nils-herrmann/clean_scirepeval huggingface dataset:
Developmental course of subclinical positive and negative psychotic symptoms and their associations with genetic risk status and impairment
Venue: Schizophrenia Research
Year: 2016

The proneness persistence impairment (PPI) model states that psychotic experiences are more likely to lead to impairment if their expression becomes persistent. Higher genetic risk for psychosis is known to affect proneness and persistence of subclinical positive symptoms. Less is known about potential effects of genetic risk on the course of subclinical negative symptoms, impairment, and their subsequent associations. The current study examined these issues in a large sample (n=1131) consisting of individuals with higher genetic risk (siblings of patients with psychotic disorders, n=703) and lower genetic risk (controls without a family member with lifetime psychosis, n=428) Psychotic experiences were assessed with the CAPE questionnaire, at two time points three years apart. Participants were allocated to one of four groups representing developmental course: stable low, decreasing, increasing or persisting subclinical positive/negative symptoms. Lifetime clinical psychosis was an exclusion criterion at baseline. Higher genetic risk status was found to be associated with a persisting course of both subclinical positive and negative symptoms, symptom related distress and functional impairment. There is no evidence for an effect of genetic risk status on the association between developmental course and impairment. The results of the current study underline the importance of assessing psychotic experiences in the context of genetic risk, multidimensional and over time. Additionally, the current findings both underscore and contribute to the PPI model: psychotic experiences are more likely to lead to impairment if their expression becomes persistent, both in individuals with higher and lower genetic risk for psychosis.
Block adaptive CNN/HEVC interframe prediction for video coding
Venue: Other Conferences
Year: 2019

This paper proposes a block adaptive CNN/HEVC prediction method for bidirectional motion compensated prediction which is one of the key technologies for video coding. We have already proposed a deep convolutional neural network (CNN) model which can predict the target block to be predicted from its spatially co located blocks existed in the temporally previous and future frames. The CNN model estimates four geometric transformation matrices, and the predicted values are produced by transforming both previous block and future block using them. We have demonstrated that the method is greatly effective for frames with complicated motion. However, the conventional CNN model often did not work well for the frames with large motion. In this paper, we introduce an adaptation method which selects the one of CNN based prediction and HEVC based prediction. Experimental results show that the prediction error using the proposed method can be reduced to 60% to 90% for many kinds of videos with complex motions compared to only HEVC prediction.
Analysis, synchronisation and circuit implementation of a novel jerk chaotic system and its application for voice encryption
Venue: Int. J. Model. Identif. Control.
Year: 2017

In this research work, a novel 3D jerk chaotic system with one quadratic nonlinearity and two cubic nonlinearities is designed to generate complex chaotic signals. We show that the novel jerk chaotic system has a unique equilibrium at the origin, which is a saddle focus and unstable. The Lyapunov exponents of the novel jerk chaotic system are obtained as L1 0.30899, L2 0 and L3 4.11304. The Kaplan Yorke dimension of the novel jerk chaotic system is obtained as DKY 2.0751. The qualitative properties of the novel jerk chaotic system are described in detail and MATLAB plots are shown. Next, we use backstepping control method to establish global chaos synchronisation of the identical novel jerk chaotic systems with unknown parameters. Next, an electronic circuit realisation of the novel jerk chaotic system is presented using MultiSIM to confirm the feasibility of the theoretical model. Finally, we present an application of the novel jerk chaotic system for voice encryption. The comparison between the MATLAB 2010 and MultiSIM 10.0 simulation results demonstrate the effectiveness of the proposed voice encryption scheme.
Hierarchical vs non hierarchical audio indexation and classification for video genres
Venue: International Conference on Machine Vision
Year: 2018

In this paper, Support Vector Machines (SVMs) are used for segmenting and indexing video genres based on only audio features extracted at block level, which has a prominent asset by capturing local temporal information. The main contribution of our study is to show the wide effect on the classification accuracies while using an hierarchical categorization structure based on Mel Frequency Cepstral Coefficients (MFCC) audio descriptor. In fact, the classification consists in three common video genres: sports videos, music clips and news scenes. The sub classification may divide each genre into several multi speaker and multi dialect sub genres. The validation of this approach was carried out on over 360 minutes of video span yielding a classification accuracy of over 99%
Genetic and environmental influences on human height from infancy through adulthood at different levels of parental education
Venue: Scientific Reports
Year: 2020

