Miracle-dz/UniFAR-FTUs huggingface dataset:
Widespread Divergence Between Incipient Anopheles gambiae Species Revealed by Whole Genome Sequences

Signals of Mosquito Speciation Malaria in Africa is transmitted by the mosquito species complex Anopheles gambiae. Neafsey et al. (p. 514) made high-resolution single-nucleotide arrays to map genetic divergence among members of the species. Differentiation between populations was observed and evidence obtained for selective sweeps within populations. Most divergence occurred within inversion regions around the centrosome and in genes associated with development, pheromone signaling, and from the X chromosome. The analysis also revealed signals of sympatric speciation occurring within similar chromosomal regions in mosquitoes from different regions in Africa. Lawniczak et al. (p. 512) sequenced the genomes of two molecular forms (known as M and S) of A. gambiae, which have distinctive behavioral phenotypes and appear to be speciating. This effort resolves problems arising from the apparently chimeric nature of the reference genome and confirms the observed genome-wide divergences. This kind of analysis has the potential to contribute to control programs that can adapt to population shifts in mosquito behavior arising from the selective effects of the control measures themselves. Gene flow among African malaria vectors is more restricted than previously thought. The Afrotropical mosquito Anopheles gambiae sensu stricto, a major vector of malaria, is currently undergoing speciation into the M and S molecular forms. These forms have diverged in larval ecology and reproductive behavior through unknown genetic mechanisms, despite considerable levels of hybridization. Previous genome-wide scans using gene-based microarrays uncovered divergence between M and S that was largely confined to gene-poor pericentromeric regions, prompting a speciation-with-ongoing-gene-flow model that implicated only about 3% of the genome near centromeres in the speciation process. Here, based on the complete M and S genome sequences, we report widespread and heterogeneous genomic divergence inconsistent with appreciable levels of interform gene flow, suggesting a more advanced speciation process and greater challenges to identify genes critical to initiating that process.
Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer

We present an efficient and accurate method for transferring annotations between two different treebanks of the same language. This method led to the creation of a new instance of the French Treebank (Abeille et al., 2003), which follows the Universal Dependency annotation scheme and which was proposed to the participants of the CoNLL 2017 Universal Dependency parsing shared task (Zeman et al., 2017). Strong results from an evaluation on our gold standard (94.75% of LAS, 99.40% UAS on the test set) demonstrate the quality of this new annotated data set and validate our approach.
Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning

This paper proposes the task of Visual COPA (VCOPA). Given a premise image and two alternative images, the task is to identify the more plausible alternative with their commonsense causal context. The VCOPA task is designed as its desirable machine system needs a more detailed understanding of the image, commonsense knowledge, and complex causal reasoning than state-of-the-art AI techniques. For that, we generate an evaluation dataset containing 380 VCOPA questions and over 1K images with various topics, which is amenable to automatic evaluation, and present the performance of baseline reasoning approaches as initial benchmarks for future systems.
Corpus Linguistics in Trademark Cases

Dictionaries and corpora may both be used as evidence to help decide legal cases. In certain trademark disputes—and in this paper—the central issue is whether a disputed term exhibits the linguistic behavior characteristic of a name (identifying the source or producer of a product or service) or the linguistic behavior characteristic of an ordinary common noun. Corpora are particularly useful in resolving such trademark disputes, but using corpora for this purpose requires attention to several important considerations, both linguistic and non-linguistic. This paper explores several of those linguistic considerations and discusses several legal cases involving well-known terms.
First Complete Genome Sequence of Yersinia massiliensis

ABSTRACT Using a combination of Illumina paired-end sequencing, Pacific Biosciences RS II sequencing, and OpGen Argus whole-genome optical mapping, we report here the first complete genome sequence of Yersinia massiliensis. The completed genome consists of a 4.99-Mb chromosome, a 121-kb megaplasmid, and a 57-kb plasmid.
Massive-Scale Deployments in Cloud: The Case of OpenStack Networking

