Serology and longevity of immunity against Echinococcus granulosus in sheep and llama induced by an oil-based EG95 vaccine.

An oil-based formulation of the EG95 vaccine to protect grazing animals against infection with Echinococcus granulosus was formulated in Argentina. The efficacy of the vaccine was monitored by serology in sheep and llama (Lama glama) and was compared to the serology in sheep previously published using a QuilA-adjuvanted vaccine. Long-term efficacy was also tested in sheep by challenging with E. granulosus eggs of the G1 strain 4 years after the beginning of the trial. The serological results for both sheep and llama were similar to those described previously, except that there was a more rapid response after the first vaccination. A third vaccination given after 1 year resulted in a transient boost in serology that lasted for about 12 months, which was similar to results previously described. Sheep challenged after 4 years with three vaccinations presented 84·2% reduction of live cysts counts compared with control group, and after a fourth vaccination prior to challenge, this reduction was 94·7%. The oil-based vaccine appeared to be bio-equivalent to the QuilA vaccine.

An analysis of training and generalization errors in shallow and deep networks.

This paper is motivated by an open problem around deep networks, namely, the apparent absence of over-fitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze this phenomenon in the case of regression problems when each unit evaluates a periodic activation function. We argue that the minimal expected value of the square loss is inappropriate to measure the generalization error in approximation of compositional functions in order to take full advantage of the compositional structure. Instead, we measure the generalization error in the sense of maximum loss, and sometimes, as a pointwise error. We give estimates on exactly how many parameters ensure both zero training error as well as a good generalization error. We prove that a solution of a regularization problem is guaranteed to yield a good training error as well as a good generalization error and estimate how much error to expect at which test data.

Image representations for visual learning.

Computer vision researchers are developing new approaches to object recognition and detection that are based almost directly on images and avoid the use of intermediate three-dimensional models. Many of these techniques depend on a representation of images that induce a linear vector space structure and in principle requires dense feature correspondence. This image representation allows the use of learning techniques for the analysis of images (for computer vision) as well as for the synthesis of images (for computer graphics).

The Müller-Lyer figure and the fly.

In the Müller-Lyer illusion two horizontal line segments of equal length are perceived by humans as unequal. The gaze of a fly presented with Müller-Lyer figures corresponds to human eye movements and human (illusionary) evaluations of the segment lengths. It is suggested that a theory similar to the phenomenological theory which accounts for the fly's gaze may account for the human eye's movement during an observation of Müller-Lyer figures.

Cooperative computation of stereo disparity.

The extraction of stereo-disparity information from two images depends upon establishing a correspondence between them. In this article we analyze the nature of the correspondence computation and derive a cooperative algorithm that implements it. We show that this algorithm successfully extracts information from random-dot stereograms, and its implications for the psychophysics and neurophysiology of the visual system are briefly discussed.

Regularization algorithms for learning that are equivalent to multilayer networks.

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function (that is, solving the problem of hypersurface reconstruction). From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. A theory is reported that shows the equivalence between regularization and a class of three-layer networks called regularization networks or hyper basis functions. These networks are not only equivalent to generalized splines but are also closely related to the classical radial basis functions used for interpolation tasks and to several pattern recognition and neural network algorithms. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage.

Parallel integration of vision modules.

Computer algorithms have been developed for several early vision processes, such as edge detection, stereopsis, motion, texture, and color, that give separate cues to the distance from the viewer of three-dimensional surfaces, their shape, and their material properties. Not surprisingly, biological vision systems still greatly outperform computer vision programs. One of the keys to the reliability, flexibility, and robustness of biological vision systems is their ability to integrate several visual cues. A computational technique for integrating different visual cues has now been developed and implemented with encouraging results on a parallel supercomputer.

Synthesizing a color algorithm from examples.

A lightness algorithm that separates surface reflectance from illumination in a Mondrian world is synthesized automatically from a set of examples, which consist of pairs of input (intensity signal) and desired output (surface reflectance) images. The algorithm, which resembles a new lightness algorithm recently proposed by Land, is approximately equivalent to filtering the image through a center-surround receptive field in individual chromatic channels. The synthesizing technique, optimal linear estimation, requires only one assumption, that the operator that transforms input into output is linear. This assumption is true for a certain class of early vision algorithms that may therefore be synthesized in a similar way from examples. Other methods of synthesizing algorithms from examples, or "learning," such as back-propagation, do not yield a significantly better lightness algorithm.

