Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction and Improvements of Bird Songs' Classification System

Detection of bird species with bird songs is a challenging and meaningful task. Two scenarios are presented in BirdCLEF challenge this year, which are monophone and soundscape. We trained convolutional neural network with both spectrograms extracted from recordings and additionally provided metadata. Focusing on the soundscape situation, we applied bird event detection to reduce false alarm. Besides, we rescored the retrievals using masks which are designed for all species being identified. In addition, context information was also taken into consideration in our system. Our system was evaluated in BirdCLEF 2018 and we achieved an official mean average precision (MAP) score of 0.6548 for monophone classification without background bird songs and 0.5882 for identification with background bird songs. For soundscape, we achieved 0.1196 in classification mean average precision (C-MAP).
Learning to Focus when Ranking Answers

One of the main challenges in ranking is embedding the query and document pairs into a joint feature space, which can then be fed to a learning-to-rank algorithm. To achieve this representation, the conventional state of the art approaches perform extensive feature engineering that encode the similarity of the query-answer pair. Recently, deep-learning solutions have shown that it is possible to achieve comparable performance, in some settings, by learning the similarity representation directly from data. Unfortunately, previous models perform poorly on longer texts, or on texts with significant portion of irrelevant information, or which are grammatically incorrect. To overcome these limitations, we propose a novel ranking algorithm for question answering, QARAT, which uses an attention mechanism to learn on which words and phrases to focus when building the mutual representation. We demonstrate superior ranking performance on several real-world question-answer ranking datasets, and provide visualization of the attention mechanism to otter more insights into how our models of attention could benefit ranking for difficult question answering challenges.
An hp-adaptive Newton-discontinuous-Galerkin finite element approach for semilinear elliptic boundary value problems

In this paper we develop an hp-adaptive procedure for the numerical solution of general second-order semilinear elliptic boundary value problems, with possible singular perturbation. Our approach combines both adaptive Newton schemes and an hp-version adaptive discontinuous Galerkin finite element discretisation, which, in turn, is based on a robust hp-version a posteriori residual analysis. Numerical experiments underline the robustness and reliability of the proposed approach for various examples.
Mixing Traditional Methods with Neural Networks for Gender Prediction: Notebook for PAN at CLEF 2018

In this paper we describe our participation in the PAN 2018 shared task on Author Proﬁling, identifying author’s gender by Tweets and images for English, Spanish and Arabic. We focused only on the textual data and left images out of scope. Our submitted model is a small feed-forward neural network. While in previous work neural networks are often used in combination with word embeddings, our best-performing system used only unigrams as features. In an unofﬁcial run, we show that extracting information from DBpedia can improve the performance. On the PAN 2018 test set our model achieved a score of 0.807, 0.792 and 0.792 for English, Arabic and Spanish respectively. With an average score of 0.797 we conclude that our model is quite robust among all three languages.
A babystep-giantstep method for faster deterministic integer factorization

In 1977, Strassen presented a deterministic and rigorous algorithm for solving the problem of computing the prime factorization of natural numbers $N$. His method is based on fast polynomial arithmetic techniques and runs in time $\widetilde{O}(N^{1/4})$, which has been state of the art for the last forty years. In this paper, we will combine Strassen's approach with a babystep-giantstep method to improve the currently best known bound by a superpolynomial factor. The runtime complexity of our algorithm is of the form \[ \widetilde{O}\left(N^{1/4}\exp(-C\log N/\log\log N)\right). \]
An expanded toolkit for gene tagging based on MiMIC and scarless CRISPR tagging in Drosophila

We generated new genetic tools to efficiently tag genes in Drosophila. Double Header (DH) utilizes intronic MiMIC/CRIMIC insertions to generate artificial exons for GFP mediated protein trapping or T2A-GAL4 gene trapping in vivo based on CRE recombinase to avoid embryo injections. DH significantly increases integration efficiency compared to previous strategies and faithfully reports the expression pattern of genes and proteins. The second technique targets genes lacking coding introns using a two-step cassette exchange. First, we replace the endogenous gene with an excisable compact dominant marker using CRISPR making a null allele. Second, the insertion is replaced with a protein::tag cassette. This sequential manipulation allows the generation of numerous tagged alleles or insertion of other DNA fragments that facilitates multiple downstream applications. Both techniques allow precise gene manipulation and facilitate detection of gene expression, protein localization and assessment of protein function, as well as numerous other applications.
Reachability Analysis Using Dissipation Inequalities For Nonlinear Dynamical Systems

We propose a method to overbound reachable sets on finite time horizons for continuous-time nonlinear systems with polynomial dynamics. The analysis considers uncertain initial conditions, parameters, L2 disturbances, and perturbations characterized by time-domain, integral quadratic constraints (IQCs). This method makes use of time-dependent polynomial storage functions that satisfy certain dissipation inequalities on local regions of the state space, intervals of time and bounded sets of uncertain parameters. The S-procedure and sum-of-square techniques are used to derive the computation algorithms, and an objective function is introduced to find an over-approximation with a desired shape. Both pedagogical and practically motivated examples are presented, including a 7-state F-18 aircraft model.
Learning to Interpret and Apply Multimodal Descriptions

Enabling computers to understand natural human communication is a goal researchers have been long aspired to in artificial intelligence. Since the concept demonstration of “Put-ThatThere” in 1980s, significant achievements have been made in developing multimodal interfaces that can process human communication such as speech, eye gaze, facial emotion, co-verbal hand gestures and pen input. State-of-the-art multimodal interfaces are able to process pointing gestures, symbolic gestures with conventional meanings, as well as gesture commands with pre-defined meanings (e.g., circling for “select”). However, in natural communication, coverbal gestures/pen input rarely convey meanings via conventions or pre-defined rules, but embody meanings relatable to the accompanied speech. For example, in route given tasks, people often describe landmarks verbally (e.g., two buildings), while demonstrating the relative position with two hands facing each other in the space. Interestingly, when the same gesture is accompanied by the utterance a ball, it may indicate the size of the ball. Hence, the interpretation of such co-verbal hand gestures largely depends on the accompanied verbal content. Similarly, when describing objects, while verbal utterances are most convenient for conveying meanings symbolically (e.g., describing colour and category with the utterance “a brown elephant”), hand-drawn sketches are often deployed to convey iconic information such as the exact shape of the elephant’s trunk, which is typically difficult to encode in language. This dissertation concerns the task of learning to interpret multimodal descriptions composed of verbal utterances and hand gestures/sketches, and apply corresponding interpretations to tasks such as image retrieval. Specifically, I aim to address following research questions: 1) For co-verbal gestures that embody meanings relatable to accompanied verbal content, how can we use natural language information to interpret the semantics of such co-verbal gestures, e.g., does a gesture indicate relative position or size? 2) As an integral system of communication, speech and gestures not only bear close semantic relations, but also close temporal relations. To what degree and on which dimensions can hand gestures benefit the interpretation of multimodal descriptions? 3) While it’s obvious that iconic information in hand-drawn sketches enriches the verbal content in object descriptions, how to model the joint contributions of such multimodal descriptions and to what degree can verbal descriptions compensate reduced iconic details in hand-drawn sketches? To address the above research questions, in this dissertation, I first introduce three multimodal description corpora: a spatial description corpus composed of natural language and placing gestures (also referred as abstract deictics), a multimodal object description corpus composed of natural language and hand-drawn sketches, and an existing corpus the Bielefeld
Interactive Sampling for Systematic Reviews. IMS Unipd At CLEF 2018 eHealth Task 2

