nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning in Label-free Cell Classification.

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.
Comparison of efficacy of tray-delivered carbamide and hydrogen peroxide for at-home bleaching: a systematic review and meta-analysis

The aim of the study was to compare the color change produced by tray-delivered carbamide peroxide [CP] versus hydrogen peroxide products [HP] for at-home bleaching through a systematic review and meta-analysis. MEDLINE via PubMeb, Scopus, Web of Science, Latin American and Caribbean Health Sciences Literature database (LILACS), Brazilian Library in Dentistry (BBO), and Cochrane Library and Grey literature were searched without restrictions. The abstracts of the International Association for Dental Research (IADR) and unpublished and ongoing trial registries were also searched. Dissertations and theses were explored using the ProQuest Dissertations and Periodicos Capes Theses databases. We included randomized clinical trials that compared tray-delivered CP versus HP for at-home dental bleaching. The color change in shade guide units (SGU) and ΔE were the primary outcomes, and tooth sensitivity and gingival irritation were the secondary outcomes. The risk of bias tool of the Cochrane Collaboration was used for quality assessment. After duplicate removal, 1379 articles were identified. However, only eight studies were considered to be at “low” risk of bias in the key domains of the risk bias tool and they were included in the analysis. For ΔE, the standardized mean difference was −0.45 (95 % CI −0.69 to −0.21), which favored tray-delivered CP products (p < 0.001). The color change in ΔSGU (p = 0.70), tooth sensitivity (p = 0.83), and gingival irritation (p = 0.62) were not significantly different between groups. Tray-delivered CP gels showed a slightly better whitening efficacy than HP-based products in terms of ΔE, but they were similar in terms of ΔSGU. Both whitening systems demonstrated equal level of gingival irritation and tooth sensitivity. Tray-delivered CP gels have a slightly better whitening efficacy than HP-based products in terms of ΔE. This should be interpreted with caution as the data of ΔSGU did not show statistical difference between the products.
Convolutional Deep Neural Networks for Document-Based Question Answering

Document-based Question Answering aims to compute the similarity or relevance between two texts: question and answer. It is a typical and core task and considered as a touchstone of natural language understanding. In this article, we present a convolutional neural network based architecture to learn feature representations of each questionanswer pair and compute its match score. By taking the interaction and attention between question and answer into consideration, as well as word overlap indices, the empirical study on Chinese Open-Domain Question Answering (DBQA) Task (document-based) demonstrates the efficacy of the proposed model, which achieves the best result on NLPCC-ICCPOL 2016 Shared Task on DBQA.
Enriching Word Vectors with Subword Information

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.
Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition

We analyze stochastic gradient descent for optimizing nonconvex functions. In many cases for nonconvex functions the goal is to find a reasonable local minimu m, and the main concern is that gradient updates are trapped in saddle points . In this paper we identifystrict saddleproperty for non-convex problem that allows for efficient optimization. Using this p ro erty we show that stochastic gradient descent converges to a local minimum in a polynomial number o f iterations. To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on nonconvex functions with exponentially many local minima and s a dle points. Our analysis can be applied to orthogonal tensor decomposit ion, which is widely used in learning a rich class of latent variable models. We propose a new optim ization formulation for the tensor decomposition problem that has strict saddle property. As a result we get the first online algorithm for orthogonal tensor decomposition with global convergen ce guarantee.
Attention Solves Your TSP

We propose a framework for solving combinatorial optimization problems of which the output can be represented as a sequence of input elements. As an alternative to the Pointer Network, we parameterize a policy by a model based entirely on (graph) attention layers, and train it efficiently using REINFORCE with a simple and robust baseline based on a deterministic (greedy) rollout of the best policy found during training. We significantly improve over state-of-the-art results for learning algorithms for the 2D Euclidean TSP, reducing the optimality gap for a single tour construction by more than 75% (to 0.33%) and 50% (to 2.28%) for instances with 20 and 50 nodes respectively.
Neural Networks Regularization Through Representation Learning

Thèse soutenue publiquement le 06 Juillet 2018 devant le jury composé de Sébastien ADAM Professeur à l'Université de Rouen Normandie Directeur de thèse Clément CHATELAIN Maître de conférence à l'INSA Rouen Normandie Encadrant de thèse Romain HÉRAULT Maître de conférence à l'INSA Rouen Normandie Encadrant de thèse Elisa FROMONT Professeur à l'Université de Rennes 1 Rapporteur de thèse Thierry ARTIÈRES Professeur à l'École Centrale Marseille Rapporteur de thèse John LEE Professeur à l'Université Catholique de Louvain Examinateur de thèse
The impact of a mass media campaign on personal risk perception, perceived self-efficacy and on other behavioural predictors.

