nils-herrmann/clean_scirepeval huggingface dataset:
The biology of fracture healing.

The biology of fracture healing is a complex biological process that follows specific regenerative patterns and involves changes in the expression of several thousand genes. Although there is still much to be learned to fully comprehend the pathways of bone regeneration, the over-all pathways of both the anatomical and biochemical events have been thoroughly investigated. These efforts have provided a general understanding of how fracture healing occurs. Following the initial trauma, bone heals by either direct intramembranous or indirect fracture healing, which consists of both intramembranous and endochondral bone formation. The most common pathway is indirect healing, since direct bone healing requires an anatomical reduction and rigidly stable conditions, commonly only obtained by open reduction and internal fixation. However, when such conditions are achieved, the direct healing cascade allows the bone structure to immediately regenerate anatomical lamellar bone and the Haversian systems without any remodelling steps necessary. In all other non-stable conditions, bone healing follows a specific biological pathway. It involves an acute inflammatory response including the production and release of several important molecules, and the recruitment of mesenchymal stem cells in order to generate a primary cartilaginous callus. This primary callus later undergoes revascularisation and calcification, and is finally remodelled to fully restore a normal bone structure. In this article we summarise the basic biology of fracture healing.
Geometrization of deep networks for the interpretability of deep learning systems

How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.
Functional Status Assessment of Patients With COPD

Presently, there is no recommendation on how to assess functional status of chronic obstructive pulmonary disease (COPD) patients. This study aimed to summarize and systematically evaluate these measures.Studies on measures of COPD patients' functional status published before the end of January 2015 were included using a search filters in PubMed and Web of Science, screening reference lists of all included studies, and cross-checking against some relevant reviews. After title, abstract, and main text screening, the remaining was appraised using the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) 4-point checklist. All measures from these studies were rated according to best-evidence synthesis and the best-rated measures were selected.A total of 6447 records were found and 102 studies were reviewed, suggesting 44 performance-based measures and 14 patient-reported measures. The majority of the studies focused on internal consistency, reliability, and hypothesis testing, but only 21% of them employed good or excellent methodology. Their common weaknesses include lack of checks for unidimensionality, inadequate sample sizes, no prior hypotheses, and improper methods. On average, patient-reported measures perform better than performance-based measures. The best-rated patient-reported measures are functional performance inventory (FPI), functional performance inventory short form (FPI-SF), living with COPD questionnaire (LCOPD), COPD activity rating scale (CARS), University of Cincinnati dyspnea questionnaire (UCDQ), shortness of breath with daily activities (SOBDA), and short-form pulmonary functional status scale (PFSS-11), and the best-rated performance-based measures are exercise testing: 6-minute walk test (6MWT), endurance treadmill test, and usual 4-meter gait speed (usual 4MGS).Further research is needed to evaluate the reliability and validity of performance-based measures since present studies failed to provide convincing evidence. FPI, FPI-SF, LCOPD, CARS, UCDQ, SOBDA, PFSS-11, 6MWT, endurance treadmill test, and usual 4MGS performed well and are preferable to assess functional status of COPD patients.
A dual-source approach for 3D human pose estimation from single images

In this work we address the challenging problem of 3D human pose estimation from single images. Recent approaches learn deep neural networks to regress 3D pose directly from images. One major challenge for such methods, however, is the collection of training data. Specifically, collecting large amounts of training data containing unconstrained images annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of accurate 3D motion capture data, and the second source consists of unconstrained images with annotated 2D poses. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient 3D pose retrieval. To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data. During inference, we estimate the 2D pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a mapping from the 3D pose space to the image and reconstruct the 3D pose. We provide a comprehensive evaluation of the proposed method and experimentally demonstrate the effectiveness of our approach, even when the skeleton structures of the two sources differ substantially.
What Makes Online Content Viral ?

Why are certain pieces of online content more viral than others? This article takes a psychological approach to understanding diffusion. Using a unique dataset of all the New York Times articles published over a three month period, the authors examine how emotion shapes virality. Results indicate that positive content is more viral than negative content, but that the relationship between emotion and social transmission is more complex than valence alone. Virality is driven, in part, by physiological arousal. Content that evokes high-arousal positive (awe) or negative (anger or anxiety) emotions is more viral. Content that evokes low arousal, or deactivating emotions (e.g., sadness) is less viral. These results hold even controlling for how surprising, interesting, or practically useful content is (all of which are positively linked to virality), as well as external drivers of attention (e.g., how prominently content was featured). Experimental results further demonstrate the causal impact of specific emotion on transmission, and illustrate that it is driven by the level of activation induced. Taken together, these findings shed light on why people share content and provide insight into designing effective viral marketing
Learning to Learn from Weak Supervision by Full Supervision

