nils-herrmann/clean_scirepeval huggingface dataset:
Periodic subgraph mining in dynamic networks

In systems of interacting entities such as social networks, interactions that occur regularly typically correspond to significant, yet often infrequent and hard to detect, interaction patterns. To identify such regular behavior in streams of dynamic interaction data, we propose a new mining problem of finding a minimal set of periodically recurring subgraphs to capture all periodic behavior in a dynamic network. We analyze the computational complexity of the problem and show that it is polynomial, unlike many related subgraph or itemset mining problems. We propose an efficient and scalable algorithm to mine all periodic subgraphs in a dynamic network. The algorithm makes a single pass over the data and is also capable of accommodating imperfect periodicity. We demonstrate the applicability of our approach on several real-world networks and extract interesting and insightful periodic interaction patterns. We also show that periodic subgraphs can be an effective way to uncover and characterize the natural periodicities in a system.
Rule-based document structure understanding with a fuzzy combination of layout and textual features

Document image processing is a crucial process in office automation and begins at the ‘OCR’ phase with difficulties in document ‘analysis’ and ‘understanding’. This paper presents a hybrid and comprehensive approach to document structure analysis. Hybrid in the sense that it makes use of layout (geometrical) as well as textual features of a given document. These features are the base for potential conditions which in turn are used to express fuzzy matched rules of an underlying rule base. Rules can be formulated based on features which might be observed within one specific layout object. However, rules can also express dependencies between different layout objects. In addition to its rule driven analysis, which allows an easy adaptation to specific domains with their specific logical objects, the system contains domain-independent markup algorithms for common objects (e.g., lists).
NELSON lung cancer screening study

The Dutch-Belgian Randomized Lung Cancer Screening Trial (Dutch acronym: NELSON study) was designed to investigate whether screening for lung cancer by low-dose multidetector computed tomography (CT) in high-risk subjects will lead to a decrease in 10-year lung cancer mortality of at least 25% compared with a control group without screening. Since the start of the NELSON study in 2003, 7557 participants underwent CT screening, with scan rounds in years 1, 2, 4 and 6. In the current review, the design of the NELSON study including participant selection and the lung nodule management protocol, as well as results on validation of CT screening and first results on lung cancer screening are described.
Data-driven hallucination of different times of day from a single outdoor photo

We introduce "time hallucination": synthesizing a plausible image at a different time of day from an input image. This challenging task often requires dramatically altering the color appearance of the picture. In this paper, we introduce the first data-driven approach to automatically creating a plausible-looking photo that appears as though it were taken at a different time of day. The time of day is specified by a semantic time label, such as "night".
 Our approach relies on a database of time-lapse videos of various scenes. These videos provide rich information about the variations in color appearance of a scene throughout the day. Our method transfers the color appearance from videos with a similar scene as the input photo. We propose a locally affine model learned from the video for the transfer, allowing our model to synthesize new color data while retaining image details. We show that this model can hallucinate a wide range of different times of day. The model generates a large sparse linear system, which can be solved by off-the-shelf solvers. We validate our methods by synthesizing transforming photos of various outdoor scenes to four times of interest: daytime, the golden hour, the blue hour, and nighttime.
qPortal: A platform for data-driven biomedical research

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software's strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.
An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic Finger

In this paper, we propose a novel electromyographic (EMG) control interface to control motion and joints compliance of a supernumerary robotic finger. The supernumerary robotic fingers are a recently introduced class of wearable robotics that provides users additional robotic limbs in order to compensate or augment the existing abilities of natural limbs without substituting them. Since supernumerary robotic fingers are supposed to closely interact and perform actions in synergy with the human limbs, the control principles of extra finger should have similar behavior as human's ones including the ability of regulating the compliance. So that, it is important to propose a control interface and to consider the actuators and sensing capabilities of the robotic extra finger compatible to implement stiffness regulation control techniques. We propose EMG interface and a control approach to regulate the compliance of the device through servo actuators. In particular, we use a commercial EMG armband for gesture recognition to be associated with the motion control of the robotic device and surface one channel EMG electrodes interface to regulate the compliance of the robotic device. We also present an updated version of a robotic extra finger where the adduction/abduction motion is realized through ball bearing and spur gears mechanism. We have validated the proposed interface with two sets of experiments related to compensation and augmentation. In the first set of experiments, different bimanual tasks have been performed with the help of the robotic device and simulating a paretic hand since this novel wearable system can be used to compensate the missing grasping abilities in chronic stroke patients. In the second set, the robotic extra finger is used to enlarge the workspace and manipulation capability of healthy hands. In both sets, the same EMG control interface has been used. The obtained results demonstrate that the proposed control interface is intuitive and can successfully be used, not only to control the motion of a supernumerary robotic finger but also to regulate its compliance. The proposed approach can be exploited also for the control of different wearable devices that has to actively cooperate with the human limbs.
The application of W-plasty combined Botox-A injection in treating sunk scar on the face

