nils-herrmann/clean_scirepeval huggingface dataset:
Time-Resolved Imaging of Single HIV-1 Uncoating In Vitro and in Living Cells

Disassembly of the cone-shaped HIV-1 capsid in target cells is a prerequisite for establishing a life-long infection. This step in HIV-1 entry, referred to as uncoating, is critical yet poorly understood. Here we report a novel strategy to visualize HIV-1 uncoating using a fluorescently tagged oligomeric form of a capsid-binding host protein cyclophilin A (CypA-DsRed), which is specifically packaged into virions through the high-avidity binding to capsid (CA). Single virus imaging reveals that CypA-DsRed remains associated with cores after permeabilization/removal of the viral membrane and that CypA-DsRed and CA are lost concomitantly from the cores in vitro and in living cells. The rate of loss is modulated by the core stability and is accelerated upon the initiation of reverse transcription. We show that the majority of single cores lose CypA-DsRed shortly after viral fusion, while a small fraction remains intact for several hours. Single particle tracking at late times post-infection reveals a gradual loss of CypA-DsRed which is dependent on reverse transcription. Uncoating occurs both in the cytoplasm and at the nuclear membrane. Our novel imaging assay thus enables time-resolved visualization of single HIV-1 uncoating in living cells, and reveals the previously unappreciated spatio-temporal features of this incompletely understood process.
Does competition improve financial stability of the banking sector in ASEAN countries? An empirical analysis

This study examines the influence of competition on the financial stability of the commercial banks of Association of Southeast Asian Nation (ASEAN) over the 1990 to 2014 period. Panzar-Rosse H-statistic, Lerner index and Herfindahl-Hirschman Index (HHI) are used as measures of competition, while Z-score, non-performing loan (NPL) ratio and equity ratio are used as measures of financial stability. Two-step system Generalized Method of Moments (GMM) estimates demonstrate that competition measured by H-statistic is positively related to Z-score and equity ratio, and negatively related to non-performing loan ratio. Conversely, market power measured by Lerner index is negatively related to Z-score and equity ratio and positively related to NPL ratio. These results strongly support the competition-stability view for ASEAN banks. We also capture the non-linear relationship between competition and financial stability by incorporating a quadratic term of competition in our models. The results show that the coefficient of the quadratic term of H-statistic is negative for the Z-score model given a positive coefficient of the linear term in the same model. These results support the non-linear relationship between competition and financial stability of the banking sector. The study contains significant policy implications for improving the financial stability of the commercial banks.
Motivation as an independent and a dependent variable in medical education: a review of the literature.

Motivation in learning behaviour and education is well-researched in general education, but less in medical education.



To answer two research questions, 'How has the literature studied motivation as either an independent or dependent variable? How is motivation useful in predicting and understanding processes and outcomes in medical education?' in the light of the Self-determination Theory (SDT) of motivation.



A literature search performed using the PubMed, PsycINFO and ERIC databases resulted in 460 articles. The inclusion criteria were empirical research, specific measurement of motivation and qualitative research studies which had well-designed methodology. Only studies related to medical students/school were included.



Findings of 56 articles were included in the review. Motivation as an independent variable appears to affect learning and study behaviour, academic performance, choice of medicine and specialty within medicine and intention to continue medical study. Motivation as a dependent variable appears to be affected by age, gender, ethnicity, socioeconomic status, personality, year of medical curriculum and teacher and peer support, all of which cannot be manipulated by medical educators. Motivation is also affected by factors that can be influenced, among which are, autonomy, competence and relatedness, which have been described as the basic psychological needs important for intrinsic motivation according to SDT.



