Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibody Epitope Specificity for dsDNA Phosphate Backbone Is an Intrinsic Property of the Heavy Chain Variable Germline Gene Segment Used

Analysis of protein sequences by the informational spectrum method (ISM) enables characterization of their specificity according to encoded information represented with defined frequency (F). Our previous data showed that F(0.367) is characteristic for variable heavy chain (VH) domains (a combination of variable (V), diversity (D) and joining (J) gene segments) of the anti-phosphocholine (PC) T15 antibodies and mostly dependent on the CDR2 region, a site for PC phosphate group binding. Because the T15 dsDNA-reactive U4 mutant also encodes F(0.367), we hypothesized that the same frequency may also be characteristic for anti-DNA antibodies. Data obtained from an analysis of 60 spontaneously produced anti-DNA antibody VH domain sequences supported our hypothesis only for antibodies, which use V gene segment in germline configuration, such as S57(VH31), MRL-DNA22, and VH11, members of the VH1 (J558) and VH7 (S107) gene families. The important finding is that out of seven V gene segments used by spontaneous anti-DNA antibodies, F(0.367) is only expressed by the germline configuration of these three V gene segments. The data suggest that antibody specificity for the phosphate group moiety delineated as F(0.367) is the intrinsic property of the V germline gene segments used, whereas paratope/epitope interaction with antigens bearing this epitope, such as PC or dsDNA, requires corresponding antibody VH conformation that is susceptible to somatic mutation(s).
SDN-RDCD: A Real-Time and Reliable Method for Detecting Compromised SDN Devices

A software-defined network (SDN) is increasingly deployed in many practical settings, bringing new security risks, e.g., SDN controller and switch hijacking. In this paper, we propose a real-time method to detect compromised SDN devices in a reliable way. The proposed method aims at solving the detection problem of compromised SDN devices when both the controller and the switch are trustless, and it is complementary with existing detection methods. Our primary idea is to employ backup controllers to audit the handling information of network update events collected from the primary controller and its switches, and to detect compromised devices by recognizing inconsistent or unexpected handling behaviors among the primary controller, backup controllers, and switches. Following this idea, we first capture each network update request and its execution result in the primary controller, collect each received network update instruction and the information of any state update in switches, and deliver these four kinds of information to those backup controllers in an auditor role. An auditor controller is designed to create an audit record for each received network update request and to add its execution result of this network update request as well as the received four kinds of matching information to the audit record. In particular, heterogeneous auditor controllers are proposed to avoid the same vulnerability with the primary controller. The audit algorithm and theoretical proof of its effectiveness for security enhancement are then presented. Finally, based on our prototype implementation, our experimental results further validate the proposed method and its low costs.
Tabular Reinforcement Learning in Real-Time Strategy Games via Options

Real-Time Strategy (RTS) games are complex domains with huge state and action spaces. In such games, humans usually pursue long-term plans, which take long sequences of actions to achieve. In this work, we implement this behavior by reasoning over options, which are temporally-extended actions in Markov Decision Processes. Our options are defined with the aid of a state aggregation scheme and a portfolio of game-playing algorithms. Experimentally, we show that our approach leverages the capabilities of traditional reinforcement-learning techniques, which become competitive against state-of-the-art search methods in µRTS.
Long-Short Term Memory Networks for Modelling Embodied Mathematical Cognition in Robots

Mathematical competence can endow robots with the necessary capability for abstract and symbolic processing, which is required for higher cognitive functions such as natural language understanding. But, so far, only few attempts have been made to model mathematical cognition in robots. This paper presents an experimental evaluation of the Long- Short Term Memory networks for modeling the simple mathematical operation of single-digits addition in a cognitive robot. To this end, the robotic model creates an association between the proprioceptive information from finger counting and the handwritten digits of the MNIST dataset. In practice, the model executes two tasks concurrently: it recognizes the handwritten digits in a sequence and sums them. The results show that the association with fingers can improve the robot precision, as observed in children. Also, the robot makes a disproportionate number of split-five errors similarly to what observed in studies with children and adults, hence giving evidence to support the hypothesis that these errors are due the use of a five-fingers counting system.
Feature extraction analysis using filter banks for faults classification in induction motors

