An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.

Identification of Robot Joint Torsional Stiffness Based on the Amplitude of the Frequency Response of Asynchronous Data

The difficulty of adding external excitation and the asynchronous data collection from the industrial robot operation limited the online parameter identification of industrial robots. In this regard, this study proposes an identification method that only uses the amplitude of the frequency response function (FRF) of the system to identify robot joint torsional stiffness and dynamic parameters. The error criterion function shows that this method is feasible and comparable to applying the complete frequency response for identification. The Levenberg–Marquardt (L-M) algorithm is used to find the global optimal value of the error criterion function. In addition, an operational excitation method is proposed to excite the system. The speed profile is set as a triangle wave to excite the system using rectangular wave electromagnetic torques. The simulation results show that using the amplitude of the FRF to identify parameters applies to asynchronous data. The experiments on a single-degree-of-freedom articulated arm test bench show that the motion excitation method is effective, and both stiffness and inertia are identifiable.

Research of a mathematical model for designing a barge-towing complex for the Republic of the Union of Myanmar

В статье описана математическая модель проектирования барже-буксирного комплекса с учётом особенности эксплуатации в условиях Республики Союз Мьянма. В статье исследуются особенности ББК как объекта оптимизации, описана математическая модель ББК, отображается его как сложную техническую систему. В рамках модели приведены математические зависимости и алгоритмы для определения элементов теоретического чертежа, мощности главных двигателей, для решения некоторых вопросов общего расположения с учетом расстояния главных водонепроницаемых переборок корпуса, для расчета нагрузки и устойчивости. Метод комплексной оценки эффективности применения барже-буксирного комплекса на стадии технико-экономического обоснования проекта в условиях развивающихся стран, в частности в условиях Республики Союз Мьянмы. В результате работы были полностью определены параметры основных типов барже-буксировочных комплексов для перспективной системы внутреннего водного транспорта Республики Союза Мьянма. На базе методов случайного поиска создан алгоритм оптимизации элементов ББК, решающий задачу математического программирования с процедурным характером функции критерия и функциональных ограничений. Математическая модель и алгоритм оптимизации реализованы в виде программы для обеспечения компьютерного эксперимента. Программа, реализующая математическую модель проектирования ББК, состоит из отдельных программных модулей, что облегчает ее совершенствование в анализе результатов решения задачи. Создан программный комплекс с использованием языка программирования Паскаль в среде Delphi для обеспечения автоматизирования проектирования. Указанные программы могут быть использованы в исследовательском проектировании на начальных стадиях проектирования. The article describes a mathematical models for designing a barge-towing complex, taking into account the peculiarities of operation in the conditions of the Republic of the Union of Myanmar. The article examines the features of the BBK as an object of optimization, describes the mathematical model of the BBK, displays it as a complex technical system. Within the framework of the model, mathematical dependencies and algorithms are given for determining the elements of the theoretical drawing, the power of the main engines, for solving some issues of the general location, taking into account the distance of the main watertight bulkheads of the hull, for calculating the load and stability. The method of comprehensive assessment of the effectiveness of the use of the barge-tow complex at the stage of the feasibility study of the project in the conditions of developing countries, in particular in the conditions of the Republic of the Union of Myanmar. As a result of the work, the parameters of the main types of barge-towing complexes for the prospective inland water transport system of the Republic of the Union of Myanmar were fully determined. On the basis of random search methods, an algorithm for optimizing the BBK elements is created, which solves the problem of mathematical programming with the procedural nature of the criterion function and functional constraints. The mathematical model and the optimization algorithm are implemented as a program to provide a computer experiment. The program that implements the mathematical model of the design of the BBK consists of separate program modules, which facilitates its improvement in the analysis of the results of solving the problem. A software package was created using the Pascal programming language in the Delphi environment to provide design automation. These programs can be used in research design at the initial stages of design.

Investigation of the Influence of the Parameters of the VFH + Method on the Navigation Efficiency of the Mobile Robot

The article focuses on verifying the effects of the VFH + navigation method parameters, proving to be very effective for the robot's reactive navigation. This research is based on our long-standing knowledge of histogram methods used in robot navigation. The article focuses on verifying the influence of crucial parameters - thresholds in a binary histogram, the smax parameter defining wide and narrow valleys, constants setting the criterion function, and the impact of robot dynamics on navigation. Many experiments were performed in a ROS simulation environment, and the article lists those significant confirming certain assumptions in setting these parameters.