The current trends in technology are shifting towards cloudification of services and massive device connectivity. Meanwhile, cloud technology is undergoing a transition from conventional full-fledged virtual machines towards lightweight container-based solutions. This results in a heavy reduction of the virtualization overhead allowing to multiply the number of running instances. There is a need for highly scalable and efficient cloud infrastructures. Our study focuses on OpenStack, as an Infrastructure-as-a-Service provider, and in particular the case of large service deployments on a single node. We conduct a comprehensive analysis of the different network building blocks used by OpenStack. This reveals important scalability limitations which negatively impact the network performance of the node and cause an unreasonably high CPU usage. We identify some of the root causes for these scaling constraints and propose a number of alternative designs. Throughout both analytical and empirical methodologies, we demonstrate the benefits of our solutions in several aspects such as scalability, efficiency and performance.
An Empirical Analysis of Causal Relationship between Stock Prices and Real Sector of the Pakistani Economy

This paper determines the causal relationship between stock prices and the macroeconomic variables representing the real sector of the Pakistani economy. In order to substantiate the purpose annual data has been acquired from the websites of State Bank of Pakistan and Federal Bureau of Statistics, from the period of 1979 to 2013. Further to get the results Unit Root, Cointegration, Error correction model has been applied. Which indicate the presence of long term relation between stock prices and macroeconomic variables including gross domestic product (GDP), Consumption and Investment. Moreover, the result shows the existence of one-way causation between macroeconomic variables such as GDP, real consumption expenditure, and real investment expenditure. This one-way causation lead to the conclusion that the stock market of Pakistan is not developed to that extinct where it could influence GDP, real consumption expenditure, and real investment expenditure. Therefore, it is finally concluded that the stock market of Pakistan is not a leading indicator of any activity of economy.
FrNewsLink : a corpus linking TV Broadcast News Segments and Press Articles

In this article, we describe FrNewsLink , a corpus allowing to address several applicative tasks that we make publicly available. It gathers several resources from TV Broadcast News (TVBN) shows and press articles such as automatic transcription of TVBN shows, text extracted from on-line press articles, manual annotations for topic segmentation of TVBN shows and linking information between topic segments and press articles. The FrNewsLink corpus is based on 112 (TVBN) shows recorded during two periods in 2014 and 2015 . Concomitantly, a set of 24,7k press articles has been gathered. Beyond topic segmentation, this corpus allows to study semantic similarity and multimedia News linking.
CATS: A Tool for Customized Alignment of Text Simplification Corpora

In text simpliﬁcation (TS), parallel corpora consisting of original sentences and their manually simpliﬁed counterparts are very scarce and small in size, which impedes building supervised automated TS systems with sufﬁcient coverage. Furthermore, the existing corpora usually do not distinguish sentence pairs which present full matches (both sentences contain the same information), and those that present only partial matches (the two sentences share the meaning only partially), thus not allowing for building customized automated TS systems which would separately model different simpliﬁcation transformations. In this paper, we present our freely available, language-independent tool for sentence alignment from parallel/comparable TS resources (document-aligned resources), which additionally offers the possibility for ﬁltering sentences depending on the level of their semantic overlap. We perform in-depth human evaluation of the tool’s performance on English and Spanish corpora, and explore its capacities for classiﬁcation of sentence pairs according to the simpliﬁcation operation they model.
Shrinkage Estimation of Linear Regression Models with GARCH Errors

This paper introduces shrinkage estimators for the parameter vector of a linear regression model with conditionally heteroscedastic errors such as the class of generalized autoregressive conditional heteroscedastic (GARCH) errors when some of the regression parameters are restricted to a subspace. We derive the asymptotic distributional biases and risks of the shrinkage estimators using a large sample theory. We show that if the shrinkage dimension exceeds two, the relative efficiency of the shrinkage estimator is strictly greater than that of the full model estimator. Furthermore, a Monte Carlo simulation study is conducted to examine the relative performance of the shrinkage estimators with the full model estimator. Our large sample theory and simulation study show that the shrinkage estimators dominate the full model estimator in the entire parameter space. We illustrate the proposed method using a real data set from econometrics.
The effect of abandonment on vegetation composition and soil properties in Molinion meadows (SW Poland)

