Exploiting medical hierarchies for concept-based information retrieval

Search technologies are critical to enable clinical sta to rapidly and e ectively access patient information contained in free-text medical records. Medical search is challenging as terms in the query are often general but those in rel- evant documents are very speci c, leading to granularity mismatch. In this paper we propose to tackle granularity mismatch by exploiting subsumption relationships de ned in formal medical domain knowledge resources. In symbolic reasoning, a subsumption (or `is-a') relationship is a parent-child rela- tionship where one concept is a subset of another concept. Subsumed concepts are included in the retrieval function. In addition, we investigate a number of initial methods for combining weights of query concepts and those of subsumed concepts. Subsumption relationships were found to provide strong indication of relevant information; their inclusion in retrieval functions yields performance improvements. This result motivates the development of formal models of rela- tionships between medical concepts for retrieval purposes.

Finding good differential patterns for attacks on SHA-1

In this paper we analyse properties of the message expansion algorithm of SHA-1 and describe a method of finding differential patterns that may be used to attack reduced versions of SHA-1. We show that the problem of finding optimal differential patterns for SHA-1 is equivalent to the problem of finding minimal weight codeword in a large linear code. Finally, we present a number of patterns of different lengths suitable for finding collisions and near-collisions and discuss some bounds on minimal weights of them.

Nutrient uptake in tropical turfgrasses growing in winter in southern Queensland

The effects of fertilisers on 8 tropical turfgrasses growing in 100-L bags of sand were studied over winter in Murrumba Downs, just north of Brisbane in southern Queensland (latitude 27.4°S, longitude 153.1°E). The species used were: Axonopus compressus (broad-leaf carpetgrass), Cynodon dactylon (bermudagrass 'Winter Green') and C. dactylon x C. transvaalensis hybrid ('Tifgreen'), Digitaria didactyla (Queensland blue couch), Paspalum notatum (bahiagrass '38824'), Stenotaphrum secundatum (buffalograss 'Palmetto'), Eremochloa ophiuroides (centipedegrass 'Centec') and Zoysia japonica (zoysiagrass 'ZT-11'). Control plots were fertilised with complete fertilisers every month from May to September (72 kg N/ha, 31 kg P/ha, 84 kg K/ha, 48 kg S/ha, 30 kg Ca/ha and 7.2 kg Mg/ha), and unfertilised plots received no fertiliser. Carpetgrass and standard bermudagrass were the most sensitive species to nutrient supply, with lower shoot dry weights in the unfertilised plots (shoots mowed to thatch level) compared with the fertilised plots in June. There were lower shoot dry weights in the unfertilised plots in July for all species, except for buffalograss, centipedegrass and zoysiagrass, and lower shoot dry weights in the unfertilised plots in August for all species, except for centipedegrass. At the end of the experiment in September, unfertilised plots were 11% of the shoot dry weights of fertilised plots, with all species affected. Mean shoot nitrogen concentrations fell from 3.2 to 1.7% in the unfertilised plots from May to August, below the sufficiency range for turfgrasses (2.8-3.5%). There were also declines in P (0.45-0.36%), K (2.4-1.5%), S (0.35-0.25%), Mg (0.24-0.18%) and B (9-6 mg/kg), which were all in the sufficiency range. The shoots in the control plots took up the following levels (kg/ha.month) of nutrients: N, 10.0-27.0; P, 1.6-4.0; K, 8.2-19.8; S, 1.0-4.2; Ca, 1.1-3.3; and Mg, 0.8-2.2, compared with applications (kg/ha.month) of: N, 72; P, 31; K, 84; S, 48; Ca, 30; and Mg, 7.2, indicating a recovery of 14-38% for N, 5-13% for P, 10-24% for K, 2-9% for S, 4-11% for Ca and 11-30% for Mg. These results suggest that buffalograss, centipedegrass and zoysiagrass are less sensitive to low nutrient supply than carpetgrass, bermudagrass, blue couch and bahiagrass. Data on nutrient uptake showed that the less sensitive species required only half or less of the nitrogen required to maintain the growth of the other grasses, indicating potential savings for turf managers in fertiliser costs and the environment in terms of nutrients entering waterways.

