nils-herrmann/clean_scirepeval huggingface dataset:
Cardiovascular effects of exercise training: molecular mechanisms.

In the natural habitat of our ancestors, physical activity was not a preventive intervention but a matter of survival. In this hostile environment with scarce food and ubiquitous dangers, human genes were selected to optimize aerobic metabolic pathways and conserve energy for potential future  Cardiac and vascular functions were continuously challenged by intermittent bouts of high-intensity physical activity and adapted to meet the metabolic demands of the working skeletal muscle under these conditions. When speaking about molecular cardiovascular effects of exercise, we should keep in mind that most of the changes from baseline are probably a return to normal values. The statistical average of physical activity in Western societies is so much below the levels normal for our genetic background that sedentary lifestyle in combination with excess food intake has surpassed smoking as the No. 1 preventable cause of death in the United States.2 Physical activity has been shown to have beneficial effects on glucose metabolism, skeletal muscle function, ventilator muscle strength, bone stability, locomotor coordination, psychological well-being, and other organ functions. However, in the context of this review, we will focus entirely on important molecular effects on the cardiovascular system. The aim of this review is to provide a bird’s-eye view on what is known and unknown about the physiological and biochemical mechanisms involved in mediating exercise-induced cardiovascular effects. The resulting map is surprisingly detailed in some areas (ie, endothelial function), whereas other areas, such as direct cardiac training effects in heart failure, are still incompletely understood. For practical purposes, we have decided to use primarily an anatomic approach to present key data on exercise effects on cardiac and vascular function. For the cardiac effects, the left ventricle and the cardiac valves will be described separately; for the vascular effects, we will follow the arterial vascular tree, addressing changes in the aorta, the large conduit arteries, the resistance vessels, and the microcirculation before turning our attention toward the venous and the pulmonary circulation . Cardiac Effects of Exercise Left Ventricular Myocardium and Ventricular Arrhythmias The maintenance of left ventricular (LV) mass and function depends on regular exercise. Prolonged periods of physical inactivity, as studied in bed rest trials, lead to significant reductions in LV mass and impaired cardiac compliance, resulting in reduced upright stroke volume and orthostatic intolerance.3 In contrast, a group of bed rest subjects randomized to regular supine lower-body negative pressure treadmill exercise showed an increase in LV mass and a preserved LV stoke  In previously sedentary healthy subjects, a 12-week moderate exercise program induced a mild cardiac hypertrophic response as measured by cardiac magnetic resonance imaging.5 These findings highlight the plasticity of LV mass and function in relation to the current level of physical activity.
X-Ray Testing by Computer Vision

X-ray imaging has been developed not only for its use in medical imaging for human beings, but also for materials or objects, where the aim is to analyze (nondestructively) those inner parts that are undetectable to the naked eye. Thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are analysis of food products, screening of baggage, inspection of automotive parts, and quality control of welds. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems are being developed to execute this task. In this paper, we present a general overview of computer vision methodologies that have been used in X-ray testing. In addition, we review some techniques that have been applied in certain relevant applications, and we introduce a public database of X-ray images that can be used for testing and evaluation of image analysis and computer vision algorithms. Finally, we conclude that the following: that there are some areas -like casting inspection- where automated systems are very effective, and other application areas -such as baggage screening- where human inspection is still used, there are certain application areas -like weld and cargo inspections- where the process is semi-automatic, and there is some research in areas -including food analysis- where processes are beginning to be characterized by the use of X-ray imaging.
A blockchain framework for smart mobility

A blockchain framework is presented for addressing the privacy and security challenges associated with the Big Data in smart mobility. It is composed of individuals, companies, government and universities where all the participants collect, own, and control their data. Each participant shares their encrypted data to the blockchain network and can make information transactions with other participants as long as both party agrees to the transaction rules (smart contract) issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility Big Data.
A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification

In this paper, we propose a joint segmentation and classification framework for sentence-level sentiment classification. It is widely recognized that phrasal information is crucial for sentiment classification. However, existing sentiment classification algorithms typically split a sentence as a word sequence, which does not effectively handle the inconsistent sentiment polarity between a phrase and the words it contains, such as {"not bad," "bad"} and {"a great deal of," "great"}. We address this issue by developing a joint framework for sentence-level sentiment classification. It simultaneously generates useful segmentations and predicts sentence-level polarity based on the segmentation results. Specifically, we develop a candidate generation model to produce segmentation candidates of a sentence; a segmentation ranking model to score the usefulness of a segmentation candidate for sentiment classification; and a classification model for predicting the sentiment polarity of a segmentation. We train the joint framework directly from sentences annotated with only sentiment polarity, without using any syntactic or sentiment annotations in segmentation level. We conduct experiments for sentiment classification on two benchmark datasets: a tweet dataset and a review dataset. Experimental results show that: 1) our method performs comparably with state-of-the-art methods on both datasets; 2) joint modeling segmentation and classification outperforms pipelined baseline methods in various experimental settings.
Understanding the Great Gatsby Curve

