nils-herrmann/clean_scirepeval huggingface dataset:
DeepAD: Alzheimer's Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

To extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer's disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer's and healthy brain data has been challenging in older adults (age 75) due to highly similar patterns of brain atrophy and image intensities. Recently, cutting edge deep learning technologies have rapidly expanded into numerous fields, including medical image analysis. This paper outlines state of the art deep learning based pipelines employed to distinguish Alzheimer's magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU based high performance computing platform, the data were strictly and carefully preprocessed. Next, scale and shift invariant low to high level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, fMRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer's disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output over other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, for clinical purposes, subject level classification was performed, resulting in an average accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipelines, respectively. Finally, a decision making algorithm designed for the subject level classification improved the rate to 97.77% for fMRI and 100% for MRI pipelines.
Content Selection in Data to Text Systems: A Survey
Venue: ArXiv

Data to text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data. Rather than presenting data using visualisation techniques, data to text systems use natural (human) language, which is the most common way for human human communication. In addition, data to text systems can adapt their output content to users' preferences, background or interests and therefore they can be pleasant for users to interact with. Content selection is an important part of every data to text system, because it is the module that determines which from the available information should be conveyed to the user. This survey initially introduces the field of data to text generation, describes the general data to text system architecture and then it reviews the state of the art content selection methods. Finally, it provides recommendations for choosing an approach and discusses opportunities for future research.
Smart Home Automation: A Literature Review

Home automation is becoming popular due to its numerous benefits. Home automation refers to the control of home appliances and domestic features by local networking or by remote control. Artificial Intelligence provides us the framework to go real time decision and automation for Internet of Things (IoT).The work deals with discussion about different intelligent home automation systems and technologies from a various features standpoint. The work focuses on concept of home automation where the monitoring and control operations are facilitating through smart devices installed in residential buildings. Heterogeneous homeautomation systems and technologies considered in review with central controller based (Arduino or Raspberry pi) web based, email based, Bluetooth based, mobile based, SMS based, ZigBee based, Dual Tone Multi Frequency based, cloud based and the Internet with performance.
End User Development Tools for the Smart Home: A Systematic Literature Review
Venue: HCI

This paper presents a systematic literature review in the Internet of Things and Ambient Intelligence areas. The goal was to identify the best software tools that allow end users, namely people without competencies in computer programming, to manage and configure the behaviors of a smart home. The review selected 48 papers out of 1049 papers found through automatic and manual search. From these papers, 11 tools have been identified and analyzed by means of eight technical characteristics. Finally, among the eleven tools, six tools have been chosen for a qualitative comparison on the basis of seven design principles for smart home control proposed in a literature paper.
A MRI CT prostate registration using sparse representation technique
Venue: SPIE Medical Imaging

To develop a new MRI CT prostate registration using patch based deformation prediction framework to improve MRI guided prostate radiotherapy by incorporating multiparametric MRI into planning CT images.  The main contribution is to estimate the deformation between prostate MRI and CT images in a patch wise fashion by using the sparse representation technique. We assume that two image patches should follow the same deformation if their patch wise appearance patterns are similar. Specifically, there are two stages in our proposed framework, i.e. the training stage and the application stage. In the training stage, each prostate MR images are carefully registered to the corresponding CT images and all training MR and CT images are carefully registered to a selected CT template. Thus, we obtain the dense deformation field for each training MR and CT image. In the application stage, for registering a new subject MR image with the same subject CT image, we first select a small number of key points at the distinctive regions of this subject CT image. Then, for each key point in the subject CT image, we extract the image patch, centered at the underlying key point. Then, we adaptively construct the coupled dictionary for the underlying point where each atom in the dictionary consists of image patches and the respective deformations obtained from training pair wise MRI CT images. Next, the subject image patch can be sparsely represented by a linear combination of training image patches in the dictionary, where we apply the same sparse coefficients to the respective deformations in the dictionary to predict the deformation for the subject MR image patch. After we repeat the same procedure for each subject CT key point, we use B splines to interpolate a dense deformation field, which is used as the initialization to allow the registration algorithm estimating the remaining small segment of deformations from MRI to CT image.  Our MRI CT registration technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using some identified landmarks in both MRI and CT images. Our proposed registration was compared with the current free form deformation (FFD) based registration method. The accuracy of the proposed method was significantly higher than the commonly used FFD based registration utilizing normalized mutual information (NMI)  We have developed a new prostate MR CT registration approach based on patch deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy with some identified landmarks. The proposed registration method may provide an accurate and robust means of estimating prostate gland deformation between MRI and CT scans, and is therefore well suited for a number of MR targeted CT based prostate radiotherapy.
Have Money and Credit Data Releases Helped Markets to Predict the Interest Rate Decisions of the European Central Bank?