Motivation is an independent variable in medical education influencing important outcomes and is also a dependent variable influenced by autonomy, competence and relatedness. This review finds some evidence in support of the validity of SDT in medical education.
Untrusted Business Process Monitoring and Execution Using Blockchain

The integration of business processes across organizations is typically beneficial for all involved parties. However, the lack of trust is often a roadblock. Blockchain is an emerging technology for decentralized and transactional data sharing across a network of untrusted participants. It can be used to find agreement about the shared state of collaborating parties without trusting a central authority or any particular participant. Some blockchain networks also provide a computational infrastructure to run autonomous programs called smart contracts. In this paper, we address the fundamental problem of trust in collaborative process execution using blockchain. We develop a technique to integrate blockchain into the choreography of processes in such a way that no central authority is needed, but trust maintained. Our solution comprises the combination of an intricate set of components, which allow monitoring or coordination of business processes. We implemented our solution and demonstrate its feasibility by applying it to three use case processes. Our evaluation includes the creation of more than 500 smart contracts and the execution over 8,000 blockchain transactions.
Personalisation How a computer can know you better than yourself

Every time you go to one of the top 100 book/music e-commerce sites, you will come into contact with personalisation systems that attempt to judge your interests to increase sales. There are 3 methods for making these personalised recommendations: Content-based filtering, Collaborative filtering and a hybrid of the two. Understanding each of these methods will give you insight as to how your personal information is used on the Internet, and remove some of the mystery associated with the systems. This will allow you understand how these systems work and how they could be improved, so as to make an informed decision as to whether this is a good thing.
Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models

Exponential growth in the number of scientific publications yields the need for effective automatic analysis of rhetorical aspects of scientific writing. Acknowledging the argumentative nature of scientific text, in this work we investigate the link between the argumentative structure of scientific publications and rhetorical aspects such as discourse categories or citation contexts. To this end, we (1) augment a corpus of scientific publications annotated with four layers of rhetoric annotations with argumentation annotations and (2) investigate neural multi-task learning architectures combining argument extraction with a set of rhetorical classification tasks. By coupling rhetorical classifiers with the extraction of argumentative components in a joint multi-task learning setting, we obtain significant performance gains for different rhetorical analysis tasks.
Mobile Edge Cloud System: Architectures, Challenges, and Approaches

Mobile edge cloud (MEC) is a model for enabling on-demand elastic access to, or an interaction with a shared pool of reconfigurable computing resources such as servers, storage, peer devices, applications, and services, at the edge of the wireless network in close proximity to mobile users. It overcomes some obstacles of traditional central clouds by offering wireless network information and local context awareness as well as low latency and bandwidth conservation. This paper presents a comprehensive survey of MEC systems, including the concept, architectures, and technical enablers. First, the MEC applications are explored and classified based on different criteria, the service models and deployment scenarios are reviewed and categorized, and the factors influencing the MEC system design are discussed. Then, the architectures and designs of MEC systems are surveyed, and the technical issues, existing solutions, and approaches are presented. The open challenges and future research directions of MEC are further discussed.
Strong sum distance in fuzzy graphs

In this paper the idea of strong sum distance which is a metric, in a fuzzy graph is introduced. Based on this metric the concepts of eccentricity, radius, diameter, center and self centered fuzzy graphs are studied. Some properties of eccentric nodes, peripheral nodes and central nodes are obtained. A characterisation of self centered complete fuzzy graph is obtained and conditions under which a fuzzy cycle is self centered are established. We have proved that based on this metric, an eccentric node of a fuzzy tree G is a fuzzy end node of G and a node is an eccentric node of a fuzzy tree if and only if it is a peripheral node of G and the center of a fuzzy tree consists of either one or two neighboring nodes. The concepts of boundary nodes and interior nodes in a fuzzy graph based on strong sum distance are introduced. Some properties of boundary nodes, interior nodes and complete nodes are studied.
The AIP Model of EMDR Therapy and Pathogenic Memories

