Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of an orthopaedic team: Insights to improve teamwork through a design thinking approach

BACKGROUND: Supporting teamwork in healthcare is a way to foster both the quality and safety of care, and better working conditions for all the team members. Although increasing attention is paid to this topic on a general level, there is less knowledge about its unfolding in orthopaedic units and its translation to interventions. OBJECTIVE: To identify concrete opportunities for teamwork intervention through a design thinking approach by analysing the teamwork dynamics of an orthopaedic team. METHODS: An adaptation of the learning history method, comprising shadowing, observations and interviews involving 26 orthopaedic team members at a top clinical teaching hospital in the Netherlands, was applied. A thematic analysis was conducted to derive themes that describe team dynamics and to subsequently extrapolate opportunities for intervention. RESULTS: We identified five themes and translated them into four design opportunities for intervention, namely: a) Improve daily rounds by reducing cognitive overload and promoting confidence; b) Improve collaboration by building empathy; c) Connect the patient with the professional team; and d) Support changes by fostering learning. Suggestions for concrete actions are presented for each opportunity. CONCLUSIONS: Opportunities to improve teamwork among healthcare professionals, specifically those in orthopaedics, revolve around the creation of common knowledge, the fostering of mutual understanding, and the design of tools and activities that support these processes.
Factors That Influence Parental Uncertainty and Health-Related Quality of Life in Children With Cancer: A Framework

The purpose of this paper is to present a conceptual-theoretical-empirical model addressing factors that influence the relation between parental uncertainty and health-related quality of life in children with cancer. The basic concepts identified and defined in the model include parental uncertainty, health-related quality of life, parental trait anxiety, parental depression, and perceived parental social support. The proposed relationships between the concepts are explained with explicit linkages to their empirical indicators. There is limited research in childhood cancer regarding the relation between parental uncertainty and health-related quality of life; therefore, the proposed model will help to better understand this relationship.
Learning to Optimize: Training Deep Neural Networks for Interference Management

Numerical optimization has played a central role in addressing key signal processing (SP) problems. Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few. However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing. In this paper, we aim at providing a new learning-based perspective to address this challenging issue. The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it. If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively—since passing the input through a DNN only requires a small number of simple operations. In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN. Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm. Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted. It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm.
The dual role of the centrosome in organizing the microtubule network in interphase

Here, we address the regulation of microtubule nucleation during interphase by genetically ablating one, or two, of three major mammalian γ‐TuRC‐binding factors namely pericentrin, CDK5Rap2, and AKAP450. Unexpectedly, we find that while all of them participate in microtubule nucleation at the Golgi apparatus, they only modestly contribute at the centrosome where CEP192 has a more predominant function. We also show that inhibiting microtubule nucleation at the Golgi does not affect centrosomal activity, whereas manipulating the number of centrosomes with centrinone modifies microtubule nucleation activity of the Golgi apparatus. In centrosome‐free cells, inhibition of Golgi‐based microtubule nucleation triggers pericentrin‐dependent formation of cytoplasmic‐nucleating structures. Further depletion of pericentrin under these conditions leads to the generation of individual microtubules in a γ‐tubulin‐dependent manner. In all cases, a conspicuous MT network forms. Strikingly, centrosome loss increases microtubule number independently of where they were growing from. Our results lead to an unexpected view of the interphase centrosome that would control microtubule network organization not only by nucleating microtubules, but also by modulating the activity of alternative microtubule‐organizing centers.
A simple developmental model recapitulates complex insect wing venation patterns

Significance The wing veins of the fruit fly Drosophila melanogaster have long been studied as an example of how signaling gradients in a growing tissue can generate precise, reproducible patterns. However, fruit fly wings represent only a small slice of wing diversity. In many insect species, wings are like human fingerprints: even the left and right wings of the same individual have unique vein patterns. We analyze wing geometry in many species and then present a minimal developmental model for how vein patterns can be formed. This model will serve as a hypothesis for future empirical work. Insect wings are typically supported by thickened struts called veins. These veins form diverse geometric patterns across insects. For many insect species, even the left and right wings from the same individual have veins with unique topological arrangements, and little is known about how these patterns form. We present a large-scale quantitative study of the fingerprint-like “secondary veins. ” We compile a dataset of wings from 232 species and 17 families from the order Odonata (dragonflies and damselflies), a group with particularly elaborate vein patterns. We characterize the geometric arrangements of veins and develop a simple model of secondary vein patterning. We show that our model is capable of recapitulating the vein geometries of species from other, distantly related winged insect clades.
Experimental evaluation of the importance of colonization history in early-life gut microbiota assembly

