Abstract: Metaheuristics are gaining increased attention as efficient solvers for hard global optimization problems arising in bioinformatics and computational systems biology. Scatter Search (SS) is one of the recent outstanding algorithms in that class. However, its application to very hard problems, like those considering parameter estimation in dynamic models of systems biology, still results in excessive computation times. In order to reduce the computational cost of the SS and improve its success, several research efforts have been made to propose dierent variants of the algorithm, including parallel approaches. This work presents an asynchronous Cooperative enhanced Scatter Search (aCeSS) based on the parallel execution of different enhanced Scatter Search threads and the cooperation between them. The main features of the proposed solution are: low overhead in the cooperation step, by means of an asynchronous protocol to exchange information between processes; more effectiveness of the cooperation step, since the exchange of information is driven by quality of the solution obtained in each process, rather than by a time elapsed; optimal use of available resources, thanks to a complete distributed approach that avoids idle processes at any moment. Several challenging parameter estimation problems from the domain of computational systems biology are used to assess the efficiency of the proposal and evaluate its scalability in a parallel environment.

Abstract: Throughout their life span plants maintain the ability to generate new organs such as leaves. This is normally done in an orderly way by activating limited groups of dormant cells to divide and grow. It is currently not understood how that process is precisely regulated. We have used the VirtualLeaf framework for plant organ growth modelling to simulate the typical developmental stages of leaves of the model plant Arabidopsis thaliana. For that purpose the Hamiltonian central to the Monte-Carlo based mechanical equilibration of VirtualLeaf was modified. A basic two-dimensional model was defined starting from a rectangular grid with a dynamic phytohormone gradient that spatially instructs the cells in the growing leaf. Our results demonstrate that such a mechanism can indeed reproduce various spatio-temporal characteristics of leaf development and provides clues for further model development.

Abstract: Environmental acoustic recordings can be used to perform avian species richness surveys, whereby a trained ornithologist can observe the species present by listening to the recording. This could be made more efficient by using computational methods for iteratively selecting the richest parts of a long recording for the human observer to listen to, a process known as “smart sampling”. This allows scaling up to much larger ecological datasets. In this paper we explore computational approaches based on information and diversity of selected samples. We propose to use an event detection algorithm to estimate the amount of information present in each sample. We further propose to cluster the detected events for a better estimate of this amount of information. Additionally, we present a time dispersal approach to estimating diversity between iteratively selected samples. Combinations of approaches were evaluated on seven one-day recordings that have been manually annotated by bird watchers. The results show that on average all the methods we have explored would allow annotators to observe more new species in fewer minutes compared to a baseline of random sampling at dawn.

Abstract: Wildlife@Home is citizen science project developed to provide wildlife biologists a way to swiftly analyze the massive quantities of data that they can amass during video surveillance studies. The project has been active for two years, with over 200 volunteers who have participated in providing observations through a web interface where they can stream video and report the occurrences of various events within that video. Wildlife@Home is currently analyzing avian nesting video from three species: Sharptailed-Grouse (Tympanuchus phasianellus) an indicator species which plays a role in determining the effect of North Dakota's oil development on the local wildlife, Interior Least Tern (Sternula antillarum) a federally listed endangered species, and Piping Plover (Charadrius Melodus) a federally listed threatened species. Video comes from 105 grouse, 61 plover and 37 tern nests from multiple nesting seasons, and consists of over 85,000 hours (13 terabytes) of 24/7 uncontrolled outdoor surveillance video. This work describes the infrastructure supporting this citizen science project, and examines the effectiveness of two different interfaces for crowd sourcing: a simpler interface where users watch short clips of video and report if an event occurred within that video, and a more involved interface where volunteers can watch entire videos and provide detailed event information including beginning and ending times for events. User observations are compared against expert observations made by wildlife biology research assistants, and are shown to be quite effective given strategies used in the project to promote accuracy and correctness.

Abstract: The goal of the study was applying machine learning methods to create rules for prediction of the surface scaling resistance of concrete modified with high-calcium fly ash. To determine the scaling durability the Bor{\aa}s method, according to European Standard procedure (PKN-CEN/TS 12390-9:2007), was used. The results of numeral experiments were utilized as a training set to generate rules indicating the relation between material composition and the scaling resistance. The classifier generated by BFT algorithm from the WEKA workbench can be used as a tool for adequate classification of plain concretes and concretes modified with high-calcium fly ash as materials resistant or not resistant to the surface scaling.