This is the second participation of the Information Management Systems (IMS) group at CLEF eHealth Task of Technologically Assisted Reviews in Empirical Medicine. This task focuses on the problem of medical systematic reviews, a problem which requires a recall close (if not equal) to 100%. Semi-Automated approaches are essential to support these type of searches when the amount of data exceed the limits of users, i.e. in terms of attention or patience. We present a variation of the two-dimensional approach which 1) sets the maximum amount of documents that the physician is willing to read, 2) takes into account a sampling strategy to estimate the 95% conﬁdence interval of the number of relevant documents present in the collection.
Deep Neural Networks and Decision Tree Classifier for Visual Question Answering in the Medical Domain

This paper presents our contribution to the problem of visual question answering in the medical domain using a combination of deep neural networks and the Decision tree classifier. In our proposed approach we consider the task of visual question answering as multi-label classification problem, where each label corresponds to a unique word in the answer dictionary that was built from the training set.
On the analysis of perfectly matched layers for a class of dispersive media and application to negative index metamaterials

This work deals with Perfectly Matched Layers (PMLs) in the context of dispersive media, and in particular for Negative Index Metamaterials (NIMs). We first present some properties of dispersive isotropic Maxwell equations that include NIMs. We then demonstrate numerically the inherent instabilities of the classical PMLs applied to NIMs. We propose and analyse the stability of very general PMLs for a large class of dispersive systems using a new change of variable. We give necessary criteria for the stability of such models. For dispersive isotropic Maxwell equations, this analysis is completed by giving necessary and sufficient conditions of stability. Finally, we propose new PMLs that satisfy these criteria and demonstrate numerically their efficiency.
Do Maternal Dietary Antioxidants Modify the Relationship Between Binge Drinking and Small for Gestational Age? Findings from a Longitudinal Cohort Study

BACKGROUND Vitamin C, vitamin E, and carotenoids are potent dietary antioxidants that have been shown to attenuate ethanol-induced harm in animal models of fetal alcohol spectrum disorders. A diet low in antioxidant-rich foods may induce a state of oxidative stress in the context of maternal alcohol consumption during pregnancy, potentially causing growth restriction in the developing fetus. METHODS We conducted a secondary analysis of a longitudinal U.K. birth cohort. The sample comprised 9,699 women and their babies in Avon, U.K., with an estimated delivery date between April 1, 1991 and December 31, 1992. Alcohol consumption data were self-reported at 18 weeks' gestation via a postal questionnaire. Women reported any binge drinking (≥4 U.K. units/occasion) during the past month. Dietary data were self-reported at 32 weeks' gestation using a food frequency questionnaire. Estimated intakes of vitamins C and E and carotenoids were categorized into quartiles. Logistic regression models with interaction terms were used to investigate relationships between maternal binge drinking, dietary antioxidants, and fetal growth. Models were adjusted for maternal sociodemographic and lifestyle characteristics. Small for gestational age (SGA; <10th percentile) was defined using customized birth centiles. RESULTS In the unadjusted models, binge drinking was associated with higher risk of SGA birth (odds ratio [OR] 1.38, 95% confidence interval [CI] 1.10, 1.72, p = 0.005), and higher maternal intakes of vitamin C (OR = 0.90, 95% CI 0.84, 0.96, p = 0.002) and vitamin E (OR = 0.90, 95% CI 0.84, 0.95, p < 0.0001) were associated with lower risk of SGA birth. However, addition of potentially confounding variables attenuated these relationships. Likelihood ratio tests indicated that interaction terms were not significant for vitamin C (p = 0.116), vitamin E (p = 0.059), or carotenoid intakes (p = 0.174). CONCLUSIONS There was no evidence of maternal intake of dietary antioxidants modifying the relationship between maternal binge drinking and SGA birth.
Detecting Misogynous Tweets

Social media companies struggle to control the quality of the content on their platforms. The sheer amount of user-generated content uploaded on a daily basis far exceeds what can be screened by human curators, fuelling the need for intelligent detection algorithms that can automatically ﬂag inappropriate content. In this paper, we present machine learning models that can identify instances of aggression and hate speech towards women in tweets. In particular, we present the system that we submitted for the shared task on automatic misogyny identiﬁ-cation at IberEval 2018.
Evaluation of the toxic potential of arecoline toward the third instar larvae of transgenic Drosophila melanogaster (hsp70-lacZ) Bg9.

