julilili42/papers huggingface dataset:
Design of a shim coil array matched to the human brain anatomy
2019-09-30

Purpose The purpose of this study is to introduce a novel design method of a shim coil array specifically optimized for whole brain shimming and to compare the performance of the resulting coils to conventional spherical harmonic shimming. Methods The proposed design approach is based on the stream function method and singular value decomposition. Eighty‐four field maps from 12 volunteers measured in seven different head positions were used during the design process. The cross validation technique was applied to find an optimal number of coil elements in the array. Additional 42 field maps from 6 further volunteers were used for an independent validation. A bootstrapping technique was used to estimate the required population size to achieve a stable coil design. Results Shimming using 12 and 24 coil elements outperforms fourth‐ and fifth‐order spherical harmonic shimming for all measured field maps, respectively. Coil elements show novel coil layouts compared to the conventional spherical harmonic coils and existing multi‐coils. Both leave‐one‐out and independent validation demonstrate the generalization ability of the designed arrays. The bootstrapping analysis predicts that field maps from approximately 140 subjects need to be acquired to arrive at a stable design. Conclusions The results demonstrate the validity of the proposed method to design a shim coil array matched to the human brain anatomy, which naturally satisfies the laws of electrodynamics. The design method may also be applied to develop new shim coil arrays matched to other human organs.
The Role of Preferences, Attitudes, and Personality Traits in Labor Market Matching
2019-09-30

We provide new evidence of worker-firm matching based on preferences, attitudes and personality traits using new, representative matched employer-employee data from Germany. Time-constant firm characteristics explain a significant proportion of total variance in a series of outcome variables commonly applied in behavioral economics research. Hence, behavioral characteristics play an important, yet under researched, role in the labor market matching process.
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
2019-10-01

Deep learning based 3D reconstruction techniques have recently achieved impressive results. However, while state-of-the-art methods are able to output complex 3D geometry, it is not clear how to extend these results to time-varying topologies. Approaches treating each time step individually lack continuity and exhibit slow inference, while traditional 4D reconstruction methods often utilize a template model or discretize the 4D space at fixed resolution. In this work, we present Occupancy Flow, a novel spatio-temporal representation of time-varying 3D geometry with implicit correspondences. Towards this goal, we learn a temporally and spatially continuous vector field which assigns a motion vector to every point in space and time. In order to perform dense 4D reconstruction from images or sparse point clouds, we combine our method with a continuous 3D representation. Implicitly, our model yields correspondences over time, thus enabling fast inference while providing a sound physical description of the temporal dynamics. We show that our method can be used for interpolation and reconstruction tasks, and demonstrate the accuracy of the learned correspondences. We believe that Occupancy Flow is a promising new 4D representation which will be useful for a variety of spatio-temporal reconstruction tasks.
Attacking Optical Flow
2019-10-01

Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-driving cars, it is important to gain insights into the robustness of those techniques. Recently, it has been shown that adversarial attacks easily fool deep neural networks to misclassify objects. The robustness of optical flow networks to adversarial attacks, however, has not been studied so far. In this paper, we extend adversarial patch attacks to optical flow networks and show that such attacks can compromise their performance. We show that corrupting a small patch of less than 1% of the image size can significantly affect optical flow estimates. Our attacks lead to noisy flow estimates that extend significantly beyond the region of the attack, in many cases even completely erasing the motion of objects in the scene. While networks using an encoder-decoder architecture are very sensitive to these attacks, we found that networks using a spatial pyramid architecture are less affected. We analyse the success and failure of attacking both architectures by visualizing their feature maps and comparing them to classical optical flow techniques which are robust to these attacks. We also demonstrate that such attacks are practical by placing a printed pattern into real scenes.
Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations
2019-10-01

