Clustering Deviation Index (CDI): A robust and accurate unsupervised measure for evaluating scRNA-seq data clustering

Single-cell RNA-sequencing (scRNA-seq) technology allows us to explore cellular heterogeneity in the transcriptome. Because most scRNA-seq data analyses begin with cell clustering, its accuracy considerably impacts the validity of downstream analyses. Although many clustering methods have been developed, few tools are available to evaluate the clustering "goodness-of-fit" to the scRNA-seq data. In this paper, we propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. Particularly, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.

Tunable band gap of layered semiconductor Zn3In2S6 under pressure

Band gap is an important property of a semiconductor, and a candidate material with highly tunable band gap under external tuning parameters will offer wider applications in optoelectronic devices and...

Investigation of Numerical Conditions of Moving Particle Semi-implicit for Two-Dimensional Wedge Slamming

The sensitivity of moving particle semi-implicit (MPS) simulations to numerical parameters is investigated in this study. Although the verification and validation (V&V) are important to ensure accurate numerical results, the MPS has poor performance in convergences with a time step size. Therefore, users of the MPS need to tune numerical parameters to fit results into benchmarks. However, such tuning parameters are not always valid for other simulations. We propose a practical numerical condition for the MPS simulation of a two-dimensional wedge slamming problem (i.e., an MPS-slamming condition). The MPS-slamming condition is represented by an MPS-slamming number, which provides the optimum time step size once the MPS-slamming number, slamming velocity, deadrise angle of the wedge, and particle size are decided. The simulation study shows that the MPS results can be characterized by the proposed MPS-slamming condition, and the use of the same MPS-slamming number provides a similar flow.

Stabilization of the Cart–Inverted-Pendulum System Using State-Feedback Pole-Independent MPC Controllers

In this paper, a pole-independent, single-input, multi-output explicit linear MPC controller is proposed to stabilize the fourth-order cart–inverted-pendulum system around the desired equilibrium points. To circumvent an obvious stability problem, a generalized prediction model is proposed that yields an MPC controller with four tuning parameters. The first two parameters, namely the horizon time and the relative cart–pendulum weight factor, are automatically adjusted to ensure a priori prescribed system gain margin and fast pendulum response while the remaining two parameters, namely the pendulum and cart velocity weight factors, are maintained as free tuning parameters. The comparison of the proposed method with some optimal control methods in the absence of disturbance input shows an obvious advantage in the average peak efficiency in favor of the proposed SIMO MPC controller at the price of slightly reduced speed efficiency. Additionally, none of the compared controllers can achieve a system gain margin greater than 1.63, while the proposed one can go beyond that limit at the price of additional degradation in the speed efficiency.

Sparse Density Estimation with Measurement Errors

This paper aims to estimate an unknown density of the data with measurement errors as a linear combination of functions from a dictionary. The main novelty is the proposal and investigation of the corrected sparse density estimator (CSDE). Inspired by the penalization approach, we propose the weighted Elastic-net penalized minimal ℓ2-distance method for sparse coefficients estimation, where the adaptive weights come from sharp concentration inequalities. The first-order conditions holding a high probability obtain the optimal weighted tuning parameters. Under local coherence or minimal eigenvalue assumptions, non-asymptotic oracle inequalities are derived. These theoretical results are transposed to obtain the support recovery with a high probability. Some numerical experiments for discrete and continuous distributions confirm the significant improvement obtained by our procedure when compared with other conventional approaches. Finally, the application is performed in a meteorology dataset. It shows that our method has potency and superiority in detecting multi-mode density shapes compared with other conventional approaches.

Development of a method for identification of the state of computer systems based on bagging classifiers

The subject of the research is methods and means of identifying the state of a computer system . The purpose of the article is to improve the quality of computer system state identification by developing a method based on ensemble classifiers. Task: to investigate methods for constructing bagging classifiers based on decision trees, to configure them and develop a method for identifying the state of the computer system. Methods used: artificial intelligence methods, machine learning, ensemble methods. The following results were obtained: the use of bagging classifiers based on meta-algorithms were investigated: Pasting Ensemble, Bootstrap Ensemble, Random Subspace Ensemble, Random Patches Ensemble and Random Forest methods and their accuracy were assessed to identify the state of the computer system. The research of tuning parameters of individual decision trees was carried out and their optimal values were found, including: the maximum number of features used in the construction of the tree; the minimum number of branches when building a tree; minimum number of leaves and maximum tree depth. The optimal number of trees in the ensemble has been determined. A method for identifying the state of the computer system is proposed, which differs from the known ones by the choice of the classification meta-algorithm and the selection of the optimal parameters for its adjustment. An assessment of the accuracy of the developed method for identifying the state of a computer system is carried out. The developed method is implemented in software and investigated when solving the problem of identifying the abnormal state of the computer system functioning. Conclusions. The scientific novelty of the results obtained lies in the development of a method for identifying the state of the computer system by choosing a meta-algorithm for classification and determining the optimal parameters for its configuration.

