Learning in ultrametric committee machines

The problem of learning by examples in ultrametric committee machines (UCMs) is studied within the framework of statistical mechanics. Using the replica formalism we calculate the average generalization error in UCMs with L hidden layers and for a large enough number of units. In most of the regimes studied we find that the generalization error, as a function of the number of examples presented, develops a discontinuous drop at a critical value of the load parameter. We also find that when L>1 a number of teacher networks with the same number of hidden layers and different overlaps induce learning processes with the same critical points.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Steps and bumps:precision extraction of discrete states of molecular machines

We report statistical time-series analysis tools providing improvements in the rapid, precision extraction of discrete state dynamics from time traces of experimental observations of molecular machines. By building physical knowledge and statistical innovations into analysis tools, we provide techniques for estimating discrete state transitions buried in highly correlated molecular noise. We demonstrate the effectiveness of our approach on simulated and real examples of steplike rotation of the bacterial flagellar motor and the F1-ATPase enzyme. We show that our method can clearly identify molecular steps, periodicities and cascaded processes that are too weak for existing algorithms to detect, and can do so much faster than existing algorithms. Our techniques represent a step in the direction toward automated analysis of high-sample-rate, molecular-machine dynamics. Modular, open-source software that implements these techniques is provided.

The effect of flying and low humidity on the admittance of the tympanic membrane and middle ear system

Many passengers experience discomfort during flight because of the effect of low humidity on the skin, eyes, throat, and nose. In this physiological study, we have investigated whether flight and low humidity also affect the tympanic membrane. From previous studies, a decrease in admittance of the tympanic membrane through drying might be expected to affect the buffering capacity of the middle ear and to disrupt automatic pressure regulation. This investigation involved an observational study onboard an aircraft combined with experiments in an environmental chamber, where the humidity could be controlled but could not be made to be as low as during flight. For the flight study, there was a linear relationship between the peak compensated static admittance of the tympanic membrane and relative humidity with a constant of proportionality of 0.00315 mmho/% relative humidity. The low humidity at cruise altitude (minimum 22.7 %) was associated with a mean decrease in admittance of about 20 % compared with measures in the airport. From the chamber study, we further found that a mean decrease in relative humidity of 23.4 % led to a significant decrease in mean admittance by 0.11 mmho [F(1,8) = 18.95, P = 0.002], a decrease of 9.4 %. The order of magnitude for the effect of humidity was similar for the flight and environmental chamber studies. We conclude that admittance changes during flight were likely to have been caused by the low humidity in the aircraft cabin and that these changes may affect the automatic pressure regulation of the middle ear during descent. © 2013 Association for Research in Otolaryngology.

The characterisation and emulation of rotary mechanical machines

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A study of stray load losses in core clamp plates of salient pole synchronous machines

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Analogue computation of the impedence of a powered flying control system

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The use of electronic techniques in the construction of machines which can learn

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Storage capacity of ultrametric committee machines

The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inputs, and K units in the first hidden layer, is analyzed using techniques from statistical mechanics. We found that the storage capacity strongly depends on the network architecture αc ∼ (log K)1-1/2L and that the number of units K limits the number of possible hidden layers L through the relationship 2L - 1 < 2log K. © 2014 IOP Publishing Ltd.

Isolation and characterisation of bacteria associated with flying insects in hospitals, with particualr emphasis on Clostridum difficile

Clostridium difficile is a bacterial healthcare-associated infection, which houseflies Musca domestica may transfer due to their synanthropic nature. The aims of this thesis were to determine the ability of M. domestica to transfer C. difficile mechanically and to collect and identify flying insects in UK hospitals and classify any associated bacteria. M. domestica exposed to independent suspensions of vegetative cells and spores of C. difficile were able to mechanically transfer the bacteria on to agar for up to 4 hours following exposure. C. difficile could be recovered from fly excreta for 96hrs and was isolated from the M. domestica alimentary canal. Also confirmed was the carriage of C. difficile by M. domestica larvae, although it was not retained in the pupae or in the adults that subsequently developed. Flying insects were collected from ultra-violet light flytraps in hospitals. Flies (order Diptera) were the most commonly identified. Chironomidae were the most common flies, Calliphora vicina were the most common synanthropic fly and ‘drain flies’ were surprisingly numerous and represent an emerging problem in hospitals. External washings and macerates of flying insects were prepared and inoculated onto a variety of agars and following incubation bacterial colonies identified by biochemical tests. A variety of flying insects, including synanthropic flies (e.g. M. domestica and C. vicina) collected from UK hospitals harboured pathogenic bacteria of different species. Enterobacteriaceae were the group of bacteria most commonly isolated, followed by Bacillus spp, Staphylococci, Clostridia, Streptococci and Micrococcus spp. This study highlights the potential for M. domestica to contribute to environmental persistence and spread of C. difficile in hospitals. Also illustrated is the potential for flying insects to contribute to environmental persistence and spread of other pathogenic bacteria in hospitals and therefore the need to implement pest control as part of infection control strategies.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

An exploration into measurement consistency on coordinate measuring machines

In high precision industry, the measurement of geometry is often performed using coordinate measuring machines (CMMs). Measurements on CMMs can occur at many places within a long and global supply chain. In this context it is a challenge to control consistency, so that measurements are applied with appropriate levels of rigour and achieve comparable results, wherever and whenever they are performed. In this paper, a framework is outlined in which consistency is controlled through measurement strategy, such as the number and location of measurement points. The framework is put to action in a case study, demonstrating the usefulness of the approach and highlighting the dangers of imposing rigid measurement strategies across the supply chain, even if linked to standardised manufacturing processes. Potential mitigations, and the requirements for future research, are outlined.

Measurement and compensation of pivot point positional error on five-axis machines

Measuring and compensating the pivot points of five-axis machine tools is always challenging and very time consuming. This paper presents a newly developed approach for automatic measurement and compensation of pivot point positional errors on five-axis machine tools. Machine rotary axis errors are measured using a circular test. This method has been tested on five-axis machine tools with swivel table configuration. Results show that up to 99% of the positional errors of the rotary axis can be compensated by using this approach.