Genetic factors explain a major proportion of human height variation, but differences in mean stature have also been found between socio economic categories suggesting a possible effect of environment. By utilizing a classical twin design which allows decomposing the variation of height into genetic and environmental components, we tested the hypothesis that environmental variation in height is greater in offspring of lower educated parents. Twin data from 29 cohorts including 65,978 complete twin pairs with information on height at ages 1 to 69 years and on parental education were pooled allowing the analyses at different ages and in three geographic cultural regions (Europe, North America and Australia, and East Asia) Parental education mostly showed a positive association with offspring height, with significant associations in mid childhood and from adolescence onwards. In variance decomposition modeling, the genetic and environmental variance components of height did not show a consistent relation to parental education. A random effects meta regression analysis of the aggregate level data showed a trend towards greater shared environmental variation of height in low parental education families. In conclusion, in our very large dataset from twin cohorts around the globe, these results provide only weak evidence for the study hypothesis.
Multi relational learning via hierarchical nonparametric Bayesian collective matrix factorization
Year: 2015

Relational learning addresses problems where the data come from multiple sources and are linked together through complex relational networks. Two important goals are pattern discovery (e.g. by (co) clustering) and predicting unknown values of a relation, given a set of entities and observed relations among entities. In the presence of multiple relations, combining information from different but related relations can lead to better insights and improved prediction. For this purpose, we propose a nonparametric hierarchical Bayesian model that improves on existing collaborative factorization models and frames a large number of relational learning problems. The proposed model naturally incorporates (co) clustering and prediction analysis in a single unified framework, and allows for the estimation of entire missing row or column vectors. We develop an efficient Gibbs algorithm and a hybrid Gibbs using Newton's method to enable fast computation in high dimensions. We demonstrate the value of our framework on simulated experiments and on two real world problems: discovering kinship systems and predicting the authors of certain articles based on article word co occurrence features.
Advanced Big Data Management and Analytics for Ubiquitous Sensors
Venue: Int. J. Distributed Sens. Networks
Year: 2015

It is estimated that, by 2020, 40 zettabytes of data will be created. The convergence of pervasive sensing with locationaware and social media technologies, along with infrastructure based sensors, will lead to the production and collection of "big data" in many areas such as transportation, healthcare, and energy. For example, today, there are 6 billion cell phone users in the world. Cell phones equipped with multiple sensors are producing large volumes of data each day. The data obtained may be structured or unstructured, ranging from GPS trajectories to text, video, still images, and others. This opens up new challenges and opportunities to address the key aspects of sensor based big data, namely, volume, velocity, variety, and veracity. This special issue aims to foster the dissemination of knowledge for advanced issues in big data management and analytics for ubiquitous sensors. This special issue will be an open international forum for researchers to summarize their latest research results. The call for papers included a number of related topics such as distributed/parallel processing of streaming data, privacy protection and security issues in sensor based big data, and data fusion techniques for distributed big data. The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. The paper by I. Ha et al. proposes a parallel approach usingMapReduce for sentiment analysis of big data in social media. The paper by Y. Yu et al. presents a parallel approach using Hadoop for density based clustering of big data. The paper by K. Omote and T. P. Thao describes a light weight network coding scheme to provide integrity of the data when stored in cloud servers. The paper by H. J. Jo and J. W. Yoon presents a countermeasure to prevent bruteforce attacks in high performance computing platforms for big data analytics. The papers by H. Kang et al. and Y. Ki et al. propose new analysis based approaches to detect malware in mobile/smart devices. Finally, the paper by S. W. Jang G.Y. Kim presents a multiple feature based image switching strategy in visual sensor networks.
[Alzheimer's disease and Helicobacter pylori infection: a possible link?
Venue: Geriatrie et psychologie neuropsychiatrie du vieillissement
Year: 2016

