nils-herrmann/clean_scirepeval huggingface dataset:
Event Extraction for Document-Level Structured Summarization

Event extraction has been well studied for more than two decades, through both the lens of document-level and sentence-level event extraction. However, event extraction methods to date do not yet offer a satisfactory solution to providing concise, structured, document-level summaries of events in news articles. Prior work on document-level event extraction methods have focused on highly specific domains, often with great reliance on handcrafted rules. Such approaches do not generalize well to new domains. In contrast, sentence-level event extraction methods have applied to a much wider variety of domains, but generate output at such fine-grained details that they cannot offer good document-level summaries of events. In this thesis, we propose a new framework for extracting document-level event summaries called macro-events, unifying together aspects of both information extraction and text summarization. The goal of this work is to extract concise, structured representations of documents that can clearly outline the main event of interest and all the necessary argument fillers to describe the event. Unlike work in abstractive and extractive summarization, we seek to create template-based, structured summaries, rather than plain text summaries. We propose three novel methods to address the macro-event extraction task. First, we introduce a structured prediction model based on the Learning to Search framework for jointly learning argument fillers both across and within event argument slots. Second, we propose a multi-layer neural network that is trained directly on macro-event annotated data. Finally, we propose a deep learning method that treats the problem as machine comprehension, which does not require training with any on-domain macro-event labeled data. Our experimental results on a variety of domains show that such algorithms can achieve stronger performance on this task compared to existing baseline approaches. On average across all datasets, neural networks can achieve a 1.76% and 3.96% improvement on micro-averaged and macro-averaged F1 respectively over baseline approaches, while Learning to Search achieves a 3.87% and 5.10% improvement over baseline approaches on the same metrics. Furthermore, under scenarios of limited training data, we find that machine comprehension models can offer very strong performance compared to directly supervised algorithms, while requiring very little human effort to adapt to new domains.
An event-triggered finite-time control scheme for unicycle robots

This paper is concerned with the event-triggered finite-time control scheme for unicycle robots. First, Lagrange method is used to model the unicycle robot at the roll and pitch axis. Second, on the basis of the established model, an event-triggered finite-time control scheme is proposed to balance the unicycle robot in finite time and to determine whether or not control input should be updated. The control input should be only updated when the triggering condition is violated. As a result, the switching energy of actor can be saved. Third, a stability criterion on unicycle robots with the proposed event-trigged finite-time control scheme is derived by using a Lyapunov method. Finally, the effectiveness of the event-triggered finite-time control scheme is illustrated for unicycle robots.
The nutrition transition in Egypt: obesity, undernutrition and the food consumption context.

To describe changing food consumption patterns in Egypt over the last several decades, current levels of overweight and obesity, and current data on obesity-related morbidity.



Secondary analysis and synthesis of existing data from national-level food consumption surveys, large recent surveys of hypertension and diabetes, and documentation of historical and policy context.



Arab Republic of Egypt.



As selected and described in primary data sources.



The nutrition transition in Egypt has occurred in the context of abundant dietary energy availability, urbanisation and moderate fat intakes. The prevalence of obesity in adults is very high, particularly among women. The prevalences of diabetes mellitus and of hypertension parallel that of obesity, and both are very high. Little information is available on physical activity, but it is likely that a large proportion of the population is quite sedentary, particularly in the cities. At the same time, rates of early childhood malnutrition remain stubbornly stable and relatively high. Public awareness of the increasing prevalence of obesity and of diet-related chronic disease is increasing, and attention has turned to documenting the problem(s).
The role of conservation agriculture in sustainable agriculture.

The paper focuses on conservation agriculture (CA), defined as minimal soil disturbance (no-till, NT) and permanent soil cover (mulch) combined with rotations, as a more sustainable cultivation system for the future. Cultivation and tillage play an important role in agriculture. The benefits of tillage in agriculture are explored before introducing conservation tillage (CT), a practice that was borne out of the American dust bowl of the 1930s. The paper then describes the benefits of CA, a suggested improvement on CT, where NT, mulch and rotations significantly improve soil properties and other biotic factors. The paper concludes that CA is a more sustainable and environmentally friendly management system for cultivating crops. Case studies from the rice-wheat areas of the Indo-Gangetic Plains of South Asia and the irrigated maize-wheat systems of Northwest Mexico are used to describe how CA practices have been used in these two environments to raise production sustainably and profitably. Benefits in terms of greenhouse gas emissions and their effect on global warming are also discussed. The paper concludes that agriculture in the next decade will have to sustainably produce more food from less land through more efficient use of natural resources and with minimal impact on the environment in order to meet growing population demands. Promoting and adopting CA management systems can help meet this goal.
Artificial Intelligence - Foundations of Computational Agents

