A note on calculating autocovariances of long-memory processes

In this paper, we consider a method (splitting) for calculating the auto- covariances of fractional integrated processes (ARFIMA) and generalized integrated processes (GARMA). The splitting method does not require any restriction on the autoregressive roots, and allows fast calculation of the autocovariances of these processes.     

TYPOLOGY OF SPACES AND TOPOLOGY OF TOLERATION: CITY,PLURALISM, OWNERSHIP

The aim of this paper is primarily to lay down a convincing typology of spaces in connection with property rights and management. Instead of the two main ownership models (private and public property) usually pinpointed, we argue that it is more fitting to speak of a plurality of property regimes. So, in this work, we identify and explain six sub‐categories: i) stricto sensu public spaces; ii) special public spaces; iii) privately run public spaces; iv) privately owned collective spaces; v) complex private spaces; vi) simple private spaces. This typology of spaces aims to draw attention to the fact that the diversity of spaces affects the type of tolerance in place: when we think about tolerance, it's important to think in a spatialized way, referring to these different property regimes.     

Optimal design of PID regulators

Tuning of three-term controllers, a widely popular device for process control, has always been accomplished by semi-empirical rules. The aim of this paper is to establish a link between PID regulators and optimal feedback design for linear systems. After showing how the optimal control theory can be tailored to the design of optimal regulators, the results are extended to the discrete-time case and the feasibility of a microprocessor implementation is assessed with the aid of benchmark tests     

THE USE OF RHEOLOGY TO CHARACTERIZE FLOW BEHAVIOR OF LIQUORICE SOLUTIONS

The rheological behavior of liquorice solutions at different concentrations (ranging between 30 and 60 Brix) and temperatures (in the range between 25 and 80C) has been investigated performing both dynamic tests in the linear viscoelastic region and steady shear tests, in 0.1–100 s^?1 shear rate range, by using a stress controlled rheometer (DSR 200, Rheometric Scientific, USA) and parallel plate geometry (φ = 40 mm). Small amplitude oscillations has shown the presence of both moduli also at the lowest tested concentration, with G′ greater than G″, except for the 30 Brix sample. It has been found that experimental viscosity data show a good agreement with a power law model, thus allowing the estimation of both the friction factor and the pressure drops in a typical pipeline used for liquorice solution conveying. It has been observed that the assumption of Newtonian behavior can lead to the erroneous design of the apparatuses that have to process liquorice extracts.     

Speaker Normalization and Model Selection of Combined Neural Networks

This paper introduces bootstrap error estimation for automatic tuning of parameters in combined networks, applied as front-end preprocessors for a speech recognition system based on hidden Markov models. The method is evaluated on a large-vocabulary (10 000 words) continuous speech recognition task. Bootstrap estimates of minimum mean squared error allow selection of speaker normalization models improving recognition performance. The procedure allows a flexible strategy for dealing with inter-speaker variability without requiring an additional validation set. Recognition results are compared for linear, generalized radial basis functions and multi-layer perceptron network architectures.     

Growth, photosynthesis and cropping of potted grapevines (Vitis vinifera L. cv. Cabernet Sauvignon) in relation to shoot trimming

The present study was conducted to evaluate how changes in leaf area affected vine growth, yield and grape quality of five‐year‐old potted Cabernet Sauvignon grapevines. At the beginning of flowering vines were randomly assigned to the following five treatments: untrimmed (control); shoot trimmed at either node 12 (T12) or node 6 (T6) with laterals either retained (R) or excised (X). Those four manipulations are abbreviated to: T12LR, T12LX, T6LR and T6LX, respectively. Total leaf area per vine was significantly lowered in T12LX and T6LX as compared to the other treatments, whereas lateral formation was able to offset foliage loss due to trimming in T12LR and T6LR with respect to control vines. T6LR also showed a more prolonged lateral node production. Yield per vine and its components did not differ significantly among treatments except for berry size, which was reduced in T6LR and T6LX. Grape ripening was severely retarded in T6LX, as shown by lower Brix, pH, colour and phenolics, and higher TA, tartrate and malate. A maturity delay was also shown in T6LR as lower soluble solids and total anthocyanins per berry in comparison with untrimmed vines. No difference in grape quality versus control was shown by the T12 treatments. Post‐trimming assimilation rates (A) clearly indicated a large compensation capacity of retained main leaves despite their mean age being higher than that calculated for main leaves sampled the same day on control vines. The assimilation rates recorded on lateral leaves increased proportionally with lateral shoot size and inversely to the number of main leaves retained with trimming. Based on present results, both the area and photosynthetic effectiveness of source leaves will drive overall vine responses to shoot trimming. For example, T6LX showed the worst performance with respect to the other treatments, in agreement with lowest leaf area, leaf‐to‐fruit ratio and oldest canopy. However, T6LR had a somewhat retarded ripening despite its non‐limiting leaf‐to‐fruit ratio, relatively young canopy and maximum whole‐canopy photosynthesis and efficiency at veraison. Under such circumstances, duration of growth and possible competition with the berry sugaring process may have played a role.     

Extracting Regularities in Space and Time Through a Cascade of Prediction Networks: The Case of a Mobile Robot Navigating in a Structured Environment

We propose that the ability to extract regularities from time series through prediction learning can be enhanced if we use a hierarchical architecture in which higher layers are trained to predict the internal state of lower layers when such states change significantly. This hierarchical organization has two functions: (a) it forces the system to recode sensory information progressively so as to enhance useful regularities and filter out useless information; and (b) it progressively reduces the length of the sequences which should be predicted going from lower to higher layers. This, in turn, allows higher levels to extract higher-level regularities which are hidden at the sensory level. By training an architecture of this type to predict the next sensory state of a robot navigating in an environment divided into two rooms, we show how the first-level prediction layer extracts low-level regularities such as 'walls', 'corners' and 'corridors', while the second-level prediction layer extracts higher-level regularities such as 'the left side wall of the large room'. The extraction of these regularities allows the robot to localize its position in the environment and to detect changes in the environment (e.g. the presence of a new object or the fact that a door has been closed).