LLukas22/scidocs huggingface dataset:
Converged social media: Identity management and engagement on Facebook Mobile and blogs

There is scant evidence to demonstrate that researchers grasp the social dimensions of convergence, and particularly, the academic and social implications of converged media on students' lives. Despite a surge in student appropriation of social media-enabled mobile phones for exchanging educational resources and social practices, little is known about this collapsing of academic and social contexts. Twelve purposively selected postgraduate students who used Facebook Mobile for exchanging learning resources and micromanagement of their daily lives were interviewed to unravel the influence of converged social media on students' lives. Identity construction and convergence as theoretical constructs were drawn upon to examine student narratives of their Facebook interactions and navigation of their identities. The findings suggest that Facebook Mobile enhanced the students’ networked exchange of knowledge and meaningful participation in learning communities. However, students expressed uncertainty about educators' judgements of their online personae, invasion of their privacy, information security and fears of jeopardising future career prospects. The study recommends best-practice pedagogical designs that emphasise deep engagement, safe communication, measures to deal with plagiarism and policy interventions to address privacy and information security.
Evaluation of right ventricular dysfunction and prediction of clinical outcomes in acute pulmonary embolism by chest computed tomography: comparisons with echocardiography

To evaluate the ability to identify right ventricular (RV) dysfunction, and to predict adverse outcomes of chest computed tomography (CT), we compared CT and echocardiography in acute pulmonary embolism patients. We analyzed 56 patients diagnosed by CT with acute pulmonary embolism, who underwent echocardiography within 48 h of CT scan from January 2004 to December 2008. From the CT scan, the ratio of RV diameter to left ventricular diameter (RVd/LVd), the presence of septal bowing and embolus location were determined. RVd/LVd (P < 0.001), septal bowing (P < 0.001) and proximal embolism (P = 0.016) were associated with echocardiographic RV hypokinesia. The odds ratio for adverse clinical outcomes was 19.2 for the combination of three CT parameters (RVd/LVd > 1, septal bowing, and proximal embolism), and 13.4 for RV hypokinesia (each P = 0.001). The positive predictive value (PPV) for adverse clinical outcomes for echocardiographic RV hypokinesia was 55.0%, and the negative predictive value (NPV) was 96.2%. The three-parameter combination predicted adverse clinical outcomes with a PPV of 54.5%, and a NPV of 94.1%. CT parameters including RV dysfunction were significantly associated with poor outcomes. Rapid risk stratification of patients with acute pulmonary embolism based on chest CT appears to be comparable with echocardiography, is clinically reliable, and may be useful in guiding management strategy.
Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method

This paper explores on the optimization of the surface roughness of milling mould 6061-T6 aluminium alloys with carbide coated inserts. Optimization of the milling is very important to reduce the cost and time for machining mould. The purposes of this study are to develop the predicting model of surface roughness, to investigate the most dominant variables among the cutting speed, feed rate, axial depth and radial depth and to optimize Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method 251 the parameters. Response surface method based optimization approach was used in this study. It can be seen from the first order model that the feed rate is the most significantly influencing factor for the surface roughness. Second-order model reveals that there is no interaction between the variables and response.
A Robust Multimodal Speech Recognition Method using Optical Flow Analysis

This paper proposes a new multi-modal speech recognition method using optical-flow analysis, evaluating its robustness to acoustic and visual noises. Optical flow is defined as the distribution of apparent velocities in the movement of brightness patterns in an image. Since the optical flow is computed without extracting speaker’s lip contours and location, robust visual features can be obtained for lip movements. Our method calculates a visual feature set in each frame consisting of maximum and minimum values of integral of the optical flow. This feature set has not only silence information but also open/close status of the speaker’s mouth. The visual feature set is combined with an acoustic feature set in the framework of HMM-based recognition. Triphone HMMs are trained using the combined parameter set extracted from clean speech data. Two multi-modal speech recognition experiments have been carried out. First, acoustic white noise was added to speech wave forms, and a speech recognition experiment was conducted using audio-visual data from 11 male speakers uttering connected Japanese digits. The following improvements of relative reduction of digit error rate over the audio-only recognition scheme were achieved, when the visual information was incorporated into silence HMM: 32% at SNR=10dB and 47% at SNR=15dB. Second, a real-world data distorted both acoustically and visually was recorded in a driving car from six male speakers and recognized. We achieved approximately 17% and 11% relative error reduction compared with audio-only results on batch and incremental MLLR-based adaptation, respectively.
Folded Recurrent Neural Networks for Future Video Prediction

