nils-herrmann/clean_scirepeval huggingface dataset:
A Deep Investigation of Deep IR Models

e eective of information retrieval (IR) systems have become more important than ever. Deep IR models have gained increasing aention for its ability to automatically learning features from raw text; thus, many deep IR models have been proposed recently. However, the learning process of these deep IR models resemble a black box. erefore, it is necessary to identify the dierence between automatically learned features by deep IR models and hand-craed features used in traditional learning to rank approaches. Furthermore, it is valuable to investigate the dierences between these deep IR models. is paper aims to conduct a deep investigation on deep IR models. Specically, we conduct an extensive empirical study on two dierent datasets, including Robust and LETOR4.0. We rst compared the automatically learned features and handcraed features on the respects of query term coverage, document length, embeddings and robustness. It reveals a number of disadvantages compared with hand-craed features. erefore, we establish guidelines for improving existing deep IR models. Furthermore, we compare two dierent categories of deep IR models, i.e. representation-focused models and interaction-focused models. It is shown that two types of deep IR models focus on dierent categories of words, including topic-related words and query-related words.
Chief complaint classification with recurrent neural networks

Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naïve Bayes (MNB) and a support vector machine (SVM). The MNB classifier is one of only two machine learning algorithms currently being used for syndromic surveillance. All four models are trained to predict diagnostic code groups as defined by Clinical Classification Software, first to predict from discharge diagnosis, then from chief complaint fields. The classifiers are trained on 3.6 million de-identified emergency department records from a single United States jurisdiction. We compare performance of these models primarily using the F1 score. We measure absolute model performance to determine which conditions are the most amenable to surveillance based on chief complaint alone. Using discharge diagnoses The LSTM classifier performs best, though all models exhibit an F1 score above 96.00. The GRU performs best on chief complaints (F1=47.38), and MNB with bigrams performs worst (F1=39.40). Certain syndrome types are easier to detect than others. For examples, chief complaints using the GRU model predicts alcohol-related disorders well (F1=78.91) but predicts influenza poorly (F1=14.80). In all instances the RNN models outperformed the bag-of-word classifiers suggesting deep learning models could substantially improve the automatic classification of unstructured text for syndromic surveillance.  Syndromic surveillance—detection and monitoring individual and population health indicators that are discernible before confirmed diagnoses are made (Mandl et al.2004)—can draw from many data sources. Electronic health records of emergency department encounters, especially the free-text chief complaint field, are a common focus for syndromic surveillance (Yoon, Ising, & Gunn 2017). In practice, a computer algorithm associates the text of the chief complaint field with predefined syndromes, often by picking out keywords or parts of keywords or a machine learning algorithm based on mathematical representation of the chief complaint text. In this paper, we explore recurrent neural networks as an alternative to existing methods for associating chief complaint text with syndromes. Overview of Chief Complaint Classifiers In a recent overview of chief complaint classifiers (Conway et al., 2013), the authors divide chief complaint classifiers into 3 categories: keyword-based classifiers, linguistic classifiers, and statistical classifiers.
MODELLING OF QUEUES BY USING OF PETRI NET SIMULATOR

In this paper we present a Petri net simulator that will be used for modeling of queues. The concept of queue is ubiquitous in the everyday life. Queues are present at the airports, banks, shops etc. Many factors are important in the studying of queues – some of them are: the average waiting time in the queue, the usage of the server, expected length of the queue etc. The paper is organized as follows: first we give a brief overview of the queuing theory. After that – we describe the Petri nets, as a tool for modeling of discrete event systems. We have implemented Petri net simulator that is capable for queue modeling. Finally we are presenting the simulation results for M/M/1 queue and its different properties. We’ve also made comparison between theoretically obtained results and our simulation results.
Stomatal development in Arabidopsis.

