Number sense and state-dependent valuation in cuttlefish.

Identifying the amount of prey available is an important part of an animal's foraging behaviour. The risk-sensitive foraging theory predicts that an organism's foraging decisions with regard to food rewards depending upon its satiation level. However, the precise interaction between optimal risk-tolerance and satiation level remains unclear. In this study, we examined, firstly, whether cuttlefish, with one of the most highly evolved nervous system among the invertebrates, have number sense, and secondly, whether their valuation of food reward is satiation state dependent. When food such as live shrimps is present, without training, cuttlefish turn toward the prey and initiate seizure behaviour. Using this visual attack behaviour as a measure, cuttlefish showed a preference for a larger quantity when faced with two-alternative forced choice tasks (1 versus 2, 2 versus 3, 3 versus 4 and 4 versus 5). However, cuttlefish preferred the small quantity when the choice was between one live and two dead shrimps. More importantly, when the choice was between one large live shrimp and two small live shrimps (a prey size and quantity trade-off), the cuttlefish chose the large single shrimp when they felt hunger, but chose the two smaller prey when they were satiated. These results demonstrate that cuttlefish are capable of number discrimination and that their choice of prey number depends on the quality of the prey and on their appetite state. The findings also suggest that cuttlefish integrate both internal and external information when making a foraging decision and that the cost of obtaining food is inversely correlated with their satiation level, a phenomenon similar to the observation that metabolic state alters economic decision making under risk among humans.

Constructive autoassociative neural network for facial recognition.

Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network). CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.

Efficient multitemplate learning for structured prediction.

Conditional random fields (CRF) and structural support vector machines (structural SVM) are two state-of-the-art methods for structured prediction that captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on all input observations. These features are usually generated by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate this issue, in this paper we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution. This paradigm can be formulated as a special multiple kernel learning problem with an exponential number of constraints. Then we introduce an efficient cutting-plane algorithm to solve this problem in the primal and present its convergence. We also evaluate the proposed learning paradigm on two widely studied structured prediction tasks, i.e., sequence labeling and dependency parsing. Extensive experimental results show that the proposed method outperforms CRFs and structural SVMs because of exploiting the importance of each template. Complexity analysis and empirical results also show that the proposed method is more efficient than Online multikernel learning on very sparse and high-dimensional data. We further extend this paradigm for structured prediction using generalized p-block norm regularization with p >; 1, and experiments show competitive performances when p ∈ [1,2).

A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions.

Particle swarm optimizer (PSO) is a powerful optimization algorithm that has been applied to a variety of problems. It can, however, suffer from premature convergence and slow convergence rate. Motivated by these two problems, a hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper. The modified BFGS method is integrated into the context of the PSOs to improve the particles' local search ability. In addition, in conjunction with the territory technique, a reposition technique to maintain the diversity of particles is proposed to improve the global search ability of PSOs. One advantage of the hybrid strategy is that it can effectively find multiple local solutions or global solutions to the multimodal functions in a box-constrained space. Based on these local solutions, a reconstruction technique can be adopted to further estimate better solutions. The proposed method is compared with several recently developed optimization algorithms on a set of 20 standard benchmark problems. Experimental results demonstrate that the proposed approach can obtain high-quality solutions on multimodal function optimization problems.

Large-scale maximum margin discriminant analysis using core vector machines.

Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) was proposed that extends the large-margin idea to feature extraction. It often outperforms traditional methods such as kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFD). However, as in the SVM, its time complexity is cubic in the number of training points m, and is thus computationally inefficient on massive data sets. In this paper, we propose an (1+epsilon)(2)-approximation algorithm for obtaining the MMDA features by extending the core vector machine. The resultant time complexity is only linear in m, while its space complexity is independent of m. Extensive comparisons with the original MMDA, KPCA, and KFD on a number of large data sets show that the proposed feature extractor can improve classification accuracy, and is also faster than these kernel-based methods by over an order of magnitude.

Linear dependency between /spl epsi/ and the input noise in /spl epsi/-support vector regression.

