nils-herrmann/clean_scirepeval huggingface dataset:
Abnormally long penis in children with vesical calculus.

A 31-month-old boy was investigated for increased frequency of urination for the past 4 months. He voided a small quantity of urine every 20 to 30 min. However, other urinary symptoms such as pain, hematuria, and fever were conspicuously absent. His physical examination was unremarkable except for an abnormally long penis . The penis in flaccid state measured 7 cm, and its stretched length was 9 cm . This is approximately equal to the average penile length of Indian adults . The stretched penis length expected for his age group, according to European standards, is 5.15±0.46 cm (range, 4.2 to 6 cm) . A 4×3×3-cm calcium oxalate stone in the urinary bladder was diagnosed by plain radiograph, and the stone was surgically removed. Sensitized by this experience, I carefully observed the length of penis in two subsequent boys with vesical calculus. In both of them, penile length was slightly more than two standard deviations of the expected mean of age-matched penile length. It is possible that penile length is influenced by the presence of a bladder stone. In vesical calculus, pain is often referred to the tip of the penis. Therefore, young boys with a bladder stone are known to repeatedly pull and squeeze the penis in a futile attempt of alleviating pain . Physical stretching of the penis or tumescence caused by repeated massage could be responsible for lengthening of the penis. This hypothesis is supported by a recent publication  wherein prolonged semi-erection of high-flow priapism in a prepubertal boy was shown to increase penile size. This interesting clinical observation definitely warrants further scientific studies. Confirmation of any correlation between penile length and vesical calculus will be truly fascinating as it will not only aid clinical diagnosis but also will widen our understanding of penis-lengthening procedures.  Clinical photograph of genitalia showing an abnormally long penis in a child with vesical calculus
Eigensolver Methods for Progressive Multidimensional Scaling of Large Data

We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.
Fine Grained Classification of Named Entities

While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications such as Question Answering and Ontology generation. We examine one coarse category of named entities, persons, and describe a method for automatically classifying person instances into eight finergrained subcategories. We present a supervised learning method that considers the local context surrounding the entity as well as more global semantic information derived from topic signatures and WordNet. We reinforce this method with an algorithm that takes advantage of the presence of entities in multiple contexts.
LightShop: interactive light field manipulation and rendering

Light fields can be used to represent an object's appearance with a high degree of realism. However, unlike their geometric counterparts, these image-based representations lack user control for manipulating them. We present a system that allows a user to interactively manipulate, composite and render multiple light fields. LightShop is a modular system consisting of three parts: 1) a set of functions that allow a user to model a scene containing multiple light fields, 2) a ray-shading language that describes how an image should be constructed from a set of light fields, and 3) a real-time light field rendering system in OpenGL that can plug into existing 3D engines as a GLSL shader.
 We show applications in digital photography and we demonstrate how to integrate light fields into a modern space-flight game using LightShop.
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018
An ultra-wideband common gate LNA with gm-boosted and noise cancelling techniques

In this paper, an ultra-wideband (UWB) common gate low-noise amplifier (LNA) with gm-boosted and noise-cancelling techniques is presented In this scheme we utilize gm-boosted stage for cancelling the noise of matching device. The bandwidth extension and flat gain are achieved by using of series and shunt peaking techniques. Simulated in .13 um Cmos technology, the proposed LNA achieved 2.38-3.4dB NF and S11 less than -11dB in the 3.1-10.6 GHz band Maximum power gain(S21) is 11dB and -3dB bandwidth is 1.25-11.33 GHz. The power consumption of LNA is
A Hybrid Machine Learning System for Stock Market Forecasting

In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. The results show that the hybrid GA-SVM system outperforms the stand alone SVM system. Genetic Algorithms, Support Vector Machines, Stock Market Forecasting.
Social network analysis: a powerful strategy, also for the information sciences

Social network analysis (SNA) is not a formal theory in sociology but rather a strategy for investigating social structures. As it is an idea that can be applied in many fields, we study, in particular, its influence in the information sciences. Information scientists study publication, citation and co-citation networks, collaboration structures and other forms of social interaction networks. Moreover, the Internet represents a social network of an unprecedented scale. We are convinced that in all these studies social network analysis can successfully be applied. SNA is further related to recent theories concerning the free market economy, geography and transport networks. The growth of SNA is documented and a co-author network of SNA is drawn. Centrality measures of this network are calculated.  To whom all correspondence should be addressed.
Loopy Belief Propagation for Approximate Inference: An Empirical Study

Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performance of "Turbo Codes" codes whose decoding algorithm is equivalent to loopy belief propagation in a chain-structured Bayesian network. In this paper we ask: is there something spe­ cial about the error-correcting code context, or does loopy propagation work as an ap­ proximate inference scheme in a more gen­ eral setting? We compare the marginals com­ puted using loopy propagation to the exact ones in four Bayesian network architectures, including two real-world networks: ALARM and QMR. We find that the loopy beliefs of­ ten converge and when they do, they give a good approximation to the correct marginals. However, on the QMR network, the loopy be­ liefs oscillated and had no obvious relation­ ship to the correct posteriors. We present some initial investigations into the cause of these oscillations, and show that some sim­ ple methods of preventing them lead to the wrong results.
RolX: structural role extraction & mining in large graphs

Given a network, intuitively two nodes belong to the same role if they have similar structural behavior. Roles should be automatically determined from the data, and could be, for example, "clique-members," "periphery-nodes," etc. Roles enable numerous novel and useful network-mining tasks, such as sense-making, searching for similar nodes, and node classification. This paper addresses the question: Given a graph, how can we automatically discover roles for nodes? We propose RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting structural roles from general network data. We demonstrate the effectiveness of RolX on several network-mining tasks: from exploratory data analysis to network transfer learning. Moreover, we compare network role discovery with network community discovery. We highlight fundamental differences between the two (e.g., roles generalize across disconnected networks, communities do not); and show that the two approaches are complimentary in nature.
Topology and Analysis of Three-Phalanx COSA Finger Based on Linkages for Humanoid Robot Hands

The design of a novel linkage three-phalanx finger was proposed in this paper. The finger is based on the concept of COSA (coupling and self-adaptation) and also two-phalanx COSA finger. As a combination of rigid coupled finger and self-adaptive under-actuated finger, the COSA finger has comprehensive grasping ability with human-like motion. Available topology designs were discussed in this paper and then mechanical design in details is given. Motion and grasping patterns of the finger were illustrated and compared with traditional coupled finger and under-actuated finger. Moreover, stability of grasp was simulated by static analysis. The three-phalanx finger with advantages of coupling and self-adaptation can be widely applied in hands for prosthetic limbs or humanoid robots.
Characterizing Political Fake News in Twitter by its Meta-Data

This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them. Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users’ exposure to the tweets, by characterizing accounts spreading fake news, and by looking at their polarization. We found significant differences on the distribution of followers, the number of URLs on tweets, and the verification of the users.
Smoothing of Piecewise Linear Paths

We present an anytime-capable fast deterministic greedy algorithm for smoothing piecewise linear paths consisting of connected linear segments. With this method, path points with only a small influence on path geometry (i.e. aligned or nearly aligned points) are successively removed. Due to the removal of less important path points, the computational and memory requirements of the paths are reduced and traversing the path is accelerated. Our algorithm can be used in many different applications, e.g. sweeping, path finding, programming-by-demonstration in a virtual environment, or 6D CNC milling. The algorithm handles points with positional and orientational coordinates of arbitrary dimension.
Practical Issues in Automatic Documentation Generation

PLANDoc, a system under joint development by Columbia and Bellcore, documents the activity of planning engineers as they study telephone routes. It takes as input a trace of the engineer’s interaction with a network planning tool and produces 1-2 page summary. In this paper, we describe the user needs analysis we performed and how it influenced the development of PLANDoc. In particular, we show how it pinpointed the need for a sublanguage specification, allowing us to identify input messages and to characterize the different sentence paraphrases for realizing them. We focus on the systematic use of conjunction in combination with paraphrase that we developed for PLANDoc, which allows for the generation of summaries that are both concise–avoiding repetition of similar information, and fluent– avoiding repetition of similar phrasing.
Augmented Virtual Environments (AVE): Dynamic Fusion of Imagery and 3D Models

An Augmented Virtual Environment (AVE) fuses dynamic imagery with 3D models. The AVE provides a unique approach to visualize and comprehend multiple streams of temporal data or images. Models are used as a 3D substrate for the visualization of temporal imagery, providing improved comprehension of scene activities. The core elements of AVE systems include model construction, sensor tracking, real-time video/image acquisition, and dynamic texture projection for 3D visualization. This paper focuses on the integration of these components and the results that illustrate the utility and benefits of the resulting augmented virtual environment.
2CAuth: A New Two Factor Authentication Scheme Using QR-Code

