Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 herd immunity in the Brazilian Amazon

The herd immunity threshold is the proportion of a population that must be immune to an infectious disease, either by natural infection or vaccination such that, in the absence of additional preventative measures, new cases decline and the effective reproduction number falls below unity. This fundamental epidemiological parameter is still unknown for the recently-emerged COVID-19, and mathematical models have predicted very divergent results. Population studies using antibody testing to infer total cumulative infections can provide empirical evidence of the level of population immunity in severely affected areas. Here we show that the transmission of SARS-CoV-2 in Manaus, located in the Brazilian Amazon, increased quickly during March and April and declined more slowly from May to September. In June, one month following the epidemic peak, 44% of the population was seropositive for SARS-CoV-2, equating to a cumulative incidence of 52%, after correcting for the false-negative rate of the antibody test. The seroprevalence fell in July and August due to antibody waning. After correcting for this, we estimate a final epidemic size of 66%. Although non-pharmaceutical interventions, plus a change in population behavior, may have helped to limit SARS-CoV-2 transmission in Manaus, the unusually high infection rate suggests that herd immunity played a significant role in determining the size of the epidemic.
Higher Initial Value for Time Demand Analysis

Time demand analysis of real-time has been an active research area and a plethora of results have been derived for the classing time demand analysis by relaxing the assumption such as making task periods harmonic, restricting the scheduling points to a subset, obtain guess values, and avoiding testing system feasibly at unnecessary points. However, till date, the complexity of the time demand analysis still remains pseudo-polynomial. In this work, we propose a higher initial value for testing the feasibility of a lower priority task based on the feasibility analysis of immediate higher priority task. The proposed technique shows improvement over closely related counterparts. Experimental results are aligned with theoretical formulations presented in this paper.
Associations Between Physical Fitness and Brain Structure in Young Adulthood

A comprehensive analysis of associations between physical fitness and brain structure in young adulthood is lacking, and further, it is unclear the degree to which associations between physical fitness and brain health can be attributed to a common genetic pathway or to environmental factors that jointly influences physical fitness and brain health. This study examined genotype-confirmed monozygotic and dizygotic twins, along with non-twin full-siblings to estimate the contribution of genetic and environmental factors to variation within, and covariation between, physical fitness and brain structure. Participants were 1,065 young adults between the ages of 22 and 36 from open-access Young Adult Human Connectome Project (YA-HCP). Physical fitness was assessed by submaximal endurance (2-min walk test), grip strength, and body mass index. Brain structure was assessed using magnetic resonance imaging on a Siemens 3T customized ‘Connectome Skyra’ at Washington University in St. Louis, using a 32-channel Siemens head coil. Acquired T1-weighted images provided measures of cortical surface area and thickness, and subcortical volume following processing by the YA-HCP structural FreeSurfer pipeline. Diffusion weighted imaging was acquired to assess white matter tract integrity, as measured by fractional anisotropy, following processing by the YA-HCP diffusion pipeline and tensor fit. Following correction for multiple testing, body mass index was negatively associated with fractional anisotropy in various white matter regions of interest (all | z| statistics > 3.9) and positively associated with cortical thickness within the right superior parietal lobe (z statistic = 4.6). Performance-based measures of fitness (i.e., endurance and grip strength) were not associated with any structural neuroimaging markers. Behavioral genetic analysis suggested that heritability of white matter integrity varied by region, but consistently explained >50% of the phenotypic variation. Heritability of right superior parietal thickness was large (∼75% variation). Heritability of body mass index was also fairly large (∼60% variation). Generally, 12 to 23 of the correlation between brain structure and body mass index could be attributed to heritability effects. Overall, this study suggests that greater body mass index is associated with lower white matter integrity, which may be due to common genetic effects that impact body composition and white matter integrity.
Visual features detection based on deep neural network in autonomous driving tasks

Navigation using visual features is one of the ways to determine automated vehicle position. In autonomous automated driving system this way can be used when other systems do not work or unavailable. Vehicle position and orientation can be determined using 2D top-down map with marked visual features, known camera and vehicle models, information about camera position on the vehicle. This paper proposes application of deep learning neural network Faster R-CNN in object detection for the autonomous driving task.
A Word Similarity Computing Method Based on Superposed Degree of Sememe

Through analysing the relationship between sememes of Hownet two sememes of equal distance in the same tree are supposed,and public nodes play a decisive role in similarity.The farther from the root node a sememe's node is,the more detailed classification and description of information the sememe take,the similarity is bigger.This paper proposes a new method on similarity computation which is based on Hownet.The method defines a similarity computation formula among Hownet's sememes.The experiments,demonstrates that the method given in this paper has improved performance in measuring word similarity.
Further Improvement on an MCMC-based Video Tracking Algorithm