This study was aimed to explore the effect of W-plasty combined Botox-A injection in improving appearance of scar.According to the inclusive and exclusive criteria, patients received W-plasty combined Botox-A injection (study group) or traditional (control group) scar repairment were enrolled in this study. After surgery, a follow-up ranged from 1 to 2 years was conducted. The effectiveness of surgery was assessed by visual analogue scale (VAS).A total of 38 patients were enrolled in this study, including 21 cases in the study group and 17 cases in the control group. There were no significant difference were identified in age (t = 0.339, P = .736), gender ratio (χ = 0.003, P = .955) and scar forming reason (χ = 0.391, P = .822) between 2 groups. After treatment, the VAS score in the study group was significantly higher than that in the control group (P < .001).W-plasty combined Botox-A injection can significantly improve the appearance of sunk scar on the face.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can cause negative effects or even manipulate the public events. One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. Unfortunately, most of the existing approaches can hardly handle this challenge, since they tend to learn event-specific features that can not be transferred to unseen events. In order to address this issue, we propose an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events. It consists of three main components: the multi-modal feature extractor, the fake news detector, and the event discriminator. The multi-modal feature extractor is responsible for extracting the textual and visual features from posts. It cooperates with the fake news detector to learn the discriminable representation for the detection of fake news. The role of event discriminator is to remove the event-specific features and keep shared features among events. Extensive experiments are conducted on multimedia datasets collected from Weibo and Twitter. The experimental results show our proposed EANN model can outperform the state-of-the-art methods, and learn transferable feature representations.
Metamaterial matching of high-permittivity coatings for 79 GHz radar sensors

Future automotive radar sensors in the 79 GHz regime are intended to be integrated behind painted, plastic fascia. This integration has to be carefully studied, concerning reflections coming from the bumper in line of sight of the sensor. To reduce these reflections, impedance matching of multilayer structures with high permittivities (e.g. metallic paints) is mandatory. Besides conventional matching techniques, metamaterials are a possibility to realize a matching layer. Based on an adopted parameter extraction algorithm for multilayer dielectrics, a bandwidth evaluation of a possible metamaterial design is shown. The measurement results show that for a bandwidth up to 3.8 GHz the metamaterial outperforms the benchmark.
Dynamic high-resolution ultrasound in vivo imaging of hyaluronic acid filler injection.

High-resolution ultrasound (HRU) imaging is a useful tool to study hyaluronic acid (HA) filler injected in the face.



To observe real-time injection of HA using HRU and describe behavior of the gel injection in various anatomic layers and the effect of massage.



Deep (preperiosteal), intermediate (subdermal), and superficial (dermal) injections of HA were performed in the supraclavicular area under ultrasonography visualization on a healthy volunteer. Videos were obtained during injection and static images at several time points, including during injection, immediately after injection, 5 minutes after massage of the treated area, and at 2 weeks after injection.



During injection, dermally injected HA stayed within the dermis, increasing its echogenicity; subdermally injected HA formed multiple anechoic pearls; and preperiosteal HA produced a single anechoic bubble with diffuse margins. No vertical transection of the planes was observed during injection or after massage. Two-week postinjection imaging showed persistence of the varying HA morphology in each plane.



High-resolution ultrasound allows in vivo study of HA injection behavior. HA adopts different morphology within the tissue depending on the density and compliance of the tissues in the plane of injection.
Feeding ecology and behavioral adjustments: flexibility of a small neotropical primate (Callithrix jacchus) to survive in a semiarid environment

Semiarid environments are known for climate extremes such as high temperatures, low humidity, irregular precipitations, and apparent resource scarcity. We aimed to investigate how a small neotropical primate (Callithrix jacchus; the common marmoset) manages to survive under the harsh conditions that a semiarid environment imposes. The study was carried out in a 400-ha area of Caatinga in the northeast of Brazil. During a 6-month period (3 months of dry season and 3 months of wet season), we collected data on the diet of 19 common marmosets (distributed in five groups) and estimated their behavioral time budget during both the dry and rainy seasons. Resting significantly increased during the dry season, while playing was more frequent during the wet season. No significant differences were detected regarding other behaviors. In relation to the diet, we recorded the consumption of prey items such as insects, spiders, and small vertebrates. We also observed the consumption of plant items, including prickly cladodes, which represents a previously undescribed food item for this species. Cladode exploitation required perceptual and motor skills to safely access the food resource, which is protected by sharp spines. Our findings show that common marmosets can survive under challenging conditions in part because of adjustments in their behavior and in part because of changes in their diet.
Gadolinium toxicity and treatment.

