Moments of Recognition:The Four Semester Studio in QUB

It is a legitimate assertion that the common ground of work of worth in architecture, whether theoretical or built comes from a firmly held position on the part of the author. In addition to delivery key competencies architectural education should act to support the formation of such a position in the student, or to make students aware of the possibility of holding such a position.

It is with this in mind perhaps that intensive unit-based diploma and masters structures are increasingly becoming the standard structure for for schools of architecture across the UK. The strengths of such a structure are most evident when the school, either by virtue of financial strength or geographic location is able to attract a diverse range of contrasting positions to bear in the formation of these units. In effect the offering to the student is a short, intensive immersion into a clear line of thought based on the position of those running the unit. Research is channeled by those running the unit to the work of the students. A single cohort of students therefore is able to observe and understand a wide range of ways of thinking about the subject whether or not they are participants in a unit or not. It is axiomatic that where this structure is applied in the absence of these resources the result can be less helpful, individual units are differentiated not to reflect the interests of those running the unit but for the sake of difference as its own end.

In structuring the M.Arch programme in Queens University Belfast the reality of our somewhat peripheral location was placed at the forefront of our considerations. A single 4 semester studio is offered. The first three semesters are carefully structured to offer a range of directed and self directed projects to the students. By interrogation of these projects, and work undertaken at undergraduate level the aim is to assist the students to identify a personal position on architecture, which is then developed in the thesis in semester four. Research and design outputs are emergent from the interest of the student body, cultivated by staff who have the time over the four semesters to get to know all aspects of a students interests.

This paper will lay out this structure and some of the projects run within it. Now having delivered two graduating years the successes and challenges of the system will be laid out by reference to several case studies of individual student experiences of the structure.

Practical automated activity recognition using standard smartphones

This paper describes a simple application for mobile devices which automatically recognizes different physical activity. This application could be used to log exercise sessions for the purpose of aiding weight management or improving sporting performance. © 2012 IEEE.

The Use of Electrospray Mass Spectrometry to Determine Speciation in a Dynamic Combinatorial Library for Anion Recognition

The composition of a dynamic mixture of similar 2,2'-bipyridine complexes of iron(II) bearing either an amide (5-benzylamido-2,2'-bipyridine and 5-(2-methoxyethane)amido-2,2'-bipyridine) or an ester (2,2'-bipyridine-5-carboxylic acid benzylester and 2,2'-bipyridine-5-carboxylic acid 2-methoxyethane ester) side chain have been evaluated by electrospray mass spectroscopy in acetonitrile. The time taken for the complexes to come to equilibrium appears to be dependent on the counteranion, with chloride causing a rapid redistribution of two preformed heteroleptic complexes (of the order of 1 hour), whereas the time it takes in the presence of tetrafluoroborate salts is in excess of 24^^h. Similarly the final distribution of products is dependent on the anion present, with the presence of chloride, and to a lesser extent bromide, preferring three amide-functionalized ligands, and a slight preference for an appended benzyl over a methoxyethyl group. Furthermore, for the first time, this study shows that the distribution of a dynamic library of metal complexes monitored by ESI-MS can adapt following the introduction of a different anion, in this case tetrabutylammonium chloride to give the most favoured heteroleptic complex despite the increasing ionic strength of the solution. 

Illumination invariant facial recognition using a piecewise-constant lighting model

In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per-person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive component modelling the mean illumination and a multiplicative component, modelling the variance within the facial area. Illumination invariant facial recognition is performed in a piecewise manner, by splitting the face image into blocks, then normalizing the illumination within each block based on the new lighting model. The assumptions underlying this novel lighting model have been verified on the YaleB face database. We show that magnitude 2D Fourier features can be used as robust facial descriptors within the new lighting model. Using only a single training image per-person, our new method achieves high (in most cases 100%) identification accuracy on the YaleB, extended YaleB and CMU-PIE face databases.

