Representing Knowledge of Large-Scale Space

This dissertation presents a model of the knowledge a person has about the spatial structure of a large-scale environment: the "cognitive map". The functions of the cognitive map are to assimilate new information about the environment, to represent the current position, and to answer route-finding and relative-position problems. This model (called the TOUR model) analyzes the cognitive map in terms of symbolic descriptions of the environment and operations on those descriptions. Knowledge about a particular environment is represented in terms of route descriptions, a topological network of paths and places, multiple frames of reference for relative positions, dividing boundaries, and a structure of containing regions. The current position is described by the "You Are Here" pointer, which acts as a working memory and a focus of attention. Operations on the cognitive map are performed by inference rules which act to transfer information among different descriptions and the "You Are Here" pointer. The TOUR model shows how the particular descriptions chosen to represent spatial knowledge support assimilation of new information from local observations into the cognitive map, and how the cognitive map solves route-finding and relative-position problems. A central theme of this research is that the states of partial knowledge supported by a representation are responsible for its ability to function with limited information of computational resources. The representations in the TOUR model provide a rich collection of states of partial knowledge, and therefore exhibit flexible, "common-sense" behavior.

Motor Control and Learning by the State Space Model

A model is presented that deals with problems of motor control, motor learning, and sensorimotor integration. The equations of motion for a limb are parameterized and used in conjunction with a quantized, multi-dimensional memory organized by state variables. Descriptions of desired trajectories are translated into motor commands which will replicate the specified motions. The initial specification of a movement is free of information regarding the mechanics of the effector system. Learning occurs without the use of error correction when practice data are collected and analyzed.

Using an Outdoor Learning Space to Teach Sustainability and Material Processes in HE product Design.

The world is facing environmental changes that are increasingly affecting how we think about manufacturing, the consumption of products and use of resources. Within the HE product design community, thinking and designing sustainability’ has evolved to become a natural part of the curriculum. Paradoxical as the rise in awareness of sustainability increases there is growing concern within HE product design of the loss of workshop facilities and as a consequence a demise in teaching traditional object-making skills and material experimentation. We suggest the loss of workshops and tangible ‘learning by making skills’ also creates a lost opportunity for a rich learning resource to address sustainable thinking, design and manufacture ‘praxis’ within HE design education. Furthermore, as learning spaces are frequently discussed in design research, there seems to be little focus on how the use of an outdoor environment might influence learning outcomes particularly with regard to material teaching and sustainability. This 'case study' of two jewellery workshops, used outdoor learning spaces to explore both its impact on learning outcomes and to introduce some key principles of sustainable working methodologies and practices. Academics and students mainly from Norway and Scotland collaborated on this international research project. Participants made models from disposable packaging materials, which were cast in tin, in the sand on a local beach, using found timber to create a heat source for melting the metal. This approach of using traditional making skills, materials and nature was found to be a relevant contribution to a sustainable discourse.

A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures

R. Daly and Q. Shen. A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 67-74.

Space saving calculation of symbolic resultants

Lloyd, Noel G., and Pearson, Jane M., 'Space saving calculation of symbolic resultants', Mathematics in Computer Science, 1 (2007), 267-290.

A Cult Film by Proxy: Space is the Place and the Sun Ra Mythos

Sexton, J. (2006). A Cult Film by Proxy: Space is the Place and the Sun Ra Mythos. New Review of Film and Television Studies. 4(3), pp.197-215. RAE2008

Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods.

Contour Generator Points for Threshold Selection and a Novel Photo-Consistency Measure for Space Carving

Space carving has emerged as a powerful method for multiview scene reconstruction. Although a wide variety of methods have been proposed, the quality of the reconstruction remains highly-dependent on the photometric consistency measure, and the threshold used to carve away voxels. In this paper, we present a novel photo-consistency measure that is motivated by a multiset variant of the chamfer distance. The new measure is robust to high amounts of within-view color variance and also takes into account the projection angles of back-projected pixels. Another critical issue in space carving is the selection of the photo-consistency threshold used to determine what surface voxels are kept or carved away. In this paper, a reliable threshold selection technique is proposed that examines the photo-consistency values at contour generator points. Contour generators are points that lie on both the surface of the object and the visual hull. To determine the threshold, a percentile ranking of the photo-consistency values of these generator points is used. This improved technique is applicable to a wide variety of photo-consistency measures, including the new measure presented in this paper. Also presented in this paper is a method to choose between photo-consistency measures, and voxel array resolutions prior to carving using receiver operating characteristic (ROC) curves.

Periodic Motion Detection and Estimation via Space-Time Sampling

A novel technique to detect and localize periodic movements in video is presented. The distinctive feature of the technique is that it requires neither feature tracking nor object segmentation. Intensity patterns along linear sample paths in space-time are used in estimation of period of object motion in a given sequence of frames. Sample paths are obtained by connecting (in space-time) sample points from regions of high motion magnitude in the first and last frames. Oscillations in intensity values are induced at time instants when an object intersects the sample path. The locations of peaks in intensity are determined by parameters of both cyclic object motion and orientation of the sample path with respect to object motion. The information about peaks is used in a least squares framework to obtain an initial estimate of these parameters. The estimate is further refined using the full intensity profile. The best estimate for the period of cyclic object motion is obtained by looking for consensus among estimates from many sample paths. The proposed technique is evaluated with synthetic videos where ground-truth is known, and with American Sign Language videos where the goal is to detect periodic hand motions.

