julilili42/papers huggingface dataset:
Camera-Based Eye Blink Detection Algorithm for Assessing Driver Drowsiness
2019-06-01

This paper presents an adaptive camera-based eye blink detection algorithm for assessing the level of drowsiness during driving. The data used in this study were collected from driving simulator experiments using a remote camera. Eye blink detection in the automotive context and for different driver states typically encounters some difficulties. It may be challenging to reliably distinguish between eye blink events and gaze-related eyelid closures, particularly the glances at the dashboard, since both exhibit a similar eyelid movement pattern. In addition, it is difficult to find comparable thresholds due to high inter-individual differences in the palpebral aperture. Furthermore, the blinking behavior is impacted by drowsiness and the blink patterns vary widely, which requires an adaptive algorithm to deal with this intra-individual variability of the blinks. These challenges are considered in the design of the presented blink detection algorithm. This algorithm is based essentially on a threshold for the maximum velocity of the eyelids. This threshold is determined using k-means clustering (k=2)and updated every five minutes of the drive. The accuracy of the algorithm is evaluated based on video labeling. The detection rates demonstrate that the algorithm performs very reliably in both awake and drowsy phases of the driving experiments.
Pelvic ring fractures in the elderly
2019-06-01

Pelvic ring fractures are rare injuries in the elderly though the incidence is increasing due to the increasing age of the population. Main goal of treatment is the quickest possible re-mobilization to prevent side-effects of immobilization such as osteopenia, pulmonary infections or thromboembolic events. Isolated anterior pelvic ring fractures are stable injuries and therefore they usually can be treated conservatively, while pelvic ring injuries with involvement of the posterior ring are considered unstable and should undergo surgical stabilization if the patient’s condition allows for it. Conservative treatment includes adequate analgesia, guided mobilization with partial weight bearing if possible and osteoanabolic medication. The appropriate surgical procedure should be discussed in an interdisciplinary round considering patient’s pre-injury condition, anaesthetic and surgical risks. Cite this article: EFORT Open Rev 2019;4 DOI: 10.1302/2058-5241.4.180062
Propagation of a Short GRB Jet in the Ejecta: Jet Launching Delay Time, Jet Structure, and GW170817/GRB 170817A
2019-06-01

Abstract We perform a series of relativistic magnetohydrodynamic simulations to investigate how a hot magnetic jet propagates within the dynamical ejecta of a binary neutron star merger, focusing on how the jet structure depends on the delay time of jet launching with respect to the merger time, Δ t jet . We find that regardless of the jet-launching delay time, a structured jet with an angle-dependent luminosity and Lorentz factor is always formed after the jet breaks out of the ejecta. On the other hand, the jet-launching delay time has an impact on the jet structure. If the jet-launching delay time is relatively long, e.g., ≥0.5 s, the line-of-sight material has a dominant contribution from the cocoon. On the other hand, for a relatively short jet-launching delay time, the jet penetrates through the ejecta early on and develops an angular structure afterward. The line-of-sight ejecta is dominated by the structured jet itself. We discuss the case of GW170817/GRB 170817A within the framework of both long and short jet-launching delay time. In the future, more observations of gravitational-wave/gamma-ray burst associations can help to differentiate between these two scenarios.
Semi-inclusive deep-inelastic scattering in Wandzura-Wilczek-type approximation
2019-06-01

A bstract We present the complete cross-section for the production of unpolarized hadrons in semi-inclusive deep-inelastic scattering up to power-suppressed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mfenced> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:msup> <mml:mi>Q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:mfenced> </mml:math> terms in the Wandzura-Wilczek-type approximation, which consists in systematically assuming that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mi>q</mml:mi> <mml:mo> ̄</mml:mo> </mml:mover> <mml:mi>g</mml:mi> <mml:mi>q</mml:mi> </mml:math> -terms are much smaller than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mi>q</mml:mi> <mml:mo> ̄</mml:mo> </mml:mover> <mml:mi>q</mml:mi> </mml:math> -correlators. We compute all twist-2 and twist-3 structure functions and the corresponding asymmetries, and discuss the applicability of the Wandzura-Wilczek-type approximations on the basis of available data. We make predictions that can be tested by data from COMPASS, HERMES, Jefferson Lab, and the future Electron-Ion Collider. The results of this paper can be readily used for phenomenology and for event generators, and will help to improve the description of semi-inclusive deep-inelastic processes in terms of transverse momentum dependent parton distribution functions and fragmentation functions beyond the leading twist.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
2019-06-01