This paper examines whether monetary data releases by the European Central Bank (ECB) have provided markets with additional clues about the future course of its monetary policy. It conducts a novel econometric approach based on a combination of an Ordered Probit model explaining future policy rate changes (sample 2000 to 2014) and the Vuong test for model selection. Overall, our results suggest that information contained in press releases on monetary developments for the euro area has helped markets in forming their expectations on the next monetary policy decision.
Determinants of Exchange Rate Risks in the Automotive Industry

The thesis details the analysis of foreign currency exposure determinants based on 21 companies in the automotive industry. The analysis confirms theoretical suggestions that the automotive industry is prone to foreign currency exposure and risks being influenced by competition intensity, functional currency, export ratio, geographic distribution of sales and production networks and operational flexibility. Analysis on company size and stock growth potential and volatility is inconclusive. The results are illustrated by a case study on Volvo Cars, Sweden. The combination of factors identified does not provide a clear explanation why some companies are more affected than others and does not allow for extrapolating economic risks in the long run. Asymmetric effects of foreign currency fluctuations on operational cash flows are deduced to result from differing hedging practices influenced by deliberate strategic moves and imperfect information. The review proposes a model of foreign currency exposure shaped by covariating currency risk determinants and hedging practices.
Fast Food Consumption in Children: A Review

Fast food refers to food that can be served ready to eat fast. Fast food and junk food are often used interchangeably. Energy dense food with high sugar/fat/salt content and low nutrient value in terms of protein, fiber, vitamin and mineral content is termed junk food. Many of our children are fond of such readymade food. Sponsorship of sports or cultural competitions with attractive gifts is the main way of promotion of first food sale. Nuclear families, working mother, socioeconomic status, close proximity of fast food shop, food test and quick service in the shop are important contributing factors of fast food consumption. This kind of food is responsible for obesity, hypertension, dyslipidemia, heart disease and diabetes. Easy availability of healthy food with reasonable prices along with its campaign, school midday food programme and health education can improve dietary habits of children. Implementation of laws for regulation of marketing and selling of fast food may be another step in controlling consumption of such food by our children.
Personalised computational cardiology: Patient specific modelling in cardiac mechanics and biomaterial injection therapies for myocardial infarction
Venue: Heart Failure Reviews

Predictive computational modelling in biomedical research offers the potential to integrate diverse data, uncover biological mechanisms that are not easily accessible through experimental methods and expose gaps in knowledge requiring further research. Recent developments in computing and diagnostic technologies have initiated the advancement of computational models in terms of complexity and specificity. Consequently, computational modelling can increasingly be utilised as enabling and complementing modality in the clinic with medical decisions and interventions being personalised. Myocardial infarction and heart failure are amongst the leading causes of death globally despite optimal modern treatment. The development of novel MI therapies is challenging and may be greatly facilitated through predictive modelling. Here, we review the advances in patient specific modelling of cardiac mechanics, distinguishing specificity in cardiac geometry, myofibre architecture and mechanical tissue properties. Thereafter, the focus narrows to the mechanics of the infarcted heart and treatment of myocardial infarction with particular attention on intramyocardial biomaterial delivery.
Geometrical aspects of patient specific modelling of the intervertebral disc: collagen fibre orientation and residual stress distribution
Venue: Biomechanics and modeling in mechanobiology