In using the /spl epsi/-support vector regression (/spl epsi/-SVR) algorithm, one has to decide a suitable value for the insensitivity parameter /spl epsi/. Smola et al. considered its "optimal" choice by studying the statistical efficiency in a location parameter estimation problem. While they successfully predicted a linear scaling between the optimal /spl epsi/ and the noise in the data, their theoretically optimal value does not have a close match with its experimentally observed counterpart in the case of Gaussian noise. In this paper, we attempt to better explain their experimental results by studying the regression problem itself. Our resultant predicted choice of /spl epsi/ is much closer to the experimentally observed optimal value, while again demonstrating a linear trend with the input noise.

Cluster size diversity, percolation, and complex systems.

Diversity of cluster size has been used as a measurement of complexity in several systems that generate a statistical distribution of clusters. Using Monte Carlo simulations, we present a statistical analysis of the cluster size diversity and the number of clusters generated on randomly occupied lattices for the Euclidean dimensions 1 to 6. A tuning effect for diversity of cluster size and critical probabilities associated with the maximum diversity and the maximum number of clusters are reported. The probability of maximum diversity is related to the percolation threshold and several scaling relations between the variables measured are reported. The statistics of cluster size diversity has important consequences in the statistical description of the Universe as a complex system.

Maternal and fetal outcomes in hyperemesis gravidarum.

OBJECTIVE: This study sought to evaluate maternal characteristics and pregnancy outcomes among women with hyperemesis gravidarum. METHODS: We performed a retrospective analysis of pregnancy records of obstetric admissions during a 6-year period. Women treated as out-patients for hyperemesis were also identified. Hyperemesis was defined as excessive nausea and vomiting resulting in dehydration, extensive medical therapy, and/or hospital admission. Statistical analysis was by t-test and chi square. RESULTS: We identified 193 women (1.5%) who developed hyperemesis among 13,053 women. Racial status, marital status, age, and gravidity were similar between the hyperemesis patients and the general population. However, there were less women with hyperemesis who were para 3 or greater. Forty-six women (24%) required hospitalization for hyperemesis, mean hospital stay 1.8 days, range 1-10 days. One patient required parenteral nutrition, two had yeast esophagitis, none had HIV infection, psychiatric pathology or thyroid disease. Pregnancy outcomes between hyperemesis patients and the general population were similar for mean birth weight, mean gestational age, deliveries less than 37 weeks, Apgar scores, perinatal mortality or incidence of fetal anomalies. Our incidence of hyperemesis (1.5%) is similar to that of other published reports. CONCLUSION: Women with hyperemesis have similar demographic characteristics to the general obstetric population, and have similar obstetric outcomes.

Perspective on low back pain.

Low back pain is a common and expensive ailment among the working population. Despite the increasing amount of basic and clinical knowledge on low back pain, the precise causes of almost all cases cannot yet be defined. The tissues that could be the sources of pain in the low back have been better identified. The validity of the commonly used diagnosis of facet syndrome is now being questioned, and a favorable response to injection of the facet joints should not be considered an indication for posterior lumbar fusion. Studies continue to indicate that total body vibration and cigarette smoking have a relationship to disk degeneration. Improved imaging techniques such as computed tomography and magnetic resonance imaging allow better visualization of the pathology in the vertebral column, but the correlation between these imaging findings and clinical symptoms has yet to be proven. Psychosocial factors are now recognized as important in patients with low back pain. Surgical procedures, even when appropriately indicated, have produced no better result than has the natural course of the condition.

Study design for microgravity human physiology experiments.

In some areas of research, such as microgravity life sciences, both the number of subjects and the opportunity for repeated observation under experimental conditions are limited. Small N study designs are appropriate for these situations since they require few subjects and few observations in the experimental condition. Small N studies compare treatment conditions separately for each subject so that between-subject variability does not obscure treatment effects. Multiple observations are collected for each individual in the baseline condition to ensure a stable reference point for comparison with the smaller number of observations collected under the experimental conditions. Individual differences can be overlooked in large N studies, but in small N studies the focus is on the individual, and differences observed between particular individuals can be suggestive of underlying causal processes. We present a step-by-step approach to designing and conducting a small N study.