Intermittently wet meadows of the Molinion alliance, as with many other grasslands of high-nature value, have become increasingly exposed to abandonment due to their low economic value. The potential consequences of land abandonment are the decrease in species diversity and environmental alterations. The issue of land-use induced changes in plant species composition and soil physico-chemical parameters have been rarely studied in species-rich intermittently wet grasslands. In this study we attempt to i) to identify determinants of plant species composition patterns and ii) to investigate the effect of cessation of mowing on vegetation composition and soil properties. The study was conducted in an area of 36 ha covered with Molinion meadows, comprising of mown sites and sites that were left unmown for 10 years. In total, 120 and 80 vegetation plots were sampled from mown and unmown sites, respectively. In these plots we measured plant community composition and soil physico-chemical parameters. The results have shown that the two groups of variables (soil properties and management) differ considerably in their ability to explain variation in plant species data. Soil variables explained four-fold more variation in plant species composition than management did. The content of soil organic matter, moisture, total nitrogen and exchangeable forms of potassium, calcium and magnesium were significantly higher in mown than in unmown grassland systems. The results revealed that soil organic matter was the component of the soil most strongly affected by management, followed by moisture, magnesium, calcium and potassium in that order. Each of these soil parameters was negatively correlated with the abundances of woody plants and invasive species. We concluded that low-intensity, late time of mowing is suitable grassland management practice to ensure high plant species diversity and sustainability of the grassland ecological system while cessation of mowing not only lead to reduced plant species richness and diversity, but also to reduced nutrient levels in grassland soils.
SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network

We propose a new convolutional neural network (CNN) which performs coarse and fine segmentation for end-to-end synthetic aperture radar (SAR) automatic target recognition (ATR) system. In recent years, many CNNs for SAR ATR using deep learning have been proposed, but most of them classify target classes from fixed size target chips extracted from SAR imagery. On the other hand, we proposed the CNN which outputs the score of the multiple target classes and a background class for each pixel from the SAR imagery of arbitrary size and multiple targets as fine segmentation. However, it was necessary for humans to judge the CNN segmentation result. In this report, we propose a CNN called SAR ATR with verification support (SAVERS), which performs region-wise (i.e. coarse) segmentation and pixel-wise segmentation. SAVERS discriminates between target and non-target, and classifies multiple target classes and non-target class by coarse segmentation. This report describes the evaluation results of SAVERS using the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.
The donor–beneficiary charity accountability paradox: a tale of two stakeholders

Using stakeholder theory, this paper explores the motivations of charities in discharging accountability and the interplay of donor and beneficiary accountability needs. It considers the extent to which concentration on one group may disadvantage another. The authors found that stakeholders commonly perceived as more salient, such as donors, cede power and impute saliency to beneficiaries.
Frequency of non-motor symptoms in Parkinson’s disease presenting to tertiary care centre in Pakistan: an observational, cross-sectional study

Objective To determine the frequency of non-motor symptoms (NMS) in patients of Parkinson’s disease (PD) presenting to a movement disorder clinic at a tertiary care centre in Pakistan, and how frequency of NMS is different in male and female patients. Study design Observational, cross-sectional study. Setting Tertiary care centre. Participants Out of 102 patients, 85 were included. Inclusion criteria were patients with PD diagnosed according to the UK Parkinson’s Disease Society Brain Bank Clinical Diagnostic Criteria, age ≥18 years, able to give consent and have no difficulty in answering questions. Exclusion criteria were diseases that resemble PD, stroke, dementia, patients unable to provide information and history of antipsychotic use. Results The NMSQuest revealed a mean of nearly seven different NMS per patient. Autonomic problems such as constipation (56%) and nocturia (49%) were the most common NMS, while urinary urgency was reported by 35% of patients. Low mood and feeling sad were reported by 47%, whereas feeling anxious/panicky was reported by 36%. Problem with memory was reported by 45% of patients. Feeling of light-headedness and dizziness was reported by 40% of patients. Problems with sexual relationship were reported by 30% of patients. The most common sleep problem was difficulty falling sleep (29%). Pain not related to the musculoskeletal system was reported by 30% of patients. Loss or change in the ability to taste or smell was reported by 29% of patients. The rest of NMS were less than 25% in frequency. Feeling sad or blue, feeling light-headed/dizzy, unexplained pain, unpleasant sensations in the legs, difficulty in swallowing and faecal incontinence were more common in female participants, while problems with sex were more common in male participants. Conclusion NMS are quite prevalent in PD in our population. Certain NMS are more common in women as compared with men. There is a need for a large-scale study to look for the association of different NMS with sex.
Geometric Primitives Based RGB-D SLAM for Low-texture Environment

