Miracle-dz/UniFAR-FTUs huggingface dataset:
Designed Organizational Search: A Comparative Analysis of Alternative Procedures for Learning from Success

Sampling on the dependent variable is unlikely to be an effective way to learn and develop the strategy. Even so, organizations spend millions of dollars on processes such as Appreciative Inquiry that make inferences about how to adapt their strategies, routines, and practices based upon only successful examples. Two techniques that are common to this kind of learning process are searching solely for successful solutions and reframing search problems (e.g., unconditionally positive questions). We build a computational model by formalizing appreciative inquiry and comparing it with other, similar processes to understand their relative effectiveness. We find that the organizations simulated in our computational model almost always improved performance over time, despite learning solely from successful observations. Their relative effectiveness depended on the complexity of the problems, the number of iterations of learning, and how much the learning process preserved variety in potential solutions. These findings suggest that appreciative inquiry may be most effective when people take the cost and complexity of organizational problems into account before engaging in the learning process and adapt the process accordingly. These findings contribute to research on organizational learning by explaining why learners may benefit from structuring the way they communicate as they search, why reframing performance measures may dissolve search problems, and how designed organizational search enables managers to be more deliberate about organizational learning.
Analysis of Distracted Driving Detection Based on Deep Learning Human Posture

Distracted driving is a phenomenon that drivers' attention points to activities unrelated to normal driving, which leads to the decline of driving operation ability. With the popularity of smart phones, traffic accidents caused by distracted driving are on the rise. Traffic accidents caused by driver's inattention due to vision deviation or distraction are particularly obvious in rear end collisions. The results of the distracted driving questionnaire show that almost all drivers have experienced distracted driving. In order to detect whether drivers are distracted when driving, this paper proposes a distracted driving analysis based on deep learning of human posture. This method uses the MMDetection framework in depth learning to build a target detection network to achieve target detection, then uses the depth neural network to estimate the geometric transformation features, and uses the stacked hourglass network to accurately predict the state features of drivers in the graph. The experimental results show that the measurement method has good accuracy, fast speed, good robustness and consistency, more accurate extraction of human posture related features, strong generalization ability and certain practical value.
Surface profile measurement of 3D-printed transparent objects using nonlinear optics

In Additive Manufacturing (AM), it is necessary to measure the surface profile of 3D-printed transparent objects in noninvasive manner, which remained, however, practically difficult to date. Nonlinear optical responses of transparent materials have been utilized in shortwavelength light generation, optical phase/intensity modulators, and non-destructive inspection of the sample characteristics. In this research, we introduced nonlinear optics to surface profile measurement of 3D-printed micro-optic structures. For in-depth understanding of third harmonic generation mechanisms, the harmonic conversion efficiency was measured with different input polarization states, incident angles, and focal depths in the transparent materials including Sapphire and Si wafers. Our results can be utilized in surface inspection required in various industrial and scientific applications, including 3D-profile measurement of printed optical components and waveguides.
Discovery and classification of variable stars

This thesis investigates the classification of variable stars and the discovery of interesting behaviour in some of them. A secondary aim was to attempt to do this without astronomical expertise (domain knowledge). The data comes from the Macho dataset which consists of time series observations of the red and blue magnitudes of about 60 million stars, each observed approximately every night. These observations are generally not equally spaced—since observations occur at different times each night—and some are missing entirely—usually due to weather. So standard time series analysis tools such as Fourier or wavelet series are difficult to compute. We explain the background to the Macho project and variable stars. The two datasets that were used in developing the techniques described in this thesis are introduced: the 20k and Wood datasets, subsets of the entire Macho project database. We design and implement a binary file format for storing the time series data which is considerably faster to access than the original ASCII format files we had been given. The very high dimensionality of the data for each star means that it is impractical to compare them directly. We develop a set of features that attempt to summarise the behaviour of each time series, including some frequency estimates based on norms of derivatives. Each star can then be directly compared with other stars based on the set of features. We use the k-means clustering algorithm to cluster the feature-set of one of our datasets and discover that while the features do group similar behaving stars they do not necessarily separate different types of star. We also present the design of a new algorithm, k-means-min-p, that limits the minimum population of each cluster to a user-defined value. We develop and test a new generic iterative classification and clustering algorithm that we call boxing. We use this to clearly show that the features do group similar stars. Using extreme-case selection we find an interesting class that was independently found by astronomers. Finally, we use the boxing algorithm together with a boosted generalised additive model classifier and relevance feedback to develop a “star-searcher” tool. This tool is very effective in allowing the user to search for examples of a particular type of variable star. Despite our success in finding an interesting class of stars with boxing and extreme-case selection, we conclude that domain knowledge is necessary for this sort of work, particularly with interpretation of the results.
How do R&D networks change? The upgrading of innovation capabilities in emerging market firms. Insights from China’s wind energy sector