Stomata consist of two guard cells around a pore and act as turgor-operated valves for gas exchange. Arabidopsis stomata develop from one or more asymmetric divisions followed by the symmetric division of the guard mother cell. Stomatal number is partly a function of the availability of smaller epidermal cells that are competent to divide asymmetrically. Stomata are spaced apart from each other by at least one neighbor cell. Pattern generation may involve cell-cell signaling that transmits spatial cues used to orient specific classes of asymmetric divisions. TOO MANY MOUTHS may function in receiving or transducing these cues to orient asymmetric divisions. TMM also is a negative or positive regulator of entry into the stomatal pathway, with the direction of the response dependent on organ and location. STOMATAL DENSITY AND DISTRIBUTION1 is a negative regulator of stomatal formation throughout the shoot and encodes a processing protease that may function in intercellular communication. FOUR LIPS apparently controls the number symmetric divisions at the guard mother cell stage. In some organs, such as the hypocotyl, the placement of stomata may be coordinated with internal features and involves genes that also regulate root hair and trichome formation. Other mutations affect guard cell morphogenesis, cytokinesis, and stomatal number in response to carbon dioxide concentration. The molecular analysis of stomatal development promises advances in understanding intercellular signaling, the control of the plane and polarity of asymmetric division, the specification of cell fate, and the regulation of cell differentiation and shape.
Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data.



We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation.



We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71).



EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.
Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate. We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,c h a n n e l) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.
Construing Experience through Meaning: A Language-based Approach to Cognition by M. A. K. Halliday and Christian M. I. M. Matthiessen

Michael Alexander Kirkwood Halliday has been actively analyzing and documenting the interactions between syntax and semantics for over forty years, and his systemic– functional theory has been a foundation for important work in computational linguistics for at least thirty years. The first major application of systemic theory was the SHRDLU system by Winograd (1972). The largest ongoing series of applications has been developed at the USC Information Sciences Institute: language generation (Mann 1982; Hovy 1988); discourse analysis and rhetorical structure (Mann and Thompson 1992); and the interface between the lexicon and world knowledge (Bateman et al. 1990; Matthiessen 1995). In this book, Halliday and Matthiessen present a comprehensive survey of semantics and its relationships to syntax and cognition. Although they present their subject from a systemic–functional point of view, they show how their approach is related to a wide range of work in both computational and theoretical linguistics. One notable omission from their 23-page bibliography is Noam Chomsky, whose period of active research almost exactly coincides with Halliday’s. They do, however, give a fair summary of semantic theories based on Chomsky’s approach, ranging from the early work of Katz and Fodor to the more recent work by Jackendoff. The book consists of 15 chapters organized in five parts. In Part I, the authors contrast the systemic approach with a view of knowledge representation as a “piecemeal accumulation” of concepts with “no overall organization.” Instead of treating language “as a kind of code in which pre-existing conceptual structures are more or less distortedly expressed,” they view language as a semiotic system that serves “as the foundation of human experience.” The goal of systemic theory is to present a comprehensive view of how humans construe experience through language. Unlike Chomsky, they do not consider grammar as “autonomous” but as an integral part of the lexicogrammar, which realizes meaning in words, phrases, sentences, and paragraphs. Part II, comprising Chapters 2 through 7, presents the meaning base, which corresponds to what many authors would call an ontology. The meaning base, however, represents categories of experience with a topmost node called phenomenon instead of categories of existence with a topmost node called entity. The first subdivision of phenomena is a three-way partitioning according to levels of complexity:
A Review of Truck Platooning Projects for Energy Savings

Technical studies on automated driving of passenger cars were started in the 1950s, but those on heavy trucks were started in the mid-1990s, and only a few projects have dealt with truck automation, which include “Chauffeur” within the EU project T-TAP from the mid-1990s, truck automation by California PATH from around 2000, “KONVOI” in Germany from 2005, and “Energy ITS” by Japan from 2008. The objectives of truck automation are energy saving and enhanced transportation capacity by platooning, and eventually possible reduction of personnel cost by unmanned operation of following vehicles. The sensing technologies for automated vehicle control are computer vision, radar, lidar, laser scanners, localization by GNSS, and vehicle to vehicle communications. Experiments of platooning of three or four heavy trucks have shown the effectiveness of platooning in achieving energy saving due to short gaps between vehicles.
APISan: Sanitizing API Usages through Semantic Cross-Checking

API misuse is a well-known source of bugs. Some of them (e.g., incorrect use of SSL API, and integer overflow of memory allocation size) can cause serious security vulnerabilities (e.g., man-in-the-middle (MITM) attack, and privilege escalation). Moreover, modern APIs, which are large, complex, and fast evolving, are error-prone. However, existing techniques to help finding bugs require manual effort by developers (e.g., providing specification or model) or are not scalable to large real-world software comprising millions of lines of code. In this paper, we present APISAN, a tool that automatically infers correct API usages from source code without manual effort. The key idea in APISAN is to extract likely correct usage patterns in four different aspects (e.g., causal relation, and semantic relation on arguments) by considering semantic constraints. APISAN is tailored to check various properties with security implications. We applied APISAN to 92 million lines of code, including Linux Kernel, and OpenSSL, found 76 previously unknown bugs, and provided patches for all the bugs.
Comparing contextual and flat representations of knowledge: a concrete case about football data