Eye Movement Desensitization and Reprocessing (EMDR) therapy has been widely recognized as an efficacious treatment for post-traumatic stress disorder (PTSD). In the last years more insight has been gained regarding the efficacy of EMDR therapy in a broad field of mental disorders beyond PTSD. The cornerstone of EMDR therapy is its unique model of pathogenesis and change: the adaptive information processing (AIP) model. The AIP model developed by F. Shapiro has found support and differentiation in recent studies on the importance of memories in the pathogenesis of a range of mental disorders beside PTSD. However, theoretical publications or research on the application of the AIP model are still rare. The increasing acceptance of ideas that relate the origin of many mental disorders to the formation and consolidation of implicit dysfunctional memory lead to formation of the theory of pathogenic memories. Within the theory of pathogenic memories these implicit dysfunctional memories are considered to form basis of a variety of mental disorders. The theory of pathogenic memories seems compatible to the AIP model of EMDR therapy, which offers strategies to effectively access and transmute these memories leading to amelioration or resolution of symptoms. Merging the AIP model with the theory of pathogenic memories may initiate research. In consequence, patients suffering from such memory-based disorders may be earlier diagnosed and treated more effectively.
FLEXION OSTEOTOMY OF THE FEMUR FOR GENU RECURVATUM

We report the results of 21 femoral osteotomies performed in 18 patients for genu recurvatum and flattening of the femoral condyles alter poiomyelltis. Before operation the average angle of recurvatum was 31#{176} and all the limbs required bracing. After a mean follow-up of four years there has been partial recurrence in only one case. Nine patients (10 limbs) needed no orthosis and the others had less discomfort and an improved gait. Complete remodelling ofthe femoral and tibial epiphyses was noted in two of the younger patients.
Mindfulness, self-compassion, and happiness in non-meditators: A theoretical and empirical examination

This study examined relationships between mindfulness and indices of happiness and explored a fivefactor model of mindfulness. Previous research using this mindfulness model has shown that several facets predicted psychological well-being (PWB) in meditating and non-meditating individuals. The current study tested the hypothesis that the prediction of PWB by mindfulness would be augmented and partially mediated by self-compassion. Participants were 27 men and 96 women (mean age = 20.9 years). All completed self-report measures of mindfulness, PWB, personality traits (NEO-PI-R), and self-compassion. Results show that mindfulness is related to psychologically adaptive variables and that self-compassion is a crucial attitudinal factor in the mindfulness–happiness relationship. Findings are interpreted from the humanistic perspective of a healthy personality.
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific "handcrafted" features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial.



This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches.



Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation (F-score of 0.83 across 12,000 nuclei), (b) epithelium segmentation (F-score of 0.84 across 1735 regions), (c) tubule segmentation (F-score of 0.83 from 795 tubules), (d) lymphocyte detection (F-score of 0.90 across 3064 lymphocytes), (e) mitosis detection (F-score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection (F-score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images).



This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data.
A Seismic Strengthening Technique for Reinforced Concrete Columns Using Sprayed FRP

Conventional methods for seismic retrofitting of concrete columns include reinforcement with steel plates or steel frame braces, as well as cross-sectional increments and in-filled walls. However, these methods have some disadvantages, such as the increase in mass and the need for precise construction. Fiber-reinforced polymer (FRP) sheets for seismic strengthening of concrete columns using new light-weight composite materials, such as carbon fiber or glass fiber, have been developed, have excellent durability and performance, and are being widely applied to overcome the shortcomings of conventional seismic strengthening methods. Nonetheless, the FRP-sheet reinforcement method also has some drawbacks, such as the need for prior surface treatment, problems at joints, and relatively expensive material costs. In the current research, the structural and material properties associated with a new method for seismic strengthening of concrete columns using FRP were investigated. The new technique is a sprayed FRP system, achieved by mixing chopped glass and carbon fibers with epoxy and vinyl ester resin in the open air and randomly spraying the resulting mixture onto the uneven surface of the concrete columns. This paper reports on the seismic resistance of reinforced concrete columns controlled by shear strengthening using the sprayed FRP system. Five shear column specimens were designed, and then strengthened with sprayed FRP by using different combinations of short carbon or glass fibers and epoxy or vinyl ester resins. There was also a non-strengthened control specimen. Cyclic loading tests were carried out, and the ultimate load carrying capacity and deformation were investigated, as well as hysteresis in the lateral load-drift relationship. The results showed that shear strengths and deformation capacities of shear columns strengthened using sprayed FRP improved markedly, compared with those of the control column. The spraying FRP technique developed in this study can be practically and effectively used for the seismic strengthening of existing concrete columns.
Exploiting Linguistic Features for Sentence Completion