The paper considers a problem of multiple person tracking. We present the approach to automatic people tracking on surveillance videos recorded by static cameras. Proposed algorithm is an extension of [1] and is based on tracking-by-detection of people heads. It performs data association using Markov chain Monte Carlo (MCMC). Tracklet postprocessing and accurate results interpolation were shown to reduce number of false positives. We use position deviations of tracklets and revised entry/exit points factor to separate pedestrians from false positives. Finally, the paper presents a new method to estimate body position. Our evaluation shows results competitive to modern tracking methods.
A SIMPLE INFINITE QUANTUM GRAPH

We study the Schrödinger equation on an infinite metric graph where the Hamiltonian is given by a suitable one-dimensional Dirichlet Laplacian. The metric structure is defined by assigning an interval In = [0, ln], n ∈ N, to each edge of the graph with ln = π n . The spectrum of this system is purely discrete with the eigenvalues given by λn = n2, n ∈ N, with multiplicities d(n), where d(n) denotes the divisor function. We thus can relate the spectral problem of this infinite quantum graph to Dirichlet’s famous divisor problem and infer the non-standard Weyl asymptotics N(λ) = √ λ 2 lnλ + O (√ λ ) for the eigenvalue counting function. Based on an exact trace formula, we derive explicit formulae for the trace of the wave group, the heat kernel, the resolvent and for various spectral zeta functions. These results enable us to establish a well-defined (renormalized) secular equation. 1. The quantum mechanical setting Our infinite quantum graph Γ consists of infinitely many edges en, n = 1 . ∞, assigned with the intervals In = [ 0, π n ] . Therefore, the total length L of the quantum graph is infinite and thus it is a noncompact quantum graph. We don’t specify the topology of the quantum graph. The corresponding Hilbert space (H, 〈·, ·〉H) consists of the vector space (1.1) H := { ψ ∈ ∞ ⊕ n=1 L2 [ 0, π n ] ; ‖ψ‖ := 〈ψ,ψ〉 1 2 H <∞ } equipped with the scalar product (1.2) 〈ψ, φ〉H := ∞ ∑ n=1 〈ψn, φn〉L2[0,π n ] , ψ = ∞ ⊕
Virtually Assisted E – Bike with Smart Connectivity

---------------------------------------------------------------------------***--------------------------------------------------------------------------- Abstract - The main objective of the project is to develop a Smart Electric Bike which is achieved by incorporating various prominent technologies. The specifications that transform an electric bike into a smart one are Global Positioning System (GPS), General Packet Radio Service/Global System for Mobile Communication (GPRS/GSM), Bluetooth connectivity, obstacle detection and vehicle friendly mobile application. In the project GPS is utilized to accomplish speed monitoring, to remotely access the vehicle location and alert the owner with GSM in case of theft. The smart electric bike is equipped with obstacle detector which triggers the UI of mobile application to intimate the driver about the vehicles in the blind spot of the electric bike and helps the driver to have a safe journey. The mobile application can provides great assistance while driving on the road by delivering proper local information, enhancing the navigation with right direction, informs about traffic, alerts the driver at odd scenario and maintains the vehicles stats. Therefore these features enhance the way a person commute in this digital era.
Keyword Detection Techniques A Comprehensive Study

—Automatic identification of influential segments from a large amount of data is an important part of topic detection and tracking (TDT). This can be done using keyword identification via collocation techniques, word co-occurrence networks, topic modeling and other machine learning techniques. This paper reviews existing traditional keyword extraction techniques and analyzes them to make useful insights and to give future directions for better automatic, unsupervised and language independent research. The paper reviews extant literature on existing traditional TDT approaches for automatic identification of influential segments from a large amount of data in keyword detection task. The current keyword detection techniques used by researchers have been discussed. Inferences have been drawn from current keyword detection techniques used by researchers, their advantages and disadvantages over the previous studies and the analysis results have been provided in tabular form. Although keyword detection has been widely explored, there is still a large scope and need for identifying topics from the uncertain user-generated data.
Iris segmentation in an image using U-Net convolutional neural network architecture

— The accuracy of segmentation plays an important role in the methods for personal identification by iris images. In this paper, we study the iris segmentation method based on the convolutional neural network of the U-Net architecture. As part of the research, manual segmentation of the images of the used dataset was performed, the optimal network training parameters were determined, and the segmentation quality was evaluated. All studies in the paper were conducted using the open MMU Iris Image Database. The results showed that the studied approach provides high precision segmentation of the iris images.
New entomopathogenic nematodes from semi-natural and small-holder farming habitats of Rwanda

