Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards an Integrated Retrieval System to Semantically Match CVs, Job Descriptions and Curricula

The job market is continuously evolving. The specific occupations, skills, competences and qualifications that people need change over time, as does their description. To deal with this, effective and intelligent communication and information exchange between the job market and the education and training sector is vital. On the other hand, and from the perspective of the individual (job seeker), especially the less privileged there is a need for approaches that combine practical tools with motivation and mentoring support since skill-matching it is not enough, skill-building is also needed. In this context, the current approach follows a bottom-up methodology investigating the problem of formalizing the lifelong learning process in a dynamic and flexible way. On the other hand, this proposal utilizes a parallel top-down approach in applying semantics and standards upon data in order to alleviate the gap among individuals, workplaces and educational contexts for the benefit of all in a transparent way. More specifically, this article reports towards an approach on tackling the complex task of interconnecting job seekers, employers and educational agents in the current European labor market. To perform this task, we implement an end-to-end service to parse resumes, job descriptions and open courses descriptions, retrieve information on the qualifications associated with the aforementioned, and semantically match them. The proposed implementation effectively detects the underlying information associated with those sources, and manages to interlink job seekers’ resumes to occupations and job vacancies, while being able to assign skill deficits to courses provided by educational agents. The performance of our implementation on CVs, job descriptions and course descriptions in English, Greek, Romanian and Bulgarian, indicate that our approach yields results on par with the state-of-the-art, however on a much larger scale: to the best of our knowledge, this is the first research work that engages with this task on three stakeholders (job seekers, employers, educational agents) and in four European languages.
A Methodology and Framework to Determine the Isolation Capabilities of Virtualisation Technologies

The capability to isolate system resources is an essential characteristic of virtualisation technologies and is therefore important for research and industry alike. It allows the co-location of experiments and workloads, the partitioning of system resources and enables multi-tenant business models such as cloud computing. Poor isolation among tenants bears the risk of noisy-neighbour and contention effects which negatively impacts all of those use-cases. These effects describe the negative impact of one tenant onto another by utilising shared resources. Both industry and research provide many different concepts and technologies to realise isolation. Yet, the isolation capabilities of all these different approaches are not well understood; nor is there an established way to measure the quality of their isolation capabilities. Such an understanding, however, is of uttermost importance in practice to elaborately decide on a suited implementation. Hence, in this work, we present a novel methodology to measure the isolation capabilities of virtualisation technologies for system resources, that fulfils all requirements to benchmarking including reliability. It relies on an immutable approach, based on Experiment-as-Code. The complete process holistically includes everything from bare metal resource provisioning to the actual experiment enactment. The results determined by this methodology help in the decision for a virtualisation technology regarding its capability to isolate given resources. Such results are presented here as a closing example in order to validate the proposed methodology.
Dog emotion recognition from images in the wild: DEBIw dataset and first results

Emotions play a transcendental role in the behavior of dogs. Their emotional response to certain stimuli and situations can be decisive in their actions. Automatic recognition of dog emotions gives ethologists and trainers the ability to monitor dog reactions and help profile them more accurately. On the other hand, providing dog-computer interaction systems with the ability to know the emotional state of the canine user can help improve the objectives of the interaction. This work presents the creation of a new database of images of dogs representing emotions such as (aggression, anxiety, contentment and fear) and a method to classify them automatically. This database consists of 15,599 images downloaded from the Internet directly. Each image was manually labeled by multiple taggers using a web-based interface. Using a variety of state-of-the-art image classification approaches, including an AutoML solution that performed the best (0.67 of the macro average f1 measure), the plausibility of automatic dog emotion recognition was assessed. This is remarkable, given that the input to the classification model is an image downloaded from the Internet without applying any cleaning, segmentation, characterization, or key point marking technique. The proposed methodology can serve as a non-invasive, easy-to-instrument, and easy-to-retrain means for the implementation of dog emotion-aware computational systems. More importantly, the created dataset will allow the in-depth study of this relevant problem. * Both authors contributed equally to this research.
Nascent evolution of recombination rate differences as a consequence of chromosomal rearrangements

