Root system traits of Norway spruce, Scots pine, and silver birch in mixed boreal forests: an analysis of root architecture, morphology, and anatomy

The aim of this thesis was to unravel the functional-structural characteristics of root systems of Betula pendula Roth., Picea abies (L.) Karst., and Pinus sylvestris L. in mixed boreal forest stands differing in their developmental stage and site fertility. The root systems of these species had similar structural regularities: horizontally-oriented shallow roots defined the horizontal area of influence, and within this area, each species placed fine roots in the uppermost soil layers, while sinker roots defined the maximum rooting depth. Large radial spread and high ramification of coarse roots, and the high specific root length (SRL) and root length density (RLD) of fine roots indicated the high belowground competitiveness and root plasticity of B. pendula. Smaller radial root spread and sparser branching of coarse roots, and low SRL and RLD of fine roots of the conifers could indicate their more conservative resource use and high association with and dependence on ectomycorrhiza-forming fungi. The vertical fine root distributions of the species were mostly overlapping, implying the possibility for intense belowground competition for nutrients. In each species, conduits tapered and their frequency increased from distal roots to the stem, from the stem to the branches, and to leaf petioles in B. pendula. Conduit tapering was organ-specific in each species violating the assumptions of the general vascular scaling model (WBE). This reflects the hierarchical organization of a tree and differences between organs in the relative importance of transport, safety, and mechanical demands. The applied root model was capable of depicting the mass, length and spread of coarse roots of B. pendula and P. abies, and to the lesser extent in P. sylvestris. The roots did not follow self-similar fractal branching, because the parameter values varied within the root systems. Model parameters indicate differences in rooting behavior, and therefore different ecophysiological adaptations between species.

A Reference Architecture for Electronic Business-to-Business Collaboration Setup and Enactment Systems

The question what a business-to-business (B2B) collaboration setup and enactment application-system should look like remains open. An important element of such collaboration constitutes the inter-organizational disclosure of business-process details so that the opposing parties may protect their business secrets. For that purpose, eSourcing [37] has been developed as a general businessprocess collaboration concept in the framework of the EU research project Cross- Work. The eSourcing characteristics are guiding for the design and evaluation of an eSourcing Reference Architecture (eSRA) that serves as a starting point for software developers of B2B-collaboration systems. In this paper we present the results of a scenario-based evaluation method conducted with the earlier specified eSourcing Architecture (eSA) that generates as results risks, sensitivity, and tradeoff points that must be paid attention to if eSA is implemented. Additionally, the evaluation method detects shortcomings of eSA in terms of integrated components that are required for electronic B2B-collaboration. The evaluation results are used for the specification of eSRA, which comprises all extensions for incorporating the results of the scenario-based evaluation, on three refinement levels.

Intraspecific variation in brain size and architecture : population divergence and phenotypic plasticity

Brain size and architecture exhibit great evolutionary and ontogenetic variation. Yet, studies on population variation (within a single species) in brain size and architecture, or in brain plasticity induced by ecologically relevant biotic factors have been largely overlooked. Here, I address the following questions: (i) do locally adapted populations differ in brain size and architecture, (ii) can the biotic environment induce brain plasticity, and (iii) do locally adapted populations differ in levels of brain plasticity? In the first two chapters I report large variation in both absolute and relative brain size, as well as in the relative sizes of brain parts, among divergent nine-spined stickleback (Pungitius pungitius) populations. Some traits show habitat-dependent divergence, implying natural selection being responsible for the observed patterns. Namely, marine sticklebacks have relatively larger bulbi olfactorii (chemosensory centre) and telencephala (involved in learning) than pond sticklebacks. Further, I demonstrate the importance of common garden studies in drawing firm evolutionary conclusions. In the following three chapters I show how the social environment and perceived predation risk shapes brain development. In common frog (Rana temporaria) tadpoles, I demonstrate that under the highest per capita predation risk, tadpoles develop smaller brains than in less risky situations, while high tadpole density results in enlarged tectum opticum (visual brain centre). Visual contact with conspecifics induces enlarged tecta optica in nine-spined sticklebacks, whereas when only olfactory cues from conspecifics are available, bulbus olfactorius become enlarged.Perceived predation risk results in smaller hypothalami (complex function) in sticklebacks. Further, group-living has a negative effect on relative brain size in the competition-adapted pond sticklebacks, but not in the predation-adapted marine sticklebacks. Perceived predation risk induces enlargement of bulbus olfactorius in pond sticklebacks, but not in marine sticklebacks who have larger bulbi olfactorii than pond fish regardless of predation. In sum, my studies demonstrate how applying a microevolutionary approach can help us to understand the enormous variation observed in the brains of wild animals a point-of-view which I high-light in the closing review chapter of my thesis.

