nils-herrmann/clean_scirepeval huggingface dataset:
Hallucinogens and dissociative agents naturally growing in the United States.

It is usually believed that drugs of abuse are smuggled into the United States or are clandestinely produced for illicit distribution. Less well known is that many hallucinogens and dissociative agents can be obtained from plants and fungi growing wild or in gardens. Some of these botanical sources can be located throughout the United States; others have a more narrow distribution. This article reviews plants containing N,N-dimethyltryptamine, reversible type A monoamine oxidase inhibitors (MAOI), lysergic acid amide, the anticholinergic drugs atropine and scopolamine, or the diterpene salvinorin-A (Salvia divinorum). Also reviewed are mescaline-containing cacti, psilocybin/psilocin-containing mushrooms, and the Amanita muscaria and Amanita pantherina mushrooms that contain muscimol and ibotenic acid. Dangerous misidentification is most common with the mushrooms, but even a novice forager can quickly learn how to properly identify and prepare for ingestion many of these plants. Moreover, through the ever-expanding dissemination of information via the Internet, this knowledge is being obtained and acted upon by more and more individuals. This general overview includes information on the geographical range, drug content, preparation, intoxication, and the special health risks associated with some of these plants. Information is also offered on the unique issue of when bona fide religions use such plants as sacraments in the United States. In addition to the Native American Church's (NAC) longstanding right to peyote, two religions of Brazilian origin, the Santo Daime and the Uniao do Vegetal (UDV), are seeking legal protection in the United States for their use of sacramental dimethyltryptamine-containing "ayahuasca."
Efficient L1 Regularized Logistic Regression

L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many practical settings. In this paper, we propose an efficient algorithm for L1 regularized logistic regression. Our algorithm iteratively approximates the objective function by a quadratic approximation at the current point, while maintaining the L1 constraint. In each iteration, it uses the efficient LARS (Least Angle Regression) algorithm to solve the resulting L1 constrained quadratic optimization problem. Our theoretical results show that our algorithm is guaranteed to converge to the global optimum. Our experiments show that our algorithm significantly outperforms standard algorithms for solving convex optimization problems. Moreover, our algorithm outperforms four previously published algorithms that were specifically designed to solve the L1 regularized logistic regression problem.
Ontology-Based Context Modeling

Ontologies are a widely accepted tool for the modeling of context information. We view the identification of the benefits and challenges of ontologybased models to be an important next step to further improve the usability of ontologies in context-aware applications. We outline a set of criteria with respect to ontology engineering and context modeling and discuss some recent achievements in the area of ontology-based context modeling in order to determine the important next steps necessary to fully exploit ontologies in pervasive computing.
Programmatic Gold: Targeted and Scalable Quality Assurance in Crowdsourcing

Crowdsourcing is an effective tool for scalable data annotation in both research and enterprise contexts. Due to crowdsourcing’s open participation model, quality assurance is critical to the success of any project. Present methods rely on EM-style post-processing or manual annotation of large gold standard sets. In this paper we present an automated quality assurance process that is inexpensive and scalable. Our novel process relies on programmatic gold creation to provide targeted training feedback to workers and to prevent common scamming scenarios. We find that it decreases the amount of manual work required to manage crowdsourced labor while improving the overall quality of the results.
Diagonal Rescaling For Neural Networks

We define a second-order neural network stochastic gradient training algorithm whose block-diagonal structure effectively amounts to normalizing the unit activations. Investigating why this algorithm lacks in robustness then reveals two interesting insights. The first insight suggests a new way to scale the stepsizes, clarifying popular algorithms such as RMSProp as well as old neural network tricks such as fanin stepsize scaling. The second insight stresses the practical importance of dealing with fast changes of the curvature of the cost.
Surgical Repair of an Impalement Genital Injury from an Inline Skating Accident in a 7-Year-Old Prepubertal Girl: A Case Report.

In girls who present with vaginal trauma, sexual abuse is often the primary diagnosis. The differential diagnosis must include patterns and the mechanism of injury that differentiate accidental injuries from inflicted trauma.



A 7-year-old prepubertal girl presented to the emergency department with genital bleeding after a serious accidental impaling injury from inline skating. After rapid abduction of the legs and a fall onto the blade of an inline skate this child incurred an impaling genital injury consistent with an accidental mechanism. The dramatic genital injuries when repaired healed with almost imperceptible residual evidence of previous trauma.



