Miracle-dz/UniFAR-FTUs huggingface dataset:
Diálogos de La Habana: una comprensión desde la paz positiva y la paz negativa

Esta investigación busca analizar el grado de inserción de las variables de paz positiva y negativa (Galtung, 1969a) de cada una de las partes durante el diálogo de paz colombiano entre el Gobierno nacional y las Fuerzas Armadas Revolucionarias de Colombia (FARC-EP), entre 2012 y 2016. La investigación espera llenar el vacío existente en el análisis de las posturas y visiones de paz de cada una de las partes en los diálogos de La Habana. Con base en los trabajos teóricos de Galtung (1965, 1969a, 1969b, 1971, 1990), Fisas (2005) y Lederach (1995, 2016), la investigación se centra en la paz positiva y negativa, y en el proceso de diálogo. La metodología cualitativa utilizada consiste en siete entrevistas en profundidad realizadas con algunas de las personas que hicieron parte de las delegaciones de paz, así como dos entrevistas con actores del componente internacional. El análisis, desarrollado a partir de la Teoría de Conjuntos Difusos, concluye que la delegación del Gobierno nacional se orientó hacia una paz negativa, mientras que las FARC-EP se orientaron hacia una paz positiva. La investigación es parte de un campo creciente dedicado al estudio del Acuerdo de Paz colombiano como garantía para la transición hacia una sociedad en paz.
Multi-modal Broad Learning System for Medical Image and Text-based Classification

Automatic classification of medical images plays an essential role in computer-aided diagnosis. However, the medical images arise from the small number of available data and the improvement of existing data-enhancement methods are limited. In order to fulfil this demand, a Multi-Modal Broad Learning System (M2-BLS) is proposed, which has two subnetworks for simultaneous learning of both medical images and the corresponding radiology reports. M2-BLS provides two advantages: i) our M2-BLS has closed-form solution and avoids iterative training, once the image feature is available; ii) benefit from the simultaneous learning of both image and text data, our M2-BLS achieves high accuracy for medical classification. Experimental results on the publicly available datasets IU X-RAY and PEIR GROSS_895 show that our M2-BLS highly improves the classification performance, compared to SOTA deep models that learn single-type of data information only.
BEHAVIOURAL STUDY OF JET IMPINGEMENT ON A STEEL ROLLED PLATE

One of the challenging problems in the steel rolling industry is the distribution of the heat flux across the geometry of the rolled plate under the cooling effect of water jet. Thus, the present work focuses on the heat transfer phenomenon of the steel rolled plate. The results obtained by the simulation studies indicate that wherein the nozzle of the jet is inclined at an angle more heat flux can be generated but the variation of the microstructure is not uniform across the width due to the impingement angle. Thus, maximum turbulence is observed at 45 0 jet angle. The results also indicated that the heat transfer coefficients are nonlinear functions of surface temperature. At the stagnation point, the surface temperature and heat transfer coefficient are not effected by the temperature of the cooling medium i.e., impingement of jet of water. Index Terms – heat transfer, impingement, heat transfer coefficient, turbulence ________________________________________________________________________________________________________
Noninvasive Cardiac Transmembrane Potential Imaging via Global Features Based FISTA Network

Noninvasive electrophysiological imaging plays an important role in the clinical diagnosis and treatment of heart diseases over recent years. Transmembrane potential (TMP) is one of the most important cardiac physiological signals, which can be used to diagnose heart disease such as premature beat and myocardial infarction. Considering the nonlocal self-similarity of TMP distribution and integrating traditional optimization strategy into deep learning, we proposed a novel global features based Fast Iterative Shrinkage/Thresholding network, named as GFISTA-Net. The proposed method has two main advantages over traditional methods, namely, the l1-norm regularization helps to avoid overfitting the model on high-dimensional but small-training data, and facilitates embedded the spatio-temporal correlation of TMP. Experiments demonstrate the power of our method.
A Closer Look at Rotation-invariant Deep Point Cloud Analysis

