Poster presentations at medical conferences: an effective way of disseminating research?

This study aimed to ascertain the value of posters at medical meetings to presenters and delegates. The usefulness of posters to presenters at national and international meetings was evaluated by assessing the numbers of delegates visiting them and the reasons why they visited. Memorability of selected posters was assessed and factors influencing their appeal to expert delegates identified. At both the national and international meetings, very few delegates (< 5%) visited posters. Only a minority read them and fewer asked useful questions. Recall of content was so poor that it prevented identification of factors improving their memorability. Factors increasing posters' visual appeal included their scientific content, pictures/graphs and limited use of words. Few delegates visit posters and those doing so recall little of their content. To engage their audience, researchers should design visually appealing posters by presenting high quality data in pictures or graphs without an excess of words.

Children with exotropia responsive to spectacle correction of hyperopia.

A series of seven exotropic children (aged 2 to 10 years) had resolution of exotropia after spectacle correction of hyperopia. Their hyperopic correction ranged from 3.00 to 7.00 diopters. Six had intermittent exotropia, which became small-angle esophoria after spectacle correction. In one patient with apparently no fusion, spectacle correction converted constant exotropia to small esotropia in the monofixational range. In all patients, Worth 4-dot and Titmus Stereo Test results, when obtainable, indicated an improvement in binocular sensory status after correction of the hyperopia. We conclude that a trial of spectacle correction is warranted in exotropic children with severe hyperopia and in those with moderate hyperopia and a low accommodative convergence/accommodation ratio or evidence of hypoaccommodation.

Imaging with rotating slit apertures and rotating collimators.

The statistical quality of conventional nuclear medical imagery is limited by the small signal collected through low-efficiency conventional apertures. Coded-aperture imaging overcomes this by employing a two-step process in which the object is first efficiently detected as an "encoded" form which does not resemble the object, and then filtered (or "decoded") to form an image. We present here the imaging properties of a class of time-modulated coded apertures which, unlike most coded apertures, encode projections of the object rather than the object itself. These coded apertures can reconstruct a volume object nontomographically, tomographically (one plane focused), or three-dimensionally. We describe a new decoding algorithm that reconstructs the object from its planar projections. Results of noise calculations are given, and the noise performance of these coded-aperture systems is compared to that of conventional counterparts. A hybrid slit-pinhole system which combines the imaging advantages of a rotating slit and a pinhole is described. A new scintillation detector which accurately measures the position of an event in one dimension only is presented, and its use in our coded-aperture system is outlined. Finally, results of imaging test objects and animals are given.

Chronic Propionibacterium acnes endophthalmitis as a cause of intermediate uveitis.

Although the pathogenesis in most cases of intermediate uveitis is unknown, a small minority of cases is associated with a variety of specific inflammatory etiologies: sarcoidosis; multiple sclerosis; Lyme disease; syphilis; ocular lymphoma; and as a rare manifestation of Behçet's disease and AIDS. A 61-year-old woman developed pars planitis after cataract surgery. A vitrectomy was performed after ten months when a white capsular plaque and an hypopyon developed. Propionibacterium acnes was isolated. The intermediate uveitis was not controlled until later removal of the intraocular lens and capsular remnants. Chronic propionibacterial endophthalmitis may be a cause of intermediate uveitis.

Pruning recurrent neural networks for improved generalization performance.

Determining the architecture of a neural network is an important issue for any learning task. For recurrent neural networks no general methods exist that permit the estimation of the number of layers of hidden neurons, the size of layers or the number of weights. We present a simple pruning heuristic that significantly improves the generalization performance of trained recurrent networks. We illustrate this heuristic by training a fully recurrent neural network on positive and negative strings of a regular grammar. We also show that rules extracted from networks trained with this pruning heuristic are more consistent with the rules to be learned. This performance improvement is obtained by pruning and retraining the networks. Simulations are shown for training and pruning a recurrent neural net on strings generated by two regular grammars, a randomly-generated 10-state grammar and an 8-state, triple-parity grammar. Further simulations indicate that this pruning method can have generalization performance superior to that obtained by training with weight decay.