To our knowledge, this case report represents the first in the medical literature of an impaling vaginal trauma from an inline skate and describes its clinical and surgical management.
Systematic literature reviews in software engineering: Preliminary results from interviews with researchers

Systematic Literature Reviews (SLRs) have been gaining significant attention from software engineering researchers since 2004. Several researchers have reported their experiences of and lessons learned from applying systematic reviews to different subject matters in software engineering. However, there has been no attempt at independently exploring experiences and perceptions of the practitioners of systematic reviews in order to gain an in-depth understanding of various aspects of systemic reviews as a new research methodology in software engineering. We assert that there is a need of evidence-based body of knowledge about the application of systematic reviews in software engineering. To address this need, we have started an empirical research program that aims to contribute to the growing body of knowledge about systematic reviews in software engineering. This paper reports the design, logistics, and results of the first phase empirical study carried out in this program. The results provide interesting insights into different aspects of systematic reviews based on the analysis of the data gathered from 17 interviewees with varying levels of knowledge of and experiences in systematic reviews. The findings from this study are expected to contribute to the existing knowledge about using systematic reviews and help further improve the state-of-the-practice of this research methodology in software engineering.
A Generative Attentional Neural Network Model for Dialogue Act Classification

We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a new attentional technique and a label-to-label connection for sequence learning, akin to Hidden Markov Models. Our experiments show that both of these innovations enable our model to outperform strong baselines for dialogue-act classification on the MapTask and Switchboard corpora. In addition, we analyse empirically the effectiveness of each of these innovations.
The Statistical Analysis of Failure Time Data.

A complete list of the titles in this series appears at the end of this volume. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print, however, may not be available in electronic format.
Memory Architecture of 3D Vertical Gate (3DVG) NAND Flash Using Plural Island-Gate SSL Decoding Method and Study of it's Program Inhibit Characteristics

The memory architecture of 3D vertical gate (3DVG) NAND Flash using plural island-gate SSL decoding method is discussed in detail. In order to provide a good array efficiency, 3DVG shares the wordlines (WL) in vertical direction, and BL's in lateral direction. In order to correctly decode the array, every channel BL has its own island-gate SSL device for the control. Meanwhile, many channel BL's are grouped together in one unit, and the staircase BL contacts are formed in order to decode various memory layers. Page operation is naturally defined by the selection of each island-gate SSL device. Due to the plural SSL devices, the 3DVG architecture inevitably has more pages when stacked layer number is increased, thus program inhibit stress is much larger than conventional 2D NAND. In this work, the program inhibit performances of 3DVG TFT NAND are discussed. Scaling capability down to 3Xnm node is also demonstrated.
Helmet Detection on Motorcyclists Using Image Descriptors and Classifiers

Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists, however many drivers do not use it. The main goal of helmet is to protect the drivers head in case of accident. In case of accident, if the motorcyclist does not use can be fatal. This paper aims to propose a system for detection of motorcyclist without helmet. For this, we have applied the circular Hough transform and the Histogram of Oriented Gradients descriptor to extract the image attributes. Then, the MultiLayer Perceptron classifier was used and the obtained results were compared with others algorithms. Traffic images were captured by cameras from public roads and constitute a database of 255 images. Indeed, the algorithm step regarding the helmet detection accomplished an accuracy rate of 91.37%.
Debunking the 'digital native': beyond digital apartheid, towards digital democracy

This paper interrogates the currently pervasive discourse of the ‘net generation’ finding the concept of the ‘digital native’ especially problematic, both empirically and conceptually. We draw on a research project of South African higher education students’ access to and use of Information and Communication Technologies (ICTs) to show that age is not a determining factor in students’ digital lives; rather, their familiarity and experience using ICTs is more relevant. We also demonstrate that the notion of a generation of ‘digital natives’ is inaccurate: those with such attributes are effectively a digital elite. Instead of a new net generation growing up to replace an older analogue generation, there is a deepening digital divide in South Africa characterized not by age but by access and opportunity; indeed, digital apartheid is alive and well. We suggest that the possibility for digital democracy does exist in the form of a mobile society which is not age specific, and which is ubiquitous. Finally, we propose redefining the concepts ‘digital’, ‘net’, ‘native’, and ‘generation’ in favour of reclaiming the term ‘digitizen’.
A survey of security techniques for the border gateway protocol (BGP)