We consider the deep point cloud analysis tasks where the inputs of the networks are randomly rotated. Recent progress in rotation-invariant point cloud analysis is mainly driven by converting point clouds into their respective canonical poses, and principal component analysis (PCA) is a practical tool to achieve this. Due to the imperfect alignment of PCA, most of the current works are devoted to developing powerful network structures and features to overcome this deficiency, without thoroughly analyzing the PCA-based canonical poses themselves. In this work, we present a detailed study w.r. t. the PCA-based canonical poses of point clouds. Our investigation reveals that the ambiguity problem associated with the PCA-based canonical poses is handled insufficiently in some recent works. To this end, we develop a simple pose selector module for disambiguation, which presents noticeable enhancement (i.e., 5.3% classification accuracy) over state-of-the-art approaches on the challenging real-world dataset.1
Muscle Activity Estimation at Drop Vertical Jump Landing Using Passive Muscle Mechanical Model

Among the various elements that facilitate the movement of the lower limbs, the anterior cruciate ligament (ACL) is prone to injury. An adequate joint control of the lower limb can prevent ACL injury. Balancing activities between the agonist and the antagonist muscles is vital for joint control. However, prior studies on muscle activities were limited since they could not determine passive muscle activities. In this study, we develop a muscle model considering the passive properties to analyze the movement mechanism of the ACL under heavy loads, such as those produced during jump landing. We estimated the muscle activities occurring during a drop vertical jump (DVJ) by applying to the proposed method the physiological constraint that muscle activities are constant during a short time around landing. In addition, the knee joint torque and muscle forces were calculated from the estimated muscle activities, which were thereafter compared with those obtained using the conventional method. The results revealed that this passive muscle model appropriately represented the knee joint torque at DVJ landing by decreasing the passive muscle strain and increasing the isometric maximum muscle force. Moreover, the estimated muscle activities were larger than those obtained using the conventional method, which may be caused by the co-contraction between agonist and antagonist muscles that cannot be represented by the conventional method. This muscle co-contraction estimation algorithm would estimate the muscle load under heavy loads, and applying this knowledge to training would help to prevent ACL injuries.
Running head: Abstractness in aging

The aim of this study is to focus on the verbal stimuli of the Vocabulary subtest of the Wechsler Adult Intelligence Scale Fourth Edition (WAIS-IV). To our knowledge, this is the first research to examine whether the abstractness/concreteness and the lexical category of the words in the WAISIV vocabulary subtest influence the ability to explain their meaning. In the first step we collected ratings of the degree of abstractness/concreteness and the lexical categoriy of the 27 verbal stimuli of vocabulary subtest. From the second step the results showed that the ability to explain the meaning of words for 497 participants (aged 19 to 89) was better with concrete concepts than with abstract ones; and the concreteness effect, i.e., the advantage in the processing of the concrete over the abstract terms, does not disappear with age. More specifically, the ability to define the abstract words does not decline with age. Still, this ability is maintained likely because linguistic knowledge is preserved in healthy elderly. Besides, the correctness in defining nouns is always superior to that in defining adjectives and verbs across all age groups. The nouns are defined more correctly than adjectives in the all age groups. The verbs are defined more correctly than adjectives in the all age groups except for the groups over 60 years of age, although at 80-89 years of age the verbs are statistically more difficult to define than abjectives.
The Kapitza’s Pendulum as a Concurrent Strategy for Maintaining Upright Posture

A Kapitza’s pendulum shows that it is possible to stabilize an inverted pendulum by making its base oscillate vertically. This action seems to introduce an inertial effect which will produce an attractor about the upright vertical position. This work shows that the upright posture of the trunk achieved while walking can be explained using a combination of a vertical oscillation and an angular stiffness regulation at the pelvis. This is shown with an estimated oscillation and stiffness obtained from video recordings of an unimpaired and a Parkinsoninan gaits. By simulating the dynamic model of the pendulum for a range of parameters, a series of stability conditions are found. They show that the introduction of the vertical oscillation results in a fast stabilization of the trunk and point to control strategies which rely on the system’s dynamics.
An Approach for Live Motion Correction for TRUS-MR Prostate Fusion Biopsy using Deep Learning