Reshuffling of genetic variation occurs both by independent assortment of chromosomes and by homologous recombination. Such reshuffling can generate novel allele combinations and break linkage between advantageous and deleterious variants which increases both the potential and the efficacy of natural selection. Here we used high-density linkage maps to characterize global and regional recombination rate variation in two populations of the wood white butterfly (Leptidea sinapis) with distinct karyotypes. The recombination data were compared to estimates of genetic diversity and measures of selection to assess the relationship between chromosomal rearrangements, crossing over, maintenance of genetic diversity and adaptation. Our data show that the recombination rate is influenced by both chromosome size and number, but that the difference in recombination rate between karyotypes is reduced as a consequence of a higher frequency of double crossovers in larger chromosomes. As expected from effects of selection on linked sites, we observed an overall positive association between recombination rate and genetic diversity in both populations. Our results also revealed a significant effect of chromosomal rearrangements on the rate of intergenic diversity change between populations, but limited effects on polymorphisms in coding sequence. We conclude that chromosomal rearrangements can have considerable effects on the recombination landscape and consequently influence both maintenance of genetic diversity and efficiency of selection in natural populations. Author summary Reshuffling genetic variation is fundamental for maintaining genetic diversity and creating novel allelic combinations. The two main processes involved are the independent assortment of chromosomes and homologous recombination. The number and size of chromosomes can influence the amount of pairwise reshuffling and local recombination patterns. However, studying this in natural populations is challenging. In this study, we used the wood white butterfly, which exhibits an extreme within-species karyotype difference. Extensive fusions and fissions have resulted in almost twice as many chromosomes in the southern populations compared to the northeast populations. This unique system allowed us to assess the relationship between karyotype differences, pairwise reshuffling, recombination rate variation and subsequent effects on diversity and linked selection. We found that a higher number of chromosomes result in a higher recombination rate, although the difference was less than expected due to multiple recombination events occuring on longer chromosomes. Both populations showed an association between recombination rate and genome-wide patterns of genetic diversity and efficacy of selection. We provide evidence that chromosomal rearrangements have considerable effects on the recombination landscape and thereby influence the maintenance of genetic diversity in populations.
Enhanced niche colonisation and competition during bacterial adaptation to a fungus

Bacterial-fungal interactions (BFIs) influence microbial community performance of most ecosystems and elicit specific microbial behaviours, including stimulating specialised metabolite production. Using a simple BFI system encompassing the Gram-positive bacterium Bacillus subtilis and the black mould fungus Aspergillus niger, we established a co-culture experimental evolution method to investigate bacterial adaptation to the presence of a fungus. In the evolving populations, B. subtilis was rapidly selected for enhanced production of the lipopeptide surfactin and accelerated surface spreading ability, leading to inhibition of fungal expansion and acidification of the environment. These phenotypes were explained by specific mutations in the DegS-DegU two-component system. In the presence of surfactin, fungal hyphae exhibited bulging cells with delocalised secretory vesicles and RlmA-dependent cell wall stress induction. Increased surfactin production typically enhances the competitive success of bacteria against fungi, which likely explains the primary adaption path in the presence of A. niger. Significance statement Experimental evolution and co-cultivation of different microbes are important and useful techniques for discovering new traits and unravelling cryptic regulatory connections. We combined these methods by evolving the Gram-positive bacterium Bacillus subtilis in the presence of the black mould fungus Aspergillus niger that were previously shown to engage in an intricate and physical interaction. Both are ubiquitous, environmentally and industrially relevant model microbes in the colonisation of rhizo- and endosphere and in the production of enzymes. Our results demonstrate how laboratory adaptation can be exploited to improve biocontrol properties of bacteria.
Chromosomal Duplications of MurZ (MurA2) or MurA (MurA1), Amino Acid Substitutions in MurZ (MurA2), and Absence of KhpAB Obviate the Requirement for Protein Phosphorylation in Streptococcus pneumoniae D39

GpsB links peptidoglycan synthases to other proteins that determine the shape of the respiratory pathogen Streptococcus pneumoniae (pneumococcus; Spn) and other low-GC Gram-positive bacteria. GpsB is also required for phosphorylation of proteins by the essential StkP(Spn) Ser/Thr protein kinase. Here we report three classes of frequently arising chromosomal duplications (≈21-176 genes) containing murZ (MurZ-family homolog of MurA) or murA that suppress ΔgpsB or ΔstkP. These duplications arose from three different repeated sequences and demonstrate the facility of pneumococcus to modulate gene dosage of numerous genes. Overproduction of MurZ or MurA alone or overexpression of MurZ caused by ΔkhpAB mutations suppressed ΔgpsB or ΔstkP phenotypes to varying extents. ΔgpsB and ΔstkP were also suppressed by MurZ amino-acid changes distant from the active site, including one in commonly studied laboratory strains, and by truncation or deletion of the homolog of IreB(ReoM). Unlike in other Gram-positive bacteria, MurZ is predominant to MurA in pneumococcal cells. However, ΔgpsB and ΔstkP were not suppressed by ΔclpCP, which did not alter MurZ or MurA amounts. These results support a model in which regulation of MurZ and MurA activity, likely by IreB(Spn), is the only essential requirement for protein phosphorylation in exponentially growing D39 pneumococcal cells.
Gain, not concomitant changes in spatial receptive field properties, improves task performance in a neural network attention model