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem
2019-06-01

Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear classifier function fail in this regard as they produce almost always high confidence predictions far away from the training data. For bounded domains like images we propose a new robust optimization technique similar to adversarial training which enforces low confidence predictions far away from the training data. We show that this technique is surprisingly effective in reducing the confidence of predictions far away from the training data while maintaining high confidence predictions and test error on the original classification task compared to standard training.
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation
2019-06-01

We propose a technique for depth estimation with a monocular structured-light camera, i.e., a calibrated stereo set-up with one camera and one laser projector. Instead of formulating the depth estimation via a correspondence search problem, we show that a simple convolutional architecture is sufficient for high-quality disparity estimates in this setting. As accurate ground-truth is hard to obtain, we train our model in a self-supervised fashion with a combination of photometric and geometric losses. Further, we demonstrate that the projected pattern of the structured light sensor can be reliably separated from the ambient information. This can then be used to improve depth boundaries in a weakly supervised fashion by modeling the joint statistics of image and depth edges. The model trained in this fashion compares favorably to the state-of-the-art on challenging synthetic and real-world datasets. In addition, we contribute a novel simulator, which allows to benchmark active depth prediction algorithms in controlled conditions.
Learning Non-Volumetric Depth Fusion Using Successive Reprojections
2019-06-01

Given a set of input views, multi-view stereopsis techniques estimate depth maps to represent the 3D reconstruction of the scene; these are fused into a single, consistent, reconstruction -- most often a point cloud. In this work we propose to learn an auto-regressive depth refinement directly from data. While deep learning has improved the accuracy and speed of depth estimation significantly, learned MVS techniques remain limited to the planesweeping paradigm. We refine a set of input depth maps by successively reprojecting information from neighbouring views to leverage multi-view constraints. Compared to learning-based volumetric fusion techniques, an image-based representation allows significantly more detailed reconstructions; compared to traditional point-based techniques, our method learns noise suppression and surface completion in a data-driven fashion. Due to the limited availability of high-quality reconstruction datasets with ground truth, we introduce two novel synthetic datasets to (pre-)train our network. Our approach is able to improve both the output depth maps and the reconstructed point cloud, for both learned and traditional depth estimation front-ends, on both synthetic and real data.
Hypothermia protects retinal ganglion cells against hypoxia‐induced cell death in a retina organ culture model
2019-06-01

Background Hypoxia contributes to retinal damage in several retinal diseases, including central retinal artery occlusion, with detrimental consequences like painless, monocular loss of vision. Currently, the treatment options are severely limited due to the short therapy window, as the neuronal cells, especially the retinal ganglion cells (RGCs), are irreversibly damaged within the first few hours. Hypothermia might be a possible treatment option or at least might increase the therapy window. Methods To investigate the neuroprotective effect of hypothermia after retinal hypoxia, an easy‐to‐use ex vivo retinal hypoxia organ culture model developed in our laboratory was used that reliably induced retinal damage on a structural, molecular and functional level. The neuroprotective effect of hypothermia after retinal hypoxia was analysed using optical coherence tomography scans, histological stainings, quantitative real‐time polymerase chain reaction, western blotting and microelectrode array recordings. Results Two different hypothermic temperatures (30°C and 20°C) were evaluated, both exhibited strong neuroprotective effects. Most importantly, hypothermia increased RGC survival after retinal hypoxia. Furthermore, hypothermia counteracted the hypoxia‐induced RGC death, reduced macroglia activation, attenuated retinal thinning and protected from loss of spontaneous RGC activity. Conclusions These results indicate that already a mild reduction in temperature protects the RGCs against damage and could function as a promising therapeutic option for hypoxic diseases.
Superquadrics Revisited: Learning 3D Shape Parsing Beyond Cuboids
2019-06-01

