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We consider the problem of finding optimal nonsequential designs for a large class of regression models involving polynomials and rational functions with heteroscedastic noise also given by a polynomial or rational weight function. Since the design weights can be found easily by existing methods once the support is known, we concentrate on determining the support of the optimal design. The proposed method treats D-, E-, A-, and Φ ₚ-optimal designs in a unified manner, and generates a polynomial whose zeros are the support points of the optimal approximate design, generalizing a number of previously known results of the same flavor. The method is based on a mathematical optimization model that can incorporate various criteria of optimality and can be solved efficiently by well-established numerical optimization methods. In contrast to optimization-based methods previously proposed for the solution of similar design problems, our method also has theoretical guarantee of its algorithmic efficiency; in concordance with the theory, the actual running times of all numerical examples considered in the paper are negligible. The numerical stability of the method is demonstrated in an example involving high-degree polynomials. As a corollary, an upper bound on the size of the support set of the minimally supported optimal designs is also found.

We present and analyze a central cutting surface algorithm for general semi-infinite convex optimization problems and use it to develop a novel algorithm for distributionally robust optimization problems in which the uncertainty set consists of probability distributions with given bounds on their moments. Moments of arbitrary order, as well as nonpolynomial moments, can be included in the formulation. We show that this gives rise to a hierarchy of optimization problems with decreasing levels of risk-aversion, with classic robust optimization at one end of the spectrum and stochastic programming at the other. Although our primary motivation is to solve distributionally robust optimization problems with moment uncertainty, the cutting surface method for general semi-infinite convex programs is also of independent interest. The proposed method is applicable to problems with nondifferentiable semi-infinite constraints indexed by an infinite dimensional index set. Examples comparing the cutting surface algorithm to the central cutting plane algorithm of Kortanek and No demonstrate the potential of our algorithm even in the solution of traditional semi-infinite convex programming problems, whose constraints are differentiable, and are indexed by an index set of low dimension. After the rate of convergence analysis of the cutting surface algorithm, we extend the authors' moment matching scenario generation algorithm to a probabilistic algorithm that finds optimal probability distributions subject to moment constraints. The combination of this distribution optimization method and the central cutting surface algorithm yields a solution to a family of distributionally robust optimization problems that are considerably more general than the ones proposed to date.

Supervised machine learning tasks often require a large number of labeled training data to set up a model, and then prediction - for example the classification - is carried out based on this model. Nowadays tremendous amount of data is available on the web or in data warehouses, although only a portion of those data is annotated and the labeling process can be tedious, expensive and time consuming. Active learning tries to overcome this problem by reducing the labeling cost through allowing the learning system to iteratively select the data from which it learns. In special case of active learning, the process starts from zero initialized scenario, where the labeled training dataset is empty, and therefore only unsupervised methods can be performed. In this paper a novel query strategy framework is presented for this problem, called Clustering Based Balanced Sampling Framework (CBBSF), which is not only select the initial labeled training dataset, but uniformly selects the items among the categories to get a balanced labeled training dataset. The framework includes an assignment technique to implicitly determine the class membership probabilities. Assignment solution is updated during CBBSF iterations, hence it simulates supervised machine learning more accurately as the process progresses. The proposed Spectral Clustering Based Sampling (SCBS) query startegy realizes the CBBSF framework, and therefore it is applicable in the special zero initialized situation. This selection approach uses ClusterGAN (Clustering using Generative Adversarial Networks) integrated in the spectral clustering algorithm and then it selects an unlabeled instance depending on the class membership probabilities. Global and local versions of SCBS were developed, furthermore, most confident and minimal entropy measures were calculated, thus four different SCBS variants were examined in total. Experimental evaluation was conducted on the MNIST dataset, and the results showed that SCBS outperforms the state-of-the-art zero initialized active learning query strategies.