First-order versus second-order single-layer recurrent neural networks.

We examine the representational capabilities of first-order and second-order single-layer recurrent neural networks (SLRNN's) with hard-limiting neurons. We show that a second-order SLRNN is strictly more powerful than a first-order SLRNN. However, if the first-order SLRNN is augmented with output layers of feedforward neurons, it can implement any finite-state recognizer, but only if state-splitting is employed. When a state is split, it is divided into two equivalent states. The judicious use of state-splitting allows for efficient implementation of finite-state recognizers using augmented first-order SLRNN's.

Learning long-term dependencies in NARX recurrent neural networks.

It has previously been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models with exogenous (NARX) recurrent neural networks, which have powerful representational capabilities. We have previously reported that gradient descent learning can be more effective in NARX networks than in recurrent neural network architectures that have "hidden states" on problems including grammatical inference and nonlinear system identification. Typically, the network converges much faster and generalizes better than other networks. The results in this paper are consistent with this phenomenon. We present some experimental results which show that NARX networks can often retain information for two to three times as long as conventional recurrent neural networks. We show that although NARX networks do not circumvent the problem of long-term dependencies, they can greatly improve performance on long-term dependency problems. We also describe in detail some of the assumptions regarding what it means to latch information robustly and suggest possible ways to loosen these assumptions.

An analysis of noise in recurrent neural networks: convergence and generalization.

Concerns the effect of noise on the performance of feedforward neural nets. We introduce and analyze various methods of injecting synaptic noise into dynamically driven recurrent nets during training. Theoretical results show that applying a controlled amount of noise during training may improve convergence and generalization performance. We analyze the effects of various noise parameters and predict that best overall performance can be achieved by injecting additive noise at each time step. Noise contributes a second-order gradient term to the error function which can be viewed as an anticipatory agent to aid convergence. This term appears to find promising regions of weight space in the beginning stages of training when the training error is large and should improve convergence on error surfaces with local minima. The first-order term is a regularization term that can improve generalization. Specifically, it can encourage internal representations where the state nodes operate in the saturated regions of the sigmoid discriminant function. While this effect can improve performance on automata inference problems with binary inputs and target outputs, it is unclear what effect it will have on other types of problems. To substantiate these predictions, we present simulations on learning the dual parity grammar from temporal strings for all noise models, and present simulations on learning a randomly generated six-state grammar using the predicted best noise model.

Face recognition: a convolutional neural-network approach.

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

On the distribution of performance from multiple neural-network trials.

The performance of neural network simulations is often reported in terms of the mean and standard deviation of a number of simulations performed with different starting conditions. However, in many cases, the distribution of the individual results does not approximate a Gaussian distribution, may not be symmetric, and may be multimodal. We present the distribution of results for practical problems and show that assuming Gaussian distributions can significantly affect the interpretation of results, especially those of comparison studies. For a controlled task which we consider, we find that the distribution of performance is skewed toward better performance for smoother target functions and skewed toward worse performance for more complex target functions. We propose new guidelines for reporting performance which provide more information about the actual distribution.

Time-delay neural networks: representation and induction of finite-state machines.

In this work, we characterize and contrast the capabilities of the general class of time-delay neural networks (TDNNs) with input delay neural networks (IDNNs), the subclass of TDNNs with delays limited to the inputs. Each class of networks is capable of representing the same set of languages, those embodied by the definite memory machines (DMMs), a subclass of finite-state machines. We demonstrate the close affinity between TDNNs and DMM languages by learning a very large DMM (2048 states) using only a few training examples. Even though both architectures are capable of representing the same class of languages, they have distinguishable learning biases. Intuition suggests that general TDNNs which include delays in hidden layers should perform well, compared to IDNNs, on problems in which the output can be expressed as a function on narrow input windows which repeat in time. On the other hand, these general TDNNs should perform poorly when the input windows are wide, or there is little repetition. We confirm these hypotheses via a set of simulations and statistical analysis.

Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution.

It is often difficult to predict the optimal neural network size for a particular application. Constructive or destructive methods that add or subtract neurons, layers, connections, etc. might offer a solution to this problem. We prove that one method, recurrent cascade correlation, due to its topology, has fundamental limitations in representation and thus in its learning capabilities. It cannot represent with monotone (i.e., sigmoid) and hard-threshold activation functions certain finite state automata. We give a "preliminary" approach on how to get around these limitations by devising a simple constructive training method that adds neurons during training while still preserving the powerful fully-recurrent structure. We illustrate this approach by simulations which learn many examples of regular grammars that the recurrent cascade correlation method is unable to learn.

Treatment failure in anterior uveitis in children.

Anterior uveitis in children can present difficult management problems. While the course of the inflammation may defy the usual treatment modalities, attention to five specific areas of clinical management may enable the ophthalmologist to better preserve visual function. Careful examination, proper adjustment of corticosteroid dosage, repeated intraocular pressure measurement, patient compliance, and adequate follow-up are the areas discussed.

Pediatric intermediate uveitis.

Clinical findings, course, treatment, and complications of intermediate uveitis in children is analyzed in a series of 60 patients. While generally a chronic indolent disease, in the pediatric age group acute anterior uveitis is the presenting symptom rather than the milder symptomatology characterized in the adult with a similar syndrome. The etiologic evaluation of these patients was unrewarding. The great majority of patients improved following the use of periocular corticosteroid injections and retinal cryopexy, and immunosuppressive agents were infrequently employed in the management of the disorder. Cataract formation, secondary glaucoma, band keratopathy, vitreous hemorrhage, and papillitis were seen as complications. A treatment algorithm is presented.

Peripheral uveitis in three children of one family.

Peripheral uveitis presenting in three siblings is described. The course and response to treatment in each youngster is detailed. Periocular and local corticosteroid therapy was effective. Etiologic evaluation and immunologic surveys proved unrewarding. Ophthalmological evaluation of families seems warranted following the detection of peripheral uveitis in any of its members.

Results of overminus lens therapy on postoperative exodeviations.

This paper is a study in the use of overminus lenses for 37 patients with postoperative exodeviations. These patients have been observed for a period ranging from three to nine years following their surgery. This study includes: (1) the immediate response to overminus treatment, (2) results at the time of cessation of overminus therapy, and (3) the follow-up results. The results are evaluated with an average follow-up of 24 months after termination of treatment and sequentially at one-year intervals following overminus therapy.

Computational capabilities of recurrent NARX neural networks.

Recently, fully connected recurrent neural networks have been proven to be computationally rich-at least as powerful as Turing machines. This work focuses on another network which is popular in control applications and has been found to be very effective at learning a variety of problems. These networks are based upon Nonlinear AutoRegressive models with eXogenous Inputs (NARX models), and are therefore called NARX networks. As opposed to other recurrent networks, NARX networks have a limited feedback which comes only from the output neuron rather than from hidden states. They are formalized by y(t)=Psi(u(t-n(u)), ..., u(t-1), u(t), y(t-n(y)), ..., y(t-1)) where u(t) and y(t) represent input and output of the network at time t, n(u) and n(y) are the input and output order, and the function Psi is the mapping performed by a Multilayer Perceptron. We constructively prove that the NARX networks with a finite number of parameters are computationally as strong as fully connected recurrent networks and thus Turing machines. We conclude that in theory one can use the NARX models, rather than conventional recurrent networks without any computational loss even though their feedback is limited. Furthermore, these results raise the issue of what amount of feedback or recurrence is necessary for any network to be Turing equivalent and what restrictions on feedback limit computational power.