Nutrient enrichment and water depth modify consumer control in rocky shore macroalgal communities

The objective of my thesis is to assess mechanisms of ecological community control in macroalgal communities in the Baltic Sea. In the top-down model, predatory fish feed on invertebrate mesograzers, releasing algae partly from grazing pressure. Such a reciprocal relationship is called trophic cascade. In the bottom-up model, nutrients increase biomass in the food chain. The nutrients are first assimilated by algae and, via food chain, increase also abundance of grazers and predators. Previous studies on oceanic shores have described these two regulative mechanisms in the grazer - alga link, but how they interact in the trophic cascades from fish to algae is still inadequately known. Because the top-down and bottom-up mechanisms are predicted to depend on environmental disturbances, such as wave stress and light, I have studied these models at two distinct water depths. There are five factorial field experiments behind the thesis, which were all conducted in the Finnish Archipelago Sea. In all the experiments, I studied macroalgal colonization - either density, filament length or biomass - on submerged colonization substrates. By excluding predatory fish and mesograzers from the algal communities, the studies compared the strength of the top-down control to natural algal communities. A part of the experimental units were, in addition, exposed to enriched nitrogen and phosphorus concentrations, which enabled testing of bottom-up control. These two models of community control were further investigated in shallow (<1 m) and deep (ca. 3 m) water. Moreover, the control mechanisms were also expected to depend on grazer species. Therefore different grazer species were enclosed into experimental units and their impacts on macroalgal communities were followed specifically. The community control in the Baltic rocky shores was found to follow theoretical predictions, which have not been confirmed by field studies before. Predatory fish limited grazing impact, which was seen as denser algal communities and longer algal filaments. Nutrient enrichment increased density and filament length of annual algae and, thus, changed the species composition of the algal community. The perennial alga Fucus vesiculosusA and the red alga Ceramium tenuicorne suffered from the increased nutrient availabilities. The enriched nutrient conditions led to denser grazer fauna, thereby causing strong top-down control over both the annual and perennial macroalgae. The strength of the top-down control seemed to depend on the density and diversity of grazers and predators as well as on the species composition of macroalgal assemblages. The nutrient enrichment led to, however, weaker limiting impact of predatory fish on grazer fauna, because fish stocks did not respond as quickly to enhanced resources in the environment as the invertebrate fauna. According to environmental stress model, environmental disturbances weaken the top-down control. For example, on a wave-exposed shore, wave stress causes more stress to animals close to the surface than deeper on the shore. Mesograzers were efficient consumers at both the depths, while predation by fish was weaker in shallow water. Thus, the results supported the environmental stress model, which predicts that environmental disturbance affects stronger the higher a species is in the food chain. This thesis assessed the mechanisms of community control in three-level food chains and did not take into account higher predators. Such predators in the Baltic Sea are, for example, cormorant, seals, white-tailed sea eagle, cod and salmon. All these predatory species were recently or are currently under intensive fishing, hunting and persecution, and their stocks have only recently increased in the region. Therefore, it is possible that future densities of top predators may yet alter the strengths of the controlling mechanisms in the Baltic littoral zone.

Of pearls and pigs : a conceptual-semantic Tiernet approach to formal representation of structure and variation of phraseological units

"Helmiä sioille", pärlor för svin, säger man på finska om någonting bra och fint som tas emot av en mottagare som inte vill eller har ingen förmåga att förstå, uppskatta eller utnyttja hela den potential som finns hos det mottagna föremålet, är ointresserad av den eller gillar den inte. För sådana relativt stabila flerordiga uttryck, som är lagrade i språkbrukarnas minnen och som demonstrerar olika slags oregelbundna drag i sin struktur använder man inom lingvistiken bl.a. termerna "idiom" eller "fraseologiska enheter". Som en oregelbundenhet kan man t.ex. beskriva det faktum att betydelsen hos uttrycket inte är densamma som man skulle komma till ifall man betraktade det som en vanlig regelbunden fras. En annan oregelbundenhet, som idiomforskare har observerat, ligger i den begränsade förmågan att varieras i form och betydelse, som många idiom har jämfört med regelbundna fraser. Därför talas det ofta om "grundform" och "grundbetydelse" hos idiom och variationen avses som avvikelse från dessa. Men när man tittar på ett stort antal förekomstexempel av idiom i språkbruk, märker man att många av dem tillåter variation, t.o.m. i sådan utsträckning att gränserna mellan en variant och en "grundform" suddas ut, och istället för ett idiom råkar vi plötsligt på en "familj" av flera besläktade uttryck. Allt detta väcker frågan om hur dessa uttryck egentligen ska vara representerade i språket. I avhandlingen utförs en kritisk granskning av olika tidigare tillvägagångssätt att beskriva fraseologiska enheter i syfte att klargöra vilka svårigheter deras struktur och variation erbjuder för den lingvistiska teorin. Samtidigt presenteras ett alternativt sätt att beskriva dessa uttryck. En systematisk och formell modell som utvecklas i denna avhandling integrerar en beskrivning av idiom på många olika språkliga nivåer och skildrar deras variation i form av ett nätverk och som ett resultat av samspel mellan idiomets struktur och kontexter där det förekommer, samt av interaktion med andra fasta uttryck. Modellen bygger på en fördjupande, språkbrukbaserad analys av det finska idiomet "X HEITTÄÄ HELMIÄ SIOILLE" (X kastar pärlor för svin).

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.