Miracle-dz/UniFAR-FTUs huggingface dataset:
A splitting method for the locality regularized semi-supervised subspace clustering

Graph-based semi-supervised learning (G-SSL) methods play an increasingly important role in machine learning systems. Recently, latent low-rank representation (LatLRR) graph has gained great success in subspace clustering. However, LatLRR only considers the global structure, while the local geometric information, which is often important to many real applications, is ignored. In this paper, we propose a locality regularized LatLRR model (LR-LatLRR) for semi-supervised subspace clustering problems. This model incorporates two regularization terms into LatLRR by taking the local structure of data into account. Then, we develop an efficient splitting algorithm for solving LR-LatLRR. In addition, we also prove the global convergence of the proposed algorithm. Furthermore, we extend the LR-LatLRR model to a case of including the non-negative constraint. Finally, we conduct experiments on a synthetic data and several real data sets for the semi-supervised clustering problems. Experimental results show that our method can obtain high classification accuracy and outperforms several state-of-the-art G-SSL methods.
Vector bundles with numerically flat reduction on rigid analytic varieties and $p$-adic local systems.

We show how to functorially attach continuous $p$-adic representations of the profinite fundamental group to vector bundles with numerically flat reduction on a proper rigid analytic variety over $\mathbb{C}_p$. This generalizes results by Deninger and Werner for vector bundles on smooth algebraic varieties. Our approach uses fundamental results on the pro-etale site of a rigid analytic variety introduced by Scholze. This enables us to get rid of the smoothness condition and to work in the analytic category. Moreover, under some mild conditions, the functor we construct gives a full embedding of the category of vector bundles with numerically flat reduction into the category of continuous $\mathbb{C}_p$-representations. This provides new insights into the $p$-adic Simpson correspondence in the case of a vanishing Higgs-field.
Design scheme of discrete time variable structure controllers applied to multi‐input‐multi‐output linear systems with chattering elimination

Designing discrete time variable structure control with sliding mode (SM) schemes implies finding an asymptotically stable sliding surface with a control policy which assures the existence of a SM. Based on a recently innovative approach, where the authors developed a flexible design of sliding surfaces applied to discrete time linear multi-input multi-output (MIMO) systems, a novelty controller design strategy is proposed, where a switching surface parameter is used for the adjustment of the control signal limitations and for the improvement of the controlled system dynamics against model uncertainties. It is the first time that these drawbacks are solved modifying the switching surface values because it has been always resolved via control law adjustment. Moreover, a new chattering elimination method using adjustable sigmoid functions is also implemented in the controller. The main advantages of the proposed methodology are: firstly, it can be easily applied to high-order linear MIMO systems; secondly, system dynamics coordinates representation transformations are not needed and thirdly, the non-ideal SM dynamics is considered in the design procedure. Both the sliding surface and the control law specification are included in the proposed methodology. An illustrative example is included.
Sliding-Mode Control for Fuzzy Stochastic Systems With Different Local-Input Matrices

By use of the method of sliding-mode control, the stabilization of the T–S fuzzy stochastic system with time delay is considered in this article. The characteristic of the proposed method is that the input matrices of the fuzzy system are not common. And the proposed control method of this article can resist the disturbance. First, a novel sliding-mode surface is introduced. Using the new surface, the stability of the fuzzy system can be studied even if the input matrices are not common. Second, a new lemma is given to judge if the surface can be utilized in the considered system. Third, a new control law is constructed according to the new sliding-mode surface. The control law is designed to deal with the problems produced by time delay and stochastic systems. At last, two simulations are provided. In this part, the input matrices of fuzzy systems are different from each other and there is a disturbance in this considered system. From the simulations, the results illustrate the validity of the proposed method.
Quantitative phase analysis from neutron powder diffraction data using the Rietveld method

