Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence Analysis of a Distributed Optimization Algorithm with a General Unbalanced Directed Communication Network

In this paper, we discuss a class of distributed constrained optimization problems in power systems where the target is to optimize the sum of all agents’ local convex objective functions over a general unbalanced directed communication network. Each local convex objective function is known exclusively to a single agent, and the agents’ variables are constrained to global coupling linear constraint and individual box constraints. To collaboratively solve the optimization problems, existing distributed methods mostly require the communication network to be balanced or have the knowledge of in-neighbors’ out-degree for all agents, which are quite restrictive and hardly inevitable in practical applications. In contrast, we investigate a novel distributed primal-dual augmented (sub)gradient algorithm which utilizes a row-stochastic matrix (does not need each agent to know its in-neighbors out-degree) and employs uncoordinated step-sizes, and yet exactly converges to the optimal solution over a general unbalanced directed communication network. Under the assumptions of the strong convexity and smoothness on the aggregate objective functions, it is proved that the algorithm geometrically converges to the optimal solution if the uncoordinated step-sizes do not exceed the upper bound. An explicit analysis for the convergence rate of the proposed algorithm is also characterized. To manifest effectiveness and applicability of the proposed algorithm, three case studies are presented to solve two practical problems in power systems.
Statistical methods for rates and proportions

Preface.Preface to the Second Edition.Preface to the First Edition.1. An Introduction to Applied Probability.2. Statistical Inference for a Single Proportion.3. Assessing Significance in a Fourfold Table.4. Determining Sample Sizes Needed to Detect a Difference Between Two Proportions.5. How to Randomize.6. Comparative Studies: Cross-Sectional, Naturalistic, or Multinomial Sampling.7. Comparative Studies: Prospective and Retrospective Sampling.8. Randomized Controlled Trials.9. The Comparison of Proportions from Several Independent Samples.10. Combining Evidence from Fourfold Tables.11. Logistic Regression.12. Poisson Regression.13. Analysis of Data from Matched Samples.14. Regression Models for Matched Samples.15. Analysis of Correlated Binary Data.16. Missing Data.17. Misclassification Errors: Effects, Control, and Adjustment.18. The Measurement of Interrater Agreement.19. The Standardization of Rates.Appendix A. Numerical Tables.Appendix B. The Basic Theory of Maximum Likelihood Estimation.Appendix C. Answers to Selected Problems.Author Index.Subject Index.
The Impact of Tax Legislation on Public Shareholding Companies Tax Disclosure: A Comparative Study Between the Jordanian Tax System and the U.S. Tax System (State of Illinois)

Purpose: the purpose of this study is to examine the impact of tax legislation frequency and awareness on tax disclosures in publicly held companies in Jordan and the USA. Theoretical Framework: the study utilizes a theoretical framework that considers the transparency, tax evasion, and availability of financial resources to the companies. The research employs an analysis of peer-reviewed journals and reports to explore the interaction between these variables. Findings: the findings reveal that frequent changes in tax legislation reduce awareness levels but have no significant effect on transparency. Companies tend to prefer minimal disclosures rather than making extensive reports. Controlled policy changes enable companies to adjust their practices and influence the availability of resources. Increased awareness of tax policies enhances corporate understanding, but its impact on transparency is limited unless there is a mandatory requirement. Higher levels of awareness also contribute to reduced corporate evasion and improved availability of financial resources. Implications of the research: the study has implications for tax legislators, providing insights into developing more effective tax legislation that enhances disclosures. Originality/value: It highlights the importance of considering other moderating factors that influence policy effectiveness across different countries.
Acceleration Methods for Classic Convex Optimization Algorithms