Web surfing is an example (and popular) Internet application where users desire services provided by servers that exist somewhere in the Internet. To provide the service, data must be routed between the user's system and the server. Local network routing (relative to the user) can not provide a complete route for the data. In the core Internet, a portion of the network controlled by a single administrative authority, called an autonomous system (AS), provides local network support and also exchanges routing information with other ASes using the border gateway protocol (BGP). Through the BGP route exchange, a complete route for the data is created. Security at this level in the Internet is challenging due to the lack of a single administration point and because there are numerous ASes which interact with one another using complex peering policies. This work reviews recent techniques to secure BGP. These security techniques are categorized as follows: 1) cryptographic/attestation, 2) database, 3) overlay/group protocols, 4) penalty, and 5) data-plane testing. The techniques are reviewed at a high level in a tutorial format, and shortcomings of the techniques are summarized as well. The depth of coverage for particular published works is intentionally kept minimal, so that the reader can quickly grasp the techniques. This survey provides a basis for evaluation of the techniques to understand coverage of published works as well as to determine the best avenues for future research.
Magpie: Online Modelling and Performance-aware Systems

Understanding the performance of distributed systems requires correlation of thousands of interactions between numerous components — a task best left to a computer. Today’s systems provide voluminous traces from each component but do not synthesise the data into concise models of system performance. We argue that online performance modelling should be a ubiquitous operating system service and outline several uses including performance debugging, capacity planning, system tuning and anomaly detection. We describe the Magpie modelling service which collates detailed traces from multiple machines in an e-commerce site, extracts request-specific audit trails, and constructs probabilistic models of request behaviour. A feasibility study evaluates the approach using an offline demonstrator. Results show that the approach is promising, but that there are many challenges to building a truly ubiquitious, online modelling infrastructure.
Persistent activity in the prefrontal cortex during working memory

The dorsolateral prefrontal cortex (DLPFC) plays a crucial role in working memory. Notably, persistent activity in the DLPFC is often observed during the retention interval of delayed response tasks. The code carried by the persistent activity remains unclear, however. We critically evaluate how well recent findings from functional magnetic resonance imaging studies are compatible with current models of the role of the DLFPC in working memory. These new findings suggest that the DLPFC aids in the maintenance of information by directing attention to internal representations of sensory stimuli and motor plans that are stored in more posterior regions.
Parametric nonlinear dimensionality reduction using kernel t-SNE

Novel non-parametric dimensionality reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) lead to a powerful and flexible visualization of high-dimensional data. One drawback of non-parametric techniques is their lack of an explicit out-of-sample extension. In this contribution, we propose an efficient extension of t-SNE to a parametric framework, kernel t-SNE, which preserves the flexibility of basic t-SNE, but enables explicit out-of-sample extensions. We test the ability of kernel t-SNE in comparison to standard t-SNE for benchmark data sets, in particular addressing the generalization ability of the mapping for novel data. In the context of large data sets, this procedure enables us to train a mapping for a fixed size subset only, mapping all data afterwards in linear time. We demonstrate that this technique yields satisfactory results also for large data sets provided missing information due to the small size of the subset is accounted for by auxiliary information such as class labels, which can be integrated into kernel t-SNE based on the Fisher information. & 2014 Elsevier B.V. All rights reserved.
RCS measurements for vehicles and pedestrian at 26 and 79GHz

The RCS of a radar target is an important factor related with the radar performance such as the detection, tracking and classification. When dealing with the design of radar system for 26/79GHz short/middle-range radars allocated for next generation perimeter surveillance radars, it is essential to know individual RCS of typical vehicles and pedestrian. However, there are few or no reports related to the RCS measurement at the band of 26GHz and 79GHz. In this paper, the RCS measurement of vehicles and pedestrian was performed in a large-scale anechoic chamber and the measured RCS characteristics are discussed comparing the 26GHz and 79GHz band.
Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach

Computed tomography angiography (CTA), a non-invasive imaging technique, is becoming increasingly popular for cardiac examination, mainly due to its superior spatial resolution compared to MRI. This imaging modality is currently widely used for the diagnosis of coronary artery disease (CAD) but it is not commonly used for the diagnosis of ventricular and atrial function. In this paper, we present a fully automatic method for segmenting the whole heart (i.e. the outer surface of the myocardium) and cardiac chambers from CTA datasets. Cardiac chamber segmentation is particularly valuable for the extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction. With our approach, we aim to improve the diagnosis of CAD by providing functional information extracted from the same CTA data, thus not requiring additional scanning. In addition, the whole heart segmentation method we propose can be used for visualization of the coronary arteries and for obtaining a region of interest for subsequent segmentation of the coronaries, ventricles and atria. Our approach is based on multi-atlas segmentation, and performed within a non-rigid registration framework. A leave-one-out quantitative validation was carried out on 8 images. The method showed a high accuracy, which is reflected in both a mean segmentation error of 1.05±1.30 mm and an average Dice coefficient of 0.93. The robustness of the method is demonstrated by successfully applying the method to 243 additional datasets, without any significant failure.
Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles

In this paper we propose the use of an illumination invariant transform to improve many aspects of visual localisation, mapping and scene classification for autonomous road vehicles. The illumination invariant colour space stems from modelling the spectral properties of the camera and scene illumination in conjunction, and requires only a single parameter derived from the image sensor specifications. We present results using a 24-hour dataset collected using an autonomous road vehicle, demonstrating increased consistency of the illumination invariant images in comparison to raw RGB images during daylight hours. We then present three example applications of how illumination invariant imaging can improve performance in the context of vision-based autonomous vehicles: 6-DoF metric localisation using monocular cameras over a 24-hour period, life-long visual localisation and mapping using stereo, and urban scene classification in changing environments. Our ultimate goal is robust and reliable vision-based perception and navigation an attractive proposition for low-cost autonomy for road vehicles.
PBIRCH: A Scalable Parallel Clustering algorithm for Incremental Data

We present a parallel version of BIRCH with the objective of enhancing the scalability without compromising on the quality of clustering. The incoming data is distributed in a cyclic manner (or block cyclic manner if the data is bursty) to balance the load among processors. The algorithm is implemented on a message passing share-nothing model. Experiments show that for very large data sets the algorithm scales nearly linearly with the increasing number of processors. Experiments also show that clusters obtained by PBIRCH are comparable to those obtained using BIRCH
Finding Overlapping Communities in Social Networks

Increasingly, methods to identify community structure in networks have been proposed which allow groups to overlap. These methods have taken a variety of forms, resulting in a lack of consensus as to what characteristics overlapping communities should have. Furthermore, overlapping community detection algorithms have been justified using intuitive arguments, rather than quantitative observations. This lack of consensus and empirical justification has limited the adoption of methods which identify overlapping communities. In this text, we distil from previous literature a minimal set of axioms which overlapping communities should satisfy. Additionally, we modify a previously published algorithm, Iterative Scan, to ensure that these properties are met. By analyzing the community structure of a large blog network, we present both structural and attribute based verification that overlapping communities naturally and frequently occur.
Visual Analysis and Dissemination of Scientific Literature Collections with SurVis

Bibliographic data such as collections of scientific articles and citation networks have been studied extensively in information visualization and visual analytics research. Powerful systems have been built to support various types of bibliographic analysis, but they require some training and cannot be used to disseminate the insights gained. In contrast, we focused on developing a more accessible visual analytics system, called SurVis, that is ready to disseminate a carefully surveyed literature collection. The authors of a survey may use our Web-based system to structure and analyze their literature database. Later, readers of the survey can obtain an overview, quickly retrieve specific publications, and reproduce or extend the original bibliographic analysis. Our system employs a set of selectors that enable users to filter and browse the literature collection as well as to control interactive visualizations. The versatile selector concept includes selectors for textual search, filtering by keywords and meta-information, selection and clustering of similar publications, and following citation links. Agreement to the selector is represented by word-sized sparkline visualizations seamlessly integrated into the user interface. Based on an analysis of the analytical reasoning process, we derived requirements for the system. We developed the system in a formative way involving other researchers writing literature surveys. A questionnaire study with 14 visual analytics experts confirms that SurVis meets the initially formulated requirements.