Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Factors of Multi-Omics Data and Clustering

Cancer is a very complex disease; often, its types cannot easily be classified simply by its location or manifested characteristics. In these circumstances, it is critical to find the group of patients who have similar underlying biological information and apply similar treatment for the person that belongs to the same group. The Cancer Genome Atlas (TCGA) database which focuses on cancer diseases with various types of omic data, such as mutations, RNA expressions and DNA methylation. Often, this data have more variables than samples which faces the problem of curse of dimensionality. With this multi-omic data, this work aims to discover unknown factors common to the three data types using factor analysis tools such as iCluster and MOFA; this dimension reduction process can select more relevant information for further analysis. This thesis proposes a methodology that compares MOFA and iCluster by finding the underlying latent factors and perform a clustering of patients who share biological similarities within the group. After testing both methods, first on the synthetic data and comparing their abilities to recover the underlying factors and clusters, we decide to apply MOFA method for the Ovarian Carcinoma (OV) data extracted from TCGA, to find latent factors and the relevant clustering results. From synthetic data analysis, we conclude that the MOFA has better performance. For real data, the genes find are cancer related but the cluster results are insignificant.
Flight control and position estimation for a tethered micro aerial robot

This work focuses on the study of tethered flights of a small quadcopter from a ground-base, which could be used to supply continuous power to the aerial robot and significantly extend its flight time. Some features that facilitate the interaction between the quadcopter and the ground-base are studied and implemented. The research starts by introducing a method capable of estimating the tension applied to a quadcopter by using the inertial information from the IMU sensors and the quadcopter’s thrust. To filter the undesired noise presented in the IMU measurements a Kalman filter is implemented. After the filtering process, the tension estimate is used to present two alternative methods to control the UAV, based on the tension applied to it. The first method allows to change the quadcopter’s position by applying a force to it. This solution is extended to implement a process of landing a UAV by pulling the tether tethered to it. Moreover, the second method allows to develop a novel position control structure, based on the tension that the tether applies to the quadcopter, and on the shape of a tether that outlines a catenary curve. At last, the position of the quadcopter is estimated taking into account the quadcopter’s tension estimates, and the shape of a tether that fits into the catenary model.
Supplementary Material for Scene Text Telescope: Text-Focused Scene Image Super-Resolution

In this section, we introduce the details of TBSRN. We first use an STN [2] to rectify the LR image, which is further processed by a CNN layer with the configuration (k : 3, s : 1, p : 1, i : 3, o : 64) to obtain a 64-channel feature map, where k, s, p, i, o denote kernel, stride, padding, input channel, output channel, respectively. The feature map goes down to a series of TBSRN blocks, then generates Figure 1. Examples of IC13, IC15, and CT80.
The Development of Feeding and Eating Disorders after Bariatric Surgery: A Systematic Review and Meta-Analysis

Background: Patients in the postoperative period following bariatric surgery are at risk of developing eating disorders. This study aims to analyze the relation between bariatric surgery and the development and recurrence of eating disorders. Material and methods: A literature review was carried out on 15 November 2020. Fourteen studies that met the eligibility criteria were included for qualitative synthesis, and 7 studies for meta-analysis. Results: The prevalence of eating disorders in the postoperative period was 7.83%, based on the 7 studies in the meta-analysis. Binge eating disorder alone was 3.81%, which was the most significant factor, and addressed in 6 of these studies. Conclusion: The investigated studies have significant methodological limitations in assessing the relation between bariatric surgery and eating disorders, since they mostly present data on prevalence. PROSPERO CRD42019135614.
SOLIDIFICATION OF THE Al ALLOY COMPOSITE REINFORCED WITH GRAPHITE PARTICLES

M. Łągiewka (lagiewka.malgorzata@wip.pcz.pl), C. Kolmasiak, Czestochowa University of Technology, Faculty of Production Engineering and Materials Technology, Czestochowa, Poland The aim of the work was to analyse the solidification of the Al matrix composite reinforced with graphite particles and to determine the influence of these particles on the solidification kinetics of metal composites. The AlMg10 alloy was used as the matrix of the tested composites. The tests covered composites containing 10 %, 20 %, 30 % graphite particles, produced in a two-stage process with the method of mechanical mixing. Solidification tests were performed by the thermal derivative analysis method (ATD). The introduction of ceramic particles into the matrix changed the solidification kinetics of the alloy.
Mix and Mask Actor-Critic Methods