Future video prediction: • Applications: Unsupervised learning scene structure and spatio-temporal relationships • Challenges: High variability and non-specificity of future frames We introduce double-mapping Gated Recurrent Units (dGRU). Standard GRUs update an output state given an input. We also consider the input as a recurrent state, using an extra set of logic gates to update it given the output, allowing for: • Lower memory and computational costs • Mitigation and recovery from temporal error propagation • An identity function during training, helping convergence • Model explainability/pruning through layer removal
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.
Histamine-2 Receptor Antagonist Cannot Prevent Recurrent Peptic Ulcers in Patients With Atherosclerotic Diseases Who Receive Platelet ADP Receptor Antagonist Monotherapy: A Randomized-Controlled, Double-Blind, and Double-Dummy Trial

OBJECTIVES:Proton pump inhibitor can effectively prevent recurrent peptic ulcers among atherosclerotic patients receiving clopidogrel monotherapy. However, the interaction between proton pump inhibitors and clopidogrel has raised concerns over the safety of combined use of the two medicines in clinical practice. The aims of this randomized-controlled, double-blind and double-dummy trial were to investigate the efficacy of histamine-2 receptor antagonist (H2RA) in the prevention of recurrent peptic ulcer in patients undergoing thienopyridine monotherapy.METHODS:From January 2012 to 2016, long-termed thienopyridine users with a peptic ulcer history who did not have peptic ulcers at initial endoscopy were randomly assigned to receive either famotidine (40 mg, before bedtime) or placebo (before bedtime) for 6 months. Follow-up endoscopy was performed at the end of the 6th month and whenever dyspepsia, hematemesis, or melena occurred.RESULTS:The cumulative incidence of recurrent peptic ulcer during the 6-month period was 7.0% in famotidine group (n=114) and 11.4% in the placebo group (n=114). The two patient groups had comparable cumulative incidence of peptic ulcer (difference, 4.4%; 95% confidence interval (CI), −11.7 to 2.9%; P=0.239). Additionally, there was no difference in the cumulative incidence of ulcer bleeding (2.6% vs. 1.8%; difference, 0.8%; 95% CI, −0.6 to 2.4%, P=1.000) between famotidine and placebo groups. However, the former had a lower incidence of gastroduodenal erosion than the latter (21.1% vs. 36.8%; difference, 15.7%; 95% CI, −27.3 to −4.1%; P=0.013).CONCLUSIONS:Famotidine cannot decrease the incidence of peptic ulcer or ulcer bleeding in thienopyridine users with atherosclerotic disease and a history of peptic ulcer.
Strategic Experimentation in Networks

This paper examines experimentation and innovation when experimental results diffuse along social links. This public-goods nature of experimentation is a feature of many areas of economics, such as consumer choice, research and development, and agriculture. The paper asks: How do different patterns of social and geographic links affect experimentation? Who experiments and who free rides? Do more links enhance or diminish social welfare? The analysis finds, first, that social networks can foster specialization. In every social network there is an equilibrium where some individuals experiment and others completely free-ride. In many networks this extreme is the only equilibrium outcome. Second, specialization can benefit society as a whole. This outcome arises when specialists are linked, collectively, to many agents. Finally, new links can reduce overall welfare. A new link increases access to new results, but also reduces an individual's incentives to conduct his own experiments. Hence, overall welfare can be higher when there are holes in a network.
Lenalidomide plus dexamethasone for high-risk smoldering multiple myeloma.

BACKGROUND
For patients with smoldering multiple myeloma, the standard of care is observation until symptoms develop. However, this approach does not identify high-risk patients who may benefit from early intervention.


METHODS
In this randomized, open-label, phase 3 trial, we randomly assigned 119 patients with high-risk smoldering myeloma to treatment or observation. Patients in the treatment group received an induction regimen (lenalidomide at a dose of 25 mg per day on days 1 to 21, plus dexamethasone at a dose of 20 mg per day on days 1 to 4 and days 12 to 15, at 4-week intervals for nine cycles), followed by a maintenance regimen (lenalidomide at a dose of 10 mg per day on days 1 to 21 of each 28-day cycle for 2 years). The primary end point was time to progression to symptomatic disease. Secondary end points were response rate, overall survival, and safety.