ABSTRACT Five field surveys for indigenous entomopathogenic nematodes (EPNs) were conducted in 22 semi-natural and 17 small-holder farming habitats across 16 districts of different altitudes in the northern, eastern, southern and Kigali city provinces of Rwanda. In 2014, 216 mixed soil samples were collected and subsamples thereof baited with Galleria mellonella or Tenebrio molitor larvae. Five samples from five locations and habitats were positive for nematodes (2.8%). Nine nematode species/strains were isolated and five successfully maintained. DNA sequence comparisons and morphological examinations revealed Steinernema carpocapsae, Heterorhabditis bacteriophora, as well as two steinernematids and one heterorhabditid with no species designation. The isolates (strains) were named Steinernema sp. RW14-M-C2a-3, Steinernema sp. RW14-M-C2b-1, Steinernema carpocapsae RW14-G-R3a-2, H. bacteriophora RW14-N-C4a and Heterorhabditis sp. RW14-K-Ca. These are the first records of naturally occurring EPNs in Rwanda. It is also the first record of S. carpocapsae from Africa. Finding H. bacteriophora from tropical rather than temperate Africa was surprising. The found nematodes will serve as the basis for efficacy screening, and for mass production in a biocontrol agent factory at Rubona Research Centre of the Rwanda Agriculture Board with the ultimate aim of delivering effective, safe and environmentally benign pest control for soil-inhabiting pests.
Improving the Stochastically Perturbed Parametrisation Tendencies Scheme using High-Resolution Model Simulations

Stochastic parametrisations are used in weather forecasts as a physically motivated way of representing model uncertainty due to unresolved processes. In particular, the “Stochastically Perturbed Parametrisation Tendencies” (SPPT) scheme has become widely implemented in operational centres. SPPT is a holistic approach that represents uncertainty in all the sub-grid physics parametrisation schemes. It is easy to implement, and has beneficial impacts on medium range, seasonal and climate timescales. In the SPPT approach, the tendencies from the different parametrisation schemes are added together, and a spatially and temporally correlated random number is used to multiply this total tendency:
Total protein determination in pigeon plasma and serum: comparison of refractometric methods with the biuret method

Total protein concentration established by the biuret method in plasma and serum from pigeons was compared with results obtained by temperature-compensated and nontemperature-com- pensated refractometers in plasma. Plasma total protein concentration and serum total protein con- centration measured by the biuret method were highly correlated (r = 0.99); plasma values were on average 1.74 g higher than serum values because of the presence of fibrinogen. Values obtained by refractometer were higher than values obtained by the biuret method. Values obtained by the temperature-compensated refractometer were significantly higher than those obtained by the non- temperature-compensated refractometer and were not considered to be accurate. Plasma protein concentration measured by refractometer can only be considered a rough estimate of total protein concentration obtained by the biuret method (r = 0.89) after applying a correction formula for the species and specific refractometer.
The Role of Pro-Organizational Motivation as Antecedent of Job Crafting: A Qualitative Exploration

The concept of “job crafting” is one of the most interesting constructs within the organizational literature about proactivity in the workplace. Several authors pointed out the need of longitudinal research on job crafting. In this paper we illustrate the results of a qualitative empirical research conducted within a large retail company. While available job crafting theory emphasizes individual goals as key motivational drivers for job crafting, we found that organizationally oriented goals and motivations are also very relevant. We also found that when a convergence of individual and organizational goals is observed, the motivation towards job crafting is significantly increased, and that work experience plays a significant role in such process.
Medical Image Segmentation Application

There is a significant increase in the number ofcases related to different spine pathologies in the recent years. For an accurate computer aided diagnostic system, an unambiguous and reliable segmentation of the human vertebral column is crucial. It aids doctors in detecting different spine pathologies with improved visualizations. Magnetic Resonance Imaging (MRI) is the best method available to visualize spine. MR images of spine are clearer and more detailed than images obtained from other imaging methods. Correct segmentation of vertebrae from MR images is a challenging task because of the high variations in shape topology, low contrast of images and different fields of view. We propose an efficient computer aided diagnostic system for fully automatic segmentation of vertebrae. Our approachuses Mask R-CNN, a deep neural network algorithm that was initially developed to detect objects and perform instance segmentation of objects in natural images. In our paper, we demonstrate the application of Mask R-CNN in segmentation of vertebrae in Magnetic Resonance images of spine. The aim of this system is only to perform segmentation and a medical professional is required to arrive at an accurate diagnosis from thesegmentation results.
Non-K¨ahler complex structures on R 4 , II

