Miracle-dz/UniFAR-FTUs huggingface dataset:
USAGE OF A GRAPHICS PROCESSOR TO ACCELERATE COREFERENCE RESOLUTION WHILE USING THE RoBERTa MODEL

"The problem of finding coreferential objects (coreference resolution) in Ukrainian-language texts is considered, examples of co-referentiality in the Ukrainian language are given. Coreference in texts means the relationship between syntactic units that point to the same object (referent) in a given context. The differences between English-language and Ukrainian-language texts in solving the problem of searching for coreferenced objects and the difficulties that arise in finding coreferenced objects are noted. In particular, there are differences in the order of words in sentences: strict word order in English and arbitrary word order in Ukrainian. It is also pointed out the need to take into account the relationship between objects that are not part of the co-reference groups (predicates and secondary parts of sentence). Created an application that searches for reference objects in Ukrainian-language texts using the Transformers library and the RoBERTa language model. The use of a graphics processor to speed up the search for referential objects by the algorithm is considered. The work of the algorithm was tested on a set containing 2500 texts. 21.3 times acceleration reached due to the use of a graphics processor (GPU) in comparison to the performance of CPU. The resulting acceleration has greatly facilitated the development and testing of the application. "
Policy generation network for zero‐shot policy learning

Lifelong reinforcement learning is able to continually accumulate shared knowledge by estimating the inter‐task relationships based on training data for the learned tasks in order to accelerate learning for new tasks by knowledge reuse. The existing methods employ a linear model to represent the inter‐task relationships by incorporating task features in order to accomplish a new task without any learning. But these methods may be ineffective for general scenarios, where linear models build inter‐task relationships from low‐dimensional task features to high‐dimensional policy parameters space. Also, the deficiency of calculating errors from objective function may arise in the lifelong reinforcement learning process when some errors of policy parameters restrain others due to inter‐parameter correlation. In this paper, we develop a policy generation network that nonlinearly models the inter‐task relationships by mapping low‐dimensional task features to the high‐dimensional policy parameters, in order to represent the shared knowledge more effectively. At the same time, we propose a novel objective function of lifelong reinforcement learning to relieve the deficiency of calculating errors by adding weight constraints for errors. We empirically demonstrate that our method improves the zero‐shot policy performance across a variety of dynamical systems.
Methods and algorithms of image segmentation and decovolution for quantitative analysis of Tissue Microarrays

This thesis aims at developing dedicated methods for quantitative analysis of Tissue Microarray (TMA) images acquired by fluorescence scanners. We addressed there issues in biomedical image processing, including segmentation of objects of interest (i.e. tissue samples), correction of acquisition artifacts during scanning process and improvement of acquired image resolution while taking into account imaging modality and scanner design. The developed algorithms allow to envisage a novel automated platform for TMA analysis, which is highly required in cancer research nowadays. On a TMA slide, multiple tissue samples which are collected from different donors are assembled according to a grid structure to facilitate their identification. In order to establish the link between each sample and its corresponding clinical data, we are not only interested in the localization of these samples but also in the computation of their array (row and column) coordinates according to the design grid because the latter is often very deformed during the manufacturing of TMA slides. However, instead of directly computing array coordinates as existing approach, we proposed to reformulate this problem as the approximation of the deformation of the theoretical TMA grid using “thin plate splines” given the result of tissue sample localization. We combined a wavelet-based detection and a ellipse-based segmentation to eliminate false alarms and thus improving the localization result of tissue samples. According to the scanner design, images are acquired pixel by pixel along each line, with a change of scan direction between two subsequent lines. Such scanning system often suffers from pixel mis-positioning (jitter) due to imperfect synchronization of mechanical and electronic components. To correct these scanning artifacts, we proposed a variational method based on the estimation of pixel displacements on subsequent lines. This method, inspired from optical flow methods, consists in estimating a dense displacement field by minimizing an energy function composed of a nonconvex data fidelity term and a convex regularization term. We used half-quadratic splitting technique to decouple the original problem into two small sub-problems: one is convex and can be solved by standard optimization algorithm, the other is non-convex but can be solved by a complete search. To improve the resolution of acquired fluorescence images, we introduced a method of image deconvolution by considering a family of convex regularizers. The considered regularizers are generalized from the concept of Sparse Variation which combines the L1 norm and Total Variation (TV) to favors the co-localization of high-intensity pixels and high-magnitude gradient. The experiments showed that the proposed regularization approach produces competitive deconvolution results on fluorescence images, compared to those obtained with other approaches such as TV or the Schatten norm of Hessian matrix.
Building Reduced Order Models for Dynamic Simulation and Optimization of Numeric Electromagnetic Models