ABSTRACT Innovative activities are increasingly decentralized and globally dispersed, which provides new upgrading opportunities for emerging market firms. However, little is known about how latecomer firms (re-)organize their research and development (R&D) over time as conditions for upgrading change. This paper systematically maps the R&D networks of China’s lead firms in the wind turbine industry. The empirical findings reveal that latecomer firms not only exploit but increasingly co-create vanguard knowledge in global R&D networks through organizational diversification. Taking an evolutionary perspective, the paper extends our understanding of the changing nature of upgrading mechanisms and provides new insights into the reorganization of innovation processes in an era of technological change.
Predicting Femoral Geometry from Anatomical Features

Knee replacement surgery is a common orthopaedic procedure that greatly benefits from a three-dimensional geometric representation of a patient’s knee bone obtained from MR or CT data. The use of these image modalities pose the following challenges: (i) high imaging cost; (ii) long wait times; (iii) limited availability and (iv) in the latter, large exposure to ionizing radiation. Traditional approaches based on planar X-ray radiography are significantly less prone to these issues; however, they only provide two-dimensional information. This work presents a proof of concept study for generating patient-specific femoral bone shapes from a statistical shape atlas using anatomical features acquired from calibrated X-ray radiographs. Our hypothesis was: three-dimensional geometry can be reconstructed, within 2 millimeters RMS, by identifying features on two calibrated radiographs. We illustrate the feasibility of our approach with regards to acquiring features and the viability of reconstructing patient-specific bony anatomy. A set of reliable and relevant features is identified for which an acquisition protocol and user-interface was devised to minimize inter-observer variability. Both the data and methods used to construct the atlas are discussed as well generating shapes from features. The reconstructions accuracy was comparable to, albeit lower than, competing approaches that rely on two-dimensional bony contours. i
High-efficiency transmission gratings for unpolarized illumination: an intelligible analysis of the diffraction process

For gaining a deeper understanding of the diffraction processes that take place in deep dielectric transmission gratings, a phenomenological explanation has been developed on the basis of a modal field, which propagates vertically through the grating region. The excitation of these modes by the incident wave, their propagation constants and how they couple to the diffraction orders determines the diffraction efficiency of the transmitted orders. The explicit modal analysis discloses the description of the highly efficient diffraction for polarized or unpolarized light by a very simple interference mechanism, which will be the subject of this paper.
Mutual dependence of Osbp and PI4KII in the maturation of regulated secretory granules

Secretory granules (SGs) are crucial for normal animal physiology due to their role in regulated exocytosis of biologically active molecules. SG membranes are enriched in phosphatidylinositol 4-phosphate (PI4P) and cholesterol, and previous studies suggest lipid composition is important for SG biogenesis and function. Nonetheless, the molecular details of how lipids are regulated during SG biogenesis remain poorly understood. Here, we identify Oxysterol binding protein (Osbp) as a novel regulator of SG biogenesis in a Drosophila model. We show Osbp expression level positively correlates with SG size and that Osbp requires type II phosphatidylinositol 4-kinase (PI4KII) to increase SG size. Moreover, Osbp is needed for proper PI4KII and PI4P distribution, autophagic resolution and formation of cholesterol-rich endosomal tubules that are positive for PI4KII. Feeding larvae food supplemented with sterol leads to partial suppression of SG size and PI4P distribution defects in Osbp mutants. Our results indicate that reciprocal regulation of Osbp and PI4KII drives formation of membrane tubules that mediate SG maturation through elevating PI4P levels on SG membranes. Graphical Abstract Highlights Osbp is needed for formation of PI4KII-positive endosomal tubules that enrich secretory granule membranes with PI4P and facilitate granule maturation. Reciprocally, PI4KII is needed for Osbp-mediated secretory granule growth in salivary gland cells. Changes in Osbp expression levels alter autophagy initiation and autophagosome resolution in salivary gland cells. PI4KII-positive endosomal tubules are enriched in sterols, and sterol feeding suppresses defects caused by loss of Osbp.
Spatial models for non‐Gaussian data with covariate measurement error