Arecoline is the key component of areca nut and has been suggested as a carcinogenic agent. In the present study, the third instar larvae of transgenic Drosophila melanogaster (hsp70-lacZ) Bg9 were allowed to feed on a diet having 5, 10, 20, 40 and 80 μM arecoline for 24 h. After the completion of 24 h, the larvae were subjected to ONPG assay, X-gal staining, trypan blue exclusion test, oxidative stress markers, and apoptotic and comet assays. A dose-dependent increase in the β-galactosidase activity, tissue damage, glutathione-S-transferase (GST) activity, lipid peroxidation assay, monoamine oxidase (MAO), caspase-9 and 3, protein carbonyl content (PCC), apoptotic index, and DNA damage and decrease in glutathione (GSH) content, delta aminolevulinic acid dehydrogenase (δ-ALA-D), and acetylcholinesterase (AChE) activity were observed in the larvae exposed to 20, 40 and 80 μM arecoline. The results suggest that arecoline is toxic at 20, 40, and 80 μM toward the third instar larvae of transgenic Drosophila melanogaster (hsp70-lacZ) Bg9 . Arecoline did not show any toxic effects at 5 and 10 μM.
A Cross Modal Deep Learning Based Approach for Caption Prediction and Concept Detection by CS Morgan State

This article describes the participation of the Computer Science Department of Morgan State University, Baltimore, Maryland, USA in the ImageCLEFcaption under ImageCLEF 2018. The problem of automatic image caption prediction involves outputting a human-readable and concise textual description of the contents of a figure appeared in a biomedical journal. It is a challenging problem in biomedical literature as it requires techniques from both the fields of Computer Vision to understand the visual contents of the image and Natural Language Processing (NLP) to turn the understanding of the image into words in the right order to generate the textual description for caption. Recently, deep learning methods have achieved state-of-the-art results on this challenging problem in general domain of natural photographic images. For the tasks of caption prediction and concept detection, we used the merge architectures for the encoder-decoder recurrent neural network (RNN) due to it’s success over inject for caption generation. The merge model combines both the encoded form of the image input with the encoded form of the text description generated so far. The combination of these two encoded inputs is then used by a very simple decoder model to generate the next word in the sequence. In this article, we present main objectives of experiments, overview of these approaches, resources employed, and describe our submitted runs and results with conclusions and future directions.
Nephrotic syndrome and adrenal insufficiency caused by a variant in SGPL1

Abstract Little is known about the molecular pathogenesis of congenital nephrotic syndrome in association with primary adrenal insufficiency. Most recently, three groups found concurrently the underlying genetic defect in the gene sphingosine-1-phosphate lyase 1 (SGPL1) and called the disease nephrotic syndrome type 14 (NPHS14). In this report we have performed whole-exome sequencing and identified a new homozygous variant in SGPL1, p.Arg340Trp, in a girl with nephrotic syndrome and Addison's disease. Her brother died previously with the same phenotype and hyperpigmentation of the skin. We reviewed the reported cases and concluded that NPHS14 is a clinically recognizable syndrome. The discovery of this syndrome may contribute to the diagnosis and description of additional patients who could benefit from treatment, genetic counseling and screening for related comorbidities. Until now, patients with congenital nephrotic syndrome associated with primary adrenal insufficiency have been treated as having two different diseases; however, the treatment for patients with NPHS14 should be unique, possibly targeting the sphingolipid metabolism.
Convolutional Long Short-Term Memory Neural Networks for Hierarchical Species Prediction

