nils-herrmann/clean_scirepeval huggingface dataset:
CARL: Content-Aware Representation Learning for Heterogeneous Networks

Heterogeneous networks not only present a challenge of heterogeneity in the types of nodes and relations, but also the attributes and content associated with the nodes. While recent works have looked at representation learning on homogeneous and heterogeneous networks, there is no work that has collectively addressed the following challenges: (a) the heterogeneous structural information of the network consisting of multiple types of nodes and relations; (b) the unstructured semantic content (e.g., text) associated with nodes; and (c) online updates due to incoming new nodes in growing network. We address these challenges by developing a Content-Aware Representation Learning model (CARL). CARL performs joint optimization of heterogeneous SkipGram and deep semantic encoding for capturing both heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network. Furthermore, an additional online update module is proposed for efficiently learning representations of incoming nodes. Extensive experiments demonstrate that CARL outperforms state-of-the-art baselines in various heterogeneous network mining tasks, such as link prediction, document retrieval, node recommendation and relevance search. We also demonstrate the effectiveness of the CARL’s online update module through a category visualization study.
The double burden of malnutrition in indigenous and nonindigenous Guatemalan populations.

As the prevalence of obesity increases in developing countries, the double burden of malnutrition (DBM) has become a public health problem, particularly in countries such as Guatemala with a high concentration of indigenous communities where the prevalence of stunting remains high.



The aim was to describe and analyze the prevalence of DBM over time (1998-2008) in indigenous and nonindigenous Guatemalan populations.



We used 3 National Maternal and Child Health Surveys conducted in Guatemala between 1998 and 2008 that include anthropometric data from children aged 0-60 mo and women of reproductive age (15-49 y). We assessed the prevalence of childhood stunting and both child and adult female overweight and obesity between 1998 and 2008. For the year 2008, we assessed the prevalence of DBM at the household (a stunted child and an overweight mother) and individual (stunting/short stature and overweight or anemia and overweight in the same individual) levels and compared the expected and observed prevalence rates to test if the coexistence of the DBM conditions corresponded to expected values.



Between 1998 and 2008, the prevalence of childhood stunting decreased in both indigenous and nonindigenous populations, whereas overweight and obesity in women increased faster in indigenous populations than in nonindigenous populations (0.91% compared with 0.38%/y; P-trend < 0.01). In 2008, the prevalence of stunted children was 28.8 percentage points higher and of overweight women 4.6 percentage points lower in indigenous compared with nonindigenous populations (63.7% compared with 34.9% and 46.7% compared with 51.3%, respectively). DBM at the household and individual levels was higher in indigenous populations and was higher in geographic areas in which most of the population was indigenous, where there was also a greater prevalence of stunting and DBM at the individual level, both in women and children.



In Guatemala, DBM is more prevalent in indigenous than in nonindigenous populations at the household and individual levels. To enhance effectiveness, current strategies of national policies and programs should consider DBM and focus on indigenous populations.
Brain Tumor Detection By Various Image Segmentation Techniques With Introducation To Non Negative Matrix Factorization

Image segmentation is one of the fundamental approaches of digital image processing. During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become a popular research area in the field of medical imaging system. MRI is used in radiology for analysing internal structures and makes easy to extract the required region. Thresholding is the simple approach to introduce to the morphological operations which are useful for the detection of the tumor but not all tumor can be specifically detected by this technique so region growing is another technique which provide seed point approach to the segmenter ROI region so the tumor is easily detected and also further used for the classification purpose. Nonnegative Matrix Factorization is one of the most promising technique to reduce the dimensionality of the data.NMF has been applied earlier to the image Processing methods such as Pattern analysis and Text mining and now in this paper it is mainly used as a uninterruptable decomposition approach for detection of tumor and to further classify into various types and also for feature extraction.NMF aims to find two non negative matrices whose product closely approximate the original matrix.NMF contains all matrices to contain only non negative elements and the NMF results shows no cancellations, linear super position only and considerable sparsity.
Precision psychiatry: a neural circuit taxonomy for depression and anxiety.