The weight of a phase in a mixture is proportional to the product of the scale factor, as derived in a multi-component Rietveld analysis of the powder diffraction pattern, with the mass and volume of the unit cell. If all phases are identified and crystalline, the weight fraction W of phase p is given by W_{p} = S_{p}(ZMV)_{p}/\sum_{i}S_{i}(ZMV)_{i},where S, Z, M and V are, respectively, the Rietveld scale factor, the number of formula units per unit cell, the mass of the formula unit and the unit-cell volume. This is the basis of a method providing accurate phase analyses without the need for standards or for laborious experimental calibration procedures. The method is demonstrated by measurements on binary mixtures of rutile, corundum, silicon and quartz.
Permeabilidad y difusión de aire en el subsuelo de un Andisol sujeto a distintas estrategias de mejoramiento de praderas

a Institute of Plant Nutrition and Soil Science, Christian-Albrechts-Universität zu Kiel Kiel University, Hermann Rodewaldstr. 2, 24118, Kiel, Germany. b Working Group “Hydropedology”, Research Area 1 “Landscape Functioning”, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, D-15374 Müncheberg, Germany. c Instituto de Ingeniería Agraria y Suelos, Facultad de Ciencias Agrarias, Universidad Austral de Chile, Casilla 567, Valdivia, Chile. d Centro de Investigación en Suelos Volcánicos, Universidad Austral de Chile, Casilla 567, Valdivia, Chile. e Escuela de Graduados, Facultad de Ciencias Agrarias, Universidad Austral de Chile, Casilla 567, Valdivia, Chile.
Adaptive Dictionary Learning for Blind Seismic Data Denoising

The data-driven tight frame (DDTF) method is a dictionary learning method which has been used widely in the adaptive sparse representation and the seismic random noise attenuation. In the DDTF method, the thresholding operator setting plays a significant role on balancing the noise removal and preservation of detail information with high frequency. The hard thresholding operator is closely related to the noise variance; however, the noise variance is unknown and unstable which varies spatially. In this letter, we propose a spatially adaptive DDTF (SA-DDTF) method to find an optimal thresholding parameter without knowing the noise variance. The thresholding is determined by the coherence between the dictionary and the reconstruction residual. Furthermore, the thresholding is chosen adaptively for each patch of the seismic data. In case of the synthetic seismic data denoising, we obtain the reconstructed seismic data with a higher signal-to-noise ratio value. Furthermore, compared with the existing random noise reduction methods, the SA-DDTF method performs much better on the amplitude preservation of weak signal when attenuating the blind random noise for field data.
Positivity-Preserving Consensus of Homogeneous Multiagent Systems

This note deals with the positivity-preserving consensus problem for undirected positive multiagent systems. The case that all agents have identical positive state-space models with multiple inputs is investigated. Using positive systems theory and analyzing the properties of the overall closed-loop system, positivity-preserving consensus conditions are derived. In order to preserve the positivity property of the agents, two conditions for positivity-preserving consensus are obtained. In contrast to some existing works that only give sufficient conditions for the solution, necessary and sufficient conditions are provided in this note. Then, the corresponding algorithm is developed for solution. Simulations are used to illustrate the effectiveness of the theoretical results and proposed algorithm.
Parametric and semiparametric copula-based models for the regression analysis of competing risks

Abstract Copula-based constructions of the joint distribution of the overall survival time and the cause-specific failure are developed for the analysis of competing risks data. Covariate effects are incorporated by characterizing the margins with parametric and semiparametric proportional hazards models for the survival times, and a logistic regression model for the cause of failure. Appealing aspects of the methods are the use of single-outcome techniques for the adequate modeling of the proportional hazards component, the ability to find parsimonious models using information-criterion metrics, and the inherent simplicity and usefulness of the interpretation of the parameters. Estimation is carried out using maximum likelihood and two-stage pseudolikelihood procedures for the parametric and semiparametric models, respectively. The performance of the estimators is evaluated in simulation studies. The methods are illustrated with the reanalysis of a follicular cell lymphoma dataset.
Efficient Algorithms for Maximum Consensus Robust Fitting