Convolutional Neural Networks (CNNs) achieve high accuracy in vision-based object detection tasks. For their usage in the automotive domain, CNNs have to be robust against various kinds of natural distortions caused by different weather conditions while state-of-the-art datasets like KITTI lack these challenging scenarios. Our approach automatically identifies corner cases where CNNs fail and improves their robustness by automated augmentation of the training data with synthetic rain variations including falling rain with brightness reduction as well as raindrops on the windshield. Our method achieves higher performance upon validation against a real rain dataset compared with state-of-the art data augmentation techniques like Gaussian noise (GN) or Salt-and-Pepper noise (SPN).
Racial differences in systemic sclerosis disease presentation: a European Scleroderma Trials and Research group study
2019-10-01

Abstract Objectives Racial factors play a significant role in SSc. We evaluated differences in SSc presentations between white patients (WP), Asian patients (AP) and black patients (BP) and analysed the effects of geographical locations. Methods SSc characteristics of patients from the EUSTAR cohort were cross-sectionally compared across racial groups using survival and multiple logistic regression analyses. Results The study included 9162 WP, 341 AP and 181 BP. AP developed the first non-RP feature faster than WP but slower than BP. AP were less frequently anti-centromere (ACA; odds ratio (OR) = 0.4, P &amp;lt; 0.001) and more frequently anti-topoisomerase-I autoantibodies (ATA) positive (OR = 1.2, P = 0.068), while BP were less likely to be ACA and ATA positive than were WP [OR(ACA) = 0.3, P &amp;lt; 0.001; OR(ATA) = 0.5, P = 0.020]. AP had less often (OR = 0.7, P = 0.06) and BP more often (OR = 2.7, P &amp;lt; 0.001) diffuse skin involvement than had WP. AP and BP were more likely to have pulmonary hypertension [OR(AP) = 2.6, P &amp;lt; 0.001; OR(BP) = 2.7, P = 0.03 vs WP] and a reduced forced vital capacity [OR(AP) = 2.5, P &amp;lt; 0.001; OR(BP) = 2.4, P &amp;lt; 0.004] than were WP. AP more often had an impaired diffusing capacity of the lung than had BP and WP [OR(AP vs BP) = 1.9, P = 0.038; OR(AP vs WP) = 2.4, P &amp;lt; 0.001]. After RP onset, AP and BP had a higher hazard to die than had WP [hazard ratio (HR) (AP) = 1.6, P = 0.011; HR(BP) = 2.1, P &amp;lt; 0.001]. Conclusion Compared with WP, and mostly independent of geographical location, AP have a faster and earlier disease onset with high prevalences of ATA, pulmonary hypertension and forced vital capacity impairment and higher mortality. BP had the fastest disease onset, a high prevalence of diffuse skin involvement and nominally the highest mortality.
Multiplicity of Asymptomatic Plasmodium falciparum Infections and Risk of Clinical Malaria: A Systematic Review and Pooled Analysis of Individual Participant Data
2019-10-01

Abstract Background The malaria parasite Plasmodium falciparum holds an extensive genetic polymorphism. In this pooled analysis, we investigate how the multiplicity in asymptomatic P. falciparum infections—that is, the number of coinfecting clones—affects the subsequent risk of clinical malaria in populations living under different levels of transmission. Methods A systematic search of the literature was performed to identify studies in which P. falciparum infections were genotyped in asymptomatic individuals who were followed up prospectively regarding the incidence of clinical malaria. Individual participant data were pooled from 15 studies (n = 3736 individuals). Results Multiclonal asymptomatic infections were associated with a somewhat increased subsequent risk of clinical malaria in the youngest children, followed by an initial declining risk with age irrespective of transmission intensity. At approximately 5 years of age, the risk continued the gradual decline with age in high-transmission settings. However, in older children in moderate-, low-, and seasonal-transmission settings, multiclonal infections were either not significantly associated with the risk of subsequent febrile malaria or were associated with an increased risk. Conclusions The number of clones in asymptomatic P. falciparum infections is associated with different risks of subsequent clinical malaria depending on age and transmission intensity.
Simulating Photo-realistic Snow and Fog on Existing Images for Enhanced CNN Training and Evaluation
2019-10-01