Although there have been tremendous advances in the understanding of human dysfunctions in the brain circuitry for self-reflection, emotion, and cognitive control, a brain-based taxonomy for mental disease is still lacking. As a result, these advances have not been translated into actionable clinical tools, and the language of brain circuits has not been incorporated into training programmes. To address this gap, I present this synthesis of published work, with a focus on functional imaging of circuit dysfunctions across the spectrum of mood and anxiety disorders. This synthesis provides the foundation for a taxonomy of putative types of dysfunction, which cuts across traditional diagnostic boundaries for depression and anxiety and includes instead distinct types of neural circuit dysfunction that together reflect the heterogeneity of depression and anxiety. This taxonomy is suited to specifying symptoms in terms of underlying neural dysfunction at the individual level and is intended as the foundation for building mechanistic research and ultimately guiding clinical practice.
AHA Scientific Statement Acute Heart Failure Syndromes : Emergency Department Presentation , Treatment , and Disposition : Current Approaches and Future Aims

Online 72514 Circulation is published by the American Heart Association. 7272 Greenville Avenue, Dallas, TX DOI: 10.1161/CIR.0b013e3181f9a223 published online Oct 11, 2010; Circulation Care, Perioperative and Resuscitation Critical Association Council on Clinical Cardiology and Council on Cardiopulmonary, Parshall, Gary S. Francis, Mihai Gheorghiade and on behalf of the American Heart Anderson, Cynthia Arslanian-Engoren, W. Brian Gibler, James K. McCord, Mark B. Neal L. Weintraub, Sean P. Collins, Peter S. Pang, Phillip D. Levy, Allen S. Statement From the American Heart Association Treatment, and Disposition: Current Approaches and Future  A Scientific Acute Heart Failure Syndromes: Emergency Department Presentation, http://circ.ahajournals.org located on the World Wide Web at: The online version of this article, along with updated information and services, is
Dynamic predictions: Oscillations and synchrony in top–down processing

Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top–down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.
Multitask Sequence-to-Sequence Models for Grapheme-to-Phoneme Conversion

Recently, neural sequence-to-sequence (Seq2Seq) models have been applied to the problem of grapheme-to-phoneme (G2P) conversion. These models offer a straightforward way of modeling the conversion by jointly learning the alignment and translation of input to output tokens in an end-to-end fashion. However, until now this approach did not show improved error rates on its own compared to traditional joint-sequence based n-gram models for G2P. In this paper, we investigate how multitask learning can improve the performance of Seq2Seq G2P models. A single Seq2Seq model is trained on multiple phoneme lexicon datasets containing multiple languages and phonetic alphabets. Although multi-language learning does not show improved error rates, combining standard datasets and crawled data with different phonetic alphabets of the same language shows promising error reductions on English and German Seq2Seq G2P conversion. Finally, combining Seq2seq G2P models with standard n-grams based models yields significant improvements over using either model alone.
CHAPTER 2 Crime Mapping : Spatial and Temporal Challenges

Crime opportunities are neither uniformly nor randomly organized in space and time. As a result, crime mappers can unlock these spatial patterns and strive for a better theoretical understanding of the role of geography and opportunity, as well as enabling practical crime prevention solutions that are tailored to specific places. The evolution of crime mapping has heralded a new era in spatial criminology, and a re-emergence of the importance of place as one of the cornerstones essential to an understanding of crime and criminality. While early criminological inquiry in France and Britain had a spatial component, much of mainstream criminology for the last century has labored to explain criminality from a dispositional perspective, trying to explain why a particular offender or group has a propensity to commit crime. This traditional perspective resulted in criminologists focusing on individuals or on communities where the community extended from the neighborhood to larger aggregations (Weisburd et al. 2004). Even when the results lacked ambiguity, the findings often lacked policy relevance. However, crime mapping has revived interest and reshaped many criminologists appreciation for the importance of local geography as a determinant of crime that may be as important as criminal motivation. Between the individual and large urban areas (such as cities and regions) lies a spatial scale where crime varies considerably and does so at a frame of reference that is often amenable to localized crime prevention techniques. For example, without the opportunity afforded by enabling environmental weaknesses, such as poorly lit streets, lack of protective surveillance, or obvious victims (such as overtly wealthy tourists or unsecured vehicles), many offenders would not be as encouraged to commit crime. This chapter seeks to make the case for crime mapping as an essential tool in the examination of criminal activity; it also charges mainstream criminology to re-engage with the practitioner interest in spatially targeted crime prevention. In the next section, I briefly outline the theoretical support for a spatial approach to the crime problem and warn of the negative outcomes that can potentially arise by ignoring the spatial dimensional of crime. After a basic primer in mapping crime locations, the chapter looks at different ways that crime hotspots can be identified. It also discusses the benefits of spatio-temporal crime mapping.
An improved 3-step contactless fingerprint image enhancement approach for minutiae detection

