limited performance at low SNR and are prone to artefacts such as generating discrete domains not found in the correct ratio image. Much more reliable restoration may be achieved with multi‐resolution denoising filters that take into account the actual noise characteristics of the detector. These filters are also capable of restoring structural details and photometric accuracy, and may serve as a general tool for retrieving reliable information from low‐light live cell images.
Biochemistry of the gene products from murine MHC mutants.

INTRODUCTION . 241 General. 241 Background on the Muri,. Major Histocompatibility Complex (H-2) . 242 Background on the ! ;-� Mutant Strains. 243 Background on the Biochemistry of the H-2 Gene Products . 246 STRUCTURAL STUDIES OF THE GENE PRODUCTS FROM THE H-2 MUTANTS . 250 The H-2J(b Mutant Series . 250 Methods for biochemical analysis of the B-2 mutants . 251 The bm3 and bmll mutants . 252 The bm1 and bm2 mutants . 256 The bm5. bm6. bm7, bm9. and bm16 mutants 258 The bm4, bm8. and bm10 mutants . 260 Specific conclusions from the studies of the B-2K' mutants . 261 The D-Region Mutants . 263 Other H-2 Mutants . 268 SUMMARY AND CONCLUSIONS . 269
Fhe-mpc Notes

A lattice consists of all linear combinations with integer coefficients of some set of linearly independent vectors in a Euclidean space. If b1, . ,bn ∈ R are linearly independent, then the lattice spanned by these vectors is given by L = Zb1 + . + Zbn = { ∑n i=1zibi : zi ∈ Z}. The vectors b1, . ,bn are called a basis of the lattice. Such a basis is generally not unique. For example, the vectors −b1,b2, . ,bn and the vectors b1, . ,bn−1, (b1 + bn) both span the same lattice as our original vectors. You can think of a basis b1, . ,bn as the matrix containing these vectors as columns, i.e., B = (b1|b2| . . . |bn). Then, the lattice can be described as the set of all vectors Bz for z ∈ Z. Now, two bases B and B′ are equivalent (they span the same lattice) if and only if there exists some unimodular matrix U (U ∈ GLn(Z)) such that B′ = BU . Since unimodular matrices U have determinant det(U) = ±1, it immediately follows that for any two equivalent bases B and B′ we have that det(B′) = det(BU) = det(B). Thus, the quantity |det(B)| is independent of the chosen basis. It can hence be written as det(L) and it is called the fundamental volume of the lattice. Because a lattice is a discrete subgroup of R, there must be a lattice vector that has a non-zero minimal length. For a given lattice L, let us define this length as the first minimum of the lattice: λ1(L) = min{‖x‖ : x ∈ L,x 6= 0}, where ‖. ‖ can be any norm (but is most commonly the Euclidean norm). Note that such a ‘shortest’ vector is not unique, because if x is a shortest lattice vector, then −x is a shortest lattice vector as well. Besides the first minimum, there is also a second minimum, a third minimum and so on. However, some care must be taken in defining these successive minima. It may very well be that the ‘second shortest’ vector in a lattice is merely a multiple of the shortest vector in the lattice. Therefore, linearly independence is used to exclude these cases. Formally, the i-th minimum is defined as
Review of Factors Influencing Employees’ Willingness to Share Knowledge

Effective management of knowledge is currently recognised as the foundation of any organisation to maximise its abilities and achieve business targets. Organisations strive to leverage knowledge stocks – mostly held in the minds of their individual employees – in order to create value and drive success. For organisations to promote effective knowledge management, they have to develop innovative methods to encourage knowledge sharing practices. Knowledge sharing (KS) is a vital organisational process which empowers individuals to confront challenges of uncertainty and complexity, instils best practices, and enables the transfer of knowledge between different parts of the organisation. Given the fact that knowledge is always a valuable asset, individuals tend to hoard knowledge for different reasons. Therefore, sharing knowledge is predominantly a voluntary process and only subject to the willingness of the individual to engage in the process. Studies have demonstrated that KS cannot be forced or mandated rather than fostered by facilitative efforts in order to motivate employees to share their tacit knowledge. The aim of the study is to evaluate the collective factors supporting the willingness of employees to share knowledge. After which, providing a summarised generic list of the terms illustrated throughout the extant literature. Previous research has shown that influencers such as the ‘perceptions’ of organisational culture, trust, infrastructure, and leadership, are among the most prominent determinants of KS within the corporate environment. There are other extrinsic motivators and social-psychological forces that can contribute to the increase of the behaviour of KS. Technology advances are also considered as an enabler because it can help in both direct and indirect transfer of knowledge. The findings provide researchers with an overall topology of the factors of KS, and equally offer useful insights for managers seeking to enhance willingness to share knowledge within their firms.
Individual Factors Influencing Career Growth Prospects in Contexts of Radical Organizational Changes

This study examined the influence of individual factors (demographic variables, self-efficacy beliefs and personal growth initiative) on career growth prospects within the context of radical organizational changes such as downsizing, mergers and acquisition. Data were collected using the questionnaire method from 199 employees in branches of a commercial bank located in a major city in South-Western Nigeria. Results of the simple multiple regression analysis showed that educational attainment (? = -.15, p < .05), tenure in the banking sector (? = -.41, p < .01), basic monthly income (? = .46, p < .001) and job status (? = .34, p < .01) are significant demographic factors in career growth prospects. The analysis of covariance which controlled for covariates revealed significant differences in the career growth prospects of employees with low levels of self-efficacy and those with high levels – in favor of the latter. In contrast, high or low levels of personal growth initiative resulted in comparable levels of career growth prospects. Self-efficacy beliefs and personal growth initiative interacted significantly to affect career growth prospect such that greater levels of career growth prospect was expressed irrespective of whether an employee is high or low on personal growth initiative when self-efficacy belief is high. Implications of findings are discussed.
STIR-Net: Deep Spatial-Temporal Image Restoration Net for Radiation Reduction in CT Perfusion

Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.