nils-herrmann/clean_scirepeval huggingface dataset:
The pathobiology of mucositis

Oral and gastrointestinal mucositis is a toxicity of many forms of radiotherapy and chemotherapy. It has a significant impact on health, quality of life and economic outcomes that are associated with treatment. It also indirectly affects the success of antineoplastic therapy by limiting the ability of patients to tolerate optimal tumoricidal treatment. The complex pathogenesis of mucositis has only recently been appreciated and reflects the dynamic interactions of all of the cell and tissue types that comprise the epithelium and submucosa. The identification of the molecular events that lead to treatment-induced mucosal injury has provided targets for mechanistically based interventions to prevent and treat mucositis.
Ovotesticular disorder of sex development with a prostatic gland and review of literature.

Ovotesticular disorder of sex development (OTDSD) is a rare condition and defined as the presence of ovarian and testicular tissue in the same individual. Most of patients with OTDSD have female internal genital organs. In this report, we present a case in which, we demonstrated prostate tissue using endoscopic and radiologic methods in a 46-XX, sex determining region of the Y chromosome negative male phenotypic patient, with no female internal genitalia. Existence of prostate in an XX male without SRY is rarely seen and reveals a complete male phenotype. This finding is critical to figure out what happens in embryonal period.
Capacity of Rayleigh fading channels under different adaptive transmission and diversity-combining techniques

We study the Shannon capacity of adaptive transmission techniques in conjunction with diversity combining. This capacity provides an upper bound on spectral efficiency using these techniques. We obtain closed-form solutions for the Rayleigh fading channel capacity under three adaptive policies: optimal power and rate adaptation, constant power with optimal rate adaptation, and channel inversion with fixed rate. Optimal power and rate adaptation yields a small increase in capacity over just rate adaptation, and this increase diminishes as the average received carrier-to-noise ratio (CNR) or the number of diversity branches increases. Channel inversion suffers the largest capacity penalty relative to the optimal technique, however, the penalty diminishes with increased diversity. Although diversity yields large capacity gains for all the techniques, the gain is most pronounced with channel inversion. For example, the capacity using channel inversion with two-branch diversity exceeds that of a single-branch system using optimal rate and power adaptation. Since channel inversion is the least complex scheme to implement, there is a tradeoff between complexity and capacity for the various adaptation methods and diversity-combining techniques.
Facebook profiles clustering

Internet social networks may be an abundant source of opportunities giving space to the “parallel world” which can and, in many ways, does surpass the realty. People share data about almost every aspect of their lives, starting with giving opinions and comments on global problems and events, friends tagging at locations up to the point of multimedia personalized content. Therefore, decentralized mini-campaigns about educational, cultural, political and sports novelties could be conducted. In this paper we have applied clustering algorithm to social network profiles with the aim of obtaining separate groups of people with different opinions about political views and parties. For network case, where some centroids are interconnected, we have implemented edge constraints into classical k-means algorithm. This approach enables fast and effective information analysis about the present state of affairs, but also discovers new tendencies in observed political sphere. All profile data, friendships, fanpage likes and statuses with interactions are collected by already developed software for neurolinguistics social network analysis “Symbols”.
A Framework for Understanding and Classifying Ontology Applications

For ontologiesto becost-efectively deployed,we requirea clearunderstandingof thevariouswaysthatontologiesarebeingusedtoday. To achieve this end,we presenta framework for understandingandclassifyingontology applications.We identify four main categoriesof ontologyapplications:1) neutralauthoring,2) ontologyasspecification, 3) commonaccessto information, and4) ontology-basedsearch. In eachcategory, we identify specific ontologyapplicationscenarios.For each,we indicatetheir intendedpurpose,therole of theontology, thesupporting technologies, who theprincipalactorsareandwhat they do. We illuminatethesimilaritiesanddifferencesbetween scenarios. We draw on work from othercommunities,suchassoftwaredevelopersandstandardsorganizations.We usea relatively broaddefinition of ‘ontology’, to show that muchof the work beingdoneby thosecommunitiesmay be viewedaspracticalapplicationsof ontologies.Thecommonthreadis theneedfor sharingthemeaningof termsin a givendomain,which is a centralrole of ontologies.An additionalaim of this paperis to draw attentionto common goalsandsupportingtechnologiesof theserelatively distinctcommunitiesto facilitateclosercooperationandfaster progress .
Multicolumn Networks for Face Recognition

