Adaptive communication: languages with more non-native speakers tend to have fewer word forms

Explaining the diversity of languages across the world is one of the central aims of typological, historical, and evolutionary linguistics. We consider the effect of language contact-the number of non-native speakers a language has-on the way languages change and evolve. By analysing hundreds of languages within and across language families, regions, and text types, we show that languages with greater levels of contact typically employ fewer word forms to encode the same information content (a property we refer to as lexical diversity). Based on three types of statistical analyses, we demonstrate that this variance can in part be explained by the impact of non-native speakers on information encoding strategies. Finally, we argue that languages are information encoding systems shaped by the varying needs of their speakers. Language evolution and change should be modeled as the co-evolution of multiple intertwined adaptive systems: On one hand, the structure of human societies and human learning capabilities, and on the other, the structure of language.

Adaptive governance: livelihood innovation for climate resilience in Uganda

Adaptive governance is the use of novel approaches within policy to support experimentation and learning. Social learning reflects the engagement of interdependent stakeholders within this learning. Much attention has focused on these concepts as a solution for resilience in governing institutions in an uncertain climate; resilience representing the ability of a system to absorb shock and to retain its function and form through reorganisation. However, there are still many questions to how these concepts enable resilience, particularly in vulnerable, developing contexts. A case study from Uganda presents how these concepts promote resilient livelihood outcomes among rural subsistence farmers within a decentralised governing framework. This approach has the potential to highlight the dynamics and characteristics of a governance system which may manage change. The paper draws from the enabling characteristics of adaptive governance, including lower scale dynamics of bonding and bridging ties and strong leadership. Central to these processes were learning platforms promoting knowledge transfer leading to improved self-efficacy, innovation and livelihood skills. However even though aspects of adaptive governance were identified as contributing to resilience in livelihoods, some barriers were identified. Reflexivity and multi-stakeholder collaboration were evident in governing institutions; however, limited self-organisation and vertical communication demonstrated few opportunities for shifts in governance, which was severely challenged by inequity, politicisation and elite capture. The paper concludes by outlining implications for climate adaptation policy through promoting the importance of mainstreaming adaptation alongside existing policy trajectories; highlighting the significance of collaborative spaces for stakeholders and the tackling of inequality and corruption.

Cross-sector surveys assessing perceptions of key stakeholders towards barriers, concerns and facilitators to the appropriate use of adaptive designs in confirmatory trials

Background Appropriately conducted adaptive designs (ADs) offer many potential advantages over conventional trials. They make better use of accruing data, potentially saving time, trial participants, and limited resources compared to conventional, fixed sample size designs. However, one can argue that ADs are not implemented as often as they should be, particularly in publicly funded confirmatory trials. This study explored barriers, concerns, and potential facilitators to the appropriate use of ADs in confirmatory trials among key stakeholders. Methods We conducted three cross-sectional, online parallel surveys between November 2014 and January 2015. The surveys were based upon findings drawn from in-depth interviews of key research stakeholders, predominantly in the UK, and targeted Clinical Trials Units (CTUs), public funders, and private sector organisations. Response rates were as follows: 30(55 %) UK CTUs, 17(68 %) private sector, and 86(41 %) public funders. A Rating Scale Model was used to rank barriers and concerns in order of perceived importance for prioritisation. Results Top-ranked barriers included the lack of bridge funding accessible to UK CTUs to support the design of ADs, limited practical implementation knowledge, preference for traditional mainstream designs, difficulties in marketing ADs to key stakeholders, time constraints to support ADs relative to competing priorities, lack of applied training, and insufficient access to case studies of undertaken ADs to facilitate practical learning and successful implementation. Associated practical complexities and inadequate data management infrastructure to support ADs were reported as more pronounced in the private sector. For funders of public research, the inadequate description of the rationale, scope, and decision-making criteria to guide the planned AD in grant proposals by researchers were all viewed as major obstacles. Conclusions There are still persistent and important perceptions of individual and organisational obstacles hampering the use of ADs in confirmatory trials research. Stakeholder perceptions about barriers are largely consistent across sectors, with a few exceptions that reflect differences in organisations’ funding structures, experiences and characterisation of study interventions. Most barriers appear connected to a lack of practical implementation knowledge and applied training, and limited access to case studies to facilitate practical learning. Keywords: Adaptive designs; flexible designs; barriers; surveys; confirmatory trials; Phase 3; clinical trials; early stopping; interim analyses

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.

The Use of On-Line Computer adaptive Testing to raise Standardized Test Scores

Nursing school graduates are under pressure to pass the RN-NCLEX Exam on the first attempt since New York State monitors the results and uses them to evaluate the school’s nursing programs. Since the RN-NCLEX Exam is a standardized test, we sought a method to make our students better test takers. The use of on-line computer adaptive testing has raised our student’s standardized test scores at the end of the nursing course.