To determine whether an AIDS prevention mass media campaign influenced risk perception, self-efficacy and other behavioural predictors. We used household survey data collected from 2,213 sexually experienced male and female Kenyans aged 15-39. Respondents were administered a questionnaire asking them about their exposure to branded and generic mass media messages concerning HIV/AIDS and condom use. They were asked questions concerning their personal risk perception, self-efficacy, condom effectiveness, condom availability, and their embarrassment in obtaining condoms. Logistic regression analysis was used to determine the impact of exposure to mass media messages on these predictors of behaviour change. Those exposed to branded advertising messages were significantly more likely to consider themselves at higher risk of acquiring HIV and to believe in the severity of AIDS. Exposure to branded messages was also associated with a higher level of personal self-efficacy, a greater belief in the efficacy of condoms, a lower level of perceived difficulty in obtaining condoms and reduced embarrassment in purchasing condoms. Moreover, there was a dose-response relationship: a higher intensity of exposure to advertising was associated with more positive outcomes. Exposure to generic advertising messages was less frequently associated with positive health beliefs and these relationships were also weaker. Branded mass media campaigns that promote condom use as an attractive lifestyle choice are likely to contribute to the development of perceptions that are conducive to the adoption of condom use.
Studying Organizations on Instagram

With the rise of social media platforms based on the sharing of pictures and videos, the question of how such platforms should be studied arises. Previous research on social media (content) has mainly focused on text (written words) and the rather text-based social media platforms Twitter and Facebook. Drawing on research in the fields of visual, political, and business communication, we introduce a methodological framework to study the fast-growing image-sharing service Instagram. This methodological framework was developed to study political parties’ Instagram accounts and tested by means of a study of Swedish political parties during the 2014 election campaign. In this article, we adapt the framework to also study other types of organizations active on Instagram by focusing on the following main questions: Do organizations only use Instagram to share one-way information, focusing on disseminating information and self-presentation? Or is Instagram used for two-way communication to establish and cultivate organization-public relationships? We introduce and discuss the coding of variables with respect to four clusters: the perception of the posting, image management, integration, and interactivity.
A survey on wearable biosensor systems for health monitoring

Wearable biosensor systems for health monitoring are an emerging trend and are expected to enable proactive personal health management and better treatment of various medical conditions. These systems, comprising various types of small physiological sensors, transmission modules and processing capabilities, promise to change the future of health care, by providing low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper presents a comprehensive survey on the research and development done so far on wearable biosensor systems for health-monitoring, by comparing a variety of current system implementations and approaches and identifying their technological shortcomings. A set of significant features, that best describe the functionality and the characteristics of wearable biosensor systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for current achievements and their maturity level and to provide direction for future research improvements.
Concept-based Short Text Classification and Ranking

Most existing approaches for text classification represent texts as vectors of words, namely ``Bag-of-Words.'' This text representation results in a very high dimensionality of feature space and frequently suffers from surface mismatching. Short texts make these issues even more serious, due to their shortness and sparsity. In this paper, we propose using ``Bag-of-Concepts'' in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem. Based on ``Bag-of-Concepts,'' a novel framework is proposed for lightweight short text classification applications. By leveraging a large taxonomy knowledgebase, it learns a concept model for each category, and conceptualizes a short text to a set of relevant concepts. A concept-based similarity mechanism is presented to classify the given short text to the most similar category. One advantage of this mechanism is that it facilitates short text ranking after classification, which is needed in many applications, such as query or ad recommendation. We demonstrate the usage of our proposed framework through a real online application: Channel-based Query Recommendation. Experiments show that our framework can map queries to channels with a high degree of precision (avg. precision=90.3%), which is critical for recommendation applications.
Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics

Today, it is not clear how the impact of research on other areas of society than science should be measured. While peer review and bibliometrics have become standard methods for measuring the impact of research in science, there is not yet an accepted framework within which to measure societal impact. Alternative metrics (called altmetrics to distinguish them from bibliometrics) are considered an interesting option for assessing the societal impact of research, as they offer new ways to measure (public) engagement with research output. Altmetrics is a term to describe web-based metrics for the impact of publications and other scholarly material by using data from social media platforms (e.g. Twitter or Mendeley). This overview of studies explores the potential of altmetrics for measuring societal impact. It deals with the definition and classification of altmetrics. Furthermore, their benefits and disadvantages for measuring impact are discussed.
On the (Statistical) Detection of Adversarial Examples

Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or malware classification. Yet, these models are vulnerable to a class of malicious inputs known as adversarial examples. These are slightly perturbed inputs that are classified incorrectly by the ML model. The mitigation of these adversarial inputs remains an open problem. As a step towards a model-agnostic defense against adversarial examples, we show that they are not drawn from the same distribution than the original data, and can thus be detected using statistical tests. As the number of malicious points included in samples presented to the test diminishes, its detection confidence decreases. Hence, we introduce a complimentary approach to identify specific inputs that are adversarial among sets of inputs flagged by the statistical test. Specifically, we augment our ML model with an additional output, in which the model is trained to classify all adversarial inputs. We evaluate our approach on multiple adversarial example crafting methods (including the fast gradient sign and Jacobian-based saliency map methods) with several datasets. The statistical test flags sample sets containing adversarial inputs with confidence above 80%. Furthermore, our augmented model either detects adversarial examples with high accuracy (> 80%) or increases the adversary’s cost—the perturbation added—by more than 150%. In this way, we show that statistical properties of adversarial examples are essential to their detection.
Can priors be trusted? Learning to anticipate roadworks

