nils-herrmann/clean_scirepeval huggingface dataset:
Echinacea reduces the risk of recurrent respiratory tract infections and complications: a meta-analysis of randomized controlled trials.

Respiratory tract infections are common, and these infections occur frequently in children, susceptible adults, and older persons. The risk for recurrences and complications relates not only to the presence of viruses but also to immune function. Therefore, modulation of the immune system and antiviral interventions such as echinacea might reduce the risk of recurrences and possibly the development of complications.



MEDLINE, EMBASE, CAplus, BIOSIS, CABA, AGRICOLA, TOXCENTER, SCISEARCH, NAHL, and NAPRALERT were searched for clinical trials that studied recurrent respiratory infections and complications on treatment with echinacea extracts in a generally healthy population. Two independent reviewers selected randomized, placebo-controlled studies of high methodological quality and a Jadad score of ≥4. Relative risks (RRs) with 95% confidence intervals (CIs) were calculated according to a fixed effect model.



Six clinical studies with a total of 2458 participants were included in the meta-analysis. Use of echinacea extracts was associated with reduced risk of recurrent respiratory infections (RR 0.649, 95% CI 0.545-0.774; P < 0.0001). Ethanolic extracts from echinacea appeared to provide superior effects over pressed juices, and increased dosing during acute episodes further enhanced these effects. Three independent studies found that in individuals with higher susceptibility, stress or a state of immunological weakness, echinacea halved the risk of recurrent respiratory infections (RR 0.501, 95% CI 0.380-0.661; P < 0.0001). Similar preventive effects were observed with virologically confirmed recurrent infections (RR 0.420, 95% CI 0.222-0.796; P = 0.005). Complications including pneumonia, otitis media/externa, and tonsillitis/pharyngitis were also less frequent with echinacea treatment (RR 0.503, 95% CI 0.384-0.658; P < 0.0001).



Evidence indicates that echinacea potently lowers the risk of recurrent respiratory infections and complications thereof. Immune modulatory, antiviral, and anti-inflammatory effects might contribute to the observed clinical benefits, which appear strongest in susceptible individuals.
From manual periodontal probing to digital 3-D imaging to endoscopic capillaroscopy: Recent advances in periodontal disease diagnosis.

Deepened periodontal pockets exert a significant pathological burden on the host and its immune system, particularly in a patient with generalized moderate to severe periodontitis. This burden is extensive and longitudinal, occurring over decades of disease development. Considerable diagnostic and prognostic successes in this regard have come from efforts to measure the depths of the pockets and their contents, including level of inflammatory mediators, cellular exudates and microbes; however, the current standard of care for measuring these pockets, periodontal probing, is an analog technology in a digital age. Measurements obtained by probing are variable, operator dependent and influenced by site-specific factors. Despite these limitations, manual probing is still the standard of care for periodontal diagnostics globally. However, it is becoming increasingly clear that this technology needs to be updated to be compatible with the digital technologies currently being used to image other orofacial structures, such as maxillary sinuses, alveolar bone, nerve foramina and endodontic canals in 3 dimensions. This review aims to summarize the existing technology, as well as new imaging strategies that could be utilized for accurate evaluation of periodontal pocket dimensions.
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

We propose a simple and general variant of the standard reparameterized gradient estimator for the variational evidence lower bound. Specifically, we remove a part of the total derivative with respect to the variational parameters that corresponds to the score function. Removing this term produces an unbiased gradient estimator whose variance approaches zero as the approximate posterior approaches the exact posterior. We analyze the behavior of this gradient estimator theoretically and empirically, and generalize it to more complex variational distributions such as mixtures and importance-weighted posteriors.
Systematic review of epidemiological studies on health effects associated with management of solid waste

Management of solid waste (mainly landfills and incineration) releases a number of toxic substances, most in small quantities and at extremely low levels. Because of the wide range of pollutants, the different pathways of exposure, long-term low-level exposure, and the potential for synergism among the pollutants, concerns remain about potential health effects but there are many uncertainties involved in the assessment. Our aim was to systematically review the available epidemiological literature on the health effects in the vicinity of landfills and incinerators and among workers at waste processing plants to derive usable excess risk estimates for health impact assessment.