Fast perceptual learning in visual hyperacuity.

In many different spatial discrimination tasks, such as in determining the sign of the offset in a vernier stimulus, the human visual system exhibits hyperacuity by evaluating spatial relations with the precision of a fraction of a photoreceptor's diameter. It is proposed that this impressive performance depends in part on a fast learning process that uses relatively few examples and that occurs at an early processing stage in the visual pathway. This hypothesis is given support by the demonstration that it is possible to synthesize, from a small number of examples of a given task, a simple network that attains the required performance level. Psychophysical experiments agree with some of the key predictions of the model. In particular, fast stimulus-specific learning is found to take place in the human visual system, and this learning does not transfer between two slightly different hyperacuity tasks.

Categorical representation of visual stimuli in the primate prefrontal cortex.

The ability to group stimuli into meaningful categories is a fundamental cognitive process. To explore its neural basis, we trained monkeys to categorize computer-generated stimuli as "cats" and "dogs." A morphing system was used to systematically vary stimulus shape and precisely define the category boundary. Neural activity in the lateral prefrontal cortex reflected the category of visual stimuli, even when a monkey was retrained with the stimuli assigned to new categories.

Models of object recognition.

Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.

Hierarchical models of object recognition in cortex.

Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.

Shape representation in the inferior temporal cortex of monkeys.

BACKGROUND: The inferior temporal cortex (IT) of the monkey has long been known to play an essential role in visual object recognition. Damage to this area results in severe deficits in perceptual learning and object recognition, without significantly affecting basic visual capacities. Consistent with these ablation studies is the discovery of IT neurons that respond to complex two-dimensional visual patterns, or objects such as faces or body parts. What is the role of these neurons in object recognition? Is such a complex configurational selectivity specific to biologically meaningful objects, or does it develop as a result of extensive exposure to any objects whose identification relies on subtle shape differences? If so, would IT neurons respond selectively to recently learned views of features of novel objects? The present study addresses this question by using combined psychophysical and electrophysiological experiments, in which monkeys learned to classify and recognize computer-generated three-dimensional objects. RESULTS: A population of IT neurons was found that responded selectively to views of previously unfamiliar objects. The cells discharged maximally to one view of an object, and their response declined gradually as the object was rotated away from this preferred view. No selective responses were ever encountered for views that the animal systematically failed to recognize. Most neurons also exhibited orientation-dependent responses during view-plane rotations. Some neurons were found to be tuned around two views of the same object, and a very small number of cells responded in a view-invariant manner. For the five different objects that were used extensively during the training of the animals, and for which behavioral performance became view-independent, multiple cells were found that were tuned around different views of the same object. A number of view-selective units showed response invariance for changes in the size of the object or the position of its image within the parafovea. CONCLUSION: Our results suggest that IT neurons can develop a complex receptive field organization as a consequence of extensive training in the discrimination and recognition of objects. None of these objects had any prior meaning for the animal, nor did they resemble anything familiar in the monkey's environment. Simple geometric features did not appear to account for the neurons' selective responses. These findings support the idea that a population of neurons--each tuned to a different object aspect, and each showing a certain degree of invariance to image transformations--may, as an ensemble, encode at least some types of complex three-dimensional objects. In such a system, several neurons may be active for any given vantage point, with a single unit acting like a blurred template for a limited neighborhood of a single view.

The importance of symmetry and virtual views in three-dimensional object recognition.

BACKGROUND: Human observers can recognize three-dimensional objects seen in novel orientations, even when they have previously seen only a relatively small number of different views of the object. How our visual system does this is a key problem in vision research. Recent theories and experiments suggest that the human visual system might store a relatively small number of sample two-dimensional views of a three-dimensional object, and recognize novel views by a process of interpolation between the stored sample views. These sample views may be collected during a training phase as the visual system familiarizes itself with the object. RESULTS: Here, we investigate whether constraints on the shapes of objects commonly encountered in the real world can reduce the number of training views required for recognition of three-dimensional objects. We are particularly concerned with the constraint of object symmetry. We show that if an object is bilaterally symmetrical, then additional 'virtual views' can automatically be generated from one sample view by symmetry transformations. These virtual views should make it more easy to recognize novel views of a symmetric than an asymmetric object, when a single sample view has been seen. Recognition should be particularly facilitated when the novel views are close to the virtual view. We present psychophysical results that bear out these predictions. CONCLUSIONS: Our results show that the human visual system can indeed exploit symmetry to facilitate object recognition, and support the model for object recognition in which a small number of two-dimensional views are remembered and combined to recognize novel views of the same object. These results raise questions about how symmetry is recognized, and symmetry transformations implemented, in real, biological neural networks.

Top-down learning of low-level vision tasks.

Perceptual tasks such as edge detection, image segmentation, lightness computation and estimation of three-dimensional structure are considered to be low-level or mid-level vision problems and are traditionally approached in a bottom-up, generic and hard-wired way. An alternative to this would be to take a top-down, object-class-specific and example-based approach. In this paper, we present a simple computational model implementing the latter approach. The results generated by our model when tested on edge-detection and view-prediction tasks for three-dimensional objects are consistent with human perceptual expectations. The model's performance is highly tolerant to the problems of sensor noise and incomplete input image information. Results obtained with conventional bottom-up strategies show much less immunity to these problems. We interpret the encouraging performance of our computational model as evidence in support of the hypothesis that the human visual system may learn to perform supposedly low-level perceptual tasks in a top-down fashion.

View-dependent object recognition by monkeys.

BACKGROUND: How do we recognize visually perceived three-dimensional objects, particularly when they are seen from novel view-points? Recent psychophysical studies have suggested that the human visual system may store a relatively small number of two-dimensional views of a three-dimensional object, recognizing novel views of the object by interpolation between the stored sample views. In order to investigate the neural mechanisms underlying this process, physiological experiments are required and, as a prelude to such experiments, we have been interested to know whether the observations made with human observers extend to monkeys. RESULTS: We trained monkeys to recognize computer-generated images of objects presented from an arbitrarily chosen training view and containing sufficient three-dimensional information to specify the object's structure. We subsequently tested the trained monkeys' ability to generalize recognition of the object to views generated by rotation of the target object around any arbitrary axis. The monkeys recognized as the target only those two-dimensional views that were close to the familiar, training view. Recognition became increasingly difficult for the monkeys as the stimulus was rotated away from the experienced viewpoint, and failed for views farther than about 40 degrees from the training view. This suggests that, in the early stages of learning to recognize a previously unfamiliar object, the monkeys build two-dimensional, viewer-centered object representations, rather than a three-dimensional model of the object. When the animals were trained with as few as three views of the object, 120 degrees apart, they could often recognize all the views of the object resulting from rotations around the same axis. CONCLUSION: Our experiments show that recognition of three-dimensional novel objects is a function of the object's retinal projection. This suggests that non-human primates, like humans, may accomplish view-invariant recognition of familiar objects by a viewer-centered system that interpolates between a small number of stored views. The measures of recognition performance can be simulated by a regularization network that stores a few familiar views, and is endowed with the ability to interpolate between these views. Our results provide the basis for physiological studies of object-recognition by monkeys and suggest that the insights gained from such studies should apply also to humans.

Models of object recognition.

Progress in the understanding of visual recognition in the past year has been signified by the demonstration of computational feasibility of and psychophysical support for two-dimensional view-interpolation methods.

Microbiology of acute arthropathies among children in Argentina: Mycoplasma pneumoniae and hominis and Ureaplasma urealyticum.

OBJECTIVE: To describe the isolation of mycoplasmas and ureaplasmas from synovial fluid in pediatric patients with joint disorders. METHODS: During 1 year 45 samples of synovial fluid, blood and urine were collected from 33 hospitalized pediatric patients up to 17 years old who had joint disorders. Mycoplasmas and ureaplasmas were isolated in joint fluid by culture methods. RESULTS: Of the 33 patients 12 (36%) had joint disorders associated with pathogens (bacteria, Mycoplasma/Ureaplasma, Chlamydia) present at the site of inflammation. Mycoplasma hominis and Ureaplasma urealyticum were isolated from 3 and 1% of joint fluid samples, respectively. M. pneumoniae was isolated from nasopharyngeal secretion in a patient with evidence of a reactive arthritis. CONCLUSION: Our results raise the question of the possible role of Mycoplasma as a cofactor in the triggering of inflammatory joint disease, as well as the hypothesis that arthropathies may be caused by chronic local infection. These findings may contribute to early diagnosis of the disease and initiation of specific treatment.

A Sparse Representation for Function Approximation.

We derive a new general representation for a function as a linear combination of local correlation kernels at optimal sparse locations (and scales) and characterize its relation to principal component analysis, regularization, sparsity principles, and support vector machines.