Alzheimer's disease (AD) is associated with Ass peptide and Tau protein deposits, but the initial process inducing the disease and ultimately neurodegeneration has not yet been elucidated. An infectious hypothesis is suggested by the alteration of the blood brain barrier and the activation of neuroinflammation in the brain, which could play a role, especially in the decrease of Ass peptide clearance. Several viral or bacterial agents have been incriminated, including Helicobacter pylori. Infection by H. pylori is acquired during childhood and often lifetime persisting, inducing a chronic gastric inflammation, which remains asymptomatic in approximately 80% of cases. However H. pylori infection can induce systemic inflammation and increase homocysteine levels, contributing to worsen AD lesions. Association between H. pylori and AD is suggested by 1) epidemiologic studies, which show higher AD prevalence and more pronounced cognitive impairment in infected than in non infected subjects; 2) experimental studies in murine models: a) in a first study we evaluated the impact of H. pylori infection on the brain of non AD predisposed C57BL/6J mice. After an 18 month infection, H. pylori induced a significant gastric inflammation but no brain Ab deposit nor increased neuroinflammation was observed in their brain; b) we currently study the impact of Helicobacter species infection on behavior and cerebral lesions of AD transgenic (APPswe/PS1dE9) mice and their wild type littermate. The results of these studies do not allow to conclude a significant association between AD and H. pylory infection but may contribute to a better understanding of the role of brain neuroinflammation in AD.
Exploratory Data Analysis of Adverse Birth Outcomes and Exposure to Oxides of Nitrogen Using Interactive Parallel Coordinates Plot Technique
Venue: Scientific Reports
Year: 2020

We propose that a parallel coordinates plot can be used to study multidimensional data particularly to explore discovery of patterns across the variables. This can assist researchers from the health sciences to visualize their cohort data with interactive data analysis. The study used data from Mother and Child in the Environment birth cohort in Durban, South Africa for the period 2013 to 2017 retrospectively registered. In this paper, we demonstrate that the exploration of multidimensional data with parallel coordinates plot and use of brushing using different colours assists with the identification of relationships and patterns. Parallel coordinates plot visualization facilitates the researcher's skills to find trends, identify outliers and perform quality checks in large multivariate data. We have identified trends in the data that provide directions for further research, and illustrated thereby the potential of parallel coordinates plot to explore patterns and relationships of prenatal oxides of nitrogen exposure with multidimensional birth outcomes. The study recognized the co occurrence of adverse birth outcomes among infants and these infants had mothers with moderate to high level of NOx exposure during pregnancy. Brushing using different colours facilitated the detection of patterns of relationships to perform basic and advanced statistical model based analysis.
Traffic scene semantic segmentation using self attention mechanism and bi directional GRU to correlate context
Venue: Neurocomputing
Year: 2020

Context information plays an important role in semantic segmentation of urban traffic scenes, which is one of the key tasks of the intelligent platform's (such as unmanned vehicles) perceiving environment, and has inspired a wide range of interests from researchers. This paper synthesizes three considerations: feature space correlation, information distributed in the long distance of image plane and long distance sequence information, and proposes a combination of self attention mechanism and bi directional gated recurrent unit (GRU) neural network to extract various contextual information on the basis of deep feature network, so as to achieve better semantic segmentation performance. In order to explore the optimal implementation, two kinds of topological connections are attempted. One is self attention branch and bi directional GRU branch in series, and the other is in parallel. In addition, in order to train the network better and achieve more precise segmentation results, a cascade refinement supervised method using two losses is proposed. Experiments carried out on Cityscapes, Mapillary, CamVid and KITTI semantic segmentation datasets demonstrate the outstanding performance and robust generalization ability of our method.
Transforming graph based sentence representations to alleviate overfitting in relation extraction
Venue: DocEng '14
Year: 2014

Relation extraction (RE) aims at finding the way entities, such as person, location, organization, date, etc. depend upon each other in a text document.  Population, Automatic Summarization, and Question Answering are fields in which relation extraction offers valuable solutions. A relation extraction method based on inductive logic programming that induces extraction rules suitable to identify semantic relations between entities was proposed by the authors in a previous work. This paper proposes a method to simplify graph based representations of sentences that replaces dependency graphs of sentences by simpler ones, keeping the target entities in it. The goal is to speed up the learning phase in a RE framework, by applying several rules for graph simplification that constrain the hypothesis space for generating extraction rules. Moreover, the direct impact on the extraction performance results is also investigated. The proposed techniques outperformed some other state of the art systems when assessed on two standard datasets for relation extraction in the biomedical domain.
Page Avenue health impact assessment: building on diverse partnerships and evidence to promote a healthy community.
Venue: Health place
Year: 2012

The Page Avenue health impact assessment (HIA) was focused on a redevelopment in Missouri. This case study describes a comprehensive HIA led by an interdisciplinary academic team with community partners, as well as compliance with North American HIA Practice Standards. Some of the key lessons learned included: (1) interdisciplinary teams are valuable but they require flexibility and organization; (2) engaging community stakeholders and decision makers prior to, during, and following the HIA is critical to a successful HIA; and (3) HIA teams should not be too closely affiliated with decision makers. It is hoped that this case study will inform future HIAs.
Incorporating Priors with Feature Attribution on Text Classification
Venue: ACL
Year: 2019