Semi-supervised oblique predictive clustering trees

Semi-supervised learning combines supervised and unsupervised learning approaches to learn predictive models from both labeled and unlabeled data. It is most appropriate for problems where labeled examples are difficult to obtain but unlabeled examples are readily available (e.g., drug repurposing). Semi-supervised predictive clustering trees (SSL-PCTs) are a prominent method for semi-supervised learning that achieves good performance on various predictive modeling tasks, including structured output prediction tasks. The main issue, however, is that the learning time scales quadratically with the number of features. In contrast to axis-parallel trees, which only use individual features to split the data, oblique predictive clustering trees (SPYCTs) use linear combinations of features. This makes the splits more flexible and expressive and often leads to better predictive performance. With a carefully designed criterion function, we can use efficient optimization techniques to learn oblique splits. In this paper, we propose semi-supervised oblique predictive clustering trees (SSL-SPYCTs). We adjust the split learning to take unlabeled examples into account while remaining efficient. The main advantage over SSL-PCTs is that the proposed method scales linearly with the number of features. The experimental evaluation confirms the theoretical computational advantage and shows that SSL-SPYCTs often outperform SSL-PCTs and supervised PCTs both in single-tree setting and ensemble settings. We also show that SSL-SPYCTs are better at producing meaningful feature importance scores than supervised SPYCTs when the amount of labeled data is limited.

Desenvolvimento de Modelo que Estima o Impacto do CO2 Atmosférico nas Precipitações do Estado de Pernambuco, utilizando ARIMA

It is known that the state of Pernambuco will suffer impacts on precipitation due to the increase in CO2 in the atmosphere. In an attempt to contribute to the prognosis of these impacts, this study aims to develop a model that makes a prognosis or creates future scenarios for the state of Pernambuco. For that, the autoregressive method of moving averages, ARIMA, was used. The model adjustment was performed using the normalized Bayesian information criterion function. The results showed that the developed model presents a strong fit. The model was better adjusted for the Agreste and West of the state. The model projects a precipitation decrease trend for the western state of Pernambuco of approximately 15% below the historical average until 2027. The model projected rainfall above the historical average for the Agreste of Pernambuco, of approximately 17%, until 2027. It concludes It is believed that rainy years will occur more frequently in the Agreste region of Pernambuco, and we will have more frequent dry years in the west of the state. In applying the results of this study and simulation with the model SUPER-System of Hydrological Response Units for Pernambuco, it is concluded that there will be more flood peaks in the Mundaú basin until 2027.

Minimum square distance thresholding method applying gray-gradient co-occurrence matrix

In image thresholding segmentation, gray level of pixels is the basic element to describe images. Besides, the gradient information of pixels is also a key feature to represent image space distribution. Therefore, the co-occurrence probability of gray and gradient of pixels is an effective information to describe image. In this paper, gray-gradient asymmetrical co-occurrence matrix is constructed, uniformity probability of image region is produced, and a minimum square distance criterion function based on gray-gradient co-occurrence matrix is proposed to measure the deviation between original and binary images. Comparing with gray-gray asymmetrical co-occurrence matrix and relative entropy-based symmetrical co-occurrence matrix method, the proposed method can obtain more complete segmentation results, especially for small-size object extraction. The peak signal to noise ratio probability also shows the better segmentation performance of our proposed method.

On the Iterated Estimation of Dynamic Discrete Choice Games

We study the first-order asymptotic properties of a class of estimators of the structural parameters in dynamic discrete choice games. We consider $K$-stage policy iteration (PI) estimators, where $K$ denotes the number of PIs employed in the estimation. This class nests several estimators proposed in the literature. By considering a “pseudo likelihood” criterion function, our estimator becomes the $K$-pseudo maximum likelihood (PML) estimator in Aguirregabiria and Mira (2002, 2007). By considering a “minimum distance” criterion function, it defines a new $K$-minimum distance (MD) estimator, which is an iterative version of the estimators in Pesendorfer and Schmidt-Dengler (2008) and Pakes et al. (2007). First, we establish that the $K$-PML estimator is consistent and asymptotically normal for any $K \in \mathbb{N}$. This complements findings in Aguirregabiria and Mira (2007), who focus on $K=1$ and $K$ large enough to induce convergence of the estimator. Furthermore, we show under certain conditions that the asymptotic variance of the $K$-PML estimator can exhibit arbitrary patterns as a function of $K$. Second, we establish that the $K$-MD estimator is consistent and asymptotically normal for any $K \in \mathbb{N}$. For a specific weight matrix, the $K$-MD estimator has the same asymptotic distribution as the $K$-PML estimator. Our main result provides an optimal sequence of weight matrices for the $K$-MD estimator and shows that the optimally weighted $K$-MD estimator has an asymptotic distribution that is invariant to $K$. The invariance result is especially unexpected given the findings in Aguirregabiria and Mira (2007) for $K$-PML estimators. Our main result implies two new corollaries about the optimal $1$-MD estimator (derived by Pesendorfer and Schmidt-Dengler (2008)). First, the optimal $1$-MD estimator is efficient in the class of $K$-MD estimators for all $K \in \mathbb{N}$. In other words, additional PIs do not provide first-order efficiency gains relative to the optimal $1$-MD estimator. Second, the optimal $1$-MD estimator is more or equally efficient than any $K$-PML estimator for all $K \in \mathbb{N}$. Finally, the Appendix provides appropriate conditions under which the optimal $1$-MD estimator is efficient among regular estimators.