Patient specific modelling of the spine is a powerful tool to explore the prevention and the treatment of injuries and pathologies. Albeit several methods have been proposed for the discretization of the bony structures, the efficient representation of the intervertebral disc anisotropy remains a challenge, especially with complex geometries. Furthermore, the swelling of the disc's nucleus pulposus is normally added to the model after geometry definition, at the cost of changes of the material properties and an unrealistic description of the prestressed state. The aim of this study was to develop techniques, which preserve the patient specific geometry of the disc and allow the representation of the system anisotropy and residual stresses, independent of the system discretization. Depending on the modelling features, the developed approaches resulted in a response of patient specific models that was in good agreement with the physiological response observed in corresponding experiments. The proposed methods represent a first step towards the development of patient specific models of the disc which respect both the geometry and the mechanical properties of the specific disc.
An original institutionalist approach to the structure, conduct, and performance of the pharmaceutical industry: The importance of intangible assets

This dissertation presents an examination of the pharmaceutical industry with a primary focus on the importance of intangible assets from the original institutional economics perspective. This is done in three main chapters. The first main chapter examines the importance of intangible assets within the context of the heterodox theory of the business enterprise. It is argued that in the early stage of the going concern where production is separate from consumption intangible assets necessitate the creation of monetary bargaining transactions. In industrial capitalism with the internal separation of the enterprise into industrial and pecuniary divisions intangible assets take the form of rationing transactions, limiting the number of sellers of a particular product. As the enterprise then evolves into its modern joint stock form in money manager capitalism, intangible assets become the basis for capitalization, upon which incorporeal property may be issued, increasing the return to shareholders. The second main chapter discusses the structure and performance of the pharmaceutical industry. Based on the concept of centralized private sector planning
The Akt/mTOR/p70S6K Pathway Is Involved in the Neuroprotective Effect of Erythropoietin on Hypoxic/Ischemic Brain Injury in a Neonatal Rat Model
Venue: Neonatology

The mTOR (mammalian target of rapamycin) signaling pathway is a master regulator of cell growth and proliferation in the nervous system. However, the effects of erythropoietin (EPO) treatment on the mTOR signaling pathway have not been elucidated in neonates with hypoxic/ischemic (H/I) brain injury.  We investigated the mechanism underlying the neuroprotective effect of EPO by analyzing the mTOR signaling pathway after H/I injury in a neonatal rat model.  Seven day old rats were subjected to left carotid artery ligation and hypoxic exposure (8% for 90 min (H/I) EPO at a dose of either 3,000 U/kg or a vehicle (V) was administered by intraperitoneal injection 0, 24 and 48 h after H/I. At 72 h after H/I (postnatal day 10) 2,3,5 triphenyltetrazolium chloride staining, myelin basic protein (MBP) immunofluorescence staining and Western blot analysis of the Akt/mTOR/p70S6K pathway were performed. Neuromotor behavioral tests included Rotarod challenge and cylinder rearing test 1 performed 3 and 6 weeks after H/I.  EPO treatment resulted in significant offsetting of MBP depletion ipsilateral (p 0.001) and contralateral (p 0.003) to ligation. Western blot analysis showed that the relative immunoreactivity of phosphorylated (p) Akt, p mTOR and p p70S6K ipsilateral to ligation was significantly decreased in the H/I+V group compared with the sham operated groups. However, EPO treatment significantly upregulated Akt/mTOR/p70S6K signals ipsilateral to ligation compared to the H/I+V group. The behavior tests showed that EPO attenuates long term impairment in Rotarod challenge and cylinder test performance from 3 6 weeks.  This study demonstrates an underlying mechanism of the mTOR signaling pathway after EPO treatment, which is a potential target for treating H/I induced brain injury.
Teacher pay and school productivity: Exploiting wage regulation

