Robust denoising of electrophoresis and mass spectrometry signals with minimum description length principle

AbstractThe need for high-throughput assays in molecular biology places increasing requirements on the applied signal processing and modelling methods. In order to be able to extract useful information from the measurements, the removal of undesirable signal characteristics such as random noise is required. This can be done in a quite elegant and efficient way by the minimum description length (MDL) principle, which treats and separates `noise' from the useful information as that part in the data that cannot be compressed. In its current form the MDL denoising method assumes the Gaussian noise model but does not require any ad hoc parameter settings. It provides a basis for high-speed automated processing systems without requiring continual user interventions to validate the results as in the conventional signal processing methods. Our analysis of the denoising problem in mass spectrometry, capillary electrophoresis genotyping, and sequencing signals suggests that the MDL denoising method produces robust and intuitively appealing results sometimes even in situations where competing approaches perform poorly

Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection

Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures

Bayesian Approach for Optimizing Forest Inventory Survey Sampling with Remote Sensing Data

In large-area forest inventories, a trade-off between the amount of data to be sampled and the corresponding collection costs is necessary. It is not always possible to have a very large data sample when dealing with sampling-based inventories. It is therefore important to optimize the sampling design with the limited resources. Whereas this sort of inventories are subject to these constraints, the availability of remote sensing (RS) data correlated with the forest inventory variables is usually much higher. For this reason, the RS and sampled field measurement data are often used in combination for improving the forest inventory estimation. In this study, we propose a model-based data sampling method founded on Bayesian optimization and machine learning algorithms which utilizes RS data to guide forest inventory sample selection. We evaluate our method in empirical experiments using real-world volume of growing stock data from the Aland region in Finland. The proposed method is compared against two baseline methods: simple random sampling and the local pivotal method. When a suitable model link is selected, the empirical experiments show on best case on average up to 22% and 79% improvement in population mean and variance estimation respectively over baselines. However, the results also illustrate the importance of model selection which has a clear effect on the results. The novelty of the study is in the application of Bayesian optimization in national forest inventory survey sampling

Työterveyspalvelujen ostajan opas maatalousyrittäjälle

Oppaassa kerrotaan, mitä työterveyshuolto voi tarjota ja mitä siltä voi odottaa. Siinä kerrotaan yrittäjän ja työnantajan vastuista, työterveyshuollon ostamisesta, sisällöstä sekä kustannusten muodostumisesta ja korvauksista

Components of defence strategies in society’s information environment: a case study based on the grounded theory

The goal of this study is to explore the components of defence strategies faced by society in its information environment, and how these strategies are inter-related. This qualitative in-depth case study applied past research and empirical evidence to identify the components of defence strategies in a society’s information environment. The data collected was analysed using the Grounded Theory approach and a conceptual framework with the components of defence strategies and the relationships between these components was developed using the Grounded Theory. This study shows that the goal of politically and militarily hostile actors is to weaken society’s information environment, and that their operations are coordinated and carried out over a long time period. The data validates past studies and reveals relationships between the components of defence strategies. These relationships increase confidence in the validity of these components and their relationships, and expand the emerging theory. First, the data and findings showed 16 inter-connected components of defence strategies. Second, they showed that the political, military, societal, power, and personal goals of the hostile actors carrying out cyber operations and cyber attacks are to weaken society’s information environment. Third, they revealed that cyber operations and cyber attacks against networks, information and infrastructures are coordinated operations, carried out over a long time period. Finally, it was revealed that the actors defending society’s information environment must rapidly change their own components of defence strategies and use the newest tools and methods for these components in networks, infrastructures and social media.</p