Different studies have been worked about induction motor bearings fault detection using digital signal processing and pattern recognition techniques. However, performance of these techniques is related with the use of correct features. This paper presents an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Classification was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23% for six considered classes related with faults in bearings.
Indoor RSSI-based Localization using Fuzzy Path Loss Models

In this paper a new approach to the construction of Bluetooth signal path loss models with a fuzzy modelling algorithm - SUHICLUST is presented. By using these models, Bluetooth beacons, a smartphone and proposed localization algorithm a very accurate low-cost localization system was developed. With the SUHICLUST algorithm fuzzy path loss models were constructed for all available beacons installed in our laboratory. The developed localization algorithm is based on the use of improved fingerprinting method and particle swarm optimization which take into account also confidence intervals of fuzzy models. Both methods are computationally efficient, which enables real-time processing and low energy consumption on a smartphone.
Non-Mercer Large Scale Multiclass Least Squares Minimal Complexity Machines

This paper extends the idea of Least Squares Minimal Complexity Machines [1] (LS-MCMs) to non-Mercer kernels. There are no efficient solvers for LS-SVMs with non-Mercer kernels for large scale datasets. Here, we propose two variants of our novel multiclass loss LS-MCMs. Firstly, with an $L_{1}$ regularizer and secondly, with an explicit margin regularizer along with $L_{1}$ norm in the Empirical Feature Space (EFS). Both these methods can be scaled to large datasets with the use of “prototype vectors” selected from the dataset. The first optimization algorithm can be solved efficiently using Stochastic Gradient Descent (SGD) directly as the problem remains convex in the parameter space. The second problem however, is solved using difference of convex functions (DC) programming with SGD due to the non-convex nature of margin regularizer. Our method also obtains a sparse solution as opposed to the one obtained using LS-SVMs which tend to be non-sparse.
Object Recognition in Traffic Monitoring Systems

In this paper we are presenting our take on object recognition in traffic monitoring systems. In the cooperation of university with company Betamont we aim to enlarge the portfolio of products for traffic monitoring with new approaches and improved technologies. Ability to reliable detect moving vehicles and correctly recognize vehicle type (car, bus, truck) make (brand) and model allows for non-invasive monitoring methods (without the use of road sensors) to be used and for additional important information about traffic to be gathered. And as such, these additional data can be utilized in the projects of smart cities and smart traffic control systems in the future. In this paper, we are focusing on the description of our current stage of research, we provide results to preliminary test done with system using local feature extraction and MF ArtMap classification on real data from monitoring point in Banská Bystrica, and we discuss its benefits and shortcomings.
Convolutional neural networks for automated targeted analysis of raw gas chromatography-mass spectrometry data

Through their breath, humans exhale hundreds of volatile organic compounds (VOCs) that can reveal pathologies, including many types of cancer at early stages. Gas chromatography-mass spectrometry (GC-MS) is an analytical method used to separate and detect compounds in the mixture contained in breath samples. The identification of VOCs is based on the recognition of their specific ion patterns in GC-MS data, which requires labour-intensive and time-consuming preprocessing and analysis by domain experts. This paper explores the original idea of applying supervised machine learning, and in particular convolutional neural networks (CNNs), to learn ion patterns directly from raw GC-MS data. The method adapts to machine specific characteristics, and once trained, can quickly analyse breath samples bypassing the time-consuming preprocessing phase. The CNN classification performance is compared to those of shallow neural networks and support vector machines. All considered machine learning tools achieved high accuracy in experiments with clinical data from participants. In particular, the CNN-based approach detected the lowest number of false positives. The results indicate that the proposed method is a promising tool to improve accuracy, specificity, and in particular speed in the detection of VOCs of interest in large-scale data analysis.
Percentile range around the mean of center distance based informative transfer for motor imagery Brain-Computer Interface