The impact of teacher pay on school productivity is a central concern for governments worldwide, yet evidence is mixed. In this paper we exploit a feature of teacher labour markets to determine the impact of teacher wages. Teacher wages are commonly set in a manner that results in flat wages across heterogeneous labour markets. This creates an exogenous gap between the outside labour market and inside (regulated) wage for teachers. We use the centralised wage regulation of teachers in England to examine the effect of pay on school performance. We use data on over 3000 schools containing around 200,000 teachers who educate around half a million children per year. We find that teachers respond to pay. A ten percent shock to the wage gap between local labour market and teacher wages results in an average loss of around 2% in average school performance in the key exams taken at the end of compulsory schooling in England.
Study of Internet of Things (IoT) A Vision, Architectural Elements, and Future Directions

Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating actuating network creates the Internet of Things (IoT) wherein, sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP) Fuelled by the recent adaptation of a variety of enabling wireless technologies such as RFID tags and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web) the need for data on demand using sophisticated intuitive queries increases significantly. This paper presents a Cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A Cloud implementation using Aneka, which is based on interaction of private and public Clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
A Secure and Efficient Off line Electronic Payment System for Wireless Networks

An electronic cash system allows the exchange of digital coins with value assured by the bank's signature and with concealed user identity. In an electronic cash system, a user can withdraw coins from the bank and then spends each coin anonymously and unlinkably. In this paper we propose a secure and efficient off line electronic payment system based on bilinear pairings and group signature schemes. The anonymity of the customer is revocable by a trustee in case of a dispute. Because the amount of communication in the payment protocol is about 480 bits, the proposed off line electronic payment system can be used in wireless networks with limited bandwidth.
Brief review of uncertainty quantification for particle image velocimetry

Metrological studies for particle image velocimetry (PIV) are recent in literature. An attempt to evaluate the uncertainty quantifications (UQ) of the PIV velocity field are in evidence. Therefore, a short review on main sources of uncertainty in PIV and available methodologies for its quantification are presented. In addition, the potential of some mathematical techniques, coming from the area of geometric mechanics and control, that could interest the fluids UQ community are highlighted as good possibilities. "We must measure what is measurable and make measurable what cannot be measured" (Galileo)
Negative Compliance as an Organizational Response to Legal Pressures: The Case of Japanese Equal Employment Opportunity Law

Scholars have conceptualized the interaction between law and organizations as a game of tug of war in which organizations try to protect those internal operations regulated by law. This suggests that organizations are likely to substantively comply if symbolic compliance alone is insufficient. This conceptualization, however, cannot capture a radical response: organizations may stop interpreting legal demands or protecting their internal operations and simply leave the game. In this case, they change their internal operations in order to disengage from the law. I term this negative compliance. In this paper, I identify conditions under which this occurs and demonstrate the unexpected consequences. Analyzing panel data on corporate responses to Japan's equal employment opportunity law and its revision, I show that after the revision strengthened the law, firms followed one of three paths: substantive compliance (hiring more women) symbolic compliance (no change in hiring practices, effectively protecting sex segregation) or negative compliance (externalizing female labor and hiring even fewer women) I discuss the implications of negative compliance, which is likely to be an increasingly common corporate response to regulations.
Matrix and Tensor Based Methods for Missing Data Estimation in Large Traffic Networks
Venue: IEEE Transactions on Intelligent Transportation Systems

Intelligent transportation systems (ITSs) gather information about traffic conditions by collecting data from a wide range of on ground sensors. The collected data usually suffer from irregular spatial and temporal resolution. Consequently, missing data is a common problem faced by ITSs. In this paper, we consider the problem of missing data in large and diverse road networks. We propose various matrix and tensor based methods to estimate these missing values by extracting common traffic patterns in large road networks. To obtain these traffic patterns in the presence of missing data, we apply fixed point continuation with approximate singular value decomposition, canonical polyadic decomposition, least squares, and variational Bayesian principal component analysis. For analysis, we consider different road networks, each of which is composed of around 1500 road segments. We evaluate the performance of these methods in terms of estimation accuracy, variance of the data set, and the bias imparted by these methods.