The objective of this work is set-based face recognition, i.e. to decide if two sets of images of a face are of the same person or not. Conventionally, the set-wise feature descriptor is computed as an average of the descriptors from individual face images within the set. In this paper, we design a neural network architecture that learns to aggregate based on both “visual” quality (resolution, illumination), and “content” quality (relative importance for discriminative classification). To this end, we propose a Multicolumn Network (MN) that takes a set of images (the number in the set can vary) as input, and learns to compute a fix-sized feature descriptor for the entire set. To encourage high-quality representations, each individual input image is first weighted by its “visual” quality, determined by a self-quality assessment module, and followed by a dynamic recalibration based on “content” qualities relative to the other images within the set. Both of these qualities are learnt implicitly during training for setwise classification. Comparing with the previous state-of-the-art architectures trained with the same dataset (VGGFace2), our Multicolumn Networks show an improvement of between 2-6% on the IARPA IJB face recognition benchmarks, and exceed the state of the art for all methods on these benchmarks.
Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks

Existing deep learning methods of video recognition usually require a large number of labeled videos for training. But for a new task, videos are often unlabeled and it is also time-consuming and labor-intensive to annotate them. Instead of human annotation, we try to make use of existing fully labeled images to help recognize those videos. However, due to the problem of domain shifts and heterogeneous feature representations, the performance of classifiers trained on images may be dramatically degraded for video recognition tasks. In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain). The HiGAN model consists of a low-level conditional GAN and a high-level conditional GAN. By taking advantage of these two-level adversarial learning, our method is capable of learning a domaininvariant feature representation of source images and target videos. Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods.
Correlated link shadow fading in multi-hop wireless networks

Accurate representation of the physical layer is required for analysis and simulation of multi-hop networking in sensor, ad hoc, and mesh networks. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. This paper presents and analyzes a non-site-specific statistical propagation model which accounts for the correlations that exist in shadow fading between links in multi-hop networks. We describe two measurement campaigns to measure a large number of multi-hop networks in an ensemble of environments. The measurements show statistically significant correlations among shadowing experienced on different links in the network, with correlation coefficients up to 0.33. Finally, we analyze multi-hop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater.
An interior-point stochastic approximation method and an L1-regularized delta rule

The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost no work on applying stochastic approximation to learning problems with general constraints. The reason for this, we hypothesize, is that no robust, widely-applicable stochastic approximation method exists for handling such problems. We propose that interior-point methods are a natural solution. We establish the stability of a stochastic interior-point approximation method both analytically and empirically, and demonstrate its utility by deriving an on-line learning algorithm that also performs feature selection via L1 regularization.
Challenges in Measuring , Understanding , and Achieving Social-Technical Congruence

Congruence, the state in which a software development organization harbors sufficient coordination capabilities to meet the coordination demands of the technical products under development, is increasingly recognized as critically important to the performance of an organization. To date, it has been shown that a variety of states of incongruence may exist in an organization, with possibly serious negative effects on product quality, development progress, cost, and so on. Exactly how to achieve congruence, or knowing what steps to take to achieve congruence, is less understood. In this paper, we introduce a series of key challenges that we believe must be comprehensively addressed in order for congruence research to result in wellunderstood approaches, tactics, and tools – so these can be infused in the day-to-day practices of development organizations to improve their coordination capabilities with better aligned social and technical structures. This effort is partially funded by the National Science Foundation under grant number IIS-0534775, IIS0329090, and the Software Industry Center and its sponsors, particularly the Alfred P. Sloan Foundation. Effort also supported by a 2007 Jazz Faculty Grant. The views and conclusions are those of the authors and do not reflect the opinions of any sponsoring organizations/agencies.
Direct WYSIWYG painting and texturing on 3D shapes

This paper describes a 3D object-space paint program. This program allows the user to directly manipulate the parameters used to shade the surface of the 3D shape by applying pigment to its surface. The pigment has all the properties normally associated with material shading models. This includes, but is not limited to, the diffuse color, the specular color, and the surface roughness. The pigment also can have thickness, which is modeled by simultaneously creating a bump map attached to the shape. The output of the paint program is a 3D model with associated texture maps. This information can be used with any rendering program with texture mapping capabilities. Almost all traditional techniques of 2D computer image painting have analogues in 3D object painting, but there are also many new techniques unique to 3D. One example is the use of solid textures to pattern the surface.
Bigtable: A Distributed Storage System for Structured Data

