nils-herrmann/clean_scirepeval huggingface dataset:
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images

This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.
Chicken embryo development detection using self-organizing maps and K-mean clustering

This paper presents a set of procedures for detecting the primary embryo development of chicken eggs using Self-Organizing Mapping (SOM) technique and K-means clustering algorithm. Our strategy consists of preprocessing of an acquired color image with color space transformation, grouping the data by Self-Organizing Mapping technique and predicting the embryo development by K-means clustering method. In our experiment, the results show that our method is more efficient. Processing with this algorithm can indicate the period of chicken embryo in on hatching. By the accuracy of the algorithm depends on the adjustment the optimum number of iterative learning. For experiment the learning rate using the example of number 4 eggs, found that the optimum learning rate to be in the range of 0.1 to 0.5. And efficiency the optimum number of iterative learning to be in the range of 250 to 300 rounds.
Renal failure in Nigerian children: factors limiting access to dialysis

A 5-year clinical and laboratory study of Nigerian children with renal failure (RF) was performed to determine the factors that limited their access to dialysis treatment and what could be done to improve access. There were 48 boys and 33 girls (aged 20 days to 15 years). Of 81 RF patients, 55 were eligible for dialysis; 33 indicated ability to afford dialysis, but only 6 were dialyzed, thus giving a dialysis access rate of 10.90% (6/55). Ability to bear dialysis cost/dialysis accessibility ratio was 5.5:1 . Factors that limited access to dialysis treatment in our patients included financial restrictions from parents (33%), no parental consent for dialysis (6%), lack or failure of dialysis equipment (45%), shortage of dialysis personnel (6%), reluctance of renal staff to dialyze (6%), and late presentation in hospital (4%). More deaths were recorded among undialyzed than dialyzed patients (P<0.01); similarly, undialyzed patients had more deaths compared with RF patients who required no dialysis (P<0.025). Since most of our patients could not be dialyzed owing to a range of factors, preventive nephrology is advocated to reduce the morbidity and mortality from RF due to preventable diseases.
Occlusion Handling via Random Subspace Classifiers for Human Detection

This paper describes a general method to address partial occlusions for human detection in still images. The random subspace method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach's capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labeling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes.
Node.fz: Fuzzing the Server-Side Event-Driven Architecture

The importance of the Event-Driven Architecture (EDA) has never been greater. Web servers and the IoT alike have begun to adopt the EDA, and the popular server-side EDA framework, Node.js, boasts the world's largest package ecosystem. While multi-threaded programming has been well studied in the literature, concurrency bug characteristics and useful development tools remain largely unexplored for server-side EDA-based applications.
 We present the first (to the best of our knowledge) concurrency bug characteristic study of real world open-source event-driven applications, based in Node.js. Like multithreaded programs, event-driven programs are prone to concurrency bugs like atomicity violations and order violations. Our study shows the forms that atomicity violations and ordering violations take in the EDA context, and points out the limitations of existing concurrency error detection tools developed for client-side EDA applications.
 Based on our bug study, we propose Node.fz, a novel testing aid for server-side event-driven programs. Node.fz is a schedule fuzzing test tool for event-driven programs, embodied for server-side Node.js programs. Node.fz randomly perturbs the execution of a Node.js program, allowing Node.js developers to explore a variety of possible schedules. Thanks to its low overhead, Node.fz enables a developer to explore a broader "schedule space" with the same test time budget, ensuring that applications will be stable in a wide variety of deployment conditions. We show that Node.fz can expose known bugs much more frequently than vanilla Node.js, and that it can uncover new bugs.
The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets

Approximate entropy (ApEn) and sample entropy (SampEn) are mathematical algorithms created to measure the repeatability or predictability within a time series. Both algorithms are extremely sensitive to their input parameters: m (length of the data segment being compared), r (similarity criterion), and N (length of data). There is no established consensus on parameter selection in short data sets, especially for biological data. Therefore, the purpose of this research was to examine the robustness of these two entropy algorithms by exploring the effect of changing parameter values on short data sets. Data with known theoretical entropy qualities as well as experimental data from both healthy young and older adults was utilized. Our results demonstrate that both ApEn and SampEn are extremely sensitive to parameter choices, especially for very short data sets, N ≤ 200. We suggest using N larger than 200, an m of 2 and examine several r values before selecting your parameters. Extreme caution should be used when choosing parameters for experimental studies with both algorithms. Based on our current findings, it appears that SampEn is more reliable for short data sets. SampEn was less sensitive to changes in data length and demonstrated fewer problems with relative consistency.
Hybrid Concolic Testing

We present hybrid concolic testing, an algorithm that interleaves random testing with concolic execution to obtain both a deep and a wide exploration of program state space. Our algorithm generates test inputs automatically by interleaving random testing until saturation with bounded exhaustive symbolic exploration of program points. It thus combines the ability of random search to reach deep program states quickly together with the ability of concolic testing to explore states in a neighborhood exhaustively. We have implemented our algorithm on top of CUTE and applied it to obtain better branch coverage for an editor implementation (VIM 5.7, 150K lines of code) as well as a data structure implementation in C. Our experiments suggest that hybrid concolic testing can handle large programs and provide, for the same testing budget, almost 4× the branch coverage than random testing and almost 2× that of concolic testing.
Metainference: A Bayesian inference method for heterogeneous systems

Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called “metainference,” that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.
Observing and participating in social interactions: Action perception and action control across the autistic spectrum

Autism is a developmental condition, characterized by difficulties of social interaction and communication, as well as restricted interests and repetitive behaviors. Although several important conceptions have shed light on specific facets, there is still no consensus about a universal yet specific theory in terms of its underlying mechanisms. While some theories have exclusively focused on sensory aspects, others have emphasized social difficulties. However, sensory and social processes in autism might be interconnected to a higher degree than what has been traditionally thought. We propose that a mismatch in sensory abilities across individuals can lead to difficulties on a social, i.e. interpersonal level and vice versa. In this article, we, therefore, selectively review evidence indicating an interrelationship between perceptual and social difficulties in autism. Additionally, we link this body of research with studies, which investigate the mechanisms of action control in social contexts. By doing so, we highlight that autistic traits are also crucially related to differences in integration, anticipation and automatic responding to social cues, rather than a mere inability to register and learn from social cues. Importantly, such differences may only manifest themselves in sufficiently complex situations, such as real-life social interactions, where such processes are inextricably linked.
Sentence retrieval for abstracts of randomized controlled trials

The practice of evidence-based medicine (EBM) requires clinicians to integrate their expertise with the latest scientific research. But this is becoming increasingly difficult with the growing numbers of published articles. There is a clear need for better tools to improve clinician's ability to search the primary literature. Randomized clinical trials (RCTs) are the most reliable source of evidence documenting the efficacy of treatment options. This paper describes the retrieval of key sentences from abstracts of RCTs as a step towards helping users find relevant facts about the experimental design of clinical studies.



Using Conditional Random Fields (CRFs), a popular and successful method for natural language processing problems, sentences referring to Intervention, Participants and Outcome Measures are automatically categorized. This is done by extending a previous approach for labeling sentences in an abstract for general categories associated with scientific argumentation or rhetorical roles: Aim, Method,  Methods are tested on several corpora of RCT abstracts. First structured abstracts with headings specifically indicating Intervention, Participant and Outcome Measures are used. Also a manually annotated corpus of structured and unstructured abstracts is prepared for testing a classifier that identifies sentences belonging to each category.



Using CRFs, sentences can be labeled for the four rhetorical roles with F-scores from 0.93-0.98. This outperforms the use of Support Vector Machines. Furthermore, sentences can be automatically labeled for Intervention, Participant and Outcome Measures, in unstructured and structured abstracts where the section headings do not specifically indicate these three topics. F-scores of up to 0.83 and 0.84 are obtained for Intervention and Outcome Measure sentences.



