Physiatric research fellows'' perceptions of the quality of their training

After being poisoned by eating the mushroom species Cortinarius speciosissimus, a twin developed interstitial nephritis with acute renal failure. He received a renal transplant from his living twin brother, who was presumed dizygotic on phenotypic grounds. Fifteen years later, the twins were zygosity tested by DNA "fingerprint analysis" and found to be monozygotic, despite important phenotypic discordances. The recipient has discontinued immunosuppression therapy and remains well after 9 months. We suggest that, for medical and other reasons, zygosity should be determined at birth on all like-sexed twins.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.     

Application of neural processing paradigm in visual landmark recognition and autonomous robot navigation

This article addresses the issue of visual landmark recognition in autonomous robot navigation along known routes, by intuitively exploiting the functions of the human visual system and its navigational ability. A feedforward–feedbackward architecture has been developed for recognising visual landmarks in real time. It integrates the theoretical concepts from the pre-attentive and attentive stages in the human visual system, the selective attention adaptive resonance theory neural network and its derivatives, and computational approaches towards object recognition in computer vision. The architecture mimics the pre-attentive and attentive stages in the context of object recognition, embedding neural network processing paradigm into a computational template-matching approach in computer vision. The real-time landmark recognition capability is achieved by mimicking the pre-attentive stage, where it models a selective attention mechanism for optimal computational resource allocation, focusing only on the regions of interest to address the computational restrictive nature of current computer processing power. Similarly, the recognition of visual landmarks in both clean and cluttered backgrounds is implemented in the attentive stage by developing a memory feedback modulation (MFM) mechanism that enables knowledge from the memory to interact and enhance the efficiency of earlier stages in the architecture. Furthermore, it also incorporates both top-down and bottom-up facilitatory and inhibition pathways between the memory and the earlier stages to enable the architecture to recognise a 2D landmark, which is partially occluded by adjacent features in the surroundings. The results show that the architecture is able to recognise objects in cluttered backgrounds using real-images in both indoor and outdoor scenes. Furthermore, the architecture application in autonomous robot navigation has been demonstrated through a number of real-time trials in both indoor and outdoor environments.     

Virtual modeling of an ankle foot orthosis for correction of foot abnormality

Clubfoot is a challenging structural foot abnormality in babies and generally occurs at birth or during childhood. Due to this the abnormal posturing of the foot causes the babies to turn the ankle inward and walk along the outside edges of the foot. In medical science it is known as “congenital talipes equino varus” (CTEV). Computer modeling of prosthesis and orthosis is a perspective method for optimal design and gait is a terminology related to walking patterns of human subject.     

Uncountable automatic classes and learning

In this paper we consider uncountable classes recognizable by ω-automata and investigate suitable learning paradigms for them. In particular, the counterparts of explanatory, vacillatory and behaviourally correct learning are introduced for this setting. Here the learner reads in parallel the data of a text for a language L from the class plus an ω-index α and outputs a sequence of ω-automata such that all but finitely many of these ω-automata accept the index α if and only if α is an index for L.It is shown that any class is behaviourally correct learnable if and only if it satisfies Angluin’s tell-tale condition. For explanatory learning, such a result needs that a suitable indexing of the class is chosen. On the one hand, every class satisfying Angluin’s tell-tale condition is vacillatorily learnable in every indexing; on the other hand, there is a fixed class such that the level of the class in the hierarchy of vacillatory learning depends on the indexing of the class chosen.We also consider a notion of blind learning. On the one hand, a class is blind explanatorily (vacillatorily) learnable if and only if it satisfies Angluin’s tell-tale condition and is countable; on the other hand, for behaviourally correct learning, there is no difference between the blind and non-blind version.This work establishes a bridge between the theory of ω-automata and inductive inference (learning theory).     

Emergent semantics through interaction in image databases

In this paper, we briefly discuss some aspects of image semantics and the role that it plays for the design of image databases. We argue that images don't have an intrinsic meaning, but that they are endowed with a meaning by placing them in the context of other images and by the user interaction. From this observation, we conclude that, in an image, database users should be allowed to manipulate not only the individual images, but also the relation between them. We present an interface model based on the manipulation of configurations of images