Maximum consensus robust fitting is a fundamental problem in many computer vision applications, such as vision-based robotic navigation and mapping. While exact search algorithms are computationally demanding, randomized algorithms are cheap but the solution quality is not guaranteed. Deterministic algorithms fill the gap between these two kinds of algorithms, which have better solution quality than randomized algorithms while being much faster than exact algorithms. In this article, we develop two highly efficient deterministic algorithms based on the alternating direction method of multipliers (ADMM) and proximal block coordinate descent (BCD) frameworks. Particularly, the proposed BCD algorithm is guaranteed convergent. Furthermore, on the slack variable in the BCD algorithm, which indicates the inliers and outliers, we establish some meaningful properties, such as support convergence within finite iterations and convergence to restricted strictly local minimizer. Compared with state-of-the-art algorithms, the new algorithms with initialization from a randomized or convex relaxed algorithm can achieve improved solution quality while being much more efficient (e.g., more than an order of magnitude faster). An application of the new ADMM algorithm in simultaneous localization and mapping (SLAM) has also been provided to demonstrate its effectiveness. Code for reproducing the results is available online.1
Knowledge, Attitudes and Utilisation of the Female Condom among High School Female Students in Kumba, Cameroon

Background: Cameroon has the highest HIV/AIDS prevalence of 5.3% in the West and Central African Sub-region, with women being the most infected. The biological and socio-cultural vulnerability of women to HIV infection is not new. A proven effective female controlled prevention tool like the female condom is relatively new compared to the male condom in the prevention of HIV in Cameroon. Data related to its use and acceptability are rare. Relatively early onset of sexual activity amongst female students in Cameroon is a sad reality. The purpose of this study was to ascertain the knowledge, attitudes and use of the female condom amongst high school female students in the Kumba Urban area, Cameroon. Methods: A descriptive design was adopted and carried out during the month of May 2014, on a representative sample of consenting high school female students, from a sample of high schools in Kumba. Pretested questionnaires in English language were self-administered to 398 students. Data
La comparaison des résultats de la réadaptation cardiaque chez les personnes ayant des comorbidités respiratoires ou cardiaques ou n’ayant aucune comorbidité : une analyse rétrospective

Faculty of Health Sciences, University of Ontario Institute of Technology, Oshawa, Ontario Correspondence: Dr Mika L Nonoyama, Faculty of Health Sciences, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, Ontario L1H 7K4. Telephone 905-721-8668 ext5329, e-mail mika.nonoyama@uoit.ca Cardiovascular and chronic respiratory disease are leading causes of morbidity and mortality in the United States and worldwide (1). In 2008, cardiovascular disease (CVD) caused 17 million deaths worldwide (48% of all noncommunicable diseases), and chronic obstructive pulmonary disease (COPD) and asthma caused 4.2 million deaths. These noncommunicable diseases, according to the World Bank/WHO, will rank in the top three of burdened diseases worldwide by 2020 (1,2). CVD and respiratory disease share many comorbid characteristics including multimorbidity (≥2 conditions) as a common feature (3). The prevalence of multimorbidity increases with age and results in poor health outcomes (4,5), stressing the importance of effective health care interventions for this population (1). The specific prevalence of respiratory comorbidity (eg, COPD, asthma) in cardiac disease has varied in the literature: 9% to 39% in patients with acute or nonacute cardiac morbidities (eg, acute myocardial infarction, cardiac surgery) (6-9). The method by which respiratory disease was defined in these studies and the cohort investigated may have contributed to this high level of variability in prevalence. Cardiac rehabilitation and pulmonary rehabilitation are common interventions for CVD and COPD (10,11). The population of patients who enter these programs share many characteristics: a common intrathoracic location of the pathology; the frequent coexistence of cardiac and pulmonary disease; and shared symptoms, such as dyspnea, fatigue, psychological disturbances, deconditioning and exercise intolerance (12-14). In addition, they share common rehabilitation goals and outcomes including improvement in exercise tolerance, which can reduce future morbidity and disability, as well as enhance quality of life (10,13,15). Although the principles of both pulmonary and cardiac rehabilitation are similar, the patients who enter these programs are functionally diverse (16). In many cardiac rehabilitation patients (without heart failure), the chief functional limitation and cause of exercise intolerance is deconditioning and, in some patients, CVD rate-limiting angina or ischemia (13-17). In many pulmonary rehabilitation patients, functional limitations are more extensive: work inefficiency due to impairment in lung mechanics; inspiratory muscle fatigue; ineffective gas exchange; original article
Advanced infrared thermography data analysis for unsteady boundary layer transition detection