The Great Gatsby Curve, the observation that for OECD countries, greater crosssectional income inequality is associated with lower mobility, has become a prominent part of scholarly and policy discussions because of its implications for the relationship between inequality of outcomes and inequality of opportunities. We explore this relationship by focusing on evidence and interpretation of an intertemporal Gatsby Curve for the United States. We consider inequality/mobility relationships that are derived from nonlinearities in the transmission process of income from parents to children and the relationship that is derived from the effects of inequality of socioeconomic segregation, which then affects children. Empirical evidence for the mechanisms we identify is strong. We find modest reduced form evidence and structural evidence of an intertemporal Gatsby Curve for the US as mediated by social influences. Steven N. Durlauf Ananth Seshadri Department of Economics Department of Economics University of Wisconsin University of Wisconsin 1180 Observatory Drive 1180 Observatory Drive Madison WI, 53706 Madison WI, 53706 durlauf@gmail.com aseshadr@ssc.wisc.edu
The FFX Mode of Operation for Format-Preserving Encryption Draft 1 . 1

Schemes for FPE enable one to encrypt Social Security numbers (SSNs), credit card numbers (CCNs), and the like, doing so in such a way that the ciphertext has the same format as the plaintext. In the case of SSNs, for example, this means that the ciphertext, like the plaintext, consists of a nine decimal-digit string. Similarly, encryption of a 16-digit CCN results in a 16-digit ciphertext. FPE is rapidly emerging as a useful cryptographic tool, with applications including financial-information security, data sanitization, and transparently encrypting fields in a legacy database.
Prophenoloxidase system and its role in shrimp immune responses against major pathogens.

The global shrimp industry still faces various serious disease-related problems that are mainly caused by pathogenic bacteria and viruses. Understanding the host defense mechanisms is likely to be beneficial in designing and implementing effective strategies to solve the current and future pathogen-related problems. Melanization, which is performed by phenoloxidase (PO) and controlled by the prophenoloxidase (proPO) activation cascade, plays an important role in the invertebrate immune system in allowing a rapid response to pathogen infection. The activation of the proPO system, by the specific recognition of microorganisms by pattern-recognition proteins (PRPs), triggers a serine proteinase cascade, eventually leading to the cleavage of the inactive proPO to the active PO that functions to produce the melanin and toxic reactive intermediates against invading pathogens. This review highlights the recent discoveries of the critical roles of the proPO system in the shrimp immune responses against major pathogens, and emphasizes the functional characterizations of four major groups of genes and proteins in the proPO cascade in penaeid shrimp, that is the PRPs, serine proteinases, proPO and inhibitors.
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2). The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data . Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid. Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing. Our extensive experimental analysis shows that our reconstruction framework i) outperforms the state-of-theart methods for single view reconstruction, and ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).
White Paper: Toward a National Framework for the Secondary Use of Health Data: An American Medical Informatics Association White Paper

Secondary use of health data applies personal health information (PHI) for uses outside of direct health care delivery. It includes such activities as analysis, research, quality and safety measurement, public health, payment, provider certification or accreditation, marketing, and other business applications, including strictly commercial activities. Secondary use of health data can enhance health care experiences for individuals, expand knowledge about disease and appropriate treatments, strengthen understanding about effectiveness and efficiency of health care systems, support public health and security goals, and aid businesses in meeting customers' needs. Yet, complex ethical, political, technical, and social issues surround the secondary use of health data. While not new, these issues play increasingly critical and complex roles given current public and private sector activities not only expanding health data volume, but also improving access to data. Lack of coherent policies and standard "good practices" for secondary use of health data impedes efforts to strengthen the U.S. health care system. The nation requires a framework for the secondary use of health data with a robust infrastructure of policies, standards, and best practices. Such a framework can guide and facilitate widespread collection, storage, aggregation, linkage, and transmission of health data. The framework will provide appropriate protections for legitimate secondary use.
Themes-based classification for Al-Quran knowledge ontology

Al-Quran knowledge representations involved classification of Al-Quran verses for providing better understanding of the readers. In the current era of social media challenges, the representation of knowledge must be understood by human and computer in order to ensure the correctness of Al-Quran semantics are persevered. Current approaches used conventional methods such as taxonomy, hierarchy or tree structure, which only provides a concept definition without linked to other sources of knowledge explanation. This research aims to develop the Al-Quran Ontology by using theme-based classification approach. The ontology model for Al-Quran is developed based on the Al-Quran knowledge theme defined in Syammil Al-Quran Miracle the Reference. The theme-based ontology approach has shown that the Al-Quran knowledge can be classified and presented systematically. This will encourage the development of applications for Al-Quran readers. Moreover, the ontology structure that representing the theme concepts in Al-Quran was reviewed and validated by the domain experts in Al-Quran knowledge.
Privacy Preserving Data Mining