Shared feature spaces for actor-critic methods aims to capture generalized latent representations to be used by the policy and value function with the hopes for a more stable and sample-efficient optimization. However, such a paradigm present a number of challenges in practice, as parameters generating a shared representation must learn off two distinct objectives, resulting in competing updates and learning perturbations. In this paper, we present a novel feature-sharing framework to address these difficulties by introducing the mix and mask mechanisms and the distributional scalarization technique. These mechanisms behaves dynamically to couple and decouple connected latent features variably between the policy and value function, while the distributional scalarization standardizes the two objectives using a probabilistic standpoint. From our experimental results, we demonstrate significant performance improvements compared to alternative methods using separate networks and networks with a shared backbone.
Deterministic Automotive Radar Channel Modeling: A Physical Optics Approach

Autonomous driving has increasingly become a very promising technological goal in the past few years, with enormous efforts invested into the development of permanent and reliable supervision of the vehicles’ surroundings. Given their essential function on long-range detection and their robustness against bad weather conditions, radars arise as one of the most relevant sensors in the automotive industry. A deterministic radar channel model that accurately predicts the behaviour of a radar is essential for the implementation of reliable and efficient autonomous driving. Since scattering plays a major role in automotive environments, and because the existing models for its prediction present some concerning limitations, an alternative approach for its deterministic simulation based on the physical optics approximation is pursued. Based on a ray tracing algorithm structure, a program in C++ is implemented that performs the diffusion of rays at the moment of scattering by a scattering surface. Radar cross section plots of a scene are obtained, with the number of incident rays representing the transmitted wave being the main focus of the analysis. A correction factor related to this parameter adjusts the range of values. For large planar surfaces, the simplified strategy of the geometrical optics approximation is still more efficient in terms of time and computational resources than the developed algorithm. However, for highly scattering obstacles, the physical optics proves to be remarkably advantageous and accurate, disclosing a medium deviation of just 0.686 dBsm.
Using early recollections to explore personality trait, self-efficacy, and insight in depressive patients

Objective: Early life experience forms a template for the self-perception of an individual, and negative representations make individuals vulnerable to depression. To explore the deep inner world of patients with depression using objective early life experience measures is not easy. In the present study, we intended to clarify the use of subjective early recollections (ER) in exploring the inner world of depressive patients. Methods: We interviewed 80 depressive inpatients with ER and evaluated whether they had a feeling sense of belonging to their caregivers, and whether they had a positive self-image. They were also assessed using the Taiwanese version Type D Scale-14, Chinese version General Self-efficacy Scale, the Taiwanese version of the Mini-International Neuropsychiatric Interview, and Insight Interview. Results: Independent t-test was used to test the significant differences between feeling belonging/not feeling belonging groups and between positive or negative self-image groups on the variables of Type D personality traits, self-efficacy, illness insight, and comorbid psychiatric disorders with effect sizes ranging from 0.4 to 1.3. Conclusion: Our study showed that ER could function as a window through which depressive patients' inner life. Thus, we suggested that ER can be considered an important part of the clinical assessment methods for depressive patients.
HEAVY-TRAFFIC LIMITS FOR A FORK-JOIN

We study a fork-join network with a single class of jobs, which are forked into a fixed number of parallel tasks upon arrival to be processed at the corresponding multi-server stations. After service completion, each task will join a buffer associated with the service station waiting for synchronization, called “unsynchronized queue”. The synchronization rule requires that all tasks from the same job must be completed, referred to as “non-exchangeable synchronization”. Once synchronized, jobs will leave the system immediately. Service times of the parallel tasks of each job can be correlated and form a sequence of i.i. d. random vectors with a general continuous joint distribution function. We study the joint dynamics of the queueing and service processes at all stations and the associated unsynchronized queueing processes. The main mathematical challenge lies in the “resequencing” of arrival orders after service completion at each station. As in Lu and Pang (2015) for the infinite-server fork-join network model, the dynamics of all the aforementioned processes can be represented via a multiparameter sequential empirical process driven by the service vectors for the parallel tasks of each job. We consider the system in the Halfin-Whitt regime, and prove a functional law of large number and a functional central limit theorem for queueing and synchronization processes. In this regime, although the delay for service at each station is asymptotically negligible, the delay for synchronization is of the same order as the service times.
Programmable-Parametric Filter with Data Acquisition