RESULTS
After a median follow-up of 40 months, the median time to progression was significantly longer in the treatment group than in the observation group (median not reached vs. 21 months; hazard ratio for progression, 0.18; 95% confidence interval [CI], 0.09 to 0.32; P<0.001). The 3-year survival rate was also higher in the treatment group (94% vs. 80%; hazard ratio for death, 0.31; 95% CI, 0.10 to 0.91; P=0.03). A partial response or better was achieved in 79% of patients in the treatment group after the induction phase and in 90% during the maintenance phase. Toxic effects were mainly grade 2 or lower.


CONCLUSIONS
Early treatment for patients with high-risk smoldering myeloma delays progression to active disease and increases overall survival. (Funded by Celgene; ClinicalTrials.gov number, NCT00480363.).
Supply chain ontology: Review, analysis and synthesis

In an environment where supply chains compete against supply chains, information sharing among supply chain partners using information systems is a competitive tool. Supply chain ontology has been proposed as an important medium for attaining information systems interoperability. Ontology has its origin in philosophy, and the computing community has adopted ontology in its language. This paper presents a study of state of the art research in supply chain ontology and identifies the outstanding research gaps. Six supply chain ontology models were identified from a systematic review of literature. A seven point comparison framework was developed to consider the underlying concepts as well as application of the ontology models. The comparison results were then synthesised into nine gaps to inform future supply chain ontology research. This work is a rigorous and systematic attempt to identify and synthesise the research in supply chain ontology. 2010 Elsevier B.V. All rights reserved.
Modified Nonparametric Approaches to Detecting Differentially Expressed Genes in Replicated Microarray Experiments

MOTIVATION
An important goal in analyzing microarray data is to determine which genes are differentially expressed across two kinds of tissue samples or samples obtained under two experimental conditions. Various parametric tests, such as the two-sample t-test, have been used, but their possibly too strong parametric assumptions or large sample justifications may not hold in practice. As alternatives, a class of three nonparametric statistical methods, including the empirical Bayes method of Efron et al. (2001), the significance analysis of microarray (SAM) method of Tusher et al. (2001) and the mixture model method (MMM) of Pan et al. (2001), have been proposed. All the three methods depend on constructing a test statistic and a so-called null statistic such that the null statistic's distribution can be used to approximate the null distribution of the test statistic. However, relatively little effort has been directed toward assessment of the performance or the underlying assumptions of the methods in constructing such test and null statistics.


RESULTS
We point out a problem of a current method to construct the test and null statistics, which may lead to largely inflated Type I errors (i.e. false positives). We also propose two modifications that overcome the problem. In the context of MMM, the improved performance of the modified methods is demonstrated using simulated data. In addition, our numerical results also provide evidence to support the utility and effectiveness of MMM.
Comparative photocross-linking analysis of the tertiary structures of Escherichia coli and Bacillus subtilis RNase P RNAs.

Bacterial ribonuclease P contains a catalytic RNA subunit that cleaves precursor sequences from the 5' ends of pre-tRNAs. The RNase P RNAs from Bacillus subtilis and Escherichia coli each contain several unique secondary structural elements not present in the other. To understand better how these phylogenetically variable elements affect the global architecture of the ribozyme, photoaffinity cross-linking studies were carried out. Photolysis of photoagents attached at homologous sites in the two RNAs results in nearly identical cross-linking patterns, consistent with the homology of the RNAs and indicating that these RNAs contain a common, core tertiary structure. Distance constraints were used to derive tertiary structure models using a molecular mechanics-based modeling protocol. The resulting superimposition of large sets of equivalent models provides a low resolution (5-10 A) structure for each RNA. Comparison of these structure models shows that the conserved core helices occupy similar positions in space. Variably present helical elements that may play a role in global structural stability are found at the periphery of the core structure. The P5.1 and P15.1 helical elements, unique to the B.subtilis RNase P RNA, and the P6/16/17 helices, unique to the E.coli RNA, occupy similar positions in the structure models and, therefore, may have analogous structural function.
A scientific theory of Ars Memoriae: Spatial view cells in a continuous attractor network with linked items.