The capability of dealing with context sensitive knowledge is recognized as a crucial aspect in the management of massive amounts of Semantic Web (SW) data. Contextual knowledge can be modelled either by adopting the primitives from RDF/OWL based SW languages or by extending such languages with new specific constructs for context representation. In this paper, we show the benefits of the context-based solution by comparing modelling and reasoning in the two approaches on the paradigmatic use case of FIFA World Cup. The comparison considers the three key aspects of engineering and exploiting knowledge: (i) simplicity and expressivity of the (formal) language; (ii) compactness of the representation; and (iii) efficiency of reasoning. As for (i), we show that the context-based language enables the construction of simpler and more intuitive models while the RDF/OWL "flat" model presents practical limitations in modelling cross-contextual knowledge. For (ii), we show that the contextualized model is more compact than the OWL based model. Finally for (iii), query answering in the context-based model outperforms in most of the cases performances on the flat model.
Multivariate pattern analysis for MEG: A comparison of dissimilarity measures

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA.
Rhombencephalitis: pictorial essay*


The term rhombencephalitis refers to inflammatory diseases affecting the hindbrain (brainstem and cerebellum). Rhombencephalitis has a wide variety of etiologies, including infections, autoimmune diseases, and paraneoplastic syndromes. Infection with bacteria of the genus Listeria is the most common cause of rhombencephalitis. Primary rhombencephalitis caused by infection with Listeria spp. occurs in healthy young adults. It usually has a biphasic time course with a flu-like syndrome, followed by brainstem dysfunction; 75% of patients have cerebrospinal fluid pleocytosis, and nearly 100% have an abnormal brain magnetic resonance imaging scan. However, other possible causes of rhombencephalitis must be borne in mind. In addition to the clinical aspects, the patterns seen in magnetic resonance imaging can be helpful in defining the possible cause. Some of the reported causes of rhombencephalitis are potentially severe and life threatening; therefore, an accurate initial diagnostic approach is important to establishing a proper early treatment regimen. This pictorial essay reviews the various causes of rhombencephalitis and the corresponding magnetic resonance imaging findings, by describing illustrative confirmed cases.
Vygotsky in a TeamRoom: An Exploratory Study on Collective Concept Formation in Electronic Environments

Effective use of distributed collaboration environments requires shared mental models that guide users in sensemaking and categorization. In Lotus Notes -based collaboration systems, such shared models are usually implemented as views and document types. TeamRoom, developed at Lotus Institute, implements in its design a theory of effective social process that creates a set of team-specific categories, which can then be used as a basis for knowledge sharing, collaboration, and team memory. This paper reports an exploratory study in collective concept formation in the TeamRoom environment. The study was run in an ecological setting, while the team members used the system for their everyday work. We apply theory developed by Lev Vygotsky, and use a modified version of an experiment on concept formation, devised by Lev Sakharov, and discussed in Vygotsky (1986). Vygotsky emphasized the role of language, cognitive artifacts, and historical and social sources in the development of thought processes. Within the Vygotskian framework it becomes clear that development of thinking does not end in adolescence. In teams of adult people, learning and knowledge creation are continuous processes. New concepts are created, shared, and developed into systems. The question, then, becomes how spontaneous concepts are collectively generated in teams, how they become integrated as systems, and how computer mediated collaboration environments affect these processes. d in ittle ons
Neurolinguists, beware! The bilingual is not two monolinguals in one person

Two views of bilingualism are presented--the monolingual or fractional view which holds that the bilingual is (or should be) two monolinguals in one person, and the bilingual or wholistic view which states that the coexistence of two languages in the bilingual has produced a unique and specific speaker-hearer. These views affect how we compare monolinguals and bilinguals, study language learning and language forgetting, and examine the speech modes--monolingual and bilingual--that characterize the bilingual's everyday interactions. The implications of the wholistic view on the neurolinguistics of bilingualism, and in particular bilingual aphasia, are discussed.