The Specialized Mappings Architecture

A probabilistic, nonlinear supervised learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA employs a set of several forward mapping functions that are estimated automatically from training data. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). The SMA can model ambiguous, one-to-many mappings that may yield multiple valid output hypotheses. Once learned, the mapping functions generate a set of output hypotheses for a given input via a statistical inference procedure. The SMA inference procedure incorporates an inverse mapping or feedback function in evaluating the likelihood of each of the hypothesis. Possible feedback functions include computer graphics rendering routines that can generate images for given hypotheses. The SMA employs a variant of the Expectation-Maximization algorithm for simultaneous learning of the specialized domains along with the mapping functions, and approximate strategies for inference. The framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human’s body or hands, given silhouettes from a single image. The accuracy and stability of the SMA are also tested using synthetic images of human bodies and hands, where ground truth is known.

Efficient End-Host Architecture for High Performance Communication Using User-level Sandboxing

Current low-level networking abstractions on modern operating systems are commonly implemented in the kernel to provide sufficient performance for general purpose applications. However, it is desirable for high performance applications to have more control over the networking subsystem to support optimizations for their specific needs. One approach is to allow networking services to be implemented at user-level. Unfortunately, this typically incurs costs due to scheduling overheads and unnecessary data copying via the kernel. In this paper, we describe a method to implement efficient application-specific network service extensions at user-level, that removes the cost of scheduling and provides protected access to lower-level system abstractions. We present a networking implementation that, with minor modifications to the Linux kernel, passes data between "sandboxed" extensions and the Ethernet device without copying or processing in the kernel. Using this mechanism, we put a customizable networking stack into a user-level sandbox and show how it can be used to efficiently process and forward data via proxies, or intermediate hosts, in the communication path of high performance data streams. Unlike other user-level networking implementations, our method makes no special hardware requirements to avoid unnecessary data copies. Results show that we achieve a substantial increase in throughput over comparable user-space methods using our networking stack implementation.

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.

Assessing the Security of a Clean-Slate Internet Architecture

The TCP/IP architecture was originally designed without taking security measures into consideration. Over the years, it has been subjected to many attacks, which has led to many patches to counter them. Our investigations into the fundamental principles of networking have shown that carefully following an abstract model of Interprocess Communication (IPC) addresses many problems [1]. Guided by this IPC principle, we designed a clean-slate Recursive INternet Architecture (RINA) [2]. In this paper, we show how, without the aid of cryptographic techniques, the bare-bones architecture of RINA can resist most of the security attacks faced by TCP/IP. We also show how hard it is for an intruder to compromise RINA. Then, we show how RINA inherently supports security policies in a more manageable, on-demand basis, in contrast to the rigid, piecemeal approach of TCP/IP.

Estimating Human Body Pose from a Single Image via the Specialized Mappings Architecture

A non-linear supervised learning architecture, the Specialized Mapping Architecture (SMA) and its application to articulated body pose reconstruction from single monocular images is described. The architecture is formed by a number of specialized mapping functions, each of them with the purpose of mapping certain portions (connected or not) of the input space, and a feedback matching process. A probabilistic model for the architecture is described along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present Expectation Maximization (EM) algorithms for two different instances of the likelihood probability. Performance is characterized by estimating human body postures from low level visual features, showing promising results.

The Cyclone Server Architecture: Streamlining Delivery of Popular Content

We propose a new technique for efficiently delivering popular content from information repositories with bounded file caches. Our strategy relies on the use of fast erasure codes (a.k.a. forward error correcting codes) to generate encodings of popular files, of which only a small sliding window is cached at any time instant, even to satisfy an unbounded number of asynchronous requests for the file. Our approach capitalizes on concurrency to maximize sharing of state across different request threads while minimizing cache memory utilization. Additional reduction in resource requirements arises from providing for a lightweight version of the network stack. In this paper, we describe the design and implementation of our Cyclone server as a Linux kernel subsystem.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

An Active Pattern Recognition Architecture for Mobile Robots

An active, attentionally-modulated recognition architecture is proposed for object recognition and scene analysis. The proposed architecture forms part of navigation and trajectory planning modules for mobile robots. Key characteristics of the system include movement planning and execution based on environmental factors and internal goal definitions. Real-time implementation of the system is based on space-variant representation of the visual field, as well as an optimal visual processing scheme utilizing separate and parallel channels for the extraction of boundaries and stimulus qualities. A spatial and temporal grouping module (VWM) allows for scene scanning, multi-object segmentation, and featural/object priming. VWM is used to modulate a tn~ectory formation module capable of redirecting the focus of spatial attention. Finally, an object recognition module based on adaptive resonance theory is interfaced through VWM to the visual processing module. The system is capable of using information from different modalities to disambiguate sensory input.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.