Advanced data processing methods for detecting unsteady boundary layer transition in periodic aerodynamic processes by means of infrared thermography measurements are presented. The thermal radiation emitted from the heated suction surface of a pitching airfoil model in subsonic flow is measured with an infrared camera. The unsteady boundary layer transition location is detected by analyzing the difference in the infrared radiation signal over short periods of time with differential infrared thermography (DIT). The DIT method is optimized and automated in the present study, which facilitates the extension of the part of the motion period where valid DIT transition measurements are produced. Additionally, a new infrared thermography data processing method is introduced in this study. The extraction of the extrema of the measured radiation signal at fixed locations on the model surface yields instants of the motion period that relate to the occurrence of boundary layer transition. The local infrared thermography (LIT) approach can be extended to measuring the two-dimensional unsteady boundary layer transition front.
Benders Subproblem Decomposition for Discrete-Continuous Bilevel Problems

Bilevel optimization formulates hierarchical decision-making processes that arise in many real-world applications such as in pricing, network design, and infrastructure defense planning. In this paper, we consider a class of bilevel optimization problems where the upper level problem features some integer variables while the lower level problem enjoys strong duality. We propose a dedicated Benders decomposition method that decomposes the Benders subproblem into two more tractable, sequentially solvable problems that can be interpreted as the upper and the lower level problems. We show that the Benders subproblem decomposition carries over to an interesting extension of bilevel problems, which connects the upper level solution with the lower level dual solution, and discuss some special cases of bilevel problems that allow sequence-independent subproblem decomposition. Several acceleration schemes are discussed and a computational study demonstrates the computational benefits of the proposed method over an up-to-date commercial solver and the standard Benders method.
An Algebraic-Geometric Approach for Linear Regression Without Correspondences

Linear regression without correspondences is the problem of performing a linear regression fit to a dataset for which the correspondences between the independent samples and the observations are unknown. Such a problem naturally arises in diverse domains such as computer vision, data mining, communications and biology. In its simplest form, it is tantamount to solving a linear system of equations, for which the entries of the right hand side vector have been permuted. This type of data corruption renders the linear regression task considerably harder, even in the absence of other corruptions, such as noise, outliers or missing entries. Existing methods are either applicable only to noiseless data or they are very sensitive to initialization or they work only for partially shuffled data. In this paper we address these issues via an algebraic geometric approach, which uses symmetric polynomials to extract permutation-invariant constraints that the parameters <inline-formula> <tex-math notation="LaTeX">$\xi ^{*} \in \mathbb {R} ^{\text {n}}$ </tex-math></inline-formula> of the linear regression model must satisfy. This naturally leads to a polynomial system of <italic>n</italic> equations in <italic>n</italic> unknowns, which contains <inline-formula> <tex-math notation="LaTeX">$\xi ^{*}$ </tex-math></inline-formula> in its root locus. Using the machinery of algebraic geometry we prove that as long as the independent samples are generic, this polynomial system is always consistent with at most <italic>n</italic>! complex roots, regardless of any type of corruption inflicted on the observations. The algorithmic implication of this fact is that one can always solve this polynomial system and use its most suitable root as initialization to the Expectation Maximization algorithm. To the best of our knowledge, the resulting method is the first working solution for small values of <italic>n</italic> able to handle thousands of fully shuffled noisy observations in milliseconds.
Outdoor Universal Thermal Comfort Index Climatology for Alaska