Abstracting complex 3D shapes with parsimonious part-based representations has been a long standing goal in computer vision. This paper presents a learning-based solution to this problem which goes beyond the traditional 3D cuboid representation by exploiting superquadrics as atomic elements. We demonstrate that superquadrics lead to more expressive 3D scene parses while being easier to learn than 3D cuboid representations. Moreover, we provide an analytical solution to the Chamfer loss which avoids the need for computational expensive reinforcement learning or iterative prediction. Our model learns to parse 3D objects into consistent superquadric representations without supervision. Results on various ShapeNet categories as well as the SURREAL human body dataset demonstrate the flexibility of our model in capturing fine details and complex poses that could not have been modelled using cuboids.
Rare mature B‐cell lymphomas in children and adolescents
2019-06-01

Abstract Pediatric‐type follicular lymphoma (PTFL), pediatric nodal marginal zone lymphoma (pnMZL), and large B‐cell lymphoma (LBCL) with IRF4 rearrangement have been introduced into the current World Health Organization (WHO) classification. They account for 5% to 10% of mature B‐cell lymphomas in children and adolescents. Both PTFL and pnMZL predominantly affect male adolescents and usually present with localized lymphadenopathy in the head and neck region. The cells within the follicles of PTFL typically show high‐grade cytology, IGH monoclonality and lack the t(14;18) chromosomal alteration. In contrast, pnMZL is characterized by progressive transformation of germinal center (PTGC)‐like features and interfollicular proliferation of the cells with expansion of the marginal zones with diffuse areas. Watch and wait after complete resection seems an adequate therapy with chemotherapy restricted to incompletely resected disease. All children with PTFL and pnMZL reported, so far, survived. B‐cell lymphomas presenting in the Waldeyer's ring are characterized by the expression of IRF4/MUM1 and often associated with IRF4 rearrangements. Because of the frequent diffuse component, treatment often follows current protocols for mature B‐NHL. The prognosis is excellent.
Local Temporal Bilinear Pooling for Fine-Grained Action Parsing
2019-06-01

Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations over a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to previous work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced without suffering from information loss nor requiring extra computation. We perform extensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art pooling work on various datasets.
Disentangling Adversarial Robustness and Generalization
2019-06-01

Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA.
Difficult Women: Changing Representations of Female Characters in Contemporary Television Series
2019-06-01

Abstract Starting out with a brief overview of recent TV series that feature complex and complicated female characters – or, as we call them, ‘difficult women’ – this introduction investigates the changing manner in which women have been represented on TV in previous decades. Demonstrating that especially TV shows of the 2010s undermine and work against traditional and stereotypical portrayals of women on TV and instead establish feminist discourses, we argue that this time period can be defined as a pivotal moment with regard to changing representations of women on TV. Using Netflix’s Orange Is the New Black as an example, we also show that TV series that feature difficult women make use of the very techniques and conventions of what Jason Mittell has described as ‘Complex TV’ in order to consciously engage with questions of female representation on TV and to create a feminist discourse that works against sexist tropes and stereotypes.
Estimation of elemental composition of agricultural soils from West Azerbaijan, Iran, using mid-infrared spectral models
2019-06-01

ABSTRACT Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2 values of 0.8, and out-of-bag mean square errors of 0.2 and 1.1, respectively. The other oxides were not predicted satisfactorily, probably due to small differences in their elemental compositions. Random Forest provided the important spectral bands related to the content of each element. For Al2O3, these bands were between 500 and 650 cm-1, which represent out-of-plane OH bending vibrations and Al-O gibbsite and alumino-silicate vibrations. For CaO, the most important bands are related to carbonate content. A combination of Fourier transform infrared spectra and Random Forest models can be used as a rapid and low-cost technique to estimate the elemental composition of arid and semi-arid soils of Northern Iran.
Experiences of a multistep process with medical and psychological interventions for patients with congenital uterine aplasia to achieve motherhood: the Gothenburg-Tübingen collaboration.
2019-06-01