Gadolinium based contrast agents (GBCAs) play an important role in the diagnostic evaluation of many patients. The safety of these agents has been once again questioned after gadolinium deposits were observed and measured in brain and bone of patients with normal renal function. This retention of gadolinium in the human body has been termed "gadolinium storage condition". The long-term and cumulative effects of retained gadolinium in the brain and elsewhere are not as yet understood. Recently, patients who report that they suffer from chronic symptoms secondary to gadolinium exposure and retention created gadolinium-toxicity on-line support groups. Their self-reported symptoms have recently been published. Bone and joint complaints, and skin changes were two of the most common complaints. This condition has been termed "gadolinium deposition disease". In this review we will address gadolinium toxicity disorders, from acute adverse reactions to GBCAs to gadolinium deposition disease, with special emphasis on the latter, as it is the most recently described and least known.
Spectral Inference Networks: Unifying Spectral Methods With Deep Learning

We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or pairs of data. We derive a training algorithm for Spectral Inference Networks that addresses the bias in the gradients due to finite batch size and allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets as well as the Arcade Learning Environment. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators, can discover interpretable representations from video and find meaningful subgoals in reinforcement learning environments.
Mapping Text with Phrase Nets

We present a new technique, the phrase net, for generating visual overviews of unstructured text. A phrase net displays a graph whose nodes are words and whose edges indicate that two words are linked by a user-specified relation. These relations may be defined either at the syntactic or lexical level; different relations often produce very different perspectives on the same text. Taken together, these perspectives often provide an illuminating visual overview of the key concepts and relations in a document or set of documents.
Spontaneous Rupture of the Imperforate Hymen in an Adolescent  Girl with Hematocolpometra

Imperforate hymen is usually treated with hymenotomy, and the management after its spontaneous rupture is not very well known.  In this paper, we present spontaneous rupture of the imperforate hymen in a 13-year-old adolescent girl with hematocolpometra just before a planned hymenotomy operation. The patient was managed conservatively with a satisfactory outcome.  Hymenotomy may not be needed in cases with spontaneous rupture of the imperforate hymen if adequate opening for menstrual discharge is warranted.
Cimetidine for chronic calcifying tendinitis of the shoulder.

Calcium deposits of the shoulder may persist for many years with resulting pain and impairment of mechanical function. The effects of different treatments vary significantly and do not show consistent and reliable long-term results. Cimetidine decreases calcium levels and improves symptoms in patients with hyperparathyroidism. We evaluated cimetidine as a treatment for chronic calcifying tendinitis of the shoulder in patients who did not respond to conservative treatment.



Cimetidine, 200 mg twice daily, was given orally for 3 months in 16 patients who did not respond to more than 6 months of conservative treatment. We recorded subjective, functional, and radiologic findings at 1 day before, at 2 weeks after, and at 2 and 3 months after the start of cimetidine. We also performed a follow-up study (4 to 24 months).



After treatment, peak pain score (visual analogue scale: 0 - 100) decreased significantly from 63 +/- 13 to 14 +/- 19 (mean +/- SD, P <.01) and 10 patients (63%) became pain free. Physical impairment was also significantly improved. Calcium deposits disappeared in 9 patients (56%), decreased in 4 patients (25%), and did not change in 3 patients (19%). Follow-up data showed that improvement of symptoms was sustained. No recurrence or enlargement of calcium deposits was observed. Plasma concentrations of calcium and parathyroid hormone did not change significantly.



Our results indicate that cimetidine is effective in treating chronic calcifying tendinitis of the shoulder; however, the mechanism by which cimetidine improves the symptoms is unknown.
Hovering flight and vertical landing control of a VTOL Unmanned Aerial Vehicle using optical flow

This paper presents a nonlinear controller for hovering flight and touchdown control for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using inertial optical flow. The VTOL vehicle is assumed to be a rigid body, equipped with a minimum sensor suite (camera and IMU), manoeuvring over a textured flat target plane. Two different tasks are considered in this paper: the first concerns the stability of hovering flight and the second one concerns regulation of automatic landing using the divergent optical flow as feedback information. Experimental results on a quad-rotor UAV demonstrate the performance of the proposed control strategy.
C4b-binding protein: a forgotten factor in thrombosis and hemostasis.

Blood coagulation and complement pathways are two important natural defense systems. The high affinity interaction between the anticoagulant vitamin K-dependent protein S and the complement regulator C4b-binding protein (C4BP) is a direct physical link between the two systems. In human plasma, ~70% of total protein S circulates in complex with C4BP; the remaining is free. The anticoagulant activity of protein S is mainly expressed by the free form, although the protein S-C4BP complex has recently been shown to have some anticoagulant activity. The high affinity binding of protein S to C4BP provides C4BP with the ability to bind to negatively charged phospholipid membranes, which serves the purpose of localizing complement regulatory activity close to the membrane. Even though C4BP does not directly affect the coagulation system, it still influences the regulation of blood coagulation through its interaction with protein S. This is particularly important in states of inherited deficiency of protein S where the tight binding of protein S to C4BP results in a pronounced and selective drop in concentration of free protein S, whereas the concentration of protein S in complex with C4BP remains relatively unchanged. This review summarizes the current knowledge on C4BP with respect to its association with thrombosis and hemostasis.
A Computational Investigation of a Turbulent Flow over a Backward Facing Step with OpenFOAM

A computational fluid dynamics study of three-dimensional turbulent flow over a backward facing step is presented. An available experimental study is investigated computationally using an open source tool. The wall static pressure distribution, the skin friction distribution and the reattachment length have been calculated and compared with the experimental data. Two different mathematical models were implemented using the OpenFOAM computational fluid dynamics (CFD) package. The long term goals for this research are to investigate and actively control the wake dynamics behind the step which will be useful to study the wake characteristics behind different types of bodies.