nils-herrmann/clean_scirepeval huggingface dataset:
Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data. The MapReduce programming framework uses two tasks common in functional programming: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation. In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.
The thought translation device (TTD) for completely paralyzed patients.

The thought translation device trains locked-in patients to self-regulate slow cortical potentials (SCP's) of their electroencephalogram (EEG). After operant learning of SCP self-control, patients select letters, words or pictograms in a computerized language support program. Results of five respirated, locked-in-patients are described, demonstrating the usefulness of the thought translation device as an alternative communication channel in motivated totally paralyzed patients with amyotrophic lateral sclerosis.
Learning Recurrent Span Representations for Extractive Question Answering

The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.’s baseline by > 50%.
Faculty at Saudi Electronic University attitudes toward using augmented reality in education

This study aims to examine the possibility of implementing the Augmented Reality (AR) application in higher education by answering three questions: Which extended faculty at the Saudi Electronic University are familiar with such applications, what the perceptions do they hold toward using it in education, and what barriers do they believe may hinder implementing this technology. An online survey was designed and distributed to faculty members at two colleges selected randomly to collect data from participants. Even though the faculty were at an accepted level of familiarity with this technology, they did not use it in their classes. Results showed that faculty have a positive attitude toward the use of AR and have confidence in its potential to enrich the learning environment. This result is connected to the AR advantages the faculty were in agreement with. Results also showed that faculty have concerns about some barriers that might impact implementation of AR in the education environment, such as the lack of technical support.
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types

We present a neural model for question generation from knowledge base triples in a “ZeroShot” setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in an encoderdecoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model sets a new state-ofthe-art for zero-shot QG.
The RADStack: Open Source Lambda Architecture for Interactive Analytics

The Real-time Analytics Data Stack, colloquially referred to as the RADStack, is an open-source data analytics stack designed to provide fast, flexible queries over up-to-the-second data. It is designed to overcome the limitations of either a purely batch processing system (it takes too long to surface new events) or a purely real-time system (it’s difficult to ensure that no data is left behind and there is often no way to correct data after initial processing). It will seamlessly return best-effort results on very recent data combined with guaranteed-correct results on older data. In this paper, we introduce the architecture of the RADStack and discuss our methods of providing interactive analytics and a flexible data processing environment to handle a variety of real-world workloads.
Theories of First Language Acquisition

Investigating the processes through which individuals acquire language is Language acquisition. In general, acquisition of language points to native language acquisition, which examines children’s acquisition of their first language, while second language acquisition concerns acquisition of extra languages in children and adults as well. The history of language learning theories can be considered as a great pendulum cycled from Skinnerian environmentalism to Piagetian constructivism to Chomskian innatism. Consequently, much of research in this field has been revolved around the debates about whether cognitive process and structure are constrained by innately predetermined mechanism or shaped by environmental input. Linguists Noam Chomsky and Eric Lenneberg, for half a century have argued for the hypothesis that children have inborn, language-specific capabilities that make possible and restrict language learning. Others, like Catherine Snow, Elizabeth Bates and Brian MacWhinney have hypothesized that language acquisition is the product of common cognitive capacities and the interface between children and their surrounding communities. William O'Grady suggests that multifaceted syntactic phenomena stem from an efficiency-driven, linear computational system. O'Grady refers to his work as "nativism without Universal Grammar. Nevertheless, these basic theories of language acquisition cannot be absolutely divorced from each other. The purpose of the present paper is reviewing some of the fundamental theories that describe how children acquire their native language. Therefore describing the strengths and weaknesses of Behaviorism, Mentalism, Rationalism, Empiricism, Emergentism, Chunking, Vygotsky’s Sociocultural Theory, Piaget’s theory of child language and thought, Statistical Language Learning, Relational Frame Theory and Activity theories are among the objectives of this study. In general these basic theories are very much complementary to each other, serving different types of learning and indicating diverse cases of language learning.
A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

Vehicle detection is a key component of environmental sensing systems for Intelligent Vehicles (IVs). The traditional shallow model and offline learning-based vehicle detection method are not able to satisfy the real-world challenges of environmental complexity and scene dynamics. Focusing on these problems, this work proposes a vehicle detection algorithm based on a multiple feature subspace distribution deep model with online transfer learning. Based on the multiple feature subspace distribution hypothesis, a deep model is established in which multiple Restricted Boltzmann Machines (RBMs) construct the lower layers and a Deep Belief Network (DBN) composes the superstructure. For this deep model, an unsupervised feature extraction method is applied, which is based on sparse constraints. Then, a transfer learning method with online sample generation is proposed based on the deep model. Finally, the entire classifier is retrained online with supervised learning. The experiment is actuated using the KITTI road image datasets. The performance of the proposed method is compared with many state-of-the-art methods and it is demonstrated that the proposed deep transfer learning-based algorithm outperformed existing state-of-the-art methods.
Circulating Mitochondrial DAMPs Cause Inflammatory Responses to Injury