Concentrates based on sorghum grain provide a basis for a finishing system for crossbred lambs

In parts of Australia, sorghum grain is a cheaper alternative to other cereal grains but its use and nutritive value in sheep feeding systems is not well understood. The aim of this work was to compare growth and carcass characteristics for crossbred lambs consuming several simple, sorghum-based diets. The treatments were: (1) whole sorghum grain, (2) whole sorghum grain + urea and ammonium sulfate, (3) cracked sorghum grain + urea and ammonium sulfate, (4) expanded sorghum grain + urea and ammonium sulfate, (5) whole sorghum grain + cottonseed meal, and (6) whole sorghum grain + whole cottonseed. Nine lambs were slaughtered initially to provide baseline carcass data and the remaining 339 lambs were gradually introduced to the concentrate diets over 14 days before being fed concentrates and wheaten hay ad libitum for 41, 56 or 76 days. Neither cracking nor expanding whole sorghum grain with added non-protein nitrogen (N) resulted in significantly (P > 0.05) increased final liveweight, growth rates or carcass weights for lambs, or in decreased days on feed to reach 18-kg carcass weight, although carcass fat depth was significantly (P < 0.05) increased compared with the whole sorghum plus non-protein N diet. However, expanding sorghum grain significantly (P < 0.05) reduced faecal starch concentrations compared with whole or cracked sorghum diets with added non-protein N (79 v. 189 g/kg DM after 59 days on feed). Lambs fed whole sorghum grain without an additional N source had significantly (P < 0.05) lower concentrate intake and required significantly (P < 0.05) more days on feed to reach a carcass weight of 18 kg than for all diets containing added N. These lambs also had significantly (P < 0.05) lower carcass weight and fat depth than for lambs consuming whole sorghum plus true protein diets. Substituting sources of true protein (cottonseed meal and whole cottonseed) for non-protein N (urea and ammonium sulfate) did not significantly (P > 0.05) affect concentrate intakes or carcass weights of lambs although carcass fat depth was significantly (P < 0.05) increased and the days to reach 18-kg carcass weight were significantly (P < 0.05) decreased for the whole sorghum plus cottonseed meal diet. In conclusion, processing sorghum grain by cracking or expanding did not significantly improve lamb performance. While providing an additional N source with sorghum grain significantly increased lamb performance, there was no benefit in final carcass weight of lambs from substituting sources of true protein for non-protein N.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Improvement in Intrusion Detection With Advances in Sensor Fusion

Various intrusion detection systems (IDSs) reported in the literature have shown distinct preferences for detecting a certain class of attack with improved accuracy, while performing moderately on the other classes. In view of the enormous computing power available in the present-day processors, deploying multiple IDSs in the same network to obtain best-of-breed solutions has been attempted earlier. The paper presented here addresses the problem of optimizing the performance of IDSs using sensor fusion with multiple sensors. The trade-off between the detection rate and false alarms with multiple sensors is highlighted. It is illustrated that the performance of the detector is better when the fusion threshold is determined according to the Chebyshev inequality. In the proposed data-dependent decision ( DD) fusion method, the performance optimization of ndividual IDSs is first addressed. A neural network supervised learner has been designed to determine the weights of individual IDSs depending on their reliability in detecting a certain attack. The final stage of this DD fusion architecture is a sensor fusion unit which does the weighted aggregation in order to make an appropriate decision. This paper theoretically models the fusion of IDSs for the purpose of demonstrating the improvement in performance, supplemented with the empirical evaluation.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Some experiments on human memory and a new neural model

An associative memory with parallel architecture is presented. The neurons are modelled by perceptrons having only binary, rather than continuous valued input. To store m elements each having n features, m neurons each with n connections are needed. The n features are coded as an n-bit binary vector. The weights of the n connections that store the n features of an element has only two values -1 and 1 corresponding to the absence or presence of a feature. This makes the learning very simple and straightforward. For an input corrupted by binary noise, the associative memory indicates the element that is closest (in terms of Hamming distance) to the noisy input. In the case where the noisy input is equidistant from two or more stored vectors, the associative memory indicates two or more elements simultaneously. From some simple experiments performed on the human memory and also on the associative memory, it can be concluded that the associative memory presented in this paper is in some respect more akin to a human memory than a Hopfield model.

Efficient external memory structures for range-aggregate queries

We present external memory data structures for efficiently answering range-aggregate queries. The range-aggregate problem is defined as follows: Given a set of weighted points in R-d, compute the aggregate of the weights of the points that lie inside a d-dimensional orthogonal query rectangle. The aggregates we consider in this paper include COUNT, sum, and MAX. First, we develop a structure for answering two-dimensional range-COUNT queries that uses O(N/B) disk blocks and answers a query in O(log(B) N) I/Os, where N is the number of input points and B is the disk block size. The structure can be extended to obtain a near-linear-size structure for answering range-sum queries using O(log(B) N) I/Os, and a linear-size structure for answering range-MAX queries in O(log(B)(2) N) I/Os. Our structures can be made dynamic and extended to higher dimensions. (C) 2012 Elsevier B.V. All rights reserved.

Local disorder investigation in NiS2-xSex using Raman and Ni K-edge x-ray absorption spectroscopies

We report on Raman and Ni K-edge x-ray absorption investigations of a NiS2-xSex (with x = 0.00, 0.50/0.55, 0.60, and 1.20) pyrite family. The Ni K-edge absorption edge shows a systematic shift going from an insulating phase (x = 0.00 and 0.50) to a metallic phase (x = 0.60 and 1.20). The near-edge absorption features show a clear evolution with Se doping. The extended x-ray absorption fine structure data reveal the evolution of the local structure with Se doping which mainly governs the local disorder. We also describe the decomposition of the NiS2-xSex Raman spectra and investigate the weights of various phonon modes using Gaussian and Lorentzian profiles. The effectiveness of the fitting models in describing the data is evaluated by means of Bayes factor estimation. The Raman analysis clearly demonstrates the disorder effects due to Se alloying in describing the phonon spectra of NiS2-xSex pyrites.