—Audio filtering systems are used in commercial applications involving equalizers. However, some of the topologies used can be modified to be used in audio signal treatment applications. Parametric filter topologies allow the user to modify independently the central frequency of the filter, the bandwidth and the gain. Using a microprocessor and programmable components will allow the user to modify the parameters of the filter and to acquire the filtered signal so it can be processed in other stages. To improve the functionality of the system, a server will be implemented to process the data sent by the user, will be sent afterwards to the microcontroller. Finally, the results obtained will be thrown back to the user.
Machine Learning and Computational Intelligence for High-Order Epistasis Detection

Genome-Wide Association Studies (GWAS) aim to discover how genetic markers like Single Nucleotide Polymorphisms (SNP) mutually interact with each other, enabling the manifestation of diseases or traits. Full exhaustive analysis is prohibitive due to the exponentially increasing, with the numbers of markers assumed to be involved and SNPs present in the human genome, of interactions to test. Common approaches use machine learning techniques to rank SNPs according to their estimated predictive power or create clusters for SNPs assumed to produce highly scoring interactions between them. The proposed approach uses a genetic algorithm to search the parameter space of a gradient boosting machine in order to ﬁnd an optimal conﬁguration. This conﬁguration will produce a model in which the most prominent features will coincide with the SNPs involved in interactions of highly predictive power for determining wether a patient will manifest a certain trait or not, prompting an exhaustive search on that subspace to ﬁnd best scoring interactions. Optimal solutions for interactions of up to 5 SNPs where found or closely approached with this approach on toy datasets where interactions of superior order where hidden with controllable prediction power for the studied trait. The algorithm is an inference tool for GWAS that works outside of common interaction orders studied, being able to ﬁnd promising higher-order interactions for further biological study without an exponential increase in time.
THE IMPACT OF AUTHENTIC LEADERSHIP ON EMPLOYEE PSYCHOLOGICAL CAPITAL IN THE HOSPITALITY INDUSTRY

Psychological capital is a focal component of authentic leadership theory. The importance and growing popularity of psychological capital stem from its positive effect on desirable employee work-related attitudes and behaviors, which is particularly pronounced in the service industries. However, empirical research on the relationship between authentic leadership and psychological capital in the hospitality industry is scarce. In order to fulfil this gap in the literature, the present study investigates the influence of authentic leadership on hotel employees’ psychological capital, additionally examining the moderating role of organizational structure. Hypotheses were tested using hierarchical multiple regression analysis on a sample of 226 employees from 24 hotels in Croatia. The findings confirmed that managers who demonstrate authentic leadership behaviors are able to positively impact hotel employees’ psychological capacities of hope, optimism, resilience and self-efficacy. However, there was no empirical evidence to support the hypothesized moderation effect of organic organizational structures in the said relationship.
Robot hand self-perception via 2D segmentation: a deep learning approach

We propose using deep learning to perform segmentation on images containing robotic hands. This serves many applications, e.g. helping the robot with tasks requiring accurate knowledge about the positioning of the robot’s hands. Detecting its own hands, through vision, is more reliable than using kinematics because, in the latter, each joint of the arm must be properly calibrated, otherwise small offsets will propagate and create large errors when estimating the hand’s pose. Maintaining all the joints properly calibrated is impractical and time consuming. To overcome the challenge of collecting and annotating large amounts of images, the datasets are generated in simulation, using domain randomization. We use a model, pre-trained on a large scale dataset, and ﬁne-tune it on our datasets. We make experiments, with different training datasets and strategies, showing results on different types of datasets, with images generated in simulation and real indoor environments, with the real robot. From these experiments we create a ﬁnal model, trained solely on synthetic images, that achieves an average IoU of 82% on synthetic validation data and 63 , 5% on real validation data. These results were achieved with only 1000 training images and 3 hours of training time on a single GPU. We do not intend to create a robust model, but rather develop a methodology requiring low amounts of data to achieve reasonable performance, on real data, and give detailed insight on how to properly generate variability in the data and how to ﬁne-tune a complex model to a very different task.
Effectiveness of Online Positive Psychology Intervention on Psychological Well-Being Among Undergraduate Students

