Parameter Estimation for Optimal Object Recognition: Theory and Application

Object recognition systems involve parameters such as thresholds, bounds and weights. These parameters have to be tuned before the system can perform successfully. A common practice is to choose such parameters manually on an ad hoc basis, which is a disadvantage. This paper presents a novel theory of parameter estimation for optimization-based object recognition where the optimal solution is defined as the global minimum of an energy function. The theory is based on supervised learning from examples. Correctness and instability are established as criteria for evaluating the estimated parameters. A correct estimate enables the labeling implied in each exemplary configuration to be encoded in a unique global energy minimum. The instability is the ease with which the minimum is replaced by a non-exemplary configuration after a perturbation. The optimal estimate minimizes the instability. Algorithms are presented for computing correct and minimal-instability estimates. The theory is applied to the parameter estimation for MRF-based recognition and promising results are obtained.     

Robust Estimation of Rotation Angles from Image Sequences Using the Annealing M-Estimator

A robust method is presented for computing rotation angles of image sequences from a set of corresponding points containing outliers. Assuming known rotation axis, a least-squares (LS) solution are derived to compute the rotation angle from a clean data set of point correspondences. Since clean data is not guaranteed, we introduce a robust solution, based on the M-estimator, to deal with outliers. Then we present an enhanced robust algorithm, called the annealing M-estimator (AM-estimator), for reliable robust estimation. The AM-estimator has several attractive advantages over the traditional M-estimator: By definition, the AM-estimator involves neither scale estimator nor free parameters and hence avoids instabilities therein. Algorithmically, it uses a deterministic annealing technique to approximate the global solution regardless of the initialization. Experimental results are presented to compare the performance of the LS, M- and AM-estimators for the angle estimation. Experiments show that in the presence of outliers, the M-estimator outperforms the LS estimator and the AM-estimator outperforms the M-estimator.     

Extraction,optimisation and characterisation of phenolics from Thymus vulgaris L.: phenolic content and profiles in relation to antioxidant,antidiabetic and antihypertensive properties

The objectives of this study were to examine varying extraction conditions of Thymus vulgaris L. as related to phenolic content and profiles of the extracts and their antioxidant, antihypertensive and antidiabetic properties. Phenolics were extracted under various conditions pertaining to free and bound phenolics, solvent type and combination of extraction time and temperature, and these extracts were evaluated in terms of their antioxidant activities and inhibitory activities of angiotensin‐converting enzyme (ACE), α‐glucosidase and α‐amylase. The acetone–water solvent mixture (1:1; v/v) produced the extract with the greatest phenolic content, antioxidant activity and inhibitory activities of ACE and α‐glucosidase. The optimal extraction temperature for maximum phenolic content and antioxidant activity associated with methanol extraction was 60 °C, whereas a lower temperature at 40 °C was required to maximise inhibitory activities for ACE, α‐glucosidase and α‐amylase. An inverse relationship was seen between antioxidant and glucosidase inhibitory activities vs. the ACE and α‐amylase inhibitory activities, which suggests the need for extractions to be directed to specific bioactivities of thyme extracts. Generally, the results indicate major differences in phenolic profiles among the tested extraction conditions with thymol as the predominant phenolic seen in most extractions, while gallic acid, rosmarinic acid or diosmin also predominated in other extracts. Extracts with the same predominant phenolic compound and similar phenolic content showed major disparities in their ACE, glucosidase and α‐amylase inhibitory activities, indicating that the major phenolic profiles of thyme extracts may not be necessarily related to the degree of inhibition of ACE, glucosidase and α‐amylase enzymes.     

Impact of precursor preparation on density of gadolinium iron garnets synthesized via reactive sintering

Gadolinium iron garnet was obtained from two different precursors, homogenized in isopropyl alcohol and in an aqueous environment with a fixed pH. In the first case, it was a mixture of goethite (FeO(OH)) and gadolinium oxide (Gd₂O₃); in the second, a mixture of GdIP (GdFeO₃) and α-Fe₂O₃. Conditions of homogenization in the aqueous environment were selected based on the zeta (ξ) potential measurements as the function of pH. DSC measurements of the output powder mixtures allowed the identification of the effects observed during the temperature rise. In the case of the material obtained from a mixture of goethite (FeO(OH)) and gadolinium oxide, with the increasing temperature, we observe three effects, the first of which corresponds to the phase transformation of goethite into α-Fe₂O₃, the second corresponds to the reaction of gadolinium iron perovskite (GdIP) formation, and the third to the reaction in which a gadolinium iron garnet (GdIG) is formed. However, in the case of heat treatment of the mixture of GdIP and α-Fe₂O₃, we only observe the effect responsible for a solid state reaction leading to the formation of gadolinium iron garnet. Dilatometric measurements allowed to determine the changes in linear dimensions at various stages of reaction sintering. The resulting materials were sintered at temperatures of 1200, 1300, and 1400 °C. In the case of the material obtained from a mixture of perovskite and iron (III) oxide, already at the temperature of 1300 °C, a density has been obtained at around 95% of the theoretical density, and the temperature of 1400 °C allowed achieving a density of 97% of the theoretical density. Whereas, for the material obtained from a mixture of goethite (FeO(OH)) and gadolinium oxide, a density above 95% of theoretical density was achieved only at 1400 °C.     

Mechanisms of molecular and structural interactions between lentil and quinoa proteins in aqueous solutions induced by pH recycling

There has been a growing interest in plant proteins due to their beneficial health effects, low cost and variety of applications in food industries. The low solubility of lentil proteins (LPs) is one of the significant factors that limit their use in food applications. Quinoa proteins (QPs), which have high water solubility, were combined with LPs at pH 12 to generate LP-QP complexes to generate pH-based soluble protein compounds. The LP-QP complexes demonstrated a large surface charge with an increase solubilisation of the protein complexes by more than 85%, together with resistance to protein aggregation. The combination of LPs to QPs led to a significant increase (P < 0.05) in unique tertiary and secondary protein structures as determined by the protein–protein interaction (PPI) technique involving pH recycling. Interactions between LPs and QPs affected the surface morphology of the protein complexes formed. Electrostatic interactions, hydrophobic forces and hydrogen bonding were indicated to play key roles in the PPIs. The capacity of pH cycling to illustrate the above protein interactions shows that this is a robust approach for assessing the emulsion and foaming properties of food proteins.     

Computational speed and high-frequency trading profitability: an ecological perspective

High-frequency traders (HFTs) account for a considerable component of equity trading but we know little about the source of their trading profits and to what extend IT based differentiators such as news processing power and ultra-low latency has contributed to competitive advantage within HFT realm. Given a fairly modest amount of empirical evidence on the subject, we study the effects of computational speed on HFTs’ profits through an experimental artificial agent-based equity market. Our approach relies on an ecological modelling inspired from the r/K selection theory, and is designed to assess the relative financial performance of two classes of aggressive HFT agents endowed with dissimilar computational capabilities. We use a discrete-event news simulation system to capture the information processing disparity and order transfer delay, and simulate the dynamics of the market. Through Monte Carlo simulation we obtain in our empirical setting robust estimates of the expected outcome.