We examined the published, peer-reviewed literature addressing health effects of waste management between 1983 and 2008. For each paper, we examined the study design and assessed potential biases in the effect estimates. We evaluated the overall evidence and graded the associated uncertainties.



In most cases the overall evidence was inadequate to establish a relationship between a specific waste process and health effects; the evidence from occupational studies was not sufficient to make an overall assessment. For community studies, at least for some processes, there was limited evidence of a causal relationship and a few studies were selected for a quantitative evaluation. In particular, for populations living within two kilometres of landfills there was limited evidence of congenital anomalies and low birth weight with excess risk of 2 percent and 6 percent, respectively. The excess risk tended to be higher when sites dealing with toxic wastes were considered. For populations living within three kilometres of old incinerators, there was limited evidence of an increased risk of cancer, with an estimated excess risk of 3.5 percent. The confidence in the evaluation and in the estimated excess risk tended to be higher for specific cancer forms such as non-Hodgkin's lymphoma and soft tissue sarcoma than for other cancers.



The studies we have reviewed suffer from many limitations due to poor exposure assessment, ecological level of analysis, and lack of information on relevant confounders. With a moderate level confidence, however, we have derived some effect estimates that could be used for health impact assessment of old landfill and incineration plants. The uncertainties surrounding these numbers should be considered carefully when health effects are estimated. It is clear that future research into the health risks of waste management needs to overcome current limitations.
Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets

Preference learning is a fundamental problem in various smart computing applications such as personalized recommendation. Collaborative filtering as a major learning technique aims to make use of users’ feedback, for which some recent works have switched from exploiting explicit feedback to implicit feedback. One fundamental challenge of leveraging implicit feedback is the lack of negative feedback, because there is only some observed relatively “positive” feedback available, making it difficult to learn a prediction model. In this paper, we propose a new and relaxed assumption of pairwise preferences over item-sets, which defines a user’s preference on a set of items (item-set) instead of on a single item only. The relaxed assumption can give us more accurate pairwise preference relationships. With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets), which contains four variants, CoFiSet(SS), CoFiSet(MOO), CoFiSet(MOS) and CoFiSet(MSO), representing “Set vs. Set,” “Many ‘One vs. One’,” “Many ‘One vs. Set”’ and “Many ‘Set vs. One”’ pairwise comparisons, respectively. Experimental results show that our CoFiSet(MSO) performs better than several state-of-the-art methods on five ranking-oriented evaluation metrics on three real-world data sets.
Cytokine and hormone responses to resistance training

This study examined the effects of heavy resistance training on physiological acute exercise-induced fatigue (5 × 10 RM leg press) changes after two loading protocols with the same relative intensity (%) (5 × 10 RMRel) and the same absolute load (kg) (5 × 10 RMAbs) as in pretraining in men (n = 12). Exercise-induced neuromuscular (maximal strength and muscle power output), acute cytokine and hormonal adaptations (i.e., total and free testosterone, cortisol, growth hormone (GH), insulin-like growth factor-1 (IGF-1), IGF binding protein-3 (IGFBP-3), interleukin-1 receptor antagonist (IL-1ra), IL-1β, IL-6, and IL-10 and metabolic responses (i.e., blood lactate) were measured before and after exercise. The resistance training induced similar acute responses in serum cortisol concentration but increased responses in anabolic hormones of FT and GH, as well as inflammation-responsive cytokine IL-6 and the anti-inflammatory cytokine IL-10, when the same relative load was used. This response was balanced by a higher release of pro-inflammatory cytokines IL-1β and cytokine inhibitors (IL-1ra) when both the same relative and absolute load was used after training. This enhanced hormonal and cytokine response to strength exercise at a given relative exercise intensity after strength training occurred with greater accumulated fatigue and metabolic demand (i.e., blood lactate accumulation). The magnitude of metabolic demand or the fatigue experienced during the resistance exercise session influences the hormonal and cytokine response patterns. Similar relative intensities may elicit not only higher exercise-induced fatigue but also an increased acute hormonal and cytokine response during the initial phase of a resistance training period.
POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses.
 Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns.
 In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks.
Strategic Management and Michael Porter: a Postmodern Reading