Positive psychology intervention is mediation that aims to promote quality of life and well-being. Current research integrating positive psychology with the Internet is called online positive psychology (OPP) which promotes and prevents mental health problems, improves well-being, and reduces depression. This experimental research aimed 1) to compare the psychological well-being of the experimental group that received online positive psychology intervention in the phase of pre-test, post-test, and follow up and 2) to compare the psychological well-being between the experimental group and the controlled group. The subjects were 24 undergraduate students from Mahasarakham University, Thailand, selected by purposive sampling. Thereafter, the subjects were equally divided into experimental and controlled groups. Measures used in this study were as follows: 1) the online positive psychology intervention to improve psychological well-being and 2) the scale of psychological well-being based on Ryff’s psychological well-being. The statistics used in the data analysis were the Friedman Test, Wilcoxon signed-rank test, and the Mann Whitney U Test. The results of the pre-test and the post-test showed that the mean scores of psychological well-being of the experimental group were significantly different at 0.05 levels. Additionally, the mean scores of psychological well-being between the experimental group and the controlled group in the phases of post-test and follow-up were significantly different at 0.05 levels. The online positive psychology intervention was effective in increasing the psychological well-being of undergraduate students.
A computational study of S. oneidensis MR-1 : discovering the role of hypothetical proteins

The genomes of many organisms have been sequenced in the last five years. The characterization of these proteomes is the next step in the process of attaining a global understanding of the workings of the pathways and protein complexes of organisms. It is difficult to computationally assign functional annotations for between thirty and fifty percent of hypothetical proteins from a newly sequenced genome. Sequence based search methods sometimes return little or no information about hypothetical proteins. In these situations three dimensional structural predictions combined with a suite of computational tools can suggest possible functions for these hypothetical proteins. The premise for development of this method is therefore that sequence-structure­ function annotations give experimentalists information that is not available using sequence searches alone. The ability to predict function for more of these hard to annotate hypotheticals will allow better characterization of proteomes, will help experimentalists choose possible candidate genes related to their area of research for further study, and will help with interpretation of microarray data.
Responses of Bat Communities (Mammalia: Chiroptera) to Forest Loss and Habitat Conversion in Southern Cameroon

We aimed to assess how bats are affected by habitat transformation by comparing bat assemblages in four habitat types: primary forest, secondary forest, cocoa plantations and human habitations in the Dja Biosphere Reserve of southern Cameroon. Bats were sampled in the four habitat types using mist nets. During 126 nights, a total of 413 bats were captured, belonging to four families, 16 genera and 24 species. Ninety three individuals (17 species) were captured in the primary forest, followed by plantations (105 individuals, 14 species), human habitations (159 individuals, 10 species), and secondary forest (55 individuals, eight species). Megaloglossus woermanni was recorded in all the four habitats, and was the most abundant species (105 individuals). The analysis of bat assemblage between habitat types showed a statistically significant difference in species composition. The distribution of the six most abundant species (Epomops franqueti, Megaloglossus woermanni, Rousettus aegyptiacus, Dohyrina cyclops, Hipposideros cf. caffer and Hipposideros cf. ruber) was influenced by habitat types. Our results suggest that the decrease in species richness observed in disturbed habitats may be due to habitat perturbations of primary forest habitats. Therefore, it is important to examine the effects of habitat conversion at species level, as responses are often species-specific.
The Calculation of High-Order Vertical Derivative in Gravity Field by Tikhonov Regularization Iterative Method

In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. The Tikhonov regularization method is widely used to solve complex problems in engineering. The vertical derivative of gravity can highlight the local anomalies and separate the horizontal superimposed abnormal bodies. The higher the order of the vertical derivative is, the stronger the resolution is. However, it is generally considered that the conversion of the high-order vertical derivative is unstable. In this paper, based on Tikhonov regularization for solving the high-order vertical derivatives of gravity field and combining with the iterative method for successive approximation, the Tikhonov regularization method for calculating the vertical high-order derivative in gravity field is proposed. The recurrence formula of Tikhonov regularization iterative method is obtained. Through the analysis of the filtering characteristics of this method, the high-order vertical derivative of gravity field calculated by this method is stable. Model tests and practical data processing also show that the method is of important theoretical significance and practical value.
Iron-associated Colitis: A Common Entity in an Uncommon Location

Iron-induced mucosal damage is a common finding within the upper gastrointestinal tract. Because of physiologic factors, however, high concentrations of iron rarely reach the distal gastrointestinal tract, thus injury there has not been reported. Here, we present a case in which multiple factors contributed to iron-induced injury in the colon.