Fingerprint enhancement is a critical step in fingerprint recognition systems. There are many existing contact-based fingerprint image enhancement methods and they have their own strengths and weaknesses. However, image enhancement approaches that can be used for contactless fingerprints are rarely considered and the number of such approaches is limited. Furthermore, the performance of existing contact-based fingerprint enhancement methods on the contactless fingerprint samples are unsatisfactory. Therefore, in this paper we propose an improved 3-step fingerprint image quality enhancement approach, which can be used for enhancing contactless fingerprint samples. The evaluation results show that, the proposed enhancement method significantly increases the number of detected minutiae, and improves the performance of fingerprint recognition system by reducing 7% and 15% EER compared to existing methods, respectively.
Association of Extracellular Membrane Vesicles with Cutaneous Wound Healing

Extracellular vesicles (EVs) are membrane-enclosed vesicles that are released into the extracellular environment by various cell types, which can be classified as apoptotic bodies, microvesicles and exosomes. EVs have been shown to carry DNA, small RNAs, proteins and membrane lipids which are derived from the parental cells. Recently, several studies have demonstrated that EVs can regulate many biological processes, such as cancer progression, the immune response, cell proliferation, cell migration and blood vessel tube formation. This regulation is achieved through the release and transport of EVs and the transfer of their parental cell-derived molecular cargo to recipient cells. This thereby influences various physiological and sometimes pathological functions within the target cells. While intensive investigation of EVs has focused on pathological processes, the involvement of EVs in normal wound healing is less clear; however, recent preliminarily investigations have produced some initial insights. This review will provide an overview of EVs and discuss the current literature regarding the role of EVs in wound healing, especially, their influence on coagulation, cell proliferation, migration, angiogenesis, collagen production and extracellular matrix remodelling.
Robotic radical prostatectomy: outcomes of 500 cases.

To report the outcomes of 500 robotically assisted laparoscopic radical prostatectomies (RALPs), a minimally invasive alternative for treating prostate cancer.



In all, 500 patients had RALP over a 30-month period. A transperitoneal six-port approach was used in each case, with the da Vinci robotic surgical system (Intuitive Surgical, Sunnyvale, CA, USA). Prospective data collection included quality-of-life questionnaires, basic demographics (height, weight and body mass index), prostate specific antigen (PSA) levels, clinical stage and Gleason grade. Variables assessed during RALP were operative duration, estimated blood loss (EBL) and complications, and after RALP were hospital stay, catheter time, pathology, PSA level, return of continence and potency.



The mean (range) duration of RALP was 130 (51-330) min; all procedures were successful, with no intraoperative transfusions or deaths. The mean EBL was 10-300 mL; 97% of patients were discharged home on the first day after RALP with a mean haematocrit of 36%. The mean duration of catheterization was 6.9 (5-21) days. The positive margin rate was 9.4% for all patients; i.e. 2.5% for T2 tumours, 23% for T3a and 53% for T4. The overall biochemical recurrence free (PSA level<0.1 ng/mL) survival was 95% at mean follow-up of 9.7 months. There was complete continence at 3 and 6 months in 89% and 95% of patients, respectively. At 1 year 78% of patients were potent (with or without the use of oral medications), 15% were not yet able to sustain erections capable of intercourse, and another 7% still required injection therapy.



