Sasaki-Einstein Manifolds and Volume Minimisation

We study a variational problem whose critical point determines the Reeb vector field for a Sasaki-Einstein manifold. This extends our previous work on Sasakian geometry by lifting the condition that the manifolds are toric. We show that the Einstein-Hilbert action, restricted to a space of Sasakian metrics on a link L in a Calabi-Yau cone X, is the volume functional, which in fact is a function on the space of Reeb vector fields. We relate this function both to the Duistermaat-Heckman formula and also to a limit of a certain equivariant index on X that counts holomorphic functions. Both formulae may be evaluated by localisation. This leads to a general formula for the volume function in terms of topological fixed point data. As a result we prove that the volume of a Sasaki-Einstein manifold, relative to that of the round sphere, is always an algebraic number. In complex dimension n=3 these results provide, via AdS/CFT, the geometric counterpart of a-maximisation in four dimensional superconformal field theories. We also show that our variational problem dynamically sets to zero the Futaki invariant of the transverse space, the latter being an obstruction to the existence of a Kahler-Einstein metric.Comment: 82 pages, 9 figures; homogeneity of the (n,0)-form now derived from the Einstein-Hilbert action of the link, example of an orbifold resolution added, various clarifications and references added; minor changes; published versio

Feasibility study in implementing shop floor management : a case study of a learning organisation

Bibliography: leaves 199-203.Most manufacturing companies in the world have solid benchmarking standards to compete against competitors all over the world. The number of tools that they possess to become World-Class Manufacturing companies is numerous. With the philosophies of Deming, Juran and Crosby on Quality and the invention of the different practical tools from the Japanese, the results should have been positive but the practical results have been poor. The next question that arises is what is so difficult in emulating the successes of these companies? The theories are the same but the implementation is different for each company. Therefore, the solution seems to be able to understand the philosophies so that modifications brought about are still within the limits of the company's core values. The case study that is being explored in this thesis will be a container tank manufacturing, a subsidiary of a bigger company, in their attempt to implement these famous concepts

Charm Production in Deep Inelastic Scattering from Threshold to High $Q^{2}

Charm final states in deep inelastic scattering constitute $\sim 25$ of the inclusive cross-section at small $x$ as measured at HERA. These data can reveal important information on the charm and gluon structure of the nucleon if they are interpreted in a consistent perturbative QCD framework which is valid over the entire energy range from threshold to the high energy limit. We describe in detail how this can be carried out order-by-order in PQCD in the generalized \msbar formalism of Collins (generally known as the ACOT approach), and demonstrate the inherent smooth transition from the 3-flavor to the 4-flavor scheme in a complete order $\alpha_s$ calculation, using a Monte Carlo implementation of this formalism. This calculation is accurate to the same order as the conventional NLO $F_2$ calculation in the limit $\frac{Q}{m_c} >> 1$. It includes the resummed large logarithm contributions of the 3-flavor scheme (generally known in this context as the fixed-flavor-number or FFN scheme) to all orders of $\alpha_s\ln(m_c^2/Q^2)$. For the inclusive structure function, comparison with recent HERA data and the existing FFN calculation reveals that the relatively simple order-$\alpha_s$ (NLO) 4-flavor ($m_c \neq 0$) calculation can, in practice, be extended to rather low energy scales, yielding good agreement with data over the full measured $Q^2$ range. The Monte Carlo implementation also allows the calculation of differential distributions with relevant kinematic cuts. Comparisons with available HERA data show qualitative agreement; however, they also indicate the need to extend the calculation to the next order to obtain better description of the differential distributions.Comment: 22 pages (LATEX), 8 figures (EPS); A few clarifying changes made; version published in JHE

Radon-Gabor Barcodes for Medical Image Retrieval

In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play a key role in describing the content of an image. Radon and Gabor transforms are both powerful techniques that have been widely studied to extract shape-texture-based information. The combined Radon-Gabor features may be more robust against scale/rotation variations, presence of noise, and illumination changes. The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes (GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we employ the IRMA x-ray dataset with 193 classes, containing 12,677 training images and 1,733 test images. A total error score as low as 322 and 330 were achieved for GRGBCs and GRIBCs, respectively. This corresponds to $\approx 81\%$ retrieval accuracy for the first hit.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

System Identification of Motion Artifact: Noise in EEG Headsets from Locomotion

Fall prevention for geriatric populations is a growing concern among clinicians and researchers due to severe risk of morbidity and loss of independence. Emerging evidence has demonstrated that mental workload while walking influences gait stability and the risk for falling. Electroencephalography (EEG) presents a potential method to provide objective measures of mental workload, particularly during daily activities. Noise introduced to the EEG signal during motion, however, is restrictive. The study presented in the following paper isolates EEG signal noise attained from gait for a commercially accessible EEG system, the Emotiv Time and spectral system identification techniques were applied to model motion-induced artifacts given head linear and angular movement data. During gait of varying speeds (1.4-5.8 km/h), frequency and time domain system identification techniques were unable to accurately model the relationship between head movement and EEG signal noise with accuracies between 1% and 11% fit. However, analysis of data obtained during activities eliciting higher amplitudes of head movement (i.e., double-footed jumping) resulted in a high accuracy in linear system modelling, ranging from 68% to 74% suggesting a dead-zone non linearity. Isolated EEG noise signals provide a ground truth measurement of the Emotiv system to estimate signal to noise ratio (SNR). Results of the SNR determined that, during gait, EEG signals are 8 to 20 times the power of gait-induced noise providing confidence in EEG recordings during ambulatory monitoring

Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these image features are only sampled randomly in the images, without considering the spatial structures or geometric information, leading to large errors or failure cases with the existence of poorly textured areas or in motion blur. Line segment features are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Bacterial Foodborne Disease: Medical Costs and Productivity Losses

Microbial pathogens in food cause an estimated 6.5-33 million cases of human illness and up to 9,000 deaths in the United States each year. Over 40 different foodborne microbial pathogens, including fungi, viruses, parasites, and bacteria, are believed to cause human illnesses. For six bacterial pathogens, the costs of human illness are estimated to be $9.3-$12.9 billion annually. Of these costs, $2.9-$6.7 billion are attributed to foodborne bacteria. These estimates were developed to provide analytical support for USDA's Hazard Analysis and Critical Control Point (HACCP) systems rule for meat and poultry. (Note that the parasite Toxoplasma gondii is not included in this report.) To estimate medical costs and productivity losses, ERS uses four severity categories for acute illnesses: those who did not visit a physician, visited a physician, were hospitalized, or died prematurely. The lifetime consequences of chronic disease are included in the cost estimates for E. coli O157:H7 and fetal listeriosis.cost-of-illness, foodborne pathogens, lost productivity, medical costs, Food Consumption/Nutrition/Food Safety, Health Economics and Policy,