Attention allows us to focus sensory processing on behaviorally relevant aspects of the visual world. One potential mechanism of attention is a change in the gain of sensory responses. However, changing gain at early stages could have multiple downstream consequences for visual processing. Which, if any, of these effects can account for the benefits of attention for detection and discrimination? Using a model of primate visual cortex we document how a Gaussian-shaped gain modulation results in changes to spatial tuning properties. Forcing the model to use only these changes failed to produce any benefit in task performance. Instead, we found that gain alone was both necessary and sufficient to explain category detection and discrimination during attention. Our results show how gain can give rise to changes in receptive fields which are not necessary for enhancing task performance.
Photoionization of disk galaxies: An explanation of the sharp edges in the H I distribution

We have reproduced the observed radial truncation of the H I distribution in isolated spiral galaxies with a model in which extragalactic radiation photoionizes the gaseous disk. For a galactic mass distribution model that reproduces the observed rotation curves, including dark matter in the disk and halo, the vertical structure of the gas is determined self-consistently. The ionization structure and column densities of H and He ions are computed by solving the radiation transfer equation for both continuum and lines. Our model is similar to that of Maloney, and the H I structure differs by less than 10%. The radial structure of the column density of H I is found to be more sensitive to the extragalactic radiation field than to the distribution of mass. For this reason, considerable progress can be made in determining the extragalactic flux of ionizing photons, phi(sub ex), with more 21 cm observations of isolated galaxies. However, owing to the uncertainty of the radial distribution of total hydrogen at large radii, inferring the extragalactic flux by comparing the observed edges to photoionization models is somewhat subjective. We find 1 x 10(exp 4)/sq cm/s is less than or approximately phi(sub ex) is less than or approximately 5 x 10(exp 4)/sq cm/s, corresponding to 2.1 is less than or approximately iota(sub 0) is less than or approximately 10.5 x 10(exp -23) ergs/sq cm/s/Hz/sr for a 1/nu spectrum. Although somewhat higher, our inferred range of iota(sub 0) is consistent with the large range of values obtained by Kulkarni & Fall from the 'proximity effect' toward Quasi-Stellar Objects (QSOs) at approximately 0.5.
Efficient Motion Planning for Wheeled Mobile Robotics

Nowadays, the field of wheeled robotics is undergoing an impressive growth and development. Different hardware and software components are being developed and applied in various contexts, including assistive robotics, industrial robotics, automotive, . Motion Planning is a fundamental aspect for the development of autonomous wheeled mobile robots. The capability of planning safe, smooth trajectories, and to locally adjust them in real-time to deal with contingent situations and avoid collisions is an essential requirement to allow robots to work and perform activities in public spaces shared with humans. Moreover, in general, efficiency is a key constraint for this kind of applications, given the limited computational power usually available on robotic platforms. In this thesis, we focus on the development of efficient algorithms to solve different kind of motion planning problems. Specifically, in the first part of the thesis, we propose a complete planning system for an assistive robot supporting the navigation of older users. The developed planner generates paths connecting different locations on the map, that are smooth and specifically tailored to optimize the comfort perceived by the human users. During the navigation, the system applies an efficient model to predict the behaviours of the surrounding pedestrians, and to locally adapt the reference path to minimise the probability of collisions. Finally, the motion planner is integrated with an “high-level” reasoning component, to generate and propose complete activities, like the visit to a museum or a shopping mall, specifically tailored to the preferences, needs and requirements of each user. In the second part of the thesis, we show how the efficient solutions and building blocks developed for the assistive robots, can be adapted and applied also to a completely different context, such as the generation of optimal trajectories for an autonomous racing vehicle. This research has received funding from the European Unions Horizon 2020 Research and Innovation Programme Societal Challenge 1 (DG CONNECT/H) under grant agreement n◦ 643644 “ACANTO A CyberphysicAl social NeTwOrk using robot friends”.
Tractable & Coherent Multi-Document Summarization: Discrete Optimization of Multiple Neural Modeling Streams via Integer Linear Programming

One key challenge in multi-document summarization is the generated summary is often less coherent compared to single document summarization due to the larger heterogeneity of the input source content. In this work, we propose a generic framework to jointly consider coherence and informativeness in multi-document summarization and offers provisions to replace individual components based on the domain of source text. In particular, the framework characterizes coherence through verb transitions and entity mentions and takes advantage of syntactic parse trees and neural modeling for intra-sentential noise pruning. The framework cast the entire problem as an integer linear programming optimization problem with neural and non-neural models as linear components. We evaluate our method in the news and legal domains. The proposed approach consistently performs better than competitive baselines for both objective metrics and human evaluation.
A Comprehensive Evaluation of Biomedical Entity-centric Search