Feature attribution methods, proposed recently, help users interpret the predictions of complex models. Our approach integrates feature attributions into the objective function to allow machine learning practitioners to incorporate priors in model building. To demonstrate the effectiveness our technique, we apply it to two tasks: (1) mitigating unintended bias in text classifiers by neutralizing identity terms; (2) improving classifier performance in scarce data setting by forcing model to focus on toxic terms. Our approach adds an L2 distance loss between feature attributions and task specific prior values to the objective. Our experiments show that i) a classifier trained with our technique reduces undesired model biases without a tradeoff on the original task; ii) incorporating prior helps model performance in scarce data settings.
Impact of writing course design based on 5E Learning Model on writing skill instruction and development.
Year: 2020

This study aims to investigate the effect of 5E Learning Model based Essay Writing course design and implementation on the achievement level of 12th grade high school students in Essay Writing Course. The teaching and development of writing skills in foreign language education is very important, but it is a known fact that our foreign language education presents problems even in the level of middle and higher education in the foreign language education system. In this respect, the development of writing skills and the improvement of the lessons are very important. The study is a case study with mixed method in which both qualitative and quantitative data collection procedures and instruments such as achievement tests, peer feedback form, students' progress check form, and teacher performance observation form are employed together. In the present study, qualitative data, collected by means of peer evaluation, student progress check and teacher performance observation, is subjected to descriptive analysis whereas quantitative data, collected by means of Essay Writing achievement tests which are applied to twenty two 12th grade students before and after the instruction and implementation of 5E Learning Model based Essay Writing Course as preand post test, is analyzed by paired sample t test. According to the results of the analysis, it can be said that the implementation of 5E Learning Model based Essay Writing course has a positive effect on the achievement level of students in Essay Writing Course.
Seizures and Epilepsy in Autism Spectrum Disorder.
Venue: Child and adolescent psychiatric clinics of North America
Year: 2020

Epilepsy and autism frequently co occur. Epilepsy confers an increased risk of autism and autism confers an increased risk of epilepsy. Specific epilepsy syndromes, intellectual disability, and female gender present a particular risk of autism in individuals with epilepsy. Epilepsy and autism are likely to share common etiologies, which predispose individuals to either or both conditions. Genetic factors, metabolic disorders, mitochondrial disorders, and immune dysfunction all can be implicated.
Development of an antimicrobial peptide loaded mineralized collagen bone scaffold for infective bone defect repair
Venue: Regenerative biomaterials
Year: 2020

The repair of infective bone defects is a great challenge in clinical work. It is of vital importance to develop a kind of bone scaffold with good osteogenic properties and long term antibacterial activity for local anti infection and bone regeneration. A porous mineralized collagen (MC) scaffold containing poly(d,l lactide co glycolic acid) (PLGA) microspheres loaded with two antibacterial synthetic peptides, Pac 525 or KSL W was developed and characterized via scanning electron microscopy (SEM) porosity measurement, swelling and mechanical tests. The results showed that the MC scaffold embedded with smooth and compact PLGA microspheres had a positive effect on cell growth and also had antibacterial properties. Through toxicity analysis, cell morphology and proliferation analysis and alkaline phosphatase evaluation, the antibacterial scaffolds showed excellent biocompatibility and osteogenic activity. The antibacterial property evaluated with Staphylococcus aureus and Escherichia coli suggested that the sustained release of Pac 525 or KSL W from the scaffolds could inhibit the bacterial growth aforementioned in the long term. Our results suggest that the antimicrobial peptides loaded MC bone scaffold has good antibacterial and osteogenic activities, thus providing a great promise for the treatment of infective bone defects.
Distributivity inequalities of monotonic operations
Venue: Fuzzy Sets Syst.
Year: 2012

The aim of this work is to complement recent results about distributive binary operations. Our investigation was motivated by non distributive logical connectives and their generalizations in fuzzy set theory. In spite of the title we neither solve the concerned functional inequalities nor look for characterization of the solutions of such inequalities. We only lay bare conditions for their fulfilment. For monotonic operations with neutral or zero element we present the sufficient conditions to obtain the subdistributivity or superdistributivity in the case of the lack of distributivity for the considered pairs of operations. In particular we describe families of increasing binary operations subdistributive or superdistributive with respect to idempotent uninorms and nullnorms.