Verification and robustness testing of machine learning algorithms for autonomous driving is crucial. Due to the increasing complexity and quantity of those systems in a single vehicle, just driving the required distance with a newly developed vehicle is not feasible anymore: billions of hours on the street without failure are necessary to qualify for industry standards like ISO 26262. That is where simulation comes into play: machine learning algorithms are trained and evaluated on well known image data sets like KITTI or Cityscapes. But today's data sets mostly contain images taken under perfect weather conditions and therefore do not harden optical object detection algorithms against various weather conditions. This paper focuses on reusing these established and labeled data sets by augmenting them with adverse weather effects like snow and fog. Those effects are rendered physically correct and life like while being added to existing real world images. Thanks to easy parametrization the weather influences may be varied as necessary and allow for finely tuned learning and optimization processes. The weather effects are evaluated with regard to realism and impact on an established object detection algorithm. These newly created weather-influenced images may be used to validate or train new object detection algorithms.
The Applicability of Cycle GANs for Pupil and Eyelid Segmentation, Data Generation and Image Refinement
2019-10-01

Eye tracking is increasingly influencing scientific areas such as psychology, cognitive science, and human-computer interaction. Many eye trackers output the gaze location and the pupil center. However, other valuable information can also be extracted from the eyelids, such as the fatigue of a person. We evaluated Generative Adversarial Networks (GAN) for eyelid and pupil area segmentation, data generation, and image refinement. While the segmentation GAN performs the desired task, the others serve as supportive Networks. The trained data generation GAN does not require simulated data to increase the dataset, it simply uses existing data and creates subsets. The purpose of the refinement GAN, in contrast, is to simplify manual annotation by removing noise and occlusion in an image without changing the eye structure and pupil position. In addition 100,000 pupil and eyelid segmentations are made publicly available for images from the labeled pupils in the wild data set (DOWNLOAD). These will support further research in this area.
New Contexts for Professional Learning: Analyzing High School Science Teachers’ Engagement on Twitter
2019-10-01

This mixed-methods observational study analyzes Advanced Placement (AP) Biology teachers’ engagement in microblogging for professional learning. Data from three hashtag-based Twitter communities—#apbiochat, #apbioleaderacad, and #apbioleaderacademy (121 users; 2,253 tweets)—are analyzed using educational data mining, qualitative two-cycle content analysis, social network analysis, linear and logistic regression analyses, and hierarchical linear modeling. Results indicate that teachers’ use of Twitter reflects aspects of high-quality professional development. Notably, Twitter’s nonhierarchical organization affords shared content creation and distribution. Additionally, Twitter allows for different temporal participation patterns and supports the personalization of learning experiences aligned to teachers’ needs and preferences. Furthermore, teachers frame their interactions on Twitter positively—thus, creating a supportive environment for professional learning that might reduce perceived professional isolation. Based on this analysis, policymakers and school leaders should feel comfortable encouraging teachers to use microblogging as part of their professional learning activities.
The functional ClpXP protease of Chlamydia trachomatis requires distinct clpP genes from separate genetic loci
2019-10-01

Abstract Clp proteases play a central role in bacterial physiology and, for some bacterial species, are even essential for survival. Also due to their conservation among bacteria including important human pathogens, Clp proteases have recently attracted considerable attention as antibiotic targets. Here, we functionally reconstituted and characterized the ClpXP protease of Chlamydia trachomatis (ctClpXP), an obligate intracellular pathogen and the causative agent of widespread sexually transmitted diseases in humans. Our in vitro data show that ctClpXP is formed by a hetero-tetradecameric proteolytic core, composed of two distinct homologs of ClpP (ctClpP1 and ctClpP2), that associates with the unfoldase ctClpX via ctClpP2 for regulated protein degradation. Antibiotics of the ADEP class interfere with protease functions by both preventing the interaction of ctClpX with ctClpP1P2 and activating the otherwise dormant proteolytic core for unregulated proteolysis. Thus, our results reveal molecular insight into ctClpXP function, validating this protease as an antibacterial target.
Tryptophan and Kynurenine Pathway Metabolites in Animal Models of Retinal and Optic Nerve Damage: Different Dynamics of Changes
2019-10-01

