nils-herrmann/clean_scirepeval huggingface dataset:
Learning in Search Advertising

Prior literature on search advertising primarily assumes that search engines know advertisers’ click-through rates, the probability that a consumer clicks on an advertiser’s ad. This information, however, is not available when a new advertiser starts search advertising for the first time. In particular, a new advertiser’s click-through rate can be learned only if the advertiser’s ad is shown to enough consumers, i.e., the advertiser wins enough auctions. Since search engines use advertisers’ expected click-through rates when calculating payments and allocations, the lack of information about a new advertiser can affect new and existing advertisers’ bidding strategies. In this paper, we use a game theory model to analyze advertisers’ strategies, their payoffs, and the search engine’s revenue when a new advertiser joins the market. Our results indicate that a new advertiser should always bid higher (sometimes above its valuation) when it starts search advertising. However, the strategy of an existing advertiser, i.e., an incumbent, depends on its valuation and click-through rate. A strong incumbent increases its bid to prevent the search engine from learning the new advertiser’s clickthrough rate, whereas a weak incumbent decreases its bid to facilitate the learning process. Interestingly, we find that, under certain conditions, the search engine benefits from not knowing the new advertiser’s click-through rate because its ignorance could induce the advertisers to bid more aggressively. Nonetheless, the search engine’s revenue sometimes decreases because of this lack of information, particularly, when the incumbent is sufficiently strong. We show that the search engine can mitigate this loss, and improve its total profit, by offering free advertising credit to new advertisers.
Treatment of the post-ICU patient in an outpatient setting.

The care of a patient in the intensive care unit extends well beyond his or her hospitalization. Evaluation of a patient after leaving the intensive care unit involves a review of the hospital stay, including principal diagnosis, exposure to medications, period spent in the intensive care unit, and history of prolonged mechanical ventilation. Fatigue should prompt evaluation for possible anemia, nutritional deficits, sleep disturbance, muscular deconditioning, and neurologic impairment. Other common problems include poor appetite with possible weight loss, falls, and sexual dysfunction. Psychological morbidities, posttraumatic stress disorder, anxiety disorder, and depression also often occur in the post-intensive care unit patient. These conditions are more common among patients with a history of delirium, prolonged sedation, mechanical ventilation, and acute respiratory distress syndrome. The physician should gain an understanding of the patient's altered quality of life, including employment status, and the state of his or her relationships with loved ones or the primary caregiver. As in many aspects of medicine, a multidisciplinary treatment approach is most beneficial to the post-intensive care unit patient.
Denoising 3D Computed Tomography Images using New Modified Coherence Enhancing Diffusion Model

The denoising step for Computed Tomography (CT) images is an important challenge in the medical image processing. These images are degraded by low resolution and noise. In this paper, we propose a new method for 3D CT denoising based on Coherence Enhancing Diffusion model. Quantitative measures such as PSNR, SSIM and RMSE are computed to a phantom CT image in order to improve the efficiently of our proposed model, compared to a number of denoising algorithms. Furthermore, experimental results on a real 3D CT data show that this approach is effective and promising in removing noise and preserving details.
Design of Fragment-Type Isolation Structures for MIMO Antennas

Fragment structure should find its application in acquiring high isolation between multipleinput multiple-output (MIMO) antennas. By gridding a design space into fragment cells, a fragmenttype isolation structure can be constructed by metalizing some of the fragment cells. For MIMO isolation design, cells to be metalized can be selected by optimization searching scheme with objectives such as isolation, return losses, and even radiation patterns of MIMO antennas. Due to the flexibility of fragment-type isolation structure, fragment-type structure has potentials to yield isolation higher than canonical isolation structures. In this paper, multi-objective evolutionary algorithm based on decomposition combined with genetic operators (MOEA/D-GO) is applied to design fragment-type isolation structures for MIMO patch antennas and MIMO PIFAs. It is demonstrated that isolation can be improved to different extents by using fragment-type isolation design. Some technique aspects related to the fragment-type isolation design, such as effects of fragment cell size, design space, density of metal cells, and efficiency consideration, are further discussed.
Variable structure-based nonlinear missile guidance/autopilot design with highly maneuverable actuators

In this brief, we propose a variable structure based nonlinear missile guidance/autopilot system with highly maneuverable actuators, mainly consisting of thrust vector control and divert control system, for the task of intercepting of a theater ballistic missile. The aim of the present work is to achieve bounded target interception under the mentioned 5 degree-of-freedom (DOF) control such that the distance between the missile and the target will enter the range of triggering the missile's explosion. First, a 3-DOF sliding-mode guidance law of the missile considering external disturbances and zero-effort-miss (ZEM) is designed to minimize the distance between the center of the missile and that of the target. Next, a quaternion-based sliding-mode attitude controller is developed to track the attitude command while coping with variation of missile's inertia and uncertain aerodynamic force/wind gusts. The stability of the overall system and ZEM-phase convergence are analyzed thoroughly via Lyapunov stability theory. Extensive simulation results are obtained to validate the effectiveness of the proposed integrated guidance/autopilot system by use of the 5-DOF inputs.
Reduced prefrontal and increased subcortical brain functioning assessed using positron emission tomography in predatory and affective murderers.