Spatial models have been widely used in the public health setup. In the case of continuous outcomes, the traditional approaches to model spatial data are based on the Gaussian distribution. This assumption might be overly restrictive to represent the data. The real data could be highly non‐Gaussian and may show features like heavy tails and/or skewness. In spatial data modeling, it is also commonly assumed that the covariates are observed without errors, but for various reasons, such as measurement techniques or instruments used, uncertainty is inherent in spatial (especially geostatistics) data, and so, these data are susceptible to measurement errors in the covariates of interest. In this paper, we introduce a general class of spatial models with covariate measurement error that can account for heavy tails, skewness, and uncertainty of the covariates. A likelihood method, which leads to the maximum likelihood estimation approach, is used for inference through the Monte Carlo expectation–maximization algorithm. The predictive distribution at nonsampled sites is approximated based on the Markov chain Monte Carlo algorithm. The proposed approach is evaluated through a simulation study and by a real application (particulate matter data set).
Analysis on Transmission Zeros in Cross-Coupled Microstrip Filters

ABSTRACT The reason of a pair of transmission zeros located near the passband of a cross-coupled microstrip filter is discussed. Moreover, with the use of tapped transmission line, another two transmission zeros are generated. To verify the analysis, a two-layer cross-coupled microstrip filter with tapped-line technique is developed, where four transmission zeros are found close to the passband, thus, realizing an enhanced filter selectivity. The two-layer cross-coupled microstrip filter is fabricated and examined experimentally. Measurements agree with the simulations.
Optimisation of pulse shape discrimination using EJ299-33 for high energy neutron detection in proton beam therapy

It is widely understood that proton beam therapy has considerable clinical benefits over photon therapy for treating certain types of tumours. Protons deposit most of their energy in a very localised area, the so-called Bragg peak, sparing surrounding healthy tissue and critical organs from radiation. However, secondary neutrons and gamma rays are generated in the beam nozzle and inside the patient. Clinically, it is highly desirable to monitor the neutron dose the patient is exposed to, and this requires a neutron detector sensitive to high energies. EJ299-33 is a solid plastic scintillator capable of discriminating neutrons from gamma rays using pulse shape analysis of scintillation light. EJ299-33 has the potential to detect neutrons with energies up to 100 MeV and does not present leakage and flammability hazards generally associated with liquid scintillators. Experimental measurements with 60Co, 137Cs and 241AmBe sources were performed to calibrate and optimise pulse shape discrimination parameters. We also performed experimental measurements at the Clatterbridge Cancer Centre in a 60 MeV passive scattered beam to detect high energy neutrons.
Controllability of nonlinear fractional Langevin delay systems

In this paper, we discuss the controllability of fractional Langevin delay dynamical systems represented by the fractional delay differential equations of order 0 < α, β (cid:54) 1 . Necessary and sufﬁcient conditions for the controllability of linear fractional Langevin delay dynamical system are obtained by using the Grammian matrix. Sufﬁcient conditions for the controllability of the nonlinear delay dynamical systems are established by using the Schauders ﬁxed-point theorem. The problem of controllability of linear and nonlinear fractional Langevin delay dynamical systems with multiple delays and distributed delays in control are studied by using the same technique. Examples are provided to illustrate the theory.
Complex holographic elements in photo-thermo-refractive glass for the visible spectral region

Planar holographic optical elements (volume Bragg gratings, VBGs) recorded in photo-thermo-refractive (PTR) glass are widely used for fine spectral filtering and laser beam control. PTR glass provides photosensitivity in near UV region. Therefore, while planar holographic elements operate in the whole window of transparency - near UV, visible and near IR spectral regions, application of complex (nonplanar) elements is restricted to near UV. A method has been proposed to create high-efficiency diffractive optical elements in PTR glass using visible light. The method employs excited state absorption in PTR glass doped with Tb3+. UV radiation was used for excitation to a metastable level of Tb3+ and pulsed radiation at 532 nm was used for hologram recording. Both planar VBGs and holographic lenses operating at 532 nm were demonstrated. Complex holographic optical elements in PTR glass can provide attractive solutions for lasers and spectroscopy replacing conventional optical components.
Optical spectroscopy of p-GaAs nanopillars on Si for monolithic integrated light sources

In this work, we study the optical properties and emission dynamics of the novel nanostructure p-GaAs nanopillars (NPs) on Si. The integration of III-V optoelectronics on Si substrates is essential for next-generation high-speed communications. NPs on Si are good candidates as gain media in monolithically integrated small-scale lasers on silicon. In order to develop this technology, an in-depth knowledge of the NP structure is necessary to resolve its optimal optical properties. The optical characterization which has been carried out consists of the emission analysis for different NP geometries. We measured NPs with different combinations of pitch (of the order of a few μm) and diameter (of the order of tens of nm). A comparison of intensities for the various NPs provides us with the most efficient geometry. The quality of the crystal grown has been studied from temperature-dependent photoluminescence (PL). A red shift and a significant reduction of the intensity of the NP emission are observed with an increase in temperature. The results also show the presence of two non-radiative recombination channels when the intensity peaks at different temperatures are analyzed with the activation energy function.
Hyperspectral unmixing using double-constrained multilayer NMF