Kynurenines, products of tryptophan (TRP) metabolism, display neurotoxic (e.g., 3-hydroxykynurenine; 3-HK), or neuroprotective (e.g., kynurenic acid; KYNA) properties. Imbalance between the enzymes constituting the kynurenine pathway (KP) plays a role in several disease, including neurodegeneration. In this study, we track changes in concentrations of tryptophan and its selected metabolites after damage to retinal ganglion cells and link this data with expression of KP enzymes. Brown-Norway rats were subjected to intravitreal <i>N</i>-methyl-D-aspartate (NMDA) injection or partial optic nerve crush (PONC). Retinas were collected 2 and 7 days after the completion of PONC or NMDA injection. Concentrations of TRP, kynurenine (KYN), and KYNA were determined by high performance liquid chromatography (HPLC). Data on gene expression in the rat retina were extracted from GEO, public microarray experiments database. Two days after NMDA injection concentration of TRP decreased, while KYN and KYNA increased. At day 7 compared to day 2 decrease of KYN, KYNA and further reduction of TRP concentration were observed, but on day 7 KYN concentration was still elevated when compared to controls. At day 2 and 7 after NMDA injection no statistically significant alterations of 3-HK were observed. TRP and 3-HK concentration was higher in PONC group than in controls. However, both KYN and KYNA were lower. At day seven concentration of TRP, 3-HK, and KYN was higher, whereas concentration of KYNA declined. <i>In vivo</i> experiments showed that retinal damage or optic nerve lesion affect TRP metabolism via KP. However, the pattern of changes in metabolite concentrations was different depending on the model. In particular, in PONC KYNA and KYN levels were decreased and 3-HK elevated. These observations correspond with data on expression of genes encoding KP enzymes assessed after optic nerve crush or transection. After intraorbital optic nerve crush downregulation of <i>KyatI</i> and <i>KyatIII</i> between 24 h and 3 days after procedure was observed. <i>Kmo</i> expression was transiently upregulated (12 h after the procedures). After intraorbital optic nerve transsection (IONT) <i>Kmo</i> expression was upregulated after 48 h and 7 days, <i>KyatI</i> and <i>KyatIII</i> were downregulated after 12, 48 h, 7 days and upregulated after 15 days. Collected data point to the conclusion that development of therapeutic strategies targeting the KP could be beneficial in diseases involving retinal neurodegeneration.
Update Breast Cancer 2019 Part 4 – Diagnostic and Therapeutic Challenges of New, Personalised Therapies for Patients with Early Breast Cancer
2019-10-01

Abstract The further development of therapies for women with early breast cancer is progressing far more slowly than in the case of patients with advanced breast cancer and is additionally delayed compared to developments in metastatic breast cancer. Nonetheless, significant advancements have been able to be recorded recently. This review summarises the latest developments in view of the most recent publications and professional conferences. For hormone-receptor-positive patients, new aspects for the duration of antihormone therapy and with regard to the benefits of multigene tests have been published. In the case of HER2-positive patients, the value of post-neoadjuvant therapy and de-escalation of the therapy is discussed. In patients with triple-negative breast cancer, there is a question of whether the knowledge of the biological background of a homologous recombination deficiency (HRD) helps develop new therapies for this subtype. In particular the “use” of a BRCA1/2 mutation or the biological characteristic HRD as a potential motive for therapy plays a role here in specifying the significance of platinum therapy and therapy with PARP inhibitors.
Effects of a probiotic treatment (Enterococcus faecalis) and open-label placebo on symptoms of allergic rhinitis: study protocol for a randomised controlled trial
2019-10-01

