How small business advisory program delivery methods (collective learning, tailored, and practice-based approaches) affect learning and innovation

Past studies relate small business advisory program effectiveness to advisory characteristics such as advisory intensity and scope. We contribute to existing literature by seeking to identify the impact of different advisory program methods of delivery on learning and subsequent firm innovation behavior. Our research is based on a survey of 257 Australian firms completing small business advisory programs in the three years preceding the research. We explore the range of small business advisory program delivery methods in which our surveyed firms participated and, with reference to the literature on organizational learning and innovation, we analyze predictors of firms' learning ability and innovativeness based on the identified delivery methods. First, we found that business advisory programs that involved high levels of collective learning and tailored approaches enhanced firms' perceptions of their learning of critical skills or capabilities. We also found that small business advisory programs that were delivered by using practice-based approaches enhanced firms' subsequent organizational innovation. We verified this finding by testing whether firms that have participated in small business advisory services subsequently demonstrate improved behavior in terms of organizational innovativeness, when compared with matched firms that have not participated in an advisory program.

Learning to lead: The development and testing of a model of leadership learning

Drawing upon an action learning perspective, we hypothesized that a leader’s learning of project leadership skills would be related to facilitative leadership, team reflexivity, and team performance. Secondly, we proposed that new and experienced leaders would differ in the amount they learn from their current and recent experience as project managers, and in the strength of the relationship between their self-reported learning, facilitative leadership, and team reflexivity. We conducted a 1-year longitudinal study of 50 R&D teams, led by 25 new and 25 experienced leaders, with 313 team members and 22 project customers, collecting both quantitative and qualitative data. We found evidence of a significant impact of the leader’s learning on subsequent facilitative leadership and team performance 8 and 12 months later, suggesting a lag between learning leadership skills and translating these skills into leadership behavior. The findings contribute to an understanding of how leaders consolidate their learned experience into facilitative leadership behavior.

Discrepancy Between Antennal and Behavioral Responses for Enantiomers of a-Pinene: Electrophysiology and Behavior of Helicoverpa Armigera (Lepidoptera)

The ability of adult cotton bollworm, Helicoverpa armigera (Hubner), to distinguish and respond to enantiomers of α-pinene was investigated with electrophysiological and behavioral methods. Electroantennogram recordings using mixtures of the enantiomers at saturating dose levels, and single unit electrophysiology, indicated that the two forms were detected by the same receptor neurons. The relative size of the electroantennogram response was higher for the (−) compared to the (+) form, indicating greater affinity for the (−) form at the level of the dendrites. Behavioral assays investigated the ability of moths to discriminate between, and respond to the (+) and (−) forms of α pinene. Moths with no odor conditioning showed an innate preference for (+)-α-pinene. This preference displayed by naıve moths was not significantly different from the preferences of moths conditioned on (+)-α-pinene. However, we found a significant difference in preference between moths conditioned on the (−) enantiomer compared to naıve moths and moths conditioned on (+)-α-pinene, showing that learning plays an important role in the behavioral response. Moths are less able to distinguish between enantiomers of α-pinene than different odors (e.g., phenylacetaldehyde versus (−)-α-pinene) in learning experiments. The relevance of receptor discrimination of enantiomers and learning ability of the moths in host plant choice is discussed.

Vineland Adaptive Behavior Scales - II profile of young children with Autism Spectrum Disorder

Adaptive behaviour is a crucial area of assessment for individuals with Autism Spectrum Disorder (ASD). This study examined the adaptive behaviour profile of 77 young children with ASD using the Vineland-II, and analysed factors associated with adaptive functioning. Consistent with previous research with the original Vineland a distinct autism profile of Vineland-II age equivalent scores, but not standard scores, was found. Highest scores were in motor skills and lowest scores were in socialisation. The addition of the Autism Diagnostic Observation Schedule (ADOS) calibrated severity score did not contribute significant variance to Vineland-II scores beyond that accounted for by age and nonverbal ability. Limitations, future directions, and implications are discussed.