Most Machine Learning models are defined in terms of a convex optimization problem. Thus, developing algorithms to quickly solve such problems its of great interest to the field. We focus in this thesis on two of the most widely used models, the Lasso and Support Vector Machines. The former belongs to the family of regularization methods, and it was introduced in 1996 to perform both variable selection and regression at the same time. This is accomplished by adding a l1-regularization term to the least squares model, achieving interpretability and also a good generalization error. Support Vector Machines were originally formulated to solve a classification problem by finding the maximum-margin hyperplane, that is, the hyperplane which separates two sets of points and its at equal distance from both of them. SVMs were later extended to handle non-separable classes and non-linear classification problems, applying the kernel-trick. A first contribution of this work is to carefully analyze all the existing algorithms to solve both problems, describing not only the theory behind them but also pointing out possible advantages and disadvantages of each one. Although the Lasso and SVMs solve very different problems, we show in this thesis that they are both equivalent. Following a recent result by Jaggi, given an instance of one model we can construct an instance of the other having the same solution, and vice versa. This equivalence allows us to translate theoretical and practical results, such as algorithms, from one field to the other, that have been otherwise being developed independently. We will give in this thesis not only the theoretical result but also a practical application, that consists on solving the Lasso problem using the SMO algorithm, the state-of-the-art solver for non-linear SVMs. We also perform experiments comparing SMO to GLMNet, one of the most popular solvers for the Lasso. The results obtained show that SMO is competitive with GLMNet, and sometimes even faster. Furthermore, motivated by a recent trend where classical optimization methods are being re-discovered in improved forms and successfully applied to many problems, we have also analyzed two classical momentum-based methods: the Heavy Ball algorithm, introduced by Polyak in 1963 and Nesterov’s Accelerated Gradient, discovered by Nesterov in 1983. In this thesis we develop practical versions of Conjugate Gradient, which is essentially equivalent to the Heavy Ball method, and Nesterov’s Acceleration for the SMO algorithm. Experiments comparing the convergence of all the methods are also carried out. The results show that the proposed algorithms can achieve a faster convergence both in terms of iterations and execution time.
Reduction of Metal/Carbon Nano-Tubes Interface Contact Resistance by Floating Catalyst Growing Method and Semimetals

In this work, we investigate the contact resistance between carbon nanotubes (CNTs) and different metal electrodes (In, Cr, Cu, Ni, W, and Bi) grown by two methods: floating catalyst chemical vapor deposition (FCCVD) and thin Fe metal chemical vapor deposition (CVD). The results demonstrate that CNTs grown by FCCVD have lower contact resistance with metal electrodes compared to those grown by thin Fe metal CVD. The study attributes this to the avoidance of metal-silicide formation between the silicon (Si) substrate and the metal catalyst film during the high-temperature growing process in FCCVD. Moreover, the research shows that the FCCVD method results in a higher CNT density grown in the through Si vias (TSVs) and lower contact resistance between metal electrodes and CNTs, moving toward the 3-D integrated circuits (3-DICs). Further, the use of metals with good wettability such as bismuth (Bi) as the metal electrode reduces contact resistance.
Residual-based variational multiscale methods for turbulent flows and fluid-structure interaction

Physical problems are often characterised by a two-way interaction of fluid flow and elastic structural deformation. The fluid part will involve fully turbulent, incompressible flow in many applications of engineering interest. The present work is on residual-based variational multiscale modelling of turbulence for such problems. In this thesis, the residual-based large-eddy approach is stated in an Arbitrary-Lagrangean-Eulerian setting. It is studied thoroughly, especially with respect to the time-dependency of the subgrid scales and the numerical dissipation of the model terms. In addition, the influence of isogeometric representations and weak boundary conditions is evaluated, and a successful application of the approach in an example computation is provided.
Testing the Reciprocal Effect between Value of Education, Time Investment, and Academic Achievement in a Large Non-Western Sample

Many theories of motivation suggest that motivation and academic achievement reinforce each other over time, yet few longitudinal studies have examined behavioral pathways that may mediate interplay from motivation to achievement. Moreover, empirical studies so far have mostly focused on Western countries. In this study, we first examined whether students’ value of education, as a measure of motivation, is reciprocally related to achievement (class rank and self-rated performance) in a sample of junior high schoolers in an East-Asian country (N = 3445, Korean Youth Panel Study). We tested this reciprocity using different statistical models. Second, we investigated whether the relation between motivation and achievement was mediated by time invested in learning. Reciprocal effects between value of education and academic achievement were found in classic cross-lagged panel models, but only unilateral effects (from achievement to value of education) were found when we used random-intercept and random-curve cross-lagged panel models. Adding the time investment variable, the reciprocal effect between value of education, time investment, and academic achievement was found with the random intercept model. In conclusion, the reciprocity between of motivation and achievement are more elusive than previous research suggested; further studies should be dedicated to scrutinizing its existence with various statistical models.
Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches

Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER−, PR+ vs. PR−, HER2+ vs. HER2−, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER− and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.
Gastrin-producing G cells of gastric mucosa in Dexamethasone-treated rats

The systemic administration of glucocorticoids has not only strong beneficial anti-inflammatory and immunosuppressive effects but also numerous adverse health effects. Therefore, this study aimed to investigate the stereological and ultrastructural characteristics and distribution of gastrin-producing G cells in rat antral mucosa after 12 days of intraperitoneal administration of 2 mg/kg dexamethasone (DEX). In both groups of rats (DEX and control), the G cells were located at the base of the antral mucosal crypts, but in the DEX-treated rats, they were also found near the gastric lumen and showed an altered morphology compared to the basal cells. There were significant differences in the number of G cells per unit area and unit volume of antral mucosa between the groups studied. In the DEX-treated rats, the modified morphology and position of the antral G cells within the gastric units indicate their modified activity and possible impaired regulation of gastric acid production.
A Case Report of Inhaled Nitric Oxide for Transfusion-Related Acute Lung Injury