TRUS-MR fusion guided biopsy highly depends on the quality of alignment between pre-operative Magnetic Resonance (MR) image and live trans-rectal ultrasound (TRUS) image during biopsy. Lot of factors influence the alignment of prostate during the biopsy like rigid motion due to patient movement and deformation of the prostate due to probe pressure. For MR-TRUS alignment during live procedure, the efficiency of the algorithm and accuracy plays an important role. In this paper, we have designed a comprehensive framework for fusion based biopsy using an end-to-end deep learning network for performing both rigid and deformation correction. Both rigid and deformation correction in one single network helps in reducing the computation time required for live TRUS-MR alignment. We have used 6500 images from 34 subjects for conducting this study. Our proposed registration pipeline provides Target Registration Error (TRE) of 2.51 mm after rigid and deformation correction on unseen patient dataset. In addition, with a total computation time of 70ms, we are able to achieve a rendering rate of 14 frames per second (FPS) that makes our network well suited for live procedures.Clinical Relevance— It is shown in the literature that systematic biopsy is the standard method for biopsy sampling in prostate that has high false negative rates. TRUS-MR fusion guided biopsy reduces the false negative rate of the sampling in prostate biopsy. Therefore, a live TRUS-MR fusion framework is helpful for prostate biopsy clinical procedures.
RWLMod—Potential Model to Study Plant Tolerance in Drought Stress Conditions

Rationale: Water loss by evaporation is a normal physiological process, in order to regulate plant temperature. Under conditions of thermal and water stress, water loss is accelerated compared to normal conditions, and the response of plants is variable. In extreme cases, it can lead to wilting and death of plants. It was found that the phenomenon of water loss behaved as a pattern in different plant species, given by two functions, logistics (first part of water loss) and hyperbola (second part of water loss) in relation to a moment m, at which the rate of water loss (RWL) has reached its maximum value. Method: We studied the water loss process for a series of plant samples on different plant species (Picea abies L., H. Karst; Juniperus communis L.; Pinus silvestris L.; Thuja occidentalis L.; Lamium purpureum L.; Veronica hederifolia L.), measuring the rate of weight loss (RWL) in controlled conditions. The drying of the samples was done in identical conditions (thermo-balance, 100 °C, standard temperature for drying the plant samples) with the real-time recording of the drying time simultaneously with the water loss rate (RWL) from the plant samples. The exposure time varied, depending on each species sample, and was approximately 1000 s. Results: The experimental data was recorded at intervals of every 10 s, during the entire drying period. RWL values varied from 0.024 to 0.054 g/min at the beginning of the drying process and reached maximum values after 70–100 s, having values between 0.258 g/min and 0.498 g/min. During the drying period, this indicator presented different graphic evolutions, difficult to be described with a single function. The first segment was described by a logistic function, and the second was described by a hyperbola, resulting in a model (RWLMod) which described the real phenomenon. This model and theoretical calculation were used to quantify the water loss in a time interval and, compared with empirical dates, no significant differences were observed, which indicated an increased degree of accuracy regarding the use of this model. Recommendation and novelty of work: The novelty of the work is given by the obtained model (RWLMod), which makes possible the description of RWL over the entire time interval, and ensures a good fit with the real data. It recommends the method and model in studies of plant behaviour under stress in relation to different influencing factors.
Locality-Aware Scheduling of Independant Tasks for Runtime Systems