Autotuning Parallel Programs by Model Checking

The paper presents a new approach to autotuning data-parallel programs. Autotuning is a search for optimal program settings which maximize its performance. The novelty of the approach lies in the use of the model checking method to find the optimal tuning parameters by the method of counterexamples. In our work, we abstract from specific programs and specific processors by defining their representative abstract patterns. Our method of counterexamples implements the following four steps. At the first step, an execution model of an abstract program on an abstract processor is described in the language of a model checking tool. At the second step, in the language of the model checking tool, we formulate the optimality property that depends on the constructed model. At the third step, we find the optimal values of the tuning parameters by using a counterexample constructed during the verification of the optimality property. In the fourth step, we extract the information about the tuning parameters from the counter-example for the optimal parameters. We apply this approach to autotuning parallel programs written in OpenCL, a popular modern language that extends the C language for programming both standard multi-core processors (CPUs) and massively parallel graphics processing units (GPUs). As a verification tool, we use the SPIN verifier and its model representation language Promela, whose formal semantics is good for modelling the execution of parallel programs on processors with different architectures.

Tuning as convex optimisation: a polynomial tuner for multi-parametric combinatorial samplers

Combinatorial samplers are algorithmic schemes devised for the approximate- and exact-size generation of large random combinatorial structures, such as context-free words, various tree-like data structures, maps, tilings, RNA molecules. They can be adapted to combinatorial specifications with additional parameters, allowing for a more flexible control over the output profile of parametrised combinatorial patterns. One can control, for instance, the number of leaves, profile of node degrees in trees or the number of certain sub-patterns in generated strings. However, such a flexible control requires an additional and nontrivial tuning procedure. Using techniques of convex optimisation, we present an efficient tuning algorithm for multi-parametric combinatorial specifications. Our algorithm works in polynomial time in the system description length, the number of tuning parameters, the number of combinatorial classes in the specification, and the logarithm of the total target size. We demonstrate the effectiveness of our method on a series of practical examples, including rational, algebraic, and so-called Pólya specifications. We show how our method can be adapted to a broad range of less typical combinatorial constructions, including symmetric polynomials, labelled sets and cycles with cardinality lower bounds, simple increasing trees or substitutions. Finally, we discuss some practical aspects of our prototype tuner implementation and provide its benchmark results.

Matrix Neo-Fuzzy-System and its Online Learning in Image Recognition Task

The paper proposes a 2D-hybrid system of computational intelligence, which is based on the generalized neo-fuzzy neuron. The system is characterised by high approximate abilities, simple computational implementation, and high learning speed. The characteristic property of the proposed system is that on its input the signal is fed not in the traditional vector form, but in the image-matrix form. Such an approach allows getting rid of additional convolution-pooling layers that are used in deep neural networks as an encoder. The main elements of the proposed system are a fuzzified multidimensional bilinear model, additional softmax layer, and multidimensional generalized neo-fuzzy neuron tuning with cross-entropy criterion. Compared to deep neural systems, the proposed matrix neo-fuzzy system contains gradually fewer tuning parameters – synaptic weights. The usage of the time-optimal algorithm for tuning synaptic weights allows implementing learning in an online mode.

Optimization of automatic control of the speed of rotation of a marine diesel engine

Annotation – The well-established method of tuning the speed governors (SG) of diesel engines during their operation under conditions of step disturbances, which are characteristic of diesel-generators, cannot be used for the main marine engines, the dynamic modes of which are associated, first of all, with heavy seas, because disturbances cannot change stepwise both along the channel for setting the rotational speed and along the load channel. In this regard, the practical need for the development of a method for tuning the SG of the main engines, which takes into account the peculiarities of their operation in heavy seas, has been determined. The study simulates the automatic speed control system (ASC) of the main marine engine HYUNDAI – MAN B&W 6G70ME-C9.2 of the large crude carrier "GOLDWAY" with the AutoChief 600 electronic SG. The minimum of instability of the controlled parameter was used as an optimality criterion, i.e. the amplitude of the oscillations of the rotational speed of the diesel engine shaft, with the most probable values of the amplitude and period of oscillations (rolling) of the disturbing effect. The study has established that changing the tuning parameters of the governor may lead to local extrema of the optimality criterion when using an electronic governor in the ACS in the factor space of disturbances on a diesel engine, which are typical for heavy seas. It means that the task, requiring finding local extrema using specialized methods, can be set when using an electronic governor in the ACS. However, a significant decrease in the instability of the rotational speed was achieved by carrying out a simple enumeration of the tuning parameters of the SG. It was also found that with a "heavy" propeller, the rotational speed stability can be increased by decreasing the proportional gain, as well as increasing the integrator time.