Biomedical information retrieval has often been studied as a task of detecting whether a system correctly detects entity spans and links these entities to concepts from a given terminology. Most academic research has focused on evaluation of named entity recognition (NER) and entity linking (EL) models which are key components to recognizing diseases and genes in PubMed abstracts. In this work, we perform a fine-grained evaluation intended to understand the efficiency of state-of-the-art BERT-based information extraction (IE) architecture as a biomedical search engine. We present a novel manually annotated dataset of abstracts for disease and gene search. The dataset contains 23K query-abstract pairs, where 152 queries are selected from logs of our target discovery platform and PubMed abstracts annotated with relevance judgments. Specifically, the query list also includes a subset of concepts with at least one ambiguous concept name. As a base-line, we use off-she-shelf Elasticsearch with BM25. Our experiments on NER, EL, and retrieval in a zero-shot setup show the neural IE architecture shows superior performance for both disease and gene concept queries.
Design and Analysis of Thin Diffractive/refractive Lens Antennas

A design method for diffractive/refractive dielectric lens antennas working at millimeter wave band is presented in this paper. One surface of this kind of lens antenna is refractive and the other is diffractive. The refractive surface is used to control the amplitude distribution of the electromagnetic wave and the diffractive surface is used to adjust the phase distribution of the electromagnetic wave. The side lobe level of this kind of lens antenna is low due to the proper distribution of the field amplitude controlled by the refractive surface, and the thickness of the lens is very thin due to the use of the diffractive surface. Two design examples of the diffractive/refractive lens antenna are given, and their radiation patterns (fed with the corrugated horns) are computed using axially symmetric FDTD algorithm and presented in this paper.
“Beyond just the four walls of the clinic”: The roles of health systems caring for refugee, immigrant and migrant communities in the United States

This article is part of the Research Topic ‘Health Systems Recovery in the Context of COVID-19 and Protracted Conflict’ Introduction Refugee, immigrant and migrant (hereafter referred to as “immigrant”) communities have been inequitably affected by the COVID-19 pandemic. There is little data to help us understand the perspectives of health systems on their role, in collaboration with public health and community-based organizations, in addressing inequities for immigrant populations. This study will address that knowledge gap. Methods This qualitative study used semi-structured video interviews of 20 leaders and providers from health systems who cared for immigrant communities during the pandemic. Interviewees were from across the US with interviews conducted between November 2020–March 2021. Data was analyzed using thematic analysis methods. Results Twenty individuals representing health systems participated with 14 (70%) community health centers, three (15%) county hospitals and three (15%) academic systems represented. The majority [16 health systems (80%)] cared specifically for immigrant communities while 14 (70%) partnered with refugee communities, and two (10%) partnered with migrant farm workers. We identified six themes (with subthemes) that represent roles health systems performed with clinical and public health implications. Two foundational themes were the roles health systems had building and maintaining trust and establishing intentionality in working with communities. On the patient-facing side, health systems played a role in developing communication strategies and reducing barriers to care and support. On the organizational side, health systems collaborated with public health and community-based organizations, in optimizing pre-existing systems and adapting roles to evolving needs throughout the pandemic. Conclusion Health systems should focus on building trusting relationships, acting intentionally, and partnering with community-based organizations and public health to handle COVID-19 and future pandemics in effective and impactful ways that center disparately affected communities. These findings have implications to mitigate disparities in current and future infectious disease outbreaks for immigrant communities who remain an essential and growing population in the US.
Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2

Introduction The routine clinical diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely restricted to real-time reverse transcription quantitative PCR (RT-qPCR), and tests that detect SARS-CoV-2 nucleocapsid antigen. Given the diagnostic delay and suboptimal sensitivity associated with these respective methods, alternative diagnostic strategies are needed for acute infection. Methods We studied the use of a clinically validated liquid chromatography triple quadrupole method (LC/MS–MS) for detection of amino acids from plasma specimens. We applied machine learning models to distinguish between SARS-CoV-2-positive and negative samples and analyzed amino acid feature importance. Results A total of 200 samples were tested, including 70 from individuals with COVID-19, and 130 from negative controls. The top performing model overall allowed discrimination between SARS-CoV-2-positive and negative control samples with an area under the receiver operating characteristic curve (AUC) of 0.96 (95%CI 0.91, 1.00), overall sensitivity of 0.99 (95%CI 0.92, 1.00), and specificity of 0.92 (95%CI 0.85, 0.95). Discussion This approach holds potential as an alternative to existing methods for the rapid and accurate diagnosis of acute SARS-CoV-2 infection.