Auditory - Visual Matching in Learning Disabilities : Intervention Studies from Finland and Sweden

The present thesis discusses relevant issues in education: 1) learning disabilities including the role of comorbidity in LDs, and 2) the use of research-based interventions. This thesis consists of a series of four studies (three articles), which deepens the knowledge of the field of special education. Intervention studies (N=242) aimed to examine whether training using a nonverbal auditory-visual matching computer program had a remedial effect in different learning disabilities, such as developmental dyslexia, Attention Deficit Disorder (ADD) and Specific Language Impairment (SLI). These studies were conducted in both Finland and Sweden. The intervention’s non-verbal character made an international perspective possible. The results of the intervention studies confirmed, that the auditory-visual matching computer program, called Audilex had positive intervention effects. In Study I of children with developmental dyslexia there were also improvements in reading skills, specifically in reading nonsense words and reading speed. These improvements in tasks, which are thought to rely on phonological processing, suggest that such reading difficulties in dyslexia may stem in part from more basic perceptual difficulties, including those required to manage the visual and auditory components of the decoding task. In Study II the intervention had a positive effect on children with dyslexia; older students with dyslexia and surprisingly, students with ADD also benefited from this intervention. In conclusion, the role of comorbidity was apparent. An intervention effect was evident also in students’ school behavior. Study III showed that children with SLI experience difficulties very similar to those of children with dyslexia in auditory-visual matching. Children with language-based learning disabilities, such as dyslexia and SLI benefited from the auditory-visual matching intervention. Also comorbidity was evident among these children; in addition to formal diagnoses, comorbidity was explored with an assessment inventory, which was developed for this thesis. Interestingly, an overview of the data of this thesis shows positive intervention effects in all studies despite learning disability, language, gender or age. These findings have been described by a concept inter-modal transpose. Self-evidently these issues need further studies. In learning disabilities the aim in the future will also be to identify individuals at risk rather than by deficit; this aim can be achieved by using research-based interventions, intensified support in general education and inclusive special education. Keywords: learning disabilities, developmental dyslexia, attention deficit disorder, specific language impairment, language-based learning disabilities, comorbidity, auditory-visual matching, research-based interventions, inter-modal transpose

Process makes perfect: High-performance design creates healthy environments for high-performance learning

Considering the staggering benefits of high-performance schools, it seems an obvious choice to “go green.” High-performance schools offer an exceptionally cost-effective means to enhance student learning, using on average 33 percent less energy than conventionally designed schools, and provide substantial health gains, including reduced respiratory problems and absenteeism. According to the 2006 study, Greening America's Schools, Costs and Benefits, co-sponsored by the American Institute of Architects (AIA) and Capital E, a green building consulting firm, high-performance lighting is a key element of healthy learning environments, contributing to improved test scores, reduced off-task behavior, and higher achievement among students. Few argue this point more convincingly than architect Heinz Rudolf, of Portland-Oregon-based Boora Architects, who has designed sustainable schools for more than 80 school districts in Oregon, Washington, Colorado, and Wyoming, and has pioneered the high-performance school movement. Boora's recently completed project, the Baker Prairie Middle School in Canby, Oregon is one of the most sustainable K-12 facilities in the state, and illustrates Rudolf's progressive and research-intensive approach to school design.

R&D in clean technology: A project choice model with learning

In this study, we investigate the qualitative and quantitative effects of an R&D subsidy for a clean technology and a Pigouvian tax on a dirty technology on environmental R&D when it is uncertain how long the research takes to complete. The model is formulated as an optimal stopping problem, in which the number of successes required to complete the R&D project is finite and learning about the probability of success is incorporated. We show that the optimal R&D subsidy with the consideration of learning is higher than that without it. We also find that an R&D subsidy performs better than a Pigouvian tax unless suppliers have sufficient incentives to continue cost-reduction efforts after the new technology success-fully replaces the old one. Moreover, by using a two-project model, we show that a uniform subsidy is better than a selective subsidy.

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.

Performance Modeling based on Multidimensional Surface Learning for Performance Predictions of Parallel Applications in Non-Dedicated Environments

Modeling the performance behavior of parallel applications to predict the execution times of the applications for larger problem sizes and number of processors has been an active area of research for several years. The existing curve fitting strategies for performance modeling utilize data from experiments that are conducted under uniform loading conditions. Hence the accuracy of these models degrade when the load conditions on the machines and network change. In this paper, we analyze a curve fitting model that attempts to predict execution times for any load conditions that may exist on the systems during application execution. Based on the experiments conducted with the model for a parallel eigenvalue problem, we propose a multi-dimensional curve-fitting model based on rational polynomials for performance predictions of parallel applications in non-dedicated environments. We used the rational polynomial based model to predict execution times for 2 other parallel applications on systems with large load dynamics. In all the cases, the model gave good predictions of execution times with average percentage prediction errors of less than 20%

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Various algorithms for optimization and learning in adaptive systems

This thesis discusses various methods for learning and optimization in adaptive systems. Overall, it emphasizes the relationship between optimization, learning, and adaptive systems; and it illustrates the influence of underlying hardware upon the construction of efficient algorithms for learning and optimization. Chapter 1 provides a summary and an overview.

Chapter 2 discusses a method for using feed-forward neural networks to filter the noise out of noise-corrupted signals. The networks use back-propagation learning, but they use it in a way that qualifies as unsupervised learning. The networks adapt based only on the raw input data-there are no external teachers providing information on correct operation during training. The chapter contains an analysis of the learning and develops a simple expression that, based only on the geometry of the network, predicts performance.

Chapter 3 explains a simple model of the piriform cortex, an area in the brain involved in the processing of olfactory information. The model was used to explore the possible effect of acetylcholine on learning and on odor classification. According to the model, the piriform cortex can classify odors better when acetylcholine is present during learning but not present during recall. This is interesting since it suggests that learning and recall might be separate neurochemical modes (corresponding to whether or not acetylcholine is present). When acetylcholine is turned off at all times, even during learning, the model exhibits behavior somewhat similar to Alzheimer's disease, a disease associated with the degeneration of cells that distribute acetylcholine.

Chapters 4, 5, and 6 discuss algorithms appropriate for adaptive systems implemented entirely in analog hardware. The algorithms inject noise into the systems and correlate the noise with the outputs of the systems. This allows them to estimate gradients and to implement noisy versions of gradient descent, without having to calculate gradients explicitly. The methods require only noise generators, adders, multipliers, integrators, and differentiators; and the number of devices needed scales linearly with the number of adjustable parameters in the adaptive systems. With the exception of one global signal, the algorithms require only local information exchange.

Establishing the C. elegans uterine seam cell (utse) as a novel model for studying cell behavior

The molecular inputs necessary for cell behavior are vital to our understanding of development and disease. Proper cell behavior is necessary for processes ranging from creating one’s face (neural crest migration) to spreading cancer from one tissue to another (invasive metastatic cancers). Identifying the genes and tissues involved in cell behavior not only increases our understanding of biology but also has the potential to create targeted therapies in diseases hallmarked by aberrant cell behavior.

A well-characterized model system is key to determining the molecular and spatial inputs necessary for cell behavior. In this work I present the C. elegans uterine seam cell (utse) as an ideal model for studying cell outgrowth and shape change. The utse is an H-shaped cell within the hermaphrodite uterus that functions in attaching the uterus to the body wall. Over L4 larval stage, the utse grows bidirectionally along the anterior-posterior axis, changing from an ellipsoidal shape to an elongated H-shape. Spatially, the utse requires the presence of the uterine toroid cells, sex muscles, and the anchor cell nucleus in order to properly grow outward. Several gene families are involved in utse development, including Trio, Nav, Rab GTPases, Arp2/3, as well as 54 other genes found from a candidate RNAi screen. The utse can be used as a model system for studying metastatic cancer. Meprin proteases are involved in promoting invasiveness of metastatic cancers and the meprin-likw genes nas-21, nas-22, and toh-1 act similarly within the utse. Studying nas-21 activity has also led to the discovery of novel upstream inhibitors and activators as well as targets of nas-21, some of which have been characterized to affect meprin activity. This illustrates that the utse can be used as an in vivo model for learning more about meprins, as well as various other proteins involved in metastasis.

Optimal Data Distributions in Machine Learning

In the first part of the thesis we explore three fundamental questions that arise naturally when we conceive a machine learning scenario where the training and test distributions can differ. Contrary to conventional wisdom, we show that in fact mismatched training and test distribution can yield better out-of-sample performance. This optimal performance can be obtained by training with the dual distribution. This optimal training distribution depends on the test distribution set by the problem, but not on the target function that we want to learn. We show how to obtain this distribution in both discrete and continuous input spaces, as well as how to approximate it in a practical scenario. Benefits of using this distribution are exemplified in both synthetic and real data sets.

In order to apply the dual distribution in the supervised learning scenario where the training data set is fixed, it is necessary to use weights to make the sample appear as if it came from the dual distribution. We explore the negative effect that weighting a sample can have. The theoretical decomposition of the use of weights regarding its effect on the out-of-sample error is easy to understand but not actionable in practice, as the quantities involved cannot be computed. Hence, we propose the Targeted Weighting algorithm that determines if, for a given set of weights, the out-of-sample performance will improve or not in a practical setting. This is necessary as the setting assumes there are no labeled points distributed according to the test distribution, only unlabeled samples.

Finally, we propose a new class of matching algorithms that can be used to match the training set to a desired distribution, such as the dual distribution (or the test distribution). These algorithms can be applied to very large datasets, and we show how they lead to improved performance in a large real dataset such as the Netflix dataset. Their computational complexity is the main reason for their advantage over previous algorithms proposed in the covariate shift literature.

In the second part of the thesis we apply Machine Learning to the problem of behavior recognition. We develop a specific behavior classifier to study fly aggression, and we develop a system that allows analyzing behavior in videos of animals, with minimal supervision. The system, which we call CUBA (Caltech Unsupervised Behavior Analysis), allows detecting movemes, actions, and stories from time series describing the position of animals in videos. The method summarizes the data, as well as it provides biologists with a mathematical tool to test new hypotheses. Other benefits of CUBA include finding classifiers for specific behaviors without the need for annotation, as well as providing means to discriminate groups of animals, for example, according to their genetic line.

Integrated treatment of first episode psychosis with online training (e-learning): study protocol for a randomised controlled trial

Background: The integrated treatment of first episode psychosis has been shown to improve functionality and negative symptoms in previous studies. In this paper, we describe a study of integrated treatment (individual psychoeducation complementary to pharmacotherapy) versus treatment as usual, comparing results at baseline with those at 6-month re-assessment (at the end of the study) for these patients, and online training of professionals to provide this complementary treatment, with the following objectives: 1) to compare the efficacy of individual psychoeducation as add-on treatment versus treatment as usual in improving psychotic and mood symptoms; 2) to compare adherence to medication, functioning, insight, social response, quality of life, and brain-derived neurotrophic factor, between both groups; and 3) to analyse the efficacy of online training of psychotherapists. Methods/design: This is a single-blind randomised clinical trial including patients with first episode psychosis from hospitals across Spain, randomly assigned to either a control group with pharmacotherapy and regular sessions with their psychiatrist (treatment as usual) or an intervention group with integrated care including treatment as usual plus a psychoeducational intervention (14 sessions). Training for professionals involved at each participating centre was provided by the coordinating centre (University Hospital of Alava) through video conferences. Patients are evaluated with an extensive battery of tests assessing clinical and sociodemographic characteristics (Positive and Negative Syndrome Scale, State-Trait Anxiety Inventory, Liebowitz Social Anxiety Scale, Hamilton Rating Scale for Depression, Scale to Assess Unawareness of Mental Disorders, Strauss and Carpenter Prognostic Scale, Global Assessment of Functioning Scale, Morisky Green Adherence Scale, Functioning Assessment Short Test, World Health Organization Quality of Life instrument WHOQOL-BREF (an abbreviated version of the WHOQOL-100), and EuroQoL questionnaire), and brain-derived neurotrophic factor levels are measured in peripheral blood at baseline and at 6 months. The statistical analysis, including bivariate analysis, linear and logistic regression models, will be performed using SPSS. Discussion: This is an innovative study that includes the assessment of an integrated intervention for patients with first episode psychosis provided by professionals who are trained online, potentially making it possible to offer the intervention to more patients.

Learning to perceive in the sensor motor approach: Piaget's theory of equilibration interpreted dynamically

Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the "laws" of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget's theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget's theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level.