ABSTRACT Hyperspectral unmixing (HU) refers to the process decomposing the entire hyperspectral image into a set of endmembers and the corresponding abundance fractions. Nonnegative matrix factorization (NMF) has been widely used in HU due to its simplicity and effectiveness. Many extensions of NMF have been also developed since traditional NMF has a large solution space. On the other hand, the multilayer structure has shown great advantages in learning data representation. Inspired by these considerations, we added sparsity and geometric structure constraints to the multilayer NMF structure and proposed a double-constrained multilayer NMF (DCMLNMF) method for HU in this paper. The multilayer NMF structure was obtained by iteratively decomposing the target matrix into a number of layers. To improve the unmixing performance, a sparsity constraint term on the abundance matrix and a graph regularization term were both incorporated to each layer. Besides, a layer-wise optimization method based on Nesterov’s optimal gradient method was further proposed to solve the multi-factor NMF problem. Experimental results based on both synthetic data and real data demonstrate that the proposed method outperforms several other state-of-art approaches.
One-dimensional self-consistent kinetic models as Vlasov equation solutions

One-dimensional self-consistent kinetic models may describe some states of intense charged particle beams. In the collisionless approximation, which is appropriate for the short current pulse duration, the kinetic distribution function may be built as a function of the motion integrals. Two situations are considered corresponding to the wide charged particle flux with the sharp front and to the sheet continuous beam freely propagating in space. In both cases, one-dimensional Vlasov equation solutions are shown to exist, which are based on algebraic functions of the motion invariants.
Improving the clinical examination for a low ankle-brachial index

We sought to determine clinical examinat ion features that predict an abnormal ankle-brachial index (ABt). Eleven United States and Canadian universityaffiliated practices par t ic ipated. Patients over age 55 (n = 218) presenting for an outpat ient appoin tment in a general medical clinic. We excluded patients with amputa t ions or acute leg pain. A s tandard clinical examinat ion was performed consist ing of historical features and physical examinat ion findings with Dopple r auscul tat ion. The most efficient findings were a. presence of only one Doppler -auscul ta ted poster ior tibial ar tery componen t [LR = 7.0; (95% CI 4.4, 11.6)], and b. absence of a pa lpable pulse [LR = 4.6; (95% CI 3.2, 6.6)]. We derived a score based on the number of auscul tated components , grade of pa lpa ted pulse, and history o f myocard ia l infarction (LRscore < 6 = 7.8; LR~core _> 6 = 0.2; c index = 0.93). Clinicians required a median 2.5 min to collect the clinical informat ion and derive the score ( interquart i le range 1.8 to 3.6 min), versus 8.5 min for the ABI (interquarti le range 7.4 to 9.4 min). Palpat ion and Dopp le r auscul ta t ion of the poster ior t ibial artery, combined with knowledge of pr ior myocard ia l infarctions, were the most effective and efficient findings for pat ients in general medical clinics. A score based on these findings appears promis ing as a screening tool for a low ABI.
2-Year Postoperative Evaluation of a Patient with a Symptomatic Full-Thickness Patellar Cartilage Defect Repaired with Particulated Juvenile Cartilage Tissue

This case report describes the early results of a 36-year-old man who underwent repair of a symptomatic full-thickness patellar cartilage defect with transplanted particu-lated juvenile articular cartilage. At 2 years postoperatively, the patient has experienced substantial clinical improvement in both pain and function when evaluated with both International Knee Documentation Committee subjective evaluation and Knee Injury and Osteoarthritis Outcome Score outcome measures. Two-year postoperative magnetic resonance imaging demonstrates ﬁll of the defect with repair tissue and near complete resolution of preoperative subchondral bone edema. To the best of the authors’ knowledge, this case report is the ﬁrst to report clinical results of this new technique at 2 years postoperatively.
High-Order Degree and Combined Degree in Complex Networks

We define several novel centrality metrics: the high-order degree and combined degree of undirected network, the high-order out-degree and in-degree and combined out out-degree and in-degree of directed network. Those are the measurement of node importance with respect to the number of the node neighbors. We also explore those centrality metrics in the context of several best-known networks. We prove that both the degree centrality and eigenvector centrality are the special cases of the high-order degree of undirected network, and both the in-degree and PageRank algorithm without damping factor are the special cases of the high-order in-degree of directed network. Finally, we also discuss the significance of high-order out-degree of directed network. Our centrality metrics work better in distinguishing nodes than degree and reduce the computation load compared with either eigenvector centrality or PageRank algorithm.