Is Porter postmodern? The project originated in my need to ‘make sense’ of the strategic management literature, and specifically the place of Michael E Porter within it. The question, what is strategic management?, often leads to the work of Porter. Strategic management texts inevitably contain his models, theories and frameworks which imply that they are ‘fundamental’ to the field. An historical journey through six prominent management\organization journals, Strategic Management Journal, Academy of Management Journal, Academy of Management Review, Journal of Management Studies, Organization Studies, Advances in Strategic Management, shows that Michael E Porter was not a constant contributor, in fact he is almost absent from the journals, but his work is often the study of empirical testing or theoretical debate (Foss 1996; Hill & Deeds 1996; Sharp & Dawson 1994; Miller & Dess 1993; Bowman 1992). This article does not attempt to account for his popularity, as others have offered substantial and convincing accounts (Barry & Elmes 1997; Whipp 1996; Knights 1992).
On-line Handwritten Signature Verification using Machine Learning Techniques with a Deep Learning Approach

The problem to be solved in this project is to distinguish two signatures from each other, with help of machine learning techniques. The main technique used is the comparison between two signatures and classifying if they are written by the same person (match) or not (no-match). The binary classification problem is then tackled with a few alternatives to better understand it. First by a simple engineered feature, then by the machine learning techniques as logistic regression, multi-layer perceptron and finally a deep learning approach with a convolutional neural network. The evaluation method for the di↵erent algorithms was a plot of true positive rate (sensitivity) versus false positive rate (fall-out). The results of the alternative algorithms gave a di↵erent understanding of the problem. The engineered feature performed unexpectedly well. The logistic regression and multi-layer perceptron performed similarly. The main results from the final model, which was a max-pooling, convolutional neural network, were a true positive rate of 96.7 % and a false positive rate of 0.6 %. The deep learning approach on the signature verification problem shows promising results but there is still room for improvement.
Imbalanced Malware Images Classification: a CNN based Approach

Deep convolutional neural networks (CNNs) can be applied to malware binary detection through images classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs. The original softmax loss is weighted, and the weight value can be determined according to class size. A scaling parameter is also included in computing the weight. Proper selection of this parameter has been studied and an empirical option is given. The weighted loss aims at alleviating the impact of data imbalance in an end-to-end learning fashion. To validate the efficacy, we deploy the proposed weighted loss in a pre-trained deep CNN model and fine-tune it to achieve promising results on malware images classification. Extensive experiments also indicate that the new loss function can fit other typical CNNs with an improved classification performance.  Deep Learning, Malware Images, Convolutional Neural Networks, CNN, Image Classification, Imbalanced Data Classification, Softmaxloss
Inner Space Preserving Generative Pose Machine

Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image “inner space” preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.
Estimation of Panel Vector Autoregression in Stata: a Package of Programs

Panel vector autoregression (VAR) models have been increasingly used in applied research. While programs specifically designed to estimate time-series VAR models are often included as standard features in most statistical packages, panel VAR model estimation and inference are often implemented with general-use routines that require some programming dexterity. In this paper, we briefly discuss model selection, estimation and inference of homogeneous panel VAR models in a generalized method of moments (GMM) framework, and present a set of Stata programs to conveniently execute them. We illustrate the pvar package of programs by using standard Stata datasets.
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

In this paper, we propose <italic>DeepCut</italic>, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known <italic>GrabCut</italic> <xref ref-type="bibr" rid="ref1">[1]</xref> method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the <italic>DeepCut</italic> method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
Predicting student personality based on a data-driven model from student behavior on LMS and social networks