The factors that govern assembly of the gut microbiota are insufficiently understood. Here, we test the hypothesis that inter-individual microbiota variation can arise solely from differences in the order and timing by which the gut is colonized early in life. Experiments in which mice were inoculated in sequence either with two complex seed communities or a cocktail of four bacterial strains and a seed community revealed that colonization order influenced both the outcome of community assembly and the ecological success of individual colonizers. Historical contingency and priority effects also occurred in Rag1-/- mice, suggesting that the adaptive immune system is not a major contributor to these processes. In conclusion, this study established a measurable effect of colonization history on gut microbiota assembly in a model in which host and environmental factors were strictly controlled, illuminating a potential cause for the high levels of unexplained individuality in host-associated microbial communities.
Conditional Kronecker Batch Normalization for Compositional Reasoning

Conditional Batch Normalization (CBN) has proved to be an effective tool for visual question answering. However, previous CBN approaches fuse the linguistic information into image features via a simple afﬁne transformation, thus they have struggled on compositional reasoning and object counting in images. In this paper, we propose a novel CBN method using the Kronecker transformation, termed as Conditional Kronecker Batch Normalization (CKBN). CKBN layer facilitates the explicit and expressive learning of compositional reasoning and robust counting in original images. Besides, we demonstrate that the Kronecker transformation in CKBN layer is a generalization of the afﬁne transformation in prior CBN approaches. It could accelerate the fusion of visual and linguistic information, and thus the convergence of overall model. Experiment result-s show that our model signiﬁcantly outperforms previous CBN methods ( e.g . FiLM) in compositional reasoning, counting as well as the convergence speed on CLEVR dataset.
Long-term object tracking with a moving event camera

This paper presents a long-term object tracking algorithm for event cameras. A ﬁrst of its kind for these revolutionary cameras, the tracking framework uses a discriminative representation for the object with online learning, and detects and re-tracks the object when it comes back into the ﬁeld-of-view. One of the key novelties is the use of an event-based local sliding window technique that performs reliably in scenes with clut-tered and textured background. In addition, Bayesian bootstrapping is used to assist real-time processing and boost the discriminative power of the object representation. Extensive experiments on a publicly available event camera dataset demonstrates the ability to track and detect arbitrary objects of various shapes and sizes. This is a signiﬁcant improvement compared to earlier works that simply track objects as long as they are visible under simpler background settings. In other words, when the object re-enters the ﬁeld-of-view of the camera, a data-driven, global sliding window based detector locates the object under different view-point conditions for subsequent tracking.
U-Fresh: An Fri-Based Single Image Super Resolution Algorithm and An Application in Image Compression

Learning based single image super resolution (SISR) methods have achieved notable results, however, they require large datasets for training, and may struggle when there is a mismatch between the testing and training data. To overcome these drawbacks, we propose an approach, named U - FRESH, which only requires a small dataset but can achieve state-of-the-art performance also in the presence of training and testing mismatches. We accomplish this by leveraging a method called FRESH, which enhances the image resolution using FRI theory. We start upscaling from the FRESH generated low resolution image. To minimize the reconstruction error, we propose a new regression selection technique to make the mapping more reliable and robust, and a wavelet based back projection technique to improve the quality of the reconstructed image. Based on U - FRESH, we also propose a new framework based on JPEG 2000 for image compression. Numerical results show that our U-FRESH method achieves state-of-the-art performance in SISR and provides better compression results than JPEG 2000.
Identifying the Resonances in Terahertz Wave Scattering from a Two-Layer Graphene Strip Grating Embedded in a Dielectric Slab

We study, using a convergent in-house numerical algorithm, the scattering and absorption of the H-polarized plane wave by a two-layer grating of identical coplanar graphene strips embedded in a lossless dielectric slab. Our instrument is the method of analytical regularization, or, more precisely, the projection of the associated singular integral equation on the set of the weighted second-kind Chebyshev polynomials, which invert the static part of the problem. We compute the reflectance, transmittance, and absorbance of such a composite metasurface versus the frequency, in the range from 0.1 to 10 THz. We reveal multiple resonances and explain their nature with the aid of the in-resonance near field portraits. Ultra-high-Q resonances on the grating (lattice) mode are paid special attention.
Recurrent Neural Networks for Automatic Replay Spoofing Attack Detection