We follow our study of non-K¨ahler complex structures on R 4 that we deﬁned in our previous paper. We prove that these complex surfaces do not admit any smooth complex compactiﬁcation. More-over, we give an explicit description of their meromorphic functions. We also prove that the Picard groups of these complex surfaces are uncountable, and give an explicit description of the canonical bundle. Finally, we show that any connected non-compact oriented 4-manifold admits complex structures without K¨ahler metrics.
The structural insight of class III of polyhydroxyalkanoate synthase from Bacillus sp. PSA10 as revealed by in silico analysis

PhaC synthase is an enzyme responsible for PHA polymerization. In this work, the catalytic mechanism class III of PhaC synthase from Bacillus sp. PSA10 (BacPhaCSynt) was reported through in silico modelling approach based on the primary sequence of the PhaC synthase. The open reading frame BacPhaCSynt has been successfully isolated, cloned and overexpressed the recombinant protein in Escherichia coli BL21(DE3). To know the global architecture and catalytic mechanism, the structural prediction of BacPhaCSynt has been carried out by using MODELLER. The recombinant BacPhaCSynt exhibited monomeric molecular weight (MW) of 43.6 kDa, when it was analyzed on 12% SDS‐PAGE gel. Based on the structural prediction, BacPhaCSynt exhibited global architecture of α/β hydrolase fold, with the root mean square deviation (r.m.s.d) value of 0. 94Å. The catalytic residues composition of BacPhaCSynt consists of C151, D307, and H336, but the H336 and D307 residues of the model have been distorted 62.8o and 175.2o from the corresponding residues of the template. Since the D307 is quite a distance from the H336, it might act as a general base for the activation of ‐OH group of the substrate. The results strongly suggested that the mode of action of BacPhaCSynt obeyed the covalent catalysis mechanism.
Geometry-based 3D Object Fitting and Localizing in Grasping Aid for Visually Impaired

—This paper presents a geometry-based method for 3D object ﬁtting and localization in the context of building a grasping aid service for visually impaired people using information from Kinect sensor. Given two constraints of this working application, (1) the interested object is on a table and (2) the geometrical form of the object is known in advance based on the query of the user, the proposed system consists of three steps: table plane detection, object detection, and object ﬁtting and localization. Our work has three contributions. First, we propose to use organized point cloud representation instead of just point cloud in order to speedup the computational time and improve the accuracy of table plane detection. Second, we employ MLESAC (Maximum LikElihood SAmple Consensus) that can give better results for object ﬁtting. Third, we introduce a new method for evaluating object localization task and make a quantitative evaluation of object localization on our captured dataset.
Pythagorean Generalization of Testing the Equality of Two Symmetric Positive Definite Matrices

We provide a new test for equality of two symmetric positive-definite matrices that leads to a convenient mechanism for testing specification using the information matrix equality and the sandwich asymptotic covariance matrix of the GMM estimator. The test relies on a new characterization of equality between two k dimensional symmetric positive-definite matrices A and B: the traces of AB−1 and BA−1 are equal to k if and only if A = B. Using this criterion, we introduce a class of omnibus test statistics for equality and examine their null and local alternative approximations under some mild regularity conditions. A preferred test in the class with good omni-directional power is recommended for practical work. Monte Carlo experiments are conducted to explore performance characteristics under the null and local as well as fixed alternatives. The test is applicable in many settings, including GMM estimation, SVAR models and high dimensional variance matrix settings.
A Fast Method of Contrast Enhancement using Histogram Equalization

Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, it tends to change the mean brightness of the image to the middle level of the gray-level range, which is not desirable in the case of images from consumer electronics products. In the latter case, preserving the input brightness of the image is required to avoid the generation of non-existing artifacts in the output image. To surmount this drawback, Bi-HE methods for brightness preserving and contrast enhancement have been proposed. Although these methods preserve the input brightness on the output image with a significant contrast enhancement, they may produce images with do not look as natural as the input ones. In order to overcome this drawback, this work proposes a novel technique called Multi-HE, which consists of decomposing the input image into several sub-images, and then applying the classical HE process to each one. This methodology performs a less intensive image contrast enhancement, in a way that the output image presents a more natural look. We propose two discrepancy functions for image decomposing, conceiving two new Multi-HE methods. A cost function is also used for automatically deciding in how many sub-images the input image will be decomposed on. Experiments show that our methods preserve more the brightness and produce more natural looking images than the other HE methods.