There appear to be no brain imaging studies investigating which brain mechanisms subserve affective, impulsive violence versus planned, predatory violence. It was hypothesized that affectively violent offenders would have lower prefrontal activity, higher subcortical activity, and reduced prefrontal/subcortical ratios relative to controls, while predatory violent offenders would show relatively normal brain functioning. Glucose metabolism was assessed using positron emission tomography in 41 comparisons, 15 predatory murderers, and nine affective murderers in left and right hemisphere prefrontal (medial and lateral) and subcortical (amygdala, midbrain, hippocampus, and thalamus) regions. Affective murderers relative to comparisons had lower left and right prefrontal functioning, higher right hemisphere subcortical functioning, and lower right hemisphere prefrontal/subcortical ratios. In contrast, predatory murderers had prefrontal functioning that was more equivalent to comparisons, while also having excessively high right subcortical activity. Results support the hypothesis that emotional, unplanned impulsive murderers are less able to regulate and control aggressive impulses generated from subcortical structures due to deficient prefrontal regulation. It is hypothesized that excessive subcortical activity predisposes to aggressive behaviour, but that while predatory murderers have sufficiently good prefrontal functioning to regulate these aggressive impulses, the affective murderers lack such prefrontal control over emotion regulation.
Multi-pass 3 D convolutional neural network segmentation of prostate MRI images

We propose a deep neural network for the segmentation of the prostate in MRI images. The segmentation is performed using a residual fully convolutional neural network. Automatic shape learning is allowed using a Compositional Pattern-Producing Network. Moreover, a multi-pass architecture is designed to foster self-consistent segmentation. The model is trained and tested on the dataset of the challenge PROMISE12.
Behavior of crocidolite asbestos during mitosis in living vertebrate lung epithelial cells.

Asbestos has been described as a physical carcinogen in that long thin fibers are generally more carcinogenic than shorter thicker ones. It has been hypothesized that long thin fibers disrupt chromosome behavior during mitosis, causing chromosome abnormalities which lead to cell transformation and neoplastic progression. Using high-resolution time lapse video-enhanced light microscopy and the uniquely suited lung epithelial cells of the newt Taricha granulosa, we have characterized for the first time the behavior of crocidolite asbestos fibers, and their interactions with chromosomes, during mitosis in living cells. We found that the keratin cage surrounding the mitotic spindle inhibited fiber migration, resulting in spindles with few fibers. As in interphase, fibers displayed microtubule-mediated saltatory movements. Fiber position was only slightly affected by the ejection forces of the spindle asters. Physical interactions between crocidolite fibers and chromosomes occurred randomly within the spindle and along its edge. Crocidolite fibers showed no affinity toward chromatin and most encounters ended with the fiber passively yielding to the chromosome. In a few encounters along the spindle edge the chromosome yielded to the fiber, which remained stationary as if anchored to the keratin cage. We suggest that fibers thin enough to be caught in the keratin cage and long enough to protrude into the spindle are those fibers with the ability to snag or block moving chromosomes.
Ontology-Based Fraud Detection

One method for detecting fraud is to check for suspicious changes in user behavior. This paper proposes a novel method, built upon ontology and ontology instance similarity. Ontology is now widely used to enable knowledge sharing and reuse, so some personality ontologies can be easily used to present user behavior. By measure the similarity of ontology instances, we can determine whether an account is defrauded. This method lows the data model cost and make the system very adaptive to different applications.
Transcutaneous lower eyelid blepharoplasty with orbitomalar suspension: retrospective review of 212 consecutive cases.

Midfacial aging is associated with increased demarcation of the nasolabial, malar, and nasojugal folds; deflation of facial soft tissues and bones; and descent of the midface. The latter is primarily attributable to attenuation of the orbitomalar ligament. Traditional surgery of the lower eyelid and midface often requires removal of excess skin, orbicularis oculi muscle, and orbital fat, which can be complicated by postoperative lower eyelid malposition. The authors describe a novel adjunct to transcutaneous lower eyelid blepharoplasty that rejuvenates the lower eyelid and midface by reconstituting the orbitomalar ligament and minimizes the development of postoperative eyelid malposition.