In order to enhance the security of automatic speaker verification (ASV) systems, automatic spoofing attack detection, which discriminates the fake audio recordings from genuine human speech, has gain much attention recently. Among various ways of spoofing attacks, replay attacks are one of the most effective and economical methods. In this paper, we explore using recurrent neural networks for automatic replay spoofing attack detection. More specifically, we focus on recurrent neural networks with more sophisticated recurrent units that involve a gating mechanism, such as a long short term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). Our experimental results on the ASVspoof 2017 showed that neural networks significantly outperform Gaussian mixture models (GMM). In addition, we achieved the best equal error rate of 9.81 % on the ASVspoof2017 and 1.077% on the BTAS 2016 by using GRU models, which outperform the best feed-forward neural networks by 19% and 46%, relatively and respectively.
Discovering Correspondence Among Image Sets with Projection View Preservation For 3D Object Detection in Point Clouds

We propose a method for detecting objects that correspond to given three-dimensional (3D) point clouds in a scene. We regard the 3D object detection as a series of optimal matching of the object and scene images that are obtained by projecting point clouds into multiple viewpoints. The key novelty of the proposed method is to introduce a constraint imposed by the spatial relationship among the image-projection directions for the object point clouds, to discover the optimal matching of the projected image sets. This constraint allows to evaluate the appearance consistency of the object in multi-viewpoint scene images. Thus, image-projection directions can be effective cues to detect objects even in cluttered scenes, where previous methods are not effective. We estimate the image-projection directions for the object point clouds by applying principal component analysis to the object point clouds and hence include highly discriminative image features. Then, we back-project reliable matching results, which are retrieved from the image set correspondence, into 3D space to achieve a point-wise object detection. Experiments using public datasets demonstrate the effectiveness and performance of the proposed method.
A Modular Multilevel Converter with Integrated Shared Capacitor Sub-Module for MV Motor Drives Incorporating Symmetrical Six-Phase Machines

This paper proposes a new modular multilevel converter (MMC) configuration as a medium-voltage drive for variable-speed applications incorporating symmetrical six-phase machines. The proposed topology employs six MMC phase-legs feeding two isolated groups of three-phase machine windings, each with 60° spatial phase-displacement. A novel concept of sharing one capacitor between each pair of adjacent-arm submodules (SMs) of MMC phase-legs, while feeding machine windings in a spatial phase-opposition, is realized through a new integrated SM arrangement. The integrated SM allows the shared capacitor to absorb and release the same energy amount in a consecutive switching scheme, where the capacitor is experiencing both charging and discharging arm currents, one after another. This results in a limited voltage variation across the SM shared capacitor, independent of the operating frequency. Also, the proposed approach allows the MMC to utilize half the number of the SM capacitors, compared to a traditional MMC topology, while further diminishes the SM capacitance requirement, reducing the volume of the MMC system and its stored energy. The proposed configuration can efficiently operate at near zero frequency, therefore a machine speed-range from zero speed to the rated speed is possible under rated torque operating condition. The proposed MMC topology is elucidated in detail, and its effective performance is verified using simulation.
Is the eye-movement field confused about fixations and saccades? A survey among 124 researchers

Eye movements have been extensively studied in a wide range of research fields. While new methods such as mobile eye tracking and eye tracking in virtual/augmented realities are emerging quickly, the eye-movement terminology has scarcely been revised. We assert that this may cause confusion about two of the main concepts: fixations and saccades. In this study, we assessed the definitions of fixations and saccades held in the eye-movement field, by surveying 124 eye-movement researchers. These eye-movement researchers held a variety of definitions of fixations and saccades, of which the breadth seems even wider than what is reported in the literature. Moreover, these definitions did not seem to be related to researcher background or experience. We urge researchers to make their definitions more explicit by specifying all the relevant components of the eye movement under investigation: (i) the oculomotor component: e.g. whether the eye moves slow or fast; (ii) the functional component: what purposes does the eye movement (or lack thereof) serve; (iii) the coordinate system used: relative to what does the eye move; (iv) the computational definition: how is the event represented in the eye-tracker signal. This should enable eye-movement researchers from different fields to have a discussion without misunderstandings.
Motor Function Test Reliability During the NeuroNEXT Spinal Muscular Atrophy Infant Biomarker Study.

