How to Hear

Let it ever be remembered that the great Bishop of souls, the Lord Jesus, who had every ministerial qualification in absolute perfection, preached the everlasting Gospel to many who were not profited by it; and that, he departed from a certain place in which he could do no mighty works, because of the people\u27s unbelief. Matt. xiii, 58. In this case it is manifest that the fault could not be in the preacher, nor in the matter of his discourse, but in the hearers only. The grand business, therefore, of the people is to inquire in the most serious manner how they are to hear so as to be saved. This tract lists 21 ways on how to hear.https://place.asburyseminary.edu/ecommonsatsdigitalresources/1254/thumbnail.jp

Magnetic-Moment Fragmentation and Monopole Crystallization

The Coulomb phase, with its dipolar correlations and pinch-point-scattering patterns, is central to discussions of geometrically frustrated systems, from water ice to binary and mixed-valence alloys, as well as numerous examples of frustrated magnets. The emergent Coulomb phase of lattice-based systems has been associated with divergence-free fields and the absence of long-range order. Here, we go beyond this paradigm, demonstrating that a Coulomb phase can emerge naturally as a persistent fluctuating background in an otherwise ordered system. To explain this behavior, we introduce the concept of the fragmentation of the field of magnetic moments into two parts, one giving rise to a magnetic monopole crystal, the other a magnetic fluid with all the characteristics of an emergent Coulomb phase. Our theory is backed up by numerical simulations, and we discuss its importance with regard to the interpretation of a number of experimental results

Representing Qualitative Action Models for Learning in Complex Virtual Worlds

This thesis addresses the problem of representing and learning qualitative models of behaviour in complex virtual worlds. It presents a novel representation, ‘Q-Systems’, that integrates two existing representation frameworks: qualitative process models and action description languages. QSystems combines the expressive power of both frameworks to allow actions and world dynamics to be modelled in a common way using a representation based on non-deterministic and probabilistic finite state machines. The representation supports learning and planning by using a modular approach that partitions world behaviour into ‘systems’ of objects with specific contexts and a related behaviour. Q-Systems was developed and tested using an agent in a rich simulated world that was created as part of the thesis. The simulation uses a rigid body physics engine to produce complex realistic interactions between objects. An action system and a qualitative vision system were also developed to allow the agent to observe and act in the simulated world. The thesis includes a proposed two stage learning process comprising an initial stage in which ‘histories’ (contextually and temporally restricted sequences of observations) are extracted from interactions with the simulation, and a second stage in which the histories are generalised to create a knowledge base of system models. An algorithm for generating histories is presented and a number of heuristics are implemented and compared. A system for learning generalised models is presented and it is used to assess the suitability of Q-Systems with respect to learning in complex environments. Planning with Q-Systems is demonstrated in an agent which reasons with generalised models to work out how to achieve goals in the simulated world. A simple planning algorithm is described and a variety of issues are explored. Planning with a single system is shown to be relatively straightforward due to the modular nature of Q-Systems. This thesis demonstrates that Q-Systems successfully integrate two different representation frameworks and that they can be used in learning and planning in complex environments. The initial results are promising, but further investigation is required to fully understand the advantages and disadvantages of the Q-System approach compared with existing learning systems. This would involve the development of benchmark problems (currently there are none for this particular domain)

Nod1 signaling overcomes resistance of S. pneumoniae to opsonophagocytic killing

Airway infection by the Gram-positive pathogen Streptococcus pneumoniae (Sp) leads to recruitment of neutrophils but limited bacterial killing by these cells. Co-colonization by Sp and a Gram-negative species, Haemophilus influenzae (Hi), provides sufficient stimulus to induce neutrophil and complement-mediated clearance of Sp from the mucosal surface in a murine model. Products from Hi, but not Sp, also promote killing of Sp by ex vivo neutrophil-enriched peritoneal exudate cells. Here we identify the stimulus from Hi as its peptidoglycan. Enhancement of opsonophagocytic killing was facilitated by signaling through nucleotide-binding oligomerization domain-1 (Nod1), which is involved in recognition of γ-D-glutamyl-meso-diaminopimelic acid (meso-DAP) contained in cell walls of Hi but not Sp. Neutrophils from mice treated with Hi or compounds containing meso-DAP, including synthetic peptidoglycan fragments, showed increased Sp killing in a Nod1-dependent manner. Moreover, Nod1-/- mice showed reduced Hi-induced clearance of Sp during co-colonization. These observations offer insight into mechanisms of microbial competition and demonstrate the importance of Nod1 in neutrophil-mediated clearance of bacteria in vivo