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target networkmodel.
Security Analysis of Vehicular Ad Hoc Networks (VANETs): A Comprehensive Study

Vehicular Ad-hoc Networks (VANETs) is a technology that has been recently emerged, and brings a lot of research interests. Security is one of the important issues in VANET, it is considered a critical point in the development of robust VANET applications. In this paper, various dimensions of VANETs including its emerging applications, security issues, challenges, security threats and the existing solutions proposed by the different researchers are studied. Also author reviewed various type of VANET simulator available and presented possible key research area of VANETs.
Incremental Global Event Extraction

Event extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This joint system achieves state-of-the-art performance in all tasks. However, as a system operating only at the sentence level, it misses valuable information from other parts of the document. In this paper, we present an incremental approach to make the global context of the entire document available to the intra-sentential, state-of-the-art event extractor. We show that our method robustly increases performance on two datasets, namely ACE 2005 and TAC 2015.
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of monoand polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.
Step-by-step guide to critiquing research. Part 2: Qualitative research.

As with a quantitative study, critical analysis of a qualitative study involves an in-depth review of how each step of the research was undertaken. Qualitative and quantitative studies are, however, fundamentally different approaches to research and therefore need to be considered differently with regard to critiquing. The different philosophical underpinnings of the various qualitative research methods generate discrete ways of reasoning and distinct terminology; however, there are also many similarities within these methods. Because of this and its subjective nature, qualitative research it is often regarded as more difficult to critique. Nevertheless, an evidenced-based profession such as nursing cannot accept research at face value, and nurses need to be able to determine the strengths and limitations of qualitative as well as quantitative research studies when reviewing the available literature on a topic.
The effect of time and frequency of static stretching on flexibility of the hamstring muscles.

Frequency and duration of static stretching have not been extensively examined. Additionally, the effect of multiple stretches per day has not been evaluated. The purpose of this study was to determine the optimal time and frequency of static stretching to increase flexibility of the hamstring muscles, as measured by knee extension range of motion (ROM).



Ninety-three subjects (61 men, 32 women) ranging in age from 21 to 39 years and who had limited hamstring muscle flexibility were randomly assigned to one of five groups. The four stretching groups stretched 5 days per week for 6 weeks. The fifth group, which served as a control, did not stretch.



Data were analyzed with a 5 x 2 (group x test) two-way analysis of variance for repeated measures on one variable (test).



The change in flexibility appeared to be dependent on the duration and frequency of stretching. Further statistical analysis of the data indicated that the groups that stretched had more ROM than did the control group, but no differences were found among the stretching groups.



The results of this study suggest that a 30-second duration is an effective amount of time to sustain a hamstring muscle stretch in order to increase ROM. No increase in flexibility occurred when the duration of stretching was increased from 30 to 60 seconds or when the frequency of stretching was increased from one to three times per day.
Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection

Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human–robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.
Contactless Vulnerability Analysis using Google and Shodan

The increasing number of attacks on internet-based systems calls for security measures on behalf those systems’ operators. Beside classical methods and tools for penetration testing, there exist additional approaches using publicly available search engines. We present an alternative approach using contactless vulnerability analysis with both classical and subject-specific search engines. Based on an extension and combination of their functionality, this approach provides a method for obtaining promising results for audits of IT systems, both quantitatively and qualitatively. We evaluate our approach and confirm its suitability for a timely determination of vulnerabilities in large-scale networks. In addition, the approach can also be used to perform vulnerability analyses of network areas or domains in unclear legal situations.
An Individual’s Connection to Nature Can Affect Perceived Restorativeness of Natural Environments. Some Observations about Biophilia

This study investigates the relationship between the level to which a person feels connected to Nature and that person's ability to perceive the restorative value of a natural environment. We assume that perceived restorativeness may depend on an individual's connection to Nature and this relationship may also vary with the biophilic quality of the environment, i.e., the functional and aesthetic value of the natural environment which presumably gave an evolutionary advantage to our species. To this end, the level of connection to Nature and the perceived restorativeness of the environment were assessed in individuals visiting three parks characterized by their high level of "naturalness" and high or low biophilic quality. The results show that the perceived level of restorativeness is associated with the sense of connection to Nature, as well as the biophilic quality of the environment: individuals with different degrees of connection to Nature seek settings with different degrees of restorativeness and biophilic quality. This means that perceived restorativeness can also depend on an individual's "inclination" towards Nature.
The enactment of socio-technical transition pathways : A reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions ( 1990 – 2014 )