An ideal noninvasive electroencephalography (EEG) based brain-computer interface (BCI) is a user-friendly plug and play system where a new user does not need to go through the long training data collection process. To reduce the amount of training data required for a new user, active learning inspired informative instance transfer is investigated in this work as one of the potential solutions. In this informative transfer learning, query by committee is applied as query method to find informative samples from subjects own domain. On the other hand, percentile range around the mean of center distance (PRMCD) query method is introduced in this work as an alternative to existing entropy criterion to find informative samples from the past user’s domain. The newly introduced PRMCD algorithm has reached the benchmark performance using only average 12% of whole subjective training set while the existing entropy-based algorithm has achieved the benchmark performance using average 17% of the whole subjective training set in case of 7 out of 9 subjects. For PRMCD algorithm, a new user can achieve the intended mean benchmark performance using reduced (only 50 which is 12.5%) amount of training data in general irrespective of subjects. Therefore, incorporation of PRMCD algorithm has added an important step towards the zero training BCI. It is a significant advancement for the practical application of motor imagery based BCI.
A mixture of experts model for predicting persistent weather patterns

Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system will be similar (or equal) to the present state. Machine learning (ML) models are widely used in different weather forecasting applications, but they need to be compared to the persistence model to analyse whether they provide a competitive solution to the problem at hand. In this paper, we devise a new model for predicting low-visibility in airports using the concepts of mixture of experts. Visibility level is coded as two different ordered categorical variables: cloud height and runway visual height. The underlying system in this application is stagnant approximately in 90% of the cases, and standard ML models fail to improve on the performance of the persistence model. Because of this, instead of trying to simply beat the persistence model using ML, we use this persistence as a baseline and learn an ordinal neural network model that refines its results by focusing on learning weather fluctuations. The results show that the proposal outperforms persistence and other ordinal autoregressive models, especially for longer time horizon predictions and for the runway visual height variable.
Laboratory Diagnosis of Respiratory Tract Infections in Children – the State of the Art

In the pediatric population, respiratory infections are the most common cause of physician visits. Although many respiratory illnesses are self-limiting viral infections that resolve with time and supportive care, it can be critical to identify the causative pathogen at an early stage of the disease in order to implement effective antimicrobial therapy and infection control. Over the last few years, diagnostics for respiratory infections have evolved substantially, with the development of novel assays and the availability of updated tests for newer strains of pathogens. Newer laboratory methods are rapid, highly sensitive and specific, and are gradually replacing the conventional gold standards, although the clinical utility of these assays is still under evaluation. This article reviews the current laboratory methods available for testing for respiratory pathogens and discusses the advantages and disadvantages of each approach.
Suecia y Dinamarca: líderes en innovación y la atracción de talentos mundiales

Este artículo analiza la importancia que han adquirido recientemente los flujos de migración altamente calificada que se dirigen hacia los países desarrollados, esto como resultado de la reconfiguración del nuevo patrón migratorio que surgió tras la crisis de 2008. Se aborda el estudio de caso de Suecia y Dinamarca, por tratarse de economías destacadas a escala mundial por el desarrollo de innovaciones tecnológicas que les han permitido tasas de crecimiento sostenidas en el largo plazo. En la medida en que ambas economías enfrentan dificultades estructurales, como el envejecimientopoblacional y la insuficiente cantidad de jóvenes egresadosde sus universidades con los perfiles necesarios para el desarrollo de las nuevas tecnologías, se restringe su oferta laboral. Debido a ello, la migración calificada y los estudiantes internacionales se han convertido en un elemento central para el impulso que requieren dichas políticas, por lo que han llevado a cabo una serie de programas para reclutar y retener a los talentos internacionales, situación que empieza a generalizarse en otros países destino, lo que puede detonar una competencia mundial por los talentos.
Genomics of speech and language disorders

There are multiple factors involved in speech and language. Investigating animal models, mainly through songbirds, have allowed a better understanding of the language process. Verbal dyspraxia, dysarthria, speech sound disorder, and stuttering are some examples of speech disorders, and specific language disorder, aphasia and, dyslexia of language disorders. More complex syndromes such as Autism-spectrum disorders, Down’s or Fragile X have more variable features. Genetic factors, such as hereditary or de novo mutations may be responsible for their development. In addition, most of them are involved in neurodevelopment with a huge range of molecular mechanisms and pathways that interact with each other, and there may be co-morbidity with other communication disorders or develop phenotypes unrelated to communication. Genes with heterogeneous functions in speech and language such as FOXP1, FOXP2, KIAA0319, ROBO1, APOE or CNTNAP2 are some examples. Epigenetic factors, especially miRNAs, influence their expressiveness. The genomics of these disorders allows us to understand language acquisition, carry out early detection strategies, genetic counseling and optimize future treatments, not only in communication disorders but also those neurological alterations that incorporate these mutations.
FORS1 is getting Blue: New Blue Optimised Detectors and High Throughput Filters