We consider the problem of nonparametric estimation of unknown smooth functions in the presence of restrictions on the shape of the estimator and on its support using polynomial splines. We provide a general computational framework that treats these estimation problems in a unified manner, without the limitations of the existing methods. Applications of our approach include computing optimal spline estimators for regression, density estimation, and arrival rate estimation problems in the presence of various shape constraints. Our approach can also handle multiple simultaneous shape constraints. The approach is based on a characterization of nonnegative polynomials that leads to semidefinite programming (SDP) and second-order cone programming (SOCP) formulations of the problems. These formulations extend and generalize a number of previous approaches in the literature, including those with piecewise linear and B-spline estimators. We also consider a simpler approach in which nonnegative splines are approximated by splines whose pieces are polynomials with nonnegative coefficients in a nonnegative basis. A condition is presented to test whether a given nonnegative basis gives rise to a spline cone that is dense in the space of nonnegative continuous functions. The optimization models formulated in the article are solvable with minimal running time using off-the-shelf software. We provide numerical illustrations for density estimation and regression problems. These examples show that the proposed approach requires minimal computational time, and that the estimators obtained using our approach often match and frequently outperform kernel methods and spline smoothing without shape constraints. Supplementary materials for this article are provided online.

Background Crop wild relatives of Malus domestica represent exceptional value as breeding resources and ornamental trees, nonetheless their natural populations are under threat due to anthropogenic factors. Malus florentina is an apple species endemic to the Balkans and Italy, and our current knowledge on its population genetics and conservation biological status is strongly limited. Results In the current study we used a 2b-RAD-seq approach to investigate the population genetic structure and diversity of four M. florentina populations from three different countries. We detected strong genetic differentiation among populations and a substantial risk of inbreeding within them. Furthermore, we identified candidate genes under selection associated with various stress responses including biotic resistance, salt stress and drought stress tolerance, further supporting local adaptation. Our enrichment analyses based on non-synonymous SNP counts at resistance loci revealed a significant 2.4-fold change, indicating that disease resistance genes are under selection. Conclusions According to this study M. florentina populations face considerable risk, and fragmentation of the studied populations cannot be ruled out. Despite this, the assessed populations exhibit considerable diversity at disease resistance loci, likely maintained by balancing selection. These findings highlight the importance of conserving the genetic diversity of wild apple populations for key agronomic traits such as disease resistance.

Despite the inherent high salinity tolerance of mature carob ( Ceratonia siliqua L., Fabaceae) trees, young seedlings are particularly vulnerable to salt stress, which can hinder their growth and development. Although hydrogen sulfide (H₂S) has been widely studied as a regulator of salinity tolerance in herbaceous plants, its role in woody legumes remains largely unexplored. This study aimed to explore the potential of hydrogen sulfide (H 2 S) pretreatment to enhance the salinity tolerance of young carob seedlings. A pot experiment was conducted using a factorial design to evaluate the effects of sodium hydrosulfide (NaHS, an H 2 S donor) at 0, 75, and 200 µM under saline conditions (0, 50, and 100 mM NaCl). The results indicated that H 2 S pretreatment enhanced the biomass of both shoots and roots under both saline and non-saline conditions, with the potential to compensate growth loss due to salt stress. Additionally, stress markers such as hydrogen peroxide (H 2 O 2 ) and malondialdehyde (MDA) increased by about 23% and 28%, respectively, in seedlings treated with 75 µM NaHS after exposure to 100 mM NaCl stress. The enhanced salinity stress tolerance was presumably associated with increased antioxidant enzyme activity, including superoxide dismutase (SOD, ≈ 24%), ascorbate peroxidase (APX, ≈ 118%), and peroxidase (POD, ≈ 50%) after 75 µM NaHS pretreatment in plants exposed to 100 mM NaCl. Moreover, NaHS pretreatment reduced sodium ion (Na⁺) accumulation in both leaves and roots. These findings indicate that H 2 S enhances the salinity tolerance of carob seedlings, helping them better withstand suboptimal growing conditions.