Congenital uterine aplasia, also known as Mayer-Rokitansky-Küster-Hauser syndrome (MRKHS) is a condition associated to a non-functional uterus in the presence of functional ovaries. In a setting where surrogacy is illegal (or not accepted) and adoption is the only alternative, neovaginoplasty and subsequent uterus transplantation (UTx) can provide a route to motherhood for women with MRKHS. This review article describes a multistep process by which patients with MRKHS can achieve motherhood with their own biological child. This process involving a careful clinical diagnosis, psychological counselling, assessment of eligibility for neovagina creation and UTx, the surgical treatment, fertility treatment, and long-term follow-up was developed at the Tübingen University Hospital and in close collaboration with Sahlgrenska Academy, University of Gothenburg, Sweden, where the basic experimental and clinical groundwork for UTx was laid and the first-ever UTx procedure was performed.
Reflective and Fluorescent Separation Under Narrow-Band Illumination
2019-06-01

In this paper, we address the separation of reflective and fluorescent components in RGB images taken under narrow-band light sources such as LEDs. First, we show that the fluorescent color per pixel can be estimated from at least two images under different light source colors, because the observed color at a surface point is represented by a convex combination of the light source color and the illumination-invariant fluorescent color. Second, we propose a method for robustly estimating the fluorescent color via MAP estimation by taking the prior knowledge with respect to fluorescent colors into consideration. We conducted a number of experiments by using both synthetic and real images, and confirmed that our proposed method works better than the closely related state-of-the-art method and enables us to separate reflective and fluorescent components even from a single image. Furthermore, we demonstrate that our method is effective for applications such as image-based material editing and relighting.
Introduction
2019-06-01

Introduced here by its guest editors, this special issue of Poetics Today focuses on multimodal media. Its purpose is twofold. On the one hand, it aims to explore the multimodal combination of semiotic resources in a range of primarily aesthetic media forms that until now have attracted comparatively little attention in multimodality studies, including not only multimodal novels, comics, and films but also current television programs, video games, and other forms of digital media. On the other hand, and no less importantly, the articles collected here engage with previously under-explored dimensions of multimodality and thus expand our understanding of what multimodality can mean, including not only perceptual and semiotic but also referential and participatory multimodality.
Exploration of Machine Learning Methods for Predicting the Operation Schedule of a Combined Heat and Power Plant
2019-06-01

In the context of sustainable energies, precise monitoring, simulation and forecasting methods are required for ensuring a sustainable and economical operation of energy systems. For combined heat and power (CHP) plants these tasks are typically performed with simulation models that model the physical behavior. With Machine Learning (ML), a data-driven modeling approach is available that can develop a reliable model without knowing detailed component characteristics. This paper compares the performance of different ML methods such as artificial neural networks, support vectors, and decision trees (including ensembles) for predicting the operation schedule of a heat-led CHP plant with heat storage. This includes the design of recursive and multiple output strategies. The experiments show that especially ensembles are well suited to solve the regression problem. They achieve an accuracy of over 98% for the forecast of a one-hour time horizon and still achieve 91% accuracy for a forecast of full days.
Hilbert series of the Grassmannian and k-Narayana numbers
2019-06-01

Abstract We compute the Hilbert series of the complex Grassmannian using invariant theoretic methods. This is made possible by showing that the denominator of the q -Hilbert series is a Vandermonde-like determinant. We show that the h -polynomial of the Grassmannian coincides with the k -Narayana polynomial. A simplified formula for the h -polynomial of Schubert varieties is given. Finally, we use a generalized hypergeometric Euler transform to find simplified formulae for the k -Narayana numbers, i.e. the h -polynomial of the Grassmannian.