This paper addresses the question of how much a previously obtained map of a road environment should be trusted for vehicle localisation during autonomous driving by assessing the probability that roadworks are being traversed. We compare two formulations of a roadwork prior: one based on Gaussian Process (GP) classification and the other on a more conventional Hidden Markov Model (HMM) in order to model correlations between nearby parts of a vehicle trajectory. Importantly, our formulation allows this prior to be updated efficiently and repeatedly to gain an ever more accurate model of the environment over time. In the absence of, or in addition to, any in-situ observations, information from dedicated web resources can readily be incorporated into the framework. We evaluate our model using real data from an autonomous car and show that although the GP and HMM are roughly commensurate in terms of mapping roadworks, the GP provides a more powerful representation and lower prediction error.
Possession and the double object construction

This paper argues that double-object verbs decompose into two heads, an external-argumentselecting CAUSE predicate (vCAUSE) and a prepositional element, PHAVE. Two primary types of argument are presented. First, a consideration of the well-known Oerhle’s generalization effects in English motivate such a decomposition, in combination with a consideration of idioms in ditransitive structures. These facts mitigate strongly against a Transform approach to the dative alternation, like that of Larson 1988, and point towards an Alternative Projection approach, similar in many respects to that of Pesetsky 1995. Second, the PHAVE prepositional element is identified with the prepositional component of verbal have, treated in the literature by Benveniste 1966; Freeze 1992; Kayne 1993; Guéron 1995. Languages without PHAVE do not allow possessors to c-command possessees, and show no evidence of a double-object construction, in which Goals c-command Themes. On the current account, these two facts receive the same explanation: PHAVE does not form part of the inventory of morphosyntactic primitives of these languages.
Qualitative Research : A Grounded Theory Example and Evaluation Criteria

The qualitative research paradigm, although occasionally applied, is not widely discussed in agribusiness and agricultural economics literature. The primary goals of this paper are (a) to present insights into qualitative research approaches and processes by outlining grounded theory as an example of a systematic and rigorous qualitative approach, and (b) to discuss criteria for scientific rigor applicable to qualitative research. In addition, assessing qualitative research is demonstrated by using a published example.
Modelado difuso Takagi-Sugeno para sintonizar un controlador de calefacción en invernaderos

Resumen. Este artículo trata sobre el modelado y el control de temperatura en un invernadero, con el fin de compensar las perturbaciones ante la presencia de heladas. Las condiciones de operación involucran una ventilación pasiva, y la irrigación se encuentra apagada. De manera que sólo se controla la temperatura mediante calefacción. Para sintonizar el controlador, un modelo difuso TakagiSugeno fue empleado y contruido usando el algoritmo fuzzy c-means para las premisas y mínimos cuadrados para los consecuentes. Mediante mediciones experimentales, donde se activó la calefacción, fue posible sintonizar un modelo difuso. Cada regla difusa corresponde a un submodelo, esta técnica es conocida como compensación paralela distribuida (CPD). El controlador propuesto está basado en el lugar geométrico de las raices para cada submodelo lineal. La adquisición de datos del microclima se realizó LabVIEW. Los resultados obtenidos son para aplicarse a una válvula proporcional para calefacción, y se muestran los resultados en simulación para dos tipos de aprendizaje: local y global para los consecuentes de los submodelos difusos del tipo Takagi-Sugeno.
Sural artery perforator flap with posterior tibial neurovascular decompression for recurrent foot ulcer in leprosy patients

The sensory loss and alteration of the shape of the foot make the foot liable to trauma and pressure, and subsequently cause more callus formation, blisters, and ulcers. Foot ulcers usually are liable to secondary infection as cellulitis or osteomyelitis, and may result in amputations. Foot ulcers are a major problem and a major cause of handicaps in leprosy patients. The current study is to present our clinical experience and evaluate the use of sural flap with posterior tibial neurovascular decompression (PTND) in recurrent foot ulcers in leprosy patients.  A total number of 9 patients were suffering from chronic sequelae of leprosy as recurrent foot ulcers. All the patients were reconstructed with the reverse sural artery fasciocutaneous flap with posterior tibial neurovascular decompression from September 2012 to August 2015. Six patients were male and three were female with a mean age of 39.8 years (range, 30-50 years). All the soft tissue defects were in the weight-bearing area of the inside of the foot. The flap sizes ranged from 15/4 to 18/6 cm. Mean follow-up period was 21.2 months (range, 35-2 months).  All the flaps healed uneventfully. There was no major complication as total flap necrosis. Only minor complications occurred which were treated without surgical intervention except in two patients who developed superficial necrosis of the skin paddle. Surgical debridement was done one week later. The flap was completely viable after surgery, and the contour of the foot was restored. We found that an improvement of sensation occurred in those patients in whom the anesthesia started one year ago or less and no sensory recovery in patient in whom the anesthesia had lasted for more than two years.  The reverse sural artery flap with posterior tibial neurovascular decompression provides a reliable method for recurrent foot soft tissue reconstruction in leprosy patients with encouraging function and aesthetic outcomes. It is a quick and easy procedure.