Codes with Locality for Two Erasures

In this paper, we study codes with locality that can recover from two erasures via a sequence of two local, parity-check computations. By a local parity-check computation, we mean recovery via a single parity-check equation associated with small Hamming weight. Earlier approaches considered recovery in parallel; the sequential approach allows us to potentially construct codes with improved minimum distance. These codes, which we refer to as locally 2-reconstructible codes, are a natural generalization along one direction, of codes with all-symbol locality introduced by Gopalan et al, in which recovery from a single erasure is considered. By studying the generalized Hamming weights of the dual code, we derive upper bounds on the minimum distance of locally 2-reconstructible codes and provide constructions for a family of codes based on Turan graphs, that are optimal with respect to this bound. The minimum distance bound derived here is universal in the sense that no code which permits all-symbol local recovery from 2 erasures can have larger minimum distance regardless of approach adopted. Our approach also leads to a new bound on the minimum distance of codes with all-symbol locality for the single-erasure case.

Improved Analysis Method for Wave-Induced Pipeline Stability on Sandy Seabed

The existing Det Norske Veritas DNV Recommended Practice RP E305 for pipeline on-bottom stability is mainly based on the pipe–soil interaction model reported by Wagner et al. in 1987, and the wake model reported by Lambrakos et al. in 1987, to calculate the soil resistance and the hydrodynamic forces upon pipeline, respectively. Unlike the methods in the DNV Practice, in this paper, an improved analysis method is proposed for the on-bottom stability of a submarine pipeline, which is based on the relationships between Um/ gD 0.5 and Ws / D2 for various restraint conditions obtained by the hydrodynamic loading experiments, taking into account the coupling effects between wave, pipeline, and sandy seabed. The analysis procedure is illustrated with a detailed flow chart. A comparison is made between the submerged weights of pipeline predicted with the DNV Practice and those with the new method. The proposed analysis method may provide a helpful tool for the engineering practice of pipeline on-bottom stability design.

Length-frequency estimation for yellowfin tuna (Thunnus albacares) caught by commercial fishing gear in the Eastern Pacific Ocean

ENGLISH: Methods of collecting samples for the purpose of estimating the numbers and weights of fish caught, by length interval, are described. Several models for two-stage sampling are described, and the equations for the estimators and their variances are given. The results from a brief simulation study are used to show the differences between estimates made with the different models. Estimators for the average weights of fish in the catch and their variances are also described. These average weights are used to provide improved estimates of the total annual catches of yellowfin taken from the eastern Pacific Ocean, east of 150°W, between 1955 and 1990. SPANISH: Se describen los métodos de recoger de muestreo para estimar el número o peso de peces capturados, por intervalo de talla. Se describen varios modelos para el muestreo de dos etapas, y se presentan las ecuaciones para los estimadores y sus varianzas. Se usan los resultados de un breve estudio de simulación para indicar las diferencias entre estimaciones realizadas con los distintosmodelos. También se describe un estimador para el peso promedio de peces en la captura y su varianza. Se usan estos estimadores para calcular estimaciones mejoradas de las capturas anuales totales de aleta amarilla tomadas del Océano Pacífico oriental, al este de 150°W, entre 1955 y 1990. (PDF contains 41 pages.)

An arithmetical theorem for partially ordered sets

The simplest multiplicative systems in which arithmetical ideas can be defined are semigroups. For such systems irreducible (prime) elements can be introduced and conditions under which the fundamental theorem of arithmetic holds have been investigated (Clifford (3)). After identifying associates, the elements of the semigroup form a partially ordered set with respect to the ordinary division relation. This suggests the possibility of an analogous arithmetical result for abstract partially ordered sets. Although nothing corresponding to product exists in a partially ordered set, there is a notion similar to g.c.d. This is the meet operation, defined as greatest lower bound. Thus irreducible elements, namely those elements not expressible as meets of proper divisors can be introduced. The assumption of the ascending chain condition then implies that each element is representable as a reduced meet of irreducibles. The central problem of this thesis is to determine conditions on the structure of the partially ordered set in order that each element have a unique such representation.

Part I contains preliminary results and introduces the principal tools of the investigation. In the second part, basic properties of the lattice of ideals and the connection between its structure and the irreducible decompositions of elements are developed. The proofs of these results are identical with the corresponding ones for the lattice case (Dilworth (2)). The last part contains those results whose proofs are peculiar to partially ordered sets and also contains the proof of the main theorem.