Transfusion-related acute lung injury (TRALI) is an acute respiratory distress syndrome (ARDS) occurring during or within six hours after transfusion. On the other hand, while inhaled nitric oxide (iNO) temporarily improves arterial oxygenation with selective pulmonary vasodilation, there is no evidence of mortality reduction in ARDS. We herein report a case in which TRALI was diagnosed with severe hypoxemia during cardiovascular surgery, and extracorporeal membrane oxygenation (ECMO) was avoided by using iNO for respiratory management. Administering iNO to patients with acute respiratory failure may be useful as a bridging therapy to help patients recover. However, further evidence is needed before this treatment can become standard practise.
PRINCIPAL GEODESIC ANALYSIS BOUNDARY DELINEATION WITH SUPERPIXEL-BASED CONSTRAINTS

In this paper an algorithm for accurate delineation of object boundaries is proposed. The method employs a superpixel algorithm to obtain an oversegmentation of the input image, used as a constraint in the task. A shape model is built by applying Principal Geodesic Analysis on angular representation of automatically placed uniformly distant landmark points. The shape model is used to detect the boundaries of an object on a given image by iterative elongation of a partial boundary along borders of superpixels. Contrary to many state-of-the-art methods, the proposed approach does not need an initial boundary. The algorithm was tested on two natural and two synthetic sets of images. Mean Dice coefficients between 0.91 and 0.97 were obtained. In almost all cases the object was found. In areas of relatively high gradient magnitude the borders are delineated very accurately, though further research is needed to improve the accuracy in areas of low gradient magnitude and automatically select the parameters of the proposed error function.
MAC-RANSAC : reconnaissance automatique d’objets multiples

Many applications in the field of computer vision (structure from motion, object recognition, image mosaicing) rely on the grouping of matchings between interest points. We propose an automatic and robust algorithm for object recognition and pose estimation. This method is based on the generalization of the a contrario approach of RANSAC algorithm proposed in [8]. The resulting framework permits the detection of multiple transformations between images.
ENHANCING UAV-SfM 3D MODELS ACCURACY OF UNIQUE HERITAGE INFRASTRUCTURES. CASE OF ISABEL II DAM, ALMERIA, SPAIN

Abstract. In recent years, resources and computer tools have devolved to allow the production of three-dimensional (3D) models that have become great sources of data for the reconstruction of elements of forgotten heritage. The main advances in the field of 3D modelling are related to terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry. Thanks to the development of image processing techniques, such as structure from motion (SfM) and multiview stereopsis (MVS), combination of both techniques offers effective and cost-efficient photogrammetric process to obtain a high-resolution data set from photographs taken from different points of view. The goal of this paper is to study the effect of the number and distribution of GCP, and the inclusion of oblique photographs on the accuracy of the 3D points clouds yielded by UAV photogrammetry. To achieve this purpose, 18 UAV photogrammetric projects, combining number and distribution of GCPs and inclusion of oblique images to the nadir images, were developed, and the accuracy, precision, and distance respect to a reference cloud were estimated. Results concluded that when only nadir images are used, the best results were found with nine GCPs placed at each of the four corners of the object under study, four in the midpoint of each line that joins two consecutive corners, and another one in the midpoint. Maintaining this arrangement of GCP and adding 12% of oblique images with respect to the total of zenith images, with an angle of 20º with respect to the nadir, improves the precision of the 3D point cloud generated by the photogrammetric project. If it is not possible to place the GCPs in this arrangement, the best option is to place them at the top of the dam.
Beam-width spreading of vortex beams in free space

Based on the extended Huygens-Fresnel principle and the definition of second-order moments of the Wigner distribution function, the analytical expression for the beam-width spreading of Gaussian Schell-model (GSM) vortex beams in free space are derived, and used to study the influence of beam parameters on the beam-width spreading of GSM vortex beams. With the increment of the propagation distance, the beam-width spreading of GSM vortex beams will increase; the bigger the topological charge, spatial correlation length, wavelength and waist width are, the smaller the beam-width spreading is.
Wickerhamomyces anomalus WO2 inoculation protects Suaeda fruticosa from salt stress by the stimulation of the production of carotenoids and sugars and the augmentation of sodium translocation