RALP is a safe, feasible and minimally invasive alternative for treating prostate cancer. Our initial experience with the procedure shows promising short-term outcomes.
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes are abundant, making them an overrepresented majority, and data of other classes are scarce, making them an underrepresented minority. This imbalance makes it challenging for a classifier to appropriately learn the discriminating boundaries of the majority and minority classes. In this paper, we propose a cost-sensitive (CoSen) deep neural network, which can automatically learn robust feature representations for both the majority and minority classes. During training, our learning procedure jointly optimizes the class-dependent costs and the neural network parameters. The proposed approach is applicable to both binary and multiclass problems without any modification. Moreover, as opposed to data-level approaches, we do not alter the original data distribution, which results in a lower computational cost during the training process. We report the results of our experiments on six major image classification data sets and show that the proposed approach significantly outperforms the baseline algorithms. Comparisons with popular data sampling techniques and CoSen classifiers demonstrate the superior performance of our proposed method.
A Survey of 5G Network: Architecture and Emerging Technologies

In the near future, i.e., beyond 4G, some of the prime objectives or demands that need to be addressed are increased capacity, improved data rate, decreased latency, and better quality of service. To meet these demands, drastic improvements need to be made in cellular network architecture. This paper presents the results of a detailed survey on the fifth generation (5G) cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of users. In this detailed survey, the prime focus is on the 5G cellular network architecture, massive multiple input multiple output technology, and device-to-device communication (D2D). Along with this, some of the emerging technologies that are addressed in this paper include interference management, spectrum sharing with cognitive radio, ultra-dense networks, multi-radio access technology association, full duplex radios, millimeter wave solutions for 5G cellular networks, and cloud technologies for 5G radio access networks and software defined networks. In this paper, a general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G cellular network architecture. A detailed survey is included regarding current research projects being conducted in different countries by research groups and institutions that are working on 5G technologies.
Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach

The associations between time spent in sleep, sedentary behaviors (SB) and physical activity with health are usually studied without taking into account that time is finite during the day, so time spent in each of these behaviors are codependent. Therefore, little is known about the combined effect of time spent in sleep, SB and physical activity, that together constitute a composite whole, on obesity and cardio-metabolic health markers. Cross-sectional analysis of NHANES 2005-6 cycle on N = 1937 adults, was undertaken using a compositional analysis paradigm, which accounts for this intrinsic codependence. Time spent in SB, light intensity (LIPA) and moderate to vigorous activity (MVPA) was determined from accelerometry and combined with self-reported sleep time to obtain the 24 hour time budget composition. The distribution of time spent in sleep, SB, LIPA and MVPA is significantly associated with BMI, waist circumference, triglycerides, plasma glucose, plasma insulin (all p<0.001), and systolic (p<0.001) and diastolic blood pressure (p<0.003), but not HDL or LDL. Within the composition, the strongest positive effect is found for the proportion of time spent in MVPA. Strikingly, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior are asymmetric. For example, re-allocating 10 minutes of SB to MVPA was associated with a lower waist circumference by 0.001% but if 10 minutes of MVPA is displaced by SB this was associated with a 0.84% higher waist circumference. The proportion of time spent in LIPA and SB were detrimentally associated with obesity and cardiovascular disease markers, but the association with SB was stronger. For diabetes risk markers, replacing SB with LIPA was associated with more favorable outcomes. Time spent in MVPA is an important target for intervention and preventing transfer of time from LIPA to SB might lessen the negative effects of physical inactivity.
Image Captioning with Deep Bidirectional LSTMs

This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are proposed to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale and vertical mirror are proposed to prevent overfitting in training deep models. We visualize the evolution of bidirectional LSTM internal states over time and qualitatively analyze how our models "translate" image to sentence. Our proposed models are evaluated on caption generation and image-sentence retrieval tasks with three benchmark datasets: Flickr8K, Flickr30K and MSCOCO datasets. We demonstrate that bidirectional LSTM models achieve highly competitive performance to the state-of-the-art results on caption generation even without integrating additional mechanism (e.g. object detection, attention model etc.) and significantly outperform recent methods on retrieval task