Ground-based observations in the ultraviolet part of the electromagnetic spectrum are notoriously difficult owing to absorption of the atmosphere, of optical elements and the poorer efficiency of detectors. Now that CCD detectors with excellent UV response and cosmetic quality have become available, it was time to optimise FORS1 for imaging, low-resolution spectroscopic and polarimetric observations in the blue-UV. As a bonus, a new set of broadband filters with very high transmission and carefully defined filter bands were installed.
An audit of teenagers who had not succeeded in elementary school: a retrospective case review

Objective: The aim was to perform a retrospective case review of pupils who had not succeeded in elementary school, in order to analyze barriers at individual and system levels. Methods: Forty-five pupils, aged 16–20 years, who had not qualified for senior high school, were consecutively assessed within the school health unit to determine their cognitive function, school history, measures taken, previous assessments, and diagnoses given. Results: School health records revealed early problems with learning, reading, mathematics, and attention, but very few students had received an appropriate diagnosis. Our review and assessment when all the data had been compiled demonstrated that the vast majority (96%) of participants had clear developmental problems. Attention problems or full attention deficit/ hyperactivity disorder dominated. About half of the students reported extensive truancy. Mean intelligence quotient in the study group was 83.9, about 1 standard deviation below the mean. Fluid intelligence was significantly superior to crystallized intelligence. Conclusion: Screening and identification are needed throughout the school years of children who present symptoms that could interfere with their academic achievement. Intervention needs to be based on skilled assessment, with consideration given to each individual’s broad panorama of cognitive deficits and assets, as well as concomitant social adversities that may underlie their school failure. Since there can be a significant negative impact of limited educational opportunities on future socioeconomic outcomes, the question of who is at risk is important both for affected individuals and more generally for public health. Limitations: The study group was small and may not have been fully representative. In spite of limited confidence about generalizing from the results, the findings call for reflection.
Patient’s perception on glaucoma and different types of treatment (medical versus surgical treatment)

Objective: To identify the meaning and impact on their quality of life of having glaucoma and to understand the patients’ perception on the different types of treatment (medical or surgical). Methods: Through a qualitative research, focus groups were conducted with patients in clinical treatment (group 1) and patients who underwent glaucoma surgery in both eyes and were without medication (group 2). The responses were analyzed using the technique of content analysis. Results: Fear of blindness and lack of information about the disease were the most cited issues in relation to how it is like to having glaucoma. Medication costs, impact of drops on patients’ daily lives and the side effects were the main points discussed in relation to medical treatment. All patients in the surgical group preferred the current situation (without medication) when compared to the need for chronic use of medication. In the two groups, both glaucoma and its treatment had a profound impact on people, not only from a psychological standpoint, but also affecting their daily lives. Patients operated on for glaucoma appear to have less impact on their daily lives, but the concern about the disease persists. Conclusion: We identified the most significant negative aspects of glaucoma and its treatment from patients’ perspectives. Confidence in the correct indication of the type of treatment, clinical or surgical, and a solid relationship between the patient the doctor are determining factors for extra peace of mind of patients being treated for glaucoma.
STELLAR DIAMETERS AND TEMPERATURES. IV. PREDICTING STELLAR ANGULAR DIAMETERS

The number of stellar angular diameter measurements has greatly increased over the past few years due to innovations and developments in the field of long baseline optical interferometry. We use a collection of high-precision angular diameter measurements for nearby, main-sequence stars to develop empirical relations that allow the prediction of stellar angular sizes as a function of observed photometric color. These relations are presented for a combination of 48 broadband color indices. We empirically show for the first time a dependence on metallicity of these relations using Johnson (B − V) and Sloan (g − r) colors. Our relations are capable of predicting diameters with a random error of less than 5% and represent the most robust and empirical determinations of stellar angular sizes to date.