This paper presents a novel approach to automated sentence completion based on pointwise mutual information (PMI). Feature sets are created by fusing the various types of input provided to other classes of language models, ultimately allowing multiple sources of both local and distant information to be considered. Furthermore, it is shown that additional precision gains may be achieved by incorporating feature sets of higher-order n-grams. Experimental results demonstrate that the PMI model outperforms all prior models and establishes a new state-of-the-art result on the Microsoft Research Sentence Completion Challenge.
Feature Selection for Classification

Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a comprehensive overview of many existing methods from the 1970’s to the present. It identifies four steps of a typical feature selection method, and categorizes the different existing methods in terms of generation procedures and evaluation functions, and reveals hitherto unattempted combinations of generation procedures and evaluation functions. Representative methods are chosen from each category for detailed explanation and discussion via example. Benchmark datasets with different characteristics are used for comparative study. The strengths and weaknesses of different methods are explained. Guidelines for applying feature selection methods are given based on data types and domain characteristics. This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications.
An Efficient Weather Forecasting System using Radial Basis Function Neural Network

Accurate weather forecasting plays a vital role fo r planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting.  A neural network model has been developed for weat her forecasting, based on various factors obtained from meteorological expert s. This study evaluates the performance of Radial Basis Function (RBF) with Back Propagation (BPN) ne ural network. The back propagation neural network and radial basis function neural network we re used to test the performance in order to investigate effective forecasting technique.  The prediction accuracy of RBF was 88.49%.  The results indicate that proposed radial basis fu nction neural network is better than back propagation neural network.
Deep Graph Kernels

In this paper, we present Deep Graph Kernels, a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.
Potential Health Benefits of Garlic ( Allium Sativum): A Narrative Review

A member of the Liliaceae family, garlic ( Allium sativum) is highly regarded throughout the world for both its medicinal and culinary value. Early men of medicine such as Hippocrates, Pliny and Aristotle encouraged a number of therapeutic uses for this botanical. Today, it is commonly used in many cultures as a seasoning or spice. Garlic also stands as the second most utilized supplement. With its sulfur containing compounds, high trace mineral content, and enzymes, garlic has shown anti-viral, anti-bacterial, anti-fungal and antioxidant abilities. Diseases that may be helped or prevented by garlic’s medicinal actions include Alzheimer’s Disease, cancer, cardiovascular disease (including atherosclerosis, strokes, hypertension, thrombosis and hyperlipidemias) children’s conditions, dermatologic applications, stress, and infections. Some research points to possible benefits in diabetes, drug toxicity, and osteoporosis.
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks. This paper proposes to decompose the convolutional filters over joint steerable bases across the space and the group geometry simultaneously, namely a rotationequivariant CNN with decomposed convolutional filters (RotDCF). This decomposition facilitates computing the joint convolution, which is proved to be necessary for the group equivariance. It significantly reduces the model size and computational complexity while preserving performance, and truncation of the bases expansion serves implicitly to regularize the filters. On datasets involving in-plane and out-of-plane object rotations, RotDCF deep features demonstrate greater robustness and interpretability than regular CNNs. The stability of the equivariant representation to input variations is also proved theoretically under generic assumptions on the filters in the decomposed form. The RotDCF framework can be extended to groups other than rotations, providing a general approach which achieves both group equivariance and representation stability at a reduced model size.