Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

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.

Laughter Induction Techniques Suitable for Generating Motion Capture Data of Laughter Associated Body Movements

Laughter is a frequently occurring social signal and an important part of human non-verbal communication. However it is often overlooked as a serious topic of scientific study. While the lack of research in this area is mostly due to laughter’s non-serious nature, it is also a particularly difficult social signal to produce on demand in a convincing manner; thus making it a difficult topic for study in laboratory settings. In this paper we provide some techniques and guidance for inducing both hilarious laughter and conversational laughter. These techniques were devised with the goal of capturing mo- tion information related to laughter while the person laughing was either standing or seated. Comments on the value of each of the techniques and general guidance as to the importance of atmosphere, environment and social setting are provided.

Comparison of 5% versus 15% sucrose intakes as part of a eucaloric diet in overweight and obese subjects:effects on insulin sensitivity, glucose metabolism, vascular compliance, body composition and lipid profile. A randomised controlled trial

AIMS: The effect of dietary sucrose on insulin resistance and the pathogenesis of diabetes and vascular disease is unclear. We assessed the effect of 5% versus 15% sucrose intakes as part of a weight maintaining, eucaloric diet in overweight/obese subjects.

METHODS: Thirteen subjects took part in a randomised controlled crossover study (M:F 9:4, median age 46 years, range 37-56 years, BMI 31.7±0.9 kg/m(2)). Subjects completed two 6 week dietary periods separated by 4 week washout. Diets were designed to have identical macronutrient profile. Insulin action was assessed using a two-step hyperinsulinaemic euglycaemic clamp; glucose tolerance, vascular compliance, body composition and lipid profiles were also assessed.

RESULTS: There was no change in weight or body composition between diets. There was no difference in peripheral glucose utilization or suppression of endogenous glucose production. Fasting glucose was significantly lower after the 5% diet. There was no demonstrated effect on lipid profiles, blood pressure or vascular compliance.

CONCLUSION: A low-sucrose diet had no beneficial effect on insulin resistance as measured by the euglycaemic glucose clamp. However, reductions in fasting glucose, one hour insulin and insulin area under the curve with the low sucrose diet on glucose tolerance testing may indicate a beneficial effect and further work is required to determine if this is the case. Clinical Trial Registration number ISRCTN50808730.

Laughter Type Recognition from Whole Body Motion

Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.

Goal Recognition through Goal Graph Analysis

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

Robust Speaker Recognition in Noisy Conditions

This paper investigates the problem of speaker identi-fication and verification in noisy conditions, assuming that speechsignals are corrupted by environmental noise, but knowledgeabout the noise characteristics is not available. This research ismotivated in part by the potential application of speaker recog-nition technologies on handheld devices or the Internet. Whilethe technologies promise an additional biometric layer of securityto protect the user, the practical implementation of such systemsfaces many challenges. One of these is environmental noise. Due tothe mobile nature of such systems, the noise sources can be highlytime-varying and potentially unknown. This raises the require-ment for noise robustness in the absence of information about thenoise. This paper describes a method that combines multicondi-tion model training and missing-feature theory to model noisewith unknown temporal-spectral characteristics. Multiconditiontraining is conducted using simulated noisy data with limitednoise variation, providing a “coarse” compensation for the noise,and missing-feature theory is applied to refine the compensationby ignoring noise variation outside the given training conditions,thereby reducing the training and testing mismatch. This paperis focused on several issues relating to the implementation of thenew model for real-world applications. These include the gener-ation of multicondition training data to model noisy speech, thecombination of different training data to optimize the recognitionperformance, and the reduction of the model’s complexity. Thenew algorithm was tested using two databases with simulated andrealistic noisy speech data. The first database is a redevelopmentof the TIMIT database by rerecording the data in the presence ofvarious noise types, used to test the model for speaker identifica-tion with a focus on the varieties of noise. The second database isa handheld-device database collected in realistic noisy conditions,used to further validate the model for real-world speaker verifica-tion. The new model is compared to baseline systems and is foundto achieve lower error rates.

The effects of season and dietary salt content on body temperature daily rhythms of common spiny mice from different micro-habitats

We compared body temperature (T-b) daily rhythms in two populations of common spiny mice, Acomys cahirinus, during summer and winter months in relation to increasing dietary salt content. Mice were collected from the North and South facing slopes (NFS and SFS) of the same valley, that are exhibiting mesic and xeric habitats, respectively. During the summer, whilst mice were offered a water source containing 0.9% NaCl, SFS individuals had T-b peak values at 24:00, whereas NFS individuals had peak values at 18:00. When the salinity of the water source was increased, from 0.9 to 2.5% and then 3.5%, the difference between maximal and minimal T-b of both populations increased. In addition, with increased salinity, the T-b daily peak of SFS mice shifted to 18:00. During the winter, the mean daily T-b values of both populations of mice were lower than during the summer. At 0.9% salinity, the NFS mice exhibited a daily T-b variation with a peak at the beginning of the night. However, we did not detect any significant variation in daily T-b in the SFS mice. At 2.5% salinity, the difference between the mean daily T-b of mice from the two slopes increased. In winter we were unable to increase the salinity to 3.5% as the animals began to lose weight rapidly. We suggest that common spiny mice that inhabit these two micro-habitats axe forming two discrete populations that respond differently to the environmental pressures prevailing in each habitat, by evolving different physiological capacities. (C) 2002 Elsevier Science Inc. All rights reserved.

A randomized controlled trial of tea tree oil (5%) body wash versus standard body wash to prevent colonization with methicillin-resistant Staphylococcus aureus (MRSA) in critically ill adults: research protocol

Background
Over the past ten years MRSA has become endemic in hospitals and is associated with increased healthcare costs. Critically ill patients are most at risk, in part because of the number of invasive therapies that they require in the intensive care unit (ICU). Washing with 5% tea tree oil (TTO) has been shown to be effective in removing MRSA on the skin. However, to date, no trials have evaluated the potential of TTO body wash to prevent MRSA colonization or infection. In addition, detecting MRSA by usual culture methods is slow. A faster method using a PCR assay has been developed in the laboratory, but requires evaluation in a large number of patients.

Methods/Design
This study protocol describes the design of a multicentre, phase II/III prospective open-label randomized controlled clinical trial to evaluate whether a concentration of 5% TTO is effective in preventing MRSA colonization in comparison with a standard body wash (Johnsons Baby Softwash) in the ICU. In addition we will evaluate the cost-effectiveness of TTO body wash and assess the effectiveness of the PCR assay in detecting MRSA in critically ill patients. On admission to intensive care, swabs from the nose and groin will be taken to screen for MRSA as per current practice. Patients will be randomly assigned to be washed with the standard body wash or TTO body wash. On discharge from the unit, swabs will be taken again to identify whether there is a difference in MRSA colonization between the two groups.

Discussion
If TTO body wash is found to be effective, widespread implementation of such a simple colonization prevention tool has the potential to impact on patient outcomes, healthcare resource use and patient confidence both nationally and internationally.

Trial Registration
[ISRCTN65190967]