An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the distribution can be defined over an arbitrary set. This allows for a reduction in the number of scenarios and allows the scenarios to be better tailored to the problem at hand. The method is based on a semi-infinite linear programming formulation of the problem that is shown to be solvable with polynomial iteration complexity. A practical column generation method is implemented. The column generation subproblems are polynomial optimization problems; however, they need not be solved to optimality. It is found that the columns in the column generation approach can be efficiently generated by random sampling. The number of scenarios generated matches a lower bound of Tchakaloff's. The rate of convergence of the approximation error is established for continuous integrands, and an improved bound is given for smooth integrands. Extensive numerical experiments are presented in which variants of the proposed method are compared to Monte Carlo and quasi-Monte Carlo methods on both numerical integration problems and stochastic optimization problems. The benefits of being able to match any prescribed set of moments, rather than all moments up to a certain order, is also demonstrated using optimization problems with 100-dimensional random vectors. Empirical results show that the proposed approach outperforms Monte Carlo and quasi-Monte Carlo based approaches on the tested problems. [PUBLICATION ABSTRACT]

The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise installed fixed position multi-camera configurations, benefit greatly from recent advances. In this paper, we propose a Multi-Camera Multi-Target (MCMT) vehicle tracking system using a constrained hierarchical clustering solution, which improves trajectory matching, and thus provides a more robust tracking of objects transitioning between cameras. YOLOv5, ByteTrack, and ResNet50-IBN ReID networks are used for vehicle detection and tracking. Static attributes such as vehicle type and vehicle color are determined from ReID features with SVM. The proposed ReID feature-based attribute categorization shows better performance, than its pure CNN counterpart. Single-camera trajectories (SCTs) are combined into multi-camera trajectories (MCTs) using hierarchical agglomerative clustering (HAC) with time and space constraints (our proposed algorithm is denoted by MCT#MAC). Similarities between SCTs are measured by comparing the mean ReID features cumulated on the trajectory. The system was evaluated on more datasets, and our experiments demonstrate that constraining HAC by manipulating the proximity matrix greatly improves the multi-camera IDF1 score.

Fungal leaf spot pathogens of cucurbits cause significant yield losses. They cause extensive leaf necroses and defoliation, reducing host photosynthesis. They increase risks of fruit sunscald, and can cause substantial crop damage. Alternaria cucumerina has been recognized as the causal agent of leaf spot disease of cucurbits, and recent studies have identified other Alternaria species, and other emerging pathogens such as Curvularia. This study characterized 25 isolates obtained from infected watermelon and cucumber leaves from Hungary, Spain, and Kosovo. Morphological characterization and molecular analyses using TEF1-α, HIS3, and ITS gene regions identified Alternaria alternata and A. arborescens, and for the first time on this host, the genus Curvularia. Detached leaf assays of ten isolates on 73 watermelon accessions showed variation in isolate pathogenicity. The tested Curvularia isolate was the most aggressive, followed by the A. arborescens  and A. alternata isolates, although A. alternata was the most frequently identified  species. These results highlight the potential for emerging fungal pathogens causing cucurbit leaf spot, such as Curvularia sp., and show that these fungi can cause damage on economically important plants. This study also showed differing resistance within the watermelon collection, indicating potential for the plant introduction (PI) accessions as sources of resistance breeding.

Mass spectrometry has become the most prominent yet evolving technology in quantitative proteomics. Today, a number of label-free and label-based approaches are available for the relative and absolute quantification of proteins and peptides. However, the label-based methods rely solely on the employment of stable isotopes, which are expensive and often limited in availability. Here we propose a label-based quantification strategy, where the mass difference is identified by the differential alkylation of cysteines using iodoacetamide and acrylamide. The alkylation reactions were performed under identical experimental conditions; therefore, the method can be easily integrated into standard proteomic workflows. Using high-resolution mass spectrometry, the feasibility of this approach was assessed with a set of tryptic peptides of human serum albumin. Several critical questions, such as the efficiency of labeling and the effect of the differential alkylation on the peptide retention and fragmentation, were addressed. The concentration of the quality control samples calculated against the calibration curves were within the ±20% acceptance range. It was also demonstrated that heavy labeled peptides exhibit a similar extraction recovery and matrix effect to light ones. Consequently, the approach presented here may be a viable and cost-effective alternative of stable isotope labeling strategies for the quantification of cysteine-containing proteins.