Results indicate that some of the methodological elements of RCTs are identifiable at the sentence level in both structured and unstructured abstract reports. This is promising in that sentences labeled automatically could potentially form concise summaries, assist in information retrieval and finer-grained extraction.
The Danish National Penile Cancer Quality database

aims to improve the quality of cancer care and monitor the diagnosis, staging, and treatment of all incident penile cancer cases in Denmark. The aim is to assure referral practice, guideline adherence, and treatment and development of the database in order to enhance research opportunities and increase knowledge and survival outcomes of penile cancer.



The DaPeCa-data registers all patients with newly diagnosed invasive squamous cell carcinoma of the penis in Denmark since June 2011.



Data are systematically registered at the time of diagnosis by a combination of automated data-linkage to the central registries as well as online registration by treating clinicians. The main variables registered relate to disease prognosis and treatment morbidity and include the presence of risk factors (phimosis, lichen sclerosus, and human papillomavirus), date of diagnosis, date of treatment decision, date of beginning of treatment, type of treatment, treating hospital, type and time of complications, date of recurrence, date of death, and cause of death.


DESCRIPTIVE DATA
Registration of these variables correlated to the unique Danish ten-digit civil registration number enables characterization of the cohort, individual patients, and patient groups with respect to age; 1-, 3-, and 5-year disease-specific and overall survival; recurrence patterns; and morbidity profile related to treatment modality. As of August 2015, more than 200 patients are registered with ∼65 new entries per year.



The DaPeCa-data has potential to provide meaningful, timely, and clinically relevant quality data for quality maintenance, development, and research purposes.
Scale Robust Multi View Stereo

We present a Multi View Stereo approach for huge unstructured image datasets that can deal with large variations in surface sampling rate of single images. Our method reconstructs surface parts always in the best available resolution. It considers scaling not only for large scale differences, but also between arbitrary small ones for a weighted merging of the best partial reconstructions. We create depth maps with our GPU based depth map algorithm, that also performs normal optimization. It matches several images that are found with a heuristic image selection method, to a reference image. We remove outliers by comparing depth maps against each other with a fast but reliable GPU approach. Then, we merge the different reconstructions from depth maps in 3D space by selecting the best points and optimizing them with not selected points. Finally, we create the surface by using a Delaunay graph cut.
GPU Computing

The graphics processing unit (GPU) has become an integral part of today's mainstream computing systems. Over the past six years, there has been a marked increase in the performance and capabilities of GPUs. The modern GPU is not only a powerful graphics engine but also a highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart. The GPU's rapid increase in both programmability and capability has spawned a research community that has successfully mapped a broad range of computationally demanding, complex problems to the GPU. This effort in general-purpose computing on the GPU, also known as GPU computing, has positioned the GPU as a compelling alternative to traditional microprocessors in high-performance computer systems of the future. We describe the background, hardware, and programming model for GPU computing, summarize the state of the art in tools and techniques, and present four GPU computing successes in game physics and computational biophysics that deliver order-of-magnitude performance gains over optimized CPU applications.
Partially transferred convolution neural network with cross-layer inheriting for posture recognition from top-view depth camera

This paper proposes a new method for human posture recognition from top-view depth maps on small training datasets. There are two strategies developed to leverage the capability of convolution neural network (CNN) in mining the fundamental and generic features for recognition. First, the early layers of CNN should serve the function to extract feature without specific representation. By applying the concept of transfer learning, the first few layers from the pre-learned VGG model can be used directly without further fine-tuning. To alleviate the computational loading and to increase the accuracy of our partially transferred model, a cross-layer inheriting feature fusion (CLIFF) is proposed by using the information from the early layer in fully connected layer without further processing. The experimental result shows that combination of partial transferred model and CLIFF can provide better performance than VGG16  model with re-trained FC layer and other hand-crafted features like RBPs .
Bootstrap Methods and Applications

