Miracle-dz/UniFAR-FTUs huggingface dataset:
A beamforming study of the linear antenna array using grey wolf optimization algorithm

The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to uniform linear antenna arrays. In this work, an amplitude is introduced as constant, while the interspacing distance between antenna array elements and the number of elements in a linear array are variables. The simulation results show that a faster convergence and likely high accurate beamforming are gained using GWO based method. Finally, it is shown that the GWO outperforms the genetic algorithm (GA) based method.
Does Super-Resolution Improve OCR Performance In The Real World? A Case Study On Images Of Receipts

Recently, many deep learning methods have been used to handle single image super-resolution (SISR) tasks and often achieve state-of-the-art performance. From a visual point of view, the results look convincing. Yet, does it mean that those techniques are reliable and robust enough to be implemented in real business cases to enhance the performance of other computer vision tasks? In this article, we investigate the use of SISR to construct higher-resolution images of real receipt photos sent by a company’s customers and evaluate its impact on the performance of an OCR task (receipt information retrieval). Using built-in task-based performance evaluation methods, we show that the use of SISR can significantly improve OCR performance in the case where recognition was poor in low-resolution, but can also deteriorate the performance for receipts that were already successfully recognized. As a conclusion, we provide recommendations on how to best use SISR in a production environment.
Three microsatellite multiplex PCR assays allowing high resolution genotyping of white spruce, Picea glauca

Abstract Fifteen previously published microsatellite primer pairs developed for and tested on Canadian white spruce were screened for amplification and polymorphy in Alaskan populations and tested for their suitability in PCR multiplexing. Eleven loci expressing polymorphisms ranging from 7 to 58 alleles were selected for development and optimization of three multiplex assays. Four natural stands containing a total of 1470 trees were used to characterize the selected loci and demonstrate their applicability for genotyping studies and parentage analysis. These assays can be used for studies focusing on population genetics, parentage analysis, provenance research, or individual genetic fingerprinting. The use of multiplex PCR facilitates large-scale studies by simultaneously enabling high resolution and reducing processing time and per sample cost.
Human factor in business process management: modeling competencies of BPM roles

PurposeThe study proposes competence models for the roles of process owners, process analysts and industrial engineers based on qualitative research.Design/methodology/approachThe research methodology is a combination of a questionnaire survey and interviewing in Czech companies, which develop the process approach. The proposed competence models can be utilized during business process management (BPM) implementation while appointing process owners, analysts and industrial engineers and their further development.FindingsThis paper emphasizes the role of human factor and presents research results concerning most important BPM roles and their competencies.Research limitations/implicationsThere is lack of research (a research gap) in the field of BPM roles, what they do and what they should do.Practical implicationsA system of competence models is thus a tool for human resource management and should increase the success rate of BPM projects. Another possible utilization is in higher education in business administration.Social implicationsAnother possible utilization is in higher education in business administration.Originality/valueIt proposes competence models for the roles of process owners, process analysts and industrial engineers based on qualitative research.
Massive Factorial Design Untangles Coding Sequences Determinants of Translation Efficacy

Comparative analyses of natural sequences or variant libraries are often used to infer mechanisms of expression, activity and evolution. Contingent selective histories and small sample sizes can profoundly bias such approaches. Both limitations can be lifted using precise design of large-scale DNA synthesis. Here, we precisely design 5 E. coli genomes worth of synthetic DNA to untangle the relative contributions of 8 interlaced sequence properties described independently as major determinants of translation in Escherichia coli. To expose hierarchical effects, we engineer an inducible translational coupling device enabling epigenetic disruption of mRNA secondary structures. We find that properties commonly believed to modulate translation generally explain less than a third of the variation in protein production. We describe dominant effects of mRNA structures over codon composition on both initiation and elongation, and previously uncharacterized relationships among factors controlling translation. These results advance our understanding of translation efficiency and expose critical design challenges.
The Effect of Private Investment on Public Expenditure in the CFA Franc Zone