Building accurate knowledge of the identity, the geographic distribution and the evolution of organisms is essential for biodiversity conservation. Automatic prediction of list of species is useful for many scenarios in biodiversity informatics. In this work, we propose a hybrid model to predict the species that are most probable to be observed at a given location, using environmental features and taxonomy of the organism. These environmental features are represented as k-dimensional image patches, where each dimension represents the value of an environmental variable, in the neighborhood of the occurrence of the species. The hybrid model Convolutional Long Short-Term Memory Neural Networks henceforth called as CLNN, is a combination of Convolutional Neural Networks(CNNs) and Long Short-Term Memory Networks(LSTMs), where the CNN forms the spatial feature generator while the LSTM focuses on finding the taxonomy. Using the dataset provided by Geo LifeCLEF 2018, the proposed method helped achieve a Mean Reciprocal Rank (MRR) score of 0.003 during the test phase.
Inequality of opportunity in health: A decomposition‐based approach

This paper presents new decomposition-based approaches to measure inequality of opportunity in health that capture Roemer's distinction between circumstances and effort and are consistent with both compensation and reward principles. Our approach is fully nonparametric in the way that it handles differences in circumstances and provides decompositions of both a rank-dependent relative (the Gini coefficient) and a rank-independent absolute inequality index (the variance). The decompositions distinguish the contribution of effort from the direct and indirect (through effort) contribution of circumstances to the total inequality. Our approach is illustrated by an empirical application that uses objectively measured biomarkers as health outcomes and as proxies for relevant effort variables. Using data from the Health Survey for England from 2003 to 2012, we find that circumstances are the leading determinant of inequality in cholesterol, glycated haemoglobin, and in a combined ill-health index whereas effort plays a substantial role in explaining inequality in fibrinogen only.
Multilingual Gender Classification with Multi-view Deep Learning: Notebook for PAN at CLEF 2018

We present the results of a gender identification performed on the data set of tweets and images prepared for the PAN 2018 Author profiling shared task. In this work we propose a hybrid neural network architecture for gender classification, capable of leveraging heterogeneous textual and image information sources. The proposed approach is based on state-of-the-art deep architectures for natural language processing, combined with a pretrained image classification architecture via a custom output combination scheme. Text classification model combines character level, word level and document level information in order to produce stable and accurate predictions on three different languages, achieving the highest accuracy of 79% on the English test set. Image classification architecture relies on the hypothesis that the authors have a gender bias when it comes to publishing images of people and has a structure of a two-phased pipeline containing two models, one for face detection and the other for face gender classification. Classifying author’s gender from posted images proved to be harder than from text, with our image classification model achieving the best accuracy of only 58.26% on the English test set. The results on the official PAN test set also confirmed slight synergy effects between the two models when combined. The proposed approach was 8 in the global ranking of PAN 2018 Author profiling shared task.
Polycystic ovary syndrome and the risk of cardiometabolic complications in longitudinal studies

The purpose of this study was to perform a review of the longitudinal studies to determine whether polycystic ovary syndrome is associated with higher prevalence of metabolic complications and cardiovascular morbidity and mortality. The primary outcomes included body mass index, metabolic syndrome and its components (waist circumference, lipid profile, arterial hypertension, abnormal glucose metabolism (impaired fasting glucose, impaired glucose tolerance, type 2 diabetes), insulin resistance, and cardiovascular diseases like stroke, angina, and coronary heart disease. Complications in pregnant women were beyond the scope of this review. PubMed database (1992‐2018) was searched to identify proper publications. Finally, data from 47 articles were analysed. Studies differed in the design (prospective, retrospective, cohort, observational), research methods, polycystic ovary syndrome diagnostic criteria, studied populations, race, and ethnicity of the participants. Based on the data collected, it appears that women with polycystic ovary syndrome have higher prevalence of obesity, abdominal fat distribution, dyslipidaemia and deterioration of glucose metabolism, but increased prevalence of cardiovascular diseases is not proven.