Search Space Reduction in QoS Routing

To provide real-time service or engineer constrained-based paths, networks require the underlying routing algorithm to be able to find low-cost paths that satisfy given Quality-of-Service (QoS) constraints. However, the problem of constrained shortest (least-cost) path routing is known to be NP-hard, and some heuristics have been proposed to find a near-optimal solution. However, these heuristics either impose relationships among the link metrics to reduce the complexity of the problem which may limit the general applicability of the heuristic, or are too costly in terms of execution time to be applicable to large networks. In this paper, we focus on solving the delay-constrained minimum-cost path problem, and present a fast algorithm to find a near-optimal solution. This algorithm, called DCCR (for Delay-Cost-Constrained Routing), is a variant of the k-shortest path algorithm. DCCR uses a new adaptive path weight function together with an additional constraint imposed on the path cost, to restrict the search space. Thus, DCCR can return a near-optimal solution in a very short time. Furthermore, we use the method proposed by Blokh and Gutin to further reduce the search space by using a tighter bound on path cost. This makes our algorithm more accurate and even faster. We call this improved algorithm SSR+DCCR (for Search Space Reduction+DCCR). Through extensive simulations, we confirm that SSR+DCCR performs very well compared to the optimal but very expensive solution.

Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions

A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an ill-posed problem as the inverse mapping is a one to many process. Hence multiple solutions exist, and it is desirable to restrict the solution space to a smaller subset of feasible solutions. For example, not all human body poses are feasible due to anthropometric constraints. Since the space of feasible solutions may not admit a closed form description, the proposed framework seeks to exploit machine learning techniques to learn an approximation that is smoothly parameterized over such a space. One such technique is Gaussian Process Latent Variable Modelling. Scaled conjugate gradient is then used find the best matching pose in the space of feasible solutions when given an input image. The formulation allows easy incorporation of various constraints, e.g. temporal consistency and anthropometric constraints. The performance of the proposed approach is evaluated in the task of upper-body pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The estimation accuracy of the Specialized Mapping Architecture is at least one standard deviation worse than the proposed approach in the experiments with synthetic data. In experiments with real video of humans performing gestures, the proposed approach produces qualitatively better estimation results.

Tracking Human Body Pose on a Learned Smooth Space

Particle filtering is a popular method used in systems for tracking human body pose in video. One key difficulty in using particle filtering is caused by the curse of dimensionality: generally a very large number of particles is required to adequately approximate the underlying pose distribution in a high-dimensional state space. Although the number of degrees of freedom in the human body is quite large, in reality, the subset of allowable configurations in state space is generally restricted by human biomechanics, and the trajectories in this allowable subspace tend to be smooth. Therefore, a framework is proposed to learn a low-dimensional representation of the high-dimensional human poses state space. This mapping can be learned using a Gaussian Process Latent Variable Model (GPLVM) framework. One important advantage of the GPLVM framework is that both the mapping to, and mapping from the embedded space are smooth; this facilitates sampling in the low-dimensional space, and samples generated in the low-dimensional embedded space are easily mapped back into the original highdimensional space. Moreover, human body poses that are similar in the original space tend to be mapped close to each other in the embedded space; this property can be exploited when sampling in the embedded space. The proposed framework is tested in tracking 2D human body pose using a Scaled Prismatic Model. Experiments on real life video sequences demonstrate the strength of the approach. In comparison with the Multiple Hypothesis Tracking and the standard Condensation algorithm, the proposed algorithm is able to maintain tracking reliably throughout the long test sequences. It also handles singularity and self occlusion robustly.

Hippocampal Modulation of Recognition, Conditioning, Timing, and Space: Why So Many Functions?

Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-24877); Office of Naval Research (N00014-91-J-1309)

Space, Time and Learning in the Hippocampus: How Fine Spatial and Temporal Scales Are Expanded into Population Codes for Behavioral Control

The hippocampus participates in multiple functions, including spatial navigation, adaptive timing, and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus, and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory.

Monasticism and its limits: rematerialising monastic space in early medieval Ireland

This thesis creates a multi-faceted archaeological context for early Irish monasticism, so as to ‘rematerialise’ a phenomenon that has been neglected by recent archaeological scholarship. Following revision of earlier models of the early Irish Church, archaeologists are now faced with redefining monasticism and distinguishing it from other diverse forms of Christian lifestyle. This research addresses this challenge, exploring the ways in which material limits can be set on the monastic phenomenon. The evidence for early Irish monasticism does not always conform to modern expectations of its character, and monastic space must be examined as culturally unique in its own right - though this thesis demonstrates that early Irish monasticism was by no means as unorthodox in its contemporary European setting as has previously been suggested. The research is informed by theories of the body, habitus and space, drawing on a wide body of archaeological, religious, sociological and anthropological thought. The data-set comprises evidences gathered through field-survey, reassessment of archaeological scholarship, historical research and cartographic research, enabling consideration of the ways in which early Irish monastics engaged with their environments. A sample of thirty-one early Irish ecclesiastical sites plus Iona forms the basis for discussion of the location and layout of monastic space, the ways in which monastics used buildings and space in their daily lives, the relationship of monasticism and material culture, the setting of mental and physical limits on monastic space and monastic bodies, and the variety of monastic lifestyles that pertained in early medieval Ireland. The study then examines the Christian landscapes of two case-studies in mid-Western Ireland in order to illustrate how monasticism functioned on the ground in these areas. As this research shows, the material complexities of early Irish monastic life are capable of archaeological definition in terms of both communal and personal lived experience.