In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with confidential patient records. Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more efficient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using efficient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048 Ã— 1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.
Breast cancer classification using deep belief networks

Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN). Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD). The classifier complex gives an accuracy of 99.68% indicating promising results over previously-published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train-test partitions.
Supercloud: A Library Cloud for Exploiting Cloud Diversity

Infrastructure-as-a-Service (IaaS) cloud providers hide available interfaces for virtual machine (VM) placement and migration, CPU capping, memory ballooning, page sharing, and I/O throttling, limiting the ways in which applications can optimally configure resources or respond to dynamically shifting workloads. Given these interfaces, applications could migrate VMs in response to diurnal workloads or changing prices, adjust resources in response to load changes, and so on. This article proposes a new abstraction that we call a Library Cloud and that allows users to customize the diverse available cloud resources to best serve their applications.
 We built a prototype of a Library Cloud that we call the Supercloud. The Supercloud encapsulates applications in a virtual cloud under users’ full control and can incorporate one or more availability zones within a cloud provider or across different providers. The Supercloud provides virtual machine, storage, and networking complete with a full set of management operations, allowing applications to optimize performance. In this article, we demonstrate various innovations enabled by the Library Cloud.
Learning principled bilingual mappings of word embeddings while preserving monolingual invariance

Mapping word embeddings of different languages into a single space has multiple applications. In order to map from a source space into a target space, a common approach is to learn a linear mapping that minimizes the distances between equivalences listed in a bilingual dictionary. In this paper, we propose a framework that generalizes previous work, provides an efficient exact method to learn the optimal linear transformation and yields the best bilingual results in translation induction while preserving monolingual performance in an analogy task.
Application of the Health Belief Model (HBM) in HIV Prevention: A Literature Review

As part of a study that explored the factors associated with consistent condom use among senior secondary school female learners in Mbonge subdivision of rural Cameroon, the Health Belief Model (HBM) was used as the framework. Literature was reviewed to ascertain how the entire HBM has been defined and what recommendations have been made as to how to apply the HBM as a framework in studies regarding HIV/AIDS prevention. To achieve this, a systemic review of literature was undertaken. Electronic databases, academic journals and books from various sources were accessed. Several key search terms relating to the HBM and HIV/AIDS prevention were used. Only references deemed useful for bibliographies of relevant texts and journal articles were included. The inclusion criteria were articles that provided information about HIV/AIDS prevention and the HBM constructs. Six constructs of the HBM (perceived susceptibility to HIV/AIDS, perceived severity of HIV/AIDS, perceived benefit of condom use, perceived barriers to condom use, cues to action for condom use and condom use self-efficacy), and modifying factors were identified and applied as the framework for the study. The HBM was identified as the most commonly used theory in health education, health promotion and disease prevention, and thus provided the framework for the study. The underlying concept of the HBM is that behaviour is determined by personal beliefs or perceptions about a disease and the strategies available to decrease its occurrence.
Jointly Learning to Extract and Compress

We learn a joint model of sentence extraction and compression for multi-document summarization. Our model scores candidate summaries according to a combined linear model whose features factor over (1) the n-gram types in the summary and (2) the compressions used. We train the model using a marginbased objective whose loss captures end summary quality. Because of the exponentially large set of candidate summaries, we use a cutting-plane algorithm to incrementally detect and add active constraints efficiently. Inference in our model can be cast as an ILP and thereby solved in reasonable time; we also present a fast approximation scheme which achieves similar performance. Our jointly extracted and compressed summaries outperform both unlearned baselines and our learned extraction-only system on both ROUGE and Pyramid, without a drop in judged linguistic quality. We achieve the highest published ROUGE results to date on the TAC 2008 data set.
Spotting fake reviewer groups in consumer reviews

Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or demote some target products. For reviews to reflect genuine user experiences and opinions, such spam reviews should be detected. Prior works on opinion spam focused on detecting fake reviews and individual fake reviewers. However, a fake reviewer group (a group of reviewers who work collaboratively to write fake reviews) is even more damaging as they can take total control of the sentiment on the target product due to its size. This paper studies spam detection in the collaborative setting, i.e., to discover fake reviewer groups. The proposed method first uses a frequent itemset mining method to find a set of candidate groups. It then uses several behavioral models derived from the collusion phenomenon among fake reviewers and relation models based on the relationships among groups, individual reviewers, and products they reviewed to detect fake reviewer groups. Additionally, we also built a labeled dataset of fake reviewer groups. Although labeling individual fake reviews and reviewers is very hard, to our surprise labeling fake reviewer groups is much easier. We also note that the proposed technique departs from the traditional supervised learning approach for spam detection because of the inherent nature of our problem which makes the classic supervised learning approach less effective. Experimental results show that the proposed method outperforms multiple strong baselines including the state-of-the-art supervised classification, regression, and learning to rank algorithms.