This paper investigates the effect of private investment on public expenditure in the CFA franc zone during the period 1980-2015. To attain this objective, the methodology adopted is the system Generalized Method of Moments (GMM). The result shows a positive and significant effect of private investment on public expenditure. This result thus justifies that private investment is an important source of increase in public expenditure. This result implies that public policies for improving private investment should be promoted: improving the business climate, improving the quality of institutions, ameliorate political environment, fight against terrorism ravaging part of Africa including Mali.
Application of Machine Learning Techniques to Delineate Homogeneous Climate Zones in River Basins of Pakistan for Hydro-Climatic Change Impact Studies

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.
The Impact of Anthropogenic Forcing and Natural Processes on Past, Present, and Future Rainfall over Victoria, Australia

Cool-season (April to October) rainfall dominates the annual average rainfall over Victoria, Australia, and is important for agriculture and replenishing reservoirs. Rainfall during the cool season has been unusually low since the beginning of the Millennium Drought in 1997 (~12% below the twentieth-century average). In this study, 24 CMIP5 climate models are used to estimate 1) the extent to which this drying is driven by external forcing and 2) future rainfall, taking both external forcing and internal natural climate variability into account. All models have preindustrial, historical, and twenty-first-century (RCP2.6, RCP4.5, and RCP8.5) simulations. It is found that rainfall in the past two decades is below the preindustrial average in two-thirds or more of model simulations. However, the magnitude of the multimodel median externally forced drying is equivalent to only 20% of the observed drying (interquartile range of 40% to −4%), suggesting that the drying is dominated by internally generated rainfall variability. Underestimation of internal variability of rainfall by the models, however, increases the uncertainties in these estimates. According to models the anthropogenically forced drying becomes dominant from 2010 to 2029, when drying is evident in over 90% of the model simulations. For the 2018–37 period, it is found that there is only a ~12% chance that internal rainfall variability could completely offset the anthropogenically forced drying. By the late twenty-first century, the anthropogenically forced drying under RCP8.5 is so large that internal variability appears too small to be able to offset it. Confidence in the projections is lowered because models have difficulty in simulating the magnitude of the observed decline in rainfall.
Dual Independent Classification For Sketch-Based 3D Shape Retrieval

Sketch-based 3D shape retrieval has received more attention by the pattern recognition and multimedia analysis community in the last few years. Sketches are simple to draw and could be an efficient tool for abstracting 3D models. However, it is very challenging to narrow the gap between 2D sketches and 3D models because of the discrepancy between their representations. Since the set of labels used to classify 3D shapes and 2D sketches is the same, the problem of sketch-based 3D shape retrieval may be reduced to a sketch classification problem and a 3D shape classification problem. By doing so, 3D shapes having the same label as the one predicted for a 2D sketch can be considered as relevant retrieved objects for the sketch. Nevertheless, the task of 2D sketch classification is also still challenging, particularly, due to the perception subjectivity of the designers, which increases intra-class variabilities. In this paper, we tackle the problem of sketch-based 3D shape retrieval and propose the dual independent classification solution which comprises two stages: First, we independently classify both 2D sketches and 3D shapes using deep neural networks. Then, we calculate pairwise cosine similarities between the softmax vectors of the query (2D sketch) and the target (3D shape). Our method has been evaluated on two commonly used datasets, namely SHREC13 and SHREC14. The results show that our method, despite its simplicity, has comparable and sometimes better performance when compared to state-of-the-art approaches.
Comparison of Market-based and DQN methods for Multi-Robot processing Task Allocation (MRpTA)

Multi-robot task allocation (MRTA) problems require that robots take complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.
Neuromeditation: The Science and Practice of Combining Neurofeedback and Meditation for Improved Mental Health

Beginning meditators often complain that they do not know if they are “doing it right” or give up before realizing significant benefits. Advanced meditators often reach a plateau and struggle to reach “the next level” of their practice. Modern researchers and practitioners are finding a possible new solution to these challenges by using EEG biofeedback to increase awareness of subtle states of consciousness and speed the learning process. By tracking brainwave activity in specific regions of the brain, we can tell if someone is focused or relaxed. We can tell if the mind is wandering, if they are engaged in body-based emotions, or if they have entered a space of internal quiet. By monitoring this activity and connecting it directly to the intent of the meditation, it is possible to help meditators learn to quickly enter a desired state of consciousness and maintain this state for increasing periods of time. This chapter will describe the early research conducted in this area along with an original case study conducted by the author. In addition, the author will describe the way this technology is being used as a treatment intervention for ADHD, anxiety, depression, and PTSD.
Mental Health and Human Rights