A note on learning chaotic sunspot equilibrium

In this paper we prove convergence to chaotic sunspot equilibrium through two learning rules used in the bounded rationality literature. The rst one shows the convergence of the actual dynamics generated by simple adaptive learning rules to a probability distribution that is close to the stationary measure of the sunspot equilibrium; since this stationary measure is absolutely continuous it results in a robust convergence to the stochastic equilibrium. The second one is based on the E-stability criterion for testing stability of rational expectations equilibrium, we show that the conditional probability distribution de ned by the sunspot equilibrium is expectational stable under a reasonable updating rule of this parameter. We also report some numerical simulations of the processes proposed.

Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process

A new implementation of population based incremental learning method for optimizations in electromagnetics

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.

A Comparison of Adaptive Strategies and Patterns of Victimization among Homeless Adolescents and Adults

One hundred and fifty-six homeless adolescents and 319 homeless adults interviewed directly on the streets and in shelters were compared for backgrounds of abuse, adaptations to life on the streets, and rates of criminal victimization when on the streets. Homeless adolescents were more likely to be from abusive family backgrounds, more likely to rely on deviant survival strategies, and more likely to be criminally victimized. A social learning model of adaptation and victimization on the streets was hypothesized. Although the model was supported for both homeless adults and adolescents, it was more strongly supported for adolescents than adults, and for males than females regardless of age.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Spike-based decision learning of nash equilibria in two-player games

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

Effect of Classroom Gender Composition on Students' Development of Self-Regulated Learning Competencies

Success in any field depends on a complex interplay among environmental and personal factors. A key set of personal factors for success in academic settings are those associated with self-regulated learners (SRL). Self-regulated learners choose their own goals, select and organize their learning strategies, and self-monitor their effectiveness. Behaviors and attitudes consistent with self-regulated learning also contribute to self-confidence, which may be important for members of underrepresented groups such as women in engineering. This exploratory study, drawing on the concept of "critical mass", examines the relationship between the personal factors that identify a self-regulated learner and the environmental factors related to gender composition of engineering classrooms. Results indicate that a relatively student gender-balanced classroom and gender match between students and their instructors provide for the development of many adaptive SRL behaviors and attitudes.

Functional neuroimaging of emotional learning and autonomic reactions

This article provides a selective overview of the functional neuroimaging literature with an emphasis on emotional activation processes. Emotions are fast and flexible response systems that provide basic tendencies for adaptive action. From the range of involved component functions, we first discuss selected automatic mechanisms that control basic adaptational changes. Second, we illustrate how neuroimaging work has contributed to the mapping of the network components associated with basic emotion families (fear, anger, disgust, happiness), and secondary dimensional concepts that organise the meaning space for subjective experience and verbal labels (emotional valence, activity/intensity, approach/withdrawal, etc.). Third, results and methodological difficulties are discussed in view of own neuroimaging experiments that investigated the component functions involved in emotional learning. The amygdala, prefrontal cortex, and striatum form a network of reciprocal connections that show topographically distinct patterns of activity as a correlate of up and down regulation processes during an emotional episode. Emotional modulations of other brain systems have attracted recent research interests. Emotional neuroimaging calls for more representative designs that highlight the modulatory influences of regulation strategies and socio-cultural factors responsible for inhibitory control and extinction. We conclude by emphasising the relevance of the temporal process dynamics of emotional activations that may provide improved prediction of individual differences in emotionality.

Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

Das intelligente Tutorensystem LARGO für die Rechtswissenschaften soll Jurastudenten helfen, Argumentationsstrategien zu lernen. Im verwendeten Ansatz werden Gerichtsprotokolle als Lernmaterialien verwendet: Studenten annotieren diese und erstellen graphische Repräsentationen des Argumentationsverlaufs. Das System kann dabei zur Reflexion über die von Anwälten vorgebrachten Argumente anregen und Lernende auf mögliche Schwächen in ihrer Analyse des Disputs hinweisen. Zur Erkennung von Schwächen verwendet das System Graphgrammatiken und kollaborative Filtermechanismen. Dieser Artikel stellt dar, wie in LARGO auf Basis der Bestimmung eines „Benutzungskontextes“ die Rückmeldungen im System benutzungsadaptiv gestaltet werden. Weiterhin diskutieren wir auf Basis der Ergebnisse einer kontrollierten Studie mit dem System, welche mit Jurastudierenden an der University of Pittsburgh stattfand, in wie weit der automatisch bestimmte Benutzungskontext zur Vorhersage von Lernerfolgen bei Studenten verwendbar ist.