Foundations of Computational Agents is about the science of artificial intelligence (AI). It presents AI as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide audience of professionals and researchers. The past decades have witnessed the emergence of AI as a serious science and engineering discipline. This book provides the first accessible synthesis of the field aimed at undergraduate and graduate students. It provides a coherent vision of the foundations of the field as it is today, in terms of a multidimensional design space that has been partially explored. As with any science worth its salt, AI has a coherent, formal theory and a rambunctious experimental wing. The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.
The new deal at the top.

What makes a company strategically agile--able to alter its strategies and business models rapidly in response to major changes in its market space, and to do so repeatedly without major trauma? Three years of in-depth case research on a dozen large companies worldwide showed the authors that one key factor is a new leadership model at the top. Senior executives at agile companies assume collective rather than individual responsibility for results. They build interdependencies among units and divisions, motivating themselves to engage with one another, and carefully manage their dealings to promote collaboration that is frequent, intense, informal, open, and focused on shared issues and the long term. Challenges to conventional thinking are encouraged. This is the new deal, and it's not easy to strike, because it requires executives to act in ways that are far from comfortable. After all, the corporate ladder at most firms favors independent types with a deep need for power and autonomy. At executive meetings, disagreement is suppressed or expressed passive-aggressively, eroding any real sense of belonging to a team. Switching to the new deal almost always requires a huge shift in the company's culture, values, and norms of interaction. The authors describe three approaches to making the shift: Executives can be given formal responsibility not for a business unit but for different stages in the company's value chain. This worked well for SAP, which has a relatively focused business portfolio. When a company's portfolio is less uniform, like Nokia's, business and functional units can be organized to crisscross on a matrix. And when a company is widely diverse, like easyGroup, it can emphasize the learning opportunities that units with common business models may share.
How Do Occupational Therapists Work with Cognitive Interventions in Psychiatry and Mental Health ?

s, Cochrane Central Register of Controlled Trials and Database of Systemic Reviews, Database of Abstracts of Effects, ACP Journal Club, and OTseeker. Experts such as librarians have been used. However, there is no mention of efforts in relation to unpublished research, but abstracts of 950 articles weres of 950 articles were scanned, which appears to be a sufficient amount.
From organic waste to biodiesel : Black soldier fly , Hermetia illucens , makes it feasible

Biodiesel is a renewable and environmentally friendly liquid fuel. However, the feedstock, predominantly crop oil, is a limited and expensive food resource which prevents large scale application of biodiesel. Development of non-food feedstocks are therefore, needed to fully utilize biodiesel’s potential. In this study, the larvae of a high fat containing insect, black soldier fly (Hermetia illucens) (BSFL), was evaluated for biodiesel production. Specifically, the BSFL was grown on organic wastes for 10 days and used for crude fat extraction by petroleum ether. The extracted crude fat was then converted into biodiesel by acid-catalyzed (1% H2SO4) esterification and alkaline-catalyzed (0.8% NaOH) transesterification, resulting in 35.5 g, 57.8 g and 91.4 g of biodiesel being produced from 1000 BSFL growing on 1 kg of cattle manure, pig manure and chicken manure, respectively. The major ester components of the resulting biodiesel were lauric acid methyl ester (35.5%), oleinic acid methyl ester (23.6%) and palmitic acid methyl ester (14.8%). Fuel properties of the BSFL fat-based biodiesel, such as density (885 kg/m), viscosity (5.8 mm/s), ester content (97.2%), flash point (123 C), and cetane number (53) were comparable to those of rapeseed-oil-based biodiesel. These results demonstrated that the organic waste-grown BSFL could be a feasible non-food feedstock for biodiesel production.
Text line extraction for historical document images