Visual Simultaneous Localization and Mapping (SLAM) is very important in various applications such as AR, Robotics, etc. A challenging problem in SLAM is the inferior tracking performance in the low-texture environment due to their low-level feature based tactic. This paper presents a novel SLAM system which leverages feature-wise alignment and layout information to handle this challenge. The key idea is to utilize the points, lines and planes with both feature-wise constraints and layout consistency constraints to obtain robust motion estimation. Firstly, we extract the points, lines and planes from color images and depth images. Then, we find some matches between these geometric primitives. Lastly, we propose a unified solution to represent the local primitives information and the layout context among the extracted primitives, thus to stabilize the camera motion estimation. Experiments on TUM datasets demonstrate that our method outperforms other RGB-D SLAM systems in the low-texture environment, and provides comparable results in the richly-textured environment.
A Lexicon of Discourse Markers for Portuguese - LDM-PT

We present LDM-PT, a lexicon of discourse markers for European Portuguese, composed of 252 pairs of discourse marker/rhetorical sense. The lexicon covers conjunctions, prepositions, adverbs, adverbial phrases and alternative lexicalizations with a connective function, as in the PDTB (Prasad et al., 2008; Prasad et al., 2010). For each discourse marker in the lexicon, there is information regarding its type, category, mood and tense restrictions over the sentence it introduces, rhetorical sense, following the PDTB 3.0 sense hierarchy (Webber et al., 2016), as well as a link to an English near-synonym and a corpus example. The lexicon is compiled in a single excel spread sheet that is later converted to an XML scheme compatible with the DiMLex format (Stede, 2002). We give a detailed description of the contents and format of the lexicon, and discuss possible applications of this resource for discourse studies and discourse processing tools for Portuguese.
Evolution of modelling languages

In model-driven engineering, evolution is inevitable over the course of the complete life cycle of complex softwareintensive systems and more importantly of entire product families. Not only instance models, but also entire modelling languages are subject to change. This is in particular true for domain-specific languages. Up to this day, modelling languages are evolved manually, with tedious and error-prone migration of for example instance models as a result. This position paper discusses the different evolution scenarios for various kinds of modelling artifacts, such as instance models, meta-models and transformation models. Subsequently, evolution is de-composed into four primitive scenarios such that all possible evolutions can be covered. We suggest that our structured approach will enable the design of (semi-)automatic model evolution solutions. Finally, the pre-requisites to allow an easy implementation of this solution in an MDE tool are discussed.1
An Image Representation for the 3D Face Synthesis

With the rapid development of the display technologies, 3D shape data is becoming another important media kind. However, most of the existing 3D shape acquisition methods are either expensive or expertise-dependent. In this paper, we present an image representation for the 3D faces to bridge the gap between the feature-lacking 3D shapes and the powerful deep neural network learning tools. To achieve this, with the training set, we first extract the radial curves for each 3D face, and reform the curves into an image matrix, which enable to apply the classical Generative Adversarial Network model for the image synthesis. Finally, we propose a refining process to transform the output images into 3D synthetic faces. Our experimental results demonstrate the capability of our method which can correctly reflect the affinities among the different facial expressions and can generate the 3D faces.
Bacteriological assessment of chevon and poultry meat from local markets of Jammu.

A study was conducted to assess the microbiological quality of chevon and poultry meat with respect to coliforms, Escherichia coli, Staphylococcus aureus and Enterococcus faecalis. A total of 167 meat samples (85 chevon and 82 poultry) collected from various localities of Jammu were processed for standard plate count (SPC), coliform count (CC), Staphylococcus aureus count (SAC) and Enterococcus faecalis count (EFC). The results are presented as log 10 cfu/g. Mean values of SPC, CC, SAC and EFC for chevon samples were 6.37±0.06, 3.85 ±0.85, 3.98 ±0.12 and 4.15 ±0.15, respectively, and for poultry meat samples, the counts were 6.64±0.06, 3.81±0.11, 4.09±0.13 and 4.02±0.12, respectively. A total of 47 E. coli isolates were obtained belonging to 7 serogroups with O92 (17.02%) being predominant, followed by serogroup O88 (14.89%). Four (8.51%) O157 E.coli isolates were also observed.
Are BLEU and Meaning Representation in Opposition?

One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted. We propose several variations of the attentive NMT architecture bringing this meeting point back. Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks.