Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.

Stress correlation function evolution in lattice solid elasto-dynamic models of shear and fracture zones and earthquake prediction

It has been argued that power-law time-to-failure fits for cumulative Benioff strain and an evolution in size-frequency statistics in the lead-up to large earthquakes are evidence that the crust behaves as a Critical Point (CP) system. If so, intermediate-term earthquake prediction is possible. However, this hypothesis has not been proven. If the crust does behave as a CP system, stress correlation lengths should grow in the lead-up to large events through the action of small to moderate ruptures and drop sharply once a large event occurs. However this evolution in stress correlation lengths cannot be observed directly. Here we show, using the lattice solid model to describe discontinuous elasto-dynamic systems subjected to shear and compression, that it is for possible correlation lengths to exhibit CP-type evolution. In the case of a granular system subjected to shear, this evolution occurs in the lead-up to the largest event and is accompanied by an increasing rate of moderate-sized events and power-law acceleration of Benioff strain release. In the case of an intact sample system subjected to compression, the evolution occurs only after a mature fracture system has developed. The results support the existence of a physical mechanism for intermediate-term earthquake forecasting and suggest this mechanism is fault-system dependent. This offers an explanation of why accelerating Benioff strain release is not observed prior to all large earthquakes. The results prove the existence of an underlying evolution in discontinuous elasto-dynamic, systems which is capable of providing a basis for forecasting catastrophic failure and earthquakes.

Load-unload response ratio and accelerating moment/energy release critical region scaling and earthquake prediction

The main idea of the Load-Unload Response Ratio (LURR) is that when a system is stable, its response to loading corresponds to its response to unloading, whereas when the system is approaching an unstable state, the response to loading and unloading becomes quite different. High LURR values and observations of Accelerating Moment/Energy Release (AMR/AER) prior to large earthquakes have led different research groups to suggest intermediate-term earthquake prediction is possible and imply that the LURR and AMR/AER observations may have a similar physical origin. To study this possibility, we conducted a retrospective examination of several Australian and Chinese earthquakes with magnitudes ranging from 5.0 to 7.9, including Australia's deadly Newcastle earthquake and the devastating Tangshan earthquake. Both LURR values and best-fit power-law time-to-failure functions were computed using data within a range of distances from the epicenter. Like the best-fit power-law fits in AMR/AER, the LURR value was optimal using data within a certain epicentral distance implying a critical region for LURR. Furthermore, LURR critical region size scales with mainshock magnitude and is similar to the AMR/AER critical region size. These results suggest a common physical origin for both the AMR/AER and LURR observations. Further research may provide clues that yield an understanding of this mechanism and help lead to a solid foundation for intermediate-term earthquake prediction.

Prediction of mortality in patients without angina: Use of an exercise score and exercise echocardiography

Background Exercise testing has limited efficacy for identifying coronary artery disease (CAD) in the absence of anginal. symptoms. Exercise echocardiography is more accurate than standard exercise testing, but its efficacy in this situation has not been defined. We sought to identify whether the Duke treadmill. score or exercise echocardiography (ExE) could be used to identify risk in patients without anginal symptoms. Methods We studied 1859 patients without typical or atypical angina, heart failure, or a history or ECG evidence of infarction or CAD, who were referred for ExE, of whom 1832 (age 51 15 years, 944 men) were followed for up to 10 years. The presence and extent of ischaemia and scar were interpreted by expert reviewers at the time of the original study. Results Exercise provoked significant (>0.1 mV) ST segment depression in 215 patients (12%), and wall motion abnormalities in 137 (8%). Seventy-eight patients (4%) died before revascularization, only 17 from known cardiac causes. The independent predictors of death were age (RR 1.1, p

Reducing the time period of steady state does not affect the accuracy of energy expenditure measurements by indirect calorimetry

Achievement of steady state during indirect calorimetry measurements of resting energy expenditure (REE) is necessary to reduce error and ensure accuracy in the measurement. Steady state is often defined as 5 consecutive min (5-min SS) during which oxygen consumption and carbon dioxide production vary by +/-10%. These criteria, however, are stringent and often difficult to satisfy. This study aimed to assess whether reducing the time period for steady state (4-min SS or 3-min SS) produced measurements of REE that were significantly different from 5-min SS. REE was measured with the use of open-circuit indirect calorimetry in 39 subjects, of whom only 21 (54%) met the 5-min SS criteria. In these 21 subjects, median biases in REE between 5-min SS and 4-min SS and between 5-min SS and 3-min SS were 0.1 and 0.01%, respectively. For individuals, 4-min SS measured REE within a clinically acceptable range of +/-2% of 5-min SS, whereas 3-min SS measured REE within a range of -2-3% of 5-min SS. Harris-Benedict prediction equations estimated REE for individuals within +/-20-30% of 5-min SS. Reducing the time period of steady state to 4 min produced measurements of REE for individuals that were within clinically acceptable, predetermined limits. The limits of agreement for 3-min SS fell outside the predefined limits of +/-2%; however, both 4-min SS and 3-min SS criteria greatly increased the proportion of subjects who satisfied steady state within smaller limits than would be achieved if relying on prediction equations.

Outbound Travel Depends on Business Returns: Australian Evidence

In an earlier note, Collins and Tisdell (2002b) explored the possibility of a long-run relationship between Australian business returns and international business travel. Using annual data they found that such a relationship exists. The purpose of this study is to further examine this relationship using quarterly data for the time frame 1974:1 to 1999:4. In addition, previous studies on international business travel have offered some but not strong evidence for the existence of a positive relationship between the level of international business travel and real GDP of the origin country. This study suggests that the aggregate return on business investments is a better predictor of international business travel than GDP. The Engle-Granger and Johansen's maximum-likelihood cointegration procedures are used to show a long-term relationship exists between Australian outbound business travel and Australian business returns, but not with Real Australian GDP. Reasons for this relationship are discussed.

Prediction of the growth of wear-type rail corrugation

The growth behaviour of the vibrational wear phenomenon known as rail corrugation is investigated analytically and numerically using mathematical models. A simplified feedback model for wear-type rail corrugation that includes a wheel pass time delay is developed with an aim to analytically distil the most critical interaction occurring between the wheel/rail structural dynamics, rolling contact mechanics and rail wear. To this end, a stability analysis on the complete system is performed to determine the growth of wear-type rail corrugations over multiple wheelset passages. This analysis indicates that although the dynamical behaviour of the system is stable for each wheel passage, over multiple wheelset passages, the growth of wear-type corrugations is shown to be the result of instability due to feedback interaction between the three primary components of the model. The corrugations are shown analytically to grow for all realistic railway parameters. From this analysis an analytical expression for the exponential growth rate of corrugations in terms of known parameters is developed. This convenient expression is used to perform a sensitivity analysis to identify critical parameters that most affect corrugation growth. The analytical predictions are shown to compare well with results from a benchmarked time-domain finite element model. (C) 2004 Elsevier B.V. All rights reserved.

Statistical tests of load-unload response ratio signals by lattice solid model: Implication to tidal triggering and earthquake prediction

Statistical tests of Load-Unload Response Ratio (LURR) signals are carried in order to verify statistical robustness of the previous studies using the Lattice Solid Model (MORA et al., 2002b). In each case 24 groups of samples with the same macroscopic parameters (tidal perturbation amplitude A, period T and tectonic loading rate k) but different particle arrangements are employed. Results of uni-axial compression experiments show that before the normalized time of catastrophic failure, the ensemble average LURR value rises significantly, in agreement with the observations of high LURR prior to the large earthquakes. In shearing tests, two parameters are found to control the correlation between earthquake occurrence and tidal stress. One is, A/(kT) controlling the phase shift between the peak seismicity rate and the peak amplitude of the perturbation stress. With an increase of this parameter, the phase shift is found to decrease. Another parameter, AT/k, controls the height of the probability density function (Pdf) of modeled seismicity. As this parameter increases, the Pdf becomes sharper and narrower, indicating a strong triggering. Statistical studies of LURR signals in shearing tests also suggest that except in strong triggering cases, where LURR cannot be calculated due to poor data in unloading cycles, the larger events are more likely to occur in higher LURR periods than the smaller ones, supporting the LURR hypothesis.

A theory for execution-time derivation in real-time programs

We provide an abstract command language for real-time programs and outline how a partial correctness semantics can be used to compute execution times. The notions of a timed command, refinement of a timed command, the command traversal condition, and the worst-case and best-case execution time of a command are formally introduced and investigated with the help of an underlying weakest liberal precondition semantics. The central result is a theory for the computation of worst-case and best-case execution times from the underlying semantics based on supremum and infimum calculations. The framework is applied to the analysis of a message transmitter program and its implementation. (c) 2005 Elsevier B.V. All rights reserved.

A glass transition temperature approach for the prediction of the surface stickiness of a drying droplet during spray drying

The development of surface stickiness of droplets of sugar and acid-rich foods during spray drying can be explained using the notion of glass transition temperature (T-g). In this work, criteria for a safe drying regime have been developed and their physical basis provided. A dimensionless time (psi) is introduced as an indicator of spray dryability and it is correlated with the recovery of powders in practical spray drying. Droplets with initial diameters of 120 mum were subjected to simulated spray drying conditions and their safe drying regime and 41 values generated. The model predicted the recovery in a pilot scale spray dryer reasonably well. (C) 2004 Elsevier B.V. All rights reserved.

Prediction of slurry transport in SAG mills using SPH fluid flow in a dynamic DEM based porous media

DEM modelling of the motion of coarse fractions of the charge inside SAG mills has now been well established for more than a decade. In these models the effect of slurry has broadly been ignored due to its complexity. Smoothed particle hydrodynamics (SPH) provides a particle based method for modelling complex free surface fluid flows and is well suited to modelling fluid flow in mills. Previous modelling has demonstrated the powerful ability of SPH to capture dynamic fluid flow effects such as lifters crashing into slurry pools, fluid draining from lifters, flow through grates and pulp lifter discharge. However, all these examples were limited by the ability to model only the slurry in the mill without the charge. In this paper, we represent the charge as a dynamic porous media through which the SPH fluid is then able to flow. The porous media properties (specifically the spatial distribution of porosity and velocity) are predicted by time averaging the mill charge predicted using a large scale DEM model. This allows prediction of transient and steady state slurry distributions in the mill and allows its variation with operating parameters, slurry viscosity and slurry volume, to be explored. (C) 2006 Published by Elsevier Ltd.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Minimising cold damage during reproductive development among temperate rice genotypes. I. Avoiding low temperature with the use of appropriate sowing time and photoperiod-sensitive varieties

Multiple-sown field trials in 4 consecutive years in the Riverina region of south-eastern Australia provided 24 different combinations of temperature and day length, which enabled the development of crop phenology models. A crop model was developed for 7 cultivars from diverse origins to identify if photoperiod sensitivity is involved in determining phenological development, and if that is advantageous in avoiding low-temperature damage. Cultivars that were mildly photoperiod-sensitive were identified from sowing to flowering and from panicle initiation to flowering. The crop models were run for 47 years of temperature data to quantify the risk of encountering low temperature during the critical young microspore stage for 5 different sowing dates. Cultivars that were mildly photoperiod-sensitive, such as Amaroo, had a reduced likelihood of encountering low temperature for a wider range of sowing dates compared with photoperiod-insensitive cultivars. The benefits of increased photoperiod sensitivity include greater sowing flexibility and reduced water use as growth duration is shortened when sowing is delayed. Determining the optimal sowing date also requires other considerations, e. g. the risk of cold damage at other sensitive stages such as flowering and the response of yield to a delay in flowering under non-limiting conditions. It was concluded that appropriate sowing time and the use of photoperiod-sensitive cultivars can be advantageous in the Riverina region in avoiding low temperature damage during reproductive development.

Relations between physiologic parameters and pulse transit time during loaded breathing

Pulse transit time (PTT) is a non-invasive measure, defined as time taken for the pulse pressure waves to travel from the R-wave of electrocardiogram to a selected peripheral site. Baseline PTT value is known to be influenced by physiologic variables like heart rate (HR), blood pressure (BP) and arterial compliance (AC). However, few quantitative data are available describing the factors which can influence PTT measurements in a child during breathing. The aim of this study was to investigate the effects of changes in breathing efforts on PTT baseline and fluctuations. Two different inspiratory resistive loading (IRL) devices were used to simulate loaded breathing in order to induce these effects. It is known that HR can influence the normative PTT value however the effect of HR variability (HRV) is not well-studied. Two groups of 3 healthy children ( 0.05) HR changes during all test activities. Results showed that HRV is not the sole contributor to PTT variations and suggest that changes in other physiologic parameters are also equally important. Hence, monitoring PTT measurement can be indicative of these associated changes during tidal or increased breathing efforts in healthy children.

Time weight collaborative filtering

Collaborative filtering is regarded as one of the most promising recommendation algorithms. The item-based approaches for collaborative filtering identify the similarity between two items by comparing users' ratings on them. In these approaches, ratings produced at different times are weighted equally. That is to say, changes in user purchase interest are not taken into consideration. For example, an item that was rated recently by a user should have a bigger impact on the prediction of future user behaviour than an item that was rated a long time ago. In this paper, we present a novel algorithm to compute the time weights for different items in a manner that will assign a decreasing weight to old data. More specifically, the users' purchase habits vary. Even the same user has quite different attitudes towards different items. Our proposed algorithm uses clustering to discriminate between different kinds of items. To each item cluster, we trace each user's purchase interest change and introduce a personalized decay factor according to the user own purchase behaviour. Empirical studies have shown that our new algorithm substantially improves the precision of item-based collaborative filtering without introducing higher order computational complexity.