The objectives of this study were to test the strain Wickerhamomyces anomalus WO2 as a plant growth-promoting yeast (PGPY), to evaluate the effect of its inoculation on the growth and physiological performance of Suaeda fruticosa subjected to salt stress, and to understand by which mechanism the yeast can protect the plant from high salinity. The results showed that the strain is halophilic, and grows at salt concentrations of up to 15%. Salinity caused decreases in chlorophyll a, b, and T in both inoculated and non-inoculated S. fruticosa. A significant increase in carotenoids was observed in W. anomalus WO2 inoculated plants. Inoculation enhanced the production of proteins, polyphenols and flavonoids at 1% of salinity, and sugars at all concentrations of NaCl. Although Na+ and K+ were higher in the leaves of non-inoculated plants compared to inoculated ones, the correlation of sodium translocation factor (TF) with salinity was very strong positive only in the inoculated plants. The production of carotenoids had a very strong positive relationship with salinity in inoculated plants, and a very strong negative correlation in non-inoculated plants. Soluble sugars were very strongly positively correlated with salinity in both inoculated and non-inoculated plants. However, a strong positive correlation of sugars with carotenoids was observed only in inoculated plants. This strain proved to be a promising candidate as a PGPY under salt stress. This work amends the PGPY bank with new strains having interesting abilities to resist high concentrations of NaCl and which can be used in the future as a biofertilizer.
Efficient Inductive Vision Transformer for Oriented Object Detection in Remote Sensing Imagery

Object detection is a fundamental task in remote sensing image analysis and scene understanding. Previous remote sensing object detectors are typically based on convolutional neural networks (CNNs), whose performance is significantly limited by the intrinsic locality of convolution operations. The emergence of vision Transformers brings potential solutions to this problem, which has the capability to be a solid alternative to CNNs. However, three crucial obstacles hinder the application and performance of Transformers in the task of remote sensing object detection, that is: 1) high computational complexity, especially for high-resolution remote sensing images; 2) training and sample inefficiency caused by lack of inductive bias; and 3) difficulty in learning arbitrary orientation knowledge of geospatial objects. To address these issues, in this article, a novel efficient inductive vision Transformer framework is proposed for oriented object detection in remote sensing imagery. This framework follows the hierarchical feature pyramid structure and makes threefold contributions as follows: 1) spatial redundancy in remote sensing images is fully explored and an adaptive multigrained routing mechanism is proposed to facilitate token sparsity in Transformers, which can dramatically reduce the computational cost without comprising the accuracy. 2) A compact dual-path encoding architecture, where both global long-range dependencies and local semantic relations are jointly and complementarily captured, is proposed to enhance inductive bias in Transformers. 3) An angle tokenization technique is proposed to promote the encoding, embedding, and learning of direction knowledge for oriented objects in remote sensing scenarios. In this work, the above-mentioned three contributions are instantiated in an advanced Transformer-based object detector, namely, EIA-pyramid vision Transformer (PVT). Comprehensive experiments on two publicly available datasets have demonstrated its effectiveness and superiority for oriented object detection in remote sensing images.
Haloaerosoltherapy and complex recovery treatment based on it as methods of physical rehabilitation for patients with chronic obstructive pulmonary disease

Rehabilitation programs for patients with COPD are based on physical exercises usage, but accounting low tolerance to physical activity, this is not always a real task. The aim is to study the effect of haloaerosoltherapy (HAT) and complex rehabilitation treatment based on it on functional recovery of COPD patients. Materials and methods: 133 patients with COPD (GOLD II-III) beyond acute period were examined. Bronchial obstruction, intensity of clinical manifestations, disturbances of cardiovascular system were investigated. The 6-minute walk test was conducted. Results: Before treatment, it has been shown that exercise tolerance significantly depended on severity of bronchial obstruction, intensity of clinical manifestations and degree of cardiovascular risk. Treatment was carried out according to three treatment complexes (TC). TC-1 was based on HAT usage with certain concentration and dispersion of aerosol, 18-20 seances per course. In TC-2, singlet oxygen therapy was included, in TC-3 mineral water was administered as drinking use and inhalation. After course of treatment, walking distance covered in 6 minutes has been increased in patients of all groups. When using complex treatment, these changes were more pronounced. Con-clusions: HAT with appropriate dispersion and concentration of haloaerosol promotes in-creasing tolerance to physical activity and may be used as a stage of physical rehabilitation with possible subsequent therapeutic physical exercises. Notably pronounced positive dy-namics of studied data was noted in COPD patients convalescents after COVID-19.