Data from 456 surface meteorological sites in Alaska, eastern Russia and northwest Canada for 1979-2017 were used to model hourly universal thermal comfort indices (UTCIs) under consideration of Alaska-appropriate clothing. The results served to determine a high-resolution climatology of thermal comfort levels for Alaska at various temporal and spatial scales as well as the frequency of thermal stress levels. On 1979-2017 average, various degrees of cold stress occurred with highest percentage on the Alaska West Coast and along the Arctic Ocean. In the continental and Inside Passage region, no thermal stress had the highest percentage of occurrence. In Interior Alaska, both strong heat and extreme cold stress occurred occasionally. At most sites and in all Alaska Koppen-Geiger bio-climate regions, the absolute range between monthly means of daily minimum and maximum UTCIs was larger than that of monthly means of daily minimum and maximum air temperatures. Major contributors to thermal discomfort (shortwave radiation, air temperature, moisture, wind speed) varied among bio-climate regions and in the diurnal and annual courses.
Drivers of Green Supply Chain Initiatives and their Impact on Economic Performance of Firms: Evidence from Pakistan’s Manufacturing Sector

The adoption and implementation of green supply chain management (GSCM) initiatives within an organization have been claimed to create specific ecological, competitiveness and monetary benefits. This study explores the drivers of GSCM initiatives and their effect on the economic performance of manufacturing companies in three sectors (textile, chemical, and pharmaceutical) of Pakistan’s economy. The three driver social pressure, competitive pressure, and institutional pressure were chosen as the enablers of GSCM initiatives in Pakistan’s manufacturing sector. The data was collected through a questionnaire distributed among managerial level employees working in the manufacturing industry of Pakistan. The study was based on the evaluation of 263 responses through partial least squares structural equation modeling (PLS-SEM) method. The results show that institutional pressures are the most significant enablers towards competitiveness and instituting GSCM initiatives, while social pressures and competitive pressures also have a substantial impact. The effect of GSCM initiatives on firm competitiveness and economic performance was also analyzed and found to be significant. The presented results may be seen as useful for managers, practitioners and policy makers, as well as for society in general in various ways.
Design of Coplanar Slotted SIW Antenna Arrays for Beam-Tilting and 5G Applications

A new solution for feeding the engraved slot antennas based on the substrate integrated waveguide (SIW) technology is presented in this letter. The structure is designed to operate at 25 GHz for beam-tilting and fifth-generation applications. Here several passive SIW slot antennas are proposed to cover an area from −56° to +56° with an offset of 10° at the operating frequency without using any phased shifter, in order to limit the losses. This new feeding approach is developed by using associated SIW holes to excite differently the centered slots located on the SIW structure broad face via coplanar lines. The electrical phase shifts are controlled by the position and number (one or two) of coplanar lines, and the combined slot configurations achieve beam-tilting function.
The faster lattice sieving for SVP

The security of lattice-based cryptography is based on the hardness of the difficult problems on lattice, especially the famous shortest vector problem. There are many famous heuristic lattice sieving algorithms to solve SVP, such as the Gauss Sieve, NV Sieve, which deal the full-rank n-dimensional lattice from start. Inspired by the idea of rank reduction, in this paper we present new technique on lattice sieving to make the algorithm solve the SVP faster. We split the basis of “bigger” lattice into several blocks according to some rule until the sublattice is small enough. Then we recursively sieved on these sub-lattices to get short vector lists. With the short vector lists, we can find the shortest vector when the algorithm recoverd the original lattice. This lead to obviously speedup with the recursive procedure without extra space overhead. Compared with the Gauss Sieve, the new method converge faster, and achieved at least 70% acceleration.