There are many available methods capable of producing High-Fidelity Models (HFM) of electromagnetic systems. If the required precision is very high or the nature of the phenomenon that is being modeled is complex, a very large number of equations may have to be solved. If the model is used for applications where many different geometries or parameters must be considered, as is the case in design optimization, solving these equations many times can be very time consuming.To avoid the burden of this computation, Model Order Reduction (MOR) algorithms have been developed. They consist in procedures of finding Reduced Order Models (ROMs) that accurately describe the input/output behavior of the High-Fidelity Model but using only a very small number of equations.In this work, MORs techniques are analyzed and improved. Special attention is paid to Moment Matching. Problems like placement of expansion points, stability and numerical robustness are investigated. This has allowed the simulation and optimization of complex electromagnetic device. As examples of application we have presented a laminates bus bar and a wave scattering problem.In addition to that, a smart adaptive way of sampling the design space to allow fast optimization has been developed. The sampled points are used to perform interpolation and approximate the objective function in a very fast manner. Classic optimization techniques have also been coupled with the Reduced Order Models, accelerating the computations. The proposed approaches have been tested mainly in electromagnetic problems obtained by the Partial Equivalent Circuit Element (PEEC) method.
New Exogeneity Tests & Causal Paths

Statistically dependent X and Y have conditional density f(Y|X) asymmetrically different from unconditional f(Y). Theorem 1 com- pares two flipped kernel regressions to derive necessary conditions for causal path X --> Y and exogeneity meaning that X is self-driven. Hausman-Wu's indirect exogeneity test diagnoses a disease (endogeneity) by showing that instrumental variables (IV) estimator remedy 'works. ' Instead, a unanimity index-based test aggregates evidence from four orders of stochastic dominance and new asymmetric partial correlation coefficients to determine the direction and strength of causal and exogenous variables. A simulation supports our decision rules. An illustration identifies exogenous variables which can help predict US economic recession.
An Application of TAM on Use of Remotely Accessible E-Resources of Dhanvantri Library, University of Jammu

This study examines how remote users used electronic resources made available by the Dhanvantri Library at the University of Jammu. Quantitative techniques were employed to investigate how effectively e-resources are being used remotely. The study focused on users’ assessments of e-resources and the satisfaction level derived from them. The Technology Acceptance Approach (TAM) model is used to explore the level of adoption of e-resources through remote access in two dimensions, i.e., PU and PEU. The results revealed a positive attitude towards e-resources and moderately adopted 2.05 ± . 83, the remote access environment of the Dhanvantri library, where the average perceived usefulness is 2.97 ± 1.17, and the perceived ease of use is 2.58 ± 1.99. Consequently, TAM is significantly related to PEU [ χ2(12, 299) = 219.06, p<. 001] and PU [χ2(10, 299) = 48.33, p<. 001].
Usnic Acid-Loaded Magnetite Nanoparticles—A Comparative Study between Synthesis Methods

Since cancer is a continuously increasing concern for the general population, more efficient treatment alternatives ought to be developed. In this regard, a promising direction is represented by the use of magnetite nanoparticles (MNPs) to act both as a nanocarrier for the targeted release of antitumoral drugs and as hyperthermia agents. Thus, the present study focused on improving the control upon the outcome properties of MNPs by using two synthesis methods, namely the co-precipitation and microwave-assisted hydrothermal method, for the incorporation of usnic acid (UA), a natural lichen-derived metabolite with proven anticancer activity. The obtained UA-loaded MNPs were thoroughly characterized regarding their morpho-structural and physicochemical properties through X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS) and zeta potential, scanning electron microscopy (SEM), and vibrating sample magnetometry (VSM). Results demonstrated the formation of magnetite as the unique mineralogical phase through both types of synthesis, with increased uniformity regarding the drug loading efficiency, size, stability, and magnetic properties obtained through the microwave-assisted hydrothermal method. Furthermore, the cytotoxicity of the nanostructures against the HEK 293T cell line was investigated through the XTT assay, which further proved their potential for anticancer treatment applications.
Continuous Prediction of Human Joint Mechanics Using EMG Signals: A Review of Model-Based and Model-Free Approaches

This paper reviews model-based and model-free approaches for continuous prediction of human joint motion using surface electromyography (EMG) signals. The review focuses on approaches that solve the regression problem of continuously decoding motion from EMG as opposed to classification or discrete gesture identification methods. Model-based approaches use physics-based models to capture the dynamics of the muscles; and musculoskeletal geometry to estimate muscle forces and joint torques. Model-free machine learning and deep learning architectures employ training to learn the statistical regularities in the data to make predictions. Model-free approaches work well for real-time applications with better scalability for multiple muscle inputs and lower computational costs while model-based approaches are insightful and capture the essence of underlying muscle activation and contraction dynamics. Integrating model-free techniques to complement parametric models is a good practice for improving generalization of EMG-based predictions. The strengths and limitations of the two paradigms are compared based on scalability, performance, computational efficiency, and robustness for applications in robotics and biomechanics. We expand on previous review works with recent studies to draw inferences about the knowledge gaps and opportunities for future research. We hope this review serves as a comprehensive guide for current and prospective researchers interested in joint mechanics prediction using EMG.
Characterization of Chitin Synthase B Gene (HvChsb) and the Effects on Feeding Behavior in Heortia vitessoides Moore