BACKGROUND The NeuroNEXT SMA Infant Biomarker Study, a two year, longitudinal, multi-center study of infants with SMA type 1 and healthy infants, presented a unique opportunity to assess multi-site rater reliability on three infant motor function tests (MFTs) commonly used to assess infants with SMA type 1. OBJECTIVE To determine the effect of prospective MFT rater training and the effect of rater experience on inter-rater and intra-rater reliability for the Test of Infant Motor Performance Screening Items (TIMPSI), the Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP-INTEND) and the Alberta Infant Motor Scale (AIMS). METHODS Training was conducted utilizing a novel set of motor function test (MFT) videos to optimize accurate MFT administration and reliability for the study duration. Inter- and intra-rater reliability of scoring for the TIMPSI and inter-rater reliability of scoring for the CHOP INTEND and the AIMS was assessed using intraclass correlation coefficients (ICC). Effect of rater experience on reliability was examined using ICC. Agreement with 'expert' consensus scores was examined using Pearson's correlation coefficients. RESULTS Inter-rater reliability on all MFTs was good to excellent. Intra-rater reliability for the primary MFT, the TIMPSI, was excellent for the study duration. Agreement with 'expert' consensus was within predetermined limits (≥85%) after training. Evaluator experience with SMA and MFTs did not affect reliability. CONCLUSIONS Reliability of scores across evaluators was demonstrated for all three study MFTs and scores were reproducible on repeated administration. Evaluator experience had no effect on reliability.
Evaluation of methodologies for microRNA biomarker detection by next generation sequencing

ABSTRACT In recent years, microRNAs (miRNAs) in tissues and biofluids have emerged as a new class of promising biomarkers for numerous diseases. Blood-based biomarkers are particularly desirable since serum or plasma is easily accessible and can be sampled repeatedly. To comprehensively explore the biomarker potential of miRNAs, sensitive, accurate and cost-efficient miRNA profiling techniques are required. Next generation sequencing (NGS) is emerging as the preferred method for miRNA profiling; offering high sensitivity, single-nucleotide resolution and the possibility to profile a considerable number of samples in parallel. Despite the excitement about miRNA biomarkers, challenges associated with insufficient characterization of the sequencing library preparation efficacy, precision and method-related quantification bias have not been addressed in detail and are generally underappreciated in the wider research community. Here, we have tested in parallel four commercially available small RNA sequencing kits against a cohort of samples comprised of human plasma, human serum, murine brain tissue and a reference library containing ~ 950 synthetic miRNAs. We discuss the advantages and limits of these methodologies for massive parallel microRNAs profiling. This work can serve as guideline for choosing an adequate library preparation method, based on sensitivity, specificity and accuracy of miRNA quantification, workflow convenience and potential for automation.
Spherical Foot Placement Estimator for Humanoid Balance Control and Recovery

One of the main challenges of bipedal gait is to avoid falling due to unknown disturbances. Compensating for these disturbances in bipeds is often achieved by leaning or stepping. In this work, the Spherical Foot Placement Estimator (SFPE) is introduced, which uses the biped's current kinematics and dynamics to predict if a step is needed, and if so where to step, to restore balance in 3D. An example of a controller using the SFPE is shown, which augments an existing optimal controller with both leaning and stepping: SFPE-based feedback is used to generate a desired momentum for momentum-based leaning while the SFPE point is used as a control reference for stepping. The new estimator outperforms existing balance criteria by providing both recovery step location prediction and momentum objectives with smooth dynamics.
A Low-Complexity Video Encoder for Equirectangular Projected 360 Video Content

360- video is gaining a lot of interest because of the immersive feeling brought by such a technology. Several projection formats are used to represent this type of content. Equirectangular projection (ERP) is one of the most widely used projection scheme for 360 panoramic content. The main drawback of ERP is its latitude dependent sampling density unlike conventional 2D content. Consequently, conventional 2D codecs such as HEVC are not optimal for the coding of ERP projected 360 content. To cope with this dependency, this work proposes an adaptation of motion vector resolution and minimum width of the coding block depending on its latitude. Experimental results show up to 0.5% BD-rate savings for motion contained sequences with 15% encoding time reduction in random access configuration.