Diurnal changes in the profile shape and range of motion of the back.

The diurnal height change reported to occur in young adults was investigated to characterize changes in the lumbar range of motion, spinal profile, and range of motion of the back. Twelve subjects aged 18-22 years were measured using stereophotography and standard clinical examinations in the evening and after a minimum of 8 hours of complete bed rest. The average increase in height was 20 mm. Forty percent of the height increase took place in the lumbar spine without change in the depth of the lordosis, and forty percent took place in the thoracic curve with a decrease in the kyphosis. The remaining 20% of the height increase was not located, but no measurements were taken of the cervical spine. Range of motion studies revealed that lumbar flexion, as measured using the lumbar flexion increment, was decreased in the morning, and straight leg raise was decreased when measured clinically but not when measured photographically. Extension, rotation, and femoral stretch test were not affected. The authors conclude that stereophotogrammetry offers an accurate, noninvasive way to study spinal profiles. Further work is needed to assess and quantify the relative motion of the skin and the underlying structures.

Back pain and spinal changes in microgravity.

We believe that investigations into back pain and spinal changes in microgravity will contribute significantly to our knowledge and understanding of factors that cause back pain and the influence of spinal cord distraction on sensory and autonomic dysfunction. This information may have clinical implications for the treatment and rehabilitation of patients with spinal cord injury, for the care of patients during and after corrective spinal surgery, for the care and treatment of patients with chronic pain syndromes, and may potentially provide us with the opportunity to learn more about demyelinating diseases.

Preliminary evaluation of a scheme for grading the gross morphology of the human intervertebral disc.

A five-category grading scheme for assessing the gross morphology of midsagittal sections of the human lumbar intervetebral disc was developed. The ability of three observers to categorize a series of 68 discs with a wide spectrum of morphologies established the comprehensiveness of the classification. Three independent observers tested the reproducibility of the procedure by assignment of grades blindly to duplicate images of 68 discs taken from 15 spines. The intraobserver agreement ranged from 87 to 91%. The interobserver agreement was 61, 64, and 88% for the three pairs, the two low values being attributable to the bias of one observer. The agreement between the assigned and average grades was 85, 92, 68, 90, and 76% for Grades I through V, respectively. Except for Grade III, the disagreements were attributable mainly to the bias of one observer. Both the increase in the grade with age and the finding that all the discs within 14 of 15 spines had a narrow range of grades demonstrated the biologic credibility of the scheme.

Update on osteoarthritis.

Osteoarthritis is the most common type of arthritis affecting humans. Its clinical features are well recognized, and its diagnosis is easily established. The etiopathogenesis of the disease is still poorly understood. The definition and classification of osteoarthritis have only recently been set up by the American Rheumatism Association and the National Institutes of Health-sponsored workshop. Management should be four-fold: educational, physiatric, medical, and surgical. The management goal should be individualized and be aimed at symptomatic relief and restoration of function.

Gray scale ultrasound in the evaluation of rheumatoid arthritis of the knee.

Gray scale ultrasound at a frequency of 5.0 MHz was used as part of a study to evaluate the results of yttrium-90 injection as therapy for rheumatoid arthritis of the knee. Popliteal cysts, suprapatellar effusions and synovial thickening in the suprapatellar pouch were demonstrated and ultrasound studies were correlated with the clinical and arthrographic findings. Gray scale ultrasound can be a useful adjunct in the evaluation and follow-up of rheumatoid arthritis of the knee.

D-penicillamine in the treatment of rheumatoid arthritis.

Forty-four patients with definite or classic rheumatoid arthritis and failure to tolerate or respond to gold therapy were treated with D-penicillamine on a so-called go-slow, go-low regime. Seventeen patients tolerated the drug and had a 3-13 month follow-up assessment; 8 were markedly improved, 6 moderately or slightly improved, and 3 unimproved. Penicillamine had to be discontinued in 9 patients because of toxic side effects.