This paper aims to make two contributions to the sustainability transitions literature, in particular the Geels and Schot (2007) transition pathways typology. First, it reformulates and differentiates the typology through the lens of endogenous enactment, identifying the main patterns for actors, formal institutions, and technologies. Second, it suggests that transitions may shift between pathways, depending on struggles over technology deployment and institutions. Both contributions are demonstrated with a comparative analysis of unfolding low-carbon electricity transitions in Germany and the UK between 1990–2014. The analysis shows that Germany is on a substitution pathway, enacted by new entrants deploying small-scale renewable electricity technologies (RETs), while the UK is on a transformation pathway, enacted by incumbent actors deploying large-scale RETs. Further analysis shows that the German transition has recently shifted from a ‘stretch-and-transform’ substitution pathway to a ‘fit-and-conform’ pathway, because of a fightback from utilities and altered institutions. It also shows that the UK transition moved from moderate to substantial incumbent reorientation, as government policies became stronger. Recent policy changes, however, substantially downscaled UK renewables support, which is likely to shift the transition back to weaker reorientation.
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring

Autonomous unpowered flight is a challenge for control and guidance systems: all the energy the aircraft might use during flight has to be harvested directly from the atmosphere. We investigate the design of an algorithm that optimizes the closed-loop control of a glider’s bank and sideslip angles, while flying in the lower convective layer of the atmosphere in order to increase its mission endurance. Using a Reinforcement Learning approach, we demonstrate the possibility for real-time adaptation of the glider’s behaviour to the time-varying and noisy conditions associated with thermal soaring flight. Our approach is online, data-based and model-free, hence avoids the pitfalls of aerological and aircraft modelling and allow us to deal with uncertainties and non-stationarity. Additionally, we put a particular emphasis on keeping low computational requirements in order to make on-board execution feasible. This article presents the stochastic, time-dependent aerological model used for simulation, together with a standard aircraft model. Then we introduce an adaptation of a Q-learning algorithm and demonstrate its ability to control the aircraft and improve its endurance by exploiting updrafts in non-stationary scenarios. Mots-clés : Reinforcement learning control, Adaptive control applications, Adaptation and learning in physical agents, UAVs.
Measuring Calorie and Nutrition From Food Image

As people across the globe are becoming more interested in watching their weight, eating more healthy, and avoiding obesity, a system that can measure calories and nutrition in every day meals can be very useful. In this paper, we propose a food calorie and nutrition measurement system that can help patients and dietitians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it to measure the consumption of calorie and nutrient components. Our results show that the accuracy of our system is acceptable and it will greatly improve and facilitate current manual calorie measurement techniques.
Incorporating Prior Knowledge into Word Embedding for Chinese Word Similarity Measurement

Word embedding-based methods have received increasing attention for their flexibility and effectiveness in many natural language-processing (NLP) tasks, including Word Similarity (WS). However, these approaches rely on high-quality corpus and neglect prior knowledge. Lexicon-based methods concentrate on human’s intelligence contained in semantic resources, e.g., Tongyici Cilin, HowNet, and Chinese WordNet, but they have the drawback of being unable to deal with unknown words. This article proposes a three-stage framework for measuring the Chinese word similarity by incorporating prior knowledge obtained from lexicons and statistics into word embedding: in the first stage, we utilize retrieval techniques to crawl the contexts of word pairs from web resources to extend context corpus. In the next stage, we investigate three types of single similarity measurements, including lexicon similarities, statistical similarities, and embedding-based similarities. Finally, we exploit simple combination strategies with math operations and the counter-fitting combination strategy using optimization method. To demonstrate our system’s efficiency, comparable experiments are conducted on the PKU-500 dataset. Our final results are 0.561/0.516 of Spearman/Pearson rank correlation coefficient, which outperform the state-of-the-art performance to the best of our knowledge. Experiment results on Chinese MC-30 and SemEval-2012 datasets show that our system also performs well on other Chinese datasets, which proves its transferability. Besides, our system is not language-specific and can be applied to other languages, e.g., English.
A research framework for the adoption of Business Intelligence by Small and Medium-sized enterprises

Due to the complexities of making effective and timely business decisions in highly competitive markets, Data-driven decision-making using Business Intelligence (BI) applications has attracted many organisations worldwide. However, despite these applications being suited for use in most organisations regardless of size, only the larger enterprises have reached a stage of maturity in BI use while small and medium-size enterprises (SMEs) still lag behind. Whilst many academic researchers have conducted BI research focused on large organisations, literature relating to BI adoption within SMEs has remained limited. To fill this research gap and support the adoption rate of BI in SMEs, this paper proposes a research framework for identifying the current state of BI adoption by SMEs and the enabling factors that impact BI adoption in SMEs. Future research directions for using this framework are also discussed.