0167-8655/$ see front matter 2013  Kafr Qarea, Israel. Fax: +972 4 6356168. E-mail addresses: saabni@cs.bgu.ac.il (R. Saabni), abedas@cs.bgu.ac.il (A. Asi), el-sana@cs.bgu.ac.il (J. El-Sana). 1 These authors contributed equally to this work. Raid Saabni a,b,⇑,1, Abedelkadir Asi , Jihad El-Sana c
DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System

Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.
Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use

This study aimed to examine smartphone use patterns, smartphone addiction characteristics, and the predictive factors of the smartphone addiction in middle school students in South Korea. According to the Smartphone Addiction Proneness Scale scores, 563 (30.9%) were classified as a risk group for smartphone addiction and 1261 (69.1%) were identified as a normal user group. The adolescents used mobile messengers for the longest, followed by Internet surfing, gaming, and social networking service use. The two groups showed significant differences in smartphone use duration, awareness of game overuse, and purposes of playing games. The predictive factors of smartphone addiction were daily smartphone and social networking service use duration, and the awareness of game overuse.
Effect of wheat grass juice in supportive care of terminally ill cancer patients- A tertiary cancer centre experience from India.

8634  The solid content of juice made from wheat grass is 70% chlorophyll. Chlorophyll is often referred to as "The blood of plant life" and has almost the same chemical structure as haemoglobin. Chlorophyll cleanses the blood by improving the supply of oxygen to the circulatory system. Wheat grass is also a complete protein with about 30 enzymes, vitamins & minerals. Wheat grass juice has been proven over many years to benefit people in numerous ways, building the blood, restoring balance in the body, removing toxic metals from the cells, nourishing the liver & kidneys and restoring vitality. The aim of our study was to see the effect of wheat grass juice in terminally ill cancer patients to improve the quality of life.



During period from January 2003 to December 2005 we selected 400 solid organ cancer patients in our palliative care unit of Netaji Subhash Chandra Bose Cancer Research Institute to see the effect of wheat grass on improvement of haemoglobin level, serum protein & performance status on terminally ill cancer patients. The age range of the patients was 22 year to 87 year (median age 42 years). The different types of cancers were Lung (25%), Breast (20%), Oesophagaus (11%), Colon (9%), Ovary (8%), Hepatocellular carcinoma (6%), Stomach (6%) and others (15%) respectively. We cultivated wheat grass in our campus. When the grasses were 5 days old we took the fresh leaves including roots and made fresh juice out of that and had given 30ml of juice to all our 400 cancer patients for continuous 6 months.



The mean levels of haemoglobin, Serum total protein, albumin and performance status were 8gm%, 5.4gm%, 2.2gm% and 50%. Fifty patients required transfusion support & those patients were excluded from the study. Other 348 patients are evaluated 6 months after giving wheat grass juice. The mean values for haemoglobin, total protein & albumin were improved significantly (pvalue < .005) and were observed mean of 9.6gm%, 7.4gm% and 3.1gm%. White blood cell & platelet count were same in both the cases. The performance status was improved from 50% to 70% (Karnofsky) after wheat grass treatment.



We concluded that wheat grass juice is an effective alternative of blood transfusion. Its use in terminally ill cancer patients should be encouraged. No significant financial relationships to disclose.
A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks

Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a model pre-trained on a source dataset can be transferred to a target dataset, resulting in the improvement of the target model. Though TL is found to be successful in the realm of imagebased applications, its impact and practical use in Natural Language Processing (NLP) applications is still a subject of research. Due to their hierarchical architecture, Deep Neural Networks (DNN) provide flexibility and customization in adjusting their parameters and depth of layers, thereby forming an apt area for exploiting the use of TL. In this paper, we report the results and conclusions obtained from extensive empirical experiments using a Convolutional Neural Network (CNN) and try to uncover thumb rules to ensure a successful positive transfer. In addition, we also highlight the flawed means that could lead to a negative transfer. We explore the transferability of various layers and describe the effect of varying hyper-parameters on the transfer performance. Also, we present a comparison of accuracy value and model size against state-of-the-art methods. Finally, we derive inferences from the empirical results and provide best practices to achieve a successful positive transfer.
Extracting Keyphrases from Research Papers Using Citation Networks

We propose CiteTextRank, a fully unsupervised graph-based algorithm that incorporates evidence from multiple sources (citation contexts as well as document content) in a flexible manner to extract keyphrases. General steps for algorithms for unsupervised keyphrase extraction: 1. Extract candidate words or lexical units from the textual content of the target document by applying stopword and parts-of-speech filters. 2. Score candidate words based on some criterion.
Gated Recursive Neural Network for Chinese Word Segmentation

Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this paper, we propose a gated recursive neural network (GRNN) for Chinese word segmentation, which contains reset and update gates to incorporate the complicated combinations of the context characters. Since GRNN is relative deep, we also use a supervised layer-wise training method to avoid the problem of gradient diffusion. Experiments on the benchmark datasets show that our model outperforms the previous neural network models as well as the state-of-the-art methods.
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training

Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multisupervised network training technique is developed to constrain the frequency domain information and reconstruction results at different levels. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.
PERFORMANCE EVALUATION OF MYSQL AND MONGODB DATABASES

A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. There are many databases commonly, relational and non relational databases. Relational databases usually work with structured data and non relational databases are work with semi structured data. In this paper, the performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational database and MongoDB is an example of non relational databases. A relational database (the concept) is a data structure that allows you to link information from different 'tables', or different types of data buckets. A non-relational database just stores data without explicit and structured mechanisms to link data from different buckets to one another.
Stability, continence and breathing: the role of fascia following pregnancy and delivery.

Pregnancy-related pelvic girdle pain (PRPGP) has a prevalence of approximately 45% during pregnancy and 20-25% in the early postpartum period. Most women become pain free in the first 12 weeks after delivery, however, 5-7% do not. In a large postpartum study of prevalence for urinary incontinence (UI) [Wilson, P.D., Herbison, P., Glazener, C., McGee, M., MacArthur, C., 2002. Obstetric practice and urinary incontinence 5-7 years after delivery. ICS Proceedings of the Neurourology and Urodynamics, vol. 21(4), pp. 284-300] found that 45% of women experienced UI at 7 years postpartum and that 27% who were initially incontinent in the early postpartum period regained continence, while 31% who were continent became incontinent. It is apparent that for some women, something happens during pregnancy and delivery that impacts the function of the abdominal canister either immediately, or over time. Current evidence suggests that the muscles and fascia of the lumbopelvic region play a significant role in musculoskeletal function as well as continence and respiration. The combined prevalence of lumbopelvic pain, incontinence and breathing disorders is slowly being understood. It is also clear that synergistic function of all trunk muscles is required for loads to be transferred effectively through the lumbopelvic region during multiple tasks of varying load, predictability and perceived threat. Optimal strategies for transferring loads will balance control of movement while maintaining optimal joint axes, maintain sufficient intra-abdominal pressure without compromising the organs (preserve continence, prevent prolapse or herniation) and support efficient respiration. Non-optimal strategies for posture, movement and/or breathing create failed load transfer which can lead to pain, incontinence and/or breathing disorders. Individual or combined impairments in multiple systems including the articular, neural, myofascial and/or visceral can lead to non-optimal strategies during single or multiple tasks. Biomechanical aspects of the myofascial piece of the clinical puzzle as it pertains to the abdominal canister during pregnancy and delivery, in particular trauma to the linea alba and endopelvic fascia and/or the consequence of postpartum non-optimal strategies for load transfer, is the focus of the first two parts of this paper. A possible physiological explanation for fascial changes secondary to altered breathing behaviour during pregnancy is presented in the third part. A case study will be presented at the end of this paper to illustrate the clinical reasoning necessary to discern whether conservative treatment or surgery is necessary for restoration of function of the abdominal canister in a woman with postpartum diastasis rectus abdominis (DRA).
An empirical study of the personality characteristics of internet sex offenders

The present study evaluated the personality characteristics and psychopathology of internet sex offenders (ISOs) using the Minnesota Multiphasic Personality Inventory, Second Edition (MMPI-2) to determine whether ISO personality profiles are different to those of general sex offenders (GSOs; e.g. child molesters and rapists). The ISOs consisted of 48 convicted males referred to a private sex offender treatment facility for a psychosexual risk assessment. The GSOs consisted of 104 incarcerated non-internet or general sex offenders. Findings indicated that ISOs scored significantly lower on the following scales: L, F, Pd and Sc. A comparison of the MMPI-2 scores of the ISO and GSO groups indicated that ISOs are a heterogeneous group with considerable withingroup differences. Current findings are consistent with the existing literature on the limited utility of the MMPI-2 in differentiating between subtypes of sex offenders.