: A now-classical way of meeting the increasing demand for computing speed by HPC applications is the use of GPUs and/or other accelerators. Such accelerators have their own memory, which is usually quite limited, and are connected to the main memory through a bus with bounded bandwidth. Thus, a particular care should be devoted to data locality in order to avoid unnecessary data movements. Task-based runtime schedulers have emerged as a convenient and e(cid:30)cient way to use such heterogeneous platforms. When processing an application, the scheduler has the knowledge of all tasks available for processing on a GPU, as well as their input data dependencies. Hence, it is able to order tasks and prefetch their input data in the GPU memory (after possibly evicting some previously-loaded data), while aiming at minimizing data movements, so as to reduce the total processing time. In this paper, we focus on how to schedule tasks that share some of their input data (but are otherwise independent) on a GPU. We provide a formal model of the problem, exhibit an optimal eviction strategy, and show that ordering tasks to minimize data movement is NP-complete. We review and adapt existing ordering strategies to this problem, and propose a new one based on task aggregation. These strategies have been implemented in the StarPU runtime system. We present their performance on tasks from tiled 2D, 3D matrix products, Cholesky factorization and randomized 2D matrix operation. Our experiments demonstrate that using our new strategy together with the optimal eviction policy reduces the amount of data movement as well as the total processing time.
Simultaneous Right Ventricle End-diastolic and End-systolic Frame Identification and Landmark Detection on Echocardiography

End-diastolic (ED) and end-systolic (ES) frame identification and landmark detection are crucial steps of estimating right ventricle function in clinic practice. However, the complex morphology of the right ventricle and low-quality echocardiography pose challenges to these tasks. This study proposes a multi-task learning (MTL) framework to simultaneously identify the right ventricle ED and ES frames and detect anatomical landmarks for echocardiography. The framework contains an encoder and two branches: frame-branch and landmark-branch. The convolution neural network (CNN) encoder is employed for extracting the shared features of two branches. The frame-branch is built with a recurrent neural network (RNN) to select ED and ES frames. A heatmap-based model is used as the landmark-branch to detect the landmarks. Furthermore, instead of directly regressing the indexes of ED/ES frames, we form the frame identification as a curve regression problem, which achieves considerable performance. Experiments performed on the echocardiography dataset of 105 patients validate the effectiveness of the proposed approach, which leads to the average frame difference of 1.59 (±1.34) frames (ED) and 1.56 (±1.35) frames (ES) on the frame identification task, and the percentage of correctly predicted landmarks is 83.3%. These results demonstrated that our method outperforms most existing methods.
Star-ECG: Visualization of Electrocardiograms for Arrhythmia and Heart Rate Variability

Conventional electrocardiograms (ECG) are displayed in one dimension. Reading one-dimensional ECG waveform becomes challenging when one wants to visualize the heart rate variability with naked eye. Some ECG visualization techniques have been proposed. However, they rely on domain knowledge to comprehend the heart rate variability. To improve the readability for patients and non-experts, we introduce Star-ECG, a novel ECG visualization approach. Such approach projects ECG waveforms onto a two-dimensional plane in a circular form. We demonstrate that Star-ECG offers not only easily deciphered visualization of cardiac abnormalities and heart rate variability, but also the application of state-of-the-art arrhythmia classification with integrated deep neural networks. We also report positive user feedback from both experts and non-experts that Star-ECG can provide readable and helpful information to monitor cardiac activities.
Hyperbaric Oxygen Therapy and Its Suitability as an Adjuvant for Treatment of Covid-19 Patients: A Review

The outbreak of the SARS CoV2 ’Coronavirus pandemic’ is believed to have originated in Wuhan in 2019 as a zoonotic spread from bats to humans. It is a highly communicable infection-causing rapid human to human transmission of the virus by virtue of its infectious and pleomorphic nature. The virus has affectedmillions of people worldwide, with numbers still rising with each passing day. Depleting oxygen saturation levels is amongst the prime concerns in the majority of infected patients. Nasal prongs, face masks, mechanical ventilation and extracorporeal membrane (ECMO) are the commonly used modes of oxygen delivery in such patients. Thesemethods thoughmostly successful, at times fail to restore the depleting oxygen levels to normal. Hyperbaric oxygen therapy (HBOT) involves the administration of 100%O2 in a special chamber whose pressure is maintained at a level greater than 1 ATP. The main purpose for raising the pressure within the chamber is that as the atmospheric pressure increases, the saturation levels of oxygen in the blood also increase, which eventually result in increased overall tissue oxygenation. This article provides a systematic and wholesome review on the basic principle of hyperbaric oxygen therapy, its effects on the body at amicroscopic andmacroscopic level, its various uses and its suitability as an adjuvant for the treatment of select COVID-19 infected patients.
Food Detection and Segmentation from Egocentric Camera Images