Optimization of Weighted Finite State Transducer for Speech Recognition

There is considerable interest in creating embedded, speech recognition hardware using the weighted finite state transducer (WFST) technique but there are performance and memory usage challenges. Two system optimization techniques are presented to address this; one approach improves token propagation by removing the WFST epsilon input arcs; another one-pass, adaptive pruning algorithm gives a dramatic reduction in active nodes to be computed. Results for memory and bandwidth are given for a 5,000 word vocabulary giving a better practical performance than conventional WFST; this is then exploited in an adaptive pruning algorithm that reduces the active nodes from 30,000 down to 4,000 with only a 2 percent sacrifice in speech recognition accuracy; these optimizations lead to a more simplified design with deterministic performance.

Robust Audio-Visual Speech Recognition under Noisy Audio-Video Conditions

This paper presents the maximum weighted stream posterior (MWSP) model as a robust and efficient stream integration method for audio-visual speech recognition in environments, where the audio or video streams may be subjected to unknown and time-varying corruption. A significant advantage of MWSP is that it does not require any specific measurements of the signal in either stream to calculate appropriate stream weights during recognition, and as such it is modality-independent. This also means that MWSP complements and can be used alongside many of the other approaches that have been proposed in the literature for this problem. For evaluation we used the large XM2VTS database for speaker-independent audio-visual speech recognition. The extensive tests include both clean and corrupted utterances with corruption added in either/both the video and audio streams using a variety of types (e.g., MPEG-4 video compression) and levels of noise. The experiments show that this approach gives excellent performance in comparison to another well-known dynamic stream weighting approach and also compared to any fixed-weighted integration approach in both clean conditions or when noise is added to either stream. Furthermore, our experiments show that the MWSP approach dynamically selects suitable integration weights on a frame-by-frame basis according to the level of noise in the streams and also according to the naturally fluctuating relative reliability of the modalities even in clean conditions. The MWSP approach is shown to maintain robust recognition performance in all tested conditions, while requiring no prior knowledge about the type or level of noise.

The lipopolysaccharide core of Brucella abortus acts as a shield against innate immunity recognition

Innate immunity recognizes bacterial molecules bearing pathogen-associated molecular patterns to launch inflammatory responses leading to the activation of adaptive immunity. However, the lipopolysaccharide (LPS) of the gram-negative bacterium Brucella lacks a marked pathogen-associated molecular pattern, and it has been postulated that this delays the development of immunity, creating a gap that is critical for the bacterium to reach the intracellular replicative niche. We found that a B. abortus mutant in the wadC gene displayed a disrupted LPS core while keeping both the LPS O-polysaccharide and lipid A. In mice, the wadC mutant induced proinflammatory responses and was attenuated. In addition, it was sensitive to killing by non-immune serum and bactericidal peptides and did not multiply in dendritic cells being targeted to lysosomal compartments. In contrast to wild type B. abortus, the wadC mutant induced dendritic cell maturation and secretion of pro-inflammatory cytokines. All these properties were reproduced by the wadC mutant purified LPS in a TLR4-dependent manner. Moreover, the core-mutated LPS displayed an increased binding to MD-2, the TLR4 co-receptor leading to subsequent increase in intracellular signaling. Here we show that Brucella escapes recognition in early stages of infection by expressing a shield against recognition by innate immunity in its LPS core and identify a novel virulence mechanism in intracellular pathogenic gram-negative bacteria. These results also encourage for an improvement in the generation of novel bacterial vaccines.

Common-sense knowledge for a computer vision system for human action recognition

This work presents a novel approach for human action recognition based on the combination of computer vision techniques and common-sense knowledge and reasoning capabilities. The emphasis of this work is on how common sense has to be leveraged to a vision-based human action recognition so that nonsensical errors can be amended at the understanding stage. The proposed framework is to be deployed in a realistic environment in which humans behave rationally, that is, motivated by an aim or a reason. © 2012 Springer-Verlag.

Common-sense reasoning for human action recognition

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm - known as bag of words - gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline. © 2012 Elsevier B.V. All rights reserved.