E-learning has become an essential factor in the modern educational system. In today's diverse student population, E-learning must recognize the differences in student personalities to make the learning process more personalized. The objective of this study is to create a data model to identify both the student personality type and the dominant preference based on the Myers-Briggs Type Indicator (MBTI) theory. The proposed model utilizes data from student engagement with the learning management system (Moodle) and the social network, Facebook. The model helps students become aware of their personality, which in turn makes them more efficient in their study habits. The model also provides vital information for educators, equipping them with a better understanding of each student's personality. With this knowledge, educators will be more capable of matching students with their respective learning styles. The proposed model was applied on a sample data collected from the Business College at the German university in Cairo, Egypt (240 students). The model was tested using 10 data mining classification algorithms which were NaiveBayes, BayesNet, Kstar, Random forest, J48, OneR, JRIP, KNN /IBK, RandomTree, Decision Table. The results showed that OneR had the best accuracy percentage of 97.40%, followed by Random forest 93.23% and J48 92.19%.
Risky Business: Emotion, Decision-Making, and Addiction

Although metabolic abnormalities in the orbitofrontal cortex have been observed in substance dependent individuals (SDI) for several years, very little attention was paid to the role of this brain region in addiction. However, patients with damage to the ventromedial (VM) sector of the prefrontal cortex and SDI show similar behaviors. (1) They often deny, or they are not aware, that they have a problem. (2) When faced with a choice to pursue a course of action that brings an immediate reward at the risk of incurring future negative consequences, they choose the immediate reward and ignore the future consequences. Studies of patients with bilateral lesions of the VM prefrontal cortex support the view that the process of decision-making depends in many important ways on neural substrates that regulate homeostasis, emotion, and feeling. Parallel lines of study have revealed that VM cortex dysfunction is also evident in subgroups of individuals who are addicted to substances. Thus, understanding the neural mechanisms of decision-making has direct implications for understanding disorders of addiction and pathological gambling, and the switch from a controlled to uncontrolled and compulsive behavior. On the clinical front, the approach to treat addictive disorders has been dominated by a diagnostic system that focuses on behaviors, physical symptoms, or choice of drugs. The article emphasizes the concept of using neurocognitive criteria for subtyping addictive disorders. This is a significant paradigm shift with significant implications for guiding diagnosis and treatment. Using neurocognitive criteria could lead to more accurate subtyping of addictive disorders, and perhaps serve as a guide for more specific, and potentially more successful, behavioral and pharmacological interventions.
From Patches to Natural Images via Hierarchical Dirichlet Processes

We propose a hierarchical generative model that captures the self-similar structure of image regions as well as how this structure is shared across image collections. Our model is based on a novel, variational interpretation of the popular expected patch log-likelihood (EPLL) method as a model for randomly positioned grids of image patches. While previous EPLL methods modeled the density of image patches with finite Gaussian mixtures, we use nonparametric Dirichlet process (DP) mixtures to create models whose complexity grows as additional images are observed. An extension based on the hierarchical DP then captures the repetitive and self-similar structure of image regions via image-specific variations in cluster frequencies. We derive a structured variational inference algorithm that uses birth and delete moves to create new patch clusters and thus more accurately model novel image textures. Our denoising performance on standard benchmarks is superior to EPLL and comparable to the state-of-the-art, while providing a novel statistical interpretation for many common image processing heuristics.
LSDSem 2017 Shared Task: The Story Cloze Test

The LSDSem’17 shared task is the Story Cloze Test, a new evaluation for story understanding and script learning. This test provides a system with a four-sentence story and two possible endings, and the system must choose the correct ending to the story. Successful narrative understanding (getting closer to human performance of 100%) requires systems to link various levels of semantics to commonsense knowledge. A total of eight systems participated in the shared task, with a variety of approaches including end-to-end neural networks, feature-based regression models, and rule-based methods. The highest performing system achieves an accuracy of 75.2%, a substantial improvement over the previous state-of-the-art.