On noncausal weighted least squares identification of nonstationary stochastic systems

In this paper, we consider the problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted (windowed) least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing bandwidth to the unknown, and possibly time-varying, rate of nonstationarity of the identified system. We optimize the window shape for a certain class of parameter variations and we derive computationally attractive recursive smoothing algorithms for such an optimized case.     

Using interesting sequences to interactively build Hidden Markov Models

The paper presents a method of interactive construction of global Hidden Markov Models (HMMs) based on local sequence patterns discovered in data. The method is based on finding interesting sequences whose frequency in the database differs from that predicted by the model. The patterns are then presented to the user who updates the model using their intelligence and their understanding of the modelled domain. It is demonstrated that such an approach leads to more understandable models than automated approaches. Two variants of the problem are considered: mining patterns occurring only at the beginning of sequences and mining patterns occurring at any position; both practically meaningful. For each variant, algorithms have been developed allowing for efficient discovery of all sequences with given minimum interestingness. Applications to modelling webpage visitors behavior and to modelling protein secondary structure are presented, validating the proposed approach.     

$$L_p$$-Support vector machines for uplift modeling

Uplift modeling is a branch of machine learning which aims to predict not the class itself, but the difference between the class variable behavior in two groups: treatment and control. Objects in the treatment group have been subjected to some action, while objects in the control group have not. By including the control group, it is possible to build a model which predicts the causal effect of the action for a given individual. In this paper, we present a variant of support vector machines designed specifically for uplift modeling. The SVM optimization task has been reformulated to explicitly model the difference in class behavior between two datasets. The model predicts whether a given object will have a positive, neutral or negative response to a given action, and by tuning a parameter of the model the analyst is able to influence the relative proportion of neutral predictions and thus the conservativeness of the model. Further, we extend \(L_p\)-SVMs to the case of uplift modeling and demonstrate that they allow for a more stable selection of the size of negative, neutral and positive groups. Finally, we present quadratic and convex optimization methods for efficiently solving the two proposed optimization tasks.     

AutoStyle: Automatic Style Transfer from Image Collections to Users' Images

Stylizing photos, to give them an antique or artistic look, has become popular in recent years. The available stylization filters, however, are usually created manually by artists, resulting in a narrow set of choices. Moreover, it can be difficult for the user to select a desired filter, since the filters’ names often do not convey their functions. We investigate an approach to photo filtering in which the user provides one or more keywords, and the desired style is defined by the set of images returned by searching the web for those keywords. Our method clusters the returned images, allows the user to select a cluster, then stylizes the user's photos by transferring vignetting, color, and local contrast from that cluster. This approach vastly expands the range of available styles, and gives each filter a meaningful name by default. We demonstrate that our method is able to robustly transfer a wide range of styles from image collections to users’ photos.     

miRNA Profiling and Its Role in Multi-Omics Regulatory Networks Connected with Somaclonal Variation in Cucumber (Cucumis sativus L.)

The role of miRNAs in connection with the phenomenon of somaclonal variation, which occurs during plant in vitro culture, remains uncertain. This study aims to investigate the possible role of miRNAs in multi-omics regulatory pathways in cucumber somaclonal lines. For this purpose, we performed sRNA sequencing (sRNA-seq) from cucumber fruit samples identified 8, 10 and 44 miRNAs that are differentially expressed between somaclones (S1, S2, S3 lines) and the reference B10 line of Cucumis sativus. For miRNA identification, we use ShortStack software designed to filter miRNAs from sRNAs according to specific program criteria. The identification of predicted in-silico targets revealed 2,886 mRNAs encoded by 644 genes. The functional annotation of miRNA’s target genes and gene ontology classification revealed their association with metabolic processes, response to stress, multicellular organism development, biosynthetic process and catalytic activity. We checked with bioinformatic analyses for possible interactions at the level of target proteins, differentially expressed genes (DEGs) and genes affected by genomic polymorphisms. We assume that miRNAs can indirectly influence molecular networks and play a role in many different regulatory pathways, leading to somaclonal variation. This regulation is supposed to occur through the process of the target gene cleavage or translation inhibition, which in turn affects the proteome, as we have shown in the example of molecular networks. This is a new approach combining levels from DNA-seq through mRNA-seq, sRNA-seq and in silico PPI in the area of plants’ somaclonal variation.     

Plant‐Derived Substances Used Against Beetles–Pests of Stored Crops and Food–and Their Mode of Action: A Review

Plants are sources of numerous active substances that are used to protect crops. Currently, due to the limitations of using synthetic insecticides, plant products have attracted increasing attention as possible pesticides. In this review, we discuss some of the most interesting plant products (for example, Solanaceae, or Asteraceae extracts, Artemisia absinthium or Citrus spp. essential oils, and single compounds like α‐chaconine, or α‐solanine) that exhibit insecticidal activity against beetles that are pests of stored food products. Next, we describe and discuss the mode of action of these products, including lethal and sublethal effects, such as antifeedant or neurotoxic activity, ultrastructural malformation, and effects on prooxidant/antioxidant balance. Furthermore, the methods of application of plant‐derived substances in food storage areas are presented.     

Effect of surface related organic vibrational modes in luminescent upconversion dynamics of rare earth ions doped nanoparticles

Physical and chemical properties of nanoparticles are known to be subject to the surface factors. For their biological/biomedical applications, typically, surface of the nanoparticles has to be modified which inevitably affects their performance. In this work we have studied the interaction between the surface related organic vibrational modes and the luminescent centers--rare earth ions--in one of the most efficient luminescence upconversion nanosystems--NaYF4. Specifically, the surface quenching centers, the surface related luminescent centers, as well as the role of shell properties, are investigated spectroscopically. Our results demonstrate that the surface related high-frequency vibrational modes can be critical to the spectral properties of the nanosystems once the surface is not well separated from the discrete luminescent centers.     

Modeling of the heating system in small building for control

Model identifications for the heating system of small building are presented. Basic problems of data acquisition and preprocessing are detailed. Specially designed wire-less data collection and control system to conduct real-world experiments are described. The model structure choice based on analysis of weather condition influences on the system performance are presented. Finally, the general structure of two-layered heating control strategy for the heating system is proposed. Instead of heating, ventilating and air conditioning (HVAC) standard, the proposed strategy controls only indoor temperature and changes the set point according to additional measurements of indoor humidity to keep thermal comfort. Non-linear compensation of outdoor temperature and wind speed is also introduced.     

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment.