Learning stable weights for data of varying dimension

In this paper we develop a data-driven weight learning method for weighted quasi-arithmetic means where the observed data may vary in dimension.

Insulin concentrations in cerebellum and body balance in diabetic male rats: Aerobic training effects

Brain insulin has had widespread metabolic, neurotrophic, and neuromodulatory functions and has been involved in the central regulation of food intake and body weight, learning and memory, neuronal development, and neuronal apoptosis. Purpose: The present study investigated the role of swimming training on cerebral metabolism on insulin concentrations in cerebellum and the body balance performance of diabetic rats. Methods: Forty Male Wistar rats were divided in four groups: sedentary control (SC), trained control (TC), sedentary diabetic (SD), and trained diabetic (TD). Diabetes was induced by alloxan (32 mg kg b.w.), single dose injection. The mean blood glucose of diabetic groups was 367 ± 40 mg/dl. Training program consisted in swimming 5 days/week, 1 h/day, 8 weeks, supporting a workload corresponding to 90% of maximal lactate steady state (MLSS). For the body balance testing rats were trained to traverse for 5 min daily for 5-7 days. All dependent variables were analyzed by one-way analysis of variance (ANOVA) and a significance level of p < 0.05 was used for all comparisons. Results: The body balance testing scores were different between groups. Insulin concentrations in cerebellum were not different between groups. Conclusion: It was concluded that in diabetic rats, aerobic training does not induce alterations on cerebellum insulin but induces important metabolic, hormonal and behavioral alterations which are associated with an improvement in glucose homeostasis, serum insulin concentrations and body balance. © 2013 Elsevier Inc.

Linear optimization for ecological indices based on aggregation functions

We consider an optimization problem in ecology where our objective is to maximize biodiversity with respect to different land-use allocations. As it turns out, the main problem can be framed as learning the weights of a weighted arithmetic mean where the objective is the geometric mean of its outputs. We propose methods for approximating solutions to this and similar problems, which are non-linear by nature, using linear and bilevel techniques.

Pattern of learning disabilities in children with extremely low birth weight and broadly average intelligence

To examine the prevalence and pattern of specific areas of learning disability (LD) in neurologically normal children with extremely low birth weight (ELBW) (<or = 800 g) who have broadly average intelligence compared with full-term children with normal birth weight of comparable sociodemographic background, and to explore concurrent cognitive correlates of the specific LDs.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Children with special health care needs : social and learning competence in the early years

The number of children with special health care needs surviving infancy and attending school has been increasing. Due to their health status, these children may be at risk of low social-emotional and learning competencies (e.g., Lightfoot, Mukherjee, & Sloper, 2000; Zehnder, Landolt, Prchal, & Vollrath, 2006). Early social problems have been linked to low levels of academic achievement (Ladd, 2005), inappropriate behaviours at school (Shiu, 2001) and strained teacher-child relationships (Blumberg, Carle, O‘Connor, Moore, & Lippmann, 2008). Early learning difficulties have been associated with mental health problems (Maughan, Rowe, Loeber, & Stouthamer-Loeber, 2003), increased behaviour issues (Arnold, 1997), delinquency (Loeber & Dishion, 1983) and later academic failure (Epstein, 2008). Considering the importance of these areas, the limited research on special health care needs in social-emotional and learning domains is a factor driving this research. The purpose of the current research is to investigate social-emotional and learning competence in the early years for Australian children who have special health care needs. The data which informed this thesis was from Growing up in Australia: The Longitudinal Study of Australian Children. This is a national, longitudinal study being conducted by the Commonwealth Department of Families, Housing, Community Services and Indigenous Affairs. The study has a national representative sample, with data collection occurring biennially, in 2004 (Wave 1), 2006 (Wave 2) and 2008 (Wave 3). Growing up in Australia uses a cross-sequential research design involving two cohorts, an Infant Cohort (0-1 at recruitment) and a Kindergarten Cohort (4-5 at recruitment). This study uses the Kindergarten Cohort, for which there were 4,983 children at recruitment. Three studies were conducted to address the objectives of this thesis. Study 1 used Wave 1 data to identify and describe Australian children with special health care needs. Children who identified as having special health care needs through the special health care needs screener were selected. From this, descriptive analyses were run. The results indicate that boys, children with low birth weight and children from families with low levels of maternal education are likely to be in the population of children with special health care needs. Further, these children are likely to be using prescription medications, have poor general health and are likely to have specific condition diagnoses. Study 2 used Wave 1 data to examine differences between children with special health care needs and their peers in social-emotional competence and learning competence prior to school. Children identified by the special health care needs screener were chosen for the case group (n = 650). A matched case control group of peers (n = 650), matched on sex, cultural and linguistic diversity, family socioeconomic position and age, were the comparison group. Social-emotional competence was measured through Social/Emotional Domain scores taken from the Growing up in Australia Outcome Index, with learning competence measured through Learning Domain scores. Results suggest statistically significant differences in scores between the two groups. Children with special health care needs have lower levels of social-emotional and learning competence prior to school compared to their peers. Study 3 used Wave 1 and Wave 2 data to examine the relationship between special health care needs at Wave 1 and social-emotional competence and learning competence at Wave 2, as children started school. The sample for this study consisted of children in the Kindergarten Cohort who had teacher data at Wave 2. Results from multiple regression models indicate that special health care needs prior to school (Wave 1) significantly predicts social-emotional competence and learning competence in the early years of school (Wave 2). These results indicate that having special health care needs prior to school is a risk factor for the social-emotional and learning domains in the early years of school. The results from these studies give valuable insight into Australian children with special health care needs and their social-emotional and learning competence in the early years. The Australia population of children with special health care needs were primarily male children, from families with low maternal education, were likely to be of poor health and taking prescription medications. It was found that children with special health care needs were likely to have lower social-emotional competence and learning competence prior to school compared to their peers. Results indicate that special health care needs prior to school were predictive of lower social-emotional and learning competencies in the early years of school. More research is required into this unique population and their competencies over time. However, the current research provides valuable insight into an under researched 'at risk' population.

Australian children with special health care needs: Social-emotional and learning competencies in the early years

This study examined the relationship between special health care needs and social-emotional and learning competence in the early years, reporting on two waves of data from the Kindergarten Cohort of Growing up in Australia: The Longitudinal Study of Australian Children (LSAC). Six hundred and fifty children were identified through the 2-question Special Health Care Needs Screener as having special health care needs. Children with special health care needs were more likely to be male, to have been of low birth weight, to be taking prescription medications, to be diagnosed with a specific health condition and to be from families where the mother was less well educated. These children scored significantly lower on teacher-rated social-emotional and learning competencies prior to school compared to a control group of children without special health care needs. Multiple regression analyses indicated that being identified with a special health care need prior to school predicted lower social-emotional and learning competencies in the early years of school. Results are discussed in terms of the implications for policy and practice.

Very Low Birth Weight Infants as Young Adults : Focus on aspects of cognition, behavior and sleep

Major advances in the treatment of preterm infants have occurred during the last three decades. Survival rates have increased, and the first generations of preterm infants born at very low birth weight (VLBW; less than 1500 g) who profited from modern neonatal intensive care are now in young adulthood. The literature shows that VLBW children achieve on average lower scores on cognitive tests, even after exclusion of individuals with obvious neurosensory deficits. Evidence also exists for an increased risk in VLBW children for various neuropsychiatric disorders such as attention-deficit hyperactivity disorder (ADHD) and related behavioral symptoms. Up till now, studies extending into adulthood are sparse, and it remains to be seen whether these problems persist into adulthood. The aim of this thesis was to study ADHD-related symptoms and cognitive and executive functioning in young adults born at VLBW. In addition, we aimed to study sleep disturbances, known to adversely affect both cognition and attention. We hypothesized that preterm birth at VLBW interferes with early brain development in a way that alters the neuropsychological phenotype; this may manifest itself as ADHD symptoms and impaired cognitive abilities in young adulthood. In this cohort study from a geographically defined region, we studied 166 VLBW adults and 172 term-born controls born from 1978 through 1985. At ages 18 to 27 years, the study participants took part in a clinic study during which their physical and psychological health was assessed in detail. Three years later, 213 of these individuals participated in a follow-up. The current study is part of a larger research project (The Helsinki Study of Very Low Birth Weight Adults), and the measurements of interest for this particular study include the following: 1) The Adult Problem Questionnaire (APQ), a self-rating scale of ADHD-related symptoms in adults; 2) A computerized cognitive test battery designed for population studies (CogState®) which measures core cognitive abilities such as reaction time, working memory, and visual learning; 3) Sleep assessment by actigraphy, the Basic Nordic Sleep Questionnaire, and the Morningness-Eveningness Questionnaire. Actigraphs are wrist-worn accelerometers that separate sleep from wakefulness by registering body movements. Contrary to expectations, VLBW adults as a group reported no more ADHD-related behavioral symptoms than did controls. Further subdivision of the VLBW group into SGA (small for gestational age) and AGA (appropriate for gestational age) subgroups, however, revealed more symptoms on ADHD subscales pertaining to executive dysfunction and emotional instability among those born SGA. Thus, it seems that intrauterine growth retardation (for which SGA served as a proxy) is a more essential predictor for self-perceived ADHD symptoms in adulthood than is VLBW birth as such. In line with observations from other cohorts, the VLBW adults reported less risk-taking behavior in terms of substance use (alcohol, smoking, and recreational drugs), a finding reassuring for the VLBW individuals and their families. On the cognitive test, VLBW adults free from neurosensory deficits had longer reaction times than did term-born peers on all tasks included in the test battery, and lower accuracy on the learning task, with no discernible effect of SGA status over and above the effect of VLBW. Altogether, on a group level, even high-functioning VLBW adults show subtle deficits in psychomotor processing speed, visual working memory, and learning abilities. The sleep studies provided no evidence for differences in sleep quality or duration between the two groups. The VLBW adults were, however, at more than two-fold higher risk for sleep-disordered breathing (in terms of chronic snoring). Given the link between sleep-disordered breathing and health sequelae, these results suggest that VLBW individuals may benefit from an increased awareness among clinicians of this potential problem area. An unexpected finding from the sleep studies was the suggestion of an advanced sleep phase: The VLBW adults went to bed earlier according to the actigraphy registrations and also reported earlier wake-up times on the questionnaire. In further study of this issue in conjunction with the follow-up three years later, the VLBW group reported higher levels of morningness propensity, further corroborating the preliminary findings of an advanced sleep phase. Although the clinical implications are not entirely clear, the issue may be worth further study, since circadian rhythms are closely related to health and well-being. In sum, we believe that increased understanding of long-term outcomes after VLBW, and identification of areas and subgroups that are particularly vulnerable, will allow earlier recognition of potential problems and ultimately lead to improved prevention strategies.

Learning optimal adaptation strategies in unpredictable motor tasks.

Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.

Optical learning in a smart spatial light modulator

A synaptic plane rendered by an array of smart pixels was described regarding its application as a complementary component for neural network implementation. The smart spatial light modulator featured auto-modification abilities. Thus, an optical system incorporating this device can show self-reliant optical learning. Furthermore, the optical system design, in the area of its optical interconnection scheme, is highly flexible since the independent weight-plane pixels eliminated the difficulty between weight update calculation and weight representation.

A Distributed Outstar Network for Spatial Pattern Learning

The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field of arbitrarily many nodes, whose activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse, whereby a path weight decreases in joint proportion to the transmitted path signal and the degree of disuse of the target node. During learning, the total signal to a target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals. Three synaptic transmission functions, by a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all. When source field activity is distributed, catastrophic forgetting may occur. Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the unit of long-term memory in such a system is an adaptive threshold, rather than the multiplicative path weight widely used in neural models.

Spatial Pattern Learning, Catastophic Forgetting and Optimal Rules of Synaptic Transmission

It is a neural network truth universally acknowledged, that the signal transmitted to a target node must be equal to the product of the path signal times a weight. Analysis of catastrophic forgetting by distributed codes leads to the unexpected conclusion that this universal synaptic transmission rule may not be optimal in certain neural networks. The distributed outstar, a network designed to support stable codes with fast or slow learning, generalizes the outstar network for spatial pattern learning. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field, of arbitrarily many nodes, where the activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse whereby a path weight decreases in joint proportion to the transmittcd path signal and the degree of disuse of the target node. During learning, the total signal to a target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals three types of synaptic transmission, a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all when source field activity is distributed, catastrophic forgetting may occur. Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the optimal unit of long-term memory in such a system is a subtractive threshold, rather than a multiplicative weight.