Tracking an individual’s food intake provides useful insight into their eating habits. Technological advancements in wearable sensors such as the automatic capture of food images from wearable cameras have made the tracking of food intake efficient and feasible. For accurate food intake monitoring, an automated food detection technique is needed to recognize foods from unstaged real-world images. This work presents a novel food detection and segmentation pipeline to detect the presence of food in images acquired from an egocentric wearable camera, and subsequently segment the food image. An ensemble of YOLOv5 detection networks is trained to detect and localize food items among other objects present in captured images. The model achieves an overall 80.6% mean average precision on four objects—Food, Beverage, Screen, and Person. Post object detection, the predicted food objects which are sufficiently sharp were considered for segmentation. The Normalized-Graph-Cut algorithm was used to segment the different parts of the food resulting in an average IoU of 82%. Clinical relevance— The automatic monitoring of food intake using wearable devices can play a pivotal role in the treatment and prevention of eating disorders, obesity, malnutrition and other related issues. It can aid in understanding the pattern of nutritional intake and make personalized adjustments to lead a healthy life.
Binary Pattern Color Doppler Shear Wave Elastography*

Some studies suggested a correlation between tissue elasticity and diseases, such as Adhesive Capsulitis (AC) of the shoulder. One category of method to measure elasticity is by utilizing Doppler imaging. This paper discusses color Doppler shear wave elastography methods and demonstrated an experiment with biological tissue mimicking phantom. A simulation with binary pattern color Doppler shear wave elastography shows that wavelength of a shear wave with suggested magnitude is equal to four multiple of pitch strip in a color flow image. However, the larger amplitude changes the duty ratio and frequency of the pattern. An experiment with biological tissue mimicking Polyvinyl Alcohol (PVA) phantoms has shown that the binary pattern color Doppler method has successfully recovered shear wave velocity map and calculate the elasticity.Clinical Relevance—The result of experiments presents a possibility of using the method for quantitatively access the stage of tissue stiffness related disease.
A rapid method of analysing the m.m.f. wave of a single or polyphase winding

The usual method of evaluating the harmonic components of the m.m. f. wave of a polyphase winding is to perform a Fourier analysis. This can, however, be a long and tedious process, especially for stepped waveforms, and further it is common practice to perform the analysis for two limiting current-vector positions, to provide a check on the results. The method described here is exceptionally simple to apply, and makes use of the well-known standard winding factors. It is shown that, by simple substitution, the magnitudes of the Fourier fundamental and harmonic components of the m.m. f. in the air-gap can be found directly, although no indication is given of their relative phase angles. The mathematics of the proof has been fitted as closely as possible to the conditions prevailing in a real machine winding, whilst at the same time maintaining generality of treatment. The value of the method is exemplified in the last Section of the paper, by carrying out the analysis for an m.m. f. wave of some complexity. The ease of the method is at once apparent.
Integration of Pedagogical and Production Factors in the Process of the Formation of Industry 4.0

The rapid development of Industry 4.0 as a result of the Fourth Industrial Revolution brings back the relationship of pedagogy and production to mutual integration that has been their characteristic since ancient times. The study revealed high digital readiness of students (4.24 according to the student assessment and 2.82 according to the teacher assessment on a 5- point scale) and very low readiness of production to accept integrated learning (1.68 and 2.67, respectively), as well as the poor digitalization of the educational process according to the students and teachers, who are more closely familiar with the reality of production (2.03 and 2.45, respectively).