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.
Wireless Sensor Networks for Industrial Process Monitoring and Control: A Survey

This paper provides a survey on implementing wireless sensor network (WSN) technology on industrial process monitoring and control. First, the existing industrial applications are explored, following with a review of the advantages of adopting WSN technology for industrial control. Then, challenging factors influencing the design and acceptance of WSNs in the process control world are outlined, and the state-of-the-art research efforts and industrial solutions are provided corresponding to each factor. Further research issues for the realization and improvement of wireless sensor network technology on process industry are also mentioned.
Self-implanted artificial nodules: a computed tomography mimic of penile pathology.

Self-introduction of foreign bodies along the penile shaft has been reported in several ethnic and social groups, mainly in Asia, and recently has been described in Europe. We present the case of a 34-year-old homeless Russian immigrant who had an abdominal CT performed during an emergency department visit. On the CT scan, several hyperdense, well-demarcated subcutaneous nodules along the penile shaft were noted. Following a focused history and physical examination, the nodules were found to represent artificial foreign bodies made of glass, which were self-introduced by the patient in order to allegedly increase the pleasure of sexual partners. Penile nodules may be a manifestation of diverse pathological entities including infectious, inflammatory, and neoplastic processes. It is important for the radiologist to be familiar with this social phenomenon and its radiological appearance in order to avoid erroneous diagnosis.
Phylogenetic Grammar Induction

We present an approach to multilingual grammar induction that exploits a phylogeny-structured model of parameter drift. Our method does not require any translated texts or token-level alignments. Instead, the phylogenetic prior couples languages at a parameter level. Joint induction in the multilingual model substantially outperforms independent learning, with larger gains both from more articulated phylogenies and as well as from increasing numbers of languages. Across eight languages, the multilingual approach gives error reductions over the standard monolingual DMV averaging 21.1% and reaching as high as 39%.
Oleic acid stimulates complete oxidation of fatty acids through protein kinase A-dependent activation of SIRT1-PGC1α complex.

Fatty acids are essential components of the dynamic lipid metabolism in cells. Fatty acids can also signal to intracellular pathways to trigger a broad range of cellular responses. Oleic acid is an abundant monounsaturated omega-9 fatty acid that impinges on different biological processes, but the mechanisms of action are not completely understood. Here, we report that oleic acid stimulates the cAMP/protein kinase A pathway and activates the SIRT1-PGC1α transcriptional complex to modulate rates of fatty acid oxidation. In skeletal muscle cells, oleic acid treatment increased intracellular levels of cyclic adenosine monophosphate (cAMP) that turned on protein kinase A activity. This resulted in SIRT1 phosphorylation at Ser-434 and elevation of its catalytic deacetylase activity. A direct SIRT1 substrate is the transcriptional coactivator peroxisome proliferator-activated receptor γ coactivator 1-α (PGC1α), which became deacetylated and hyperactive after oleic acid treatment. Importantly, oleic acid, but not other long chain fatty acids such as palmitate, increased the expression of genes linked to fatty acid oxidation pathway in a SIRT1-PGC1α-dependent mechanism. As a result, oleic acid potently accelerated rates of complete fatty acid oxidation in skeletal muscle cells. These results illustrate how a single long chain fatty acid specifically controls lipid oxidation through a signaling/transcriptional pathway. Pharmacological manipulation of this lipid signaling pathway might provide therapeutic possibilities to treat metabolic diseases associated with lipid dysregulation.
Compact UWB and Low-RCS Vivaldi Antenna Using Ultrathin Microwave-Absorbing Materials

In this letter, we present an improved Vivaldi antenna by using ultrathin microwave-absorbing materials (MAMs). The reverse currents at the outer side edges of the Vivaldi antenna always distort its radiation patterns and worsen its VSWR in some frequencies. To solve this problem, the ultrathin MAMs are loaded at the side edges of the antenna to absorb the energy of the current, and also to reduce its radar cross section (RCS). Simulated and measured results show that the proposed antenna operates from 0.8 to 15.5 GHz with VSWR < 2.15, and its gains are more stable than those of the antenna without MAMs in a very wide frequency band. The average monostatic RCSs of the proposed antenna in 2–18-GHz band and $\theta \in , + 30^{\circ}],\; \phi = [- 45, + 45^{\circ}]$ angle range are less than 0.001 m2, which make it very attractive for a variety of applications.
Authorship Analysis and Identification Techniques: A Review