Password based schemes has been the standard means of authentication over decades. Enhancements use entities like ownership (something one possess), knowledge (something one knows), and inherence (something one is) as first factor and mobile phones as token less second factor, in combinations, to offer different levels of security assurances, trading off usability. In this paper we present ‘2CAuth’ a new two factor authentication scheme that enhances secure usage of application information and preserves usability, without sacrificing user’s privacy. A significant feature of the scheme is that it DOES NOT call for any synchronization between Mobile Network Operator (MNO) and users. The analysis of the scheme clearly brings out its effectiveness in terms of its usability even at times of peak loads on mobile networks. The scheme has the dual advantage of carrying out the verification of transactions which involve the physical presence of the user as well as those to be done in his absence.
A Power-Control Scheme With Constant Switching Frequency in Class-D Inverter for Induction-Heating Jar Application

In this paper, a simple power-control scheme for a constant-frequency class-D inverter with variable duty cycle at constant frequency is proposed. It is more suitable and acceptable for high-frequency induction-heating (IH) jar applications. The proposed control scheme has the advantages of not only wide power-regulation range but also ease of output-power control. In addition, it can achieve stable and efficient zero-voltage switching in the whole load range. The control principles of the proposed method are described in detail, and the validity is verified through the simulated and experimental results on the 42.8-kHz insulated gate bipolar transistor for an IH rated on 1.6 kW with constant-frequency variable power
Gore Galore: Literary Theory and Computer Games

Computer games have not been adequately theorized within the humanities. In this paper a brief history of computer games is presented as a starting point for developing a topology of games and a theory of computer games as rhetorical artifacts suitable for critical study. The paper addresses the question of why games should be treated seriously and suggests a theoretical approach based on Backhtin’s poetics of the novel where the experience of time and space (the chronotope) provides a framework of questions for discussing computer games.
Privacy-Preserving Data Mining

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.
Active learning increases student performance in science, engineering, and mathematics.

To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineering, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories increased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes--although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.
High Performance Single-Chip FPGA Rijndael Algorithm Implementations

This paper describes high performance single-chip FPGA implementations of the new Advanced Encryption Standard (AES) algorithm, Rijndael. The designs are implemented on the Virtex-E FPGA family of devices. FPGAs have proven to be very effective in implementing encryption algorithms. They provide more flexibility than ASIC implementations and produce higher data-rates than equivalent software implementations. A novel, generic, parameterisable Rijndael encryptor core capable of supporting varying key sizes is presented. The 192-bit key and 256-bit key designs run at data rates of 5.8 Gbits/sec and 5.1 Gbits/sec respectively. The 128-bit key encryptor core has a throughput of 7 Gbits/sec which is 3.5 times faster than similar existing hardware designs and 21 times faster than known software implementations, making it the fastest single-chip FPGA Rijndael encryptor core reported to date. A fully pipelined single-chip 128-bit key Rijndael encryptor/decryptor core is also presented. This design runs at a data rate of 3.2 Gbits/sec on a Xilinx Virtex-E XCV3200E-8-CG1156 FPGA device. There are no known singlechip FPGA implementations of an encryptor/decryptor Rijndael design.
Identifying medical terms in patient-authored text: a crowdsourcing-based approach

As people increasingly engage in online health-seeking behavior and contribute to health-oriented websites, the volume of medical text authored by patients and other medical novices grows rapidly. However, we lack an effective method for automatically identifying medical terms in patient-authored text (PAT). We demonstrate that crowdsourcing PAT medical term identification tasks to non-experts is a viable method for creating large, accurately-labeled PAT datasets; moreover, such datasets can be used to train classifiers that outperform existing medical term identification tools.



To evaluate the viability of using non-expert crowds to label PAT, we compare expert (registered nurses) and non-expert (Amazon Mechanical Turk workers; Turkers) responses to a PAT medical term identification task. Next, we build a crowd-labeled dataset comprising 10 000 sentences from MedHelp. We train two models on this dataset and evaluate their performance, as well as that of MetaMap, Open Biomedical Annotator (OBA), and NaCTeM's TerMINE, against two gold standard datasets: one from MedHelp and the other from CureTogether.



When aggregated according to a corroborative voting policy, Turker responses predict expert responses with an F1 score of 84%. A conditional random field (CRF) trained on 10 000 crowd-labeled MedHelp sentences achieves an F1 score of 78% against the CureTogether gold standard, widely outperforming OBA (47%), TerMINE (43%), and MetaMap (39%). A failure analysis of the CRF suggests that misclassified terms are likely to be either generic or rare.



Our results show that combining statistical models sensitive to sentence-level context with crowd-labeled data is a scalable and effective technique for automatically identifying medical terms in PAT.