This Handbook is part of the celebration of the seventieth anniversary of the adoption of the Universal Declaration of Human Rights (UDHR) (UN, 1948). Not only was health not mentioned in the UDHR beyond a brief reference to “medical care” in the context of the right to an adequate standard of living, but mental health was not mentioned at all. Nevertheless, the UDHR inaugurated a process of normative expansion and practical application of human rights deemed essential for life with dignity and social participation. Recent decades have witnessed an increased focus on mental health as an essential component of optimal living. The human rights framework is especially relevant for persons with mental disorders insofar as their vulnerabilities are addressed and protected within the meaning, scope, and practical significance of human rights. In other words, the limitations on autonomy and agency associated with mental disabilities challenge the equality in dignity and rights of all human beings, which is the operating premise of human rights. Mental health conditions can be broadly divided into two large categories: common mental disorders (CMDs) – which encompass depression, anxiety, and somatoform disorders – and severe mental disorders (SMDs) – which include schizophrenia and other psychoses, as well as severe forms of mood and other disorders (e.g., bipolar) (WHO, 2018a). Although the discourse on human rights in mental health usually revolves around SMDs, where the most frequent and visible violations of human rights take place, other 12 Mental Health and Human Rights
Cytotoxic T lymphocyte immunodominance to Epstein-Barr virus infection

EBV lytic replication involves the sequential expression of a large array of antigens that potentially provides a complex antigenic challenge. Despite this number, the primary CD8+ T cell response in infectious mononucleosis (IM) patients appears focused against epitopes found in immediate-early and some early expressed antigens with responses to late antigens typically subdominant. However previous approaches have only focused on a limited number of lytic antigens. To resolve these issues, this study examines the CD8+ T cell repertoire to all EBY lytic antigens in different phases of infection. Polyelonal CD8+ T cell lines were mitogenically expanded from IM patients or expanded in an antigenically unbiased way from post-1M patients and healthy carriers using autologous dendritic cells loaded with lysates of lytically infected cells. Target cells expressing individual lytic antigens along with donor HLA class I molecules were used to challenge polyclonal lines with responses measured by the release of IFNγ. These studies show that the pattern of target antigen choice varies with the phase of infection and is consistent with the idea that CD8+ T cell responses in primary infection are driven by lytically infected B cells. However over time the repertoire of responses may be influenced through antigen cross-presentation.
CEO Pay Sensitivity (Delta and Vega) and Corporate Social Responsibility

We use CEO pay sensitivity to stock performance (delta) and stock volatility (vega) to provide empirical evidence that CEO compensation structure influences firm Corporate Social Responsibility (CSR) performance. We find that delta has no significant effect on CSR, while vega has a strong, causal relationship with CSR. Our findings suggest that CEOs do not view CSR as value enhancing, but as a way to increase their own compensation through vega. Firms that want to improve their social performance should consider vega as an important compensation incentive for executives.
Estrogen Reduced Blood Glucose in High Fat-Fed Mice: An Animal Model of Type 2 Diabetes

Objective: This study aims to produce a mouse model of type 2 diabetes by using high fat diet. The C57BL/6J mouse strain can develop type 2 diabetes by putting on high fat diet. Methods: A group of C57BL/6J male mice were fed with a high fat diet (53% energy by fat) while another group was fed with normal diet (4.5% energy by fat). Results: At the 16th week of feeding study, the high fat-fed mice developed type 2 diabetes and had higher fat-pad weight than the normal diet-fed mice. However, plasma triglyceride (TG) levels of the two groups were not different. High fat-induced diabetic mice were administered 0.2 μg/g body weight of 17-β estradiol for 2 weeks. Their fasting blood levels were reduced to become lesser than the levels in high-fat fed mice without estrogen. A trend of decrease in plasma TG level of 17-β estradiol treated mice was observed. Conclusion: This study demonstrated that high fat diet could induce type 2 diabetes in a mouse model and that estrogen could reduce the fasting blood glucose in these mice.
On Learned Operator Correction in Inverse Problems