Trends in data mining are increasing over the time. Current world is of internet and everything is available over internet, which leads to criminal and malicious activity. So the identity of available content is now a need. Available content is always in the form of text data. Authorship analysis is the statistical study of linguistic and computational characteristics of the written documents of individuals. This paper describes review of various methods for authorship analysis and identification for a set of provided text. Surely research in authorship analysis and identification will continue and even increase over decades. In this article, we put our vision of future authorship analysis and identification with high performance and solution for behavioral feature extraction from set of text documents.
Collaborative writing: the effects of metacognitive prompting and structured peer interaction.

The structured system for peer assisted learning in writing named Paired Writing (Topping, 1995) incorporates both metacognitive prompting and scaffolding for the interactive process.



This study sought to evaluate the relative contribution of these two components to student gain in quality of writing and attitudes to writing, while controlling for amount of writing practice and teacher effects.


SAMPLE
Participants were 28 ten- and eleven-year-old students forming a problematic mixed ability class.



All received training in Paired Writing and its inherent metacognitive prompting. Students matched by gender and pre-test writing scores were assigned randomly to Interaction or No Interaction conditions. In the Interaction condition, the more able writers became 'tutors' for the less able. In the No Interaction condition, the more able writers acted as controls for the tutors and the less able as controls for the tutees. Over six weeks, the paired writers produced five pieces of personal writing collaboratively, while children in the No Interaction condition did so alone.



On pre- and post-project analyses of the quality of individual writing, all groups showed statistically significant improvements in writing. However, the pre-post gains of the children who wrote interactively were significantly greater than those of the lone writers. There was some evidence that the paired writers also had more positive self-esteem as writers.



The operation and durability of the Paired Writing system are discussed.
Brain Points: A Deeper Look at a Growth Mindset Incentive Structure for an Educational Game

Student retention is a central challenge in systems for learning at scale. It has been argued that educational video games could improve student retention by providing engaging experiences and informing the design of other online learning environments. However, educational games are not uniformly effective. Our recent research shows that player retention can be increased by using a brain points incentive structure that rewards behaviors associated with growth mindset, or the belief that intelligence can grow. In this paper, we expand on our prior work by providing new insights into how growth mindset behaviors can be effectively promoted in the educational game Refraction. We present results from an online study of 25,000 children who were exposed to five different versions of the brain points intervention. We find that growth mindset animations cause a large number of players to quit, while brain points encourage persistence. Most importantly, we find that awarding brain points randomly is ineffective; the incentive structure is successful specifically because it rewards desirable growth mindset behaviors. These findings have important implications that can support the future generalization of the brain points intervention to new educational contexts.
Cross-Project Transfer Representation Learning for Vulnerable Function Discovery

Machine learning is now widely used to detect security vulnerabilities in the software, even before the software is released. But its potential is often severely compromised at the early stage of a software project when we face a shortage of high-quality training data and have to rely on overly generic hand-crafted features. This paper addresses this cold-start problem of machine learning, by learning rich features that generalize across similar projects. To reach an optimal balance between feature-richness and generalizability, we devise a data-driven method including the following innovative ideas. First, the code semantics are revealed through serialized abstract syntax trees (ASTs), with tokens encoded by Continuous Bag-of-Words neural embeddings. Next, the serialized ASTs are fed to a sequential deep learning classifier (Bi-LSTM) to obtain a representation indicative of software vulnerability. Finally, the neural representation obtained from existing software projects is then transferred to the new project to enable early vulnerability detection even with a small set of training labels. To validate this vulnerability detection approach, we manually labeled 457 vulnerable functions and collected 30 000+ nonvulnerable functions from six open-source projects. The empirical results confirmed that the trained model is capable of generating representations that are indicative of program vulnerability and is adaptable across multiple projects. Compared with the traditional code metrics, our transfer-learned representations are more effective for predicting vulnerable functions, both within a project and across multiple projects.