Introduction Several studies suggest that gut microbiota may play an important role in allergic diseases. The present trial aims to examine effects of the probiotic Enterococcus faecalis on symptoms of allergic rhinitis in patients. Effects of this probiotic on the immune system have been reported by several studies, but the majority of the previous trials were animal studies. In addition, it is well known that symptoms in allergic rhinitis are prone to exhibit high placebo responses. Moreover, recent studies report that even placebos without deception (open-label placebos) are highly effective in reducing symptoms of allergic rhinitis. Our study design combines both new approaches to assess effects on allergic symptoms in patients. The objective of this study is to compare the effects of a probiotic treatment ( E. faecalis ) with effects seen by open-label placebo, concealed placebo treatment and no treatment control. Methods and analysis A total of 120 patients with allergic rhinitis will be randomly assigned to one of four different groups: a double-blind probiotic/placebo group (groups 1 and 2), an open-label placebo group (group 3) and a no-treatment group (group 4) to control for spontaneous variation of symptoms. The primary outcome is the evaluation of allergic symptoms using the Combined Symptoms Medication Score. Furthermore, health-related quality of life is examined (Rhinitis Quality of Life Questionnaire). Secondary outcomes include a visual analogue scale on allergic burden and a second quality of life questionnaire. This report describes the study design of the randomised controlled trial. Ethics and dissemination The study design was approved by the ethical committee of the UKT Department of Psychosomatic Medicine and Psychotherapy, Tübingen, Germany. The trial is registered at the German Clinical Trials Register ( www.drks.de , DRKS00015804). The trial results will be published in peer-reviewed journals and at conferences. Trial registration number German Clinical Trials Register ( www.drks.de , DRKS00015804); Pre-results.
End-to-End Learning for Graph Decomposition
2019-10-01

Deep neural networks provide powerful tools for pattern recognition, while classical graph algorithms are widely used to solve combinatorial problems. In computer vision, many tasks combine elements of both pattern recognition and graph reasoning. In this paper, we study how to connect deep networks with graph decomposition into an end-to-end trainable framework. More specifically, the minimum cost multicut problem is first converted to an unconstrained binary cubic formulation where cycle consistency constraints are incorporated into the objective function. The new optimization problem can be viewed as a Conditional Random Field (CRF) in which the random variables are associated with the binary edge labels. Cycle constraints are introduced into the CRF as high-order potentials. A standard Convolutional Neural Network (CNN) provides the front-end features for the fully differentiable CRF. The parameters of both parts are optimized in an end-to-end manner. The efficacy of the proposed learning algorithm is demonstrated via experiments on clustering MNIST images and on the challenging task of real-world multi-people pose estimation.
Update Breast Cancer 2019 Part 5 – Diagnostic and Therapeutic Challenges of New, Personalised Therapies in Patients with Advanced Breast Cancer
2019-10-01

Abstract Significant advancements have been made in recent years in advanced breast cancer and nearly all of them have been in the field of targeted therapy. Pertuzumab and trastuzumab-emtansine (T-DM1) have been able to be introduced in HER2-positive breast cancer. Now other anti-HER2 therapies are being developed (e.g. margetuximab, DS-8201a, pyrotinib) which can overcome other resistance mechanisms in the HER2 signalling pathway. In the field of hormone-receptor-positive breast cancer, an mTOR inhibitor and CDK4/6 inhibitors were introduced in the past. Now the introduction of the first PI3K inhibitor is forthcoming and this inhibitor will involve genetic testing of the tumour for a mutation in the PIK3CA gene. There are also significant advancements in triple-negative breast cancer: By combining chemotherapy and immunotherapy, an advantage for overall survival was able to be demonstrated in a subgroup (immune cells PD-L1-positive). The PARP inhibitor therapy for HER2-negative patients with a germ line mutation in BRCA1 or BRCA2 was also associated with an improved overall survival in a subgroup. These promising new study results are summarised in this review.
Sparse and Imperceivable Adversarial Attacks
2019-10-01

Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On the other hand the pixelwise perturbations of sparse attacks are typically large and thus can be potentially detected. We propose a new black-box technique to craft adversarial examples aiming at minimizing -distance to the original image. Extensive experiments show that our attack is better or competitive to the state of the art. Moreover, we can integrate additional bounds on the componentwise perturbation. Allowing pixels to change only in region of high variation and avoiding changes along axis-aligned edges makes our adversarial examples almost non-perceivable. Moreover, we adapt the Projected Gradient Descent attack to the -norm integrating componentwise constraints. This allows us to do adversarial training to enhance the robustness of classifiers against sparse and imperceivable adversarial manipulations.