The art of memory (ars memoriae) used since classical times includes using a well-known scene to associate each view or part of the scene with a different item in a speech. This memory technique is also known as the "method of loci." The new theory is proposed that this type of memory is implemented in the CA3 region of the hippocampus where there are spatial view cells in primates that allow a particular view to be associated with a particular object in an event or episodic memory. Given that the CA3 cells with their extensive recurrent collateral system connecting different CA3 cells, and associative synaptic modifiability, form an autoassociation or attractor network, the spatial view cells with their approximately Gaussian view fields become linked in a continuous attractor network. As the view space is traversed continuously (e.g., by self-motion or imagined self-motion across the scene), the views are therefore successively recalled in the correct order, with no view missing, and with low interference between the items to be recalled. Given that each spatial view has been associated with a different discrete item, the items are recalled in the correct order, with none missing. This is the first neuroscience theory of ars memoriae. The theory provides a foundation for understanding how a key feature of ars memoriae, the ability to use a spatial scene to encode a sequence of items to be remembered, is implemented. © 2017 Wiley Periodicals, Inc.
An advanced ACO algorithm for feature subset selection

Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are treated as graph nodes to construct a graph model and are fully connected to each other. In this graph, each node has two sub-nodes, one for selecting and the other for deselecting the feature. Ant colony algorithm is used to select nodes while ants should visit all features. The use of several statistical measures is examined as the heuristic function for visibility of the edges in the graph. At the end of a tour, each ant has a binary vector with the same length as the number of features, where 1 implies selecting and 0 implies deselecting the corresponding feature. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), CatfishBPSO, Improved Binary Gravitational Search Algorithm (IBGSA), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods. KeywordsFeature selection; Wrraper; Ant colony optimization (ACO); Binary ACO; Classification.
Underwater Target Recognition Using Artificial Fish-Swarm Algorithm

基金项目:863 基金资助项目(2008AA092301); 水下智能机器人技术国防 科技重点实验室开放课题研究基金资助(2008003) 作者简介:张赫(1982-),女,辽宁葫芦岛人,博士研究生. 主要研究方向:水下 图像处理与模式识别. 具体应用。它能很好地解决非线性函数优化等问题。 其主要特点是不需要了解问题的特殊信息,只需要对问 题进行优劣的比较,有着较快的收敛速度。本文应用人 工鱼群算法结合神经网络建立水下目标的识别系统。该 优化方法利于发挥神经网络分类器对于处理环境信息 复杂、背景知识模糊的分类问题的优越性,同时能够提 高网络收敛速度及获取全局极值的能力。文中识别系统 的输入向量为水下目标不变性特征,采用了基于区域的 组合不变矩进行特征提取。通过对水池试验获取的样本 图像(四类不同目标图像和一类干扰图像)进行特征提 取和模式识别实验,并对比其他优化算法的识别性能, 结果表明建立的系统具有较优的聚类性能和较高的识 别精度。 基于人工鱼群神经网络的水下目标识别
Apache Hadoop YARN: yet another resource negotiator

The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agorá---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler.
 In this paper, we summarize the design, development, and current state of deployment of the next generation of Hadoop's compute platform: YARN. The new architecture we introduced decouples the programming model from the resource management infrastructure, and delegates many scheduling functions (e.g., task fault-tolerance) to per-application components. We provide experimental evidence demonstrating the improvements we made, confirm improved efficiency by reporting the experience of running YARN on production environments (including 100% of Yahoo! grids), and confirm the flexibility claims by discussing the porting of several programming frameworks onto YARN viz. Dryad, Giraph, Hoya, Hadoop MapReduce, REEF, Spark, Storm, Tez.
An extension of the technology acceptance model in an ERP implementation environment

This paper presents an extension to the technology acceptance model (TAM) and empirically examines it in an enterprise resource planning (ERP) implementation environment. The study evaluated the impact of one belief construct (shared beliefs in the benefits of a technology) and two widely recognized technology implementation success factors (training and communication) on the perceived usefulness and perceived ease of use during technology implementation. Shared beliefs refer to the beliefs that organizational participants share with their peers and superiors on the benefits of the ERP system. Using data gathered from the implementation of an ERP system, we showed that both training and project communication influence the shared beliefs that users form about the benefits of the technology and that the shared beliefs influence the perceived usefulness and ease of use of the technology. Thus, we provided empirical and theoretical support for the use of managerial interventions, such as training and communication, to influence the acceptance of technology, since perceived usefulness and ease of use contribute to behavioral intention to use the technology. # 2003 Elsevier B.V. All rights reserved.