Injury causes a systemic inflammatory response syndrome (SIRS) that is clinically much like sepsis. Microbial pathogen-associated molecular patterns (PAMPs) activate innate immunocytes through pattern recognition receptors. Similarly, cellular injury can release endogenous ‘damage’-associated molecular patterns (DAMPs) that activate innate immunity. Mitochondria are evolutionary endosymbionts that were derived from bacteria and so might bear bacterial molecular motifs. Here we show that injury releases mitochondrial DAMPs (MTDs) into the circulation with functionally important immune consequences. MTDs include formyl peptides and mitochondrial DNA. These activate human polymorphonuclear neutrophils (PMNs) through formyl peptide receptor-1 and Toll-like receptor (TLR) 9, respectively. MTDs promote PMN Ca2+ flux and phosphorylation of mitogen-activated protein (MAP) kinases, thus leading to PMN migration and degranulation in vitro and in vivo. Circulating MTDs can elicit neutrophil-mediated organ injury. Cellular disruption by trauma releases mitochondrial DAMPs with evolutionarily conserved similarities to bacterial PAMPs into the circulation. These signal through innate immune pathways identical to those activated in sepsis to create a sepsis-like state. The release of such mitochondrial ‘enemies within’ by cellular injury is a key link between trauma, inflammation and SIRS.
Learning Representations by Recirculation

We describe a new learning procedure for networks that contain groups of nonlinear units arranged in a closed loop. The aim of the learning is to discover codes that allow the activity vectors in a "visible" group to be represented by activity vectors in a "hidden" group. One way to test whether a code is an accurate representation is to try to reconstruct the visible vector from the hidden vector. The difference between the original and the reconstructed visible vectors is called the reconstruction error, and the learning procedure aims to minimize this error. The learning procedure has two passes. On the fust pass, the original visible vector is passed around the loop, and on the second pass an average of the original vector and the reconstructed vector is passed around the loop. The learning procedure changes each weight by an amount proportional to the product of the "presynaptic" activity and the difference in the post-synaptic activity on the two passes. This procedure is much simpler to implement than methods like back-propagation. Simulations in simple networks show that it usually converges rapidly on a good set of codes, and analysis shows that in certain restricted cases it performs gradient descent in the squared reconstruction error.
Approximate max-flow min-(multi)cut theorems and their applications

Consider the multicommodity flow problem in which the object is to maximize the sum of commodities routed. We prove the following approximate max-flow min-multicut theorem: min multicut < max flow < min multicut, O(log k) where k is the number of commodities. Our proof is constructive; it enables us to find a multicut within O(logk) of the max flow (and hence also the optimal multicut). In addition, the proof technique provides a unified framework in which one can also analyse the case of flows with specified demands of Leighton and Rao and Klein et al. and thereby obtain an improved bound for the latter problem. Key words, approximation algorithm, multicommodity flow, minimum multicut AMS subject classifications. 68Q25, 90B10
OpenCV based disease identification of mango leaves

This paper aims in classifying and identifying the diseases of mango leaves for Indian agriculture. K-means algorithm is chosen for the disease segmentation, and the disease classification and identification is carried out using the SVM classifier. Disease identification based on analysis of patches or discoloring of leaf will hold good for some of the plant diseases, but some other diseases which will deform the leaf shape cannot be identified based on the same method. In this case leaf shape based disease identification has to be performed. Based on this analysis two topics are addressed in this research paper. (1) Disease identification using the OpenCV libraries (2) Leaf shape based disease identification. Keywordk-means,Principal Component Analysis (PCA), feature extraction, shape detection, disease identification, Elliptic fourier analysis, Support Vector Machine(SVM), Artificial Neural Network (ANN)
Autonomous vehicles : Pedestrian heaven or pedestrian hell ?

Autonomous vehicles are an emerging technology that will play a significant role in shaping transport systems in the decades to come. Consequently, implications are anticipated for societal issues ranging from transport safety to congestion, from energy consumption to the allocation of public and private lands. Driverless technology will undoubtedly influence other transport modes, too. With this in mind, the implications for pedestrians have hardly been researched. If the introduction of driverless cars could ultimately result in the disappearance of on-street parking spaces and in crossing streets without so much as glancing towards approaching cars, this could potentially mean that walking becomes much more attractive. At the same time, one could imagine the opposite outcome if society decides that the ability of being picked up and dropped off at any front door or location imaginable renders walking as a state-supported transport mode superfluous. The purpose of this paper is to discuss the impact of autonomous vehicles on pedestrian transport in all its aspects, including the range of possible outcomes, interdependencies, and opposing effects. With the state-of-the-art of pedestrian transport research as a starting point, how can the needs of pedestrians be taken into consideration when fully autonomous vehicles become ubiquitous in daily life?
High-speed pattern projection for three-dimensional shape measurement using laser speckles.