We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions. This paper discusses the conceptual difficulty to learn such a forward model correction and proceeds to present a possible solution as forward-adjoint correction that explicitly corrects in both data and solution spaces. We then derive conditions under which solutions to the variational problem with a learned correction converge to solutions obtained with the correct operator. The proposed approach is evaluated on an application to limited view photoacoustic tomography and compared to the established framework of Bayesian approximation error method.
Algorithmes de sortie du Piège de la Zone Ennuyeuse en apprentissage par renforcement

Les algorithmes d'apprentissage par renforcement ont reussi au fil des annees a obtenir des resultats impressionnants dans divers domaines. Cependant, ces algorithmes souffrent de certaines faiblesses mises en evidence par Refael Vivanti et al. qui peuvent expliquer la regression des agents meme bien entraines dans certains environnements : la difference de variance sur les recompenses entre les zones de l'environnement. Cette difference de variance conduit a deux problemes : le Piege de la Zone Ennuyeuse (Boring Area Trap) et le Consultant Manipulateur. Nous observons que l'algorithme Adaptive Symmetric Reward Noising (ASRN) propose par Refael Vivanti et al. pre-sente des limites pour des environnements ayant les caracteristiques suivantes : longues durees de jeu et environnement a zones ennuyeuses multiples. Pour pallier ces problemes, nous proposons trois algorithmes derives de l'algorithme ASRN nommes Rebooted Adaptive Symmetric Reward Noi-sing (RASRN) : Continuous e decay RASRN, Full RASRN et Stepwise α decay RASRN. Grâce a deux series d'experimentations menees sur le probleme du bandit a k bras, nous montrons que ces algorithmes permettent de mieux corriger le probleme du piege de la zone ennuyeuse.
H7N1 Low Pathogenicity Avian Influenza Viruses in Poultry in the United States During 2018

SUMMARY. Here, we report three detections of H7N1 low pathogenicity avian influenza viruses (LPAIV) from poultry in Missouri (n = 2) and Texas (n = 1) during February and March 2018. Complete genome sequencing and comparative phylogenetic analysis suggest that the H7 LPAIV precursor viruses were circulating in wild birds in North America during the fall and winter of 2017 and spilled over into domestic poultry in Texas and Missouri independently during the spring of 2018.
Comparison of Size, Terminal Fall Velocity, and Density of Bighead Carp, Silver Carp, and Grass Carp Eggs for Use in Drift Modeling

AbstractInvasive Asian carp established in the United States spawn in the turbulent water of rivers, and their eggs and early larvae develop while drifting in the current. The eggs, which are believed to perish if they settle before hatching, are slightly denser than water and are held in suspension by water turbulence. It is possible to use egg drift modeling to assess the capability of a river to support the survival of Asian carp eggs. Detection of spawning and estimation of egg abundance in the drift are typically assessed by ichthyoplankton trawls. Correct sampling design and interpretation of trawl data require knowledge of the vertical distribution of eggs in the drift, which can be accomplished with particle transport models. Data that are required to populate models of egg drift and vertical distribution include physical properties of assessed rivers and information on egg size, density, and terminal fall velocity, but data on these egg characteristics have not been previously available. Physical.
What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020

Most studies analyzing political traffic on Social Networks focus on a single platform, while campaigns and reactions to political events produce interactions across different social media. Ignoring such cross-platform traffic may lead to analytical errors, missing important interactions across social media that e.g. explain the cause of trending or viral discussions. This work links Twitter and YouTube social networks using cross-postings of video URLs on Twitter to discover the main tendencies and preferences of the electorate, distinguish users and communities’ favouritism towards an ideology or candidate, study the sentiment towards candidates and political events, and measure political homophily. This study shows that Twitter communities correlate with YouTube comment communities: that is, Twitter users belonging to the same community in the Retweet graph tend to post YouTube video links with comments from YouTube users belonging to the same community in the YouTube Comment graph. Specifically, we identify Twitter and YouTube communities, we measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter. To achieve that, we have gather a dataset of approximately 20M tweets and the comments of 29K YouTube videos; we present the volume, the sentiment, and the communities formed in YouTube and Twitter graphs, and publish a representative sample of the dataset, as allowed by the corresponding Twitter policy restrictions.