This study was a retrospective, consecutive, nonrandomized, interventional case series. The authors reviewed the medical records of 212 consecutive patients who underwent transcutaneous lower eyelid blepharoplasty with orbitomalar suspension. The aesthetic outcome, patient satisfaction, and development of eyelid malposition were evaluated.



Transcutaneous lower eyelid blepharoplasty with orbitomalar suspension resulted in improved lower eyelid dermatochalasis, contour, midfacial ptosis, and appearance of the nasojugal and malar folds. All patients reported satisfaction with the aesthetic outcome. One patient (0.5 percent) developed lower eyelid retraction requiring subsequent lower eyelid tightening. Three patients (1.4 percent) developed transient lagophthalmos from lower eyelid orbicularis paresis that resolved spontaneously.



Transcutaneous lower eyelid blepharoplasty combined with orbitomalar suspension is a powerful technique that can be performed concomitantly with facial rejuvenative procedures. Orbitomalar suspension addresses midfacial ptosis by restoring the natural function of the orbitomalar ligament and minimizes the development of postoperative lower eyelid malposition.
Molecular mechanisms and animal models of spinal muscular atrophy.

Spinal muscular atrophy (SMA), the leading genetic cause of infant mortality, is characterized by the degeneration of spinal motor neurons and muscle atrophy. Although the genetic cause of SMA has been mapped to the Survival Motor Neuron1 (SMN1) gene, mechanisms underlying selective motor neuron degeneration in SMA remain largely unknown. Here we review the latest developments and our current understanding of the molecular mechanisms underlying SMA pathogenesis, focusing on the animal model systems that have been developed, as well as new diagnostic and treatment strategies that have been identified using these model systems. This article is part of a special issue entitled: Neuromuscular Diseases: Pathology and Molecular Pathogenesis.
Analysis of Customer Churn prediction in Logistic Industry using Machine Learning

Customer churn prediction in logistics industry is one of the most prominent research topics in recent years. It consists of detecting customers who are likely to cancel a subscription to a service. Recently, logistics market has changed from a rapidly growing market into a state of saturation and fierce competition. The focus of the logistic companies has therefore shifted from building a large customer base into keeping customers in house. For that reason, it is valuable to know which customers are likely to switch to a competitor in the near future. The data extracted from the industry can help analyse the reasons of customer churn and use that information to retain the customers. We have proposed to build a model for churn prediction for a company using data mining and machine learning techniques namely logistic regression and decision trees. A comparison is made based on efficiency of these algorithms on the available dataset.
Emerging avenues linking inflammation and cancer.

The role of inflammation in carcinogenesis has been extensively investigated and well documented. Many biochemical processes that are altered during chronic inflammation have been implicated in tumorigenesis. These include shifting cellular redox balance toward oxidative stress; induction of genomic instability; increased DNA damage; stimulation of cell proliferation, metastasis, and angiogenesis; deregulation of cellular epigenetic control of gene expression; and inappropriate epithelial-to-mesenchymal transition. A wide array of proinflammatory cytokines, prostaglandins, nitric oxide, and matricellular proteins are closely involved in premalignant and malignant conversion of cells in a background of chronic inflammation. Inappropriate transcription of genes encoding inflammatory mediators, survival factors, and angiogenic and metastatic proteins is the key molecular event in linking inflammation and cancer. Aberrant cell signaling pathways comprising various kinases and their downstream transcription factors have been identified as the major contributors in abnormal gene expression associated with inflammation-driven carcinogenesis. The posttranscriptional regulation of gene expression by microRNAs also provides the molecular basis for linking inflammation to cancer. This review highlights the multifaceted role of inflammation in carcinogenesis in the context of altered cellular redox signaling.
Laser bioprinting of human induced pluripotent stem cells-the effect of printing and biomaterials on cell survival, pluripotency, and differentiation.

Research on human induced pluripotent stem cells (hiPSCs) is one of the fastest growing fields in biomedicine. Generated from patient's own somatic cells, hiPSCs can be differentiated towards all functional cell types and returned to the patient without immunological concerns. 3D printing of hiPSCs could enable the generation of functional organs for replacement therapies or realization of organ-on-chip systems for individualized medicine. Printing of living cells was demonstrated with immortalized cell lines, primary cells, and adult stem cells with different printing technologies and biomaterials. However, hiPSCs are more sensitive to handling procedures, in particular, when dissociated into single cells. Both pluripotency and directed differentiation are influenced by numerous environmental factors including culture media, biomaterials, and cell density. Notably, existing literature on the effect of applied biomaterials on pluripotency is rather ambiguous. In this study, laser bioprinting of undifferentiated hiPSCs in combination with different biomaterials was performed and the impact on cells' behavior, pluripotency, and differentiation was investigated. Our findings suggest that hiPSCs are indeed more sensitive to the applied biomaterials, but not to laser printing itself. With appropriate biomaterials, such as the hyaluronic acid based solutions applied in this study, hiPSCs can be successfully laser printed without losing their pluripotency.
What Trust means in the Sharing Economy: A provider perspective on Airbnb.com