Simple Summary Heortia vitessoides Moore is a leaf-eating pest that affects Aquilaria sinensis. In the outbreak period, the leaves of Aquilaria sinensis can be eaten up in a short time, resulting in the death of trees and great economic losses. Chitin is the main component of insect cuticle, peritrophic membrane, and tracheal intima. Chitin synthesis in insects is a complex process that requires the cooperation of many enzymes. Chitin synthase is one of the key enzymes in the process. Chitin synthase is divided into two types: chitin synthase A gene (Chsa) and chitin synthase B gene (Chsb). Chsb is mainly responsible for the tissue synthesis of chitin in midgut peritrophic membrane. It was found that the expression of HvChsb was inhibited, the growth and development were abnormal, and the mortality rate was increased. These findings provide a reference for the prevention and control of the pest from the perspective of gene manipulation. Abstract The chitin synthase B gene is a key enzyme in the chitin synthesis of insect peritrophic matrix (PM), which affects insects’ feeding behavior. The chitin synthase B gene was cloned from the transcription library of Heortia vitessoides Moore. RT-qPCR showed that HvChsb was highly expressed in the larval stage of H. vitessoides, especially on the first day of the pre-pupal stage, as well as in the midgut of larvae and the abdomen of adults. After starvation treatment, HvChsb was found to be significantly inhibited over time. After 48 h of starvation, the feeding experiment showed that HvChsb increased with the prolongation of the re-feeding time. The experimental data showed that feeding affected the expression of HvChsb. HvChsb was effectively silenced via RNA interference; thus, its function was lost, significantly decreasing the survival rate of H. vitessoides. The survival rate from larval-to-pupal stages was only 43.33%, and this rate was accompanied by abnormal phenotypes. It can be seen that HvChsb plays a key role in the average growth and development of H. vitessoides.
Family Victimization Among Canadian Sexual and Gender Minority Adolescents and Emerging Adults

Objectives: This paper examines family victimization, well-being, and resilience among sexual and gender minority (SGM) adolescents and emerging adults aged 15 to 29 years. Methods: Self-reported data were collected online (2019-2020) from 1,971 Canadian SGM youths. We used logistic regression to estimate the odds of: (a) having experienced family victimization over the past 12 months; (b) reporting well-being across the various frequencies of family victimization; and (c) thriving (i.e., flourishing despite having experienced family victimization). Results: About 36% of participants experienced family victimization within the previous 12 months, with 13% reporting recurrent family victimization. Recurrent victimization was more prevalent among trans and nonbinary youths as compared to cisgender men, and was also more prevalent among socioeconomically disadvantaged participants. Recurrent victimization was significantly associated with higher odds of reporting internalized heterosexism, efforts to conceal gender and sexual orientation, languishing mental health, social anxiety, loneliness, and post-traumatic stress symptoms. Thriving participants were less likely to experience activity restrictions or to live with at least one parent, and more likely to score higher on authenticity scales, to report proactive norms against violence within their family, and to have food and economic security. Conclusion: Despite recent advances in SGM rights and acceptance, SGM youths still face family victimization and compromised well-being. Implications: These findings underline the importance of screening for family violence among SGM youths, particularly among trans youths and those of lower socioeconomic status. Findings also underline the importance of providing SGM youth both a safe family environment and material security.
Genome sequence of Bacillus velezensis LZN01 inhibiting Fusarium oxysporum f. sp. niveum and producing myriocin

Abstract The bacterial strain LZN01 is a potential biocontrol agent that produces myriocin and inhibits the growth of Fusarium oxysporum f. sp. niveum (Fon). To characterize strain LZN01, the Illumina HiSeq + PacBio sequencing platform was used to sequence its genome. Unicycler software was used to perform genome assembly and antiSMASH to predict gene clusters responsible for secondary metabolite biosynthesis. The genome comprises 3973236 bp and has a 46.52% G + C content. Genome annotation predicts 4031 coding DNA sequences (CDSs), 27 rRNAs and 86 tRNAs. Strain LZN01 was identified as Bacillus velezensis based on phylogenomic analysis. We identified 14 gene clusters associated with the synthesis of antimicrobial secondary metabolites, including surfactin, fengycin, bacillibactin and bacilysin, among others. There are also genes associated with induced systemic resistance and plant growth promotion, such as biofilm formation, swarming motility, root colonisation, phosphate solubilisation, phytohormone production, acetoin and 2,3-butanediol synthesis. Many genes are associated with plant–bacteria interactions, including pectinase and galactosidase genes. We inferred that the genes epsN, capD, dagK, glk and manA are associated with myriocin biosynthesis. Overall, our data provide a valuable reference for further studies of the biocontrol mechanisms of strain LZN01 as a promising agent for application in agricultural practice.
Circadian regulation of metabolic, cell division, and cation transport promoters in the gastrointestinal bacterium Klebsiella aerogenes