Given the wealth of literature on the topic supported by solutions to practical problems, we would expect the bootstrap to be an off-the-shelf tool for signal processing problems as are maximum likelihood and least-squares methods. This is not the case, and we wonder why a signal processing practitioner would not resort to the bootstrap for inferential problems. We may attribute the situation to some confusion when the engineer attempts to discover the bootstrap paradigm in an overwhelming body of statistical literature. Our aim is to give a short tutorial of bootstrap methods supported by real-life applications. This pragmatic approach is to serve as a practical guide rather than a comprehensive treatment, which can be found elsewhere. However, for the bootstrap to be successful, we need to identify which resampling scheme is most appropriate.
Morphological and molecular characterization of sclerotium rolfsii associated with fruit rot of Cucurbita maxima

In the present study, fruit rot disease of Cucurbita maxima observed in many crop fields of Southern Karnataka, India is reported. The fruit rot disease on Pumpkin appears as water soaked lesion on the surface of the fruit followed by the development of large rotten areas with abundant white mycelial growth. The fungal pathogen was isolated on potato dextrose agar (PDA) medium and development of whitish fast-growing mycelia with numerous reddish brown sclerotia was observed. The sclerotia were initially white and turned dark brown upon maturation. Genomic DNA was isolated and internal transcribed region of ribosomal DNA was amplified using ITS1-ITS4 universal primers. Based on cultural, morphological and molecular characteristics, the fungal pathogen was identified as Sclerotium rolfsii. The rDNA sequence results also showed 99–100 % similarity with reference sequence AB075298.1 and JF966208.1 confirming pathogen identity. The pathogenicity test conducted on healthy detached fruits produced fruit rot symptoms after 6–8 days of post inoculation. S. rolfsii is known to infect many economically important crop plants at various stages of its growth and development. To the best of our knowledge, this is the first report on the association of S. rolfsii with fruit rot of C. maxima in India.
Identification of pesticides residues in different samples of milk

Different pesticides have been used in tropical countries to control agricultural pests. Problem of the presence of pesticides in milk contributes to the serious issue of human health. The identification and quantification of organophosphorous (chlorpyrifos), pyrethroid (bifenthrin, deltamethrin and lambda cyhalothrin), insecticide (imidacloprid) and carbamate (carbofuran) pesticides in cow, buffalo, goat, and camel and sheep milk collected from different localities of Lahore, Pakistan was performed. Milk was monitored by using HPTLC technique for six pesticides residues to determine the degree of contamination. Extraction was performed with ethanol, petroleum ether and sodium oxalate. Extracted milk samples run with pesticides standards. The analysis showed that most of the milk samples were contaminated by the pesticides. Identification of pesticides was done through UV light examination at 254nm and with the standards. Confirmation of pesticides was done through GC-MS. Results showed that 50 % of the milk samples were contaminated with pesticides residues. Deltamethrin was most significantly present in milk samples. Sheep milk samples were mostly contaminated with the pesticides residues. The intake of the pesticide contaminated milk might pose health hazardous to humans in this locality.
Improved In Vitro Quantification of the Force Exerted by the Papillary Muscle on the Left Ventricular Wall: Three-Dimensional Force Vector Measurement System

Recent developments indicate that the forces acting on the papillary muscles can be a measure of the severity of mitral valve regurgitation. Pathological conditions, such as ischemic heart disease, cause changes in the geometry of the left ventricle and the mitral valve annulus, often resulting in displacement of the papillary muscles relative to the annulus. This can lead to increased tension in the chordae tendineae. This increased tension is transferred to the leaflets, and can disturb the coaptation pattern of the mitral valve. The force balance on the individual components governs the function of the mitral valve. The ability to measure changes in the force distribution from normal to pathological conditions may give insight into the mechanisms of mitral valve insufficiency. A unique in vitro model has been developed that allows quantification of the papillary muscle spatial position and quantification of the three-dimensional force vector applied to the left ventricular wall by the papillary muscles. This system allows for the quantification of the global force exerted on the posterior left ventricular wall from the papillary muscles during simulation of normal and diseased conditions.