Information and communications technology has fostered the rise of the sharing economy, enabling individuals to share excess capacity. In this paper, we focus on Airbnb.com, which is among the most prominent examples of the sharing economy. We take the perspective of an accommodation provider and investigate the concept of trust, which facilitates complete strangers to form temporal C2C relationships on Airbnb.com. In fact, the implications of trust in the sharing economy fundamentally differ to related online industries. In our research model, we investigate the formation of trust by incorporating two antecedents – ‘Disposition to trust’ and ‘Familiarity with Airbnb.com’. Furthermore, we differentiate between ‘Trust in Airbnb.com’ and ‘Trust in renters’ and examine their implications on two provider intentions. To seek support for our research model, we conducted a survey with 189 participants. The results show that both trust constructs are decisive to successfully initiate a sharing deal between two parties.
Erratum to: FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algorithm that allocates resources to jobs with optimal execution time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this paper, we present a hybrid approach called FUGE that is based on fuzzy theory and a genetic algorithm (GA) that aims to perform optimal load balancing considering execution time and cost. We modify the standard genetic algorithm (SGA) and use fuzzy theory to devise a fuzzy-based steady-state GA in order to improve SGA performance in term of makespan. In details, the FUGE algorithm assigns jobs to resources by considering virtual machine (VM) processing speed, VM memory, VM bandwidth, and the job lengths. We mathematically prove our M. Shojafar (B) · N. Cordeschi Department of Information Engineering Electronics and Telecommunications (DIET), University Sapienza of Rome, via Eudossiana 18, 00184 Rome, Italy e-mail: m.shojafar@yahoo.com; shojafar@diet.uniroma1.it URL: http://www.mshojafar.com N. Cordeschi e-mail: cordeschi@diet.uniroma1.it S. Javanmardi Research and Education center, Nikan network Company, Shiraz, Fars, Iran e-mail: info@nikannetwork.com; saeedjavanmardi@gmail.com S. Abolfazli Center for Mobile Cloud Computing, University of Malaya, Kuala Lumpur, Malaysia e-mail: Abolfazli@ieee.org optimization problem which is convex with well-known analytical conditions (specifically, Karush–Kuhn–Tucker conditions). We compare the performance of our approach to several other cloud scheduling models. The results of the experiments show the efficiency of the FUGE approach in terms of execution time, execution cost, and average degree of imbalance.
Time-Frequency Filtering with the S-Transform of ECG Signals

A key feature of the S-transform is that it uniquely combines a frequency dependent resolution of the time-frequency space and absolutely referenced local phase information. The S-transform is a time-frequency representation known for its local spectral phase properties. In this paper, a method to process non-stationary signal, such as electrocardiograms (ECG) based on Stransform, in which a filter is applied to a time frequency distribution instead of the Fourier spectrum. Such distribution is the Stransform, a modified short-time Fourier transform whose window scales with frequency, as in wavelets.
Black spot poison ivy: under the cover of darkness.

A 16-year-old boy presented to the Emergency Department having noted the pictured skin markings on his left forearm several hours earlier. He stated that the markings had not been present earlier that afternoon, and had remained unchanged since first noted after track and field practice. There was no history of trauma, ingestions, or any systemic symptoms. The markings were neither tender nor pruritic. His parents denied any family history of malignancy. Physical examination revealed the raised black markings with minimal surrounding erythema, as seen in Figure 1. The rest of the dermatologic and remaining physical examinations were, and remained, unremarkable.
Modelling Dependencies between Variation Points in Use Case Diagrams

Software product family variability facilitates the constructive and pro-active reuse of assets during the development of software applications. The variability is typically represented by variation points, the variants and their interdependencies. Those variation points and their variants have to be considered when defining the requirements for the applications of the software product family. To facilitate the communication of the variability to the customer, an extension to UML-use case diagrams has been proposed in . In this paper we identify common dependency types used within the feature modelling community to express interdependencies between variation points and variants. We then propose a differentiation of those interdependencies to be used to reduce complexity and to facilitate selective retrieval of interdependencies between variation points and the variants. Finally, we extend the notations proposed in  for representing interdependencies between variation points and variants in use case diagrams and use a simple example to illustrate those extensions.