We propose a high-speed projection system that is able to project statistical speckle patterns at a rate of 500Hz. Its purpose is to generate structured light for a real-time photogrammetry stereo vision setup. As conventional digital light projector (DLP) projection setups are limited in their maximum projection rate to 250Hz for gray-value patterns, stripe projection systems are usually applied for real-time three-dimensional (3D) measurements. However, these techniques can only be used on steady surfaces as phase unwrapping has to be done. In contrast, the proposed setup is able to measure the shape of multiple spatially separated objects at once. We compare the speckle setup with a system using a DLP projector and with other fast 3D shape measurement setups, like the widely used stripe projection methods, qualitatively and quantitatively.
Biological synthesis of copper nanoparticles using plant extract

Copper nanoparticles were biologically synthesized using plant leaf extract as reducing agent. On treatment of aqueous solution of CuSO4∙5H2O with Magnolia leaf extract, stable copper nanoparticles were formed. UV-vis spectroscopy was used to monitor the quantitative formation of copper nanoparticles. The synthesized nanoparticles were characterized with inductively coupled plasma spectrometry (ICP), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and highresolution transmission electron microscopy (HR-TEM). Electron microscopy analysis of copper nanoparticles indicated that they ranged in size from 40 to 100 nm. Antibacterial tests were carried out by counting viable Escherichia coli cells after 24 h growth in shake flasks containing latex foams coated with copper nanoparticles. As a result, foams coated with biologically synthesized copper nanoparticles showed higher antibacterial activity compared with foams untreated. As possible ecofriendly alternatives to chemical and physical methods, biologically synthesized nanoparticles using plant extracts may have applications in various human body-contacting areas.
Demonstration of an optical chip-to-chip link in a 3D integrated electronic-photonic platform

A full optical chip-to-chip link is demonstrated for the first time in a wafer-scale heterogeneous platform, where the photonics and CMOS chips are 3D integrated using wafer bonding and low-parasitic capacitance thru-oxide vias (TOVs). This development platform yields 1000s of functional photonic components as well as 16M transistors per chip module. The transmitter operates at 6Gb/s with an energy cost of 100fJ/bit and the receiver at 7Gb/s with a sensitivity of 26μA (-14.5dBm) and 340fJ/bit energy consumption. A full 5Gb/s chip-to-chip link, with the on-chip calibration and self-test, is demonstrated over a 100m single mode optical fiber with 560fJ/bit of electrical and 4.2pJ/bit of optical energy.
Norton Zone: Symantec's Secure Cloud Storage System

Cloud storage services are the way of the future, if not the present, but broad adoption is limited by a stark trade-off between privacy and functionality. Many popular cloud services provide search capabilities, but make only nominal efforts to keep user data fully private. Alternatives that search private user data on an untrusted server sacrifice functionality and/or scalability. We describe Norton Zone, Symantec's secure and scalable public storage system based on our valet security model. Whereas most commercial cloud storage systems secure user data with access control and legal mechanisms, Zone's cryptographic techniques provide proven privacy guarantees. This gives users an extra layer of security without compromising functionality. Zone's performance is comparable to unencrypted cloud storage systems that support search and sharing. We report on the design of Zone and the lessons learned in developing and deploying it in commercial, distributed datacenters scalable to millions of users.
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation

We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly quantifies similarity rather than association or relatedness so that pairs of entities that are associated but not actually similar (Freud, psychology) have a low rating. We show that, via this focus on similarity, SimLex-999 incentivizes the development of models with a different, and arguably wider, range of applications than those which reflect conceptual association. Second, SimLex-999 contains a range of concrete and abstract adjective, noun, and verb pairs, together with an independent rating of concreteness and (free) association strength for each pair. This diversity enables fine-grained analyses of the performance of models on concepts of different types, and consequently greater insight into how architectures can be improved. Further, unlike existing gold standard evaluations, for which automatic approaches have reached or surpassed the inter-annotator agreement ceiling, state-of-the-art models perform well below this ceiling on SimLex-999. There is therefore plenty of scope for SimLex-999 to quantify future improvements to distributional semantic models, guiding the development of the next generation of representation-learning architectures.
Unsupervised Learning for Physical Interaction through Video Prediction

A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real-world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action-conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real-world interactive agents, we also introduce a dataset of 50, 000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot’s future actions amounts to learning a “visual imagination” of different futures based on different courses of action. Our experiments show that our proposed method not only produces more accurate video predictions, but also more accurately predicts object motion, when compared to prior methods.
A Space Vector Modulation Scheme of the Quasi-Z-Source Three-Level T-Type Inverter for Common-Mode Voltage Reduction

The conventional three-level inverter suffers the limitation of voltage buck operation. In order to give both voltage buck and boost operation capability, the quasi-Z-source three-level T-type inverter (3LT<inline-formula> <tex-math notation="LaTeX">$^2$</tex-math></inline-formula>I) has been proposed. This paper further proposes a space vector modulation (SVM) scheme for the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX">$^2$</tex-math> </inline-formula>I to reduce the magnitude and slew rate of common-mode voltage (CMV). By properly selecting the shoot-through phase, the shoot-through states are inserted within zero vector in order not to affect the active states and output voltage. Doing so, the CMV generated by the quasi-Z-source 3LT<inline-formula><tex-math notation="LaTeX"> $^2$</tex-math></inline-formula>I is restricted within one-sixth of dc-link voltage, and voltage boosting and CMV reduction can be simultaneously realized. In addition, high dc-link voltage utilization can be maintained. The proposed scheme has been verified in both simulations and experiments. Comparisons are conducted with the conventional SVM method and the phase-shifted sinusoidal PWM method.