Introduction All eukaryotes and at least some prokaryotes express the capacity to anticipate and adapt to daily changes of light and temperature in their environments. These circadian programs are fundamental features of many forms of life. Cyanobacteria were the first prokaryotes to have demonstrated circadian gene expression. Recently, a circadian rhythm was also discovered in an unrelated bacterium, Klebsiella aerogenes, a human gut commensal and nosocomial pathogen. Methods Here we characterize new clock-controlled genes with spatial differences in expression using a bacterial luciferase reporter. These include dephospho-coenzyme A kinase (coaE), manganese transporter, H-dependent (mntH) and a gene identified as filamenting temperature-sensitive mutant Z (ftsZ). Results and Discussion The data show that all three reporter constructs exhibited circadian variation, although only PmntH::luxCDABE reporter strains were synchronized by melatonin. Additionally, we show that K. aerogenes divides rhythmically in vitro and that these bacteria may alternate between exponential and stationary cells. Together, these findings provide a deeper understanding of K. aerogenes.
Point-to-Region Co-learning for Poverty Mapping at High Resolution Using Satellite Imagery

Despite improvements in safe water and sanitation services in low-income countries, a substantial proportion of the population in Africa still does not have access to these essential services. Up-to-date fine-scale maps of low-income settlements are urgently needed by authorities to improve service provision. We aim to develop a cost-effective solution to generate fine-scale maps of these vulnerable populations using multi-source public information. The problem is challenging as ground-truth maps are available at only a limited number of cities, and the patterns are heterogeneous across cities. Recent attempts tackling the spatial heterogeneity issue focus on scenarios where true labels partially exist for each input region, which are unavailable for the present problem. We propose a dynamic point-to-region co-learning framework to learn heterogeneity patterns that cannot be reflected by point-level information and generalize deep learners to new areas with no labels. We also propose an attention-based correction layer to remove spurious signatures, and a region-gate to capture both region-invariant and variant patterns. Experiment results on real-world fine-scale data in three cities of Kenya show that the proposed approach can largely improve model performance on various base network architectures.
A RAPID PIPELINE FOR PERIODIC INSPECTION AND MAINTENANCE OF ARCHITECTURAL SURFACES

Abstract. The contribution fits into the framework of Digital Heritage, within the diagnostic process, leading up to restoration. Control and monitoring of architectural heritage are still open topics, with respect to requirements as simplicity, non-invasiveness and cost-effectiveness of both procedures and equipment. A peculiar gap concerns the accessibility to the artefact, which could be compromised by bad state of preservation, poor hygienic/sanitary conditions, or unsafe post-emergency circumstances, as well as by considerable extent, complex morphology, or incidental access conditions, due to the simultaneous presence of multiple working groups.In recent years, literature is moving towards an optimization of the investigation phases, by implementing digital technologies for the acquisition, elaboration and interpretation of 2D/3D data, to assess the state of conservation of a building or its main components. Besides, digital image processing and artificial intelligence are progressively rationalizing the analysis of the collected data. Furthermore, easy common devices, like spherical cameras or smartphones, have been introduced for the virtual reconstruction and representation of architectural environments, with the purpose of assessing their morphology and conditions. However, there is still an absence of harmonized standard procedures.To address these issues, the paper proposes a rapid pipeline, involving easy-to-use devices and expeditious procedures, to remotely assess, quantify, and monitor the extension of surface decay of historical buildings. A workflow based on the use of 360° images and videogrammetry has been defined and tested on a representative case study, both for its cultural value and for its limited accessibility, demonstrating a great suitability to the topic.
Common errors on using convex modelling and linear matrix inequalities for nonlinear control

This note discusses common errors on using convex modelling and linear matrix inequalities for nonlinear control, a methodology that has become increasingly popular due to its systematicness and numerical implementability. Illustrations on common problems are made from existing literature: they are classified and discussed; advices are given to prevent them. Convex modelling is employed in linear parameter varying, Takagi-Sugeno models, and other convex structures in order to subsume or rewrite a nonlinear system for analysis or design via the direct Lyapunov method. Convexity plays a central role in allowing a finite set of vertex conditions in the form of linear matrix inequalities to be sufficient for the corresponding task. In contrast with other nonlinear methodologies, this one produces expressions resembling linear results, which makes it easier to grasp while often inducing subtle mistakes.